From 391d7b2763df0b90a975c7232f38c4de4be2ff85 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Tue, 10 Dec 2024 13:45:47 +0800 Subject: [PATCH 001/155] [Bugfix] Fix usage of `deprecated` decorator (#11025) Signed-off-by: DarkLight1337 --- vllm/engine/llm_engine.py | 8 +-- vllm/engine/multiprocessing/__init__.py | 8 +-- vllm/engine/multiprocessing/client.py | 16 +++--- vllm/entrypoints/llm.py | 72 ++++++++++++------------- 4 files changed, 52 insertions(+), 52 deletions(-) diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 560f84a008291..8fc69d96d321e 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -677,12 +677,10 @@ def stop_remote_worker_execution_loop(self) -> None: self.model_executor.stop_remote_worker_execution_loop() @overload - @deprecated("'inputs' will be renamed to 'prompt") def add_request( self, request_id: str, - *, - inputs: PromptType, + prompt: PromptType, params: Union[SamplingParams, PoolingParams], arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, @@ -693,10 +691,12 @@ def add_request( ... @overload + @deprecated("'inputs' will be renamed to 'prompt") def add_request( self, request_id: str, - prompt: PromptType, + *, + inputs: PromptType, params: Union[SamplingParams, PoolingParams], arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, diff --git a/vllm/engine/multiprocessing/__init__.py b/vllm/engine/multiprocessing/__init__.py index 7020012e8bb86..420f540d0b5f4 100644 --- a/vllm/engine/multiprocessing/__init__.py +++ b/vllm/engine/multiprocessing/__init__.py @@ -35,11 +35,9 @@ class RPCProcessRequest: priority: int = 0 @overload - @deprecated("'inputs' will be renamed to 'prompt") def __init__( self, - *, - inputs: PromptType, + prompt: PromptType, params: Union[SamplingParams, PoolingParams], request_id: str, lora_request: Optional[LoRARequest] = None, @@ -50,9 +48,11 @@ def __init__( ... @overload + @deprecated("'inputs' will be renamed to 'prompt") def __init__( self, - prompt: PromptType, + *, + inputs: PromptType, params: Union[SamplingParams, PoolingParams], request_id: str, lora_request: Optional[LoRARequest] = None, diff --git a/vllm/engine/multiprocessing/client.py b/vllm/engine/multiprocessing/client.py index 7e4f81b2cf8e2..32bd83305bb8f 100644 --- a/vllm/engine/multiprocessing/client.py +++ b/vllm/engine/multiprocessing/client.py @@ -415,11 +415,9 @@ def dead_error(self) -> BaseException: return ENGINE_DEAD_ERROR(self._errored_with) @overload - @deprecated("'inputs' will be renamed to 'prompt") def generate( self, - *, - inputs: PromptType, + prompt: PromptType, sampling_params: SamplingParams, request_id: str, lora_request: Optional[LoRARequest] = None, @@ -430,9 +428,11 @@ def generate( ... @overload + @deprecated("'inputs' will be renamed to 'prompt") def generate( self, - prompt: PromptType, + *, + inputs: PromptType, sampling_params: SamplingParams, request_id: str, lora_request: Optional[LoRARequest] = None, @@ -487,11 +487,9 @@ def generate( prompt_adapter_request, priority) @overload - @deprecated("'inputs' will be renamed to 'prompt") def encode( self, - *, - inputs: PromptType, + prompt: PromptType, pooling_params: PoolingParams, request_id: str, lora_request: Optional[LoRARequest] = None, @@ -501,9 +499,11 @@ def encode( ... @overload + @deprecated("'inputs' will be renamed to 'prompt") def encode( self, - prompt: PromptType, + *, + inputs: PromptType, pooling_params: PoolingParams, request_id: str, lora_request: Optional[LoRARequest] = None, diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py index 8de30ccd18a11..2a02187223a33 100644 --- a/vllm/entrypoints/llm.py +++ b/vllm/entrypoints/llm.py @@ -252,8 +252,21 @@ def set_tokenizer(self, tokenizer: AnyTokenizer) -> None: else: tokenizer_group.tokenizer = get_cached_tokenizer(tokenizer) + @overload + def generate( + self, + prompts: Union[PromptType, Sequence[PromptType]], + /, + *, + sampling_params: Optional[Union[SamplingParams, + Sequence[SamplingParams]]] = None, + use_tqdm: bool = True, + lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None, + ) -> List[RequestOutput]: + ... + @overload # LEGACY: single (prompt + optional token ids) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: str, @@ -266,7 +279,7 @@ def generate( ... @overload # LEGACY: multi (prompt + optional token ids) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: List[str], @@ -279,7 +292,7 @@ def generate( ... @overload # LEGACY: single (token ids + optional prompt) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: Optional[str] = None, @@ -293,7 +306,7 @@ def generate( ... @overload # LEGACY: multi (token ids + optional prompt) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: Optional[List[str]] = None, @@ -307,7 +320,7 @@ def generate( ... @overload # LEGACY: single or multi token ids [pos-only] - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: None, @@ -318,19 +331,6 @@ def generate( ) -> List[RequestOutput]: ... - @overload - def generate( - self, - prompts: Union[PromptType, Sequence[PromptType]], - /, - *, - sampling_params: Optional[Union[SamplingParams, - Sequence[SamplingParams]]] = None, - use_tqdm: bool = True, - lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None, - ) -> List[RequestOutput]: - ... - @deprecate_kwargs( "prompt_token_ids", is_deprecated=lambda: LLM.DEPRECATE_LEGACY, @@ -672,8 +672,21 @@ def chat( lora_request=lora_request, ) + @overload + def encode( + self, + prompts: Union[PromptType, Sequence[PromptType]], + /, + *, + pooling_params: Optional[Union[PoolingParams, + Sequence[PoolingParams]]] = None, + use_tqdm: bool = True, + lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None, + ) -> List[PoolingRequestOutput]: + ... + @overload # LEGACY: single (prompt + optional token ids) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: str, @@ -686,7 +699,7 @@ def encode( ... @overload # LEGACY: multi (prompt + optional token ids) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: List[str], @@ -699,7 +712,7 @@ def encode( ... @overload # LEGACY: single (token ids + optional prompt) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: Optional[str] = None, @@ -713,7 +726,7 @@ def encode( ... @overload # LEGACY: multi (token ids + optional prompt) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: Optional[List[str]] = None, @@ -727,7 +740,7 @@ def encode( ... @overload # LEGACY: single or multi token ids [pos-only] - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: None, @@ -738,19 +751,6 @@ def encode( ) -> List[PoolingRequestOutput]: ... - @overload - def encode( - self, - prompts: Union[PromptType, Sequence[PromptType]], - /, - *, - pooling_params: Optional[Union[PoolingParams, - Sequence[PoolingParams]]] = None, - use_tqdm: bool = True, - lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None, - ) -> List[PoolingRequestOutput]: - ... - @deprecate_kwargs( "prompt_token_ids", is_deprecated=lambda: LLM.DEPRECATE_LEGACY, From 980ad394a83a6f12c576a035922db3c2e743beff Mon Sep 17 00:00:00 2001 From: Joe Runde Date: Mon, 9 Dec 2024 22:46:29 -0700 Subject: [PATCH 002/155] [Frontend] Use request id from header (#10968) Signed-off-by: Joe Runde --- docs/requirements-docs.txt | 1 + vllm/entrypoints/openai/api_server.py | 4 ++-- vllm/entrypoints/openai/serving_chat.py | 3 ++- vllm/entrypoints/openai/serving_completion.py | 4 ++-- vllm/entrypoints/openai/serving_embedding.py | 4 ++-- vllm/entrypoints/openai/serving_engine.py | 11 ++++++++++- vllm/entrypoints/openai/serving_score.py | 4 ++-- vllm/entrypoints/openai/serving_tokenization.py | 9 ++++++--- 8 files changed, 27 insertions(+), 13 deletions(-) diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt index 5c80645b405ae..ca2da4cd66d2d 100644 --- a/docs/requirements-docs.txt +++ b/docs/requirements-docs.txt @@ -16,5 +16,6 @@ mistral_common >= 1.5.0 aiohttp starlette openai # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args +fastapi # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args partial-json-parser # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args requests diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py index c7bc30040279c..0f93eb54111ad 100644 --- a/vllm/entrypoints/openai/api_server.py +++ b/vllm/entrypoints/openai/api_server.py @@ -305,7 +305,7 @@ async def health(raw_request: Request) -> Response: async def tokenize(request: TokenizeRequest, raw_request: Request): handler = tokenization(raw_request) - generator = await handler.create_tokenize(request) + generator = await handler.create_tokenize(request, raw_request) if isinstance(generator, ErrorResponse): return JSONResponse(content=generator.model_dump(), status_code=generator.code) @@ -319,7 +319,7 @@ async def tokenize(request: TokenizeRequest, raw_request: Request): async def detokenize(request: DetokenizeRequest, raw_request: Request): handler = tokenization(raw_request) - generator = await handler.create_detokenize(request) + generator = await handler.create_detokenize(request, raw_request) if isinstance(generator, ErrorResponse): return JSONResponse(content=generator.model_dump(), status_code=generator.code) diff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py index 54ca0463bcab1..0af7613a473a4 100644 --- a/vllm/entrypoints/openai/serving_chat.py +++ b/vllm/entrypoints/openai/serving_chat.py @@ -176,7 +176,8 @@ async def create_chat_completion( logger.exception("Error in preprocessing prompt inputs") return self.create_error_response(str(e)) - request_id = f"chatcmpl-{request.request_id}" + request_id = "chatcmpl-" \ + f"{self._base_request_id(raw_request, request.request_id)}" request_metadata = RequestResponseMetadata(request_id=request_id) if raw_request: diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py index fc1c4908d6650..c54d5f07cf58c 100644 --- a/vllm/entrypoints/openai/serving_completion.py +++ b/vllm/entrypoints/openai/serving_completion.py @@ -30,7 +30,7 @@ from vllm.sampling_params import BeamSearchParams, SamplingParams from vllm.sequence import Logprob from vllm.transformers_utils.tokenizer import AnyTokenizer -from vllm.utils import merge_async_iterators, random_uuid +from vllm.utils import merge_async_iterators logger = init_logger(__name__) @@ -86,7 +86,7 @@ async def create_completion( "suffix is not currently supported") model_name = self.base_model_paths[0].name - request_id = f"cmpl-{random_uuid()}" + request_id = f"cmpl-{self._base_request_id(raw_request)}" created_time = int(time.time()) request_metadata = RequestResponseMetadata(request_id=request_id) diff --git a/vllm/entrypoints/openai/serving_embedding.py b/vllm/entrypoints/openai/serving_embedding.py index 2cbb252610e39..3f7b75e893cad 100644 --- a/vllm/entrypoints/openai/serving_embedding.py +++ b/vllm/entrypoints/openai/serving_embedding.py @@ -19,7 +19,7 @@ from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing from vllm.logger import init_logger from vllm.outputs import PoolingOutput, PoolingRequestOutput -from vllm.utils import merge_async_iterators, random_uuid +from vllm.utils import merge_async_iterators logger = init_logger(__name__) @@ -110,7 +110,7 @@ async def create_embedding( "dimensions is currently not supported") model_name = request.model - request_id = f"embd-{random_uuid()}" + request_id = f"embd-{self._base_request_id(raw_request)}" created_time = int(time.monotonic()) truncate_prompt_tokens = None diff --git a/vllm/entrypoints/openai/serving_engine.py b/vllm/entrypoints/openai/serving_engine.py index 8232c6116c1bd..63f27b955461e 100644 --- a/vllm/entrypoints/openai/serving_engine.py +++ b/vllm/entrypoints/openai/serving_engine.py @@ -6,6 +6,7 @@ from typing import (Any, Callable, Dict, Iterable, Iterator, List, Mapping, Optional, Sequence, Tuple, TypedDict, Union) +from fastapi import Request from pydantic import Field from starlette.datastructures import Headers from typing_extensions import Annotated @@ -47,7 +48,7 @@ from vllm.tracing import (contains_trace_headers, extract_trace_headers, log_tracing_disabled_warning) from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer -from vllm.utils import AtomicCounter, is_list_of, make_async +from vllm.utils import AtomicCounter, is_list_of, make_async, random_uuid logger = init_logger(__name__) @@ -565,6 +566,14 @@ async def _get_trace_headers( return None + @staticmethod + def _base_request_id(raw_request: Request, + default: Optional[str] = None) -> Optional[str]: + """Pulls the request id to use from a header, if provided""" + default = default or random_uuid() + return raw_request.headers.get( + "X-Request-Id", default) if raw_request is not None else default + @staticmethod def _get_decoded_token(logprob: Logprob, token_id: int, diff --git a/vllm/entrypoints/openai/serving_score.py b/vllm/entrypoints/openai/serving_score.py index a1f14449ba9c3..fed06fa452955 100644 --- a/vllm/entrypoints/openai/serving_score.py +++ b/vllm/entrypoints/openai/serving_score.py @@ -15,7 +15,7 @@ from vllm.logger import init_logger from vllm.outputs import PoolingRequestOutput from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer -from vllm.utils import make_async, merge_async_iterators, random_uuid +from vllm.utils import make_async, merge_async_iterators logger = init_logger(__name__) @@ -102,7 +102,7 @@ async def create_score( return error_check_ret model_name = request.model - request_id = f"score-{random_uuid()}" + request_id = f"score-{self._base_request_id(raw_request)}" created_time = int(time.monotonic()) truncate_prompt_tokens = request.truncate_prompt_tokens diff --git a/vllm/entrypoints/openai/serving_tokenization.py b/vllm/entrypoints/openai/serving_tokenization.py index 9c3dc2c98b2dd..2e849333680d4 100644 --- a/vllm/entrypoints/openai/serving_tokenization.py +++ b/vllm/entrypoints/openai/serving_tokenization.py @@ -1,5 +1,7 @@ from typing import Final, List, Optional, Union +from fastapi import Request + from vllm.config import ModelConfig from vllm.engine.protocol import EngineClient from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption @@ -17,7 +19,6 @@ LoRAModulePath, OpenAIServing) from vllm.logger import init_logger -from vllm.utils import random_uuid logger = init_logger(__name__) @@ -48,12 +49,13 @@ def __init__( async def create_tokenize( self, request: TokenizeRequest, + raw_request: Request, ) -> Union[TokenizeResponse, ErrorResponse]: error_check_ret = await self._check_model(request) if error_check_ret is not None: return error_check_ret - request_id = f"tokn-{random_uuid()}" + request_id = f"tokn-{self._base_request_id(raw_request)}" try: ( @@ -112,12 +114,13 @@ async def create_tokenize( async def create_detokenize( self, request: DetokenizeRequest, + raw_request: Request, ) -> Union[DetokenizeResponse, ErrorResponse]: error_check_ret = await self._check_model(request) if error_check_ret is not None: return error_check_ret - request_id = f"tokn-{random_uuid()}" + request_id = f"tokn-{self._base_request_id(raw_request)}" ( lora_request, From bc192a2b099558ec94864974b2a91b84c271a84d Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Tue, 10 Dec 2024 07:09:32 +0100 Subject: [PATCH 003/155] [Pixtral] Improve loading (#11040) --- vllm/model_executor/models/pixtral.py | 56 ++++++++++++--------------- 1 file changed, 25 insertions(+), 31 deletions(-) diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index c6786c363ab4a..94a4ab882c1a9 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -1,6 +1,5 @@ from dataclasses import dataclass, fields from functools import cached_property -from itertools import tee from typing import Iterable, List, Mapping, Optional, Set, Tuple, Union import numpy @@ -359,38 +358,33 @@ def is_vision_encoder_weights(weight: Tuple[str, torch.Tensor]): def is_vision_lang_adapter_weights(weight: Tuple[str, torch.Tensor]): return weight[0].startswith("vision_language_adapter") - def is_vision_weights(weight: Tuple[str, torch.Tensor]): - return is_vision_encoder_weights( - weight) or is_vision_lang_adapter_weights(weight) - - llm_weights, vision_encoder_weights, vision_lang_adapter_weights = tee( - weights, 3) - - # llm - llm_weights = filter(lambda x: not is_vision_weights(x), llm_weights) - self.language_model.load_weights(llm_weights) - - # vision encoder - vision_encoder_weights = filter(is_vision_encoder_weights, - vision_encoder_weights) + # Get references to parameters for direct loading vision_encoder_dict = dict(self.vision_encoder.named_parameters()) - for name, loaded_weight in vision_encoder_weights: - # cut 'vision_encoder.' - name = '.'.join(name.split(".")[1:]) - param = vision_encoder_dict[name] - - default_weight_loader(param, loaded_weight) - - # adapter - vision_lang_adapter_weights = filter(is_vision_lang_adapter_weights, - vision_lang_adapter_weights) - vision_lang_adpter_dict = dict( + vision_lang_adapter_dict = dict( self.vision_language_adapter.named_parameters()) - for name, loaded_weight in vision_lang_adapter_weights: - # cut 'vision_language_adapter.' - name = '.'.join(name.split(".")[1:]) - param = vision_lang_adpter_dict[name] - default_weight_loader(param, loaded_weight) + + def llm_weights_generator(): + # Single pass over weights + for name, w in weights: + if is_vision_encoder_weights((name, w)): + # Load vision encoder weights directly + trimmed_name = '.'.join(name.split(".")[1:]) + param = vision_encoder_dict[trimmed_name] + with torch.no_grad(): + default_weight_loader(param, w) + elif is_vision_lang_adapter_weights((name, w)): + # Load vision-language adapter weights directly + trimmed_name = '.'.join(name.split(".")[1:]) + param = vision_lang_adapter_dict[trimmed_name] + with torch.no_grad(): + default_weight_loader(param, w) + else: + # LLM weights: yield them to be loaded + # by language_model.load_weights + yield (name, w) + + # Now we call the language model load with the generator + self.language_model.load_weights(llm_weights_generator()) # Vision encoder From 28b3a1c7e596c08efac0fcfa59a629d16197be30 Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Tue, 10 Dec 2024 01:28:14 -0500 Subject: [PATCH 004/155] [V1] Multiprocessing Tensor Parallel Support for v1 (#9856) Signed-off-by: Tyler Michael Smith --- .../test_basic_correctness.py | 16 + tests/conftest.py | 11 +- .../device_communicators/shm_broadcast.py | 76 ++-- vllm/executor/multiproc_gpu_executor.py | 47 +-- vllm/executor/multiproc_worker_utils.py | 42 ++ .../model_executor/layers/logits_processor.py | 5 +- vllm/platforms/cuda.py | 28 +- vllm/utils.py | 26 ++ vllm/v1/core/scheduler.py | 4 +- vllm/v1/engine/async_llm.py | 18 +- vllm/v1/engine/core.py | 74 ++-- vllm/v1/engine/core_client.py | 13 +- vllm/v1/engine/llm_engine.py | 19 +- vllm/v1/executor/abstract.py | 48 +++ vllm/v1/executor/multiproc_executor.py | 375 ++++++++++++++++++ .../{gpu_executor.py => uniproc_executor.py} | 12 +- vllm/v1/outputs.py | 6 +- vllm/v1/sample/sampler.py | 3 +- vllm/v1/utils.py | 33 +- vllm/v1/worker/gpu_model_runner.py | 12 +- vllm/v1/worker/gpu_worker.py | 11 +- 21 files changed, 733 insertions(+), 146 deletions(-) create mode 100644 vllm/v1/executor/abstract.py create mode 100644 vllm/v1/executor/multiproc_executor.py rename vllm/v1/executor/{gpu_executor.py => uniproc_executor.py} (90%) diff --git a/tests/basic_correctness/test_basic_correctness.py b/tests/basic_correctness/test_basic_correctness.py index fcba253d159f3..11d05cefb7313 100644 --- a/tests/basic_correctness/test_basic_correctness.py +++ b/tests/basic_correctness/test_basic_correctness.py @@ -26,6 +26,14 @@ TARGET_TEST_SUITE = os.environ.get("TARGET_TEST_SUITE", "L4") +@pytest.fixture(autouse=True) +def v1(run_with_both_engines): + # Simple autouse wrapper to run both engines for each test + # This can be promoted up to conftest.py to run for every + # test in a package + pass + + def test_vllm_gc_ed(): """Verify vllm instance is GC'ed when it is deleted""" llm = LLM("facebook/opt-125m") @@ -36,6 +44,7 @@ def test_vllm_gc_ed(): assert weak_llm() is None +@pytest.mark.skip_v1 @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS", "FLASHINFER"]) @pytest.mark.parametrize("dtype", ["half"]) @@ -118,6 +127,11 @@ def test_models_distributed( if attention_backend: os.environ["VLLM_ATTENTION_BACKEND"] = attention_backend + # Import VLLM_USE_V1 dynamically to handle patching + from vllm.envs import VLLM_USE_V1 + if VLLM_USE_V1 and distributed_executor_backend != "mp": + pytest.skip(f"Skip {distributed_executor_backend} for V1") + dtype = "half" max_tokens = 5 @@ -143,6 +157,7 @@ def test_models_distributed( ) +@pytest.mark.skip_v1 def test_model_with_failure(vllm_runner) -> None: try: with patch("vllm.model_executor.models.opt.OPTForCausalLM.forward", @@ -169,6 +184,7 @@ def test_model_with_failure(vllm_runner) -> None: os.remove(filename) +@pytest.mark.skip_v1 def test_failure_with_async_out_proc(vllm_runner) -> None: filename = None diff --git a/tests/conftest.py b/tests/conftest.py index d6be8f5b00af8..7606e0f11dfeb 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,7 +5,6 @@ from enum import Enum from typing import (Any, Callable, Dict, List, Optional, Tuple, Type, TypedDict, TypeVar, Union) -from unittest.mock import patch import numpy as np import pytest @@ -110,7 +109,7 @@ def prompts(self, prompts: _VideoAssetPrompts) -> List[str]: @pytest.fixture(params=[True, False]) -def run_with_both_engines(request): +def run_with_both_engines(request, monkeypatch): # Automatically runs tests twice, once with V1 and once without use_v1 = request.param # Tests decorated with `@skip_v1` are only run without v1 @@ -119,11 +118,11 @@ def run_with_both_engines(request): if use_v1: if skip_v1: pytest.skip("Skipping test on vllm V1") - with patch('vllm.envs.VLLM_USE_V1', True): - yield + monkeypatch.setenv('VLLM_USE_V1', '1') else: - with patch('vllm.envs.VLLM_USE_V1', False): - yield + monkeypatch.setenv('VLLM_USE_V1', '0') + + yield @pytest.fixture(autouse=True) diff --git a/vllm/distributed/device_communicators/shm_broadcast.py b/vllm/distributed/device_communicators/shm_broadcast.py index 2ff1a1ead99c1..9a2d8918d96e5 100644 --- a/vllm/distributed/device_communicators/shm_broadcast.py +++ b/vllm/distributed/device_communicators/shm_broadcast.py @@ -1,10 +1,11 @@ import os import pickle +import sys import time from contextlib import contextmanager from dataclasses import dataclass, field from multiprocessing import shared_memory -from typing import List, Optional +from typing import List, Optional, Tuple from unittest.mock import patch import torch @@ -21,6 +22,20 @@ logger = init_logger(__name__) +# We prefer to use os.sched_yield as it results in tighter polling loops, +# measured to be around 3e-7 seconds. However on earlier versions of Python +# os.sched_yield() does not release the GIL, so we fall back to time.sleep(0) +USE_SCHED_YIELD = ((sys.version_info[:3] >= (3, 11, 1)) + or (sys.version_info[:2] == (3, 10) + and sys.version_info[2] >= 8)) + + +def sched_yield(): + if USE_SCHED_YIELD: + os.sched_yield() + else: + time.sleep(0) + class ShmRingBuffer: @@ -114,11 +129,14 @@ def __init__(self, # and we should suppress the error pass + def handle(self): + return (self.n_reader, self.max_chunk_bytes, self.max_chunks, + self.shared_memory.name) + def __reduce__(self): return ( self.__class__, - (self.n_reader, self.max_chunk_bytes, self.max_chunks, - self.shared_memory.name), + self.handle(), ) def __del__(self): @@ -147,7 +165,7 @@ class Handle: connect_ip: str local_reader_ranks: List[int] = field(default_factory=list) - buffer: Optional[ShmRingBuffer] = None + buffer_handle: Optional[Tuple[int, int, int, str]] = None local_subscribe_port: Optional[int] = None remote_subscribe_port: Optional[int] = None @@ -228,7 +246,7 @@ def __init__( self.handle = Handle( connect_ip=connect_ip, local_reader_ranks=local_reader_ranks, - buffer=self.buffer, + buffer_handle=self.buffer.handle(), local_subscribe_port=local_subscribe_port, remote_subscribe_port=remote_subscribe_port, ) @@ -247,8 +265,8 @@ def create_from_handle(handle: Handle, rank) -> "MessageQueue": context = Context() if rank in handle.local_reader_ranks: - assert handle.buffer is not None - self.buffer = handle.buffer + assert handle.buffer_handle is not None + self.buffer = ShmRingBuffer(*handle.buffer_handle) self.current_idx = 0 self.local_reader_rank = handle.local_reader_ranks.index(rank) self._is_local_reader = True @@ -314,7 +332,7 @@ def wait_until_ready(self): assert recv == b"READY" @contextmanager - def acquire_write(self): + def acquire_write(self, timeout: Optional[float] = None): assert self._is_writer, "Only writers can acquire write" start_time = time.monotonic() n_warning = 1 @@ -329,16 +347,20 @@ def acquire_write(self): # we need to wait until it is read by all readers # Release the processor to other threads - os.sched_yield() + sched_yield() - # if we wait for a long time, we should warn the user + # if we wait for a long time, log a message if (time.monotonic() - start_time > VLLM_RINGBUFFER_WARNING_INTERVAL * n_warning): - logger.warning( - "No available block found in %s second. ", - VLLM_RINGBUFFER_WARNING_INTERVAL) + logger.debug("No available block found in %s second. ", + VLLM_RINGBUFFER_WARNING_INTERVAL) n_warning += 1 + # if we time out, raise an exception + if (timeout is not None + and time.monotonic() - start_time > timeout): + raise TimeoutError + continue # found a block that is either # (1) not written @@ -365,7 +387,7 @@ def acquire_write(self): break @contextmanager - def acquire_read(self): + def acquire_read(self, timeout: Optional[float] = None): assert self._is_local_reader, "Only readers can acquire read" start_time = time.monotonic() n_warning = 1 @@ -383,16 +405,20 @@ def acquire_read(self): # we need to wait until it is written # Release the processor to other threads - os.sched_yield() + sched_yield() - # if we wait for a long time, we should warn the user + # if we wait for a long time, log a message if (time.monotonic() - start_time > VLLM_RINGBUFFER_WARNING_INTERVAL * n_warning): - logger.warning( - "No available block found in %s second. ", - VLLM_RINGBUFFER_WARNING_INTERVAL) + logger.debug("No available block found in %s second. ", + VLLM_RINGBUFFER_WARNING_INTERVAL) n_warning += 1 + # if we time out, raise an exception + if (timeout is not None + and time.monotonic() - start_time > timeout): + raise TimeoutError + continue # found a block that is not read by this reader # let caller read from the buffer @@ -406,24 +432,26 @@ def acquire_read(self): 1) % self.buffer.max_chunks break - def enqueue(self, obj): + def enqueue(self, obj, timeout: Optional[float] = None): + """ Write to message queue with optional timeout (in seconds) """ assert self._is_writer, "Only writers can enqueue" serialized_obj = pickle.dumps(obj, protocol=pickle.HIGHEST_PROTOCOL) if self.n_local_reader > 0: if len(serialized_obj) >= self.buffer.max_chunk_bytes: - with self.acquire_write() as buf: + with self.acquire_write(timeout) as buf: buf[0] = 1 # overflow self.local_socket.send(serialized_obj) else: - with self.acquire_write() as buf: + with self.acquire_write(timeout) as buf: buf[0] = 0 # not overflow buf[1:len(serialized_obj) + 1] = serialized_obj if self.n_remote_reader > 0: self.remote_socket.send(serialized_obj) - def dequeue(self): + def dequeue(self, timeout: Optional[float] = None): + """ Read from message queue with optional timeout (in seconds) """ if self._is_local_reader: - with self.acquire_read() as buf: + with self.acquire_read(timeout) as buf: overflow = buf[0] == 1 if not overflow: # no need to know the size of serialized object diff --git a/vllm/executor/multiproc_gpu_executor.py b/vllm/executor/multiproc_gpu_executor.py index c450209f0eb91..fc58163cade64 100644 --- a/vllm/executor/multiproc_gpu_executor.py +++ b/vllm/executor/multiproc_gpu_executor.py @@ -3,25 +3,19 @@ from functools import partial from typing import Any, List, Optional -import torch - from vllm.executor.distributed_gpu_executor import ( # yapf: disable DistributedGPUExecutor, DistributedGPUExecutorAsync) from vllm.executor.gpu_executor import create_worker -from vllm.executor.multiproc_worker_utils import (ProcessWorkerWrapper, - ResultHandler, WorkerMonitor) +from vllm.executor.multiproc_worker_utils import ( + ProcessWorkerWrapper, ResultHandler, WorkerMonitor, + set_multiprocessing_worker_envs) from vllm.logger import init_logger from vllm.model_executor.layers.sampler import SamplerOutput from vllm.sequence import ExecuteModelRequest -from vllm.triton_utils.importing import HAS_TRITON from vllm.utils import (_run_task_with_lock, cuda_device_count_stateless, - cuda_is_initialized, get_distributed_init_method, - get_open_port, make_async, + get_distributed_init_method, get_open_port, make_async, update_environment_variables) -if HAS_TRITON: - from vllm.triton_utils import maybe_set_triton_cache_manager - logger = init_logger(__name__) @@ -37,30 +31,8 @@ def _init_executor(self) -> None: world_size = self.parallel_config.world_size tensor_parallel_size = self.parallel_config.tensor_parallel_size - # Disable torch async compiling which won't work with daemonic processes - os.environ["TORCHINDUCTOR_COMPILE_THREADS"] = "1" - - # Configure thread parallelism if OMP_NUM_THREADS isn't set - # - # Helps to avoid CPU contention. The default of spawning a thread per - # core combined with multiprocessing for each GPU can have a negative - # impact on performance. The contention is amplified when running in a - # container where CPU limits can cause throttling. - default_omp_num_threads = 1 - if "OMP_NUM_THREADS" not in os.environ and ( - current_parallelism := - torch.get_num_threads()) > default_omp_num_threads: - logger.warning( - "Reducing Torch parallelism from %d threads to %d to avoid " - "unnecessary CPU contention. Set OMP_NUM_THREADS in the " - "external environment to tune this value as needed.", - current_parallelism, default_omp_num_threads) - os.environ["OMP_NUM_THREADS"] = str(default_omp_num_threads) - torch.set_num_threads(default_omp_num_threads) - - # workaround for https://github.com/vllm-project/vllm/issues/6103 - if HAS_TRITON and world_size > 1: - maybe_set_triton_cache_manager() + # Set multiprocessing envs that are common to V0 and V1 + set_multiprocessing_worker_envs(self.parallel_config) # Multiprocessing-based executor does not support multi-node setting. # Since it only works for single node, we can use the loopback address @@ -122,13 +94,6 @@ def _check_executor_parameters(self): "CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size)))) }) - if (cuda_is_initialized() - and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") != "spawn"): - logger.warning("CUDA was previously initialized. We must use " - "the `spawn` multiprocessing start method. Setting " - "VLLM_WORKER_MULTIPROC_METHOD to 'spawn'.") - os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" - cuda_device_count = cuda_device_count_stateless() # Use confusing message for more common TP-only case. assert tensor_parallel_size <= cuda_device_count, ( diff --git a/vllm/executor/multiproc_worker_utils.py b/vllm/executor/multiproc_worker_utils.py index 884267d23dfc8..fe475db6d3f57 100644 --- a/vllm/executor/multiproc_worker_utils.py +++ b/vllm/executor/multiproc_worker_utils.py @@ -11,8 +11,15 @@ from typing import (Any, Callable, Dict, Generic, List, Optional, TextIO, TypeVar, Union) +import torch + import vllm.envs as envs from vllm.logger import init_logger +from vllm.triton_utils.importing import HAS_TRITON +from vllm.utils import cuda_is_initialized + +if HAS_TRITON: + from vllm.triton_utils import maybe_set_triton_cache_manager logger = init_logger(__name__) @@ -270,3 +277,38 @@ def write_with_prefix(s: str): def get_mp_context(): mp_method = envs.VLLM_WORKER_MULTIPROC_METHOD return multiprocessing.get_context(mp_method) + + +def set_multiprocessing_worker_envs(parallel_config): + """ Set up environment variables that should be used when there are workers + in a multiprocessing environment. This should be called by the parent + process before worker processes are created""" + + if (cuda_is_initialized() + and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") != "spawn"): + logger.warning("CUDA was previously initialized. We must use " + "the `spawn` multiprocessing start method. Setting " + "VLLM_WORKER_MULTIPROC_METHOD to 'spawn'.") + os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" + + # Configure thread parallelism if OMP_NUM_THREADS isn't set + # + # Helps to avoid CPU contention. The default of spawning a thread per + # core combined with multiprocessing for each GPU can have a negative + # impact on performance. The contention is amplified when running in a + # container where CPU limits can cause throttling. + default_omp_num_threads = 1 + if "OMP_NUM_THREADS" not in os.environ and ( + current_parallelism := + torch.get_num_threads()) > default_omp_num_threads: + logger.warning( + "Reducing Torch parallelism from %d threads to %d to avoid " + "unnecessary CPU contention. Set OMP_NUM_THREADS in the " + "external environment to tune this value as needed.", + current_parallelism, default_omp_num_threads) + os.environ["OMP_NUM_THREADS"] = str(default_omp_num_threads) + torch.set_num_threads(default_omp_num_threads) + + # workaround for https://github.com/vllm-project/vllm/issues/6103 + if HAS_TRITON and parallel_config.world_size > 1: + maybe_set_triton_cache_manager() diff --git a/vllm/model_executor/layers/logits_processor.py b/vllm/model_executor/layers/logits_processor.py index fb76b1b17925e..2bc7e458494f7 100644 --- a/vllm/model_executor/layers/logits_processor.py +++ b/vllm/model_executor/layers/logits_processor.py @@ -5,6 +5,7 @@ import torch import torch.nn as nn +import vllm.envs as envs from vllm.distributed import (tensor_model_parallel_all_gather, tensor_model_parallel_gather) from vllm.model_executor.layers.vocab_parallel_embedding import ( @@ -42,7 +43,9 @@ def __init__(self, # Soft cap the logits. Used in Gemma 2. self.soft_cap = soft_cap # Whether to use gather or all-gather to gather the logits. - self.use_gather = not current_platform.is_tpu() + + self.use_gather = not current_platform.is_tpu( + ) and not envs.VLLM_USE_V1 def forward( self, diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index edaf377b501df..10f83fd304281 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -12,6 +12,7 @@ # import custom ops, trigger op registration import vllm._C # noqa +import vllm.envs as envs from vllm.logger import init_logger from .interface import DeviceCapability, Platform, PlatformEnum @@ -110,17 +111,28 @@ def log_warnings(cls): def check_and_update_config(cls, vllm_config: VllmConfig) -> None: parallel_config = vllm_config.parallel_config scheduler_config = vllm_config.scheduler_config + if parallel_config.worker_cls == "auto": if scheduler_config.is_multi_step: - parallel_config.worker_cls = \ - "vllm.worker.multi_step_worker.MultiStepWorker" + if envs.VLLM_USE_V1: + raise NotImplementedError + else: + parallel_config.worker_cls = \ + "vllm.worker.multi_step_worker.MultiStepWorker" elif vllm_config.speculative_config: - parallel_config.worker_cls = \ - "vllm.spec_decode.spec_decode_worker.create_spec_worker" - parallel_config.sd_worker_cls = \ - "vllm.worker.worker.Worker" + if envs.VLLM_USE_V1: + raise NotImplementedError + else: + parallel_config.worker_cls = \ + "vllm.spec_decode.spec_decode_worker.create_spec_worker" + parallel_config.sd_worker_cls = \ + "vllm.worker.worker.Worker" else: - parallel_config.worker_cls = "vllm.worker.worker.Worker" + if envs.VLLM_USE_V1: + parallel_config.worker_cls = \ + "vllm.v1.worker.gpu_worker.Worker" + else: + parallel_config.worker_cls = "vllm.worker.worker.Worker" # NVML utils @@ -249,4 +261,4 @@ def is_full_nvlink(cls, physical_device_ids: List[int]) -> bool: if not isinstance(pynvml, _MockModule): CudaPlatform.log_warnings() except ModuleNotFoundError: - CudaPlatform.log_warnings() \ No newline at end of file + CudaPlatform.log_warnings() diff --git a/vllm/utils.py b/vllm/utils.py index 2bb1fb2af40f4..7cdb2cb320b05 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -10,6 +10,7 @@ import inspect import ipaddress import os +import signal import socket import subprocess import sys @@ -1652,3 +1653,28 @@ def resolve_obj_by_qualname(qualname: str) -> Any: module_name, obj_name = qualname.rsplit(".", 1) module = importlib.import_module(module_name) return getattr(module, obj_name) + + +def kill_process_tree(pid: int): + """ + Kills all descendant processes of the given pid by sending SIGKILL. + + Args: + pid (int): Process ID of the parent process + """ + try: + parent = psutil.Process(pid) + except psutil.NoSuchProcess: + return + + # Get all children recursively + children = parent.children(recursive=True) + + # Send SIGKILL to all children first + for child in children: + with contextlib.suppress(ProcessLookupError): + os.kill(child.pid, signal.SIGKILL) + + # Finally kill the parent + with contextlib.suppress(ProcessLookupError): + os.kill(pid, signal.SIGKILL) diff --git a/vllm/v1/core/scheduler.py b/vllm/v1/core/scheduler.py index 1203d35fc985f..a3e85c20cc664 100644 --- a/vllm/v1/core/scheduler.py +++ b/vllm/v1/core/scheduler.py @@ -5,6 +5,8 @@ from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig from vllm.logger import init_logger +from vllm.multimodal import MultiModalKwargs +from vllm.multimodal.base import PlaceholderRange from vllm.sampling_params import SamplingParams from vllm.v1.core.encoder_cache_manager import EncoderCacheManager from vllm.v1.core.kv_cache_manager import KVCacheManager @@ -383,7 +385,7 @@ def update_from_output( model_runner_output: "ModelRunnerOutput", ) -> List[EngineCoreOutput]: # NOTE(woosuk): This method doesn't consider speculative decoding. - sampled_token_ids = model_runner_output.sampled_token_ids_cpu.tolist() + sampled_token_ids = model_runner_output.sampled_token_ids num_scheduled_tokens = scheduler_output.num_scheduled_tokens new_running: List[Request] = [] engine_core_outputs: List[EngineCoreOutput] = [] diff --git a/vllm/v1/engine/async_llm.py b/vllm/v1/engine/async_llm.py index 0bcccda2bf329..26fd650aee4b7 100644 --- a/vllm/v1/engine/async_llm.py +++ b/vllm/v1/engine/async_llm.py @@ -20,7 +20,7 @@ from vllm.v1.engine.core_client import EngineCoreClient from vllm.v1.engine.detokenizer import Detokenizer from vllm.v1.engine.processor import Processor -from vllm.v1.executor.gpu_executor import GPUExecutor +from vllm.v1.executor.abstract import Executor logger = init_logger(__name__) @@ -30,7 +30,7 @@ class AsyncLLM(EngineClient): def __init__( self, vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, @@ -119,14 +119,24 @@ def from_engine_args( def shutdown(self): """Shutdown, cleaning up the background proc and IPC.""" - self.engine_core.shutdown() + if engine_core := getattr(self, "engine_core", None): + engine_core.shutdown() if handler := getattr(self, "output_handler", None): handler.cancel() @classmethod def _get_executor_cls(cls, vllm_config: VllmConfig): - return GPUExecutor + distributed_executor_backend = ( + vllm_config.parallel_config.distributed_executor_backend) + if distributed_executor_backend == "mp": + from vllm.v1.executor.multiproc_executor import MultiprocExecutor + executor_class = MultiprocExecutor + else: + assert (distributed_executor_backend is None) + from vllm.v1.executor.uniproc_executor import UniprocExecutor + executor_class = UniprocExecutor + return executor_class async def add_request( self, diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py index 751eb3b40a68d..fdb241e6753fb 100644 --- a/vllm/v1/engine/core.py +++ b/vllm/v1/engine/core.py @@ -1,12 +1,12 @@ import multiprocessing import pickle import queue +import signal import threading import time -from contextlib import contextmanager from multiprocessing.process import BaseProcess from multiprocessing.sharedctypes import Synchronized -from typing import Any, Iterator, List, Tuple, Type, Union +from typing import List, Tuple, Type, Union import zmq import zmq.asyncio @@ -20,9 +20,10 @@ EngineCoreProfile, EngineCoreRequest, EngineCoreRequestType) from vllm.v1.engine.mm_input_mapper import MMInputMapper -from vllm.v1.executor.gpu_executor import GPUExecutor +from vllm.v1.executor.abstract import Executor from vllm.v1.request import Request, RequestStatus from vllm.v1.serial_utils import PickleEncoder +from vllm.v1.utils import make_zmq_socket from vllm.version import __version__ as VLLM_VERSION logger = init_logger(__name__) @@ -38,7 +39,7 @@ class EngineCore: def __init__( self, vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], usage_context: UsageContext, ): assert vllm_config.model_config.task != "embedding" @@ -80,7 +81,7 @@ def _initialize_kv_caches(self, num_gpu_blocks = num_gpu_blocks_override num_cpu_blocks = 0 - self.model_executor.initialize_cache(num_gpu_blocks) + self.model_executor.initialize(num_gpu_blocks) elapsed = time.time() - start logger.info(("init engine (profile, create kv cache, " "warmup model) took %.2f seconds"), elapsed) @@ -112,8 +113,11 @@ def step(self) -> List[EngineCoreOutput]: scheduler_output, output) return engine_core_outputs + def shutdown(self): + self.model_executor.shutdown() + def profile(self, is_start=True): - self.model_executor.worker.profile(is_start) + self.model_executor.profile(is_start) class EngineCoreProc(EngineCore): @@ -124,7 +128,7 @@ class EngineCoreProc(EngineCore): def __init__( self, vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], usage_context: UsageContext, input_path: str, output_path: str, @@ -151,32 +155,9 @@ def __init__( daemon=True).start() # Send Readiness signal to EngineClient. - with self.make_socket(ready_path, zmq.constants.PUSH) as ready_socket: + with make_zmq_socket(ready_path, zmq.constants.PUSH) as ready_socket: ready_socket.send_string(EngineCoreProc.READY_STR) - @contextmanager - def make_socket(self, path: str, type: Any) -> Iterator[zmq.Socket]: - """Context manager for use """ - - ctx = zmq.Context() - try: - socket = ctx.socket(type) - - if type == zmq.constants.PULL: - socket.connect(path) - elif type == zmq.constants.PUSH: - socket.bind(path) - else: - raise ValueError(f"Unknown Socket Type: {type}") - - yield socket - - except KeyboardInterrupt: - logger.debug("EngineCore had Keyboard Interrupt.") - - finally: - ctx.destroy(linger=0) - @staticmethod def wait_for_startup( proc: BaseProcess, @@ -209,7 +190,7 @@ def wait_for_startup( @staticmethod def make_engine_core_process( vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], usage_context: UsageContext, input_path: str, output_path: str, @@ -244,17 +225,38 @@ def make_engine_core_process( def run_engine_core(*args, **kwargs): """Launch EngineCore busy loop in background process.""" + # Signal handler used for graceful termination. + # SystemExit exception is only raised once to allow this and worker + # processes to terminate without error + shutdown_requested = False + + def signal_handler(signum, frame): + nonlocal shutdown_requested + if not shutdown_requested: + shutdown_requested = True + raise SystemExit() + + # Either SIGTERM or SIGINT will terminate the engine_core + signal.signal(signal.SIGTERM, signal_handler) + signal.signal(signal.SIGINT, signal_handler) + + engine_core = None try: engine_core = EngineCoreProc(*args, **kwargs) engine_core.run_busy_loop() - except KeyboardInterrupt: + except SystemExit: logger.debug("EngineCore interrupted.") except BaseException as e: logger.exception(e) raise e + finally: + if engine_core is not None: + engine_core.shutdown() + engine_core = None + def run_busy_loop(self): """Core busy loop of the EngineCore.""" @@ -272,6 +274,8 @@ def run_busy_loop(self): logger.debug("EngineCore busy loop waiting.") if self.should_shutdown: return + except BaseException: + raise # 2) Handle any new client requests (Abort or Add). while not self.input_queue.empty(): @@ -321,7 +325,7 @@ def process_input_socket(self, input_path: str): decoder_add_req = PickleEncoder() decoder_abort_req = PickleEncoder() - with self.make_socket(input_path, zmq.constants.PULL) as socket: + with make_zmq_socket(input_path, zmq.constants.PULL) as socket: while True: # (RequestType, RequestData) type_frame, data_frame = socket.recv_multipart(copy=False) @@ -349,7 +353,7 @@ def process_output_socket(self, output_path: str): # Reuse send buffer. buffer = bytearray() - with self.make_socket(output_path, zmq.constants.PUSH) as socket: + with make_zmq_socket(output_path, zmq.constants.PUSH) as socket: while True: engine_core_outputs = self.output_queue.get() outputs = EngineCoreOutputs(outputs=engine_core_outputs) diff --git a/vllm/v1/engine/core_client.py b/vllm/v1/engine/core_client.py index 835963f7ee86c..ee89cece73141 100644 --- a/vllm/v1/engine/core_client.py +++ b/vllm/v1/engine/core_client.py @@ -1,5 +1,4 @@ import multiprocessing -import time from typing import List, Union import msgspec @@ -7,7 +6,7 @@ import zmq.asyncio from vllm.logger import init_logger -from vllm.utils import get_open_zmq_ipc_path +from vllm.utils import get_open_zmq_ipc_path, kill_process_tree from vllm.v1.engine import (EngineCoreOutput, EngineCoreOutputs, EngineCoreProfile, EngineCoreRequest, EngineCoreRequestType) @@ -99,6 +98,12 @@ def add_request(self, request: EngineCoreRequest) -> None: def abort_requests(self, request_ids: List[str]) -> None: self.engine_core.abort_requests(request_ids) + def shutdown(self): + self.engine_core.shutdown() + + def __del__(self): + self.shutdown() + async def profile(self, is_start=True) -> None: self.engine_core.profile(is_start) @@ -163,10 +168,10 @@ def shutdown(self): # Shutdown the process if needed. if hasattr(self, "proc") and self.proc.is_alive(): self.proc.terminate() + self.proc.join(5) - time.sleep(5) if self.proc.is_alive(): - self.proc.kill() + kill_process_tree(self.proc.pid) def __del__(self): self.shutdown() diff --git a/vllm/v1/engine/llm_engine.py b/vllm/v1/engine/llm_engine.py index 994e68669108e..1b3a9f12d009e 100644 --- a/vllm/v1/engine/llm_engine.py +++ b/vllm/v1/engine/llm_engine.py @@ -20,7 +20,7 @@ from vllm.v1.engine.core_client import EngineCoreClient from vllm.v1.engine.detokenizer import Detokenizer from vllm.v1.engine.processor import Processor -from vllm.v1.executor.gpu_executor import GPUExecutor +from vllm.v1.executor.abstract import Executor logger = init_logger(__name__) @@ -33,7 +33,7 @@ class LLMEngine: def __init__( self, vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, @@ -104,10 +104,17 @@ def from_engine_args( @classmethod def _get_executor_cls(cls, vllm_config: VllmConfig): - return GPUExecutor - - def stop_remote_worker_execution_loop(self) -> None: - raise NotImplementedError("TP not implemented yet.") + distributed_executor_backend = ( + vllm_config.parallel_config.distributed_executor_backend) + if distributed_executor_backend == "mp": + from vllm.v1.executor.multiproc_executor import MultiprocExecutor + executor_class = MultiprocExecutor + else: + assert (distributed_executor_backend is None) + from vllm.v1.executor.uniproc_executor import UniprocExecutor + executor_class = UniprocExecutor + + return executor_class def get_num_unfinished_requests(self) -> int: return self.detokenizer.get_num_unfinished_requests() diff --git a/vllm/v1/executor/abstract.py b/vllm/v1/executor/abstract.py new file mode 100644 index 0000000000000..9cd267581ad18 --- /dev/null +++ b/vllm/v1/executor/abstract.py @@ -0,0 +1,48 @@ +from abc import ABC, abstractmethod +from typing import Dict, Optional, Tuple + +from vllm.config import VllmConfig +from vllm.v1.outputs import ModelRunnerOutput + + +class Executor(ABC): + """Abstract class for executors.""" + + @abstractmethod + def __init__(self, vllm_config: VllmConfig) -> None: + raise NotImplementedError + + @abstractmethod + def initialize(self, num_gpu_blocks: int) -> None: + raise NotImplementedError + + @abstractmethod + def determine_num_available_blocks(self) -> Tuple[int, int]: + raise NotImplementedError + + @abstractmethod + def execute_model( + self, + scheduler_output, + ) -> ModelRunnerOutput: + raise NotImplementedError + + @abstractmethod + def profile(self, is_start=True): + raise NotImplementedError + + @abstractmethod + def shutdown(self): + pass + + @abstractmethod + def check_health(self) -> None: + raise NotImplementedError + + @abstractmethod + def collective_rpc(self, + method: str, + timeout: Optional[float] = None, + args: Tuple = (), + kwargs: Optional[Dict] = None) -> []: + raise NotImplementedError diff --git a/vllm/v1/executor/multiproc_executor.py b/vllm/v1/executor/multiproc_executor.py new file mode 100644 index 0000000000000..f8f3d583618cf --- /dev/null +++ b/vllm/v1/executor/multiproc_executor.py @@ -0,0 +1,375 @@ +import atexit +import os +import pickle +import signal +import sys +import time +from dataclasses import dataclass +from enum import Enum, auto +from multiprocessing.process import BaseProcess +from typing import Dict, List, Optional, Tuple + +import zmq + +from vllm.config import VllmConfig +from vllm.distributed import (destroy_distributed_environment, + destroy_model_parallel) +from vllm.distributed.device_communicators.shm_broadcast import (Handle, + MessageQueue) +from vllm.executor.multiproc_worker_utils import ( + _add_prefix, get_mp_context, set_multiprocessing_worker_envs) +from vllm.logger import init_logger +from vllm.utils import (get_distributed_init_method, get_open_port, + get_open_zmq_ipc_path) +from vllm.v1.outputs import ModelRunnerOutput +from vllm.v1.utils import make_zmq_socket +from vllm.worker.worker_base import WorkerWrapperBase + +logger = init_logger(__name__) + +POLLING_TIMEOUT_MS = 5000 +POLLING_TIMEOUT_S = POLLING_TIMEOUT_MS // 1000 + + +class MultiprocExecutor: + + def __init__(self, vllm_config: VllmConfig) -> None: + # Call self.shutdown at exit to clean up + # and ensure workers will be terminated. + atexit.register(self.shutdown) + + self.vllm_config = vllm_config + self.parallel_config = vllm_config.parallel_config + + self.world_size = self.parallel_config.world_size + tensor_parallel_size = self.parallel_config.tensor_parallel_size + assert self.world_size == tensor_parallel_size, ( + f"world_size ({self.world_size}) must be equal to the " + f"tensor_parallel_size ({tensor_parallel_size}). " + f"Pipeline parallelism is not yet implemented in v1") + + # Set multiprocessing envs that are common to V0 and V1 + set_multiprocessing_worker_envs(self.parallel_config) + + # Multiprocessing-based executor does not support multi-node setting. + # Since it only works for single node, we can use the loopback address + # 127.0.0.1 for communication. + distributed_init_method = get_distributed_init_method( + "127.0.0.1", get_open_port()) + + # Initialize worker and set up message queues for SchedulerOutputs + # and ModelRunnerOutputs + self.rpc_broadcast_mq = MessageQueue(self.world_size, self.world_size) + scheduler_output_handle = self.rpc_broadcast_mq.export_handle() + + # Create workers + self.workers: List[WorkerProcHandle] = [] + for rank in range(self.world_size): + worker = WorkerProc.make_worker_process(vllm_config, rank, rank, + distributed_init_method, + scheduler_output_handle) + self.workers.append(worker) + + # Ensure message queues are ready. Will deadlock if re-ordered + # Must be kept consistent with the WorkerProc + self.rpc_broadcast_mq.wait_until_ready() + for w in self.workers: + w.worker_response_mq.wait_until_ready() + + def initialize(self, num_gpu_blocks: int) -> None: + """ + Initialize the KV caches and begin the model execution loop of the + underlying workers. + """ + self.collective_rpc("initialize_cache", args=(num_gpu_blocks, )) + self.collective_rpc("compile_or_warm_up_model") + + def determine_num_available_blocks(self) -> Tuple[int, int]: + """ + Determine the number of available KV blocks by invoking the + underlying worker. + """ + num_blocks = self.collective_rpc("determine_num_available_blocks") + + # Since we use a shared centralized controller, we take the minimum + # number of blocks across all workers to make sure all the memory + # operators can be applied to all workers. + num_gpu_blocks = min(b[0] for b in num_blocks) + num_cpu_blocks = min(b[1] for b in num_blocks) + + return num_gpu_blocks, num_cpu_blocks + + def collective_rpc(self, + method: str, + timeout: Optional[float] = None, + args: Tuple = (), + kwargs: Optional[Dict] = None) -> []: + """ + Execute an RPC call on workers. + + Args: + method: Name of the worker method to execute + timeout: Maximum time in seconds to wait for execution. Rases a + TimeoutError on timeout. None means wait indefinitely. + args: Positional arguments to pass to the worker method + kwargs: Keyword arguments to pass to the worker method + + Returns: + List of results from each worker + """ + start_time = time.monotonic() + kwargs = kwargs or {} + + try: + self.rpc_broadcast_mq.enqueue((method, args, kwargs)) + + responses = [None] * self.world_size + for w in self.workers: + dequeue_timeout = timeout - (time.monotonic() - start_time() + ) if timeout is not None else None + status, result = w.worker_response_mq.dequeue( + timeout=dequeue_timeout) + + if status != WorkerProc.ResponseStatus.SUCCESS: + if isinstance(result, Exception): + raise result + else: + raise RuntimeError("Worker failed") + + responses[w.rank] = result + + return responses + except TimeoutError as e: + raise TimeoutError(f"RPC call to {method} timed out.") from e + except Exception as e: + # Re-raise any other exceptions + raise e + + def execute_model( + self, + scheduler_output, + ) -> ModelRunnerOutput: + model_output = self.collective_rpc("execute_model", + args=(scheduler_output, ))[0] + return model_output + + def profile(self, is_start=True): + self.collective_rpc("profile", args=(is_start, )) + return + + def _ensure_worker_termination(self): + """Ensure that all worker processes are terminated. Assumes workers have + received termination requests. Waits for processing, then sends + termination and kill signals if needed.""" + + def wait_for_termination(procs, timeout): + start_time = time.time() + while time.time() - start_time < timeout: + if all(not proc.is_alive() for proc in procs): + return True + time.sleep(0.1) + return False + + # Send SIGTERM if still running + active_procs = [w.proc for w in self.workers if w.proc.is_alive()] + self.workers = None + for p in active_procs: + p.terminate() + if wait_for_termination(active_procs, 4): + return + + # Send SIGKILL if still running + active_procs = [p for p in active_procs if p.is_alive()] + for p in active_procs: + p.kill() + + def shutdown(self): + """Properly shut down the executor and its workers""" + if (hasattr(self, 'workers') and self.workers is not None): + for w in self.workers: #TODO: not sure if needed + w.worker_response_mq = None + self._ensure_worker_termination() + + self.rpc_broadcast_mq = None + + def check_health(self) -> None: + self.collective_rpc("check_health", timeout=10) + return + + +@dataclass +class WorkerProcHandle: + proc: BaseProcess + rank: int + ready_path: str + worker_response_mq: MessageQueue # The worker process writes to this MQ + + +class WorkerProc: + """Wrapper that runs one Worker in a separate process.""" + + READY_STR = "READY" + + def __init__( + self, + vllm_config: VllmConfig, + local_rank: int, + rank: int, + distributed_init_method: str, + input_shm_handle: Handle, + ready_path: str, + ): + self.rank = rank + wrapper = WorkerWrapperBase(vllm_config=vllm_config) + wrapper.init_worker(vllm_config, local_rank, rank, + distributed_init_method) + self.worker = wrapper.worker + + pid = os.getpid() + _add_prefix(sys.stdout, f"VllmWorker rank={rank}", pid) + _add_prefix(sys.stderr, f"VllmWorker rank={rank}", pid) + + # Initialize MessageQueue for receiving SchedulerOutput + self.rpc_broadcast_mq = MessageQueue.create_from_handle( + input_shm_handle, self.worker.rank) + + # Initializes a message queue for sending the model output + self.worker_response_mq = MessageQueue(1, 1) + worker_response_mq_handle = self.worker_response_mq.export_handle() + + # Send Readiness signal to EngineCore process. + with make_zmq_socket(ready_path, zmq.constants.PUSH) as ready_socket: + payload = pickle.dumps(worker_response_mq_handle, + protocol=pickle.HIGHEST_PROTOCOL) + ready_socket.send_string(WorkerProc.READY_STR) + ready_socket.send(payload) + + self.worker.initialize() + self.worker.load_model() + + @staticmethod + def make_worker_process( + vllm_config: VllmConfig, + local_rank: int, + rank: int, + distributed_init_method: str, + input_shm_handle, # Receive SchedulerOutput + ) -> WorkerProcHandle: + context = get_mp_context() + + # ZMQ path for worker to send ready message and shm_broadcast handle + # back to core process. + ready_path = get_open_zmq_ipc_path() + + process_kwargs = { + "vllm_config": vllm_config, + "local_rank": local_rank, + "rank": rank, + "distributed_init_method": distributed_init_method, + "input_shm_handle": input_shm_handle, + "ready_path": ready_path, + } + # Run EngineCore busy loop in background process. + proc = context.Process(target=WorkerProc.worker_main, + kwargs=process_kwargs, + daemon=True) + proc.start() + + # Wait for startup + worker_response_mq_handle = WorkerProc.wait_for_startup( + proc, ready_path) + + worker_response_mq = MessageQueue.create_from_handle( + worker_response_mq_handle, 0) + + return WorkerProcHandle(proc, rank, ready_path, worker_response_mq) + + def shutdown(self): + self.rpc_broadcast_mq = None + self.worker_response_mq = None + destroy_model_parallel() + destroy_distributed_environment() + + @staticmethod + def worker_main(*args, **kwargs): + """ Worker initialization and execution loops. + This runs a background process """ + + # Signal handler used for graceful termination. + # SystemExit exception is only raised once to allow this and worker + # processes to terminate without error + shutdown_requested = False + + def signal_handler(signum, frame): + nonlocal shutdown_requested + if not shutdown_requested: + shutdown_requested = True + raise SystemExit() + + # Either SIGTERM or SIGINT will terminate the worker + signal.signal(signal.SIGTERM, signal_handler) + signal.signal(signal.SIGINT, signal_handler) + + worker = None + try: + worker = WorkerProc(*args, **kwargs) + + # Ensure message queues are ready. Will deadlock if re-ordered. + # Must be kept consistent with the Executor + worker.rpc_broadcast_mq.wait_until_ready() + worker.worker_response_mq.wait_until_ready() + + worker.worker_busy_loop() + + except SystemExit: + logger.debug("Worker interrupted.") + + except BaseException as e: + logger.exception(e) + raise + + finally: + # Clean up once worker exits busy loop + if worker is not None: + worker.shutdown() + worker = None + + @staticmethod + def wait_for_startup( + proc: BaseProcess, + ready_path: str, + ) -> Optional[Handle]: + """Wait until the Worker is ready.""" + with make_zmq_socket(ready_path, zmq.constants.PULL) as socket: + + # Wait for Worker to send READY. + while socket.poll(timeout=POLLING_TIMEOUT_MS) == 0: + logger.debug("Waiting for WorkerProc to startup.") + + if not proc.is_alive(): + raise RuntimeError("WorkerProc failed to start.") + + message = socket.recv_string() + assert message == WorkerProc.READY_STR + handle_frame = socket.recv(copy=False) + handle = pickle.loads(handle_frame.buffer) + return handle + + class ResponseStatus(Enum): + SUCCESS = auto() + FAILURE = auto() + + def worker_busy_loop(self): + """Main busy loop for Multiprocessing Workers""" + while True: + method, args, kwargs = self.rpc_broadcast_mq.dequeue() + + try: + output = getattr(self.worker, method)(*args, **kwargs) + except BaseException as e: + self.worker_response_mq.enqueue( + (WorkerProc.ResponseStatus.FAILURE, e)) + continue + + self.worker_response_mq.enqueue( + (WorkerProc.ResponseStatus.SUCCESS, output)) diff --git a/vllm/v1/executor/gpu_executor.py b/vllm/v1/executor/uniproc_executor.py similarity index 90% rename from vllm/v1/executor/gpu_executor.py rename to vllm/v1/executor/uniproc_executor.py index f71fa16b16e27..9b1d9a40950c6 100644 --- a/vllm/v1/executor/gpu_executor.py +++ b/vllm/v1/executor/uniproc_executor.py @@ -10,7 +10,7 @@ logger = init_logger(__name__) -class GPUExecutor: +class UniprocExecutor: def __init__(self, vllm_config: VllmConfig) -> None: self.vllm_config = vllm_config @@ -54,7 +54,7 @@ def determine_num_available_blocks(self) -> Tuple[int, int]: """ return self.worker.determine_num_available_blocks() - def initialize_cache(self, num_gpu_blocks: int) -> None: + def initialize(self, num_gpu_blocks: int) -> None: """Initialize the KV cache by invoking the underlying worker. """ # NOTE: This is logged in the executor because there can be >1 worker @@ -71,7 +71,13 @@ def execute_model( output = self.worker.execute_model(scheduler_output) return output + def profile(self, is_start: bool = True): + self.worker.profile(is_start) + + def shutdown(self): + self.worker = None + def check_health(self) -> None: - # GPUExecutor will always be healthy as long as + # UniprocExecutor will always be healthy as long as # it's running. return diff --git a/vllm/v1/outputs.py b/vllm/v1/outputs.py index 8574987728844..acc3a944e21b9 100644 --- a/vllm/v1/outputs.py +++ b/vllm/v1/outputs.py @@ -8,7 +8,7 @@ class SamplerOutput: # [num_reqs] - sampled_token_ids: torch.Tensor + sampled_token_ids: List[int] # [num_reqs, max_num_logprobs + 1] logprob_token_ids: Optional[torch.Tensor] @@ -20,6 +20,8 @@ class SamplerOutput: prompt_logprobs: Optional[torch.Tensor] +# ModelRunnerOutput is serialized and sent to the scheduler process. +# This is expensive for torch.Tensor so prefer to use List instead. @dataclass class ModelRunnerOutput: @@ -29,7 +31,7 @@ class ModelRunnerOutput: req_id_to_index: Dict[str, int] # [num_reqs] - sampled_token_ids_cpu: torch.Tensor + sampled_token_ids: List[int] # [num_reqs, max_num_logprobs + 1] logprob_token_ids_cpu: Optional[torch.Tensor] diff --git a/vllm/v1/sample/sampler.py b/vllm/v1/sample/sampler.py index 927f274541c4d..d1a755be01ff7 100644 --- a/vllm/v1/sample/sampler.py +++ b/vllm/v1/sample/sampler.py @@ -37,8 +37,9 @@ def forward( topk_logprobs = None topk_indices = None + # NOTE: CPU-GPU synchronization happens here. sampler_output = SamplerOutput( - sampled_token_ids=sampled, + sampled_token_ids=sampled.tolist(), logprob_token_ids=topk_indices, logprobs=topk_logprobs, prompt_logprob_token_ids=None, diff --git a/vllm/v1/utils.py b/vllm/v1/utils.py index 4b26749712e32..6e7a7d4fe12cd 100644 --- a/vllm/v1/utils.py +++ b/vllm/v1/utils.py @@ -1,4 +1,11 @@ -from typing import Generic, List, TypeVar, overload +from contextlib import contextmanager +from typing import Any, Generic, Iterator, List, TypeVar, overload + +import zmq + +from vllm.logger import init_logger + +logger = init_logger(__name__) T = TypeVar("T") @@ -62,3 +69,27 @@ def __contains__(self, item): def __len__(self): return len(self._x) + + +@contextmanager +def make_zmq_socket(path: str, type: Any) -> Iterator[zmq.Socket]: + """Context manager for a ZMQ socket""" + + ctx = zmq.Context() + try: + socket = ctx.socket(type) + + if type == zmq.constants.PULL: + socket.connect(path) + elif type == zmq.constants.PUSH: + socket.bind(path) + else: + raise ValueError(f"Unknown Socket Type: {type}") + + yield socket + + except KeyboardInterrupt: + logger.debug("Worker had Keyboard Interrupt.") + + finally: + ctx.destroy(linger=0) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index c601aca13feaf..0a5adfb28c9bd 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -34,6 +34,7 @@ class GPUModelRunner: def __init__( self, vllm_config: VllmConfig, + device: torch.device, input_registry: InputRegistry = INPUT_REGISTRY, ): self.vllm_config = vllm_config @@ -43,7 +44,6 @@ def __init__( self.load_config = vllm_config.load_config self.parallel_config = vllm_config.parallel_config self.scheduler_config = vllm_config.scheduler_config - self.device_config = vllm_config.device_config self.speculative_config = vllm_config.speculative_config self.prompt_adapter_config = vllm_config.prompt_adapter_config self.observability_config = vllm_config.observability_config @@ -52,7 +52,7 @@ def __init__( cache_config = self.cache_config scheduler_config = self.scheduler_config parallel_config = self.parallel_config - self.device = self.device_config.device + self.device = device self.pin_memory = is_pin_memory_available() self.dtype = self.model_config.dtype if cache_config.cache_dtype == "auto": @@ -477,9 +477,7 @@ def execute_model( sampling_metadata=sampling_metadata, ) - # NOTE: CPU-GPU synchronization happens here. - sampled_token_ids = sampler_output.sampled_token_ids.cpu() - sampled_token_ids_list = sampled_token_ids.tolist() + sampled_token_ids = sampler_output.sampled_token_ids # TODO(woosuk): The following loop can be slow since it iterates over # the requests one by one. Optimize. num_reqs = self.input_batch.num_reqs @@ -490,7 +488,7 @@ def execute_model( assert seq_len <= req_state.num_tokens if seq_len == req_state.num_tokens: # Append the sampled token to the output token ids. - token_id = sampled_token_ids_list[i] + token_id = sampled_token_ids[i] self.input_batch.token_ids_cpu[i, seq_len] = token_id req_state.output_token_ids.append(token_id) else: @@ -512,7 +510,7 @@ def execute_model( model_runner_output = ModelRunnerOutput( req_ids=self.input_batch.req_ids[:num_reqs], req_id_to_index=self.input_batch.req_id_to_index, - sampled_token_ids_cpu=sampled_token_ids, + sampled_token_ids=sampled_token_ids, logprob_token_ids_cpu=logprob_token_ids, logprobs_cpu=logprobs, ) diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py index d33b55a8a9f9a..d32848c3775ae 100644 --- a/vllm/v1/worker/gpu_worker.py +++ b/vllm/v1/worker/gpu_worker.py @@ -15,6 +15,7 @@ from vllm.model_executor import set_random_seed from vllm.platforms import current_platform from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, get_dtype_size +from vllm.v1.core.scheduler import SchedulerOutput from vllm.v1.outputs import ModelRunnerOutput from vllm.v1.worker.gpu_model_runner import GPUModelRunner @@ -56,7 +57,6 @@ def __init__( from vllm.utils import init_cached_hf_modules init_cached_hf_modules() - self.model_runner = GPUModelRunner(vllm_config) # Torch profiler. Enabled and configured through env vars: # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace if envs.VLLM_TORCH_PROFILER_DIR: @@ -103,6 +103,9 @@ def initialize(self): # Set random seed. set_random_seed(self.model_config.seed) + # Construct the model runner + self.model_runner = GPUModelRunner(self.vllm_config, self.device) + def load_model(self) -> None: self.model_runner.load_model() @@ -198,7 +201,7 @@ def execute_model( scheduler_output: "SchedulerOutput", ) -> ModelRunnerOutput: output = self.model_runner.execute_model(scheduler_output) - # TODO(woosuk): Send the output to the engine process. + return output if self.rank == 0 else None return output def profile(self, is_start=True): @@ -209,6 +212,10 @@ def profile(self, is_start=True): else: self.profiler.stop() + def check_health(self) -> None: + # worker will always be healthy as long as it's running. + return + def init_worker_distributed_environment( parallel_config: ParallelConfig, From ebf778061db4e67c6903f8d6e8ad97c3db0174d8 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 9 Dec 2024 22:35:36 -0800 Subject: [PATCH 005/155] monitor metrics of tokens per step using cudagraph batchsizes (#11031) Signed-off-by: youkaichao --- tests/metrics/test_metrics.py | 2 +- vllm/engine/llm_engine.py | 6 ++++-- vllm/engine/metrics.py | 25 ++++++++++++++++--------- vllm/engine/metrics_types.py | 3 ++- 4 files changed, 23 insertions(+), 13 deletions(-) diff --git a/tests/metrics/test_metrics.py b/tests/metrics/test_metrics.py index 4a824c7acef21..b3c7850556f90 100644 --- a/tests/metrics/test_metrics.py +++ b/tests/metrics/test_metrics.py @@ -411,7 +411,7 @@ def log(self, *args, **kwargs): logger = _RayPrometheusStatLogger( local_interval=0.5, labels=dict(model_name=engine.model_config.served_model_name), - max_model_len=engine.model_config.max_model_len) + vllm_config=engine.vllm_config) engine.add_logger("ray", logger) for i, prompt in enumerate(example_prompts): engine.add_request( diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 8fc69d96d321e..6eca304b45f07 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -232,6 +232,7 @@ def __init__( use_cached_outputs: bool = False, ) -> None: + self.vllm_config = vllm_config self.model_config = vllm_config.model_config self.cache_config = vllm_config.cache_config self.lora_config = vllm_config.lora_config @@ -385,13 +386,14 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer: self.stat_loggers = { "logging": LoggingStatLogger( - local_interval=_LOCAL_LOGGING_INTERVAL_SEC), + local_interval=_LOCAL_LOGGING_INTERVAL_SEC, + vllm_config=vllm_config), "prometheus": PrometheusStatLogger( local_interval=_LOCAL_LOGGING_INTERVAL_SEC, labels=dict( model_name=self.model_config.served_model_name), - max_model_len=self.model_config.max_model_len), + vllm_config=vllm_config), } self.stat_loggers["prometheus"].info("cache_config", self.cache_config) diff --git a/vllm/engine/metrics.py b/vllm/engine/metrics.py index a5ae21c3966a7..c8aec8dd3afa3 100644 --- a/vllm/engine/metrics.py +++ b/vllm/engine/metrics.py @@ -6,6 +6,7 @@ import numpy as np import prometheus_client +from vllm.config import VllmConfig from vllm.engine.metrics_types import (StatLoggerBase, Stats, SupportsMetricsInfo) from vllm.executor.ray_utils import ray @@ -44,10 +45,12 @@ class Metrics: _counter_cls = prometheus_client.Counter _histogram_cls = prometheus_client.Histogram - def __init__(self, labelnames: List[str], max_model_len: int): + def __init__(self, labelnames: List[str], vllm_config: VllmConfig): # Unregister any existing vLLM collectors (for CI/CD) self._unregister_vllm_metrics() + max_model_len = vllm_config.model_config.max_model_len + # System stats # Scheduler State self.gauge_scheduler_running = self._gauge_cls( @@ -115,11 +118,15 @@ def __init__(self, labelnames: List[str], max_model_len: int): name="vllm:tokens_total", documentation="Number of prefill plus generation tokens processed.", labelnames=labelnames) + buckets = [1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8096] + if not vllm_config.model_config.enforce_eager: + buckets = vllm_config.compilation_config.capture_sizes.copy() + buckets.sort() self.histogram_iteration_tokens = self._histogram_cls( name="vllm:iteration_tokens_total", documentation="Histogram of number of tokens per engine_step.", labelnames=labelnames, - buckets=[1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8096]) + buckets=buckets) self.histogram_time_to_first_token = self._histogram_cls( name="vllm:time_to_first_token_seconds", documentation="Histogram of time to first token in seconds.", @@ -361,10 +368,10 @@ class RayMetrics(Metrics): _histogram_cls: Type[prometheus_client.Histogram] = cast( Type[prometheus_client.Histogram], _RayHistogramWrapper) - def __init__(self, labelnames: List[str], max_model_len: int): + def __init__(self, labelnames: List[str], vllm_config: VllmConfig): if ray_metrics is None: raise ImportError("RayMetrics requires Ray to be installed.") - super().__init__(labelnames, max_model_len) + super().__init__(labelnames, vllm_config) def _unregister_vllm_metrics(self) -> None: # No-op on purpose @@ -421,8 +428,8 @@ def get_throughput(tracked_stats: List[int], now: float, class LoggingStatLogger(StatLoggerBase): """LoggingStatLogger is used in LLMEngine to log to Stdout.""" - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) + def __init__(self, local_interval: float, vllm_config: VllmConfig) -> None: + super().__init__(local_interval, vllm_config) self.last_prompt_throughput: Optional[float] = None self.last_generation_throughput: Optional[float] = None @@ -515,12 +522,12 @@ class PrometheusStatLogger(StatLoggerBase): _gauge_cls = prometheus_client.Gauge def __init__(self, local_interval: float, labels: Dict[str, str], - max_model_len: int) -> None: - super().__init__(local_interval) + vllm_config: VllmConfig) -> None: + super().__init__(local_interval, vllm_config) # Prometheus metrics self.labels = labels self.metrics = self._metrics_cls(labelnames=list(labels.keys()), - max_model_len=max_model_len) + vllm_config=vllm_config) def _log_gauge(self, gauge, data: Union[int, float]) -> None: # Convenience function for logging to gauge. diff --git a/vllm/engine/metrics_types.py b/vllm/engine/metrics_types.py index 5f7ec3bbcb269..5c7a430d11c5a 100644 --- a/vllm/engine/metrics_types.py +++ b/vllm/engine/metrics_types.py @@ -16,6 +16,7 @@ from dataclasses import dataclass from typing import Dict, List, Optional, Protocol +from vllm.config import VllmConfig from vllm.spec_decode.metrics import SpecDecodeWorkerMetrics @@ -77,7 +78,7 @@ def metrics_info(self) -> Dict[str, str]: class StatLoggerBase(ABC): """Base class for StatLogger.""" - def __init__(self, local_interval: float) -> None: + def __init__(self, local_interval: float, vllm_config: VllmConfig) -> None: # Tracked stats over current local logging interval. self.num_prompt_tokens: List[int] = [] self.num_generation_tokens: List[int] = [] From e35879c27601b09aab49e054786ce2a459f7a384 Mon Sep 17 00:00:00 2001 From: Jeff Cook Date: Mon, 9 Dec 2024 23:54:22 -0700 Subject: [PATCH 006/155] [Bugfix] Fix xgrammar failing to read a vocab_size from LlavaConfig on PixtralHF. (#11043) --- vllm/model_executor/guided_decoding/xgrammar_decoding.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/vllm/model_executor/guided_decoding/xgrammar_decoding.py b/vllm/model_executor/guided_decoding/xgrammar_decoding.py index b59a2269d2cd5..80e88dd5b4b37 100644 --- a/vllm/model_executor/guided_decoding/xgrammar_decoding.py +++ b/vllm/model_executor/guided_decoding/xgrammar_decoding.py @@ -148,7 +148,7 @@ def from_guided_params(cls, else: json_str = guided_params.json return cls(json_str=json_str, - vocab_size=model_config.hf_config.vocab_size, + vocab_size=model_config.hf_text_config.vocab_size, encoded_vocab=encoded_vocab, stop_token_ids=stop_token_ids, backend_str=backend_str, @@ -168,7 +168,7 @@ def from_guided_params(cls, else: grammar_str = guided_params.grammar return cls(grammar_str=grammar_str, - vocab_size=model_config.hf_config.vocab_size, + vocab_size=model_config.hf_text_config.vocab_size, encoded_vocab=encoded_vocab, stop_token_ids=stop_token_ids, backend_str=backend_str, @@ -176,7 +176,7 @@ def from_guided_params(cls, max_threads=max_threads) elif guided_params.json_object: return cls(json_object=True, - vocab_size=model_config.hf_config.vocab_size, + vocab_size=model_config.hf_text_config.vocab_size, encoded_vocab=encoded_vocab, stop_token_ids=stop_token_ids, backend_str=backend_str, From bfd610430c04d2962a03a2db304fb13b09b4f1b3 Mon Sep 17 00:00:00 2001 From: Diego Marinho Date: Tue, 10 Dec 2024 18:08:10 +1100 Subject: [PATCH 007/155] Update README.md (#11034) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index cfeb24cbb5823..ed5161ccffb45 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,7 @@ Easy, fast, and cheap LLM serving for everyone --- *Latest News* 🔥 +- [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone! - [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing). - [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there! - [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://www.youtube.com/playlist?list=PLzTswPQNepXl6AQwifuwUImLPFRVpksjR) from other vLLM contributors and users! From 82c73fd5104e010c2c98820f3e761e1e4f36c135 Mon Sep 17 00:00:00 2001 From: Gene Der Su Date: Mon, 9 Dec 2024 23:41:11 -0800 Subject: [PATCH 008/155] [Bugfix] cuda error running llama 3.2 (#11047) --- vllm/platforms/cuda.py | 35 ++++++++++++++++++++++++++++------- 1 file changed, 28 insertions(+), 7 deletions(-) diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index 10f83fd304281..ae1fd6d5ce068 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -4,7 +4,8 @@ import os from functools import lru_cache, wraps -from typing import TYPE_CHECKING, Callable, List, Optional, TypeVar +from typing import (TYPE_CHECKING, Callable, List, Optional, Tuple, TypeVar, + Union) import pynvml import torch @@ -78,7 +79,9 @@ class CudaPlatformBase(Platform): dispatch_key: str = "CUDA" @classmethod - def get_device_capability(cls, device_id: int = 0) -> DeviceCapability: + def get_device_capability(cls, + device_id: int = 0 + ) -> Optional[DeviceCapability]: raise NotImplementedError @classmethod @@ -144,11 +147,29 @@ class NvmlCudaPlatform(CudaPlatformBase): @classmethod @lru_cache(maxsize=8) @with_nvml_context - def get_device_capability(cls, device_id: int = 0) -> DeviceCapability: - physical_device_id = device_id_to_physical_device_id(device_id) - handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id) - major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle) - return DeviceCapability(major=major, minor=minor) + def get_device_capability(cls, + device_id: int = 0 + ) -> Optional[DeviceCapability]: + try: + physical_device_id = device_id_to_physical_device_id(device_id) + handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id) + major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle) + return DeviceCapability(major=major, minor=minor) + except RuntimeError: + return None + + @classmethod + @lru_cache(maxsize=8) + @with_nvml_context + def has_device_capability( + cls, + capability: Union[Tuple[int, int], int], + device_id: int = 0, + ) -> bool: + try: + return super().has_device_capability(capability, device_id) + except RuntimeError: + return False @classmethod @lru_cache(maxsize=8) From fe2e10c71b98a43ccde0e8aba0d4fe0d23369538 Mon Sep 17 00:00:00 2001 From: Maxime Fournioux <55544262+mfournioux@users.noreply.github.com> Date: Tue, 10 Dec 2024 10:19:27 +0100 Subject: [PATCH 009/155] Add example of helm chart for vllm deployment on k8s (#9199) Signed-off-by: Maxime Fournioux <55544262+mfournioux@users.noreply.github.com> --- .github/workflows/lint-and-deploy.yaml | 81 ++++++ docs/source/index.rst | 1 + .../serving/architecture_helm_deployment.png | Bin 0 -> 991484 bytes docs/source/serving/deploying_with_helm.rst | 253 +++++++++++++++++ examples/chart-helm/.helmignore | 6 + examples/chart-helm/Chart.yaml | 21 ++ examples/chart-helm/ct.yaml | 3 + examples/chart-helm/lintconf.yaml | 42 +++ examples/chart-helm/templates/_helpers.tpl | 164 +++++++++++ examples/chart-helm/templates/configmap.yaml | 11 + .../chart-helm/templates/custom-objects.yaml | 6 + examples/chart-helm/templates/deployment.yaml | 122 ++++++++ examples/chart-helm/templates/hpa.yaml | 31 ++ examples/chart-helm/templates/job.yaml | 37 +++ .../templates/poddisruptionbudget.yaml | 7 + examples/chart-helm/templates/pvc.yaml | 13 + examples/chart-helm/templates/secrets.yaml | 10 + examples/chart-helm/templates/service.yaml | 14 + examples/chart-helm/values.schema.json | 265 ++++++++++++++++++ examples/chart-helm/values.yaml | 119 ++++++++ 20 files changed, 1206 insertions(+) create mode 100644 .github/workflows/lint-and-deploy.yaml create mode 100644 docs/source/serving/architecture_helm_deployment.png create mode 100644 docs/source/serving/deploying_with_helm.rst create mode 100644 examples/chart-helm/.helmignore create mode 100644 examples/chart-helm/Chart.yaml create mode 100644 examples/chart-helm/ct.yaml create mode 100644 examples/chart-helm/lintconf.yaml create mode 100644 examples/chart-helm/templates/_helpers.tpl create mode 100644 examples/chart-helm/templates/configmap.yaml create mode 100644 examples/chart-helm/templates/custom-objects.yaml create mode 100644 examples/chart-helm/templates/deployment.yaml create mode 100644 examples/chart-helm/templates/hpa.yaml create mode 100644 examples/chart-helm/templates/job.yaml create mode 100644 examples/chart-helm/templates/poddisruptionbudget.yaml create mode 100644 examples/chart-helm/templates/pvc.yaml create mode 100644 examples/chart-helm/templates/secrets.yaml create mode 100644 examples/chart-helm/templates/service.yaml create mode 100644 examples/chart-helm/values.schema.json create mode 100644 examples/chart-helm/values.yaml diff --git a/.github/workflows/lint-and-deploy.yaml b/.github/workflows/lint-and-deploy.yaml new file mode 100644 index 0000000000000..ab6f6e5d2060d --- /dev/null +++ b/.github/workflows/lint-and-deploy.yaml @@ -0,0 +1,81 @@ +name: Lint and Deploy Charts + +on: pull_request + +jobs: + lint-and-deploy: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + fetch-depth: 0 + + - name: Set up Helm + uses: azure/setup-helm@fe7b79cd5ee1e45176fcad797de68ecaf3ca4814 # v4.2.0 + with: + version: v3.14.4 + + #Python is required because ct lint runs Yamale and yamllint which require Python. + - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0 + with: + python-version: '3.13' + + - name: Set up chart-testing + uses: helm/chart-testing-action@e6669bcd63d7cb57cb4380c33043eebe5d111992 # v2.6.1 + with: + version: v3.10.1 + + - name: Run chart-testing (lint) + run: ct lint --target-branch ${{ github.event.repository.default_branch }} --chart-dirs examples/chart-helm --charts examples/chart-helm + + - name: Setup minio + run: | + docker network create vllm-net + docker run -d -p 9000:9000 --name minio --net vllm-net \ + -e "MINIO_ACCESS_KEY=minioadmin" \ + -e "MINIO_SECRET_KEY=minioadmin" \ + -v /tmp/data:/data \ + -v /tmp/config:/root/.minio \ + minio/minio server /data + export AWS_ACCESS_KEY_ID=minioadmin + export AWS_SECRET_ACCESS_KEY=minioadmin + export AWS_EC2_METADATA_DISABLED=true + mkdir opt-125m + cd opt-125m && curl -O -Ls "https://huggingface.co/facebook/opt-125m/resolve/main/{pytorch_model.bin,config.json,generation_config.json,merges.txt,special_tokens_map.json,tokenizer_config.json,vocab.json}" && cd .. + aws --endpoint-url http://127.0.0.1:9000/ s3 mb s3://testbucket + aws --endpoint-url http://127.0.0.1:9000/ s3 cp opt-125m/ s3://testbucket/opt-125m --recursive + + - name: Create kind cluster + uses: helm/kind-action@0025e74a8c7512023d06dc019c617aa3cf561fde # v1.10.0 + + - name: Build the Docker image vllm cpu + run: docker buildx build -f Dockerfile.cpu -t vllm-cpu-env . + + - name: Configuration of docker images, network and namespace for the kind cluster + run: | + docker pull amazon/aws-cli:2.6.4 + kind load docker-image amazon/aws-cli:2.6.4 --name chart-testing + kind load docker-image vllm-cpu-env:latest --name chart-testing + docker network connect vllm-net "$(docker ps -aqf "name=chart-testing-control-plane")" + kubectl create ns ns-vllm + + - name: Run chart-testing (install) + run: | + export AWS_ACCESS_KEY_ID=minioadmin + export AWS_SECRET_ACCESS_KEY=minioadmin + helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/chart-helm -f examples/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env" + + - name: curl test + run: | + kubectl -n ns-vllm port-forward service/test-vllm-service 8001:80 & + sleep 10 + CODE="$(curl -v -f --location http://localhost:8001/v1/completions \ + --header "Content-Type: application/json" \ + --data '{ + "model": "opt-125m", + "prompt": "San Francisco is a", + "max_tokens": 7, + "temperature": 0 + }'):$CODE" + echo "$CODE" \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index c45c941b00e20..ebf1361976c5e 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -82,6 +82,7 @@ Documentation serving/openai_compatible_server serving/deploying_with_docker serving/deploying_with_k8s + serving/deploying_with_helm serving/deploying_with_nginx serving/distributed_serving serving/metrics diff 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zFdK8kdJ7<_hjkOjT9ue%rzQYVbfa|sV9RMBpJ5+)T@Tbq8upO@2H`|?sv|C2CEGe82#D+u6?$ZFG)^8}jXZ1+j6Nlf8}g(AU`5mkzy2sXOAxr9F9E X&+FC3YT^A1{ZqZHrIfFD!~cH(b|Z=* literal 0 HcmV?d00001 diff --git a/docs/source/serving/deploying_with_helm.rst b/docs/source/serving/deploying_with_helm.rst new file mode 100644 index 0000000000000..21b17e881b945 --- /dev/null +++ b/docs/source/serving/deploying_with_helm.rst @@ -0,0 +1,253 @@ +.. _deploying_with_helm: + +Deploying with Helm +=================== + +A Helm chart to deploy vLLM for Kubernetes + +Helm is a package manager for Kubernetes. It will help you to deploy vLLM on k8s and automate the deployment of vLLMm Kubernetes applications. With Helm, you can deploy the same framework architecture with different configurations to multiple namespaces by overriding variables values. + +This guide will walk you through the process of deploying vLLM with Helm, including the necessary prerequisites, steps for helm install and documentation on architecture and values file. + +Prerequisites +------------- +Before you begin, ensure that you have the following: + +- A running Kubernetes cluster +- NVIDIA Kubernetes Device Plugin (``k8s-device-plugin``): This can be found at `https://github.com/NVIDIA/k8s-device-plugin `__ +- Available GPU resources in your cluster +- S3 with the model which will be deployed + +Installing the chart +-------------------- + +To install the chart with the release name ``test-vllm``: + +.. code-block:: console + + helm upgrade --install --create-namespace --namespace=ns-vllm test-vllm . -f values.yaml --set secrets.s3endpoint=$ACCESS_POINT --set secrets.s3buckername=$BUCKET --set secrets.s3accesskeyid=$ACCESS_KEY --set secrets.s3accesskey=$SECRET_KEY + +Uninstalling the Chart +---------------------- + +To uninstall the ``test-vllm`` deployment: + +.. code-block:: console + + helm uninstall test-vllm --namespace=ns-vllm + +The command removes all the Kubernetes components associated with the +chart **including persistent volumes** and deletes the release. + +Architecture +------------ + +.. image:: architecture_helm_deployment.png + +Values +------ + +.. list-table:: Values + :widths: 25 25 25 25 + :header-rows: 1 + + * - Key + - Type + - Default + - Description + * - autoscaling + - object + - {"enabled":false,"maxReplicas":100,"minReplicas":1,"targetCPUUtilizationPercentage":80} + - Autoscaling configuration + * - autoscaling.enabled + - bool + - false + - Enable autoscaling + * - autoscaling.maxReplicas + - int + - 100 + - Maximum replicas + * - autoscaling.minReplicas + - int + - 1 + - Minimum replicas + * - autoscaling.targetCPUUtilizationPercentage + - int + - 80 + - Target CPU utilization for autoscaling + * - configs + - object + - {} + - Configmap + * - containerPort + - int + - 8000 + - Container port + * - customObjects + - list + - [] + - Custom Objects configuration + * - deploymentStrategy + - object + - {} + - Deployment strategy configuration + * - externalConfigs + - list + - [] + - External configuration + * - extraContainers + - list + - [] + - Additional containers configuration + * - extraInit + - object + - {"pvcStorage":"1Gi","s3modelpath":"relative_s3_model_path/opt-125m", "awsEc2MetadataDisabled": true} + - Additional configuration for the init container + * - extraInit.pvcStorage + - string + - "50Gi" + - Storage size of the s3 + * - extraInit.s3modelpath + - string + - "relative_s3_model_path/opt-125m" + - Path of the model on the s3 which hosts model weights and config files + * - extraInit.awsEc2MetadataDisabled + - boolean + - true + - Disables the use of the Amazon EC2 instance metadata service + * - extraPorts + - list + - [] + - Additional ports configuration + * - gpuModels + - list + - ["TYPE_GPU_USED"] + - Type of gpu used + * - image + - object + - {"command":["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"],"repository":"vllm/vllm-openai","tag":"latest"} + - Image configuration + * - image.command + - list + - ["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"] + - Container launch command + * - image.repository + - string + - "vllm/vllm-openai" + - Image repository + * - image.tag + - string + - "latest" + - Image tag + * - livenessProbe + - object + - {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":15,"periodSeconds":10} + - Liveness probe configuration + * - livenessProbe.failureThreshold + - int + - 3 + - Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not alive + * - livenessProbe.httpGet + - object + - {"path":"/health","port":8000} + - Configuration of the Kubelet http request on the server + * - livenessProbe.httpGet.path + - string + - "/health" + - Path to access on the HTTP server + * - livenessProbe.httpGet.port + - int + - 8000 + - Name or number of the port to access on the container, on which the server is listening + * - livenessProbe.initialDelaySeconds + - int + - 15 + - Number of seconds after the container has started before liveness probe is initiated + * - livenessProbe.periodSeconds + - int + - 10 + - How often (in seconds) to perform the liveness probe + * - maxUnavailablePodDisruptionBudget + - string + - "" + - Disruption Budget Configuration + * - readinessProbe + - object + - {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":5,"periodSeconds":5} + - Readiness probe configuration + * - readinessProbe.failureThreshold + - int + - 3 + - Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not ready + * - readinessProbe.httpGet + - object + - {"path":"/health","port":8000} + - Configuration of the Kubelet http request on the server + * - readinessProbe.httpGet.path + - string + - "/health" + - Path to access on the HTTP server + * - readinessProbe.httpGet.port + - int + - 8000 + - Name or number of the port to access on the container, on which the server is listening + * - readinessProbe.initialDelaySeconds + - int + - 5 + - Number of seconds after the container has started before readiness probe is initiated + * - readinessProbe.periodSeconds + - int + - 5 + - How often (in seconds) to perform the readiness probe + * - replicaCount + - int + - 1 + - Number of replicas + * - resources + - object + - {"limits":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1},"requests":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1}} + - Resource configuration + * - resources.limits."nvidia.com/gpu" + - int + - 1 + - Number of gpus used + * - resources.limits.cpu + - int + - 4 + - Number of CPUs + * - resources.limits.memory + - string + - "16Gi" + - CPU memory configuration + * - resources.requests."nvidia.com/gpu" + - int + - 1 + - Number of gpus used + * - resources.requests.cpu + - int + - 4 + - Number of CPUs + * - resources.requests.memory + - string + - "16Gi" + - CPU memory configuration + * - secrets + - object + - {} + - Secrets configuration + * - serviceName + - string + - + - Service name + * - servicePort + - int + - 80 + - Service port + * - labels.environment + - string + - test + - Environment name + * - labels.release + - string + - test + - Release name diff --git a/examples/chart-helm/.helmignore b/examples/chart-helm/.helmignore new file mode 100644 index 0000000000000..2d1303b784cb8 --- /dev/null +++ b/examples/chart-helm/.helmignore @@ -0,0 +1,6 @@ +*.png +.git/ +ct.yaml +lintconf.yaml +values.schema.json +/workflows \ No newline at end of file diff --git a/examples/chart-helm/Chart.yaml b/examples/chart-helm/Chart.yaml new file mode 100644 index 0000000000000..fb0f06f6d2701 --- /dev/null +++ b/examples/chart-helm/Chart.yaml @@ -0,0 +1,21 @@ +apiVersion: v2 +name: chart-vllm +description: Chart vllm + +# A chart can be either an 'application' or a 'library' chart. +# +# Application charts are a collection of templates that can be packaged into versioned archives +# to be deployed. +# +# Library charts provide useful utilities or functions for the chart developer. They're included as +# a dependency of application charts to inject those utilities and functions into the rendering +# pipeline. Library charts do not define any templates and therefore cannot be deployed. +type: application + +# This is the chart version. This version number should be incremented each time you make changes +# to the chart and its templates, including the app version. +# Versions are expected to follow Semantic Versioning (https://semver.org/) +version: 0.0.1 + +maintainers: + - name: mfournioux diff --git a/examples/chart-helm/ct.yaml b/examples/chart-helm/ct.yaml new file mode 100644 index 0000000000000..d273e118203ad --- /dev/null +++ b/examples/chart-helm/ct.yaml @@ -0,0 +1,3 @@ +chart-dirs: + - charts +validate-maintainers: false \ No newline at end of file diff --git a/examples/chart-helm/lintconf.yaml b/examples/chart-helm/lintconf.yaml new file mode 100644 index 0000000000000..c8e8c5d7d9767 --- /dev/null +++ b/examples/chart-helm/lintconf.yaml @@ -0,0 +1,42 @@ +--- +rules: + braces: + min-spaces-inside: 0 + max-spaces-inside: 0 + min-spaces-inside-empty: -1 + max-spaces-inside-empty: -1 + brackets: + min-spaces-inside: 0 + max-spaces-inside: 0 + min-spaces-inside-empty: -1 + max-spaces-inside-empty: -1 + colons: + max-spaces-before: 0 + max-spaces-after: 1 + commas: + max-spaces-before: 0 + min-spaces-after: 1 + max-spaces-after: 1 + comments: + require-starting-space: true + min-spaces-from-content: 2 + document-end: disable + document-start: disable # No --- to start a file + empty-lines: + max: 2 + max-start: 0 + max-end: 0 + hyphens: + max-spaces-after: 1 + indentation: + spaces: consistent + indent-sequences: whatever # - list indentation will handle both indentation and without + check-multi-line-strings: false + key-duplicates: enable + line-length: disable # Lines can be any length + new-line-at-end-of-file: disable + new-lines: + type: unix + trailing-spaces: enable + truthy: + level: warning \ No newline at end of file diff --git a/examples/chart-helm/templates/_helpers.tpl b/examples/chart-helm/templates/_helpers.tpl new file mode 100644 index 0000000000000..a9690bad3c945 --- /dev/null +++ b/examples/chart-helm/templates/_helpers.tpl @@ -0,0 +1,164 @@ +{{/* +Define ports for the pods +*/}} +{{- define "chart.container-port" -}} +{{- default "8000" .Values.containerPort }} +{{- end }} + +{{/* +Define service name +*/}} +{{- define "chart.service-name" -}} +{{- if .Values.serviceName }} +{{- .Values.serviceName | lower | trim }} +{{- else }} +"{{ .Release.Name }}-service" +{{- end }} +{{- end }} + +{{/* +Define service port +*/}} +{{- define "chart.service-port" -}} +{{- if .Values.servicePort }} +{{- .Values.servicePort }} +{{- else }} +{{- include "chart.container-port" . }} +{{- end }} +{{- end }} + +{{/* +Define service port name +*/}} +{{- define "chart.service-port-name" -}} +"service-port" +{{- end }} + +{{/* +Define container port name +*/}} +{{- define "chart.container-port-name" -}} +"container-port" +{{- end }} + +{{/* +Define deployment strategy +*/}} +{{- define "chart.strategy" -}} +strategy: +{{- if not .Values.deploymentStrategy }} + rollingUpdate: + maxSurge: 100% + maxUnavailable: 0 +{{- else }} +{{ toYaml .Values.deploymentStrategy | indent 2 }} +{{- end }} +{{- end }} + +{{/* +Define additional ports +*/}} +{{- define "chart.extraPorts" }} +{{- with .Values.extraPorts }} +{{ toYaml . }} +{{- end }} +{{- end }} + +{{/* +Define chart external ConfigMaps and Secrets +*/}} +{{- define "chart.externalConfigs" -}} +{{- with .Values.externalConfigs -}} +{{ toYaml . }} +{{- end }} +{{- end }} + + +{{/* +Define liveness et readiness probes +*/}} +{{- define "chart.probes" -}} +{{- if .Values.readinessProbe }} +readinessProbe: +{{- with .Values.readinessProbe }} +{{- toYaml . | nindent 2 }} +{{- end }} +{{- end }} +{{- if .Values.livenessProbe }} +livenessProbe: +{{- with .Values.livenessProbe }} +{{- toYaml . | nindent 2 }} +{{- end }} +{{- end }} +{{- end }} + +{{/* +Define resources +*/}} +{{- define "chart.resources" -}} +requests: + memory: {{ required "Value 'resources.requests.memory' must be defined !" .Values.resources.requests.memory | quote }} + cpu: {{ required "Value 'resources.requests.cpu' must be defined !" .Values.resources.requests.cpu | quote }} + {{- if and (gt (int (index .Values.resources.requests "nvidia.com/gpu")) 0) (gt (int (index .Values.resources.limits "nvidia.com/gpu")) 0) }} + nvidia.com/gpu: {{ required "Value 'resources.requests.nvidia.com/gpu' must be defined !" (index .Values.resources.requests "nvidia.com/gpu") | quote }} + {{- end }} +limits: + memory: {{ required "Value 'resources.limits.memory' must be defined !" .Values.resources.limits.memory | quote }} + cpu: {{ required "Value 'resources.limits.cpu' must be defined !" .Values.resources.limits.cpu | quote }} + {{- if and (gt (int (index .Values.resources.requests "nvidia.com/gpu")) 0) (gt (int (index .Values.resources.limits "nvidia.com/gpu")) 0) }} + nvidia.com/gpu: {{ required "Value 'resources.limits.nvidia.com/gpu' must be defined !" (index .Values.resources.limits "nvidia.com/gpu") | quote }} + {{- end }} +{{- end }} + + +{{/* +Define User used for the main container +*/}} +{{- define "chart.user" }} +{{- if .Values.image.runAsUser }} +runAsUser: +{{- with .Values.runAsUser }} +{{- toYaml . | nindent 2 }} +{{- end }} +{{- end }} +{{- end }} + +{{- define "chart.extraInitImage" -}} +"amazon/aws-cli:2.6.4" +{{- end }} + +{{- define "chart.extraInitEnv" -}} +- name: S3_ENDPOINT_URL + valueFrom: + secretKeyRef: + name: {{ .Release.Name }}-secrets + key: s3endpoint +- name: S3_BUCKET_NAME + valueFrom: + secretKeyRef: + name: {{ .Release.Name }}-secrets + key: s3bucketname +- name: AWS_ACCESS_KEY_ID + valueFrom: + secretKeyRef: + name: {{ .Release.Name }}-secrets + key: s3accesskeyid +- name: AWS_SECRET_ACCESS_KEY + valueFrom: + secretKeyRef: + name: {{ .Release.Name }}-secrets + key: s3accesskey +- name: S3_PATH + value: "{{ .Values.extraInit.s3modelpath }}" +- name: AWS_EC2_METADATA_DISABLED + value: "{{ .Values.extraInit.awsEc2MetadataDisabled }}" +{{- end }} + +{{/* + Define chart labels +*/}} +{{- define "chart.labels" -}} +{{- with .Values.labels -}} +{{ toYaml . }} +{{- end }} +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/configmap.yaml b/examples/chart-helm/templates/configmap.yaml new file mode 100644 index 0000000000000..cc5d03782f878 --- /dev/null +++ b/examples/chart-helm/templates/configmap.yaml @@ -0,0 +1,11 @@ +{{- if .Values.configs -}} +apiVersion: v1 +kind: ConfigMap +metadata: + name: "{{ .Release.Name }}-configs" + namespace: {{ .Release.Namespace }} +data: + {{- with .Values.configs }} + {{- toYaml . | nindent 2 }} + {{- end }} +{{- end -}} \ No newline at end of file diff --git a/examples/chart-helm/templates/custom-objects.yaml b/examples/chart-helm/templates/custom-objects.yaml new file mode 100644 index 0000000000000..8a65ffd0e552d --- /dev/null +++ b/examples/chart-helm/templates/custom-objects.yaml @@ -0,0 +1,6 @@ +{{- if .Values.customObjects }} +{{- range .Values.customObjects }} +{{- tpl (. | toYaml) $ }} +--- +{{- end }} +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/deployment.yaml b/examples/chart-helm/templates/deployment.yaml new file mode 100644 index 0000000000000..536983b587be2 --- /dev/null +++ b/examples/chart-helm/templates/deployment.yaml @@ -0,0 +1,122 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: "{{ .Release.Name }}-deployment-vllm" + namespace: {{ .Release.Namespace }} + labels: + {{- include "chart.labels" . | nindent 4 }} +spec: + replicas: {{ .Values.replicaCount }} + {{- include "chart.strategy" . | nindent 2 }} + selector: + matchLabels: + environment: "test" + release: "test" + progressDeadlineSeconds: 1200 + template: + metadata: + labels: + environment: "test" + release: "test" + spec: + containers: + - name: "vllm" + image: "{{ required "Required value 'image.repository' must be defined !" .Values.image.repository }}:{{ required "Required value 'image.tag' must be defined !" .Values.image.tag }}" + {{- if .Values.image.command }} + command : + {{- with .Values.image.command }} + {{- toYaml . | nindent 10 }} + {{- end }} + {{- end }} + securityContext: + {{- if .Values.image.securityContext }} + {{- with .Values.image.securityContext }} + {{- toYaml . | nindent 12 }} + {{- end }} + {{- else }} + runAsNonRoot: false + {{- include "chart.user" . | indent 12 }} + {{- end }} + imagePullPolicy: IfNotPresent + {{- if .Values.image.env }} + env : + {{- with .Values.image.env }} + {{- toYaml . | nindent 10 }} + {{- end }} + {{- else }} + env: [] + {{- end }} + {{- if or .Values.externalConfigs .Values.configs .Values.secrets }} + envFrom: + {{- if .Values.configs }} + - configMapRef: + name: "{{ .Release.Name }}-configs" + {{- end }} + {{- if .Values.secrets}} + - secretRef: + name: "{{ .Release.Name }}-secrets" + {{- end }} + {{- include "chart.externalConfigs" . | nindent 12 }} + {{- end }} + ports: + - name: {{ include "chart.container-port-name" . }} + containerPort: {{ include "chart.container-port" . }} + {{- include "chart.extraPorts" . | nindent 12 }} + {{- include "chart.probes" . | indent 10 }} + resources: {{- include "chart.resources" . | nindent 12 }} + volumeMounts: + - name: {{ .Release.Name }}-storage + mountPath: /data + + {{- with .Values.extraContainers }} + {{ toYaml . | nindent 8 }} + {{- end }} + + {{- if .Values.extraInit }} + initContainers: + - name: wait-download-model + image: {{ include "chart.extraInitImage" . }} + command: + - /bin/bash + args: + - -eucx + - while aws --endpoint-url $S3_ENDPOINT_URL s3 sync --dryrun s3://$S3_BUCKET_NAME/$S3_PATH /data | grep -q download; do sleep 10; done + env: {{- include "chart.extraInitEnv" . | nindent 10 }} + resources: + requests: + cpu: 200m + memory: 1Gi + limits: + cpu: 500m + memory: 2Gi + volumeMounts: + - name: {{ .Release.Name }}-storage + mountPath: /data + {{- end }} + volumes: + - name: {{ .Release.Name }}-storage + persistentVolumeClaim: + claimName: {{ .Release.Name }}-storage-claim + + {{- with .Values.nodeSelector }} + nodeSelector: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.tolerations }} + tolerations: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- if and (gt (int (index .Values.resources.requests "nvidia.com/gpu")) 0) (gt (int (index .Values.resources.limits "nvidia.com/gpu")) 0) }} + runtimeClassName: nvidia + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: nvidia.com/gpu.product + operator: In + {{- with .Values.gpuModels }} + values: + {{- toYaml . | nindent 20 }} + {{- end }} + {{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/hpa.yaml b/examples/chart-helm/templates/hpa.yaml new file mode 100644 index 0000000000000..5ca94c8213541 --- /dev/null +++ b/examples/chart-helm/templates/hpa.yaml @@ -0,0 +1,31 @@ +{{- if .Values.autoscaling.enabled }} +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: "{{ .Release.Name }}-hpa" + namespace: {{ .Release.Namespace }} +spec: + scaleTargetRef: + apiVersion: apps/v1 + kind: Deployment + name: vllm + minReplicas: {{ .Values.autoscaling.minReplicas }} + maxReplicas: {{ .Values.autoscaling.maxReplicas }} + metrics: + {{- if .Values.autoscaling.targetCPUUtilizationPercentage }} + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: {{ .Values.autoscaling.targetCPUUtilizationPercentage }} + {{- end }} + {{- if .Values.autoscaling.targetMemoryUtilizationPercentage }} + - type: Resource + resource: + name: memory + target: + type: Utilization + averageUtilization: {{ .Values.autoscaling.targetMemoryUtilizationPercentage }} + {{- end }} +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/job.yaml b/examples/chart-helm/templates/job.yaml new file mode 100644 index 0000000000000..f9ea3541e78d2 --- /dev/null +++ b/examples/chart-helm/templates/job.yaml @@ -0,0 +1,37 @@ +{{- if .Values.extraInit }} +apiVersion: batch/v1 +kind: Job +metadata: + name: "{{ .Release.Name }}-init-vllm" + namespace: {{ .Release.Namespace }} +spec: + ttlSecondsAfterFinished: 100 + template: + metadata: + name: init-vllm + spec: + containers: + - name: job-download-model + image: {{ include "chart.extraInitImage" . }} + command: + - /bin/bash + args: + - -eucx + - aws --endpoint-url $S3_ENDPOINT_URL s3 sync s3://$S3_BUCKET_NAME/$S3_PATH /data + env: {{- include "chart.extraInitEnv" . | nindent 8 }} + volumeMounts: + - name: {{ .Release.Name }}-storage + mountPath: /data + resources: + requests: + cpu: 200m + memory: 1Gi + limits: + cpu: 500m + memory: 2Gi + restartPolicy: OnFailure + volumes: + - name: {{ .Release.Name }}-storage + persistentVolumeClaim: + claimName: "{{ .Release.Name }}-storage-claim" +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/poddisruptionbudget.yaml b/examples/chart-helm/templates/poddisruptionbudget.yaml new file mode 100644 index 0000000000000..512bac727da87 --- /dev/null +++ b/examples/chart-helm/templates/poddisruptionbudget.yaml @@ -0,0 +1,7 @@ +apiVersion: policy/v1 +kind: PodDisruptionBudget +metadata: + name: "{{ .Release.Name }}-pdb" + namespace: {{ .Release.Namespace }} +spec: + maxUnavailable: {{ default 1 .Values.maxUnavailablePodDisruptionBudget }} \ No newline at end of file diff --git a/examples/chart-helm/templates/pvc.yaml b/examples/chart-helm/templates/pvc.yaml new file mode 100644 index 0000000000000..e8d203a7a5ace --- /dev/null +++ b/examples/chart-helm/templates/pvc.yaml @@ -0,0 +1,13 @@ +{{- if .Values.extraInit }} +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: "{{ .Release.Name }}-storage-claim" + namespace: {{ .Release.Namespace }} +spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: {{ .Values.extraInit.pvcStorage }} +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/secrets.yaml b/examples/chart-helm/templates/secrets.yaml new file mode 100644 index 0000000000000..4e88e747b616a --- /dev/null +++ b/examples/chart-helm/templates/secrets.yaml @@ -0,0 +1,10 @@ +apiVersion: v1 +kind: Secret +metadata: + name: "{{ .Release.Name }}-secrets" + namespace: {{ .Release.Namespace }} +type: Opaque +data: + {{- range $key, $val := .Values.secrets }} + {{ $key }}: {{ $val | b64enc | quote }} + {{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/service.yaml b/examples/chart-helm/templates/service.yaml new file mode 100644 index 0000000000000..12d0f68b03a35 --- /dev/null +++ b/examples/chart-helm/templates/service.yaml @@ -0,0 +1,14 @@ +apiVersion: v1 +kind: Service +metadata: + name: "{{ .Release.Name }}-service" + namespace: {{ .Release.Namespace }} +spec: + type: ClusterIP + ports: + - name: {{ include "chart.service-port-name" . }} + port: {{ include "chart.service-port" . }} + targetPort: {{ include "chart.container-port-name" . }} + protocol: TCP + selector: + {{- include "chart.labels" . | nindent 4 }} \ No newline at end of file diff --git a/examples/chart-helm/values.schema.json b/examples/chart-helm/values.schema.json new file mode 100644 index 0000000000000..812d54bde1397 --- /dev/null +++ b/examples/chart-helm/values.schema.json @@ -0,0 +1,265 @@ +{ + "$schema": "http://json-schema.org/schema#", + "type": "object", + "properties": { + "image": { + "type": "object", + "properties": { + "repository": { + "type": "string" + }, + "tag": { + "type": "string" + }, + "command": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "required": [ + "command", + "repository", + "tag" + ] + }, + "containerPort": { + "type": "integer" + }, + "serviceName": { + "type": "null" + }, + "servicePort": { + "type": "integer" + }, + "extraPorts": { + "type": "array" + }, + "replicaCount": { + "type": "integer" + }, + "deploymentStrategy": { + "type": "object" + }, + "resources": { + "type": "object", + "properties": { + "requests": { + "type": "object", + "properties": { + "cpu": { + "type": "integer" + }, + "memory": { + "type": "string" + }, + "nvidia.com/gpu": { + "type": "integer" + } + }, + "required": [ + "cpu", + "memory", + "nvidia.com/gpu" + ] + }, + "limits": { + "type": "object", + "properties": { + "cpu": { + "type": "integer" + }, + "memory": { + "type": "string" + }, + "nvidia.com/gpu": { + "type": "integer" + } + }, + "required": [ + "cpu", + "memory", + "nvidia.com/gpu" + ] + } + }, + "required": [ + "limits", + "requests" + ] + }, + "gpuModels": { + "type": "array", + "items": { + "type": "string" + } + }, + "autoscaling": { + "type": "object", + "properties": { + "enabled": { + "type": "boolean" + }, + "minReplicas": { + "type": "integer" + }, + "maxReplicas": { + "type": "integer" + }, + "targetCPUUtilizationPercentage": { + "type": "integer" + } + }, + "required": [ + "enabled", + "maxReplicas", + "minReplicas", + "targetCPUUtilizationPercentage" + ] + }, + "configs": { + "type": "object" + }, + "secrets": { + "type": "object" + }, + "externalConfigs": { + "type": "array" + }, + "customObjects": { + "type": "array" + }, + "maxUnavailablePodDisruptionBudget": { + "type": "string" + }, + "extraInit": { + "type": "object", + "properties": { + "s3modelpath": { + "type": "string" + }, + "pvcStorage": { + "type": "string" + }, + "awsEc2MetadataDisabled": { + "type": "boolean" + } + }, + "required": [ + "pvcStorage", + "s3modelpath", + "awsEc2MetadataDisabled" + ] + }, + "extraContainers": { + "type": "array" + }, + "readinessProbe": { + "type": "object", + "properties": { + "initialDelaySeconds": { + "type": "integer" + }, + "periodSeconds": { + "type": "integer" + }, + "failureThreshold": { + "type": "integer" + }, + "httpGet": { + "type": "object", + "properties": { + "path": { + "type": "string" + }, + "port": { + "type": "integer" + } + }, + "required": [ + "path", + "port" + ] + } + }, + "required": [ + "failureThreshold", + "httpGet", + "initialDelaySeconds", + "periodSeconds" + ] + }, + "livenessProbe": { + "type": "object", + "properties": { + "initialDelaySeconds": { + "type": "integer" + }, + "failureThreshold": { + "type": "integer" + }, + "periodSeconds": { + "type": "integer" + }, + "httpGet": { + "type": "object", + "properties": { + "path": { + "type": "string" + }, + "port": { + "type": "integer" + } + }, + "required": [ + "path", + "port" + ] + } + }, + "required": [ + "failureThreshold", + "httpGet", + "initialDelaySeconds", + "periodSeconds" + ] + }, + "labels": { + "type": "object", + "properties": { + "environment": { + "type": "string" + }, + "release": { + "type": "string" + } + }, + "required": [ + "environment", + "release" + ] + } + }, + "required": [ + "autoscaling", + "configs", + "containerPort", + "customObjects", + "deploymentStrategy", + "externalConfigs", + "extraContainers", + "extraInit", + "extraPorts", + "gpuModels", + "image", + "labels", + "livenessProbe", + "maxUnavailablePodDisruptionBudget", + "readinessProbe", + "replicaCount", + "resources", + "secrets", + "servicePort" + ] +} \ No newline at end of file diff --git a/examples/chart-helm/values.yaml b/examples/chart-helm/values.yaml new file mode 100644 index 0000000000000..9c48e7d061bf7 --- /dev/null +++ b/examples/chart-helm/values.yaml @@ -0,0 +1,119 @@ +# -- Default values for chart vllm +# -- Declare variables to be passed into your templates. + +# -- Image configuration +image: + # -- Image repository + repository: "vllm/vllm-openai" + # -- Image tag + tag: "latest" + # -- Container launch command + command: ["vllm", "serve", "/data/", "--served-model-name", "opt-125m", "--dtype", "bfloat16", "--host", "0.0.0.0", "--port", "8000"] + +# -- Container port +containerPort: 8000 +# -- Service name +serviceName: +# -- Service port +servicePort: 80 +# -- Additional ports configuration +extraPorts: [] + +# -- Number of replicas +replicaCount: 1 + +# -- Deployment strategy configuration +deploymentStrategy: {} + +# -- Resource configuration +resources: + requests: + # -- Number of CPUs + cpu: 4 + # -- CPU memory configuration + memory: 16Gi + # -- Number of gpus used + nvidia.com/gpu: 1 + limits: + # -- Number of CPUs + cpu: 4 + # -- CPU memory configuration + memory: 16Gi + # -- Number of gpus used + nvidia.com/gpu: 1 + +# -- Type of gpu used +gpuModels: + - "TYPE_GPU_USED" + +# -- Autoscaling configuration +autoscaling: + # -- Enable autoscaling + enabled: false + # -- Minimum replicas + minReplicas: 1 + # -- Maximum replicas + maxReplicas: 100 + # -- Target CPU utilization for autoscaling + targetCPUUtilizationPercentage: 80 + # targetMemoryUtilizationPercentage: 80 + +# -- Configmap +configs: {} + +# -- Secrets configuration +secrets: {} + +# -- External configuration +externalConfigs: [] + +# -- Custom Objects configuration +customObjects: [] + +# -- Disruption Budget Configuration +maxUnavailablePodDisruptionBudget: "" + +# -- Additional configuration for the init container +extraInit: + # -- Path of the model on the s3 which hosts model weights and config files + s3modelpath: "relative_s3_model_path/opt-125m" + # -- Storage size of the s3 + pvcStorage: "1Gi" + awsEc2MetadataDisabled: true + +# -- Additional containers configuration +extraContainers: [] + +# -- Readiness probe configuration +readinessProbe: + # -- Number of seconds after the container has started before readiness probe is initiated + initialDelaySeconds: 5 + # -- How often (in seconds) to perform the readiness probe + periodSeconds: 5 + # -- Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not ready + failureThreshold: 3 + # -- Configuration of the Kubelet http request on the server + httpGet: + # -- Path to access on the HTTP server + path: /health + # -- Name or number of the port to access on the container, on which the server is listening + port: 8000 + +# -- Liveness probe configuration +livenessProbe: + # -- Number of seconds after the container has started before liveness probe is initiated + initialDelaySeconds: 15 + # -- Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not alive + failureThreshold: 3 + # -- How often (in seconds) to perform the liveness probe + periodSeconds: 10 + # -- Configuration of the Kubelet http request on the server + httpGet: + # -- Path to access on the HTTP server + path: /health + # -- Name or number of the port to access on the container, on which the server is listening + port: 8000 + +labels: + environment: "test" + release: "test" From beb16b2c810a87b28e7b8a7aa29d26f842f654b9 Mon Sep 17 00:00:00 2001 From: Travis Johnson Date: Tue, 10 Dec 2024 03:27:11 -0700 Subject: [PATCH 010/155] [Bugfix] Handle <|tool_call|> token in granite tool parser (#11039) Signed-off-by: Travis Johnson --- vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py b/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py index b5854ca39ab47..00917c866e496 100644 --- a/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py +++ b/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py @@ -35,11 +35,13 @@ class GraniteToolParser(ToolParser): def __init__(self, tokenizer: AnyTokenizer): super().__init__(tokenizer) + self.bot_token = "<|tool_call|>" def extract_tool_calls( self, model_output: str, request: ChatCompletionRequest) -> ExtractedToolCallInformation: - stripped = model_output.strip() + # remove whitespace and the BOT token if it exists + stripped = model_output.strip().removeprefix(self.bot_token).lstrip() if not stripped or stripped[0] != '[': return ExtractedToolCallInformation(tools_called=False, tool_calls=[], From d05f88679bedd73939251a17c3d785a354b2946c Mon Sep 17 00:00:00 2001 From: Jee Jee Li Date: Tue, 10 Dec 2024 19:12:01 +0800 Subject: [PATCH 011/155] [Misc][LoRA] Add PEFTHelper for LoRA (#11003) Signed-off-by: Jee Jee Li --- tests/lora/test_lora_manager.py | 58 +++++++++++++++++++++++++-- vllm/lora/lora.py | 18 +++++++++ vllm/lora/models.py | 42 ++++++++------------ vllm/lora/peft_helper.py | 70 +++++++++++++++++++++++++++++++++ 4 files changed, 160 insertions(+), 28 deletions(-) create mode 100644 vllm/lora/peft_helper.py diff --git a/tests/lora/test_lora_manager.py b/tests/lora/test_lora_manager.py index 8d109b2c81503..0b76f466702fc 100644 --- a/tests/lora/test_lora_manager.py +++ b/tests/lora/test_lora_manager.py @@ -1,3 +1,4 @@ +import json import os from typing import Dict, List @@ -13,6 +14,7 @@ from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights from vllm.lora.models import (LoRAMapping, LoRAModel, LoRAModelManager, LRUCacheLoRAModelManager) +from vllm.lora.peft_helper import PEFTHelper from vllm.lora.request import LoRARequest from vllm.lora.worker_manager import (LRUCacheWorkerLoRAManager, WorkerLoRAManager) @@ -30,18 +32,68 @@ ] +def test_peft_helper(sql_lora_files): + lora_config_path = os.path.join(sql_lora_files, "adapter_config.json") + with open(lora_config_path) as f: + config = json.load(f) + peft_helper = PEFTHelper.from_dict(config) + assert peft_helper.r == 8 + assert peft_helper.lora_alpha == 16 + assert peft_helper.target_modules == [ + "q_proj", + "v_proj", + "k_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + "embed_tokens", + "lm_head", + ] + + expected_error = "vLLM only supports modules_to_save being None." + with pytest.raises(ValueError, match=expected_error): + config = dict( + r=8, + lora_alpha=16, + target_modules=["gate_proj"], + modules_to_save=["lm_head"], + ) + PEFTHelper.from_dict(config) + expected_error = "vLLM does not yet support RSLoRA." + with pytest.raises(ValueError, match=expected_error): + config = dict(r=8, + lora_alpha=16, + target_modules=["gate_proj"], + use_rslora=True) + PEFTHelper.from_dict(config) + + expected_error = "vLLM does not yet support DoRA." + with pytest.raises(ValueError, match=expected_error): + config = dict(r=8, + lora_alpha=16, + target_modules=["gate_proj"], + use_dora=True) + PEFTHelper.from_dict(config) + + @pytest.mark.parametrize("device", CUDA_DEVICES) def test_from_lora_tensors(sql_lora_files, device): tensors = load_file( os.path.join(sql_lora_files, "adapter_model.safetensors")) new_embeddings = load_file( os.path.join(sql_lora_files, "new_embeddings.safetensors")) + + lora_config_path = os.path.join(sql_lora_files, "adapter_config.json") + with open(lora_config_path) as f: + config = json.load(f) + + peft_helper = PEFTHelper.from_dict(config) lora_model = LoRAModel.from_lora_tensors( 1, - 8, - 16, tensors, - device, + peft_helper=peft_helper, + device=device, embeddings=new_embeddings, embedding_modules=EMBEDDING_MODULES, embedding_padding_modules=EMBEDDING_PADDING_MODULES) diff --git a/vllm/lora/lora.py b/vllm/lora/lora.py index b648312ba76ec..dde347b78bf81 100644 --- a/vllm/lora/lora.py +++ b/vllm/lora/lora.py @@ -4,6 +4,7 @@ import torch import torch.types +from vllm.lora.peft_helper import PEFTHelper from vllm.utils import is_pin_memory_available @@ -59,6 +60,23 @@ def extra_vocab_size(self) -> int: return self.embeddings_tensor.shape[ 0] if self.embeddings_tensor is not None else 0 + @classmethod + def from_config( + cls, + module_name: str, + peft_helper: PEFTHelper, + embeddings_tensor: Optional[torch.Tensor] = None, + ) -> "LoRALayerWeights": + return cls( + module_name, + peft_helper.r, + peft_helper.lora_alpha, + None, + None, + None, + embeddings_tensor, + ) + @classmethod def create_dummy_lora_weights( cls, diff --git a/vllm/lora/models.py b/vllm/lora/models.py index 49cd9f0c236ad..70806a77b9fff 100644 --- a/vllm/lora/models.py +++ b/vllm/lora/models.py @@ -21,6 +21,7 @@ LinearScalingRotaryEmbeddingWithLora, LoRAMapping) from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights +from vllm.lora.peft_helper import PEFTHelper from vllm.lora.punica_wrapper import get_punica_wrapper from vllm.lora.utils import (from_layer, from_layer_logits_processor, is_regex_target_modules, @@ -104,14 +105,12 @@ def get_lora(self, module_name: str) -> Optional[LoRALayerWeights]: def from_lora_tensors( cls, lora_model_id: int, - rank: int, - lora_alpha: int, tensors: Dict[str, torch.Tensor], + peft_helper: PEFTHelper, device: str = "cuda", dtype: Optional[torch.dtype] = None, embeddings: Optional[Dict[str, torch.Tensor]] = None, target_embedding_padding: Optional[int] = None, - scaling_factor: Optional[float] = None, embedding_modules: Optional[Dict[str, str]] = None, embedding_padding_modules: Optional[List[str]] = None, ) -> "LoRAModel": @@ -135,10 +134,9 @@ def from_lora_tensors( if pin_memory: lora_embeddings_tensor = ( lora_embeddings_tensor.pin_memory()) - loras[module_name] = LoRALayerWeights(module_name, rank, - lora_alpha, None, None, - None, - lora_embeddings_tensor) + loras[module_name] = LoRALayerWeights.from_config( + module_name, peft_helper, lora_embeddings_tensor) + if is_bias: loras[module_name].bias = tensor.to(device=device, dtype=dtype).t() @@ -170,7 +168,11 @@ def from_lora_tensors( for lora in loras.values(): lora.optimize() - return cls(lora_model_id, rank, loras, scaling_factor=scaling_factor) + + return cls(lora_model_id, + peft_helper.r, + loras, + scaling_factor=peft_helper.vllm_scaling_factor) @classmethod def from_local_checkpoint( @@ -212,6 +214,9 @@ def from_local_checkpoint( "new_embeddings.bin") with open(lora_config_path) as f: config = json.load(f) + + config["vllm_max_position_embeddings"] = max_position_embeddings + peft_helper = PEFTHelper.from_dict(config) if os.path.isfile(lora_tensor_path): tensors: Dict[str, torch.Tensor] = {} # Find unexpected modules. @@ -242,7 +247,7 @@ def from_local_checkpoint( # When a bin file is provided, we rely on config to find unexpected # modules. unexpected_modules = [] - target_modules = config["target_modules"] + target_modules = peft_helper.target_modules if not isinstance(target_modules, list): target_modules = [target_modules] for module in target_modules: @@ -256,7 +261,7 @@ def from_local_checkpoint( # https://github.com/vllm-project/vllm/pull/5909. But there's no # other better mechanism. if unexpected_modules and not is_regex_target_modules( - config["target_modules"], expected_lora_modules): + peft_helper.target_modules, expected_lora_modules): raise ValueError( f"While loading {lora_dir}, expected" f" target modules in {expected_lora_modules}" @@ -274,30 +279,17 @@ def from_local_checkpoint( embeddings = torch.load(new_embeddings_bin_file_path, map_location=device) - rank = config["r"] - lora_alpha = config["lora_alpha"] - context_length = config.get("context_length", None) - scaling_factor = None - if context_length: - if max_position_embeddings is None: - max_position_embeddings = context_length - scaling_factor = float( - math.ceil(context_length / max_position_embeddings)) - return cls.from_lora_tensors( lora_model_id=get_lora_id() if lora_model_id is None else lora_model_id, - rank=rank, - lora_alpha=lora_alpha, tensors=tensors, + peft_helper=peft_helper, device=device, dtype=dtype, embeddings=embeddings, target_embedding_padding=target_embedding_padding, - scaling_factor=scaling_factor, embedding_modules=embedding_modules, - embedding_padding_modules=embedding_padding_modules, - ) + embedding_padding_modules=embedding_padding_modules) class LoRAModelManager(AdapterModelManager): diff --git a/vllm/lora/peft_helper.py b/vllm/lora/peft_helper.py new file mode 100644 index 0000000000000..edf4ba5659575 --- /dev/null +++ b/vllm/lora/peft_helper.py @@ -0,0 +1,70 @@ +# Adapted from: https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/config.py + +import math +from dataclasses import MISSING, dataclass, field, fields +from typing import Literal, Optional, Union + + +@dataclass +class PEFTHelper: + # Required fields + r: int + lora_alpha: int + target_modules: Union[list[str], str] + + bias: Literal["none", "all", "lora_only"] = field(default="none") + modules_to_save: Optional[list[str]] = field(default=None) + use_rslora: bool = field(default=False) + use_dora: bool = field(default=False) + # long lora field + context_length: int = field(default=0) + # Extra vllm field, start with 'vllm_' to avoid conflict + vllm_max_position_embeddings: Optional[int] = field(default=False) + vllm_scaling_factor: Optional[float] = field(default=None) + + def _validate_features(self): + error_msg = [] + + if self.modules_to_save: + error_msg.append("vLLM only supports modules_to_save being None.") + if self.use_rslora: + error_msg.append("vLLM does not yet support RSLoRA.") + + if self.use_dora: + error_msg.append("vLLM does not yet support DoRA.") + + if error_msg: + raise ValueError(f"{', '.join(error_msg)}") + + def __post_init__(self): + self._validate_features() + if self.context_length: + if self.vllm_max_position_embeddings is None: + self.vllm_max_position_embeddings = self.context_length + self.vllm_scaling_factor = float( + math.ceil(self.context_length / + self.vllm_max_position_embeddings)) + + @classmethod + def from_dict(cls, config_dict: dict) -> "PEFTHelper": + # Get all field information from the class + class_fields = {f.name: f for f in fields(cls)} + # Check for required fields + required_fields = { + name + for name, f in class_fields.items() + if f.default is MISSING and f.default_factory is MISSING + } + + # Identify any missing required fields + missing_fields = required_fields - set(config_dict.keys()) + if missing_fields: + raise ValueError( + f"Missing required configuration fields: {missing_fields}") + + # Filter out fields that aren't defined in the class + filtered_dict = { + k: v + for k, v in config_dict.items() if k in class_fields + } + return cls(**filtered_dict) From 9b9cef3145381721fa950c89718fe71849ac2a55 Mon Sep 17 00:00:00 2001 From: Joe Runde Date: Tue, 10 Dec 2024 09:38:23 -0700 Subject: [PATCH 012/155] [Bugfix] Backport request id validation to v0 (#11036) Signed-off-by: Joe Runde --- vllm/engine/multiprocessing/client.py | 4 ++++ vllm/v1/engine/async_llm.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/vllm/engine/multiprocessing/client.py b/vllm/engine/multiprocessing/client.py index 32bd83305bb8f..a729023bc00bb 100644 --- a/vllm/engine/multiprocessing/client.py +++ b/vllm/engine/multiprocessing/client.py @@ -576,6 +576,10 @@ async def _process_request( if self._errored_with is not None: raise ENGINE_DEAD_ERROR(self._errored_with) + # Ensure the request id is unique among running requests + if request_id in self.output_queues: + raise ValueError(f"Request {request_id} already exists") + # Constructing guided decoding logits processors is expensive, so we do # it here to avoid contending with cpu resources and the GIL on the # backend process. diff --git a/vllm/v1/engine/async_llm.py b/vllm/v1/engine/async_llm.py index 26fd650aee4b7..24cafeff63d1e 100644 --- a/vllm/v1/engine/async_llm.py +++ b/vllm/v1/engine/async_llm.py @@ -152,7 +152,7 @@ async def add_request( """Add new request to the AsyncLLM.""" if self.detokenizer.is_request_active(request_id): - raise KeyError(f"Request {request_id} already exists.") + raise ValueError(f"Request {request_id} already exists.") # 1) Create a new AsyncStream for the request. stream = self._add_request_to_streams(request_id) From 250ee65d72a0c7b86ec5cea9cbe9377da21d6439 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fl=C3=A1via=20B=C3=A9o?= <119421251+flaviabeo@users.noreply.github.com> Date: Tue, 10 Dec 2024 14:38:15 -0300 Subject: [PATCH 013/155] [BUG] Remove token param #10921 (#11022) Signed-off-by: Flavia Beo --- vllm/transformers_utils/config.py | 63 ++++++++++++++----------------- 1 file changed, 29 insertions(+), 34 deletions(-) diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index 3da99bcbee9ae..4529cf27ef565 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -1,5 +1,6 @@ import enum import json +import os from pathlib import Path from typing import Any, Dict, Optional, Type, Union @@ -41,6 +42,7 @@ from transformers import AutoConfig MISTRAL_CONFIG_NAME = "params.json" +HF_TOKEN = os.getenv('HF_TOKEN', None) logger = init_logger(__name__) @@ -77,8 +79,8 @@ class ConfigFormat(str, enum.Enum): MISTRAL = "mistral" -def file_or_path_exists(model: Union[str, Path], config_name, revision, - token) -> bool: +def file_or_path_exists(model: Union[str, Path], config_name: str, + revision: Optional[str]) -> bool: if Path(model).exists(): return (Path(model) / config_name).is_file() @@ -93,7 +95,10 @@ def file_or_path_exists(model: Union[str, Path], config_name, revision, # NB: file_exists will only check for the existence of the config file on # hf_hub. This will fail in offline mode. try: - return file_exists(model, config_name, revision=revision, token=token) + return file_exists(model, + config_name, + revision=revision, + token=HF_TOKEN) except huggingface_hub.errors.OfflineModeIsEnabled: # Don't raise in offline mode, all we know is that we don't have this # file cached. @@ -161,7 +166,6 @@ def get_config( revision: Optional[str] = None, code_revision: Optional[str] = None, config_format: ConfigFormat = ConfigFormat.AUTO, - token: Optional[str] = None, **kwargs, ) -> PretrainedConfig: # Separate model folder from file path for GGUF models @@ -173,19 +177,20 @@ def get_config( if config_format == ConfigFormat.AUTO: if is_gguf or file_or_path_exists( - model, HF_CONFIG_NAME, revision=revision, token=token): + model, HF_CONFIG_NAME, revision=revision): config_format = ConfigFormat.HF - elif file_or_path_exists(model, - MISTRAL_CONFIG_NAME, - revision=revision, - token=token): + elif file_or_path_exists(model, MISTRAL_CONFIG_NAME, + revision=revision): config_format = ConfigFormat.MISTRAL else: # If we're in offline mode and found no valid config format, then # raise an offline mode error to indicate to the user that they # don't have files cached and may need to go online. # This is conveniently triggered by calling file_exists(). - file_exists(model, HF_CONFIG_NAME, revision=revision, token=token) + file_exists(model, + HF_CONFIG_NAME, + revision=revision, + token=HF_TOKEN) raise ValueError(f"No supported config format found in {model}") @@ -194,7 +199,7 @@ def get_config( model, revision=revision, code_revision=code_revision, - token=token, + token=HF_TOKEN, **kwargs, ) @@ -206,7 +211,7 @@ def get_config( model, revision=revision, code_revision=code_revision, - token=token, + token=HF_TOKEN, **kwargs, ) else: @@ -216,7 +221,7 @@ def get_config( trust_remote_code=trust_remote_code, revision=revision, code_revision=code_revision, - token=token, + token=HF_TOKEN, **kwargs, ) except ValueError as e: @@ -234,7 +239,7 @@ def get_config( raise e elif config_format == ConfigFormat.MISTRAL: - config = load_params_config(model, revision, token=token, **kwargs) + config = load_params_config(model, revision, token=HF_TOKEN, **kwargs) else: raise ValueError(f"Unsupported config format: {config_format}") @@ -256,8 +261,7 @@ def get_config( def get_hf_file_to_dict(file_name: str, model: Union[str, Path], - revision: Optional[str] = 'main', - token: Optional[str] = None): + revision: Optional[str] = 'main'): """ Downloads a file from the Hugging Face Hub and returns its contents as a dictionary. @@ -266,7 +270,6 @@ def get_hf_file_to_dict(file_name: str, - file_name (str): The name of the file to download. - model (str): The name of the model on the Hugging Face Hub. - revision (str): The specific version of the model. - - token (str): The Hugging Face authentication token. Returns: - config_dict (dict): A dictionary containing @@ -276,8 +279,7 @@ def get_hf_file_to_dict(file_name: str, if file_or_path_exists(model=model, config_name=file_name, - revision=revision, - token=token): + revision=revision): if not file_path.is_file(): try: @@ -296,9 +298,7 @@ def get_hf_file_to_dict(file_name: str, return None -def get_pooling_config(model: str, - revision: Optional[str] = 'main', - token: Optional[str] = None): +def get_pooling_config(model: str, revision: Optional[str] = 'main'): """ This function gets the pooling and normalize config from the model - only applies to @@ -315,8 +315,7 @@ def get_pooling_config(model: str, """ modules_file_name = "modules.json" - modules_dict = get_hf_file_to_dict(modules_file_name, model, revision, - token) + modules_dict = get_hf_file_to_dict(modules_file_name, model, revision) if modules_dict is None: return None @@ -332,8 +331,7 @@ def get_pooling_config(model: str, if pooling: pooling_file_name = "{}/config.json".format(pooling["path"]) - pooling_dict = get_hf_file_to_dict(pooling_file_name, model, revision, - token) + pooling_dict = get_hf_file_to_dict(pooling_file_name, model, revision) pooling_type_name = next( (item for item, val in pooling_dict.items() if val is True), None) @@ -368,8 +366,8 @@ def get_pooling_config_name(pooling_name: str) -> Union[str, None]: def get_sentence_transformer_tokenizer_config(model: str, - revision: Optional[str] = 'main', - token: Optional[str] = None): + revision: Optional[str] = 'main' + ): """ Returns the tokenization configuration dictionary for a given Sentence Transformer BERT model. @@ -379,7 +377,6 @@ def get_sentence_transformer_tokenizer_config(model: str, BERT model. - revision (str, optional): The revision of the m odel to use. Defaults to 'main'. - - token (str): A Hugging Face access token. Returns: - dict: A dictionary containing the configuration parameters @@ -394,7 +391,7 @@ def get_sentence_transformer_tokenizer_config(model: str, "sentence_xlm-roberta_config.json", "sentence_xlnet_config.json", ]: - encoder_dict = get_hf_file_to_dict(config_name, model, revision, token) + encoder_dict = get_hf_file_to_dict(config_name, model, revision) if encoder_dict: break @@ -474,16 +471,14 @@ def _reduce_config(config: VllmConfig): exc_info=e) -def load_params_config(model: Union[str, Path], - revision: Optional[str], - token: Optional[str] = None, +def load_params_config(model: Union[str, Path], revision: Optional[str], **kwargs) -> PretrainedConfig: # This function loads a params.json config which # should be used when loading models in mistral format config_file_name = "params.json" - config_dict = get_hf_file_to_dict(config_file_name, model, revision, token) + config_dict = get_hf_file_to_dict(config_file_name, model, revision) assert isinstance(config_dict, dict) config_mapping = { From e7391949267a4eff3d84f02119f442f46b16d163 Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Tue, 10 Dec 2024 15:08:16 -0500 Subject: [PATCH 014/155] [Core] Update to outlines >= 0.1.8 (#10576) Signed-off-by: Russell Bryant --- requirements-common.txt | 2 +- .../guided_decoding/outlines_logits_processors.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements-common.txt b/requirements-common.txt index 112528880c0ac..c71fc458aca13 100644 --- a/requirements-common.txt +++ b/requirements-common.txt @@ -18,7 +18,7 @@ prometheus_client >= 0.18.0 prometheus-fastapi-instrumentator >= 7.0.0 tiktoken >= 0.6.0 # Required for DBRX tokenizer lm-format-enforcer >= 0.10.9, < 0.11 -outlines >= 0.0.43, < 0.1 +outlines >= 0.1.8 xgrammar >= 0.1.6; platform_machine == "x86_64" typing_extensions >= 4.10 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317 diff --git a/vllm/model_executor/guided_decoding/outlines_logits_processors.py b/vllm/model_executor/guided_decoding/outlines_logits_processors.py index e1309c31f77e7..1f0dbe024609d 100644 --- a/vllm/model_executor/guided_decoding/outlines_logits_processors.py +++ b/vllm/model_executor/guided_decoding/outlines_logits_processors.py @@ -99,7 +99,7 @@ class RegexLogitsProcessor(BaseLogitsProcessor): def _get_guide(cls, regex_string: str, tokenizer: PreTrainedTokenizerBase) -> Guide: tokenizer = _adapt_tokenizer(tokenizer) - return RegexGuide(regex_string, tokenizer) + return RegexGuide.from_regex(regex_string, tokenizer) def __init__(self, regex_string: str, tokenizer: PreTrainedTokenizerBase): """Compile the FSM that drives the regex-structured generation. From 75f89dc44c6e44cc28bae59d5b40a588735b507b Mon Sep 17 00:00:00 2001 From: youkaichao Date: Tue, 10 Dec 2024 12:40:52 -0800 Subject: [PATCH 015/155] [torch.compile] add a flag to track batchsize statistics (#11059) Signed-off-by: youkaichao --- vllm/envs.py | 3 +++ vllm/forward_context.py | 32 +++++++++++++++++++++++- vllm/v1/attention/backends/flash_attn.py | 1 + vllm/v1/worker/gpu_model_runner.py | 2 ++ 4 files changed, 37 insertions(+), 1 deletion(-) diff --git a/vllm/envs.py b/vllm/envs.py index ab12a7b48dc53..be5d9985b63a4 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -69,6 +69,7 @@ VLLM_DISABLED_KERNELS: List[str] = [] VLLM_USE_V1: bool = False VLLM_ENABLE_V1_MULTIPROCESSING: bool = False + VLLM_LOG_BATCHSIZE_INTERVAL: float = -1 def get_default_cache_root(): @@ -452,6 +453,8 @@ def get_default_config_root(): # If set, enable multiprocessing in LLM for the V1 code path. "VLLM_ENABLE_V1_MULTIPROCESSING": lambda: bool(int(os.getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0"))), + "VLLM_LOG_BATCHSIZE_INTERVAL": + lambda: float(os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")), } # end-env-vars-definition diff --git a/vllm/forward_context.py b/vllm/forward_context.py index aaa3e4bb3a1e8..cd136f43c0c57 100644 --- a/vllm/forward_context.py +++ b/vllm/forward_context.py @@ -1,8 +1,19 @@ +import time +from collections import Counter from contextlib import contextmanager from dataclasses import dataclass from typing import Any, Dict, Optional +import vllm.envs as envs from vllm.config import VllmConfig +from vllm.logger import init_logger + +logger = init_logger(__name__) + +track_batchsize: bool = envs.VLLM_LOG_BATCHSIZE_INTERVAL >= 0 +batchsize_counter: Counter = Counter() +last_logging_time: float = 0 +batchsize_logging_interval: float = envs.VLLM_LOG_BATCHSIZE_INTERVAL @dataclass @@ -26,7 +37,26 @@ def get_forward_context() -> ForwardContext: @contextmanager def set_forward_context(context: Any, vllm_config: VllmConfig): """A context manager that stores the current forward context, - can be attention metadata, etc.""" + can be attention metadata, etc. + Here we can inject common logic for every model forward pass. + """ + global track_batchsize, batchsize_counter + global last_logging_time, batchsize_logging_interval + if track_batchsize and context is not None: + if hasattr(context, "num_prefill_tokens"): + # for v0 attention backends + batchsize = context.num_prefill_tokens + context.num_decode_tokens + else: + # for v1 attention backends + batchsize = context.num_input_tokens + batchsize_counter[batchsize] += 1 + if time.monotonic() - last_logging_time > batchsize_logging_interval: + last_logging_time = time.monotonic() + sorted_data = sorted(batchsize_counter.items(), + key=lambda x: x[1], + reverse=True) + logger.info("Batchsize distribution (batchsize, count): %s", + sorted_data) global _forward_context prev_context = _forward_context _forward_context = ForwardContext( diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py index 251a103e60f06..c9f04ace644c7 100644 --- a/vllm/v1/attention/backends/flash_attn.py +++ b/vllm/v1/attention/backends/flash_attn.py @@ -56,6 +56,7 @@ class FlashAttentionMetadata: seq_start_loc: torch.Tensor block_table: torch.Tensor slot_mapping: torch.Tensor + num_input_tokens: int = 0 # Number of tokens including padding. class FlashAttentionImpl(AttentionImpl): diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 0a5adfb28c9bd..a3335fa838352 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -445,6 +445,8 @@ def execute_model( # Eager mode. num_input_tokens = num_scheduled_tokens + attn_metadata.num_input_tokens = num_input_tokens + # Get the inputs embeds. if encoder_outputs: inputs_embeds = self.model.get_input_embeddings( From 134810b3d9a05510622282479f0f9e2114b88017 Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Tue, 10 Dec 2024 14:41:23 -0800 Subject: [PATCH 016/155] [V1][Bugfix] Always set enable_chunked_prefill = True for V1 (#11061) Signed-off-by: Woosuk Kwon --- vllm/engine/arg_utils.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 3db069ec64ee4..7b9adc401abcf 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -122,7 +122,7 @@ class EngineArgs: cpu_offload_gb: float = 0 # GiB gpu_memory_utilization: float = 0.90 max_num_batched_tokens: Optional[int] = None - max_num_seqs: int = 256 + max_num_seqs: Optional[int] = None max_logprobs: int = 20 # Default value for OpenAI Chat Completions API disable_log_stats: bool = False revision: Optional[str] = None @@ -205,6 +205,9 @@ def __post_init__(self): # by user. if self.enable_prefix_caching is None: self.enable_prefix_caching = bool(envs.VLLM_USE_V1) + # Override max_num_seqs if it's not set by user. + if self.max_num_seqs is None: + self.max_num_seqs = 256 if not envs.VLLM_USE_V1 else 1024 # support `EngineArgs(compilation_config={...})` # without having to manually construct a @@ -1225,19 +1228,19 @@ def _override_v1_engine_args(self, usage_context: UsageContext) -> None: """ assert envs.VLLM_USE_V1, "V1 is not enabled" + # V1 always uses chunked prefills. + self.enable_chunked_prefill = True + # When no user override, set the default values based on the usage + # context. + # TODO(woosuk): Tune the default values for different hardware. if self.max_num_batched_tokens is None: - # When no user override, set the default values based on the - # usage context. if usage_context == UsageContext.LLM_CLASS: - logger.warning("Setting max_num_batched_tokens to 8192 " - "for LLM_CLASS usage context.") - self.max_num_seqs = 1024 self.max_num_batched_tokens = 8192 elif usage_context == UsageContext.OPENAI_API_SERVER: - logger.warning("Setting max_num_batched_tokens to 2048 " - "for OPENAI_API_SERVER usage context.") - self.max_num_seqs = 1024 self.max_num_batched_tokens = 2048 + logger.warning( + "Setting max_num_batched_tokens to %d for %s usage context.", + self.max_num_batched_tokens, usage_context.value) def _override_v1_engine_config(self, engine_config: VllmConfig) -> None: """ From 9a93973708d7f52f1d1439f8f32b8c1514d18b86 Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Tue, 10 Dec 2024 19:16:22 -0500 Subject: [PATCH 017/155] [Bugfix] Fix Mamba multistep (#11071) Signed-off-by: Tyler Michael Smith --- vllm/attention/backends/placeholder_attn.py | 64 ++++++++++++++++++++- vllm/worker/multi_step_model_runner.py | 4 +- 2 files changed, 66 insertions(+), 2 deletions(-) diff --git a/vllm/attention/backends/placeholder_attn.py b/vllm/attention/backends/placeholder_attn.py index 888adbffb8578..658039bfc3365 100644 --- a/vllm/attention/backends/placeholder_attn.py +++ b/vllm/attention/backends/placeholder_attn.py @@ -11,7 +11,8 @@ from vllm.multimodal import MultiModalPlaceholderMap if TYPE_CHECKING: - from vllm.worker.model_runner import ModelInputForGPUBuilder + from vllm.worker.model_runner import (ModelInputForGPUBuilder, + ModelInputForGPUWithSamplingMetadata) # Placeholder attention backend for models like Mamba and embedding models that # lack attention. @@ -186,6 +187,67 @@ def decode_metadata(self) -> Optional["PlaceholderAttentionMetadata"]: ) return self._cached_decode_metadata + def advance_step(self, + model_input: "ModelInputForGPUWithSamplingMetadata", + sampled_token_ids: Optional[torch.Tensor], + block_size: int, + num_seqs: int, + num_queries: int, + turn_prefills_into_decodes: bool = False): + """ + Update metadata in-place to advance one decode step. + """ + # When using cudagraph, the num_seqs is padded to the next captured + # batch sized, but num_queries tracks the actual number of requests in + # the batch. For --enforce-eager mode, num_seqs == num_queries + if num_seqs != num_queries: + assert num_seqs > num_queries + assert self.use_cuda_graph + + assert not turn_prefills_into_decodes, \ + ("Multi-Step + Chunked-Prefill is not supported for attention-free" + "models. turn_prefills_into_decodes is a " + "Multi-Step + Chunked-Prefill specific parameter.") + + assert self.seq_lens is not None + assert self.max_decode_seq_len == max(self.seq_lens) + + assert self.num_prefills == 0 + assert self.num_prefill_tokens == 0 + assert self.num_decode_tokens == num_seqs + + assert self.seq_lens is not None + assert len(self.seq_lens) == num_seqs + assert self.seq_lens_tensor is not None + assert self.seq_lens_tensor.shape == (num_seqs, ) + assert self.max_query_len == 1 + assert self.max_prefill_seq_len == 0 + + assert self.query_start_loc is not None + assert self.query_start_loc.shape == (num_queries + 1, ) + assert self.seq_start_loc is not None + assert self.seq_start_loc.shape == (num_seqs + 1, ) + + assert self.context_lens_tensor is not None + assert self.context_lens_tensor.shape == (num_queries, ) + + assert self.block_tables is not None + + # Update query lengths. Note that we update only queries and not seqs, + # since tensors may be padded due to captured cuda graph batch size + for i in range(num_queries): + self.seq_lens[i] += 1 + self.max_decode_seq_len = max(self.seq_lens) + + # Update sequences, masking off entries greater than num_queries + device = self.seq_lens_tensor.device + mask = torch.arange(self.seq_lens_tensor.size(0), + device=device) < num_queries + self.seq_lens_tensor += mask.to(self.seq_lens_tensor.dtype) + if sampled_token_ids is not None: + model_input.input_tokens.masked_scatter_( + mask, sampled_token_ids[:num_queries]) + class PlaceholderAttentionMetadataBuilder( AttentionMetadataBuilder[PlaceholderAttentionMetadata]): diff --git a/vllm/worker/multi_step_model_runner.py b/vllm/worker/multi_step_model_runner.py index 3ca0d88a42183..e08a61e31fe42 100644 --- a/vllm/worker/multi_step_model_runner.py +++ b/vllm/worker/multi_step_model_runner.py @@ -29,7 +29,9 @@ logger = init_logger(__name__) -MULTI_STEP_ATTENTION_BACKENDS = ["FLASH_ATTN", "ROCM_FLASH", "FLASHINFER"] +MULTI_STEP_ATTENTION_BACKENDS = [ + "FLASH_ATTN", "ROCM_FLASH", "FLASHINFER", "NO_ATTENTION" +] MULTI_STEP_CHUNKED_PREFILL_ATTENTION_BACKENDS = ["FLASH_ATTN"] def _get_supported_attention_backends(chunked_prefill_enabled: bool) \ From d5c5154fcf4c5d65551c98e458cbb027e5f4b672 Mon Sep 17 00:00:00 2001 From: Aurick Qiao Date: Tue, 10 Dec 2024 21:09:20 -0500 Subject: [PATCH 018/155] [Misc] LoRA + Chunked Prefill (#9057) --- tests/lora/test_chatglm3_tp.py | 9 ++++++--- tests/lora/test_gemma.py | 3 ++- tests/lora/test_llama_tp.py | 6 +++++- tests/lora/test_long_context.py | 3 ++- tests/lora/test_minicpmv.py | 3 ++- tests/lora/test_minicpmv_tp.py | 2 ++ tests/lora/test_mixtral.py | 1 + tests/lora/test_phi.py | 3 ++- tests/lora/test_quant_model.py | 9 ++++++--- vllm/config.py | 3 ++- vllm/core/scheduler.py | 15 ++++++++++++--- vllm/worker/model_runner.py | 12 +++++++----- 12 files changed, 49 insertions(+), 20 deletions(-) diff --git a/tests/lora/test_chatglm3_tp.py b/tests/lora/test_chatglm3_tp.py index f17464573459f..49a527b99ac16 100644 --- a/tests/lora/test_chatglm3_tp.py +++ b/tests/lora/test_chatglm3_tp.py @@ -53,7 +53,8 @@ def test_chatglm3_lora(chatglm3_lora_files): max_loras=4, max_lora_rank=64, tensor_parallel_size=1, - trust_remote_code=True) + trust_remote_code=True, + enable_chunked_prefill=True) output1 = do_sample(llm, chatglm3_lora_files, lora_id=1) for i in range(len(EXPECTED_LORA_OUTPUT)): @@ -73,7 +74,8 @@ def test_chatglm3_lora_tp4(chatglm3_lora_files): max_lora_rank=64, tensor_parallel_size=4, trust_remote_code=True, - fully_sharded_loras=False) + fully_sharded_loras=False, + enable_chunked_prefill=True) output1 = do_sample(llm, chatglm3_lora_files, lora_id=1) for i in range(len(EXPECTED_LORA_OUTPUT)): @@ -93,7 +95,8 @@ def test_chatglm3_lora_tp4_fully_sharded_loras(chatglm3_lora_files): max_lora_rank=64, tensor_parallel_size=4, trust_remote_code=True, - fully_sharded_loras=True) + fully_sharded_loras=True, + enable_chunked_prefill=True) output1 = do_sample(llm, chatglm3_lora_files, lora_id=1) for i in range(len(EXPECTED_LORA_OUTPUT)): assert output1[i] == EXPECTED_LORA_OUTPUT[i] diff --git a/tests/lora/test_gemma.py b/tests/lora/test_gemma.py index 15ec66b0f5502..5ae705e474ec6 100644 --- a/tests/lora/test_gemma.py +++ b/tests/lora/test_gemma.py @@ -37,7 +37,8 @@ def test_gemma_lora(gemma_lora_files): llm = vllm.LLM(MODEL_PATH, max_model_len=1024, enable_lora=True, - max_loras=4) + max_loras=4, + enable_chunked_prefill=True) expected_lora_output = [ "more important than knowledge.\nAuthor: Albert Einstein\n", diff --git a/tests/lora/test_llama_tp.py b/tests/lora/test_llama_tp.py index d3ca7f878191a..dfeac380951d8 100644 --- a/tests/lora/test_llama_tp.py +++ b/tests/lora/test_llama_tp.py @@ -78,7 +78,8 @@ def test_llama_lora(sql_lora_files): enable_lora=True, max_num_seqs=16, max_loras=4, - tensor_parallel_size=1) + tensor_parallel_size=1, + enable_chunked_prefill=True) generate_and_test(llm, sql_lora_files) @@ -120,6 +121,7 @@ def test_llama_lora_tp4(sql_lora_files): max_num_seqs=16, max_loras=4, tensor_parallel_size=4, + enable_chunked_prefill=True, ) generate_and_test(llm, sql_lora_files) @@ -135,6 +137,7 @@ def test_llama_lora_tp4_fully_sharded_loras(sql_lora_files): max_loras=4, tensor_parallel_size=4, fully_sharded_loras=True, + enable_chunked_prefill=True, ) generate_and_test(llm, sql_lora_files) @@ -151,5 +154,6 @@ def test_llama_lora_tp4_fully_sharded_enable_bias(sql_lora_files): tensor_parallel_size=4, fully_sharded_loras=True, enable_lora_bias=True, + enable_chunked_prefill=True, ) generate_and_test(llm, sql_lora_files) diff --git a/tests/lora/test_long_context.py b/tests/lora/test_long_context.py index eada902c891f7..e7a34f2ced7ed 100644 --- a/tests/lora/test_long_context.py +++ b/tests/lora/test_long_context.py @@ -124,7 +124,8 @@ def lora_llm(long_context_infos): tensor_parallel_size=4, # FIXME enable async output processor disable_async_output_proc=True, - distributed_executor_backend="mp") + distributed_executor_backend="mp", + enable_chunked_prefill=True) yield llm del llm diff --git a/tests/lora/test_minicpmv.py b/tests/lora/test_minicpmv.py index 2c45ce5141f7d..1f3de9edc0d0f 100644 --- a/tests/lora/test_minicpmv.py +++ b/tests/lora/test_minicpmv.py @@ -67,7 +67,8 @@ def test_minicpmv_lora(minicpmv_lora_files): max_loras=4, max_lora_rank=64, trust_remote_code=True, - gpu_memory_utilization=0.97 # This model is pretty big for CI gpus + gpu_memory_utilization=0.97, # This model is pretty big for CI gpus + enable_chunked_prefill=True, ) output1 = do_sample(llm, minicpmv_lora_files, lora_id=1) for i in range(len(EXPECTED_OUTPUT)): diff --git a/tests/lora/test_minicpmv_tp.py b/tests/lora/test_minicpmv_tp.py index ba29e562e58ec..930f177953a5f 100644 --- a/tests/lora/test_minicpmv_tp.py +++ b/tests/lora/test_minicpmv_tp.py @@ -69,6 +69,7 @@ def test_minicpmv_tp2(minicpmv_lora_files, fully_sharded): tensor_parallel_size=2, trust_remote_code=True, fully_sharded_loras=fully_sharded, + enable_chunked_prefill=True, ) output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1) @@ -89,6 +90,7 @@ def test_minicpmv_tp4(minicpmv_lora_files, fully_sharded): tensor_parallel_size=4, trust_remote_code=True, fully_sharded_loras=fully_sharded, + enable_chunked_prefill=True, ) output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1) for i in range(len(EXPECTED_OUTPUT)): diff --git a/tests/lora/test_mixtral.py b/tests/lora/test_mixtral.py index dddc299da446b..150221dfce6ab 100644 --- a/tests/lora/test_mixtral.py +++ b/tests/lora/test_mixtral.py @@ -47,6 +47,7 @@ def test_mixtral_lora(mixtral_lora_files, tp_size): max_loras=4, distributed_executor_backend="ray", tensor_parallel_size=tp_size, + enable_chunked_prefill=True, ) expected_lora_output = [ diff --git a/tests/lora/test_phi.py b/tests/lora/test_phi.py index 733eff48a9bf3..5a3fcb8d690d9 100644 --- a/tests/lora/test_phi.py +++ b/tests/lora/test_phi.py @@ -53,7 +53,8 @@ def test_phi2_lora(phi2_lora_files): max_model_len=1024, enable_lora=True, max_loras=2, - enforce_eager=True) + enforce_eager=True, + enable_chunked_prefill=True) expected_lora_output = [ "SELECT catalog_publisher, COUNT(*) as num_catalogs FROM catalogs GROUP BY catalog_publisher ORDER BY num_catalogs DESC LIMIT 1;", # noqa: E501 diff --git a/tests/lora/test_quant_model.py b/tests/lora/test_quant_model.py index 5432fa4ad0d3a..026269667b473 100644 --- a/tests/lora/test_quant_model.py +++ b/tests/lora/test_quant_model.py @@ -84,7 +84,8 @@ def test_quant_model_lora(tinyllama_lora_files, num_gpus_available, model, tensor_parallel_size=tp_size, gpu_memory_utilization=0.2, #avoid OOM quantization=model.quantization, - trust_remote_code=True) + trust_remote_code=True, + enable_chunked_prefill=True) if model.quantization is None: expected_no_lora_output = [ @@ -176,7 +177,8 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available, tensor_parallel_size=1, gpu_memory_utilization=0.2, #avoid OOM quantization=model.quantization, - trust_remote_code=True) + trust_remote_code=True, + enable_chunked_prefill=True) output_tp1 = do_sample(llm_tp1, tinyllama_lora_files, lora_id=1) del llm_tp1 @@ -189,7 +191,8 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available, max_loras=4, tensor_parallel_size=2, gpu_memory_utilization=0.2, #avoid OOM - quantization=model.quantization) + quantization=model.quantization, + enable_chunked_prefill=True) output_tp2 = do_sample(llm_tp2, tinyllama_lora_files, lora_id=1) del llm_tp2 diff --git a/vllm/config.py b/vllm/config.py index 5fb9563fcf3a3..c66ddbb47f22e 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -1698,7 +1698,8 @@ def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig): # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if scheduler_config.chunked_prefill_enabled: - raise ValueError("LoRA is not supported with chunked prefill yet.") + logger.warning("LoRA with chunked prefill is still experimental " + "and may be unstable.") @dataclass diff --git a/vllm/core/scheduler.py b/vllm/core/scheduler.py index d23009dae01ee..94c62743883ec 100644 --- a/vllm/core/scheduler.py +++ b/vllm/core/scheduler.py @@ -166,9 +166,18 @@ def is_empty(self) -> bool: and not self.blocks_to_swap_out and not self.blocks_to_copy) def _sort_by_lora_ids(self): - self.scheduled_seq_groups = sorted( - self.scheduled_seq_groups, - key=lambda g: (g.seq_group.lora_int_id, g.seq_group.request_id)) + assert 0 <= self.num_prefill_groups <= len(self.scheduled_seq_groups) + + def key_fn(group: ScheduledSequenceGroup): + key = (group.seq_group.lora_int_id, group.seq_group.request_id) + if 0 < self.num_prefill_groups < len(self.scheduled_seq_groups): + # Sort sequence groups so that all prefills come before all + # decodes as required by chunked prefill. + return (not group.seq_group.is_prefill(), *key) + return key + + self.scheduled_seq_groups = sorted(self.scheduled_seq_groups, + key=key_fn) @property def lora_requests(self) -> Set[LoRARequest]: diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index 1bc5f65c7127f..551b84435fdc0 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -622,11 +622,13 @@ def _compute_lora_input(self, inter_data: InterDataForSeqGroup, inter_data.lora_requests.add(seq_group_metadata.lora_request) query_len = inter_data.query_lens[seq_idx] inter_data.lora_index_mapping.append([lora_id] * query_len) - inter_data.lora_prompt_mapping.append( - [lora_id] * - (query_len if seq_group_metadata.sampling_params - and seq_group_metadata.sampling_params.prompt_logprobs is not None - else 1)) + sampling_params = seq_group_metadata.sampling_params + if sampling_params and sampling_params.prompt_logprobs is not None: + inter_data.lora_prompt_mapping.append([lora_id] * query_len) + elif not self.chunked_prefill_enabled or seq_group_metadata.do_sample: + inter_data.lora_prompt_mapping.append([lora_id]) + else: + inter_data.lora_prompt_mapping.append([]) def _compute_prompt_adapter_input( self, inter_data: InterDataForSeqGroup, From ffa48c9146fda1e8810d1cfa159e1d70aadae6c6 Mon Sep 17 00:00:00 2001 From: Mor Zusman Date: Wed, 11 Dec 2024 04:53:37 +0200 Subject: [PATCH 019/155] [Model] PP support for Mamba-like models (#10992) Signed-off-by: mzusman --- docs/source/models/supported_models.rst | 6 +- tests/distributed/test_pipeline_parallel.py | 6 +- vllm/config.py | 58 +++++++++---- vllm/model_executor/models/interfaces.py | 37 ++++++++ vllm/model_executor/models/jamba.py | 93 ++++++++++++++------- vllm/model_executor/models/mamba.py | 68 ++++++++++----- vllm/model_executor/models/registry.py | 11 ++- vllm/utils.py | 5 ++ vllm/v1/worker/gpu_model_runner.py | 8 +- vllm/v1/worker/gpu_worker.py | 6 +- vllm/worker/cache_engine.py | 12 +-- 11 files changed, 229 insertions(+), 81 deletions(-) diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 4e5b10967e3bb..6540e023c1ab0 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -128,7 +128,7 @@ Text Generation - FalconMamba - :code:`tiiuae/falcon-mamba-7b`, :code:`tiiuae/falcon-mamba-7b-instruct`, etc. - ✅︎ - - + - ✅︎ * - :code:`GemmaForCausalLM` - Gemma - :code:`google/gemma-2b`, :code:`google/gemma-7b`, etc. @@ -193,7 +193,7 @@ Text Generation - Jamba - :code:`ai21labs/AI21-Jamba-1.5-Large`, :code:`ai21labs/AI21-Jamba-1.5-Mini`, :code:`ai21labs/Jamba-v0.1`, etc. - ✅︎ - - + - ✅︎ * - :code:`LlamaForCausalLM` - Llama 3.1, Llama 3, Llama 2, LLaMA, Yi - :code:`meta-llama/Meta-Llama-3.1-405B-Instruct`, :code:`meta-llama/Meta-Llama-3.1-70B`, :code:`meta-llama/Meta-Llama-3-70B-Instruct`, :code:`meta-llama/Llama-2-70b-hf`, :code:`01-ai/Yi-34B`, etc. @@ -203,7 +203,7 @@ Text Generation - Mamba - :code:`state-spaces/mamba-130m-hf`, :code:`state-spaces/mamba-790m-hf`, :code:`state-spaces/mamba-2.8b-hf`, etc. - - - + - ✅︎ * - :code:`MiniCPMForCausalLM` - MiniCPM - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, :code:`openbmb/MiniCPM-S-1B-sft`, etc. diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py index b818ca921fcb0..85d408efafe96 100644 --- a/tests/distributed/test_pipeline_parallel.py +++ b/tests/distributed/test_pipeline_parallel.py @@ -156,13 +156,13 @@ def iter_params(self, model_name: str): # "internlm/internlm-chat-7b": PPTestSettings.fast(), "internlm/internlm2-chat-7b": PPTestSettings.fast(trust_remote_code=True), "inceptionai/jais-13b-chat": PPTestSettings.fast(), - # TODO: Implement PP - # "ai21labs/AI21-Jamba-1.5-Mini": PPTestSettings.fast(), + "ai21labs/Jamba-tiny-dev": PPTestSettings.fast(), "meta-llama/Meta-Llama-3-8B": PPTestSettings.detailed(), "openbmb/MiniCPM-2B-sft-bf16": PPTestSettings.fast(trust_remote_code=True), "openbmb/MiniCPM3-4B": PPTestSettings.fast(trust_remote_code=True), # Uses Llama # "mistralai/Mistral-7B-Instruct-v0.1": PPTestSettings.fast(), + "state-spaces/mamba-130m-hf": PPTestSettings.fast(), "mistralai/Mixtral-8x7B-Instruct-v0.1": PPTestSettings.fast(tp_base=4), "mosaicml/mpt-7b": PPTestSettings.fast(), "nvidia/Minitron-8B-Base": PPTestSettings.fast(), @@ -234,6 +234,8 @@ def iter_params(self, model_name: str): "OpenGVLab/InternVL2-1B", "microsoft/Phi-3-vision-128k-instruct", "fixie-ai/ultravox-v0_3", + # [LANGUAGE GENERATION - HYBRID ARCH] + "ai21labs/Jamba-tiny-dev", ] diff --git a/vllm/config.py b/vllm/config.py index c66ddbb47f22e..2a9f0ebae997d 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -27,8 +27,8 @@ ConfigFormat, get_config, get_hf_image_processor_config, get_hf_text_config, get_pooling_config, get_sentence_transformer_tokenizer_config, is_encoder_decoder, uses_mrope) -from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory, - print_warning_once, random_uuid, +from vllm.utils import (GiB_bytes, LayerBlockType, cuda_device_count_stateless, + get_cpu_memory, print_warning_once, random_uuid, resolve_obj_by_qualname) if TYPE_CHECKING: @@ -284,6 +284,7 @@ def __init__( self._verify_tokenizer_mode() self.is_attention_free = self._init_attention_free() + self.is_hybrid = self._init_is_hybrid() self.has_inner_state = self._init_has_inner_state() if current_platform.is_neuron(): @@ -340,6 +341,10 @@ def _init_attention_free(self) -> bool: architectures = getattr(self.hf_config, "architectures", []) return ModelRegistry.is_attention_free_model(architectures) + def _init_is_hybrid(self) -> bool: + architectures = getattr(self.hf_config, "architectures", []) + return ModelRegistry.is_hybrid_model(architectures) + def _init_has_inner_state(self) -> bool: architectures = getattr(self.hf_config, "architectures", []) return ModelRegistry.model_has_inner_state(architectures) @@ -669,26 +674,51 @@ def get_num_attention_heads(self, num_heads = getattr(self.hf_text_config, "num_attention_heads", 0) return num_heads // parallel_config.tensor_parallel_size - def get_num_layers(self, parallel_config: "ParallelConfig") -> int: + def get_layers_start_end_indices( + self, parallel_config: "ParallelConfig") -> Tuple[int, int]: from vllm.distributed.utils import get_pp_indices total_num_hidden_layers = getattr(self.hf_text_config, "num_hidden_layers", 0) pp_rank = parallel_config.rank // parallel_config.tensor_parallel_size pp_size = parallel_config.pipeline_parallel_size start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size) - return end - start - - def get_num_attention_layers(self, - parallel_config: "ParallelConfig") -> int: - if self.is_attention_free: - return 0 + return start, end - num_layers = self.get_num_layers(parallel_config) + def get_num_layers(self, parallel_config: "ParallelConfig") -> int: + start, end = self.get_layers_start_end_indices(parallel_config) + return end - start - # Transformers supports layers_block_type @property - layers = getattr(self.hf_config, "layers_block_type", - ["attention"] * num_layers) - return len([t for t in layers if t == "attention"]) + def get_num_layers_by_block_type( + self, + parallel_config: "ParallelConfig", + block_type: LayerBlockType = LayerBlockType.attention, + ) -> int: + # This function relies on 'layers_block_type' in hf_config, + # for w/o this attribute, we will need to have workarounds like so + attn_block_type = block_type == LayerBlockType.attention + is_transformer = not self.is_hybrid and not self.is_attention_free + start, end = self.get_layers_start_end_indices(parallel_config) + + if is_transformer: + # Handle the basic case first + return end - start if attn_block_type else 0 + elif self.is_attention_free: + # Attention free + # Note that this code assumes there + # is only one type of attention-free block type. + return 0 if attn_block_type else end - start + else: + # Hybrid model + layers_block_type_value = getattr(self.hf_config, + "layers_block_type", None) + if layers_block_type_value is None: + raise ValueError("The model is an hybrid without a" + "layers_block_type in the hf_config," + "cannot determine the num of " + f"{block_type.value} layers") + + return sum(t == block_type.value + for t in layers_block_type_value[start:end]) def get_multimodal_config(self) -> "MultiModalConfig": """ diff --git a/vllm/model_executor/models/interfaces.py b/vllm/model_executor/models/interfaces.py index c3979eab905db..70b78fe64f2d8 100644 --- a/vllm/model_executor/models/interfaces.py +++ b/vllm/model_executor/models/interfaces.py @@ -363,6 +363,43 @@ def is_attention_free( return isinstance(model, IsAttentionFree) +@runtime_checkable +class IsHybrid(Protocol): + """The interface required for all models like Jamba that have both + attention and mamba blocks, indicates that + hf_config has 'layers_block_type'""" + + is_hybrid: ClassVar[Literal[True]] = True + """ + A flag that indicates this model has both mamba and attention blocks + , also indicates that the model's hf_config has + 'layers_block_type' """ + + +@runtime_checkable +class _IsHybridType(Protocol): + is_hybrid: ClassVar[Literal[True]] + + +@overload +def is_hybrid(model: object) -> TypeIs[IsHybrid]: + ... + + +@overload +def is_hybrid(model: Type[object]) -> TypeIs[Type[IsHybrid]]: + ... + + +def is_hybrid( + model: Union[Type[object], object] +) -> Union[TypeIs[Type[IsHybrid]], TypeIs[IsHybrid]]: + if isinstance(model, type): + return isinstance(model, _IsHybridType) + + return isinstance(model, IsHybrid) + + @runtime_checkable class SupportsCrossEncoding(Protocol): """The interface required for all models that support cross encoding.""" diff --git a/vllm/model_executor/models/jamba.py b/vllm/model_executor/models/jamba.py index 5d5e8ae1ee532..6bb4c13ab35df 100644 --- a/vllm/model_executor/models/jamba.py +++ b/vllm/model_executor/models/jamba.py @@ -9,6 +9,7 @@ from vllm.attention.layer import Attention from vllm.config import _BATCH_SIZES_TO_CAPTURE, CacheConfig, VllmConfig from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.distributed.parallel_state import get_pp_group from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (QKVParallelLinear, @@ -25,9 +26,12 @@ MambaCacheParams) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors +from vllm.utils import LayerBlockType -from .interfaces import HasInnerState, SupportsLoRA -from .utils import maybe_prefix +from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP +from .utils import (is_pp_missing_parameter, + make_empty_intermediate_tensors_factory, make_layers, + maybe_prefix) KVCache = Tuple[torch.Tensor, torch.Tensor] @@ -281,16 +285,24 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): org_num_embeddings=config.vocab_size, ) - decoder_layers = [] - for i in range(config.num_hidden_layers): - layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]] - decoder_layers.append( - layer_class(config, - layer_idx=i, - cache_config=cache_config, - quant_config=quant_config, - prefix=f"{prefix}.layers.{i}")) - self.layers = nn.ModuleList(decoder_layers) + def get_layer(prefix: str): + layer_idx = int(prefix.rsplit(".", 1)[1]) + layer_class = ALL_DECODER_LAYER_TYPES[ + config.layers_block_type[layer_idx]] + return layer_class( + config, + layer_idx, + cache_config, + quant_config=quant_config, + prefix=prefix, + ) + + self.start_layer, self.end_layer, self.layers = make_layers( + config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers") + self.make_empty_intermediate_tensors = ( + make_empty_intermediate_tensors_factory( + ["hidden_states", "residual"], config.hidden_size)) + self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) @@ -304,26 +316,34 @@ def forward( kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, mamba_cache_params: MambaCacheParams, + intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: - if inputs_embeds is not None: - hidden_states = inputs_embeds + if get_pp_group().is_first_rank: + if inputs_embeds is not None: + hidden_states = inputs_embeds + else: + hidden_states = self.get_input_embeddings(input_ids) + residual = None else: - hidden_states = self.get_input_embeddings(input_ids) - residual = None - for i in range(len(self.layers)): + assert intermediate_tensors is not None + hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] + + kv_cache_index = 0 + mamba_cache_index = 0 + for i in range(self.start_layer, self.end_layer): layer = self.layers[i] kv_cache = None layer_mamba_cache_params = None if isinstance(layer, JambaAttentionDecoderLayer): - kv_cache = kv_caches[(i - self.config.attn_layer_offset) // - self.config.attn_layer_period] + kv_cache = kv_caches[kv_cache_index] + kv_cache_index += 1 if isinstance(layer, JambaMambaDecoderLayer): - current_state_layer = i - (1 + - (i - self.config.attn_layer_offset) - // self.config.attn_layer_period) + current_state_layer = mamba_cache_index layer_mamba_cache_params = mamba_cache_params.at_layer_idx( current_state_layer) + mamba_cache_index += 1 hidden_states, residual = layer( positions=positions, @@ -332,11 +352,17 @@ def forward( attn_metadata=attn_metadata, residual=residual, mamba_cache_params=layer_mamba_cache_params) + if not get_pp_group().is_last_rank: + return IntermediateTensors({ + "hidden_states": hidden_states, + "residual": residual + }) hidden_states, _ = self.final_layernorm(hidden_states, residual) return hidden_states -class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA): +class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP, + IsHybrid): packed_modules_mapping = { "qkv_proj": [ "q_proj", @@ -368,6 +394,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() self.config = config + self.vllm_config = vllm_config + self.model_config = vllm_config.model_config self.scheduler_config = scheduler_config self.model = JambaModel(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")) @@ -390,6 +418,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): config.vocab_size) self.sampler = get_sampler() + self.make_empty_intermediate_tensors = ( + self.model.make_empty_intermediate_tensors) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.get_input_embeddings(input_ids) @@ -406,10 +437,8 @@ def forward(self, self.scheduler_config.max_num_seqs) if self.scheduler_config else max(_BATCH_SIZES_TO_CAPTURE) + 2) - layers_type = self.config.layers_block_type - num_mamba_layers = sum( - [layer_type == "mamba" for layer_type in layers_type]) - + num_mamba_layers = self.model_config.get_num_layers_by_block_type( + self.vllm_config.parallel_config, LayerBlockType.mamba) self.mamba_cache = MambaCacheManager( self.lm_head.weight.dtype, num_mamba_layers, max_batch_size, *self._get_mamba_cache_shape()) @@ -423,7 +452,7 @@ def forward(self, state_indices_tensor) hidden_states = self.model(input_ids, positions, kv_caches, attn_metadata, mamba_cache_params, - inputs_embeds) + intermediate_tensors, inputs_embeds) return hidden_states def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs): @@ -504,8 +533,12 @@ def load_weights(self, weights: Iterable[Tuple[str, continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: continue + # Skip layers on other devices. + if is_pp_missing_parameter(name, self): + continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) @@ -520,6 +553,8 @@ def load_weights(self, weights: Iterable[Tuple[str, if weight_name not in name: continue + if is_pp_missing_parameter(name, self): + continue name = name.replace(weight_name, param_name) param = params_dict[name] weight_loader = param.weight_loader @@ -533,6 +568,8 @@ def load_weights(self, weights: Iterable[Tuple[str, # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue + if is_pp_missing_parameter(name, self): + continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", diff --git a/vllm/model_executor/models/mamba.py b/vllm/model_executor/models/mamba.py index 8bdcd2c5aad1f..1f5cd02711899 100644 --- a/vllm/model_executor/models/mamba.py +++ b/vllm/model_executor/models/mamba.py @@ -8,6 +8,7 @@ from vllm.attention.backends.abstract import AttentionMetadata from vllm.config import _BATCH_SIZES_TO_CAPTURE, CacheConfig, VllmConfig from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.distributed.parallel_state import get_pp_group from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer @@ -18,13 +19,16 @@ DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.interfaces import (HasInnerState, - IsAttentionFree) + IsAttentionFree, SupportsPP) from vllm.model_executor.models.mamba_cache import (MambaCacheManager, MambaCacheParams) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors +from vllm.utils import LayerBlockType -from .utils import maybe_prefix +from .utils import (is_pp_missing_parameter, + make_empty_intermediate_tensors_factory, make_layers, + maybe_prefix) KVCache = Tuple[torch.Tensor, torch.Tensor] @@ -95,15 +99,17 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): org_num_embeddings=config.vocab_size, ) - decoder_layers = [] - for i in range(config.num_hidden_layers): - decoder_layers.append( - MambaDecoderLayer(config, - cache_config=cache_config, - quant_config=quant_config)) - self.layers = nn.ModuleList(decoder_layers) + self.start_layer, self.end_layer, self.layers = make_layers( + config.num_hidden_layers, + lambda prefix: MambaDecoderLayer( + config, cache_config=cache_config, quant_config=quant_config), + prefix=f"{prefix}.layers") + self.norm_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) + self.make_empty_intermediate_tensors = ( + make_empty_intermediate_tensors_factory( + ["hidden_states", "residual"], config.hidden_size)) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embeddings(input_ids) @@ -114,29 +120,40 @@ def forward( positions: torch.Tensor, attn_metadata: AttentionMetadata, mamba_cache_params: MambaCacheParams, + intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: - - if inputs_embeds is not None: - hidden_states = inputs_embeds + if get_pp_group().is_first_rank: + if inputs_embeds is not None: + hidden_states = inputs_embeds + else: + hidden_states = self.get_input_embeddings(input_ids) + residual = None else: - hidden_states = self.get_input_embeddings(input_ids) - residual = None + assert intermediate_tensors is not None + hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] - for i in range(len(self.layers)): + for i in range(self.start_layer, self.end_layer): layer = self.layers[i] hidden_states, residual = layer( positions=positions, hidden_states=hidden_states, attn_metadata=attn_metadata, residual=residual, - mamba_cache_params=mamba_cache_params.at_layer_idx(i)) + mamba_cache_params=mamba_cache_params.at_layer_idx( + i - self.start_layer)) + if not get_pp_group().is_last_rank: + return IntermediateTensors({ + "hidden_states": hidden_states, + "residual": residual + }) hidden_states, _ = self.norm_f(hidden_states, residual) return hidden_states -class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree): +class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): config = vllm_config.model_config.hf_config @@ -148,7 +165,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() self.config = config + self.vllm_config = vllm_config self.scheduler_config = scheduler_config + self.model_config = vllm_config.model_config self.backbone = MambaModel(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "backbone")) self.unpadded_vocab_size = config.vocab_size @@ -174,6 +193,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): config.vocab_size) self.sampler = get_sampler() + self.make_empty_intermediate_tensors = ( + self.backbone.make_empty_intermediate_tensors) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.backbone.get_input_embeddings(input_ids) @@ -189,9 +211,12 @@ def forward(self, max_batch_size = (VllmConfig.get_graph_batch_size( self.scheduler_config.max_num_seqs) if self.scheduler_config else max(_BATCH_SIZES_TO_CAPTURE) + 2) + + num_mamba_layers = self.model_config.get_num_layers_by_block_type( + self.vllm_config.parallel_config, LayerBlockType.mamba) self.mamba_cache = MambaCacheManager( - self.lm_head.weight.dtype, self.config.num_hidden_layers, - max_batch_size, *self._get_mamba_cache_shape()) + self.lm_head.weight.dtype, num_mamba_layers, max_batch_size, + *self._get_mamba_cache_shape()) ( mamba_cache_tensors, @@ -204,7 +229,8 @@ def forward(self, state_indices_tensor) hidden_states = self.backbone(input_ids, positions, attn_metadata, - mamba_cache_params, inputs_embeds) + mamba_cache_params, intermediate_tensors, + inputs_embeds) return hidden_states @@ -252,6 +278,8 @@ def load_weights(self, weights: Iterable[Tuple[str, # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue + if is_pp_missing_parameter(name, self): + continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index e69596aa915b5..4beea4641f5ab 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -21,7 +21,7 @@ from vllm.platforms import current_platform from .adapters import as_embedding_model -from .interfaces import (has_inner_state, is_attention_free, +from .interfaces import (has_inner_state, is_attention_free, is_hybrid, supports_cross_encoding, supports_multimodal, supports_pp) from .interfaces_base import is_pooling_model, is_text_generation_model @@ -218,6 +218,7 @@ class _ModelInfo: supports_pp: bool has_inner_state: bool is_attention_free: bool + is_hybrid: bool @staticmethod def from_model_cls(model: Type[nn.Module]) -> "_ModelInfo": @@ -239,6 +240,7 @@ def from_model_cls(model: Type[nn.Module]) -> "_ModelInfo": supports_pp=supports_pp(model), has_inner_state=has_inner_state(model), is_attention_free=is_attention_free(model), + is_hybrid=is_hybrid(model), ) @@ -484,6 +486,13 @@ def is_attention_free_model( model_cls, _ = self.inspect_model_cls(architectures) return model_cls.is_attention_free + def is_hybrid_model( + self, + architectures: Union[str, List[str]], + ) -> bool: + model_cls, _ = self.inspect_model_cls(architectures) + return model_cls.is_hybrid + ModelRegistry = _ModelRegistry({ model_arch: _LazyRegisteredModel( diff --git a/vllm/utils.py b/vllm/utils.py index 7cdb2cb320b05..1882264c19775 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -170,6 +170,11 @@ class Device(enum.Enum): CPU = enum.auto() +class LayerBlockType(enum.Enum): + attention = "attention" + mamba = "mamba" + + class Counter: def __init__(self, start: int = 0) -> None: diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index a3335fa838352..8d9976ded7c5e 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -15,8 +15,8 @@ from vllm.model_executor.model_loader import get_model from vllm.multimodal import MultiModalKwargs from vllm.sampling_params import SamplingType -from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler, cdiv, - is_pin_memory_available) +from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler, + LayerBlockType, cdiv, is_pin_memory_available) from vllm.v1.attention.backends.flash_attn import (FlashAttentionBackend, FlashAttentionMetadata) from vllm.v1.outputs import ModelRunnerOutput @@ -68,8 +68,8 @@ def __init__( self.max_num_tokens = scheduler_config.max_num_batched_tokens # Model-related. - self.num_attn_layers = model_config.get_num_attention_layers( - parallel_config) + self.num_attn_layers = model_config.get_num_layers_by_block_type( + parallel_config, LayerBlockType.attention) self.num_kv_heads = model_config.get_num_kv_heads(parallel_config) self.head_size = model_config.get_head_size() self.hidden_size = model_config.get_hidden_size() diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py index d32848c3775ae..49e415ab72e0b 100644 --- a/vllm/v1/worker/gpu_worker.py +++ b/vllm/v1/worker/gpu_worker.py @@ -14,7 +14,7 @@ from vllm.logger import init_logger from vllm.model_executor import set_random_seed from vllm.platforms import current_platform -from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, get_dtype_size +from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, LayerBlockType, get_dtype_size from vllm.v1.core.scheduler import SchedulerOutput from vllm.v1.outputs import ModelRunnerOutput from vllm.v1.worker.gpu_model_runner import GPUModelRunner @@ -260,8 +260,8 @@ def _get_cache_block_size( ) -> int: head_size = model_config.get_head_size() num_heads = model_config.get_num_kv_heads(parallel_config) - num_attention_layers = model_config.get_num_attention_layers( - parallel_config) + num_attention_layers = model_config.get_num_layers_by_block_type( + parallel_config, LayerBlockType.attention) key_cache_block = cache_config.block_size * num_heads * head_size value_cache_block = key_cache_block diff --git a/vllm/worker/cache_engine.py b/vllm/worker/cache_engine.py index ac3270d1c9909..7ccd4571b19df 100644 --- a/vllm/worker/cache_engine.py +++ b/vllm/worker/cache_engine.py @@ -6,8 +6,8 @@ from vllm.attention import get_attn_backend from vllm.config import CacheConfig, DeviceConfig, ModelConfig, ParallelConfig from vllm.logger import init_logger -from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, get_dtype_size, - is_pin_memory_available) +from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, LayerBlockType, + get_dtype_size, is_pin_memory_available) logger = init_logger(__name__) @@ -34,8 +34,8 @@ def __init__( self.head_size = model_config.get_head_size() # Models like Jamba, have mixed typed layers, E.g Mamba - self.num_attention_layers = model_config.get_num_attention_layers( - parallel_config) + self.num_attention_layers = model_config.get_num_layers_by_block_type( + parallel_config, LayerBlockType.attention) self.num_kv_heads = model_config.get_num_kv_heads(parallel_config) self.block_size = cache_config.block_size @@ -105,8 +105,8 @@ def get_cache_block_size( ) -> int: head_size = model_config.get_head_size() num_heads = model_config.get_num_kv_heads(parallel_config) - num_attention_layers = model_config.get_num_attention_layers( - parallel_config) + num_attention_layers = model_config.get_num_layers_by_block_type( + parallel_config, LayerBlockType.attention) key_cache_block = cache_config.block_size * num_heads * head_size value_cache_block = key_cache_block From e39400a4b60d28ff5c0a1a5194068c928adcaf98 Mon Sep 17 00:00:00 2001 From: Maximilien de Bayser Date: Wed, 11 Dec 2024 01:51:40 -0300 Subject: [PATCH 020/155] Fix streaming for granite tool call when <|tool_call|> is present (#11069) Signed-off-by: Max de Bayser --- vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py b/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py index 00917c866e496..dae481a2154a1 100644 --- a/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py +++ b/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py @@ -88,7 +88,11 @@ def extract_tool_calls_streaming( ) -> Union[DeltaMessage, None]: start_idx = consume_space(0, current_text) - if not current_text or current_text[start_idx] != '[': + if current_text[start_idx:].startswith(self.bot_token): + start_idx = consume_space(start_idx + len(self.bot_token), + current_text) + if not current_text or start_idx >= len(current_text)\ + or current_text[start_idx] != '[': return DeltaMessage(content=delta_text) # bit mask flags for partial JSON parsing. If the name hasn't been From 2e33fe419186c65a18da6668972d61d7bbc31564 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 11 Dec 2024 13:02:02 +0800 Subject: [PATCH 021/155] [CI/Build] Check transformers v4.47 (#10991) Signed-off-by: DarkLight1337 --- requirements-test.txt | 4 ++-- .../vision_language/mm_processor_kwargs/test_idefics3.py | 9 --------- .../models/embedding/vision_language/test_llava_next.py | 2 +- 3 files changed, 3 insertions(+), 12 deletions(-) diff --git a/requirements-test.txt b/requirements-test.txt index 38a064bca449a..8ceb705cdffd7 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -506,7 +506,7 @@ tiktoken==0.7.0 # mistral-common timm==1.0.11 # via -r requirements-test.in -tokenizers==0.20.3 +tokenizers==0.21.0 # via transformers torch==2.5.1 # via @@ -534,7 +534,7 @@ tqdm==4.66.6 # transformers tqdm-multiprocess==0.0.11 # via lm-eval -transformers==4.46.3 +transformers==4.47.0 # via # lm-eval # peft diff --git a/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_idefics3.py b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_idefics3.py index 31896bfd13e8c..c71a2d359043d 100644 --- a/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_idefics3.py +++ b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_idefics3.py @@ -3,7 +3,6 @@ import pytest import torch -import transformers from transformers import AutoImageProcessor, AutoTokenizer from vllm.inputs import InputContext, token_inputs @@ -36,8 +35,6 @@ def get_max_idefics3_image_tokens(): return get_max_idefics3_image_tokens -@pytest.mark.skipif(transformers.__version__ < "4.46.0", - reason="Model introduced in HF >= 4.46.0") @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("longest_edge", [None, 168, 336, 400, 2 * 336]) def test_input_mapper_override(model: str, image_assets: _ImageAssets, @@ -77,8 +74,6 @@ def test_input_mapper_override(model: str, image_assets: _ImageAssets, assert torch.all(hf_result["pixel_values"] == vllm_result["pixel_values"]) -@pytest.mark.skipif(transformers.__version__ < "4.46.0", - reason="Model introduced in HF >= 4.46.0") @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("longest_edge, expected_max_tokens", [ (None, 2873), @@ -107,8 +102,6 @@ def test_max_tokens_override(get_max_idefics3_image_tokens, model: str, assert expected_max_tokens == actual_max_tokens -@pytest.mark.skipif(transformers.__version__ < "4.46.0", - reason="Model introduced in HF >= 4.46.0") @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("longest_edge, toks_per_img, num_imgs", [ (168, 169, 1), @@ -143,8 +136,6 @@ def test_dummy_data_override(dummy_data_for_idefics3, model: str, assert img_tok_count == toks_per_img * num_imgs -@pytest.mark.skipif(transformers.__version__ < "4.46.0", - reason="Model introduced in HF >= 4.46.0") @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("longest_edge,expected_toks_per_img,num_imgs", [ (336, 169 * (1**2 + 1), 1), diff --git a/tests/models/embedding/vision_language/test_llava_next.py b/tests/models/embedding/vision_language/test_llava_next.py index 329c6ba279f89..693abd7252d5e 100644 --- a/tests/models/embedding/vision_language/test_llava_next.py +++ b/tests/models/embedding/vision_language/test_llava_next.py @@ -86,7 +86,7 @@ def _run_test( ) -@pytest.mark.skipif(transformers.__version__.startswith("4.46"), +@pytest.mark.skipif(transformers.__version__ >= "4.46", reason="Model broken with changes in transformers 4.46") @pytest.mark.core_model @pytest.mark.parametrize("model", MODELS) From 3fb4b4f1634a896653acc12c72b8e5d6d87a8f82 Mon Sep 17 00:00:00 2001 From: "Kevin H. Luu" Date: Wed, 11 Dec 2024 00:39:53 -0800 Subject: [PATCH 022/155] [ci/build] Fix AMD CI dependencies (#11087) --- requirements-rocm.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements-rocm.txt b/requirements-rocm.txt index 121123611d2da..ccc9062341772 100644 --- a/requirements-rocm.txt +++ b/requirements-rocm.txt @@ -5,7 +5,8 @@ awscli boto3 botocore +datasets ray >= 2.10.0 peft pytest-asyncio -tensorizer>=2.9.0 \ No newline at end of file +tensorizer>=2.9.0 From 9974fca047bb332ec68377be4579ea515a300d69 Mon Sep 17 00:00:00 2001 From: "Kevin H. Luu" Date: Wed, 11 Dec 2024 01:01:53 -0800 Subject: [PATCH 023/155] [ci/build] Fix entrypoints test and pin outlines version (#11088) --- requirements-common.txt | 2 +- .../guided_decoding/outlines_logits_processors.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements-common.txt b/requirements-common.txt index c71fc458aca13..792cd58e80669 100644 --- a/requirements-common.txt +++ b/requirements-common.txt @@ -18,7 +18,7 @@ prometheus_client >= 0.18.0 prometheus-fastapi-instrumentator >= 7.0.0 tiktoken >= 0.6.0 # Required for DBRX tokenizer lm-format-enforcer >= 0.10.9, < 0.11 -outlines >= 0.1.8 +outlines == 0.1.9 xgrammar >= 0.1.6; platform_machine == "x86_64" typing_extensions >= 4.10 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317 diff --git a/vllm/model_executor/guided_decoding/outlines_logits_processors.py b/vllm/model_executor/guided_decoding/outlines_logits_processors.py index 1f0dbe024609d..b63fed1c8a8c3 100644 --- a/vllm/model_executor/guided_decoding/outlines_logits_processors.py +++ b/vllm/model_executor/guided_decoding/outlines_logits_processors.py @@ -25,7 +25,7 @@ from outlines import grammars from outlines.caching import cache from outlines.fsm.guide import CFGGuide, Generate, Guide, RegexGuide, Write -from outlines.fsm.json_schema import build_regex_from_schema +from outlines_core.fsm.json_schema import build_regex_from_schema from pydantic import BaseModel from transformers import PreTrainedTokenizerBase From 61b1d2f6aef8e29c6a0d795a9c6682d525f4d8cc Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Wed, 11 Dec 2024 04:26:36 -0500 Subject: [PATCH 024/155] [Core] v1: Use atexit to handle engine core client shutdown (#11076) Signed-off-by: Russell Bryant --- vllm/v1/engine/core_client.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/vllm/v1/engine/core_client.py b/vllm/v1/engine/core_client.py index ee89cece73141..4d96b323d1662 100644 --- a/vllm/v1/engine/core_client.py +++ b/vllm/v1/engine/core_client.py @@ -1,3 +1,4 @@ +import atexit import multiprocessing from typing import List, Union @@ -157,6 +158,7 @@ def __init__( should_shutdown=self.should_shutdown, **kwargs, ) + atexit.register(self.shutdown) def shutdown(self): # Send shutdown signal to background process. From 2e32f5d28db3cd79f6a421f640e083be1f9468b7 Mon Sep 17 00:00:00 2001 From: B-201 Date: Wed, 11 Dec 2024 17:27:07 +0800 Subject: [PATCH 025/155] [Bugfix] Fix Idefics3 fails during multi-image inference (#11080) Signed-off-by: B-201 --- vllm/model_executor/models/idefics3.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/vllm/model_executor/models/idefics3.py b/vllm/model_executor/models/idefics3.py index e5d2edbd81eb1..17e772e7faa32 100644 --- a/vllm/model_executor/models/idefics3.py +++ b/vllm/model_executor/models/idefics3.py @@ -60,7 +60,8 @@ class Idefics3ImagePixelInputs(TypedDict): type: Literal["pixel_values"] data: torch.Tensor """ - Shape: `(batch_size * num_images, num_channels, height, width)` + Shape: `(batch_size * num_images * num_patches, + num_channels, height, width)` """ pixel_attention_mask: Optional[torch.BoolTensor] @@ -520,13 +521,17 @@ def _parse_and_validate_image_input( raise ValueError("Incorrect type of pixel values. " f"Got type: {type(pixel_values)}") - return Idefics3ImagePixelInputs(type="pixel_values", - data=self._validate_pixel_values( - flatten_bn(pixel_values, - concat=True)), - pixel_attention_mask=flatten_bn( - pixel_attention_mask, - concat=True)) + if isinstance(pixel_values, list): + pixel_values = torch.cat(pixel_values, dim=1) + pixel_attention_mask = torch.cat(pixel_attention_mask, dim=1) + else: + pixel_values = flatten_bn(pixel_values) + pixel_attention_mask = flatten_bn(pixel_attention_mask) + + return Idefics3ImagePixelInputs( + type="pixel_values", + data=self._validate_pixel_values(pixel_values), + pixel_attention_mask=pixel_attention_mask) raise AssertionError("This line should be unreachable.") From 40766ca1b8b0ef92e220595bda96c4336b597e5b Mon Sep 17 00:00:00 2001 From: Rafael Vasquez Date: Wed, 11 Dec 2024 04:27:39 -0500 Subject: [PATCH 026/155] [Bugfix]: Clamp `-inf` logprob values in prompt_logprobs (#11073) Signed-off-by: Rafael Vasquez --- vllm/entrypoints/openai/serving_completion.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py index c54d5f07cf58c..ee97d35f2b087 100644 --- a/vllm/entrypoints/openai/serving_completion.py +++ b/vllm/entrypoints/openai/serving_completion.py @@ -392,6 +392,12 @@ def request_output_to_completion_response( prompt_token_ids = final_res.prompt_token_ids assert prompt_token_ids is not None prompt_logprobs = final_res.prompt_logprobs + if prompt_logprobs: + for logprob_dict in prompt_logprobs: + if logprob_dict: + for logprob_values in logprob_dict.values(): + if logprob_values.logprob == float('-inf'): + logprob_values.logprob = -9999.0 prompt_text = final_res.prompt token_ids: GenericSequence[int] From 8f10d5e3930f05c2057a831cd80ba24c52b8ceef Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 11 Dec 2024 17:28:00 +0800 Subject: [PATCH 027/155] [Misc] Split up pooling tasks (#10820) Signed-off-by: DarkLight1337 --- docs/source/index.rst | 2 + docs/source/models/generative_models.rst | 146 ++++++++++++++++ docs/source/models/pooling_models.rst | 99 +++++++++++ docs/source/models/supported_models.rst | 157 ++++++++++++------ docs/source/usage/compatibility_matrix.rst | 12 +- examples/offline_inference_embedding.py | 7 +- ...ine_inference_vision_language_embedding.py | 4 +- tests/compile/test_basic_correctness.py | 4 +- tests/core/test_scheduler_encoder_decoder.py | 2 +- .../openai/test_vision_embedding.py | 2 +- .../embedding/language/test_embedding.py | 2 +- .../models/embedding/language/test_scoring.py | 12 +- .../vision_language/test_dse_qwen2_vl.py | 2 +- .../vision_language/test_llava_next.py | 2 +- .../embedding/vision_language/test_phi3v.py | 2 +- tests/test_config.py | 17 +- vllm/config.py | 137 ++++++++++----- vllm/core/scheduler.py | 2 +- vllm/engine/arg_utils.py | 7 +- vllm/engine/llm_engine.py | 4 +- vllm/entrypoints/llm.py | 53 +++--- vllm/entrypoints/openai/api_server.py | 8 +- vllm/entrypoints/openai/run_batch.py | 4 +- vllm/model_executor/model_loader/utils.py | 2 +- vllm/v1/engine/core.py | 2 +- vllm/worker/cpu_worker.py | 2 +- vllm/worker/worker.py | 2 +- 27 files changed, 527 insertions(+), 168 deletions(-) create mode 100644 docs/source/models/generative_models.rst create mode 100644 docs/source/models/pooling_models.rst diff --git a/docs/source/index.rst b/docs/source/index.rst index ebf1361976c5e..842013d6d49c4 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -94,6 +94,8 @@ Documentation :caption: Models models/supported_models + models/generative_models + models/pooling_models models/adding_model models/enabling_multimodal_inputs diff --git a/docs/source/models/generative_models.rst b/docs/source/models/generative_models.rst new file mode 100644 index 0000000000000..fb71185600863 --- /dev/null +++ b/docs/source/models/generative_models.rst @@ -0,0 +1,146 @@ +.. _generative_models: + +Generative Models +================= + +vLLM provides first-class support for generative models, which covers most of LLMs. + +In vLLM, generative models implement the :class:`~vllm.model_executor.models.VllmModelForTextGeneration` interface. +Based on the final hidden states of the input, these models output log probabilities of the tokens to generate, +which are then passed through :class:`~vllm.model_executor.layers.Sampler` to obtain the final text. + +Offline Inference +----------------- + +The :class:`~vllm.LLM` class provides various methods for offline inference. +See :ref:`Engine Arguments ` for a list of options when initializing the model. + +For generative models, the only supported :code:`task` option is :code:`"generate"`. +Usually, this is automatically inferred so you don't have to specify it. + +``LLM.generate`` +^^^^^^^^^^^^^^^^ + +The :class:`~vllm.LLM.generate` method is available to all generative models in vLLM. +It is similar to `its counterpart in HF Transformers `__, +except that tokenization and detokenization are also performed automatically. + +.. code-block:: python + + llm = LLM(model="facebook/opt-125m") + outputs = llm.generate("Hello, my name is") + + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + +You can optionally control the language generation by passing :class:`~vllm.SamplingParams`. +For example, you can use greedy sampling by setting :code:`temperature=0`: + +.. code-block:: python + + llm = LLM(model="facebook/opt-125m") + params = SamplingParams(temperature=0) + outputs = llm.generate("Hello, my name is", params) + + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + +A code example can be found in `examples/offline_inference.py `_. + +``LLM.beam_search`` +^^^^^^^^^^^^^^^^^^^ + +The :class:`~vllm.LLM.beam_search` method implements `beam search `__ on top of :class:`~vllm.LLM.generate`. +For example, to search using 5 beams and output at most 50 tokens: + +.. code-block:: python + + llm = LLM(model="facebook/opt-125m") + params = BeamSearchParams(beam_width=5, max_tokens=50) + outputs = llm.generate("Hello, my name is", params) + + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + +``LLM.chat`` +^^^^^^^^^^^^ + +The :class:`~vllm.LLM.chat` method implements chat functionality on top of :class:`~vllm.LLM.generate`. +In particular, it accepts input similar to `OpenAI Chat Completions API `__ +and automatically applies the model's `chat template `__ to format the prompt. + +.. important:: + + In general, only instruction-tuned models have a chat template. + Base models may perform poorly as they are not trained to respond to the chat conversation. + +.. code-block:: python + + llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct") + conversation = [ + { + "role": "system", + "content": "You are a helpful assistant" + }, + { + "role": "user", + "content": "Hello" + }, + { + "role": "assistant", + "content": "Hello! How can I assist you today?" + }, + { + "role": "user", + "content": "Write an essay about the importance of higher education.", + }, + ] + outputs = llm.chat(conversation) + + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + +A code example can be found in `examples/offline_inference_chat.py `_. + +If the model doesn't have a chat template or you want to specify another one, +you can explicitly pass a chat template: + +.. code-block:: python + + from vllm.entrypoints.chat_utils import load_chat_template + + # You can find a list of existing chat templates under `examples/` + custom_template = load_chat_template(chat_template="") + print("Loaded chat template:", custom_template) + + outputs = llm.chat(conversation, chat_template=custom_template) + +Online Inference +---------------- + +Our `OpenAI Compatible Server <../serving/openai_compatible_server>`__ can be used for online inference. +Please click on the above link for more details on how to launch the server. + +Completions API +^^^^^^^^^^^^^^^ + +Our Completions API is similar to ``LLM.generate`` but only accepts text. +It is compatible with `OpenAI Completions API `__ +so that you can use OpenAI client to interact with it. +A code example can be found in `examples/openai_completion_client.py `_. + +Chat API +^^^^^^^^ + +Our Chat API is similar to ``LLM.chat``, accepting both text and :ref:`multi-modal inputs `. +It is compatible with `OpenAI Chat Completions API `__ +so that you can use OpenAI client to interact with it. +A code example can be found in `examples/openai_chat_completion_client.py `_. diff --git a/docs/source/models/pooling_models.rst b/docs/source/models/pooling_models.rst new file mode 100644 index 0000000000000..7fa66274c3c5a --- /dev/null +++ b/docs/source/models/pooling_models.rst @@ -0,0 +1,99 @@ +.. _pooling_models: + +Pooling Models +============== + +vLLM also supports pooling models, including embedding, reranking and reward models. + +In vLLM, pooling models implement the :class:`~vllm.model_executor.models.VllmModelForPooling` interface. +These models use a :class:`~vllm.model_executor.layers.Pooler` to aggregate the final hidden states of the input +before returning them. + +.. note:: + + We currently support pooling models primarily as a matter of convenience. + As shown in the :ref:`Compatibility Matrix `, most vLLM features are not applicable to + pooling models as they only work on the generation or decode stage, so performance may not improve as much. + +Offline Inference +----------------- + +The :class:`~vllm.LLM` class provides various methods for offline inference. +See :ref:`Engine Arguments ` for a list of options when initializing the model. + +For pooling models, we support the following :code:`task` options: + +- Embedding (:code:`"embed"` / :code:`"embedding"`) +- Classification (:code:`"classify"`) +- Sentence Pair Scoring (:code:`"score"`) +- Reward Modeling (:code:`"reward"`) + +The selected task determines the default :class:`~vllm.model_executor.layers.Pooler` that is used: + +- Embedding: Extract only the hidden states corresponding to the last token, and apply normalization. +- Classification: Extract only the hidden states corresponding to the last token, and apply softmax. +- Sentence Pair Scoring: Extract only the hidden states corresponding to the last token, and apply softmax. +- Reward Modeling: Extract all of the hidden states and return them directly. + +When loading `Sentence Transformers `__ models, +we attempt to override the default pooler based on its Sentence Transformers configuration file (:code:`modules.json`). + +You can customize the model's pooling method via the :code:`override_pooler_config` option, +which takes priority over both the model's and Sentence Transformers's defaults. + +``LLM.encode`` +^^^^^^^^^^^^^^ + +The :class:`~vllm.LLM.encode` method is available to all pooling models in vLLM. +It returns the aggregated hidden states directly. + +.. code-block:: python + + llm = LLM(model="intfloat/e5-mistral-7b-instruct", task="embed") + outputs = llm.encode("Hello, my name is") + + outputs = model.encode(prompts) + for output in outputs: + embeddings = output.outputs.embedding + print(f"Prompt: {prompt!r}, Embeddings (size={len(embeddings)}: {embeddings!r}") + +A code example can be found in `examples/offline_inference_embedding.py `_. + +``LLM.score`` +^^^^^^^^^^^^^ + +The :class:`~vllm.LLM.score` method outputs similarity scores between sentence pairs. +It is primarily designed for `cross-encoder models `__. +These types of models serve as rerankers between candidate query-document pairs in RAG systems. + +.. note:: + + vLLM can only perform the model inference component (e.g. embedding, reranking) of RAG. + To handle RAG at a higher level, you should use integration frameworks such as `LangChain `_. + +You can use `these tests `_ as reference. + +Online Inference +---------------- + +Our `OpenAI Compatible Server <../serving/openai_compatible_server>`__ can be used for online inference. +Please click on the above link for more details on how to launch the server. + +Embeddings API +^^^^^^^^^^^^^^ + +Our Embeddings API is similar to ``LLM.encode``, accepting both text and :ref:`multi-modal inputs `. + +The text-only API is compatible with `OpenAI Embeddings API `__ +so that you can use OpenAI client to interact with it. +A code example can be found in `examples/openai_embedding_client.py `_. + +The multi-modal API is an extension of the `OpenAI Embeddings API `__ +that incorporates `OpenAI Chat Completions API `__, +so it is not part of the OpenAI standard. Please see :ref:`this page ` for more details on how to use it. + +Score API +^^^^^^^^^ + +Our Score API is similar to ``LLM.score``. +Please see `this page <../serving/openai_compatible_server.html#score-api-for-cross-encoder-models>`__ for more details on how to use it. diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 6540e023c1ab0..b9957cf9563b1 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -3,11 +3,21 @@ Supported Models ================ -vLLM supports a variety of generative and embedding models from `HuggingFace (HF) Transformers `_. -This page lists the model architectures that are currently supported by vLLM. +vLLM supports generative and pooling models across various tasks. +If a model supports more than one task, you can set the task via the :code:`--task` argument. + +For each task, we list the model architectures that have been implemented in vLLM. Alongside each architecture, we include some popular models that use it. -For other models, you can check the :code:`config.json` file inside the model repository. +Loading a Model +^^^^^^^^^^^^^^^ + +HuggingFace Hub ++++++++++++++++ + +By default, vLLM loads models from `HuggingFace (HF) Hub `_. + +To determine whether a given model is supported, you can check the :code:`config.json` file inside the HF repository. If the :code:`"architectures"` field contains a model architecture listed below, then it should be supported in theory. .. tip:: @@ -17,38 +27,57 @@ If the :code:`"architectures"` field contains a model architecture listed below, from vllm import LLM - llm = LLM(model=...) # Name or path of your model + # For generative models (task=generate) only + llm = LLM(model=..., task="generate") # Name or path of your model output = llm.generate("Hello, my name is") print(output) - If vLLM successfully generates text, it indicates that your model is supported. + # For pooling models (task={embed,classify,reward}) only + llm = LLM(model=..., task="embed") # Name or path of your model + output = llm.encode("Hello, my name is") + print(output) + + If vLLM successfully returns text (for generative models) or hidden states (for pooling models), it indicates that your model is supported. Otherwise, please refer to :ref:`Adding a New Model ` and :ref:`Enabling Multimodal Inputs ` for instructions on how to implement your model in vLLM. Alternatively, you can `open an issue on GitHub `_ to request vLLM support. -.. note:: - To use models from `ModelScope `_ instead of HuggingFace Hub, set an environment variable: +ModelScope +++++++++++ - .. code-block:: shell +To use models from `ModelScope `_ instead of HuggingFace Hub, set an environment variable: - $ export VLLM_USE_MODELSCOPE=True +.. code-block:: shell - And use with :code:`trust_remote_code=True`. + $ export VLLM_USE_MODELSCOPE=True - .. code-block:: python +And use with :code:`trust_remote_code=True`. - from vllm import LLM +.. code-block:: python - llm = LLM(model=..., revision=..., trust_remote_code=True) # Name or path of your model - output = llm.generate("Hello, my name is") - print(output) + from vllm import LLM + + llm = LLM(model=..., revision=..., task=..., trust_remote_code=True) -Text-only Language Models -^^^^^^^^^^^^^^^^^^^^^^^^^ + # For generative models (task=generate) only + output = llm.generate("Hello, my name is") + print(output) -Text Generation ---------------- + # For pooling models (task={embed,classify,reward}) only + output = llm.encode("Hello, my name is") + print(output) + +List of Text-only Language Models +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Generative Models ++++++++++++++++++ + +See :ref:`this page ` for more information on how to use generative models. + +Text Generation (``--task generate``) +------------------------------------- .. list-table:: :widths: 25 25 50 5 5 @@ -328,8 +357,24 @@ Text Generation .. note:: Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096. -Text Embedding --------------- +Pooling Models +++++++++++++++ + +See :ref:`this page ` for more information on how to use pooling models. + +.. important:: + Since some model architectures support both generative and pooling tasks, + you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode. + +Text Embedding (``--task embed``) +--------------------------------- + +Any text generation model can be converted into an embedding model by passing :code:`--task embed`. + +.. note:: + To get the best results, you should use pooling models that are specifically trained as such. + +The following table lists those that are tested in vLLM. .. list-table:: :widths: 25 25 50 5 5 @@ -371,13 +416,6 @@ Text Embedding - - -.. important:: - Some model architectures support both generation and embedding tasks. - In this case, you have to pass :code:`--task embedding` to run the model in embedding mode. - -.. tip:: - You can override the model's pooling method by passing :code:`--override-pooler-config`. - .. note:: :code:`ssmits/Qwen2-7B-Instruct-embed-base` has an improperly defined Sentence Transformers config. You should manually set mean pooling by passing :code:`--override-pooler-config '{"pooling_type": "MEAN"}'`. @@ -389,8 +427,8 @@ Text Embedding On the other hand, its 1.5B variant (:code:`Alibaba-NLP/gte-Qwen2-1.5B-instruct`) uses causal attention despite being described otherwise on its model card. -Reward Modeling ---------------- +Reward Modeling (``--task reward``) +----------------------------------- .. list-table:: :widths: 25 25 50 5 5 @@ -416,11 +454,8 @@ Reward Modeling For process-supervised reward models such as :code:`peiyi9979/math-shepherd-mistral-7b-prm`, the pooling config should be set explicitly, e.g.: :code:`--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`. -.. note:: - As an interim measure, these models are supported in both offline and online inference via Embeddings API. - -Classification ---------------- +Classification (``--task classify``) +------------------------------------ .. list-table:: :widths: 25 25 50 5 5 @@ -437,11 +472,8 @@ Classification - ✅︎ - ✅︎ -.. note:: - As an interim measure, these models are supported in both offline and online inference via Embeddings API. - -Sentence Pair Scoring ---------------------- +Sentence Pair Scoring (``--task score``) +---------------------------------------- .. list-table:: :widths: 25 25 50 5 5 @@ -468,13 +500,10 @@ Sentence Pair Scoring - - -.. note:: - These models are supported in both offline and online inference via Score API. - .. _supported_mm_models: -Multimodal Language Models -^^^^^^^^^^^^^^^^^^^^^^^^^^ +List of Multimodal Language Models +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The following modalities are supported depending on the model: @@ -491,8 +520,15 @@ On the other hand, modalities separated by :code:`/` are mutually exclusive. - e.g.: :code:`T / I` means that the model supports text-only and image-only inputs, but not text-with-image inputs. -Text Generation ---------------- +See :ref:`this page ` on how to pass multi-modal inputs to the model. + +Generative Models ++++++++++++++++++ + +See :ref:`this page ` for more information on how to use generative models. + +Text Generation (``--task generate``) +------------------------------------- .. list-table:: :widths: 25 25 15 20 5 5 5 @@ -696,8 +732,24 @@ Text Generation The official :code:`openbmb/MiniCPM-V-2` doesn't work yet, so we need to use a fork (:code:`HwwwH/MiniCPM-V-2`) for now. For more details, please see: https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630 -Multimodal Embedding --------------------- +Pooling Models +++++++++++++++ + +See :ref:`this page ` for more information on how to use pooling models. + +.. important:: + Since some model architectures support both generative and pooling tasks, + you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode. + +Text Embedding (``--task embed``) +--------------------------------- + +Any text generation model can be converted into an embedding model by passing :code:`--task embed`. + +.. note:: + To get the best results, you should use pooling models that are specifically trained as such. + +The following table lists those that are tested in vLLM. .. list-table:: :widths: 25 25 15 25 5 5 @@ -728,12 +780,7 @@ Multimodal Embedding - - ✅︎ -.. important:: - Some model architectures support both generation and embedding tasks. - In this case, you have to pass :code:`--task embedding` to run the model in embedding mode. - -.. tip:: - You can override the model's pooling method by passing :code:`--override-pooler-config`. +---- Model Support Policy ===================== diff --git a/docs/source/usage/compatibility_matrix.rst b/docs/source/usage/compatibility_matrix.rst index a93632ff36fb8..04dd72b1e3527 100644 --- a/docs/source/usage/compatibility_matrix.rst +++ b/docs/source/usage/compatibility_matrix.rst @@ -39,13 +39,13 @@ Feature x Feature - :abbr:`prmpt adptr (Prompt Adapter)` - :ref:`SD ` - CUDA graph - - :abbr:`emd (Embedding Models)` + - :abbr:`pooling (Pooling Models)` - :abbr:`enc-dec (Encoder-Decoder Models)` - :abbr:`logP (Logprobs)` - :abbr:`prmpt logP (Prompt Logprobs)` - :abbr:`async output (Async Output Processing)` - multi-step - - :abbr:`mm (Multimodal)` + - :abbr:`mm (Multimodal Inputs)` - best-of - beam-search - :abbr:`guided dec (Guided Decoding)` @@ -151,7 +151,7 @@ Feature x Feature - - - - * - :abbr:`emd (Embedding Models)` + * - :abbr:`pooling (Pooling Models)` - ✗ - ✗ - ✗ @@ -253,7 +253,7 @@ Feature x Feature - - - - * - :abbr:`mm (Multimodal)` + * - :abbr:`mm (Multimodal Inputs)` - ✅ - `✗ `__ - `✗ `__ @@ -386,7 +386,7 @@ Feature x Hardware - ✅ - ✗ - ✅ - * - :abbr:`emd (Embedding Models)` + * - :abbr:`pooling (Pooling Models)` - ✅ - ✅ - ✅ @@ -402,7 +402,7 @@ Feature x Hardware - ✅ - ✅ - ✗ - * - :abbr:`mm (Multimodal)` + * - :abbr:`mm (Multimodal Inputs)` - ✅ - ✅ - ✅ diff --git a/examples/offline_inference_embedding.py b/examples/offline_inference_embedding.py index ae158eef2ca4c..17f6d992073d7 100644 --- a/examples/offline_inference_embedding.py +++ b/examples/offline_inference_embedding.py @@ -9,7 +9,12 @@ ] # Create an LLM. -model = LLM(model="intfloat/e5-mistral-7b-instruct", enforce_eager=True) +model = LLM( + model="intfloat/e5-mistral-7b-instruct", + task="embed", # You should pass task="embed" for embedding models + enforce_eager=True, +) + # Generate embedding. The output is a list of PoolingRequestOutputs. outputs = model.encode(prompts) # Print the outputs. diff --git a/examples/offline_inference_vision_language_embedding.py b/examples/offline_inference_vision_language_embedding.py index e1732d045f949..bf466109f0981 100644 --- a/examples/offline_inference_vision_language_embedding.py +++ b/examples/offline_inference_vision_language_embedding.py @@ -59,7 +59,7 @@ def run_e5_v(query: Query): llm = LLM( model="royokong/e5-v", - task="embedding", + task="embed", max_model_len=4096, ) @@ -88,7 +88,7 @@ def run_vlm2vec(query: Query): llm = LLM( model="TIGER-Lab/VLM2Vec-Full", - task="embedding", + task="embed", trust_remote_code=True, mm_processor_kwargs={"num_crops": 4}, ) diff --git a/tests/compile/test_basic_correctness.py b/tests/compile/test_basic_correctness.py index 99781c55b672e..87d5aefea6cb4 100644 --- a/tests/compile/test_basic_correctness.py +++ b/tests/compile/test_basic_correctness.py @@ -55,7 +55,7 @@ class TestSetting: # embedding model TestSetting( model="BAAI/bge-multilingual-gemma2", - model_args=["--task", "embedding"], + model_args=["--task", "embed"], pp_size=1, tp_size=1, attn_backend="FLASHINFER", @@ -65,7 +65,7 @@ class TestSetting: # encoder-based embedding model (BERT) TestSetting( model="BAAI/bge-base-en-v1.5", - model_args=["--task", "embedding"], + model_args=["--task", "embed"], pp_size=1, tp_size=1, attn_backend="XFORMERS", diff --git a/tests/core/test_scheduler_encoder_decoder.py b/tests/core/test_scheduler_encoder_decoder.py index 7cd0416d321ef..16bea54936bc8 100644 --- a/tests/core/test_scheduler_encoder_decoder.py +++ b/tests/core/test_scheduler_encoder_decoder.py @@ -37,7 +37,7 @@ def test_scheduler_schedule_simple_encoder_decoder(): num_seq_group = 4 max_model_len = 16 scheduler_config = SchedulerConfig( - task="generate", + "generate", max_num_batched_tokens=64, max_num_seqs=num_seq_group, max_model_len=max_model_len, diff --git a/tests/entrypoints/openai/test_vision_embedding.py b/tests/entrypoints/openai/test_vision_embedding.py index 425f2a10ec855..43c63daacb17f 100644 --- a/tests/entrypoints/openai/test_vision_embedding.py +++ b/tests/entrypoints/openai/test_vision_embedding.py @@ -27,7 +27,7 @@ def server(): args = [ "--task", - "embedding", + "embed", "--dtype", "bfloat16", "--max-model-len", diff --git a/tests/models/embedding/language/test_embedding.py b/tests/models/embedding/language/test_embedding.py index 5ef8540265d14..f458ef5ef556d 100644 --- a/tests/models/embedding/language/test_embedding.py +++ b/tests/models/embedding/language/test_embedding.py @@ -54,7 +54,7 @@ def test_models( hf_outputs = hf_model.encode(example_prompts) with vllm_runner(model, - task="embedding", + task="embed", dtype=dtype, max_model_len=None, **vllm_extra_kwargs) as vllm_model: diff --git a/tests/models/embedding/language/test_scoring.py b/tests/models/embedding/language/test_scoring.py index 30fa5ea7b36c0..0c3115d195fc1 100644 --- a/tests/models/embedding/language/test_scoring.py +++ b/tests/models/embedding/language/test_scoring.py @@ -35,9 +35,7 @@ def test_llm_1_to_1(vllm_runner, hf_runner, model_name, dtype: str): with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model: hf_outputs = hf_model.predict([text_pair]).tolist() - with vllm_runner(model_name, - task="embedding", - dtype=dtype, + with vllm_runner(model_name, task="score", dtype=dtype, max_model_len=None) as vllm_model: vllm_outputs = vllm_model.score(text_pair[0], text_pair[1]) @@ -58,9 +56,7 @@ def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str): with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model: hf_outputs = hf_model.predict(text_pairs).tolist() - with vllm_runner(model_name, - task="embedding", - dtype=dtype, + with vllm_runner(model_name, task="score", dtype=dtype, max_model_len=None) as vllm_model: vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2) @@ -82,9 +78,7 @@ def test_llm_N_to_N(vllm_runner, hf_runner, model_name, dtype: str): with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model: hf_outputs = hf_model.predict(text_pairs).tolist() - with vllm_runner(model_name, - task="embedding", - dtype=dtype, + with vllm_runner(model_name, task="score", dtype=dtype, max_model_len=None) as vllm_model: vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2) diff --git a/tests/models/embedding/vision_language/test_dse_qwen2_vl.py b/tests/models/embedding/vision_language/test_dse_qwen2_vl.py index 3dd8cb729f8a6..2641987b25a3a 100644 --- a/tests/models/embedding/vision_language/test_dse_qwen2_vl.py +++ b/tests/models/embedding/vision_language/test_dse_qwen2_vl.py @@ -93,7 +93,7 @@ def _run_test( # if we run HF first, the cuda initialization will be done and it # will hurt multiprocessing backend with fork method (the default method). with vllm_runner(model, - task="embedding", + task="embed", dtype=dtype, enforce_eager=True, max_model_len=8192) as vllm_model: diff --git a/tests/models/embedding/vision_language/test_llava_next.py b/tests/models/embedding/vision_language/test_llava_next.py index 693abd7252d5e..f4cd8b81a0d7d 100644 --- a/tests/models/embedding/vision_language/test_llava_next.py +++ b/tests/models/embedding/vision_language/test_llava_next.py @@ -47,7 +47,7 @@ def _run_test( # if we run HF first, the cuda initialization will be done and it # will hurt multiprocessing backend with fork method (the default method). with vllm_runner(model, - task="embedding", + task="embed", dtype=dtype, max_model_len=4096, enforce_eager=True) as vllm_model: diff --git a/tests/models/embedding/vision_language/test_phi3v.py b/tests/models/embedding/vision_language/test_phi3v.py index 6145aff1a5ea2..9374c23dd6ffe 100644 --- a/tests/models/embedding/vision_language/test_phi3v.py +++ b/tests/models/embedding/vision_language/test_phi3v.py @@ -39,7 +39,7 @@ def _run_test( # vLLM needs a fresh new process without cuda initialization. # if we run HF first, the cuda initialization will be done and it # will hurt multiprocessing backend with fork method (the default method). - with vllm_runner(model, task="embedding", dtype=dtype, + with vllm_runner(model, task="embed", dtype=dtype, enforce_eager=True) as vllm_model: vllm_outputs = vllm_model.encode(input_texts, images=input_images) diff --git a/tests/test_config.py b/tests/test_config.py index 45b0b938af215..4518adfc31bfc 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -7,11 +7,17 @@ from vllm.platforms import current_platform -@pytest.mark.parametrize(("model_id", "expected_task"), [ - ("facebook/opt-125m", "generate"), - ("intfloat/e5-mistral-7b-instruct", "embedding"), -]) -def test_auto_task(model_id, expected_task): +@pytest.mark.parametrize( + ("model_id", "expected_runner_type", "expected_task"), + [ + ("facebook/opt-125m", "generate", "generate"), + ("intfloat/e5-mistral-7b-instruct", "pooling", "embed"), + ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify"), + ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "score"), + ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "reward"), + ], +) +def test_auto_task(model_id, expected_runner_type, expected_task): config = ModelConfig( model_id, task="auto", @@ -22,6 +28,7 @@ def test_auto_task(model_id, expected_task): dtype="float16", ) + assert config.runner_type == expected_runner_type assert config.task == expected_task diff --git a/vllm/config.py b/vllm/config.py index 2a9f0ebae997d..2d9a76fe7ddb1 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -45,13 +45,27 @@ logger = init_logger(__name__) -_EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768 +_POOLING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768 _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120 -TaskOption = Literal["auto", "generate", "embedding"] +TaskOption = Literal["auto", "generate", "embedding", "embed", "classify", + "score", "reward"] -# "draft" is only used internally for speculative decoding -_Task = Literal["generate", "embedding", "draft"] +_ResolvedTask = Literal["generate", "embed", "classify", "score", "reward", + "draft"] + +RunnerType = Literal["generate", "pooling", "draft"] + +_RUNNER_TASKS: Dict[RunnerType, List[_ResolvedTask]] = { + "generate": ["generate"], + "pooling": ["embed", "classify", "score", "reward"], + "draft": ["draft"], +} + +_TASK_RUNNER: Dict[_ResolvedTask, RunnerType] = { + task: runner + for runner, tasks in _RUNNER_TASKS.items() for task in tasks +} HfOverrides = Union[Dict[str, Any], Callable[[PretrainedConfig], PretrainedConfig]] @@ -144,7 +158,7 @@ class ModelConfig: def __init__( self, model: str, - task: Union[TaskOption, _Task], + task: Union[TaskOption, Literal["draft"]], tokenizer: str, tokenizer_mode: str, trust_remote_code: bool, @@ -295,6 +309,7 @@ def __init__( supported_tasks, task = self._resolve_task(task, self.hf_config) self.supported_tasks = supported_tasks self.task: Final = task + self.pooler_config = self._init_pooler_config(override_pooler_config) self._verify_quantization() @@ -323,7 +338,7 @@ def _init_pooler_config( override_pooler_config: Optional["PoolerConfig"], ) -> Optional["PoolerConfig"]: - if self.task == "embedding": + if self.runner_type == "pooling": user_config = override_pooler_config or PoolerConfig() base_config = get_pooling_config(self.model, self.revision) @@ -357,60 +372,90 @@ def _verify_tokenizer_mode(self) -> None: "either 'auto', 'slow' or 'mistral'.") self.tokenizer_mode = tokenizer_mode + def _get_preferred_task( + self, + architectures: List[str], + supported_tasks: Set[_ResolvedTask], + ) -> Optional[_ResolvedTask]: + model_id = self.model + if get_pooling_config(model_id, self.revision): + return "embed" + if ModelRegistry.is_cross_encoder_model(architectures): + return "score" + + suffix_to_preferred_task: List[Tuple[str, _ResolvedTask]] = [ + # Other models follow this pattern + ("ForCausalLM", "generate"), + ("ForConditionalGeneration", "generate"), + ("ForSequenceClassification", "classify"), + ("ChatModel", "generate"), + ("LMHeadModel", "generate"), + ("EmbeddingModel", "embed"), + ("RewardModel", "reward"), + ] + _, arch = ModelRegistry.inspect_model_cls(architectures) + + for suffix, pref_task in suffix_to_preferred_task: + if arch.endswith(suffix) and pref_task in supported_tasks: + return pref_task + + return None + def _resolve_task( self, - task_option: Union[TaskOption, _Task], + task_option: Union[TaskOption, Literal["draft"]], hf_config: PretrainedConfig, - ) -> Tuple[Set[_Task], _Task]: + ) -> Tuple[Set[_ResolvedTask], _ResolvedTask]: if task_option == "draft": return {"draft"}, "draft" architectures = getattr(hf_config, "architectures", []) - task_support: Dict[_Task, bool] = { + runner_support: Dict[RunnerType, bool] = { # NOTE: Listed from highest to lowest priority, # in case the model supports multiple of them "generate": ModelRegistry.is_text_generation_model(architectures), - "embedding": ModelRegistry.is_pooling_model(architectures), + "pooling": ModelRegistry.is_pooling_model(architectures), } - supported_tasks_lst: List[_Task] = [ - task for task, is_supported in task_support.items() if is_supported + supported_runner_types_lst: List[RunnerType] = [ + runner_type + for runner_type, is_supported in runner_support.items() + if is_supported + ] + + supported_tasks_lst: List[_ResolvedTask] = [ + task for runner_type in supported_runner_types_lst + for task in _RUNNER_TASKS[runner_type] ] supported_tasks = set(supported_tasks_lst) if task_option == "auto": selected_task = next(iter(supported_tasks_lst)) - if len(supported_tasks) > 1: - suffix_to_preferred_task: List[Tuple[str, _Task]] = [ - # Hardcode the models that are exceptions - ("AquilaModel", "generate"), - ("ChatGLMModel", "generate"), - # Other models follow this pattern - ("ForCausalLM", "generate"), - ("ForConditionalGeneration", "generate"), - ("ChatModel", "generate"), - ("LMHeadModel", "generate"), - ("EmbeddingModel", "embedding"), - ("RewardModel", "embedding"), - ("ForSequenceClassification", "embedding"), - ] - info, arch = ModelRegistry.inspect_model_cls(architectures) - - for suffix, pref_task in suffix_to_preferred_task: - if arch.endswith(suffix) and pref_task in supported_tasks: - selected_task = pref_task - break - else: - if (arch.endswith("Model") - and info.architecture.endswith("ForCausalLM") - and "embedding" in supported_tasks): - selected_task = "embedding" + if len(supported_tasks_lst) > 1: + preferred_task = self._get_preferred_task( + architectures, supported_tasks) + if preferred_task is not None: + selected_task = preferred_task logger.info( "This model supports multiple tasks: %s. " "Defaulting to '%s'.", supported_tasks, selected_task) else: + # Aliases + if task_option == "embedding": + preferred_task = self._get_preferred_task( + architectures, supported_tasks) + if preferred_task != "embed": + msg = ("The 'embedding' task will be restricted to " + "embedding models in a future release. Please " + "pass `--task classify`, `--task score`, or " + "`--task reward` explicitly for other pooling " + "models.") + warnings.warn(msg, DeprecationWarning, stacklevel=2) + + task_option = preferred_task or "embed" + if task_option not in supported_tasks: msg = ( f"This model does not support the '{task_option}' task. " @@ -533,7 +578,7 @@ def verify_async_output_proc(self, parallel_config, speculative_config, # Async postprocessor is not necessary with embedding mode # since there is no token generation - if self.task == "embedding": + if self.runner_type == "pooling": self.use_async_output_proc = False # Reminder: Please update docs/source/usage/compatibility_matrix.rst @@ -750,6 +795,14 @@ def is_cross_encoder(self) -> bool: architectures = getattr(self.hf_config, "architectures", []) return ModelRegistry.is_cross_encoder_model(architectures) + @property + def supported_runner_types(self) -> Set[RunnerType]: + return {_TASK_RUNNER[task] for task in self.supported_tasks} + + @property + def runner_type(self) -> RunnerType: + return _TASK_RUNNER[self.task] + class CacheConfig: """Configuration for the KV cache. @@ -1096,7 +1149,7 @@ def _verify_args(self) -> None: class SchedulerConfig: """Scheduler configuration.""" - task: str = "generate" # The task to use the model for. + runner_type: str = "generate" # The runner type to launch for the model. # Maximum number of tokens to be processed in a single iteration. max_num_batched_tokens: int = field(default=None) # type: ignore @@ -1164,11 +1217,11 @@ def __post_init__(self) -> None: # for higher throughput. self.max_num_batched_tokens = max(self.max_model_len, 2048) - if self.task == "embedding": - # For embedding, choose specific value for higher throughput + if self.runner_type == "pooling": + # Choose specific value for higher throughput self.max_num_batched_tokens = max( self.max_num_batched_tokens, - _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS, + _POOLING_MODEL_MAX_NUM_BATCHED_TOKENS, ) if self.is_multimodal_model: # The value needs to be at least the number of multimodal tokens diff --git a/vllm/core/scheduler.py b/vllm/core/scheduler.py index 94c62743883ec..c3bc6becf0995 100644 --- a/vllm/core/scheduler.py +++ b/vllm/core/scheduler.py @@ -337,7 +337,7 @@ def __init__( self.lora_config = lora_config version = "selfattn" - if (self.scheduler_config.task == "embedding" + if (self.scheduler_config.runner_type == "pooling" or self.cache_config.is_attention_free): version = "placeholder" diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 7b9adc401abcf..d485c2a9e7208 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -1066,7 +1066,7 @@ def create_engine_config(self, if (is_gpu and not use_sliding_window and not use_spec_decode and not self.enable_lora and not self.enable_prompt_adapter - and model_config.task != "embedding"): + and model_config.runner_type != "pooling"): self.enable_chunked_prefill = True logger.warning( "Chunked prefill is enabled by default for models with " @@ -1083,7 +1083,8 @@ def create_engine_config(self, "errors during the initial memory profiling phase, or result " "in low performance due to small KV cache space. Consider " "setting --max-model-len to a smaller value.", max_model_len) - elif self.enable_chunked_prefill and model_config.task == "embedding": + elif (self.enable_chunked_prefill + and model_config.runner_type == "pooling"): msg = "Chunked prefill is not supported for embedding models" raise ValueError(msg) @@ -1144,7 +1145,7 @@ def create_engine_config(self, " please file an issue with detailed information.") scheduler_config = SchedulerConfig( - task=model_config.task, + runner_type=model_config.runner_type, max_num_batched_tokens=self.max_num_batched_tokens, max_num_seqs=self.max_num_seqs, max_model_len=model_config.max_model_len, diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 6eca304b45f07..9be30c635cb2c 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -288,7 +288,7 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer: self.model_executor = executor_class(vllm_config=vllm_config, ) - if self.model_config.task != "embedding": + if self.model_config.runner_type != "pooling": self._initialize_kv_caches() # If usage stat is enabled, collect relevant info. @@ -1123,7 +1123,7 @@ def _process_model_outputs(self, seq_group.metrics.model_execute_time = ( o.model_execute_time) - if self.model_config.task == "embedding": + if self.model_config.runner_type == "pooling": self._process_sequence_group_outputs(seq_group, output) else: self.output_processor.process_prompt_logprob(seq_group, output) diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py index 2a02187223a33..0bec978c4869c 100644 --- a/vllm/entrypoints/llm.py +++ b/vllm/entrypoints/llm.py @@ -381,19 +381,20 @@ def generate( considered legacy and may be deprecated in the future. You should instead pass them via the ``inputs`` parameter. """ - task = self.llm_engine.model_config.task - if task != "generate": + runner_type = self.llm_engine.model_config.runner_type + if runner_type != "generate": messages = [ "LLM.generate() is only supported for (conditional) generation " "models (XForCausalLM, XForConditionalGeneration).", ] - supported_tasks = self.llm_engine.model_config.supported_tasks - if "generate" in supported_tasks: + supported_runner_types = self.llm_engine.model_config \ + .supported_runner_types + if "generate" in supported_runner_types: messages.append( - "Your model supports the 'generate' task, but is " - f"currently initialized for the '{task}' task. Please " - "initialize the model using `--task generate`.") + "Your model supports the 'generate' runner, but is " + f"currently initialized for the '{runner_type}' runner. " + "Please initialize vLLM using `--task generate`.") raise ValueError(" ".join(messages)) @@ -793,16 +794,18 @@ def encode( considered legacy and may be deprecated in the future. You should instead pass them via the ``inputs`` parameter. """ - task = self.llm_engine.model_config.task - if task != "embedding": - messages = ["LLM.encode() is only supported for embedding models."] + runner_type = self.llm_engine.model_config.runner_type + if runner_type != "pooling": + messages = ["LLM.encode() is only supported for pooling models."] - supported_tasks = self.llm_engine.model_config.supported_tasks - if "embedding" in supported_tasks: + supported_runner_types = self.llm_engine.model_config \ + .supported_runner_types + if "pooling" in supported_runner_types: messages.append( - "Your model supports the 'embedding' task, but is " - f"currently initialized for the '{task}' task. Please " - "initialize the model using `--task embedding`.") + "Your model supports the 'pooling' runner, but is " + f"currently initialized for the '{runner_type}' runner. " + "Please initialize vLLM using `--task embed`, " + "`--task classify`, `--task score` etc.") raise ValueError(" ".join(messages)) @@ -864,21 +867,23 @@ def score( A list of ``PoolingRequestOutput`` objects containing the generated scores in the same order as the input prompts. """ - task = self.llm_engine.model_config.task - if task != "embedding": - messages = ["LLM.score() is only supported for embedding models."] + runner_type = self.llm_engine.model_config.runner_type + if runner_type != "pooling": + messages = ["LLM.score() is only supported for pooling models."] - supported_tasks = self.llm_engine.model_config.supported_tasks - if "embedding" in supported_tasks: + supported_runner_types = self.llm_engine.model_config \ + .supported_runner_types + if "pooling" in supported_runner_types: messages.append( - "Your model supports the 'embedding' task, but is " - f"currently initialized for the '{task}' task. Please " - "initialize the model using `--task embedding`.") + "Your model supports the 'pooling' runner, but is " + f"currently initialized for the '{runner_type}' runner. " + "Please initialize vLLM using `--task embed`, " + "`--task classify`, `--task score` etc.") raise ValueError(" ".join(messages)) if not self.llm_engine.model_config.is_cross_encoder: - raise ValueError("Your model does not support the cross encoding") + raise ValueError("Your model does not support cross encoding") tokenizer = self.llm_engine.get_tokenizer() diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py index 0f93eb54111ad..a345f8caeeed2 100644 --- a/vllm/entrypoints/openai/api_server.py +++ b/vllm/entrypoints/openai/api_server.py @@ -573,7 +573,7 @@ def init_app_state( enable_auto_tools=args.enable_auto_tool_choice, tool_parser=args.tool_call_parser, enable_prompt_tokens_details=args.enable_prompt_tokens_details, - ) if model_config.task == "generate" else None + ) if model_config.runner_type == "generate" else None state.openai_serving_completion = OpenAIServingCompletion( engine_client, model_config, @@ -582,7 +582,7 @@ def init_app_state( prompt_adapters=args.prompt_adapters, request_logger=request_logger, return_tokens_as_token_ids=args.return_tokens_as_token_ids, - ) if model_config.task == "generate" else None + ) if model_config.runner_type == "generate" else None state.openai_serving_embedding = OpenAIServingEmbedding( engine_client, model_config, @@ -590,13 +590,13 @@ def init_app_state( request_logger=request_logger, chat_template=resolved_chat_template, chat_template_content_format=args.chat_template_content_format, - ) if model_config.task == "embedding" else None + ) if model_config.runner_type == "pooling" else None state.openai_serving_scores = OpenAIServingScores( engine_client, model_config, base_model_paths, request_logger=request_logger - ) if (model_config.task == "embedding" \ + ) if (model_config.runner_type == "pooling" \ and model_config.is_cross_encoder) else None state.openai_serving_tokenization = OpenAIServingTokenization( engine_client, diff --git a/vllm/entrypoints/openai/run_batch.py b/vllm/entrypoints/openai/run_batch.py index 00cdb3b6839f5..675daf54c0d0d 100644 --- a/vllm/entrypoints/openai/run_batch.py +++ b/vllm/entrypoints/openai/run_batch.py @@ -224,7 +224,7 @@ async def main(args): chat_template=None, chat_template_content_format="auto", enable_prompt_tokens_details=args.enable_prompt_tokens_details, - ) if model_config.task == "generate" else None + ) if model_config.runner_type == "generate" else None openai_serving_embedding = OpenAIServingEmbedding( engine, model_config, @@ -232,7 +232,7 @@ async def main(args): request_logger=request_logger, chat_template=None, chat_template_content_format="auto", - ) if model_config.task == "embedding" else None + ) if model_config.runner_type == "pooling" else None tracker = BatchProgressTracker() logger.info("Reading batch from %s...", args.input_file) diff --git a/vllm/model_executor/model_loader/utils.py b/vllm/model_executor/model_loader/utils.py index cfb89e0f336bc..f15e7176b3d50 100644 --- a/vllm/model_executor/model_loader/utils.py +++ b/vllm/model_executor/model_loader/utils.py @@ -35,7 +35,7 @@ def get_model_architecture( architectures = ["QuantMixtralForCausalLM"] model_cls, arch = ModelRegistry.resolve_model_cls(architectures) - if model_config.task == "embedding": + if model_config.runner_type == "pooling": model_cls = as_embedding_model(model_cls) return model_cls, arch diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py index fdb241e6753fb..55a5c4dff3a5c 100644 --- a/vllm/v1/engine/core.py +++ b/vllm/v1/engine/core.py @@ -42,7 +42,7 @@ def __init__( executor_class: Type[Executor], usage_context: UsageContext, ): - assert vllm_config.model_config.task != "embedding" + assert vllm_config.model_config.runner_type != "pooling" logger.info("Initializing an LLM engine (v%s) with config: %s", VLLM_VERSION, vllm_config) diff --git a/vllm/worker/cpu_worker.py b/vllm/worker/cpu_worker.py index 4fad1a3f4caeb..ba3d4a130a80b 100644 --- a/vllm/worker/cpu_worker.py +++ b/vllm/worker/cpu_worker.py @@ -163,7 +163,7 @@ def __init__( not in ["medusa", "mlp_speculator", "eagle"]) \ else {"return_hidden_states": True} ModelRunnerClass: Type[CPUModelRunnerBase] = CPUModelRunner - if self.model_config.task == "embedding": + if self.model_config.runner_type == "pooling": ModelRunnerClass = CPUPoolingModelRunner elif self.model_config.is_encoder_decoder: ModelRunnerClass = CPUEncoderDecoderModelRunner diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py index 094dd5a5d08b3..832b9903b7abc 100644 --- a/vllm/worker/worker.py +++ b/vllm/worker/worker.py @@ -75,7 +75,7 @@ def __init__( else {"return_hidden_states": True} ModelRunnerClass: Type[GPUModelRunnerBase] = ModelRunner - if model_config.task == "embedding": + if model_config.runner_type == "pooling": ModelRunnerClass = PoolingModelRunner elif self.model_config.is_encoder_decoder: ModelRunnerClass = EncoderDecoderModelRunner From cad5c0a6eda057eeece87a42fff49fef3e18a2ac Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 11 Dec 2024 21:36:27 +0800 Subject: [PATCH 028/155] [Doc] Update docs to refer to pooling models (#11093) Signed-off-by: DarkLight1337 --- docs/source/usage/faq.rst | 7 ++++++- vllm/attention/backends/placeholder_attn.py | 2 +- vllm/config.py | 8 ++++---- vllm/core/placeholder_block_space_manager.py | 2 +- vllm/engine/arg_utils.py | 4 ++-- vllm/engine/async_llm_engine.py | 2 +- vllm/engine/multiprocessing/client.py | 2 +- vllm/engine/protocol.py | 2 +- vllm/entrypoints/openai/serving_score.py | 2 +- vllm/sequence.py | 6 +++--- vllm/v1/engine/processor.py | 2 +- vllm/worker/cpu_worker.py | 2 +- vllm/worker/hpu_worker.py | 4 ++-- vllm/worker/worker.py | 2 +- 14 files changed, 26 insertions(+), 21 deletions(-) diff --git a/docs/source/usage/faq.rst b/docs/source/usage/faq.rst index ce327abd5fa20..d88da32092924 100644 --- a/docs/source/usage/faq.rst +++ b/docs/source/usage/faq.rst @@ -11,7 +11,12 @@ A: Assuming that you're referring to using OpenAI compatible server to serve mul Q: Which model to use for offline inference embedding? -A: If you want to use an embedding model, try: https://huggingface.co/intfloat/e5-mistral-7b-instruct. Instead models, such as Llama-3-8b, Mistral-7B-Instruct-v0.3, are generation models rather than an embedding model +A: You can try `e5-mistral-7b-instruct `__ and `BAAI/bge-base-en-v1.5 `__; +more are listed :ref:`here `. + +By extracting hidden states, vLLM can automatically convert text generation models like `Llama-3-8B `__, +`Mistral-7B-Instruct-v0.3 `__ into embedding models, +but they are expected be inferior to models that are specifically trained on embedding tasks. ---------------------------------------- diff --git a/vllm/attention/backends/placeholder_attn.py b/vllm/attention/backends/placeholder_attn.py index 658039bfc3365..534f79b3a60bf 100644 --- a/vllm/attention/backends/placeholder_attn.py +++ b/vllm/attention/backends/placeholder_attn.py @@ -14,7 +14,7 @@ from vllm.worker.model_runner import (ModelInputForGPUBuilder, ModelInputForGPUWithSamplingMetadata) -# Placeholder attention backend for models like Mamba and embedding models that +# Placeholder attention backend for models like Mamba and pooling models that # lack attention. diff --git a/vllm/config.py b/vllm/config.py index 2d9a76fe7ddb1..322c8f8990a40 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -152,7 +152,7 @@ class ModelConfig: this argument will be used to configure the neuron config that can not be gathered from the vllm arguments. override_pooler_config: Initialize non default pooling config or - override default pooling config for the embedding model. + override default pooling config for the pooling model. """ def __init__( @@ -576,7 +576,7 @@ def verify_async_output_proc(self, parallel_config, speculative_config, self.use_async_output_proc = False return - # Async postprocessor is not necessary with embedding mode + # Async postprocessor is not necessary for pooling models # since there is no token generation if self.runner_type == "pooling": self.use_async_output_proc = False @@ -1825,11 +1825,11 @@ class MultiModalConfig: @dataclass class PoolerConfig: - """Controls the behavior of output pooling in embedding models.""" + """Controls the behavior of output pooling in pooling models.""" pooling_type: Optional[str] = None """ - The pooling method of the embedding model. This should be a key in + The pooling method of the pooling model. This should be a key in :class:`vllm.model_executor.layers.pooler.PoolingType`. """ diff --git a/vllm/core/placeholder_block_space_manager.py b/vllm/core/placeholder_block_space_manager.py index 26d42b7f1790e..a47e594518534 100644 --- a/vllm/core/placeholder_block_space_manager.py +++ b/vllm/core/placeholder_block_space_manager.py @@ -8,7 +8,7 @@ class PlaceholderBlockSpaceManager(BlockSpaceManager): """A version of BlockSpaceManager for use in environments where block management is not required. - For example: embedding models or attention-free models like Mamba. + For example: pooling models or attention-free models like Mamba. This class provides the same interface as BlockSpaceManager, but its methods perform no actions or return simple values like True in specific diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index d485c2a9e7208..7337522bc9952 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -893,7 +893,7 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: '--override-pooler-config', type=PoolerConfig.from_json, default=None, - help="Override or set the pooling method in the embedding model. " + help="Override or set the pooling method for pooling models. " "e.g. {\"pooling_type\": \"mean\", \"normalize\": false}.'") parser.add_argument('--compilation-config', @@ -1085,7 +1085,7 @@ def create_engine_config(self, "setting --max-model-len to a smaller value.", max_model_len) elif (self.enable_chunked_prefill and model_config.runner_type == "pooling"): - msg = "Chunked prefill is not supported for embedding models" + msg = "Chunked prefill is not supported for pooling models" raise ValueError(msg) diff --git a/vllm/engine/async_llm_engine.py b/vllm/engine/async_llm_engine.py index 60dccd7a0812c..32396fd10188d 100644 --- a/vllm/engine/async_llm_engine.py +++ b/vllm/engine/async_llm_engine.py @@ -1085,7 +1085,7 @@ async def encode( trace_headers: Optional[Mapping[str, str]] = None, priority: int = 0, ) -> AsyncGenerator[PoolingRequestOutput, None]: - """Generate outputs for a request from an embedding model. + """Generate outputs for a request from a pooling model. Generate outputs for a request. This method is a coroutine. It adds the request into the waiting queue of the LLMEngine and streams the outputs diff --git a/vllm/engine/multiprocessing/client.py b/vllm/engine/multiprocessing/client.py index a729023bc00bb..0a046c71e86e8 100644 --- a/vllm/engine/multiprocessing/client.py +++ b/vllm/engine/multiprocessing/client.py @@ -527,7 +527,7 @@ def encode( *, inputs: Optional[PromptType] = None # DEPRECATED ) -> AsyncGenerator[PoolingRequestOutput, None]: - """Generate outputs for a request from an embedding model. + """Generate outputs for a request from a pooling model. Generate outputs for a request. This method is a coroutine. It adds the request into the waiting queue of the LLMEngine and streams the outputs diff --git a/vllm/engine/protocol.py b/vllm/engine/protocol.py index 4079de7d36793..a066836b92708 100644 --- a/vllm/engine/protocol.py +++ b/vllm/engine/protocol.py @@ -209,7 +209,7 @@ def encode( trace_headers: Optional[Mapping[str, str]] = None, priority: int = 0, ) -> AsyncGenerator[PoolingRequestOutput, None]: - """Generate outputs for a request from an embedding model.""" + """Generate outputs for a request from a pooling model.""" ... @abstractmethod diff --git a/vllm/entrypoints/openai/serving_score.py b/vllm/entrypoints/openai/serving_score.py index fed06fa452955..4929e720c00e4 100644 --- a/vllm/entrypoints/openai/serving_score.py +++ b/vllm/entrypoints/openai/serving_score.py @@ -119,7 +119,7 @@ async def create_score( if prompt_adapter_request is not None: raise NotImplementedError("Prompt adapter is not supported " - "for embedding models") + "for scoring models") if isinstance(tokenizer, MistralTokenizer): raise ValueError( diff --git a/vllm/sequence.py b/vllm/sequence.py index 669124319c4f4..b0f3c1cc3609f 100644 --- a/vllm/sequence.py +++ b/vllm/sequence.py @@ -618,9 +618,9 @@ class SequenceGroup: arrival_time: The arrival time of the request. lora_request: LoRA request. embeddings: The embeddings vectors of the prompt of the sequence group - for an embedding model. + for a pooling model. pooling_params: The pooling parameters used to generate the pooling - for an embedding model. + for a pooling model. encoder_seq: Optional, the single encoder sequence. Should be None unless you are working with an encoder/decoder model. trace_headers: OpenTelemetry trace headers. @@ -1102,7 +1102,7 @@ class PoolerOutput( msgspec.Struct, omit_defaults=True, # type: ignore[call-arg] array_like=True): # type: ignore[call-arg] - """The output from a pooling operation in the embedding model.""" + """The output from a pooling operation in the pooling model.""" outputs: List[EmbeddingSequenceGroupOutput] # lazy import to avoid circular import diff --git a/vllm/v1/engine/processor.py b/vllm/v1/engine/processor.py index 120fc64969552..e0e525b30a767 100644 --- a/vllm/v1/engine/processor.py +++ b/vllm/v1/engine/processor.py @@ -59,7 +59,7 @@ def process_inputs( priority: int = 0, ) -> Tuple[DetokenizerRequest, EngineCoreRequest]: - # TODO(woosuk): Support embedding mode. + # TODO(woosuk): Support pooling models. # TODO(woosuk): Check max_logprobs # TODO(woosuk): Support encoder-decoder models. diff --git a/vllm/worker/cpu_worker.py b/vllm/worker/cpu_worker.py index ba3d4a130a80b..09758a5d9accf 100644 --- a/vllm/worker/cpu_worker.py +++ b/vllm/worker/cpu_worker.py @@ -178,7 +178,7 @@ def __init__( # Uninitialized cache engine. Will be initialized by # initialize_cache. self.cache_engine: List[CPUCacheEngine] - # Initialize cpu_cache as embedding models don't initialize kv_caches + # Initialize cpu_cache as pooling models don't initialize kv_caches self.cpu_cache: Optional[List[List[torch.Tensor]]] = None # Torch profiler. Enabled and configured through env vars: diff --git a/vllm/worker/hpu_worker.py b/vllm/worker/hpu_worker.py index 493f7a9fad098..cca7cd50bfc7b 100644 --- a/vllm/worker/hpu_worker.py +++ b/vllm/worker/hpu_worker.py @@ -65,8 +65,8 @@ def __init__( # Uninitialized cache engine. Will be initialized by # initialize_cache. self.cache_engine: List[HPUCacheEngine] - # Initialize gpu_cache as embedding models don't initialize kv_caches - self.hpu_cache: Optional[List[List[torch.tensor]]] = None + # Initialize gpu_cache as pooling models don't initialize kv_caches + self.hpu_cache: Optional[List[List[torch.Tensor]]] = None # Torch profiler. Enabled and configured through env vars: # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace if envs.VLLM_TORCH_PROFILER_DIR: diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py index 832b9903b7abc..a368bb9ee9a5b 100644 --- a/vllm/worker/worker.py +++ b/vllm/worker/worker.py @@ -91,7 +91,7 @@ def __init__( # Uninitialized cache engine. Will be initialized by # initialize_cache. self.cache_engine: List[CacheEngine] - # Initialize gpu_cache as embedding models don't initialize kv_caches + # Initialize gpu_cache as pooling models don't initialize kv_caches self.gpu_cache: Optional[List[List[torch.Tensor]]] = None self._seq_group_metadata_cache: Dict[str, SequenceGroupMetadata] = {} From b2f775456e4af7412308320a9c11e4dac3086205 Mon Sep 17 00:00:00 2001 From: hissu-hyvarinen Date: Wed, 11 Dec 2024 17:23:37 +0200 Subject: [PATCH 029/155] [CI/Build] Enable prefix caching test for AMD (#11098) Signed-off-by: Hissu Hyvarinen --- .buildkite/test-pipeline.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 8f57006214c88..df4fa7a6ee9ba 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -201,7 +201,7 @@ steps: - python3 offline_profile.py --model facebook/opt-125m - label: Prefix Caching Test # 9min - #mirror_hardwares: [amd] + mirror_hardwares: [amd] source_file_dependencies: - vllm/ - tests/prefix_caching From fd22220687af5ccd89d9f8f2812069ef0422244c Mon Sep 17 00:00:00 2001 From: bingps <46775742+bingps@users.noreply.github.com> Date: Wed, 11 Dec 2024 23:43:24 +0800 Subject: [PATCH 030/155] [Doc] Installed version of llmcompressor for int8/fp8 quantization (#11103) Signed-off-by: Guangda Liu Co-authored-by: Guangda Liu --- docs/source/quantization/fp8.rst | 2 +- docs/source/quantization/int8.rst | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/quantization/fp8.rst b/docs/source/quantization/fp8.rst index aacd07a34ad46..4dbf8e9d346e1 100644 --- a/docs/source/quantization/fp8.rst +++ b/docs/source/quantization/fp8.rst @@ -45,7 +45,7 @@ To produce performant FP8 quantized models with vLLM, you'll need to install the .. code-block:: console - $ pip install llmcompressor==0.1.0 + $ pip install llmcompressor Quantization Process -------------------- diff --git a/docs/source/quantization/int8.rst b/docs/source/quantization/int8.rst index 04fa308449507..aa5b251becb1c 100644 --- a/docs/source/quantization/int8.rst +++ b/docs/source/quantization/int8.rst @@ -19,7 +19,7 @@ To use INT8 quantization with vLLM, you'll need to install the `llm-compressor < .. code-block:: console - $ pip install llmcompressor==0.1.0 + $ pip install llmcompressor Quantization Process -------------------- @@ -142,4 +142,4 @@ Best Practices Troubleshooting and Support --------------------------- -If you encounter any issues or have feature requests, please open an issue on the ``vllm-project/llm-compressor`` GitHub repository. \ No newline at end of file +If you encounter any issues or have feature requests, please open an issue on the ``vllm-project/llm-compressor`` GitHub repository. From 91642db952458fbb6ae7c2d167757dc86b105991 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Wed, 11 Dec 2024 10:43:05 -0800 Subject: [PATCH 031/155] [torch.compile] use depyf to dump torch.compile internals (#10972) Signed-off-by: youkaichao --- requirements-common.txt | 1 + vllm/compilation/backends.py | 69 ++++++++++++++++++---------------- vllm/compilation/decorators.py | 2 +- vllm/compilation/monitor.py | 23 ++++++++++-- vllm/compilation/wrapper.py | 4 +- vllm/config.py | 6 ++- vllm/worker/model_runner.py | 3 +- 7 files changed, 66 insertions(+), 42 deletions(-) diff --git a/requirements-common.txt b/requirements-common.txt index 792cd58e80669..850b8f4101701 100644 --- a/requirements-common.txt +++ b/requirements-common.txt @@ -33,3 +33,4 @@ six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that need setuptools>=74.1.1; python_version > '3.11' # Setuptools is used by triton, we need to ensure a modern version is installed for 3.12+ so that it does not try to import distutils, which was removed in 3.12 einops # Required for Qwen2-VL. compressed-tensors == 0.8.0 # required for compressed-tensors +depyf==0.18.0 # required for profiling and debugging torch.compile diff --git a/vllm/compilation/backends.py b/vllm/compilation/backends.py index f002a8ff905b1..09a3daa731829 100644 --- a/vllm/compilation/backends.py +++ b/vllm/compilation/backends.py @@ -9,7 +9,7 @@ import torch.fx as fx import vllm.envs as envs -from vllm.config import CompilationConfig +from vllm.config import CompilationConfig, VllmConfig from vllm.logger import init_logger from vllm.utils import weak_ref_tensors @@ -149,14 +149,15 @@ class PiecewiseCompileInterpreter(torch.fx.Interpreter): """ def __init__(self, module: torch.fx.GraphModule, - compile_submod_names: List[str], - compilation_configs: CompilationConfig, graph_pool): + compile_submod_names: List[str], vllm_config: VllmConfig, + graph_pool): super().__init__(module) from torch._guards import detect_fake_mode self.fake_mode = detect_fake_mode() self.compile_submod_names = compile_submod_names - self.compilation_configs = compilation_configs + self.compilation_config = vllm_config.compilation_config self.graph_pool = graph_pool + self.vllm_config = vllm_config def run(self, *args): fake_args = [ @@ -182,15 +183,15 @@ def call_module(self, target: torch.fx.node.Target, compiled_graph_for_general_shape = wrap_inductor( submod, args, - self.compilation_configs.inductor_compile_config, - self.compilation_configs, + self.compilation_config.inductor_compile_config, + self.compilation_config, graph_index=index, num_graphs=len(self.compile_submod_names), runtime_shape=None, - use_inductor=self.compilation_configs.use_inductor) + use_inductor=self.compilation_config.use_inductor) self.module.__dict__[target] = PiecewiseBackend( - submod, self.compilation_configs, self.graph_pool, index, + submod, self.vllm_config, self.graph_pool, index, len(self.compile_submod_names), sym_shape_indices, compiled_graph_for_general_shape) @@ -211,7 +212,8 @@ class VllmBackend: which handles the post-grad passes. """ - compilation_configs: CompilationConfig + vllm_config: VllmConfig + compilation_config: CompilationConfig graph_pool: Any _called: bool = False # the graph we compiled @@ -227,7 +229,7 @@ class VllmBackend: def __init__( self, - compilation_configs: CompilationConfig, + vllm_config: VllmConfig, ): global global_graph_pool if global_graph_pool is None: @@ -244,13 +246,14 @@ def __init__( self.sym_tensor_indices = [] self.input_buffers = [] - self.compilation_configs = compilation_configs + self.vllm_config = vllm_config + self.compilation_config = vllm_config.compilation_config # `torch.compile` is JIT compiled, so we don't need to # do anything here def configure_post_pass(self): - config = self.compilation_configs + config = self.compilation_config self.post_grad_pass_manager.configure(config.pass_config) # Post-grad custom passes are run using the post_grad_custom_post_pass @@ -271,7 +274,7 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable: from .monitor import torch_compile_start_time dynamo_time = time.time() - torch_compile_start_time logger.info("Dynamo bytecode transform time: %.2f s", dynamo_time) - self.compilation_configs.compilation_time += dynamo_time + self.compilation_config.compilation_time += dynamo_time # we control the compilation process, each instance can only be # called once @@ -281,7 +284,7 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable: self.configure_post_pass() self.split_gm, self.piecewise_graphs = split_graph( - graph, self.compilation_configs.splitting_ops) + graph, self.compilation_config.splitting_ops) from torch._dynamo.utils import lazy_format_graph_code logger.debug("%s", lazy_format_graph_code("before split", self.graph)) @@ -298,13 +301,13 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable: # propagate the split graph to the piecewise backend, # compile submodules with symbolic shapes PiecewiseCompileInterpreter(self.split_gm, submod_names_to_compile, - self.compilation_configs, + self.vllm_config, self.graph_pool).run(*example_inputs) self._called = True - if not self.compilation_configs.use_cudagraph or \ - not self.compilation_configs.cudagraph_copy_inputs: + if not self.compilation_config.use_cudagraph or \ + not self.compilation_config.cudagraph_copy_inputs: return self.split_gm # if we need to copy input buffers for cudagraph @@ -364,10 +367,9 @@ class ConcreteSizeEntry: class PiecewiseBackend: - def __init__(self, graph: fx.GraphModule, - compilation_configs: CompilationConfig, graph_pool: Any, - piecewise_compile_index: int, total_piecewise_compiles: int, - sym_shape_indices: List[int], + def __init__(self, graph: fx.GraphModule, vllm_config: VllmConfig, + graph_pool: Any, piecewise_compile_index: int, + total_piecewise_compiles: int, sym_shape_indices: List[int], compiled_graph_for_general_shape: Callable): """ The backend for piecewise compilation. @@ -375,7 +377,7 @@ def __init__(self, graph: fx.GraphModule, We will compile `self.graph` once for the general shape, and then compile for different shapes specified in - `compilation_configs.compile_sizes`. + `compilation_config.compile_sizes`. Independently, we will capture cudagraph for different shapes. @@ -383,7 +385,8 @@ def __init__(self, graph: fx.GraphModule, compile it first, and then capture cudagraph. """ self.graph = graph - self.compilation_configs = compilation_configs + self.vllm_config = vllm_config + self.compilation_config = vllm_config.compilation_config self.graph_pool = graph_pool self.piecewise_compile_index = piecewise_compile_index self.total_piecewise_compiles = total_piecewise_compiles @@ -393,10 +396,10 @@ def __init__(self, graph: fx.GraphModule, piecewise_compile_index == total_piecewise_compiles - 1) self.compile_sizes: Set[int] = set( - self.compilation_configs.compile_sizes) + self.compilation_config.compile_sizes) self.capture_sizes: Set[int] = set( - self.compilation_configs.capture_sizes - ) if self.compilation_configs.use_cudagraph else set() + self.compilation_config.capture_sizes + ) if self.compilation_config.use_cudagraph else set() self.first_run_finished = False @@ -423,7 +426,7 @@ def __call__(self, *args) -> Any: self.first_run_finished = True # no specific sizes to compile if self.is_last_graph and not self.to_be_compiled_sizes: - end_monitoring_torch_compile(self.compilation_configs) + end_monitoring_torch_compile(self.vllm_config) return self.compiled_graph_for_general_shape(*args) runtime_shape = args[self.sym_shape_indices[0]] @@ -443,28 +446,28 @@ def __call__(self, *args) -> Any: entry.runnable = wrap_inductor( self.graph, args, - self.compilation_configs.inductor_compile_config, - self.compilation_configs, + self.compilation_config.inductor_compile_config, + self.compilation_config, graph_index=self.piecewise_compile_index, num_graphs=self.total_piecewise_compiles, runtime_shape=runtime_shape, - use_inductor=self.compilation_configs.use_inductor) + use_inductor=self.compilation_config.use_inductor) # finished compilations for all required shapes if self.is_last_graph and not self.to_be_compiled_sizes: - end_monitoring_torch_compile(self.compilation_configs) + end_monitoring_torch_compile(self.vllm_config) if not entry.use_cudagraph: return entry.runnable(*args) if entry.cudagraph is None: - if entry.num_finished_warmup < self.compilation_configs.cudagraph_num_of_warmups: # noqa + if entry.num_finished_warmup < self.compilation_config.cudagraph_num_of_warmups: # noqa entry.num_finished_warmup += 1 if self.is_first_graph: logger.debug( "Warming up %s/%s for shape %s", entry.num_finished_warmup, - self.compilation_configs.cudagraph_num_of_warmups, + self.compilation_config.cudagraph_num_of_warmups, runtime_shape) return entry.runnable(*args) diff --git a/vllm/compilation/decorators.py b/vllm/compilation/decorators.py index 938430fe2a501..805a217ee6ca1 100644 --- a/vllm/compilation/decorators.py +++ b/vllm/compilation/decorators.py @@ -185,7 +185,7 @@ def __call__(self, *args, **kwargs): "Unsupported dynamic dimensions" f" {dims} for argument {k} with type {type(arg)}.") # here, it is the starting point of the `torch.compile` process - start_monitoring_torch_compile(self.vllm_config.compilation_config) + start_monitoring_torch_compile(self.vllm_config) # if we don't use custom dispatcher, we can directly call the # compiled function and let torch.compile handle the dispatching, diff --git a/vllm/compilation/monitor.py b/vllm/compilation/monitor.py index 3348674b09af2..b97e40415b41b 100644 --- a/vllm/compilation/monitor.py +++ b/vllm/compilation/monitor.py @@ -1,19 +1,36 @@ +import os import time -from vllm.config import CompilationConfig, CompilationLevel +from vllm.config import CompilationConfig, CompilationLevel, VllmConfig from vllm.logger import init_logger logger = init_logger(__name__) +context_manager = None torch_compile_start_time: float = 0.0 -def start_monitoring_torch_compile(compilation_config: CompilationConfig): +def start_monitoring_torch_compile(vllm_config: VllmConfig): global torch_compile_start_time torch_compile_start_time = time.time() + compilation_config: CompilationConfig = vllm_config.compilation_config + if compilation_config.level == CompilationLevel.PIECEWISE and \ + compilation_config.debug_dump_path: + import depyf + path = os.path.join(compilation_config.debug_dump_path, + f"rank_{vllm_config.parallel_config.rank}") + global context_manager + context_manager = depyf.prepare_debug(path) + context_manager.__enter__() -def end_monitoring_torch_compile(compilation_config: CompilationConfig): + +def end_monitoring_torch_compile(vllm_config: VllmConfig): + compilation_config: CompilationConfig = vllm_config.compilation_config if compilation_config.level == CompilationLevel.PIECEWISE: logger.info("torch.compile takes %.2f s in total", compilation_config.compilation_time) + global context_manager + if context_manager is not None: + context_manager.__exit__(None, None, None) + context_manager = None diff --git a/vllm/compilation/wrapper.py b/vllm/compilation/wrapper.py index bc4d292fef402..c10241b483169 100644 --- a/vllm/compilation/wrapper.py +++ b/vllm/compilation/wrapper.py @@ -32,8 +32,8 @@ def __init__(self, # default compilation settings # compiling the forward method - backend = get_current_vllm_config( - ).compilation_config.init_backend() + vllm_config = get_current_vllm_config() + backend = vllm_config.compilation_config.init_backend(vllm_config) compiled_callable = torch.compile( self.forward, diff --git a/vllm/config.py b/vllm/config.py index 322c8f8990a40..7f9be5a3a98bc 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -2222,6 +2222,7 @@ class CompilationConfig(BaseModel): - 1: dynamo as is. - 2: dynamo once. - 3: piecewise compilation. + - debug_dump_path: the path to dump the debug information. - backend: the backend for compilation. It needs to be a string. - "" (empty string): use the default backend. - "eager"/"openxla"/...: use the specified backend registered in PyTorch. @@ -2289,6 +2290,7 @@ class CompilationConfig(BaseModel): certain small batchsizes, where inductor is good at optimizing. """ # noqa level: int = 0 + debug_dump_path: str = "" backend: str = "" custom_ops: List[str] = Field(default_factory=list) splitting_ops: List[str] = Field(default_factory=lambda: [ @@ -2394,7 +2396,7 @@ def model_post_init(self, __context: Any) -> None: self.static_forward_context = {} self.compilation_time = 0.0 - def init_backend(self) -> Union[str, Callable]: + def init_backend(self, vllm_config: "VllmConfig") -> Union[str, Callable]: if self.level == CompilationLevel.NO_COMPILATION: raise ValueError("No compilation level is set.") @@ -2413,7 +2415,7 @@ def init_backend(self) -> Union[str, Callable]: # merge with the config use_inductor assert self.level == CompilationLevel.PIECEWISE from vllm.compilation.backends import VllmBackend - return VllmBackend(self) + return VllmBackend(vllm_config) def init_with_cudagraph_sizes(self, sizes_to_specialize: List[int]): """To complete the initialization of config, diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index 551b84435fdc0..26fd486130ce6 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -1162,7 +1162,8 @@ def load_model(self) -> None: if self.vllm_config.compilation_config.level ==\ CompilationLevel.DYNAMO_AS_IS and supports_dynamo(): - backend = self.vllm_config.compilation_config.init_backend() + backend = self.vllm_config.compilation_config.init_backend( + self.vllm_config) self.model = torch.compile( self.model, fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE, From d643c2aba1cd5421200f3a3bad1813dd067233b4 Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Wed, 11 Dec 2024 10:49:23 -0800 Subject: [PATCH 032/155] [V1] Use input_ids as input for text-only models (#11032) Signed-off-by: Woosuk Kwon --- vllm/v1/worker/gpu_model_runner.py | 68 +++++++++++++++++++++--------- 1 file changed, 47 insertions(+), 21 deletions(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 8d9976ded7c5e..e75be21ef2d91 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -61,6 +61,7 @@ def __init__( self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[ cache_config.cache_dtype] + self.is_multimodal_model = model_config.is_multimodal_model self.sliding_window = model_config.get_sliding_window() self.block_size = cache_config.block_size self.max_model_len = model_config.max_model_len @@ -103,6 +104,11 @@ def __init__( # The batch sizes in the config are in descending order. self.cudagraph_batch_sizes = list( reversed(self.vllm_config.compilation_config.capture_sizes)) + + # Persistent buffers for CUDA graphs. + self.input_ids = torch.zeros(self.max_num_tokens, + dtype=torch.int32, + device=self.device) self.positions = torch.zeros(self.max_num_tokens, dtype=torch.int64, device=self.device) @@ -310,7 +316,8 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"): seq_start_loc_np[0] = 0 np.cumsum(seq_lens, out=seq_start_loc_np[1:]) - input_ids = input_ids.to(self.device, non_blocking=True) + self.input_ids[:total_num_scheduled_tokens].copy_(input_ids, + non_blocking=True) self.positions[:total_num_scheduled_tokens].copy_(positions, non_blocking=True) query_start_loc = query_start_loc.to(self.device, non_blocking=True) @@ -331,7 +338,7 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"): # token from the partial request. # TODO: Support prompt logprobs. logits_indices = query_start_loc[1:] - 1 - return input_ids, attn_metadata, logits_indices + return attn_metadata, logits_indices def _prepare_sampling( self, @@ -427,13 +434,15 @@ def execute_model( ) -> ModelRunnerOutput: self._update_states(scheduler_output) - # Run the encoder. - self._execute_encoder(scheduler_output) - encoder_outputs = self._gather_encoder_outputs(scheduler_output) + if self.is_multimodal_model: + # Run the multimodal encoder if any. + self._execute_encoder(scheduler_output) + encoder_outputs = self._gather_encoder_outputs(scheduler_output) + else: + encoder_outputs = [] # Prepare the decoder inputs. - input_ids, attn_metadata, logits_indices = self._prepare_inputs( - scheduler_output) + attn_metadata, logits_indices = self._prepare_inputs(scheduler_output) num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens if (self.use_cuda_graph and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]): @@ -444,29 +453,39 @@ def execute_model( else: # Eager mode. num_input_tokens = num_scheduled_tokens - attn_metadata.num_input_tokens = num_input_tokens - # Get the inputs embeds. - if encoder_outputs: - inputs_embeds = self.model.get_input_embeddings( - input_ids, encoder_outputs) + if self.is_multimodal_model: + # NOTE(woosuk): To unify token ids and soft tokens (vision + # embeddings), we always use embeddings (rather than token ids) + # as input to the multimodal model, even when the input is text. + input_ids = self.input_ids[:num_scheduled_tokens] + if encoder_outputs: + inputs_embeds = self.model.get_input_embeddings( + input_ids, encoder_outputs) + else: + inputs_embeds = self.model.get_input_embeddings(input_ids) + # TODO(woosuk): Avoid the copy. Optimize. + self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds) + inputs_embeds = self.inputs_embeds[:num_input_tokens] + input_ids = None else: - inputs_embeds = self.model.get_input_embeddings(input_ids) - # NOTE(woosuk): To unify token ids and soft tokens (vision embeddings), - # always use embeddings (rather than token ids) as input to the model. - # TODO(woosuk): Avoid the copy. Optimize. - self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds) + # For text-only models, we use token ids as input. + # While it is possible to use embeddings as input just like the + # multimodal models, it is not desirable for performance since + # then the embedding layer is not included in the CUDA graph. + input_ids = self.input_ids[:num_input_tokens] + inputs_embeds = None # Run the decoder. # Use persistent buffers for CUDA graphs. with set_forward_context(attn_metadata, self.vllm_config): hidden_states = self.model( - input_ids=None, + input_ids=input_ids, positions=self.positions[:num_input_tokens], kv_caches=self.kv_caches, attn_metadata=None, - inputs_embeds=self.inputs_embeds[:num_input_tokens], + inputs_embeds=inputs_embeds, ) hidden_states = hidden_states[:num_scheduled_tokens] hidden_states = hidden_states[logits_indices] @@ -534,13 +553,20 @@ def _dummy_run( num_tokens: int, kv_caches: List[torch.Tensor], ) -> torch.Tensor: + if self.is_multimodal_model: + input_ids = None + inputs_embeds = self.inputs_embeds[:num_tokens] + else: + input_ids = self.input_ids[:num_tokens] + inputs_embeds = None with set_forward_context(None, self.vllm_config): hidden_states = model( - input_ids=None, + input_ids=input_ids, positions=self.positions[:num_tokens], kv_caches=kv_caches, attn_metadata=None, - inputs_embeds=self.inputs_embeds[:num_tokens]) + inputs_embeds=inputs_embeds, + ) return hidden_states def profile_run(self) -> None: From 66aaa7722df3d7ef9e9bd2942cab5cd0d7473174 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Wed, 11 Dec 2024 10:59:50 -0800 Subject: [PATCH 033/155] [torch.compile] remove graph logging in ci (#11110) Signed-off-by: youkaichao --- vllm/compilation/backends.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/vllm/compilation/backends.py b/vllm/compilation/backends.py index 09a3daa731829..4a5dc337d01b8 100644 --- a/vllm/compilation/backends.py +++ b/vllm/compilation/backends.py @@ -287,9 +287,11 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable: graph, self.compilation_config.splitting_ops) from torch._dynamo.utils import lazy_format_graph_code - logger.debug("%s", lazy_format_graph_code("before split", self.graph)) - logger.debug("%s", lazy_format_graph_code("after split", - self.split_gm)) + + # depyf will hook lazy_format_graph_code and dump the graph + # for debugging, no need to print the graph here + lazy_format_graph_code("before split", self.graph) + lazy_format_graph_code("after split", self.split_gm) compilation_counter.num_piecewise_graphs_seen += len( self.piecewise_graphs) From 72ff3a968682e6a3f7620ab59f2baf5e8eb2777b Mon Sep 17 00:00:00 2001 From: Rui Qiao <161574667+ruisearch42@users.noreply.github.com> Date: Wed, 11 Dec 2024 11:36:35 -0800 Subject: [PATCH 034/155] [core] Bump ray to use _overlap_gpu_communication in compiled graph tests (#10410) Signed-off-by: Rui Qiao Signed-off-by: Rui Qiao Co-authored-by: Rui Qiao --- requirements-test.in | 2 +- requirements-test.txt | 2 +- vllm/envs.py | 8 ++++++++ vllm/executor/ray_gpu_executor.py | 17 ++++++++++------- 4 files changed, 20 insertions(+), 9 deletions(-) diff --git a/requirements-test.in b/requirements-test.in index c0b228148ab31..57fddb416317e 100644 --- a/requirements-test.in +++ b/requirements-test.in @@ -13,7 +13,7 @@ einops # required for MPT, qwen-vl and Mamba httpx librosa # required for audio tests peft -ray[adag]==2.35 +ray[adag]==2.40.0 sentence-transformers # required for embedding tests soundfile # required for audio tests timm # required for internvl test diff --git a/requirements-test.txt b/requirements-test.txt index 8ceb705cdffd7..c786a1249bddb 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -410,7 +410,7 @@ pyyaml==6.0.2 # ray # timm # transformers -ray[adag]==2.35.0 +ray[adag]==2.40.0 # via -r requirements-test.in redis==5.2.0 # via tensorizer diff --git a/vllm/envs.py b/vllm/envs.py index be5d9985b63a4..bc8c1499e9534 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -45,6 +45,7 @@ VLLM_USE_RAY_SPMD_WORKER: bool = False VLLM_USE_RAY_COMPILED_DAG: bool = False VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL: bool = True + VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = True VLLM_WORKER_MULTIPROC_METHOD: str = "fork" VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets") VLLM_IMAGE_FETCH_TIMEOUT: int = 5 @@ -337,6 +338,13 @@ def get_default_config_root(): lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL", "1")) ), + # If the env var is set, it enables GPU communication overlap in + # Ray's compiled DAG. This flag is ignored if + # VLLM_USE_RAY_COMPILED_DAG is not set. + "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM": + lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "1")) + ), + # Use dedicated multiprocess context for workers. # Both spawn and fork work "VLLM_WORKER_MULTIPROC_METHOD": diff --git a/vllm/executor/ray_gpu_executor.py b/vllm/executor/ray_gpu_executor.py index 4263fb27265f6..4bf5cbbd18ffe 100644 --- a/vllm/executor/ray_gpu_executor.py +++ b/vllm/executor/ray_gpu_executor.py @@ -414,12 +414,10 @@ def _check_ray_adag_installation(self): import pkg_resources from packaging import version - required_version = version.parse("2.35") + required_version = version.parse("2.40") current_version = version.parse( pkg_resources.get_distribution("ray").version) - # TODO: update the constraint once we adapt to the backward - # incompatible API change from ray 2.36 - if current_version != required_version: + if current_version < required_version: raise ValueError(f"Ray version {required_version} is " f"required, but found {current_version}") @@ -445,6 +443,8 @@ def _compiled_ray_dag(self, enable_asyncio: bool): logger.info("VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL = %s", envs.VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL) + logger.info("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM = %s", + envs.VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM) with InputNode() as input_data: # Example DAG: PP=2, TP=4 # (ExecuteModelReq, None) -> 0 -> (ExecuteModelReq, IntermediateOutput) -> 4 -> SamplerOutput # noqa: E501 @@ -480,7 +480,10 @@ def _compiled_ray_dag(self, enable_asyncio: bool): forward_dag = MultiOutputNode(outputs) - return forward_dag.experimental_compile(enable_asyncio=enable_asyncio) + return forward_dag.experimental_compile( + enable_asyncio=enable_asyncio, + _overlap_gpu_communication=envs. + VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM) def __del__(self): self.shutdown() @@ -507,8 +510,8 @@ async def execute_model_async( serialized_data = self.input_encoder.encode(execute_model_req) dag_future = await self.forward_dag.execute_async(serialized_data) - outputs = await dag_future - return self.output_decoder.decode(outputs[0]) + output = await dag_future[0] + return self.output_decoder.decode(output) async def _driver_execute_model_async( self, From d1e21a979bba4712f48dac1bbf410e0b57c92e7a Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Thu, 12 Dec 2024 06:18:16 +0800 Subject: [PATCH 035/155] [CI/Build] Split up VLM tests (#11083) Signed-off-by: DarkLight1337 --- .buildkite/test-pipeline.yaml | 32 ++++++--- pyproject.toml | 3 +- .../vision_language/test_models.py | 72 ++++++++++++------- tests/utils.py | 37 ++++++---- 4 files changed, 94 insertions(+), 50 deletions(-) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index df4fa7a6ee9ba..aca505178df06 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -321,7 +321,7 @@ steps: ##### models test ##### -- label: Basic Models Test # 30min +- label: Basic Models Test # 24min source_file_dependencies: - vllm/ - tests/models @@ -331,7 +331,7 @@ steps: - pytest -v -s models/test_registry.py - pytest -v -s models/test_initialization.py -- label: Language Models Test (Standard) # 42min +- label: Language Models Test (Standard) # 32min #mirror_hardwares: [amd] source_file_dependencies: - vllm/ @@ -342,7 +342,7 @@ steps: - pytest -v -s models/decoder_only/language -m 'core_model or quant_model' - pytest -v -s models/embedding/language -m core_model -- label: Language Models Test (Extended) # 50min +- label: Language Models Test (Extended) # 1h10min optional: true source_file_dependencies: - vllm/ @@ -353,7 +353,7 @@ steps: - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model' - pytest -v -s models/embedding/language -m 'not core_model' -- label: Multi-Modal Models Test (Standard) # 26min +- label: Multi-Modal Models Test (Standard) # 28min #mirror_hardwares: [amd] source_file_dependencies: - vllm/ @@ -369,7 +369,7 @@ steps: - pytest -v -s models/encoder_decoder/language -m core_model - pytest -v -s models/encoder_decoder/vision_language -m core_model -- label: Multi-Modal Models Test (Extended) # 1h15m +- label: Multi-Modal Models Test (Extended) 1 # 1h16m optional: true source_file_dependencies: - vllm/ @@ -380,14 +380,24 @@ steps: commands: - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git - pytest -v -s models/decoder_only/audio_language -m 'not core_model and not quant_model' + - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=0) and not core_model and not quant_model' # HACK - run phi3v tests separately to sidestep this transformers bug # https://github.com/huggingface/transformers/issues/34307 - pytest -v -s models/decoder_only/vision_language/test_phi3v.py - - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model' + - pytest -v -s --ignore models/decoder_only/vision_language/test_models.py --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model' - pytest -v -s models/embedding/vision_language -m 'not core_model' - pytest -v -s models/encoder_decoder/language -m 'not core_model' - pytest -v -s models/encoder_decoder/vision_language -m 'not core_model' +- label: Multi-Modal Models Test (Extended) 2 # 38m + optional: true + source_file_dependencies: + - vllm/ + - tests/models/decoder_only/vision_language + commands: + - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git + - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=1) and not core_model and not quant_model' + # This test is used only in PR development phase to test individual models and should never run on main - label: Custom Models Test optional: true @@ -446,11 +456,11 @@ steps: - pytest -v -s ./compile/test_basic_correctness.py - pytest -v -s ./compile/test_wrapper.py - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep -q 'Same node test passed' - - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m distributed_2_gpus + - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)' # Avoid importing model tests that cause CUDA reinitialization error - - pytest models/encoder_decoder/language/test_bart.py -v -s -m distributed_2_gpus - - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m distributed_2_gpus - - pytest models/decoder_only/vision_language/test_models.py -v -s -m distributed_2_gpus + - pytest models/encoder_decoder/language/test_bart.py -v -s -m 'distributed(num_gpus=2)' + - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m 'distributed(num_gpus=2)' + - pytest models/decoder_only/vision_language/test_models.py -v -s -m 'distributed(num_gpus=2)' - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py - pip install -e ./plugins/vllm_add_dummy_model - pytest -v -s distributed/test_distributed_oot.py @@ -540,7 +550,7 @@ steps: # see https://github.com/vllm-project/vllm/pull/5689 for details - pytest -v -s distributed/test_custom_all_reduce.py - torchrun --nproc_per_node=2 distributed/test_ca_buffer_sharing.py - - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m distributed_2_gpus + - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)' - pytest -v -s -x lora/test_mixtral.py - label: LM Eval Large Models # optional diff --git a/pyproject.toml b/pyproject.toml index 253b706a774a7..c5a14ecf5aea9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -96,7 +96,8 @@ markers = [ "core_model: enable this model test in each PR instead of only nightly", "cpu_model: enable this model test in CPU tests", "quant_model: run this model test under Quantized category", - "distributed_2_gpus: run this test only in distributed tests for 2 GPUs", + "split: run this test as part of a split", + "distributed: run this test only in distributed GPU tests", "skip_v1: do not run this test with v1", "optional: optional tests that are automatically skipped, include --optional to run them", ] diff --git a/tests/models/decoder_only/vision_language/test_models.py b/tests/models/decoder_only/vision_language/test_models.py index ed8f34a677f84..3101d1d2ea831 100644 --- a/tests/models/decoder_only/vision_language/test_models.py +++ b/tests/models/decoder_only/vision_language/test_models.py @@ -1,7 +1,9 @@ """Common tests for testing .generate() functionality for single / multiple image, embedding, and video support for different VLMs in vLLM. """ +import math import os +from collections import defaultdict from pathlib import PosixPath from typing import Type @@ -10,11 +12,12 @@ from transformers.utils import is_flash_attn_2_available from vllm.platforms import current_platform -from vllm.utils import cuda_device_count_stateless, identity +from vllm.utils import identity from ....conftest import (IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets, _VideoAssets) -from ....utils import fork_new_process_for_each_test, large_gpu_mark +from ....utils import (fork_new_process_for_each_test, large_gpu_mark, + multi_gpu_marks) from ...utils import check_outputs_equal from .vlm_utils import custom_inputs, model_utils, runners from .vlm_utils.case_filtering import get_parametrized_options @@ -382,7 +385,7 @@ prompt_path_encoder=model_utils.qwen_prompt_path_encoder, ), ### Tensor parallel / multi-gpu broadcast tests - "broadcast-chameleon": VLMTestInfo( + "chameleon-broadcast": VLMTestInfo( models=["facebook/chameleon-7b"], prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:", max_model_len=4096, @@ -393,43 +396,25 @@ vllm_output_post_proc = lambda vllm_output, model: vllm_output[:2], hf_output_post_proc = lambda hf_output, model: hf_output[:2], comparator=check_outputs_equal, - marks=[ - pytest.mark.distributed_2_gpus, - pytest.mark.skipif( - cuda_device_count_stateless() < 2, - reason="Need at least 2 GPUs to run the test.", - ), - ], + marks=multi_gpu_marks(num_gpus=2), **COMMON_BROADCAST_SETTINGS # type: ignore ), - "broadcast-llava": VLMTestInfo( + "llava-broadcast": VLMTestInfo( models=["llava-hf/llava-1.5-7b-hf"], prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:", max_model_len=4096, auto_cls=AutoModelForVision2Seq, vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output, - marks=[ - pytest.mark.distributed_2_gpus, - pytest.mark.skipif( - cuda_device_count_stateless() < 2, - reason="Need at least 2 GPUs to run the test.", - ) - ], + marks=multi_gpu_marks(num_gpus=2), **COMMON_BROADCAST_SETTINGS # type: ignore ), - "broadcast-llava_next": VLMTestInfo( + "llava_next-broadcast": VLMTestInfo( models=["llava-hf/llava-v1.6-mistral-7b-hf"], prompt_formatter=lambda img_prompt: f"[INST] {img_prompt} [/INST]", max_model_len=10240, auto_cls=AutoModelForVision2Seq, vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output, - marks=[ - pytest.mark.distributed_2_gpus, - pytest.mark.skipif( - cuda_device_count_stateless() < 2, - reason="Need at least 2 GPUs to run the test.", - ) - ], + marks=multi_gpu_marks(num_gpus=2), **COMMON_BROADCAST_SETTINGS # type: ignore ), ### Custom input edge-cases for specific models @@ -468,6 +453,41 @@ # yapf: enable +def _mark_splits( + test_settings: dict[str, VLMTestInfo], + *, + num_groups: int, +) -> dict[str, VLMTestInfo]: + name_by_test_info_id = {id(v): k for k, v in test_settings.items()} + test_infos_by_model = defaultdict[str, list[VLMTestInfo]](list) + + for info in test_settings.values(): + for model in info.models: + test_infos_by_model[model].append(info) + + models = sorted(test_infos_by_model.keys()) + split_size = math.ceil(len(models) / num_groups) + + new_test_settings = dict[str, VLMTestInfo]() + + for i in range(num_groups): + models_in_group = models[i * split_size:(i + 1) * split_size] + + for model in models_in_group: + for info in test_infos_by_model[model]: + new_marks = (info.marks or []) + [pytest.mark.split(group=i)] + new_info = info._replace(marks=new_marks) + new_test_settings[name_by_test_info_id[id(info)]] = new_info + + missing_keys = test_settings.keys() - new_test_settings.keys() + assert not missing_keys, f"Missing keys: {missing_keys}" + + return new_test_settings + + +VLM_TEST_SETTINGS = _mark_splits(VLM_TEST_SETTINGS, num_groups=2) + + ### Test wrappers # Wrappers around the core test running func for: # - single image diff --git a/tests/utils.py b/tests/utils.py index a893667e144a6..afeb708f3bcdc 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -682,10 +682,12 @@ def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None: def large_gpu_mark(min_gb: int) -> pytest.MarkDecorator: - """Gets a pytest skipif mark, which triggers ig the the device doesn't have - meet a minimum memory requirement in gb; can be leveraged via - @large_gpu_test to skip tests in environments without enough resources, or - called when filtering tests to run directly. + """ + Get a pytest mark, which skips the test if the GPU doesn't meet + a minimum memory requirement in GB. + + This can be leveraged via `@large_gpu_test` to skip tests in environments + without enough resources, or called when filtering tests to run directly. """ try: if current_platform.is_cpu(): @@ -712,26 +714,37 @@ def large_gpu_test(*, min_gb: int): Currently, the CI machine uses L4 GPU which has 24 GB VRAM. """ - test_skipif = large_gpu_mark(min_gb) + mark = large_gpu_mark(min_gb) def wrapper(f: Callable[_P, None]) -> Callable[_P, None]: - return test_skipif(f) + return mark(f) return wrapper -def multi_gpu_test(*, num_gpus: int): - """ - Decorate a test to be run only when multiple GPUs are available. - """ - test_selector = getattr(pytest.mark, f"distributed_{num_gpus}_gpus") +def multi_gpu_marks(*, num_gpus: int): + """Get a collection of pytest marks to apply for `@multi_gpu_test`.""" + test_selector = pytest.mark.distributed(num_gpus=num_gpus) test_skipif = pytest.mark.skipif( cuda_device_count_stateless() < num_gpus, reason=f"Need at least {num_gpus} GPUs to run the test.", ) + return [test_selector, test_skipif] + + +def multi_gpu_test(*, num_gpus: int): + """ + Decorate a test to be run only when multiple GPUs are available. + """ + marks = multi_gpu_marks(num_gpus=num_gpus) + def wrapper(f: Callable[_P, None]) -> Callable[_P, None]: - return test_selector(test_skipif(fork_new_process_for_each_test(f))) + func = fork_new_process_for_each_test(f) + for mark in reversed(marks): + func = mark(func) + + return func return wrapper From 452a723bf2e8410ee9b47f82f90c7ea48aa6d14f Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Wed, 11 Dec 2024 18:34:54 -0500 Subject: [PATCH 036/155] [V1][Core] Remove should_shutdown to simplify core process termination (#11113) Signed-off-by: Tyler Michael Smith --- vllm/v1/engine/core.py | 13 ++----------- vllm/v1/engine/core_client.py | 6 ------ 2 files changed, 2 insertions(+), 17 deletions(-) diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py index 55a5c4dff3a5c..a26ffe74a3ae8 100644 --- a/vllm/v1/engine/core.py +++ b/vllm/v1/engine/core.py @@ -5,7 +5,6 @@ import threading import time from multiprocessing.process import BaseProcess -from multiprocessing.sharedctypes import Synchronized from typing import List, Tuple, Type, Union import zmq @@ -133,13 +132,9 @@ def __init__( input_path: str, output_path: str, ready_path: str, - should_shutdown: Synchronized, ): super().__init__(vllm_config, executor_class, usage_context) - # Signal from main process to shutdown (multiprocessing.Value). - self.should_shutdown = should_shutdown - # Background Threads and Queues for IO. These enable us to # overlap ZMQ socket IO with GPU since they release the GIL, # and to overlap some serialization/deserialization with the @@ -195,7 +190,6 @@ def make_engine_core_process( input_path: str, output_path: str, ready_path: str, - should_shutdown: Synchronized, ) -> BaseProcess: # The current process might have CUDA context, # so we need to spawn a new process. @@ -210,7 +204,6 @@ def make_engine_core_process( "vllm_config": vllm_config, "executor_class": executor_class, "usage_context": usage_context, - "should_shutdown": should_shutdown } # Run EngineCore busy loop in background process. proc = context.Process(target=EngineCoreProc.run_engine_core, @@ -260,8 +253,8 @@ def signal_handler(signum, frame): def run_busy_loop(self): """Core busy loop of the EngineCore.""" - # Loop until we get a shutdown signal. - while not self.should_shutdown: + # Loop until process is sent a SIGINT or SIGTERM + while True: # 1) Poll the input queue until there is work to do. if not self.scheduler.has_unfinished_requests(): while True: @@ -272,8 +265,6 @@ def run_busy_loop(self): except queue.Empty: self._log_stats() logger.debug("EngineCore busy loop waiting.") - if self.should_shutdown: - return except BaseException: raise diff --git a/vllm/v1/engine/core_client.py b/vllm/v1/engine/core_client.py index 4d96b323d1662..1d5ddf4db4d7c 100644 --- a/vllm/v1/engine/core_client.py +++ b/vllm/v1/engine/core_client.py @@ -1,5 +1,4 @@ import atexit -import multiprocessing from typing import List, Union import msgspec @@ -149,21 +148,16 @@ def __init__( self.input_socket.bind(input_path) # Start EngineCore in background process. - self.should_shutdown = multiprocessing.Value('b', False, lock=False) self.proc = EngineCoreProc.make_engine_core_process( *args, input_path=input_path, output_path=output_path, ready_path=ready_path, - should_shutdown=self.should_shutdown, **kwargs, ) atexit.register(self.shutdown) def shutdown(self): - # Send shutdown signal to background process. - self.should_shutdown = True - # Shut down the zmq context. self.ctx.destroy(linger=0) From 4e116833686f3e0c0a223b05b5859ad76843a017 Mon Sep 17 00:00:00 2001 From: Alexander Matveev <59768536+alexm-neuralmagic@users.noreply.github.com> Date: Wed, 11 Dec 2024 19:55:30 -0500 Subject: [PATCH 037/155] [V1] VLM preprocessor hashing (#11020) Signed-off-by: Roger Wang Signed-off-by: Alexander Matveev Co-authored-by: Michael Goin Co-authored-by: Roger Wang --- examples/offline_inference_vision_language.py | 126 ++++++++++++-- requirements-common.txt | 1 + tests/v1/engine/test_engine_core.py | 1 + tests/v1/engine/test_engine_core_client.py | 1 + vllm/config.py | 10 +- vllm/engine/arg_utils.py | 8 + vllm/v1/engine/__init__.py | 3 +- vllm/v1/engine/core.py | 18 +- vllm/v1/engine/mm_input_mapper.py | 156 ++++++++++++++++-- vllm/v1/engine/processor.py | 35 ++-- vllm/v1/utils.py | 21 +++ 11 files changed, 332 insertions(+), 48 deletions(-) diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py index c6a274ee5894b..5e210126dc8fe 100644 --- a/examples/offline_inference_vision_language.py +++ b/examples/offline_inference_vision_language.py @@ -5,6 +5,8 @@ For most models, the prompt format should follow corresponding examples on HuggingFace model repository. """ +import random + from transformers import AutoTokenizer from vllm import LLM, SamplingParams @@ -23,7 +25,9 @@ def run_llava(question: str, modality: str): prompt = f"USER: \n{question}\nASSISTANT:" - llm = LLM(model="llava-hf/llava-1.5-7b-hf", max_model_len=4096) + llm = LLM(model="llava-hf/llava-1.5-7b-hf", + max_model_len=4096, + mm_cache_preprocessor=args.mm_cache_preprocessor) stop_token_ids = None return llm, prompt, stop_token_ids @@ -33,7 +37,9 @@ def run_llava_next(question: str, modality: str): assert modality == "image" prompt = f"[INST] \n{question} [/INST]" - llm = LLM(model="llava-hf/llava-v1.6-mistral-7b-hf", max_model_len=8192) + llm = LLM(model="llava-hf/llava-v1.6-mistral-7b-hf", + max_model_len=8192, + mm_cache_preprocessor=args.mm_cache_preprocessor) stop_token_ids = None return llm, prompt, stop_token_ids @@ -44,7 +50,9 @@ def run_llava_next_video(question: str, modality: str): assert modality == "video" prompt = f"USER: 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z_+wN3-xGcRttL!$q4lu1Tda=JU=XQr`}42`@z)ZykD>A~S! zZyor8_&a2u0EK?mH9PV!{N1h}vd$Se%-@31vv_{~e?|-aVRg{&tiS&$fB(IR`l0^- DLO^x_ literal 0 HcmV?d00001 diff --git a/docs/source/index.rst b/docs/source/index.rst index 8ac09f6988893..fd741ea5e9766 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -114,6 +114,7 @@ Documentation usage/engine_args usage/env_vars usage/usage_stats + usage/disagg_prefill .. toctree:: :maxdepth: 1 diff --git a/docs/source/usage/disagg_prefill.rst b/docs/source/usage/disagg_prefill.rst new file mode 100644 index 0000000000000..9fe714b4fd856 --- /dev/null +++ b/docs/source/usage/disagg_prefill.rst @@ -0,0 +1,69 @@ +.. _disagg_prefill: + +Disaggregated prefilling (experimental) +======================================= + +This page introduces you the disaggregated prefilling feature in vLLM. This feature is experimental and subject to change. + +Why disaggregated prefilling? +----------------------------- + +Two main reasons: + +* **Tuning time-to-first-token (TTFT) and inter-token-latency (ITL) separately**. Disaggregated prefilling put prefill and decode phase of LLM inference inside different vLLM instances. This gives you the flexibility to assign different parallel strategies (e.g. ``tp`` and ``pp``) to tune TTFT without affecting ITL, or to tune ITL without affecting TTFT. +* **Controlling tail ITL**. Without disaggregated prefilling, vLLM may insert some prefill jobs during the decoding of one request. This results in higher tail latency. Disaggregated prefilling helps you solve this issue and control tail ITL. Chunked prefill with a proper chunk size also can achieve the same goal, but in practice it's hard to figure out the correct chunk size value. So disaggregated prefilling is a much more reliable way to control tail ITL. + +.. note:: + Disaggregated prefill DOES NOT improve throughput. + +Usage example +------------- + +Please refer to ``examples/disaggregated_prefill.sh`` for the example usage of disaggregated prefilling. + + +Benchmarks +---------- + +Please refer to ``benchmarks/disagg_benchmarks/`` for disaggregated prefilling benchmarks. + + +Development +----------- + +We implement disaggregated prefilling by running 2 vLLM instances. One for prefill (we call it prefill instance) and one for decode (we call it decode instance), and then use a connector to transfer the prefill KV caches and results from prefill instance to decode instance. + +All disaggregated prefilling implementation is under ``vllm/distributed/kv_transfer``. + +Key abstractions for disaggregated prefilling: + +* **Connector**: Connector allows **kv consumer** to retrieve the KV caches of a batch of request from **kv producer**. +* **LookupBuffer**: LookupBuffer provides two API: ``insert`` KV cache and ``drop_select`` KV cache. The semantics of ``insert`` and ``drop_select`` are similar to SQL, where ``insert`` inserts a KV cache into the buffer, and ``drop_select`` returns the KV cache that matches the given condition and drop it from the buffer. +* **Pipe**: A single-direction FIFO pipe for tensor transmission. It supports ``send_tensor`` and ``recv_tensor``. + +.. note:: + ``insert`` is non-blocking operation but ``drop_select`` is blocking operation. + +Here is a figure illustrating how the above 3 abstractions are organized: + +.. image:: /assets/usage/disagg_prefill/abstraction.jpg + :alt: Disaggregated prefilling abstractions + +The workflow of disaggregated prefilling is as follows: + +.. image:: /assets/usage/disagg_prefill/overview.jpg + :alt: Disaggregated prefilling workflow + +The ``buffer`` corresponds to ``insert`` API in LookupBuffer, and the ``drop_select`` corresponds to ``drop_select`` API in LookupBuffer. + + +Third-party contributions +------------------------- + +Disaggregated prefilling is highly related to infrastructure, so vLLM relies on third-party connectors for production-level disaggregated prefilling (and vLLM team will actively review and merge new PRs for third-party connectors). + +We recommend three ways of implementations: + +* **Fully-customized connector**: Implement your own ``Connector``, and call third-party libraries to send and receive KV caches, and many many more (like editing vLLM's model input to perform customized prefilling, etc). This approach gives you the most control, but at the risk of being incompatible with future vLLM versions. +* **Database-like connector**: Implement your own ``LookupBuffer`` and support the ``insert`` and ``drop_select`` APIs just like SQL. +* **Distributed P2P connector**: Implement your own ``Pipe`` and support the ``send_tensor`` and ``recv_tensor`` APIs, just like `torch.distributed`. From d263bd9df7b2f5586910e5d006a11ff11ba7c310 Mon Sep 17 00:00:00 2001 From: shangmingc Date: Mon, 16 Dec 2024 05:28:18 +0800 Subject: [PATCH 087/155] [Core] Support disaggregated prefill with Mooncake Transfer Engine (#10884) Signed-off-by: Shangming Cai --- vllm/config.py | 7 +- .../kv_transfer/kv_connector/factory.py | 3 +- .../kv_connector/simple_connector.py | 101 +++++-- .../kv_transfer/kv_pipe/mooncake_pipe.py | 272 ++++++++++++++++++ 4 files changed, 352 insertions(+), 31 deletions(-) create mode 100644 vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py diff --git a/vllm/config.py b/vllm/config.py index 37d062f7eb079..fce8011be4015 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -2171,13 +2171,14 @@ def from_cli(cls, cli_value: str) -> "KVTransferConfig": return KVTransferConfig.model_validate_json(cli_value) def model_post_init(self, __context: Any) -> None: + supported_kv_connector = ["PyNcclConnector", "MooncakeConnector"] if all([ - self.kv_connector is not None, - self.kv_connector != "PyNcclConnector" + self.kv_connector is not None, self.kv_connector + not in supported_kv_connector ]): raise ValueError(f"Unsupported kv_connector: {self.kv_connector}. " f"Supported connectors are " - f"`PyNcclConnector`.") + f"{supported_kv_connector}.") if self.kv_role is not None and self.kv_role not in [ "kv_producer", "kv_consumer", "kv_both" diff --git a/vllm/distributed/kv_transfer/kv_connector/factory.py b/vllm/distributed/kv_transfer/kv_connector/factory.py index 015f892cec933..3e2bb436d24b5 100644 --- a/vllm/distributed/kv_transfer/kv_connector/factory.py +++ b/vllm/distributed/kv_transfer/kv_connector/factory.py @@ -11,7 +11,8 @@ class KVConnectorFactory: @staticmethod def create_connector(rank: int, local_rank: int, config: "VllmConfig") -> KVConnectorBase: - if config.kv_transfer_config.kv_connector == 'PyNcclConnector': + supported_kv_connector = ["PyNcclConnector", "MooncakeConnector"] + if config.kv_transfer_config.kv_connector in supported_kv_connector: from .simple_connector import SimpleConnector return SimpleConnector(rank, local_rank, config) else: diff --git a/vllm/distributed/kv_transfer/kv_connector/simple_connector.py b/vllm/distributed/kv_transfer/kv_connector/simple_connector.py index bf4f40ca94e29..4ace03ff1184e 100644 --- a/vllm/distributed/kv_transfer/kv_connector/simple_connector.py +++ b/vllm/distributed/kv_transfer/kv_connector/simple_connector.py @@ -2,7 +2,8 @@ Simple KV Cache Connector for Distributed Machine Learning Inference The SimpleConnector transfers KV caches between prefill vLLM worker (KV cache -producer) and decode vLLM worker (KV cache consumer) using PyNcclPipe. +producer) and decode vLLM worker (KV cache consumer) using PyNcclPipe or +MooncakePipe. But the logic can be extended to support other pipe and lookup buffer. """ @@ -15,7 +16,6 @@ from vllm.distributed.kv_transfer.kv_connector.base import KVConnectorBase from vllm.distributed.kv_transfer.kv_lookup_buffer.simple_buffer import ( SimpleBuffer) -from vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe import PyNcclPipe from vllm.logger import init_logger from vllm.sequence import IntermediateTensors @@ -36,7 +36,27 @@ def __init__( self.config = config.kv_transfer_config - logger.info("Initializing PyNcclConfig under kv_transfer_config %s", + if self.config.kv_connector == "PyNcclConnector": + from vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe import ( + PyNcclPipe) + logger.info( + "Initializing PyNcclConfig under kv_transfer_config %s", + self.config) + elif self.config.kv_connector == "MooncakeConnector": + # Check if MOONCAKE_CONFIG_PATH is set + import os + use_mooncake_distributed_pipe = os.getenv( + 'MOONCAKE_CONFIG_PATH') is not None + + if not use_mooncake_distributed_pipe: + raise ValueError( + "To use MooncakeConnector, you need to pass the ENV: " + "'MOONCAKE_CONFIG_PATH=/path/to/mooncake_config.json'.") + else: + from vllm.distributed.kv_transfer.kv_pipe.mooncake_pipe import ( # noqa: E501 + MooncakePipe) + logger.info( + "Initializing MooncakeConfig under kv_transfer_config %s", self.config) self.lookup_buffer_size = self.config.kv_buffer_size @@ -44,6 +64,11 @@ def __init__( self.producer_buffer: Optional[SimpleBuffer] = None self.consumer_buffer: Optional[SimpleBuffer] = None + self.producer_data_pipe: Union[PyNcclPipe, MooncakePipe] + self.consumer_data_pipe: Union[PyNcclPipe, MooncakePipe] + self.producer_signal_pipe: Union[PyNcclPipe, MooncakePipe] + self.consumer_signal_pipe: Union[PyNcclPipe, MooncakePipe] + # 2 pipes for every rank in the world port_offset_base = 2 * rank @@ -51,17 +76,26 @@ def __init__( # and the decode vLLM only uses recv pipe if self.config.is_kv_producer: - self.producer_data_pipe = PyNcclPipe( - local_rank=local_rank, - config=self.config, - port_offset=port_offset_base, - ) - self.producer_signal_pipe = PyNcclPipe( - local_rank=local_rank, - config=self.config, - port_offset=port_offset_base + 1, - device="cpu", - ) + if self.config.kv_connector == "PyNcclConnector": + self.producer_data_pipe = PyNcclPipe( + local_rank=local_rank, + config=self.config, + port_offset=port_offset_base, + ) + self.producer_signal_pipe = PyNcclPipe( + local_rank=local_rank, + config=self.config, + port_offset=port_offset_base + 1, + device="cpu", + ) + elif self.config.kv_connector == "MooncakeConnector": + self.producer_data_pipe = MooncakePipe( + local_rank=local_rank, + config=self.config, + ) + # We only need to initialize MooncakePipe once + self.producer_signal_pipe = self.producer_data_pipe + self.producer_buffer = SimpleBuffer(self.producer_signal_pipe, self.producer_data_pipe, self.config.kv_buffer_size) @@ -70,17 +104,25 @@ def __init__( # the current vLLM instance is KV consumer, so it needs to connect # its recv pipe to the send pipe of KV producder - self.consumer_data_pipe = PyNcclPipe( - local_rank=local_rank, - config=self.config, - port_offset=port_offset_base, - ) - self.consumer_signal_pipe = PyNcclPipe( - local_rank=local_rank, - config=self.config, - port_offset=port_offset_base + 1, - device="cpu", - ) + if self.config.kv_connector == "PyNcclConnector": + self.consumer_data_pipe = PyNcclPipe( + local_rank=local_rank, + config=self.config, + port_offset=port_offset_base, + ) + self.consumer_signal_pipe = PyNcclPipe( + local_rank=local_rank, + config=self.config, + port_offset=port_offset_base + 1, + device="cpu", + ) + elif self.config.kv_connector == "MooncakeConnector": + self.consumer_data_pipe = MooncakePipe( + local_rank=local_rank, + config=self.config, + ) + self.consumer_signal_pipe = self.consumer_data_pipe + self.consumer_buffer = SimpleBuffer( self.consumer_signal_pipe, self.consumer_data_pipe, @@ -260,6 +302,11 @@ def recv_kv_caches_and_hidden_states( def close(self): self.producer_data_pipe.close() - self.producer_signal_pipe.close() self.consumer_data_pipe.close() - self.consumer_signal_pipe.close() + if self.config.kv_connector == "PyNcclConnector": + self.producer_signal_pipe.close() + self.consumer_signal_pipe.close() + elif self.config.kv_connector == "MooncakeConnector": + # MooncakePipe reuses data_pipe for signal_pipe, so we only have to + # close the data_pipe. + pass diff --git a/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py b/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py new file mode 100644 index 0000000000000..8e4358672b74d --- /dev/null +++ b/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py @@ -0,0 +1,272 @@ +import json +import os +import pickle +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass +from typing import Optional, Union + +import torch +import zmq + +from vllm.config import KVTransferConfig +from vllm.distributed.kv_transfer.kv_pipe.base import KVPipeBase +from vllm.logger import init_logger + +logger = init_logger(__name__) +NONE_INT = -150886311 + + +@dataclass +class MooncakeTransferEngineConfig: + prefill_url: str + decode_url: str + metadata_backend: Union[str, None] + metadata_server: str + protocol: str + device_name: str + + @staticmethod + def from_file(file_path: str) -> 'MooncakeTransferEngineConfig': + """Load the config from a JSON file.""" + with open(file_path) as fin: + config = json.load(fin) + return MooncakeTransferEngineConfig( + prefill_url=config.get("prefill_url"), + decode_url=config.get("decode_url"), + metadata_backend=config.get("metadata_backend", None), + metadata_server=config.get("metadata_server"), + protocol=config.get("protocol", "tcp"), + device_name=config.get("device_name", ""), + ) + + @staticmethod + def load_from_env() -> 'MooncakeTransferEngineConfig': + """Load config from a file specified in the environment variable.""" + config_file_path = os.getenv('MOONCAKE_CONFIG_PATH') + if config_file_path is None: + raise ValueError( + "The environment variable 'MOONCAKE_CONFIG_PATH' is not set.") + return MooncakeTransferEngineConfig.from_file(config_file_path) + + +class MooncakeTransferEngine: + """Handles the transfer of data using mooncake_vllm_adaptor and ZeroMQ.""" + + def __init__(self, kv_rank: int, local_rank: int): + try: + import mooncake_vllm_adaptor as mva + except ImportError as e: + raise ImportError( + "Please install mooncake by following the instructions at " + "https://github.com/kvcache-ai/Mooncake/blob/main/doc/en/build.md " # noqa: E501 + "to run vLLM with MooncakeConnector.") from e + + self.engine = mva.mooncake_vllm_adaptor() + self.local_rank = local_rank + + try: + self.config = MooncakeTransferEngineConfig.load_from_env() + logger.info("Mooncake Configuration loaded successfully.") + except ValueError as e: + logger.error(e) + raise + except Exception as exc: + logger.error( + "An error occurred while loading the configuration: %s", exc) + raise + prefill_host, base_prefill_port = self.config.prefill_url.split(':') + decode_host, base_decode_port = self.config.decode_url.split(':') + + # Avoid ports conflict when running prefill and decode on the same node + if prefill_host == decode_host and \ + base_prefill_port == base_decode_port: + base_decode_port = str(int(base_decode_port) + 100) + + prefill_port = int(base_prefill_port) + self.local_rank + decode_port = int(base_decode_port) + self.local_rank + self.prefill_url = ':'.join([prefill_host, str(prefill_port)]) + self.decode_url = ':'.join([decode_host, str(decode_port)]) + + self.initialize(self.prefill_url if kv_rank == 0 else self.decode_url, + self.config.metadata_server, self.config.protocol, + self.config.device_name, self.config.metadata_backend) + + self.remote_url = (self.decode_url + if kv_rank == 0 else self.prefill_url) + + # Initialize ZeroMQ context and sockets + self.context = zmq.Context() # type: ignore[attr-defined] + self.sender_socket = self.context.socket(zmq.constants.PUSH) + self.receiver_socket = self.context.socket(zmq.constants.PULL) + self.sender_ack = self.context.socket(zmq.constants.PULL) + self.receiver_ack = self.context.socket(zmq.constants.PUSH) + + self.buffer_cleaner = ThreadPoolExecutor(max_workers=1) + self._setup_metadata_sockets(kv_rank, prefill_host, base_prefill_port, + decode_host, base_decode_port) + + def _setup_metadata_sockets(self, kv_rank: int, p_host: str, p_port: str, + d_host: str, d_port: str) -> None: + """Set up ZeroMQ sockets for sending and receiving data.""" + # Offsets < 8 are left for initialization in case tp and pp are enabled + p_rank_offset = int(p_port) + 8 + self.local_rank * 2 + d_rank_offset = int(d_port) + 8 + self.local_rank * 2 + if kv_rank == 0: + self.sender_socket.bind(f"tcp://*:{p_rank_offset + 1}") + self.receiver_socket.connect(f"tcp://{d_host}:{d_rank_offset + 1}") + self.sender_ack.connect(f"tcp://{d_host}:{d_rank_offset + 2}") + self.receiver_ack.bind(f"tcp://*:{p_rank_offset + 2}") + else: + self.receiver_socket.connect(f"tcp://{p_host}:{p_rank_offset + 1}") + self.sender_socket.bind(f"tcp://*:{d_rank_offset + 1}") + self.receiver_ack.bind(f"tcp://*:{d_rank_offset + 2}") + self.sender_ack.connect(f"tcp://{p_host}:{p_rank_offset + 2}") + + def initialize(self, local_hostname: str, metadata_server: str, + protocol: str, device_name: str, + metadata_backend: Union[str, None]) -> None: + """Initialize the mooncake instance.""" + if metadata_backend is None: + self.engine.initialize(local_hostname, metadata_server, protocol, + device_name) + else: + supported_backend = ["etcd", "redis"] + metadata_backend = metadata_backend.lower() + if metadata_backend not in supported_backend: + raise ValueError( + "Mooncake Configuration error. `metadata_backend`" + f"should be one of {supported_backend}.") + + self.engine.initializeExt(local_hostname, metadata_server, + protocol, device_name, metadata_backend) + + def allocate_managed_buffer(self, length: int) -> int: + """Allocate a managed buffer of the specified length.""" + ret = self.engine.allocateManagedBuffer(length) + if ret <= 0: + logger.error("Allocation Return Error") + raise Exception("Allocation Return Error") + return ret + + def free_managed_buffer(self, buffer: int, length: int) -> int: + """Free a previously allocated managed buffer.""" + return self.engine.freeManagedBuffer(buffer, length) + + def transfer_sync(self, buffer: int, peer_buffer_address: int, + length: int) -> int: + """Synchronously transfer data to the specified address.""" + ret = self.engine.transferSync(self.remote_url, buffer, + peer_buffer_address, length) + if ret < 0: + logger.error("Transfer Return Error") + raise Exception("Transfer Return Error") + return ret + + def write_bytes_to_buffer(self, buffer: int, user_data: bytes, + length: int) -> int: + """Write bytes to the allocated buffer.""" + return self.engine.writeBytesToBuffer(buffer, user_data, length) + + def read_bytes_from_buffer(self, buffer: int, length: int) -> bytes: + """Read bytes from the allocated buffer.""" + return self.engine.readBytesFromBuffer(buffer, length) + + def wait_for_ack(self, src_ptr: int, length: int) -> None: + """Asynchronously wait for ACK from the receiver.""" + ack = self.sender_ack.recv_pyobj() + if ack != b'ACK': + logger.error("Failed to receive ACK from the receiver") + + self.free_managed_buffer(src_ptr, length) + + def send_bytes(self, user_data: bytes) -> None: + """Send bytes to the remote process.""" + length = len(user_data) + src_ptr = self.allocate_managed_buffer(length) + self.write_bytes_to_buffer(src_ptr, user_data, length) + self.sender_socket.send_pyobj((src_ptr, length)) + self.buffer_cleaner.submit(self.wait_for_ack, src_ptr, length) + + def recv_bytes(self) -> bytes: + """Receive bytes from the remote process.""" + src_ptr, length = self.receiver_socket.recv_pyobj() + dst_ptr = self.allocate_managed_buffer(length) + self.transfer_sync(dst_ptr, src_ptr, length) + ret = self.read_bytes_from_buffer(dst_ptr, length) + + # Buffer cleanup + self.receiver_ack.send_pyobj(b'ACK') + self.free_managed_buffer(dst_ptr, length) + + return ret + + +class MooncakePipe(KVPipeBase): + """MooncakeTransferEngine based Pipe implementation.""" + + def __init__(self, + local_rank: int, + config: KVTransferConfig, + device: Optional[str] = None): + """Initialize the mooncake pipe and set related parameters.""" + self.config = config + self.local_rank = local_rank + self.kv_rank = self.config.kv_rank + if device is None: + self.device = self._select_device(self.config.kv_buffer_device) + else: + self.device = self._select_device(device) + + self.transfer_engine = MooncakeTransferEngine(self.kv_rank, + self.local_rank) + self.transport_thread: Optional[ThreadPoolExecutor] = None + self.none_tensor = torch.tensor([NONE_INT], device=self.device) + + def _select_device(self, device: str) -> torch.device: + """Select available device (CUDA or CPU).""" + logger.info("Selecting device: %s", device) + if device == "cuda": + return torch.device(f"cuda:{self.local_rank}") + else: + return torch.device("cpu") + + def tensor_hash(self, tensor: torch.Tensor) -> int: + """Calculate the hash value of the tensor.""" + return hash(tensor.data_ptr()) + + def _send_impl(self, tensor: torch.Tensor) -> None: + """Implement the tensor sending logic.""" + value_bytes = pickle.dumps(tensor) + self.transfer_engine.send_bytes(value_bytes) + + def _recv_impl(self) -> torch.Tensor: + """Implement the tensor receiving logic.""" + data = self.transfer_engine.recv_bytes() + return pickle.loads(data) + + def send_tensor(self, tensor: Optional[torch.Tensor]) -> None: + """Send tensor to the target process.""" + if self.transport_thread is None: + self.transport_thread = ThreadPoolExecutor(max_workers=1) + tensor = tensor if tensor is not None else self.none_tensor + assert (len(tensor.shape) > 0) + self.transport_thread.submit(self._send_impl, tensor) + + def recv_tensor(self) -> Optional[torch.Tensor]: + """Receive tensor from other processes.""" + if self.transport_thread is None: + self.transport_thread = ThreadPoolExecutor(max_workers=1) + tensor = self.transport_thread.submit(self._recv_impl).result() + if tensor.numel() == 1 and tensor.item() == NONE_INT: + return None + else: + return tensor + + def close(self) -> None: + """Cleanup logic when closing the pipe.""" + self.transfer_engine.sender_socket.close() + self.transfer_engine.receiver_socket.close() + self.transfer_engine.sender_ack.close() + self.transfer_engine.receiver_ack.close() + self.transfer_engine.context.term() # Terminate the ZMQ context + logger.info("Closed the transfer engine and cleaned up resources.") From 25ebed2f8ca6d747d63f2be9ede023c561851ac8 Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Sun, 15 Dec 2024 13:33:00 -0800 Subject: [PATCH 088/155] [V1][Minor] Cache np arange to reduce input preparation overhead (#11214) Signed-off-by: Woosuk Kwon --- vllm/v1/worker/gpu_model_runner.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index abcd4b007a326..67166fb05085c 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -118,6 +118,12 @@ def __init__( dtype=self.dtype, device=self.device) + # OPTIMIZATION: Cache the tensors rather than creating them every step. + self.arange_np = np.arange(max(self.max_num_reqs, self.max_model_len), + dtype=np.int32) + # NOTE(woosuk): These tensors are "stateless", i.e., they are literally + # a faster version of creating a new tensor every time. Thus, we should + # not make any assumptions about the values in these tensors. self.input_ids_cpu = torch.zeros(self.max_num_tokens, dtype=torch.int32, device="cpu", @@ -269,11 +275,13 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"): # Get request indices. # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2] - req_indices = np.repeat(np.arange(num_reqs), num_scheduled_tokens) + req_indices = np.repeat(self.arange_np[:num_reqs], + num_scheduled_tokens) # Get batched arange. # E.g., [2, 5, 3] -> [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] - arange = np.concatenate([np.arange(n) for n in num_scheduled_tokens]) + arange = np.concatenate( + [self.arange_np[:n] for n in num_scheduled_tokens]) # Get positions. positions_np = self.positions_np[:total_num_scheduled_tokens] From da6f40924609e084ced486cae5b4ddf97133acd9 Mon Sep 17 00:00:00 2001 From: AlexHe99 Date: Mon, 16 Dec 2024 08:33:58 +0800 Subject: [PATCH 089/155] Update deploying_with_k8s.rst (#10922) --- docs/source/serving/deploying_with_k8s.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/serving/deploying_with_k8s.rst b/docs/source/serving/deploying_with_k8s.rst index 7dc076dc709df..cc3606f0df851 100644 --- a/docs/source/serving/deploying_with_k8s.rst +++ b/docs/source/serving/deploying_with_k8s.rst @@ -162,7 +162,7 @@ To test the deployment, run the following ``curl`` command: curl http://mistral-7b.default.svc.cluster.local/v1/completions \ -H "Content-Type: application/json" \ -d '{ - "model": "facebook/opt-125m", + "model": "mistralai/Mistral-7B-Instruct-v0.3", "prompt": "San Francisco is a", "max_tokens": 7, "temperature": 0 @@ -172,4 +172,4 @@ If the service is correctly deployed, you should receive a response from the vLL Conclusion ---------- -Deploying vLLM with Kubernetes allows for efficient scaling and management of ML models leveraging GPU resources. By following the steps outlined above, you should be able to set up and test a vLLM deployment within your Kubernetes cluster. If you encounter any issues or have suggestions, please feel free to contribute to the documentation. \ No newline at end of file +Deploying vLLM with Kubernetes allows for efficient scaling and management of ML models leveraging GPU resources. By following the steps outlined above, you should be able to set up and test a vLLM deployment within your Kubernetes cluster. If you encounter any issues or have suggestions, please feel free to contribute to the documentation. From 69ba344de8683ec4d3d42d11ae4e147a2a302da8 Mon Sep 17 00:00:00 2001 From: chenqianfzh <51831990+chenqianfzh@users.noreply.github.com> Date: Sun, 15 Dec 2024 16:38:40 -0800 Subject: [PATCH 090/155] [Bugfix] Fix block size validation (#10938) --- vllm/engine/arg_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 5a73c6ee02e0c..0aa367a173b6c 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -425,7 +425,7 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: parser.add_argument('--block-size', type=int, default=EngineArgs.block_size, - choices=[8, 16, 32, 64, 128], + choices=[8, 16, 32], help='Token block size for contiguous chunks of ' 'tokens. This is ignored on neuron devices and ' 'set to max-model-len') From 17138af7c45eba3aba3e9b84a3852b4ba81e460f Mon Sep 17 00:00:00 2001 From: yansh97 Date: Mon, 16 Dec 2024 16:15:40 +0800 Subject: [PATCH 091/155] [Bugfix] Fix the default value for temperature in ChatCompletionRequest (#11219) --- vllm/entrypoints/openai/protocol.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py index dfb7c977dbd43..6ed7c2e9dcd6b 100644 --- a/vllm/entrypoints/openai/protocol.py +++ b/vllm/entrypoints/openai/protocol.py @@ -211,7 +211,7 @@ class ChatCompletionRequest(OpenAIBaseModel): stop: Optional[Union[str, List[str]]] = Field(default_factory=list) stream: Optional[bool] = False stream_options: Optional[StreamOptions] = None - temperature: Optional[float] = 0.7 + temperature: Optional[float] = 1.0 top_p: Optional[float] = 1.0 tools: Optional[List[ChatCompletionToolsParam]] = None tool_choice: Optional[Union[Literal["none"], Literal["auto"], From b3b1526f03906c935e6ef80a2cdc971a65fdf7e2 Mon Sep 17 00:00:00 2001 From: cennn <61925104+cennn@users.noreply.github.com> Date: Mon, 16 Dec 2024 17:20:49 +0800 Subject: [PATCH 092/155] WIP: [CI/Build] simplify Dockerfile build for ARM64 / GH200 (#11212) Signed-off-by: drikster80 Co-authored-by: drikster80 --- Dockerfile | 40 +++++++++++++++---- docs/source/serving/deploying_with_docker.rst | 26 ++++++++++++ requirements-build.txt | 2 +- requirements-cuda-arm64.txt | 3 ++ requirements-cuda.txt | 4 +- 5 files changed, 64 insertions(+), 11 deletions(-) create mode 100644 requirements-cuda-arm64.txt diff --git a/Dockerfile b/Dockerfile index c1b6e1bbfe354..123703848749c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -11,6 +11,7 @@ ARG CUDA_VERSION=12.4.1 FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base ARG CUDA_VERSION=12.4.1 ARG PYTHON_VERSION=3.12 +ARG TARGETPLATFORM ENV DEBIAN_FRONTEND=noninteractive # Install Python and other dependencies @@ -46,9 +47,14 @@ WORKDIR /workspace # install build and runtime dependencies COPY requirements-common.txt requirements-common.txt COPY requirements-cuda.txt requirements-cuda.txt +COPY requirements-cuda-arm64.txt requirements-cuda-arm64.txt RUN --mount=type=cache,target=/root/.cache/pip \ python3 -m pip install -r requirements-cuda.txt +RUN --mount=type=cache,target=/root/.cache/pip \ + if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \ + python3 -m pip install -r requirements-cuda-arm64.txt; \ + fi # cuda arch list used by torch # can be useful for both `dev` and `test` @@ -63,6 +69,7 @@ ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches} #################### WHEEL BUILD IMAGE #################### FROM base AS build +ARG TARGETPLATFORM # install build dependencies COPY requirements-build.txt requirements-build.txt @@ -70,6 +77,11 @@ COPY requirements-build.txt requirements-build.txt RUN --mount=type=cache,target=/root/.cache/pip \ python3 -m pip install -r requirements-build.txt +RUN --mount=type=cache,target=/root/.cache/pip \ + if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \ + python3 -m pip install -r requirements-cuda-arm64.txt; \ + fi + COPY . . ARG GIT_REPO_CHECK=0 RUN --mount=type=bind,source=.git,target=.git \ @@ -134,8 +146,8 @@ COPY requirements-test.txt requirements-test.txt COPY requirements-dev.txt requirements-dev.txt RUN --mount=type=cache,target=/root/.cache/pip \ python3 -m pip install -r requirements-dev.txt - #################### DEV IMAGE #################### + #################### vLLM installation IMAGE #################### # image with vLLM installed FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base @@ -143,6 +155,9 @@ ARG CUDA_VERSION=12.4.1 ARG PYTHON_VERSION=3.12 WORKDIR /vllm-workspace ENV DEBIAN_FRONTEND=noninteractive +ARG TARGETPLATFORM + +COPY requirements-cuda-arm64.txt requirements-cuda-arm64.txt RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \ echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment @@ -168,18 +183,25 @@ RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \ # or future versions of triton. RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/ -# install vllm wheel first, so that torch etc will be installed +# Install vllm wheel first, so that torch etc will be installed. RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \ --mount=type=cache,target=/root/.cache/pip \ python3 -m pip install dist/*.whl --verbose RUN --mount=type=cache,target=/root/.cache/pip \ - . /etc/environment && \ - python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl + if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \ + pip uninstall -y torch && \ + python3 -m pip install -r requirements-cuda-arm64.txt; \ + fi + +RUN --mount=type=cache,target=/root/.cache/pip \ +. /etc/environment && \ +if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \ + python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \ +fi COPY examples examples #################### vLLM installation IMAGE #################### - #################### TEST IMAGE #################### # image to run unit testing suite # note that this uses vllm installed by `pip` @@ -209,7 +231,6 @@ COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1 RUN mkdir test_docs RUN mv docs test_docs/ RUN mv vllm test_docs/ - #################### TEST IMAGE #################### #################### OPENAI API SERVER #################### @@ -218,8 +239,11 @@ FROM vllm-base AS vllm-openai # install additional dependencies for openai api server RUN --mount=type=cache,target=/root/.cache/pip \ - pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' timm==0.9.10 - + if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \ + pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10'; \ + else \ + pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10'; \ + fi ENV VLLM_USAGE_SOURCE production-docker-image ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"] diff --git a/docs/source/serving/deploying_with_docker.rst b/docs/source/serving/deploying_with_docker.rst index 14d94b09e9b9c..11a9f12fd17cd 100644 --- a/docs/source/serving/deploying_with_docker.rst +++ b/docs/source/serving/deploying_with_docker.rst @@ -37,6 +37,32 @@ You can build and run vLLM from source via the provided `Dockerfile =61 setuptools-scm>=8 -torch==2.5.1 +torch==2.5.1; platform_machine != 'aarch64' wheel jinja2 diff --git a/requirements-cuda-arm64.txt b/requirements-cuda-arm64.txt new file mode 100644 index 0000000000000..bbcb5cb7012ce --- /dev/null +++ b/requirements-cuda-arm64.txt @@ -0,0 +1,3 @@ +--index-url https://download.pytorch.org/whl/nightly/cu124 +torchvision==0.22.0.dev20241215; platform_machine == 'aarch64' +torch==2.6.0.dev20241210+cu124; platform_machine == 'aarch64' diff --git a/requirements-cuda.txt b/requirements-cuda.txt index 058ab7c1ee9df..5d4dee8c7129a 100644 --- a/requirements-cuda.txt +++ b/requirements-cuda.txt @@ -4,7 +4,7 @@ # Dependencies for NVIDIA GPUs ray >= 2.9 nvidia-ml-py >= 12.560.30 # for pynvml package -torch == 2.5.1 +torch == 2.5.1; platform_machine != 'aarch64' # These must be updated alongside torch -torchvision == 0.20.1 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version +torchvision == 0.20.1; platform_machine != 'aarch64' # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version xformers == 0.0.28.post3; platform_system == 'Linux' and platform_machine == 'x86_64' # Requires PyTorch 2.5.1 From bddbbcb132429084ede62855bcd6a1023a3645c1 Mon Sep 17 00:00:00 2001 From: Jani Monoses Date: Mon, 16 Dec 2024 11:56:19 +0200 Subject: [PATCH 093/155] [Model] Support Cohere2ForCausalLM (Cohere R7B) (#11203) --- docs/source/models/supported_models.rst | 4 ++-- tests/models/registry.py | 2 ++ tests/models/test_initialization.py | 4 ++++ vllm/model_executor/models/commandr.py | 19 +++++++++++++++++-- vllm/model_executor/models/registry.py | 1 + 5 files changed, 26 insertions(+), 4 deletions(-) diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index cae4a88de1638..3bef3f3226062 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -118,9 +118,9 @@ Text Generation (``--task generate``) - :code:`THUDM/chatglm2-6b`, :code:`THUDM/chatglm3-6b`, etc. - ✅︎ - ✅︎ - * - :code:`CohereForCausalLM` + * - :code:`CohereForCausalLM`,:code:`Cohere2ForCausalLM` - Command-R - - :code:`CohereForAI/c4ai-command-r-v01`, etc. + - :code:`CohereForAI/c4ai-command-r-v01`, :code:`CohereForAI/c4ai-command-r7b-12-2024`, etc. - ✅︎ - ✅︎ * - :code:`DbrxForCausalLM` diff --git a/tests/models/registry.py b/tests/models/registry.py index 6a8b1742ceae3..fac8c4b2e9b19 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -53,6 +53,8 @@ class _HfExamplesInfo: # ChatGLMModel supports multimodal "CohereForCausalLM": _HfExamplesInfo("CohereForAI/c4ai-command-r-v01", trust_remote_code=True), + "Cohere2ForCausalLM": _HfExamplesInfo("CohereForAI/c4ai-command-r7b-12-2024", # noqa: E501 + trust_remote_code=True), "DbrxForCausalLM": _HfExamplesInfo("databricks/dbrx-instruct"), "DeciLMForCausalLM": _HfExamplesInfo("Deci/DeciLM-7B-instruct", trust_remote_code=True), diff --git a/tests/models/test_initialization.py b/tests/models/test_initialization.py index 3b728f2744fca..a4eea7f035c91 100644 --- a/tests/models/test_initialization.py +++ b/tests/models/test_initialization.py @@ -1,6 +1,7 @@ from unittest.mock import patch import pytest +import transformers from transformers import PretrainedConfig from vllm import LLM @@ -11,6 +12,9 @@ @pytest.mark.parametrize("model_arch", HF_EXAMPLE_MODELS.get_supported_archs()) def test_can_initialize(model_arch): model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch) + if (model_arch == "Cohere2ForCausalLM" + and transformers.__version__ < "4.48.0"): + pytest.skip(reason="Model introduced in HF >= 4.48.0") if not model_info.is_available_online: pytest.skip("Model is not available online") diff --git a/vllm/model_executor/models/commandr.py b/vllm/model_executor/models/commandr.py index 85e24ca660686..c846e42f1b0c3 100644 --- a/vllm/model_executor/models/commandr.py +++ b/vllm/model_executor/models/commandr.py @@ -48,7 +48,7 @@ from vllm.sequence import IntermediateTensors from .interfaces import SupportsLoRA, SupportsPP -from .utils import (is_pp_missing_parameter, +from .utils import (extract_layer_index, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) @@ -171,12 +171,26 @@ def __init__( rope_scaling=self.rope_scaling, is_neox_style=False, ) + + sliding_window = getattr(config, "sliding_window", None) + # Model v2 has sliding windows, v1 does not + self.v1 = sliding_window is None + + layer_idx = extract_layer_index(prefix) + layer_has_sliding_window = ( + getattr(config, "sliding_window_pattern", False) + and (layer_idx + 1) % self.config.sliding_window_pattern != 0) + + self.sliding_window = (sliding_window + if layer_has_sliding_window else None) + self.attn = Attention(self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, cache_config=cache_config, quant_config=quant_config, + per_layer_sliding_window=self.sliding_window, prefix=f"{prefix}.attn") if self.use_qk_norm: self.q_norm = LayerNorm(param_shape=(self.num_heads, @@ -206,7 +220,8 @@ def forward( q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) if self.use_qk_norm: q, k = self._apply_qk_norm(q, k) - q, k = self.rotary_emb(positions, q, k) + if self.v1 or self.sliding_window: + q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, kv_cache, attn_metadata) output, _ = self.o_proj(attn_output) return output diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 4e77746f312e3..68a2467a813a1 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -41,6 +41,7 @@ "BloomForCausalLM": ("bloom", "BloomForCausalLM"), # ChatGLMModel supports multimodal "CohereForCausalLM": ("commandr", "CohereForCausalLM"), + "Cohere2ForCausalLM": ("commandr", "CohereForCausalLM"), "DbrxForCausalLM": ("dbrx", "DbrxForCausalLM"), "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"), "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"), From d927dbcd889fb2476cb61ea477ff51e5dd9e1ae3 Mon Sep 17 00:00:00 2001 From: Isotr0py Date: Mon, 16 Dec 2024 18:09:53 +0800 Subject: [PATCH 094/155] [Model] Refactor Ultravox to use merged input processor (#11198) Signed-off-by: Isotr0py <2037008807@qq.com> Co-authored-by: Cyrus Leung --- examples/offline_inference_audio_language.py | 10 +- tests/distributed/test_pipeline_parallel.py | 2 +- tests/entrypoints/openai/test_audio.py | 1 + .../audio_language/test_ultravox.py | 5 +- vllm/entrypoints/chat_utils.py | 2 +- vllm/model_executor/models/ultravox.py | 244 ++++++++---------- vllm/multimodal/processing.py | 19 +- 7 files changed, 129 insertions(+), 154 deletions(-) diff --git a/examples/offline_inference_audio_language.py b/examples/offline_inference_audio_language.py index 050b791b62adb..68b786961b14a 100644 --- a/examples/offline_inference_audio_language.py +++ b/examples/offline_inference_audio_language.py @@ -25,16 +25,16 @@ def run_ultravox(question: str, audio_count: int): tokenizer = AutoTokenizer.from_pretrained(model_name) messages = [{ - 'role': - 'user', - 'content': - "<|reserved_special_token_0|>\n" * audio_count + question + 'role': 'user', + 'content': "<|audio|>\n" * audio_count + question }] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) - llm = LLM(model=model_name, limit_mm_per_prompt={"audio": audio_count}) + llm = LLM(model=model_name, + trust_remote_code=True, + limit_mm_per_prompt={"audio": audio_count}) stop_token_ids = None return llm, prompt, stop_token_ids diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py index 85d408efafe96..ddbf40f089407 100644 --- a/tests/distributed/test_pipeline_parallel.py +++ b/tests/distributed/test_pipeline_parallel.py @@ -214,7 +214,7 @@ def iter_params(self, model_name: str): "Qwen/Qwen-VL-Chat": PPTestSettings.fast(trust_remote_code=True), "Qwen/Qwen2-Audio-7B-Instruct": PPTestSettings.fast(), "Qwen/Qwen2-VL-2B-Instruct": PPTestSettings.fast(), - "fixie-ai/ultravox-v0_3": PPTestSettings.fast(), + "fixie-ai/ultravox-v0_3": PPTestSettings.fast(trust_remote_code=True), # [Encoder-decoder] # TODO: Implement PP # "meta-llama/Llama-3.2-11B-Vision-Instruct": PPTestSettings.fast(), diff --git a/tests/entrypoints/openai/test_audio.py b/tests/entrypoints/openai/test_audio.py index a74109e2f5120..b579dcbb5c402 100644 --- a/tests/entrypoints/openai/test_audio.py +++ b/tests/entrypoints/openai/test_audio.py @@ -25,6 +25,7 @@ def server(): "--max-num-seqs", "5", "--enforce-eager", + "--trust-remote-code", ] with RemoteOpenAIServer(MODEL_NAME, args) as remote_server: diff --git a/tests/models/decoder_only/audio_language/test_ultravox.py b/tests/models/decoder_only/audio_language/test_ultravox.py index e100c6b9bb906..c548cfdf53414 100644 --- a/tests/models/decoder_only/audio_language/test_ultravox.py +++ b/tests/models/decoder_only/audio_language/test_ultravox.py @@ -16,7 +16,7 @@ AudioTuple = Tuple[np.ndarray, int] -VLLM_PLACEHOLDER = "<|reserved_special_token_0|>" +VLLM_PLACEHOLDER = "<|audio|>" HF_PLACEHOLDER = "<|audio|>" CHUNKED_PREFILL_KWARGS = { @@ -46,7 +46,8 @@ def audio(request): def server(request, audio_assets): args = [ "--dtype=bfloat16", "--max-model-len=4096", "--enforce-eager", - f"--limit-mm-per-prompt=audio={len(audio_assets)}" + f"--limit-mm-per-prompt=audio={len(audio_assets)}", + "--trust-remote-code" ] + [ f"--{key.replace('_','-')}={value}" for key, value in request.param.items() diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index c2054dcbfce0e..aaa5cd759366a 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -418,7 +418,7 @@ def _placeholder_str(self, modality: ModalityStr, raise TypeError(f"Unknown {modality} model type: {model_type}") elif modality == "audio": if model_type == "ultravox": - return "<|reserved_special_token_0|>" + return "<|audio|>" if model_type == "qwen2_audio": return (f"Audio {current_count}: " f"<|audio_bos|><|AUDIO|><|audio_eos|>") diff --git a/vllm/model_executor/models/ultravox.py b/vllm/model_executor/models/ultravox.py index ea1e5401d42c0..ebaa8a4c4f38a 100644 --- a/vllm/model_executor/models/ultravox.py +++ b/vllm/model_executor/models/ultravox.py @@ -3,41 +3,39 @@ import math from functools import cached_property, lru_cache -from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple, - TypedDict, Union, cast) +from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional, Set, + Tuple, TypedDict, Union) import numpy as np import torch import torch.utils.checkpoint from torch import nn from torch.nn import functional as F +from transformers import BatchFeature from transformers.models.whisper import WhisperFeatureExtractor from transformers.models.whisper.modeling_whisper import WhisperEncoder from vllm.attention import AttentionMetadata from vllm.config import VllmConfig -from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData, - InputContext, token_inputs) +from vllm.inputs import InputContext from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler from vllm.model_executor.model_loader.loader import DefaultModelLoader from vllm.model_executor.sampling_metadata import SamplingMetadata -from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalKwargs, - NestedTensors) -from vllm.multimodal.utils import (cached_get_tokenizer, - consecutive_placeholder_ranges, - repeat_and_pad_placeholder_tokens) -from vllm.sequence import IntermediateTensors, SequenceData +from vllm.multimodal import MULTIMODAL_REGISTRY, NestedTensors +from vllm.multimodal.processing import (BaseMultiModalProcessor, + MultiModalDataDict, + MultiModalDataItems, ProcessorInputs, + PromptReplacement) +from vllm.sequence import IntermediateTensors from vllm.transformers_utils.configs.ultravox import UltravoxConfig -from vllm.utils import is_list_of from .interfaces import SupportsMultiModal, SupportsPP from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, init_vllm_registered_model, maybe_prefix, merge_multimodal_embeddings_from_map) -_AUDIO_PLACEHOLDER_TOKEN = 128002 _AUDIO_TOKENS_PER_SECOND = 6.25 @@ -72,64 +70,18 @@ def get_ultravox_max_audio_tokens(ctx: InputContext): return math.ceil(feature_extractor.chunk_length * _AUDIO_TOKENS_PER_SECOND) -def dummy_seq_data_for_ultravox( - ctx: InputContext, - seq_len: int, - audio_count: int, -): - audio_length = min(get_ultravox_max_audio_tokens(ctx), - seq_len // audio_count) +class UltravoxMultiModalProcessor(BaseMultiModalProcessor): - return SequenceData.from_prompt_token_counts( - (_AUDIO_PLACEHOLDER_TOKEN, audio_length * audio_count), - (0, seq_len - audio_length * audio_count)), { - "audio": - consecutive_placeholder_ranges(num_items=audio_count, - item_size=audio_length) - } - - -def dummy_audio_for_ultravox( - ctx: InputContext, - audio_count: int, -): - feature_extractor = whisper_feature_extractor(ctx) - audio_and_sr = (np.array([0.0] * feature_extractor.chunk_length), 1) - return {"audio": [audio_and_sr] * audio_count} - - -def dummy_data_for_ultravox( - ctx: InputContext, - seq_len: int, - mm_counts: Mapping[str, int], -): - audio_count = mm_counts["audio"] - seq_data, ranges = dummy_seq_data_for_ultravox(ctx, seq_len, audio_count) - mm_dict = dummy_audio_for_ultravox(ctx, audio_count) - - return DummyData(seq_data, mm_dict, ranges) - - -def input_mapper_for_ultravox(ctx: InputContext, data: object): - if not isinstance(data, list): - data = [data] - - if len(data) == 0: - return MultiModalKwargs() - - # If the audio inputs are embeddings, no need for preprocessing - if is_list_of(data, torch.Tensor, check="all"): - return MultiModalKwargs({"audio_embeds": data}) - - audio_features = [] - for audio_input in data: - if not isinstance(audio_input, tuple): - raise NotImplementedError( - f"Unsupported data type: {type(audio_input)}") - - (audio, sr) = cast(Tuple[np.ndarray, Union[float, int]], audio_input) - feature_extractor = whisper_feature_extractor(ctx) + def _get_feature_extractor(self) -> WhisperFeatureExtractor: + return self._get_hf_processor().audio_processor.feature_extractor + def _resample_audio( + self, + audio: np.ndarray, + sr: int, + ) -> Dict[str, Union[np.ndarray, int]]: + # resample audio to the model's sampling rate + feature_extractor = self._get_feature_extractor() if sr != feature_extractor.sampling_rate: try: import librosa @@ -140,78 +92,92 @@ def input_mapper_for_ultravox(ctx: InputContext, data: object): orig_sr=sr, target_sr=feature_extractor.sampling_rate) sr = feature_extractor.sampling_rate + return {"audio": audio, "sampling_rate": sr} - minimum_audio_length = feature_extractor.n_fft // 2 + 1 - if len(audio) < minimum_audio_length: - # Not enough audio; pad it. - audio = np.pad(audio, (0, minimum_audio_length - len(audio))) - - single_audio_features = feature_extractor( - audio, sampling_rate=sr, padding="longest", - return_tensors="pt")["input_features"] - - # Remove the batch dimension because we're wrapping it in a list. - audio_features.append(single_audio_features.squeeze(0)) - - return MultiModalKwargs({"audio_features": audio_features}) - - -def input_processor_for_ultravox(ctx: InputContext, inputs: DecoderOnlyInputs): - multi_modal_data = inputs.get("multi_modal_data") - if multi_modal_data is None or "audio" not in multi_modal_data: - return inputs + def _apply_hf_processor( + self, + prompt: str, + mm_data: MultiModalDataDict, + mm_processor_kwargs: Mapping[str, object], + ) -> BatchFeature: + if not mm_data or not mm_data.get("audio", None): + return super()._apply_hf_processor(prompt, mm_data, + mm_processor_kwargs) + + audio_data = mm_data["audio"] + if not isinstance(audio_data, list): + audio_data = [audio_data] + + # Ultravox processor doesn't support multiple inputs, + # therefore we need to input text and audio one by one + tokenizer = self._get_tokenizer() + audio_features, audio_token_len = [], [] + processed_inputs = {} + for audio, sr in audio_data: + data = self._resample_audio(audio, sr) + processed_inputs = super()._apply_hf_processor( + prompt, data, mm_processor_kwargs) + prompt = tokenizer.decode(processed_inputs["input_ids"][0], + skip_special_tokens=False) + audio_features.append( + processed_inputs.pop("audio_values").squeeze(0)) + audio_token_len.append( + processed_inputs.pop("audio_token_len").item()) + + return dict( + **processed_inputs, + audio_features=audio_features, + audio_token_len=audio_token_len, + ) - if "multi_modal_placeholders" in inputs and "audio" in inputs[ - "multi_modal_placeholders"]: - # The inputs already have placeholders. - return inputs + def _get_processor_data( + self, + mm_data: MultiModalDataDict, + ) -> Tuple[Dict[str, Any], Dict[str, Any]]: + # Ultravox uses "audio" instead of "audios" as calling keyword + processor_data, passthrough_data = super()._get_processor_data(mm_data) + if "audios" in processor_data: + processor_data["audio"] = processor_data.pop("audios") + return processor_data, passthrough_data + + def _get_prompt_replacements( + self, + mm_items: MultiModalDataItems, + hf_inputs: BatchFeature, + mm_processor_kwargs: Mapping[str, object], + ) -> list[PromptReplacement]: + hf_processor = self._get_hf_processor() + placeholder = hf_processor.audio_token_replacement + + def get_replacement_ultravox(item_idx: int): + audio_token_len = hf_inputs["audio_token_len"][item_idx] + return placeholder * audio_token_len + + return [ + PromptReplacement( + modality="audio", + target="<|audio|>", + replacement=get_replacement_ultravox, + ) + ] - feature_extractor = whisper_feature_extractor(ctx) - audios = multi_modal_data["audio"] - if not isinstance(audios, list): - audios = [audios] - - audio_token_counts = [] - for audio in audios: - if isinstance(audio, torch.Tensor): - audio_num_tokens = audio.shape[1] - audio_token_counts.append(audio_num_tokens) - else: - audio_data, sample_rate = audio - audio_length = audio_data.shape[0] - if sample_rate != feature_extractor.sampling_rate: - # Account for resampling. - adjustment = feature_extractor.sampling_rate / sample_rate - audio_length = math.ceil(adjustment * audio_length) - - feature_extractor_output_length = math.ceil( - (audio_length - (feature_extractor.hop_length - 1)) / - feature_extractor.hop_length) - - uv_config = ctx.get_hf_config(UltravoxConfig) - audio_num_tokens = min( - max( - 1, - math.ceil(feature_extractor_output_length / - (uv_config.stack_factor * 2))), - get_ultravox_max_audio_tokens(ctx)) - audio_token_counts.append(audio_num_tokens) - - tokenizer = cached_get_tokenizer(ctx.model_config.tokenizer) - - new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens( - tokenizer, - inputs.get("prompt"), - inputs["prompt_token_ids"], - placeholder_token_id=_AUDIO_PLACEHOLDER_TOKEN, - repeat_count=audio_token_counts, - ) - - # NOTE: Create a defensive copy of the original inputs - return token_inputs(prompt_token_ids=new_token_ids, - prompt=new_prompt, - multi_modal_data=multi_modal_data, - multi_modal_placeholders={"audio": ranges}) + def _get_dummy_mm_inputs( + self, + mm_counts: Mapping[str, int], + ) -> ProcessorInputs: + feature_extractor = self._get_feature_extractor() + sampling_rate = feature_extractor.sampling_rate + audio_len = feature_extractor.chunk_length * sampling_rate + + audio_count = mm_counts["audio"] + audio = np.zeros(audio_len) + data = {"audio": [(audio, sampling_rate)] * audio_count} + + return ProcessorInputs( + prompt_text="<|audio|>" * audio_count, + mm_data=data, + mm_processor_kwargs={}, + ) class StackAudioFrames(nn.Module): @@ -332,11 +298,9 @@ def forward( return hidden_states -@MULTIMODAL_REGISTRY.register_input_mapper("audio", input_mapper_for_ultravox) @MULTIMODAL_REGISTRY.register_max_multimodal_tokens( "audio", get_ultravox_max_audio_tokens) -@INPUT_REGISTRY.register_dummy_data(dummy_data_for_ultravox) -@INPUT_REGISTRY.register_input_processor(input_processor_for_ultravox) +@MULTIMODAL_REGISTRY.register_processor(UltravoxMultiModalProcessor) class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): diff --git a/vllm/multimodal/processing.py b/vllm/multimodal/processing.py index ce6bec1d49aac..339e193eefe20 100644 --- a/vllm/multimodal/processing.py +++ b/vllm/multimodal/processing.py @@ -594,14 +594,10 @@ def _find_placeholders( return list( iter_placeholders(all_prompt_repls, new_token_ids, mm_item_counts)) - def _apply_hf_processor( + def _get_processor_data( self, - prompt: str, mm_data: MultiModalDataDict, - mm_processor_kwargs: Mapping[str, object], ) -> BatchFeature: - hf_processor = self._get_hf_processor(**mm_processor_kwargs) - processor_data = dict[str, Any]() passthrough_data = dict[str, Any]() for k, v in mm_data.items(): @@ -619,6 +615,19 @@ def _apply_hf_processor( processor_data[f"{k}s"] = v else: processor_data[k] = v + return processor_data, passthrough_data + + def _apply_hf_processor( + self, + prompt: str, + mm_data: MultiModalDataDict, + mm_processor_kwargs: Mapping[str, object], + ) -> BatchFeature: + # some mm_processor_kwargs may be used in processor initialization + # instead of processor call + hf_processor = self._get_hf_processor(**mm_processor_kwargs) + + processor_data, passthrough_data = self._get_processor_data(mm_data) assert callable(hf_processor) mm_processor_kwargs = self.ctx.resolve_hf_processor_call_kwargs( From 2ca830dbaa1a7c30b8ff4d7c860c63f87dc18be3 Mon Sep 17 00:00:00 2001 From: Isotr0py Date: Mon, 16 Dec 2024 19:23:33 +0800 Subject: [PATCH 095/155] [Doc] Reorder vision language examples in alphabet order (#11228) Signed-off-by: Isotr0py <2037008807@qq.com> --- examples/offline_inference_vision_language.py | 486 +++++++++--------- ...e_inference_vision_language_multi_image.py | 288 +++++------ 2 files changed, 387 insertions(+), 387 deletions(-) diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py index 7bc43242b717e..6d0495fdd4054 100644 --- a/examples/offline_inference_vision_language.py +++ b/examples/offline_inference_vision_language.py @@ -19,6 +19,159 @@ # Unless specified, these settings have been tested to work on a single L4. +# Aria +def run_aria(question: str, modality: str): + assert modality == "image" + model_name = "rhymes-ai/Aria" + + llm = LLM(model=model_name, + tokenizer_mode="slow", + trust_remote_code=True, + dtype="bfloat16", + mm_cache_preprocessor=args.mm_cache_preprocessor) + + prompt = (f"<|im_start|>user\n<|img|>\n{question}" + "<|im_end|>\n<|im_start|>assistant\n") + + stop_token_ids = [93532, 93653, 944, 93421, 1019, 93653, 93519] + return llm, prompt, stop_token_ids + + +# BLIP-2 +def run_blip2(question: str, modality: str): + assert modality == "image" + + # BLIP-2 prompt format is inaccurate on HuggingFace model repository. + # See https://huggingface.co/Salesforce/blip2-opt-2.7b/discussions/15#64ff02f3f8cf9e4f5b038262 #noqa + prompt = f"Question: {question} Answer:" + llm = LLM(model="Salesforce/blip2-opt-2.7b", + mm_cache_preprocessor=args.mm_cache_preprocessor) + stop_token_ids = None + return llm, prompt, stop_token_ids + + +# Chameleon +def run_chameleon(question: str, modality: str): + assert modality == "image" + + prompt = f"{question}" + llm = LLM(model="facebook/chameleon-7b", + max_model_len=4096, + mm_cache_preprocessor=args.mm_cache_preprocessor) + stop_token_ids = None + return llm, prompt, stop_token_ids + + +# Fuyu +def run_fuyu(question: str, modality: str): + assert modality == "image" + + prompt = f"{question}\n" + llm = LLM(model="adept/fuyu-8b", + max_model_len=2048, + max_num_seqs=2, + mm_cache_preprocessor=args.mm_cache_preprocessor) + stop_token_ids = None + return llm, prompt, stop_token_ids + + +# GLM-4v +def run_glm4v(question: str, modality: str): + assert modality == "image" + model_name = "THUDM/glm-4v-9b" + + llm = LLM(model=model_name, + max_model_len=2048, + max_num_seqs=2, + trust_remote_code=True, + enforce_eager=True, + mm_cache_preprocessor=args.mm_cache_preprocessor) + prompt = question + stop_token_ids = [151329, 151336, 151338] + return llm, prompt, stop_token_ids + + +# H2OVL-Mississippi +def run_h2ovl(question: str, modality: str): + assert modality == "image" + + model_name = "h2oai/h2ovl-mississippi-2b" + + llm = LLM( + model=model_name, + trust_remote_code=True, + max_model_len=8192, + mm_cache_preprocessor=args.mm_cache_preprocessor, + ) + + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + messages = [{'role': 'user', 'content': f"\n{question}"}] + prompt = tokenizer.apply_chat_template(messages, + tokenize=False, + add_generation_prompt=True) + + # Stop tokens for H2OVL-Mississippi + # https://huggingface.co/h2oai/h2ovl-mississippi-2b + stop_token_ids = [tokenizer.eos_token_id] + return llm, prompt, stop_token_ids + + +# Idefics3-8B-Llama3 +def run_idefics3(question: str, modality: str): + assert modality == "image" + model_name = "HuggingFaceM4/Idefics3-8B-Llama3" + + llm = LLM( + model=model_name, + max_model_len=8192, + max_num_seqs=2, + enforce_eager=True, + # if you are running out of memory, you can reduce the "longest_edge". + # see: https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3#model-optimizations + mm_processor_kwargs={ + "size": { + "longest_edge": 3 * 364 + }, + }, + mm_cache_preprocessor=args.mm_cache_preprocessor, + ) + prompt = ( + f"<|begin_of_text|>User:{question}\nAssistant:" + ) + stop_token_ids = None + return llm, prompt, stop_token_ids + + +# InternVL +def run_internvl(question: str, modality: str): + assert modality == "image" + + model_name = "OpenGVLab/InternVL2-2B" + + llm = LLM( + model=model_name, + trust_remote_code=True, + max_model_len=4096, + mm_cache_preprocessor=args.mm_cache_preprocessor, + ) + + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + messages = [{'role': 'user', 'content': f"\n{question}"}] + prompt = tokenizer.apply_chat_template(messages, + tokenize=False, + add_generation_prompt=True) + + # Stop tokens for InternVL + # models variants may have different stop tokens + # please refer to the model card for the correct "stop words": + # https://huggingface.co/OpenGVLab/InternVL2-2B/blob/main/conversation.py + stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|end|>"] + stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens] + return llm, prompt, stop_token_ids + + # LLaVA-1.5 def run_llava(question: str, modality: str): assert modality == "image" @@ -75,83 +228,20 @@ def run_llava_onevision(question: str, modality: str): return llm, prompt, stop_token_ids -# Fuyu -def run_fuyu(question: str, modality: str): - assert modality == "image" - - prompt = f"{question}\n" - llm = LLM(model="adept/fuyu-8b", - max_model_len=2048, - max_num_seqs=2, - mm_cache_preprocessor=args.mm_cache_preprocessor) - stop_token_ids = None - return llm, prompt, stop_token_ids - - -# Phi-3-Vision -def run_phi3v(question: str, modality: str): +# Mantis +def run_mantis(question: str, modality: str): assert modality == "image" - prompt = f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n" + llama3_template = '<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' # noqa: E501 + prompt = llama3_template.format(f"{question}\n") - # num_crops is an override kwarg to the multimodal image processor; - # For some models, e.g., Phi-3.5-vision-instruct, it is recommended - # to use 16 for single frame scenarios, and 4 for multi-frame. - # - # Generally speaking, a larger value for num_crops results in more - # tokens per image instance, because it may scale the image more in - # the image preprocessing. Some references in the model docs and the - # formula for image tokens after the preprocessing - # transform can be found below. - # - # https://huggingface.co/microsoft/Phi-3.5-vision-instruct#loading-the-model-locally - # https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/main/processing_phi3_v.py#L194 llm = LLM( - model="microsoft/Phi-3.5-vision-instruct", - trust_remote_code=True, + model="TIGER-Lab/Mantis-8B-siglip-llama3", max_model_len=4096, - max_num_seqs=2, - # Note - mm_processor_kwargs can also be passed to generate/chat calls - mm_processor_kwargs={"num_crops": 16}, + hf_overrides={"architectures": ["MantisForConditionalGeneration"]}, mm_cache_preprocessor=args.mm_cache_preprocessor, ) - stop_token_ids = None - return llm, prompt, stop_token_ids - - -# PaliGemma -def run_paligemma(question: str, modality: str): - assert modality == "image" - - # PaliGemma has special prompt format for VQA - prompt = "caption en" - llm = LLM(model="google/paligemma-3b-mix-224", - mm_cache_preprocessor=args.mm_cache_preprocessor) - stop_token_ids = None - return llm, prompt, stop_token_ids - - -# PaliGemma 2 -def run_paligemma2(question: str, modality: str): - assert modality == "image" - - # PaliGemma 2 has special prompt format for VQA - prompt = "caption en" - llm = LLM(model="google/paligemma2-3b-ft-docci-448", - mm_cache_preprocessor=args.mm_cache_preprocessor) - stop_token_ids = None - return llm, prompt, stop_token_ids - - -# Chameleon -def run_chameleon(question: str, modality: str): - assert modality == "image" - - prompt = f"{question}" - llm = LLM(model="facebook/chameleon-7b", - max_model_len=4096, - mm_cache_preprocessor=args.mm_cache_preprocessor) - stop_token_ids = None + stop_token_ids = [128009] return llm, prompt, stop_token_ids @@ -199,58 +289,45 @@ def run_minicpmv(question: str, modality: str): return llm, prompt, stop_token_ids -# H2OVL-Mississippi -def run_h2ovl(question: str, modality: str): +# LLama 3.2 +def run_mllama(question: str, modality: str): assert modality == "image" - model_name = "h2oai/h2ovl-mississippi-2b" + model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct" + # Note: The default setting of max_num_seqs (256) and + # max_model_len (131072) for this model may cause OOM. + # You may lower either to run this example on lower-end GPUs. + + # The configuration below has been confirmed to launch on a single L40 GPU. llm = LLM( model=model_name, - trust_remote_code=True, - max_model_len=8192, + max_model_len=4096, + max_num_seqs=16, + enforce_eager=True, mm_cache_preprocessor=args.mm_cache_preprocessor, ) - tokenizer = AutoTokenizer.from_pretrained(model_name, - trust_remote_code=True) - messages = [{'role': 'user', 'content': f"\n{question}"}] - prompt = tokenizer.apply_chat_template(messages, - tokenize=False, - add_generation_prompt=True) - - # Stop tokens for H2OVL-Mississippi - # https://huggingface.co/h2oai/h2ovl-mississippi-2b - stop_token_ids = [tokenizer.eos_token_id] + prompt = f"<|image|><|begin_of_text|>{question}" + stop_token_ids = None return llm, prompt, stop_token_ids -# InternVL -def run_internvl(question: str, modality: str): +# Molmo +def run_molmo(question, modality): assert modality == "image" - model_name = "OpenGVLab/InternVL2-2B" + model_name = "allenai/Molmo-7B-D-0924" llm = LLM( model=model_name, trust_remote_code=True, - max_model_len=4096, + dtype="bfloat16", mm_cache_preprocessor=args.mm_cache_preprocessor, ) - tokenizer = AutoTokenizer.from_pretrained(model_name, - trust_remote_code=True) - messages = [{'role': 'user', 'content': f"\n{question}"}] - prompt = tokenizer.apply_chat_template(messages, - tokenize=False, - add_generation_prompt=True) - - # Stop tokens for InternVL - # models variants may have different stop tokens - # please refer to the model card for the correct "stop words": - # https://huggingface.co/OpenGVLab/InternVL2-2B/blob/main/conversation.py - stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|end|>"] - stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens] + prompt = question + stop_token_ids = None return llm, prompt, stop_token_ids @@ -279,58 +356,57 @@ def run_nvlm_d(question: str, modality: str): return llm, prompt, stop_token_ids -# BLIP-2 -def run_blip2(question: str, modality: str): +# PaliGemma +def run_paligemma(question: str, modality: str): assert modality == "image" - # BLIP-2 prompt format is inaccurate on HuggingFace model repository. - # See https://huggingface.co/Salesforce/blip2-opt-2.7b/discussions/15#64ff02f3f8cf9e4f5b038262 #noqa - prompt = f"Question: {question} Answer:" - llm = LLM(model="Salesforce/blip2-opt-2.7b", + # PaliGemma has special prompt format for VQA + prompt = "caption en" + llm = LLM(model="google/paligemma-3b-mix-224", mm_cache_preprocessor=args.mm_cache_preprocessor) stop_token_ids = None return llm, prompt, stop_token_ids -# Qwen -def run_qwen_vl(question: str, modality: str): +# PaliGemma 2 +def run_paligemma2(question: str, modality: str): assert modality == "image" - llm = LLM( - model="Qwen/Qwen-VL", - trust_remote_code=True, - max_model_len=1024, - max_num_seqs=2, - mm_cache_preprocessor=args.mm_cache_preprocessor, - ) - - prompt = f"{question}Picture 1: \n" + # PaliGemma 2 has special prompt format for VQA + prompt = "caption en" + llm = LLM(model="google/paligemma2-3b-ft-docci-448", + mm_cache_preprocessor=args.mm_cache_preprocessor) stop_token_ids = None return llm, prompt, stop_token_ids -# Qwen2-VL -def run_qwen2_vl(question: str, modality: str): +# Phi-3-Vision +def run_phi3v(question: str, modality: str): assert modality == "image" - model_name = "Qwen/Qwen2-VL-7B-Instruct" + prompt = f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n" + # num_crops is an override kwarg to the multimodal image processor; + # For some models, e.g., Phi-3.5-vision-instruct, it is recommended + # to use 16 for single frame scenarios, and 4 for multi-frame. + # + # Generally speaking, a larger value for num_crops results in more + # tokens per image instance, because it may scale the image more in + # the image preprocessing. Some references in the model docs and the + # formula for image tokens after the preprocessing + # transform can be found below. + # + # https://huggingface.co/microsoft/Phi-3.5-vision-instruct#loading-the-model-locally + # https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/main/processing_phi3_v.py#L194 llm = LLM( - model=model_name, + model="microsoft/Phi-3.5-vision-instruct", + trust_remote_code=True, max_model_len=4096, - max_num_seqs=5, + max_num_seqs=2, # Note - mm_processor_kwargs can also be passed to generate/chat calls - mm_processor_kwargs={ - "min_pixels": 28 * 28, - "max_pixels": 1280 * 28 * 28, - }, + mm_processor_kwargs={"num_crops": 16}, mm_cache_preprocessor=args.mm_cache_preprocessor, ) - - prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n" - "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>" - f"{question}<|im_end|>\n" - "<|im_start|>assistant\n") stop_token_ids = None return llm, prompt, stop_token_ids @@ -352,149 +428,73 @@ def run_pixtral_hf(question: str, modality: str): return llm, prompt, stop_token_ids -# LLama 3.2 -def run_mllama(question: str, modality: str): - assert modality == "image" - - model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct" - - # Note: The default setting of max_num_seqs (256) and - # max_model_len (131072) for this model may cause OOM. - # You may lower either to run this example on lower-end GPUs. - - # The configuration below has been confirmed to launch on a single L40 GPU. - llm = LLM( - model=model_name, - max_model_len=4096, - max_num_seqs=16, - enforce_eager=True, - mm_cache_preprocessor=args.mm_cache_preprocessor, - ) - - prompt = f"<|image|><|begin_of_text|>{question}" - stop_token_ids = None - return llm, prompt, stop_token_ids - - -# Molmo -def run_molmo(question, modality): +# Qwen +def run_qwen_vl(question: str, modality: str): assert modality == "image" - model_name = "allenai/Molmo-7B-D-0924" - llm = LLM( - model=model_name, + model="Qwen/Qwen-VL", trust_remote_code=True, - dtype="bfloat16", + max_model_len=1024, + max_num_seqs=2, mm_cache_preprocessor=args.mm_cache_preprocessor, ) - prompt = question + prompt = f"{question}Picture 1: \n" stop_token_ids = None return llm, prompt, stop_token_ids -# GLM-4v -def run_glm4v(question: str, modality: str): +# Qwen2-VL +def run_qwen2_vl(question: str, modality: str): assert modality == "image" - model_name = "THUDM/glm-4v-9b" - llm = LLM(model=model_name, - max_model_len=2048, - max_num_seqs=2, - trust_remote_code=True, - enforce_eager=True, - mm_cache_preprocessor=args.mm_cache_preprocessor) - prompt = question - stop_token_ids = [151329, 151336, 151338] - return llm, prompt, stop_token_ids - - -# Idefics3-8B-Llama3 -def run_idefics3(question: str, modality: str): - assert modality == "image" - model_name = "HuggingFaceM4/Idefics3-8B-Llama3" + model_name = "Qwen/Qwen2-VL-7B-Instruct" llm = LLM( model=model_name, - max_model_len=8192, - max_num_seqs=2, - enforce_eager=True, - # if you are running out of memory, you can reduce the "longest_edge". - # see: https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3#model-optimizations + max_model_len=4096, + max_num_seqs=5, + # Note - mm_processor_kwargs can also be passed to generate/chat calls mm_processor_kwargs={ - "size": { - "longest_edge": 3 * 364 - }, + "min_pixels": 28 * 28, + "max_pixels": 1280 * 28 * 28, }, mm_cache_preprocessor=args.mm_cache_preprocessor, ) - prompt = ( - f"<|begin_of_text|>User:{question}\nAssistant:" - ) - stop_token_ids = None - return llm, prompt, stop_token_ids - -# Aria -def run_aria(question: str, modality: str): - assert modality == "image" - model_name = "rhymes-ai/Aria" - - llm = LLM(model=model_name, - tokenizer_mode="slow", - trust_remote_code=True, - dtype="bfloat16", - mm_cache_preprocessor=args.mm_cache_preprocessor) - - prompt = (f"<|im_start|>user\n<|img|>\n{question}" - "<|im_end|>\n<|im_start|>assistant\n") - - stop_token_ids = [93532, 93653, 944, 93421, 1019, 93653, 93519] - return llm, prompt, stop_token_ids - - -# Mantis -def run_mantis(question: str, modality: str): - assert modality == "image" - - llama3_template = '<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' # noqa: E501 - prompt = llama3_template.format(f"{question}\n") - - llm = LLM( - model="TIGER-Lab/Mantis-8B-siglip-llama3", - max_model_len=4096, - hf_overrides={"architectures": ["MantisForConditionalGeneration"]}, - mm_cache_preprocessor=args.mm_cache_preprocessor, - ) - stop_token_ids = [128009] + prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n" + "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>" + f"{question}<|im_end|>\n" + "<|im_start|>assistant\n") + stop_token_ids = None return llm, prompt, stop_token_ids model_example_map = { + "aria": run_aria, + "blip-2": run_blip2, + "chameleon": run_chameleon, + "fuyu": run_fuyu, + "glm4v": run_glm4v, + "h2ovl_chat": run_h2ovl, + "idefics3": run_idefics3, + "internvl_chat": run_internvl, "llava": run_llava, "llava-next": run_llava_next, "llava-next-video": run_llava_next_video, "llava-onevision": run_llava_onevision, - "fuyu": run_fuyu, - "phi3_v": run_phi3v, - "paligemma": run_paligemma, - "paligemma2": run_paligemma2, - "chameleon": run_chameleon, + "mantis": run_mantis, "minicpmv": run_minicpmv, - "blip-2": run_blip2, - "h2ovl_chat": run_h2ovl, - "internvl_chat": run_internvl, + "mllama": run_mllama, + "molmo": run_molmo, "NVLM_D": run_nvlm_d, + "paligemma": run_paligemma, + "paligemma2": run_paligemma2, + "phi3_v": run_phi3v, + "pixtral_hf": run_pixtral_hf, "qwen_vl": run_qwen_vl, "qwen2_vl": run_qwen2_vl, - "pixtral_hf": run_pixtral_hf, - "mllama": run_mllama, - "molmo": run_molmo, - "glm4v": run_glm4v, - "idefics3": run_idefics3, - "aria": run_aria, - "mantis": run_mantis, } diff --git a/examples/offline_inference_vision_language_multi_image.py b/examples/offline_inference_vision_language_multi_image.py index 928bbef54eab7..6af8d7768e75d 100644 --- a/examples/offline_inference_vision_language_multi_image.py +++ b/examples/offline_inference_vision_language_multi_image.py @@ -33,78 +33,23 @@ class ModelRequestData(NamedTuple): # Unless specified, these settings have been tested to work on a single L4. -def load_qwenvl_chat(question: str, image_urls: List[str]) -> ModelRequestData: - model_name = "Qwen/Qwen-VL-Chat" - llm = LLM( - model=model_name, - trust_remote_code=True, - max_model_len=1024, - max_num_seqs=2, - limit_mm_per_prompt={"image": len(image_urls)}, - ) - placeholders = "".join(f"Picture {i}: \n" - for i, _ in enumerate(image_urls, start=1)) - - # This model does not have a chat_template attribute on its tokenizer, - # so we need to explicitly pass it. We use ChatML since it's used in the - # generation utils of the model: - # https://huggingface.co/Qwen/Qwen-VL-Chat/blob/main/qwen_generation_utils.py#L265 - tokenizer = AutoTokenizer.from_pretrained(model_name, - trust_remote_code=True) - - # Copied from: https://huggingface.co/docs/transformers/main/en/chat_templating - chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" # noqa: E501 - - messages = [{'role': 'user', 'content': f"{placeholders}\n{question}"}] - prompt = tokenizer.apply_chat_template(messages, - tokenize=False, - add_generation_prompt=True, - chat_template=chat_template) - - stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>"] - stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens] - return ModelRequestData( - llm=llm, - prompt=prompt, - stop_token_ids=stop_token_ids, - image_data=[fetch_image(url) for url in image_urls], - chat_template=chat_template, - ) - - -def load_phi3v(question: str, image_urls: List[str]) -> ModelRequestData: - # num_crops is an override kwarg to the multimodal image processor; - # For some models, e.g., Phi-3.5-vision-instruct, it is recommended - # to use 16 for single frame scenarios, and 4 for multi-frame. - # - # Generally speaking, a larger value for num_crops results in more - # tokens per image instance, because it may scale the image more in - # the image preprocessing. Some references in the model docs and the - # formula for image tokens after the preprocessing - # transform can be found below. - # - # https://huggingface.co/microsoft/Phi-3.5-vision-instruct#loading-the-model-locally - # https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/main/processing_phi3_v.py#L194 - llm = LLM( - model="microsoft/Phi-3.5-vision-instruct", - trust_remote_code=True, - max_model_len=4096, - max_num_seqs=2, - limit_mm_per_prompt={"image": len(image_urls)}, - mm_processor_kwargs={"num_crops": 4}, - ) - placeholders = "\n".join(f"<|image_{i}|>" - for i, _ in enumerate(image_urls, start=1)) - prompt = f"<|user|>\n{placeholders}\n{question}<|end|>\n<|assistant|>\n" - stop_token_ids = None - +def load_aria(question, image_urls: List[str]) -> ModelRequestData: + model_name = "rhymes-ai/Aria" + llm = LLM(model=model_name, + tokenizer_mode="slow", + trust_remote_code=True, + dtype="bfloat16", + limit_mm_per_prompt={"image": len(image_urls)}) + placeholders = "<|img|>\n" * len(image_urls) + prompt = (f"<|im_start|>user\n{placeholders}{question}<|im_end|>\n" + "<|im_start|>assistant\n") + stop_token_ids = [93532, 93653, 944, 93421, 1019, 93653, 93519] return ModelRequestData( llm=llm, prompt=prompt, stop_token_ids=stop_token_ids, image_data=[fetch_image(url) for url in image_urls], - chat_template=None, - ) + chat_template=None) def load_h2onvl(question: str, image_urls: List[str]) -> ModelRequestData: @@ -141,6 +86,37 @@ def load_h2onvl(question: str, image_urls: List[str]) -> ModelRequestData: ) +def load_idefics3(question, image_urls: List[str]) -> ModelRequestData: + model_name = "HuggingFaceM4/Idefics3-8B-Llama3" + + # The configuration below has been confirmed to launch on a single L40 GPU. + llm = LLM( + model=model_name, + max_model_len=8192, + max_num_seqs=16, + enforce_eager=True, + limit_mm_per_prompt={"image": len(image_urls)}, + # if you are running out of memory, you can reduce the "longest_edge". + # see: https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3#model-optimizations + mm_processor_kwargs={ + "size": { + "longest_edge": 2 * 364 + }, + }, + ) + + placeholders = "\n".join(f"Image-{i}: \n" + for i, _ in enumerate(image_urls, start=1)) + prompt = f"<|begin_of_text|>User:{placeholders}\n{question}\nAssistant:" # noqa: E501 + return ModelRequestData( + llm=llm, + prompt=prompt, + stop_token_ids=None, + image_data=[fetch_image(url) for url in image_urls], + chat_template=None, + ) + + def load_internvl(question: str, image_urls: List[str]) -> ModelRequestData: model_name = "OpenGVLab/InternVL2-2B" @@ -178,6 +154,28 @@ def load_internvl(question: str, image_urls: List[str]) -> ModelRequestData: ) +def load_mllama(question, image_urls: List[str]) -> ModelRequestData: + model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct" + + # The configuration below has been confirmed to launch on a single L40 GPU. + llm = LLM( + model=model_name, + max_model_len=4096, + max_num_seqs=16, + enforce_eager=True, + limit_mm_per_prompt={"image": len(image_urls)}, + ) + + prompt = f"<|image|><|image|><|begin_of_text|>{question}" + return ModelRequestData( + llm=llm, + prompt=prompt, + stop_token_ids=None, + image_data=[fetch_image(url) for url in image_urls], + chat_template=None, + ) + + def load_nvlm_d(question: str, image_urls: List[str]): model_name = "nvidia/NVLM-D-72B" @@ -211,6 +209,80 @@ def load_nvlm_d(question: str, image_urls: List[str]): ) +def load_phi3v(question: str, image_urls: List[str]) -> ModelRequestData: + # num_crops is an override kwarg to the multimodal image processor; + # For some models, e.g., Phi-3.5-vision-instruct, it is recommended + # to use 16 for single frame scenarios, and 4 for multi-frame. + # + # Generally speaking, a larger value for num_crops results in more + # tokens per image instance, because it may scale the image more in + # the image preprocessing. Some references in the model docs and the + # formula for image tokens after the preprocessing + # transform can be found below. + # + # https://huggingface.co/microsoft/Phi-3.5-vision-instruct#loading-the-model-locally + # https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/main/processing_phi3_v.py#L194 + llm = LLM( + model="microsoft/Phi-3.5-vision-instruct", + trust_remote_code=True, + max_model_len=4096, + max_num_seqs=2, + limit_mm_per_prompt={"image": len(image_urls)}, + mm_processor_kwargs={"num_crops": 4}, + ) + placeholders = "\n".join(f"<|image_{i}|>" + for i, _ in enumerate(image_urls, start=1)) + prompt = f"<|user|>\n{placeholders}\n{question}<|end|>\n<|assistant|>\n" + stop_token_ids = None + + return ModelRequestData( + llm=llm, + prompt=prompt, + stop_token_ids=stop_token_ids, + image_data=[fetch_image(url) for url in image_urls], + chat_template=None, + ) + + +def load_qwenvl_chat(question: str, image_urls: List[str]) -> ModelRequestData: + model_name = "Qwen/Qwen-VL-Chat" + llm = LLM( + model=model_name, + trust_remote_code=True, + max_model_len=1024, + max_num_seqs=2, + limit_mm_per_prompt={"image": len(image_urls)}, + ) + placeholders = "".join(f"Picture {i}: \n" + for i, _ in enumerate(image_urls, start=1)) + + # This model does not have a chat_template attribute on its tokenizer, + # so we need to explicitly pass it. We use ChatML since it's used in the + # generation utils of the model: + # https://huggingface.co/Qwen/Qwen-VL-Chat/blob/main/qwen_generation_utils.py#L265 + tokenizer = AutoTokenizer.from_pretrained(model_name, + trust_remote_code=True) + + # Copied from: https://huggingface.co/docs/transformers/main/en/chat_templating + chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" # noqa: E501 + + messages = [{'role': 'user', 'content': f"{placeholders}\n{question}"}] + prompt = tokenizer.apply_chat_template(messages, + tokenize=False, + add_generation_prompt=True, + chat_template=chat_template) + + stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>"] + stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens] + return ModelRequestData( + llm=llm, + prompt=prompt, + stop_token_ids=stop_token_ids, + image_data=[fetch_image(url) for url in image_urls], + chat_template=chat_template, + ) + + def load_qwen2_vl(question, image_urls: List[str]) -> ModelRequestData: try: from qwen_vl_utils import process_vision_info @@ -268,88 +340,16 @@ def load_qwen2_vl(question, image_urls: List[str]) -> ModelRequestData: ) -def load_mllama(question, image_urls: List[str]) -> ModelRequestData: - model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct" - - # The configuration below has been confirmed to launch on a single L40 GPU. - llm = LLM( - model=model_name, - max_model_len=4096, - max_num_seqs=16, - enforce_eager=True, - limit_mm_per_prompt={"image": len(image_urls)}, - ) - - prompt = f"<|image|><|image|><|begin_of_text|>{question}" - return ModelRequestData( - llm=llm, - prompt=prompt, - stop_token_ids=None, - image_data=[fetch_image(url) for url in image_urls], - chat_template=None, - ) - - -def load_idefics3(question, image_urls: List[str]) -> ModelRequestData: - model_name = "HuggingFaceM4/Idefics3-8B-Llama3" - - # The configuration below has been confirmed to launch on a single L40 GPU. - llm = LLM( - model=model_name, - max_model_len=8192, - max_num_seqs=16, - enforce_eager=True, - limit_mm_per_prompt={"image": len(image_urls)}, - # if you are running out of memory, you can reduce the "longest_edge". - # see: https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3#model-optimizations - mm_processor_kwargs={ - "size": { - "longest_edge": 2 * 364 - }, - }, - ) - - placeholders = "\n".join(f"Image-{i}: \n" - for i, _ in enumerate(image_urls, start=1)) - prompt = f"<|begin_of_text|>User:{placeholders}\n{question}\nAssistant:" # noqa: E501 - return ModelRequestData( - llm=llm, - prompt=prompt, - stop_token_ids=None, - image_data=[fetch_image(url) for url in image_urls], - chat_template=None, - ) - - -def load_aria(question, image_urls: List[str]) -> ModelRequestData: - model_name = "rhymes-ai/Aria" - llm = LLM(model=model_name, - tokenizer_mode="slow", - trust_remote_code=True, - dtype="bfloat16", - limit_mm_per_prompt={"image": len(image_urls)}) - placeholders = "<|img|>\n" * len(image_urls) - prompt = (f"<|im_start|>user\n{placeholders}{question}<|im_end|>\n" - "<|im_start|>assistant\n") - stop_token_ids = [93532, 93653, 944, 93421, 1019, 93653, 93519] - return ModelRequestData( - llm=llm, - prompt=prompt, - stop_token_ids=stop_token_ids, - image_data=[fetch_image(url) for url in image_urls], - chat_template=None) - - model_example_map = { - "phi3_v": load_phi3v, + "aria": load_aria, "h2ovl_chat": load_h2onvl, + "idefics3": load_idefics3, "internvl_chat": load_internvl, + "mllama": load_mllama, "NVLM_D": load_nvlm_d, - "qwen2_vl": load_qwen2_vl, + "phi3_v": load_phi3v, "qwen_vl_chat": load_qwenvl_chat, - "mllama": load_mllama, - "idefics3": load_idefics3, - "aria": load_aria, + "qwen2_vl": load_qwen2_vl, } From efbce85f4d375d7851a491a0126a224e25d9f91d Mon Sep 17 00:00:00 2001 From: Varun Sundar Rabindranath Date: Mon, 16 Dec 2024 13:14:57 -0500 Subject: [PATCH 096/155] [misc] Layerwise profile updates (#10242) Signed-off-by: Varun Sundar Rabindranath Co-authored-by: Varun Sundar Rabindranath --- .buildkite/test-pipeline.yaml | 2 +- examples/offline_profile.py | 236 +++++++++++++++--- tools/profiler/print_layerwise_table.py | 9 +- tools/profiler/visualize_layerwise_profile.py | 92 ++++++- vllm/profiler/layerwise_profile.py | 22 +- 5 files changed, 314 insertions(+), 47 deletions(-) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 97aae233db105..44f47fac1c1b3 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -201,7 +201,7 @@ steps: - python3 offline_inference_classification.py - python3 offline_inference_embedding.py - python3 offline_inference_scoring.py - - python3 offline_profile.py --model facebook/opt-125m + - python3 offline_profile.py --model facebook/opt-125m run_num_steps --num-steps 2 - label: Prefix Caching Test # 9min mirror_hardwares: [amd] diff --git a/examples/offline_profile.py b/examples/offline_profile.py index 1d415b82cddb6..46afe8aa2604b 100644 --- a/examples/offline_profile.py +++ b/examples/offline_profile.py @@ -4,9 +4,10 @@ import sys from argparse import RawTextHelpFormatter from dataclasses import asdict, dataclass -from typing import Optional +from typing import Any, Dict, Generator, List, Optional, TypeAlias import torch +import tqdm from vllm import LLM, SamplingParams from vllm.engine.arg_utils import EngineArgs @@ -15,16 +16,21 @@ BATCH_SIZE_DEFAULT = 1 PROMPT_LEN_DEFAULT = 256 -OUTPUT_LEN_DEFAULT = 2 @dataclass class ProfileContext: engine_args: EngineArgs prompt_len: int - output_len: int batch_size: int - save_chrome_traces_folder: Optional[str] + + # The profiler can run in 2 modes, + # 1. Run profiler for user specified num_steps + num_steps: Optional[int] = None + # 2. Run profiler until all requests complete + complete_num_requests_per_step: Optional[int] = None + + save_chrome_traces_folder: Optional[str] = None def get_dtype(dtype: str): @@ -34,23 +40,155 @@ def get_dtype(dtype: str): return dtype +OutputLen_NumReqs_Map: TypeAlias = Dict[int, int] +def compute_request_output_lengths(batch_size: int, step_requests: List[int]) \ + -> OutputLen_NumReqs_Map: + """ + Given the number of requests, batch_size, and the number of requests + that each engine-step should process, step_requests, determine the + output lengths of the requests such that step_request is honoured. + + Example: + if batch size = 128 and step_request = [128, 128, 96, 64, 32, 1] + then return, + {2 : 32, 3 : 32, 4 : 32, 5 : 31, 6 : 1}, meaning, + 32 requests should have output length 2, + 32 requests should have output length 3, + 32 requests should have output length 4, + 31 requests should have output length 5, + 1 request should have output length 6. + + Args: + batch_size (int): Number of requests submitted for profile. This is + args.batch_size. + step_requests (List[int]): step_requests[i] is the number of requests + that the ith engine step should process. + + Returns: + OutputLen_NumReqs_Map : A dictionary with output-length as keys and the + number of requests required to have that output-length as values. + """ + ol_nr: OutputLen_NumReqs_Map = {} + + # Number of request that are assigned an output-length + num_reqs_assigned: int = 0 + num_steps: int = len(step_requests) + + # sanity check. The first step (prefill-step), must process all requests. + assert step_requests[0] == batch_size + + # Begin assignments from the last step. + output_length: int = num_steps + for num_requests_at_step in reversed(step_requests): + if num_reqs_assigned == batch_size: + break + + assert num_reqs_assigned < batch_size + + # Remove the number of requests that have been determined + # to participate in this step and beyond. + num_reqs_unassigned_at_step = num_requests_at_step - num_reqs_assigned + assert num_reqs_unassigned_at_step >= 0 + + if num_reqs_unassigned_at_step > 0: + ol_nr[output_length] = num_reqs_unassigned_at_step + num_reqs_assigned += num_reqs_unassigned_at_step + + output_length -= 1 + + # sanity checks. + assert sum(ol_nr.values()) == batch_size, \ + ("Number of requests in output-length assignment does not match " + f"batch-size.\n batch size {batch_size} - " + f"step requests {step_requests} - assignments {ol_nr}") + + # Check that the output-length is in [1, num-steps]. Output length must be + # at least 1 as all requests must participate in the prefill-step. + assert all(ol >= 1 and ol <= num_steps for ol in ol_nr), \ + ("Output lengths of requests should be in range " + f"[1, num-engine-steps].\n batch size {batch_size} - " + f"step requests {step_requests} - assignments {ol_nr}") + + return ol_nr + + +def determine_requests_per_step(context: ProfileContext) -> List[int]: + """ + Determine number of requests each engine step should process. + If context.num_steps is set, then all engine steps process the + same number of requests and the output list is of length + context.num_steps. + + If context.complete_num_requests_per_step is set, then each decode step + processes fewer and fewer requests until there are no requests to process. + In this case, the output list is as big as the number of steps + required to process all requests. + + Args: + context: ProfileContext object. + + Returns: + List[int]: Number of requests to process for all engine-steps. + output[i], contains the number of requests that the ith step + should process. + """ + if context.num_steps: + # All requests must run until num_engine_steps. This implies + # that their output lengths must be equal to num_engine_steps. + return [context.batch_size] * context.num_steps + + assert context.complete_num_requests_per_step and \ + context.complete_num_requests_per_step > 0, \ + (f"Expected a positive complete_num_requests_per_step argument." + f"Instead got {context.complete_num_requests_per_step}") + + # We start dropping after the first decode step. + step_requests = [ + context.batch_size, # prefill + context.batch_size, # decode + ] + + num_running_requests = context.batch_size + num_running_requests -= context.complete_num_requests_per_step + while num_running_requests > 0: + step_requests.append(num_running_requests) + num_running_requests -= context.complete_num_requests_per_step + + if step_requests[-1] != 1: + # have 1 request running at the last step. This is often + # useful + step_requests.append(1) + + return step_requests + + def run_profile(context: ProfileContext, csv_output: Optional[str], json_output: Optional[str]): print("Run profile with:") for key, value in asdict(context).items(): print(f" {key} = {value}") + requests_per_step: List[int] = determine_requests_per_step(context) + + ol_nr: OutputLen_NumReqs_Map = compute_request_output_lengths( + context.batch_size, requests_per_step) + + num_steps_to_profile: int = len(requests_per_step) + max_output_len: int = max(ol_nr.keys()) + assert max_output_len >= 1 + # Create sampling params - sampling_params = SamplingParams(temperature=0.8, - top_p=0.95, - max_tokens=args.output_len, - ignore_eos=True) + sampling_params = SamplingParams( + temperature=0.8, + top_p=0.95, + # max_tokens is set on a per-request basis. + max_tokens=None, + ignore_eos=True) # Create LLM llm = LLM(**asdict(context.engine_args)) batch_size = context.batch_size prompt_len = context.prompt_len - output_len = context.output_len scheduler_config = llm.llm_engine.scheduler_config max_model_len = llm.llm_engine.model_config.max_model_len @@ -65,7 +203,7 @@ def run_profile(context: ProfileContext, csv_output: Optional[str], f"choose a smaller batch size or prompt length, or increase " f"--max-num-batched-tokens") sys.exit(-1) - if batch_size >= max_num_seqs: + if batch_size > max_num_seqs: print( f"ERROR: chosen batch_size ({batch_size}) is larger than " f"max_num_seqs ({max_num_seqs}) and therefore cannot be run in a " @@ -73,16 +211,26 @@ def run_profile(context: ProfileContext, csv_output: Optional[str], sys.exit(-1) print("llm.llm_engine.model_config.max_model_len: ", llm.llm_engine.model_config.max_model_len) - if prompt_len + output_len > llm.llm_engine.model_config.max_model_len: - print( - f"ERROR: chosen prompt_len + output_len ({prompt_len} + " - f"{output_len} = {prompt_len + output_len}) is larger than the " - f"model's max_model_len ({max_model_len}), please choose a smaller " - f"prompt_len or output_len, or increase --max-model-len") + if prompt_len + max_output_len > llm.llm_engine.model_config.max_model_len: + print(f"ERROR: chosen prompt_len + max_output_len ({prompt_len} + " + f"{max_output_len} = {prompt_len + max_output_len}) is larger " + f"than the model's max_model_len ({max_model_len}), please " + f"choose a smaller prompt_len or max_output_len, or increase " + f"--max-model-len") sys.exit(-1) def add_requests(): + + def get_output_len_generator() -> Generator[int, Any, Any]: + for output_len, num_reqs in ol_nr.items(): + for _ in range(num_reqs): + yield output_len + + output_len_generator = get_output_len_generator() for i in range(batch_size): + sampling_params.max_tokens = next(output_len_generator) + assert isinstance(sampling_params.max_tokens, int) + prompt_token_ids = torch.randint( llm.llm_engine.model_config.get_vocab_size(), size=(prompt_len, )).tolist() @@ -110,8 +258,11 @@ def abort_requests(): llm.llm_engine.step() # First step is prefill decode_profs = [] - for x in range(args.output_len - 1): - with layerwise_profile() as decode_prof: + for _ in tqdm.tqdm(range(num_steps_to_profile - 1)): + num_running_seqs = llm.llm_engine.scheduler[ + 0].get_num_unfinished_seq_groups() + with layerwise_profile( + num_running_seqs=num_running_seqs) as decode_prof: llm.llm_engine.step() decode_profs.append(decode_prof) @@ -154,7 +305,8 @@ def abort_requests(): decode_results_list[0].print_summary_table() if csv_output: - csv_filename_base = csv_output.rstrip(".csv") + csv_filename_base = csv_output[:-4] \ + if csv_output.endswith('.csv') else csv_output prefill_results.export_model_stats_table_csv( csv_filename_base + "_prefill_model_table.csv") prefill_results.export_summary_stats_table_csv( @@ -187,10 +339,10 @@ def abort_requests(): for idx, dr in enumerate(decode_results_list): json_dict[f"decode_{idx + 1}"] = dr.convert_stats_to_dict() - for idx, dr in enumerate(decode_results_list[1:]): - json_dict[f"decode_{idx + 1}"] = dr.convert_stats_to_dict() - - with open(json_output.rstrip(".json") + ".json", "w+") as f: + # Add .json to json_output filename if it doesn't exist already. + json_output_file = json_output if json_output.endswith( + '.json') else json_output + '.json' + with open(json_output_file, "w+") as f: json.dump(json_dict, f, indent=2) pass @@ -214,7 +366,7 @@ def abort_requests(): python examples/offline_profile.py \\ --model neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8 --batch-size 4 \\ --prompt-len 512 --max-num-batched-tokens 8196 --json Llama31-8b-FP8 \\ - --enforce-eager + --enforce-eager run_num_steps -n 2 ``` then you can use various tools to analyze the json output @@ -261,17 +413,41 @@ def abort_requests(): default=BATCH_SIZE_DEFAULT, help=f"Number of requests to run as a single batch, " f"default={BATCH_SIZE_DEFAULT}") - parser.add_argument( - "--output-len", + + subparsers = parser.add_subparsers(dest="cmd") + + run_num_steps_parser = subparsers.add_parser( + "run_num_steps", + help="This variation profiles n engine.step() invocations.") + run_num_steps_parser.add_argument( + '-n', + '--num-steps', type=int, - default=OUTPUT_LEN_DEFAULT, - help="Number of llm steps to run (includes prefill and decode) " - "- default={OUTPUT_LEN_DEFAULT}") + help="Number of engine steps to profile.\n" + "Setting it to 1, profiles only the prefill step.\n" + "Setting it to 2, profiles the prefill and first decode step\n" + "Setting it to 3, profiles the prefill, 1st and 2nd decode steps\n" + "and so on ...") + + run_to_completion_parser = subparsers.add_parser( + "run_to_completion", + help="This variation profiles all the engine.step() invocations" + "until the engine exhausts all submitted requests.") + run_to_completion_parser.add_argument( + '-n', + '--complete-num-requests-per-step', + type=int, + help= + "Complete complete_num_requests_per_step requests every decode step." + "For e.g., with batch_size 128 and complete_num_requests_per_step 32," + "the profiler is run for 6 engine steps, with the steps processing, " + "128, 128, 96, 64, 32, 1 requests respectively.\n" + "Note that we tack-on a one-request step at the end as it is often " + "useful.") EngineArgs.add_cli_args(parser) args = parser.parse_args() - context = ProfileContext( engine_args=EngineArgs.from_cli_args(args), **{ diff --git a/tools/profiler/print_layerwise_table.py b/tools/profiler/print_layerwise_table.py index 081076ad7dbdc..394ca8663e189 100644 --- a/tools/profiler/print_layerwise_table.py +++ b/tools/profiler/print_layerwise_table.py @@ -34,9 +34,10 @@ def get_entries(node, curr_depth=0): "examples/offline_profile.py") parser.add_argument("--phase", type=str, - choices=["prefill", "decode_1"], required=True, - help="The phase to print the table for.") + help="The phase to print the table for. This is either" + "prefill or decode_n, where n is the decode step " + "number") parser.add_argument("--table", type=str, choices=["summary", "model"], @@ -49,6 +50,10 @@ def get_entries(node, curr_depth=0): with open(args.json_trace) as f: profile_data = json.load(f) + assert args.phase in profile_data, \ + (f"Cannot find phase {args.phase} in profile data. Choose one among" + f'{[x for x in profile_data.keys() if "prefill" in x or "decode" in x]}') #noqa + if args.table == "summary": entries_and_depths = flatten_entries( SummaryStatsEntry, profile_data[args.phase]["summary_stats"]) diff --git a/tools/profiler/visualize_layerwise_profile.py b/tools/profiler/visualize_layerwise_profile.py index adc44474aa4c1..da7a28da15c19 100644 --- a/tools/profiler/visualize_layerwise_profile.py +++ b/tools/profiler/visualize_layerwise_profile.py @@ -151,16 +151,31 @@ def is_quant(op_name: str): "scaled_int8_quant" in op_name: return True + # LoRA ops + def is_sgmv_shrink(op_name: str): + return "sgmv_shrink" in op_name + + def is_sgmv_expand(op_name: str): + return "sgmv_expand" in op_name + + def is_bgmv_shrink(op_name: str): + return "bgmv_shrink" in op_name + + def is_bgmv_expand(op_name: str): + return "bgmv_expand" in op_name + + def is_cutlass_gemm_op(op_name: str): + return "void cutlass::Kernel" in op_name or \ + "void cutlass::device_kernel" in op_name + def is_gemm_op(op_name: str): if is_quant(op_name): return False - if "xmma_gemm" in op_name or \ + return is_cutlass_gemm_op(op_name) or \ + "xmma_gemm" in op_name or \ "gemv2T_kernel" in op_name or \ "splitKreduce" in op_name or \ - "void cutlass::Kernel" in op_name or \ - "void cutlass::device_kernel" in op_name or \ - "s16816gemm" in op_name: - return True + "s16816gemm" in op_name def is_elementwise_op(op_name: str): return "elementwise_kernel" in op_name @@ -211,6 +226,18 @@ def is_reduce_kernel(op_name: str): quant_ops = list(filter(lambda x: is_quant(x), ops)) ops = list(filter(lambda x: x not in quant_ops, ops)) + sgmv_shrink_ops = list(filter(lambda x: is_sgmv_shrink(x), ops)) + ops = list(filter(lambda x: x not in sgmv_shrink_ops, ops)) + sgmv_expand_ops = list(filter(lambda x: is_sgmv_expand(x), ops)) + ops = list(filter(lambda x: x not in sgmv_expand_ops, ops)) + bgmv_shrink_ops = list(filter(lambda x: is_bgmv_shrink(x), ops)) + ops = list(filter(lambda x: x not in bgmv_shrink_ops, ops)) + bgmv_expand_ops = list(filter(lambda x: is_bgmv_expand(x), ops)) + ops = list(filter(lambda x: x not in bgmv_expand_ops, ops)) + + cutlass_gemm_ops = list(filter(lambda x: is_cutlass_gemm_op(x), ops)) + ops = list(filter(lambda x: x not in cutlass_gemm_ops, ops)) + gemm_ops = list(filter(lambda x: is_gemm_op(x), ops)) ops = list(filter(lambda x: x not in gemm_ops, ops)) @@ -257,6 +284,24 @@ def is_reduce_kernel(op_name: str): trace_df['attention'] = trace_df[attention_ops].agg("sum", axis=1) if len(quant_ops): trace_df['quant_ops'] = trace_df[quant_ops].agg("sum", axis=1) + + if len(sgmv_shrink_ops): + trace_df['sgmv_shrink_ops'] = trace_df[sgmv_shrink_ops].agg("sum", + axis=1) + if len(sgmv_expand_ops): + trace_df['sgmv_expand_ops'] = trace_df[sgmv_expand_ops].agg("sum", + axis=1) + if len(bgmv_shrink_ops): + trace_df['bgmv_shrink_ops'] = trace_df[bgmv_shrink_ops].agg("sum", + axis=1) + if len(bgmv_expand_ops): + trace_df['bgmv_expand_ops'] = trace_df[bgmv_expand_ops].agg("sum", + axis=1) + + if len(cutlass_gemm_ops): + trace_df['cutlass_gemm_ops'] = trace_df[cutlass_gemm_ops].agg("sum", + axis=1) + if len(gemm_ops): trace_df['gemm_ops'] = trace_df[gemm_ops].agg("sum", axis=1) if len(rms_norm_ops): @@ -296,7 +341,9 @@ def is_reduce_kernel(op_name: str): trace_df['reduce_kernel_ops'] = trace_df[reduce_kernel_ops].agg("sum", axis=1) - trace_df.drop(attention_ops + quant_ops + gemm_ops + rms_norm_ops + + trace_df.drop(attention_ops + quant_ops + sgmv_shrink_ops + + sgmv_expand_ops + bgmv_shrink_ops + bgmv_expand_ops + + cutlass_gemm_ops + gemm_ops + rms_norm_ops + vocab_embed_ops + mem_ops + elementwise_ops + nccl_all_reduce_ops + nccl_gather_ops + nccl_broadcast_ops + nccl_other_ops + cross_device_reduce_1stage_ops + @@ -315,7 +362,14 @@ def plot_trace_df(traces_df: pd.DataFrame, plot_title: str, output: Optional[Path] = None): + def get_phase_description(traces_df: pd.DataFrame, phase: str) -> str: + phase_df = traces_df.query(f'phase == "{phase}"') + descs = phase_df['phase_desc'].to_list() + assert all([desc == descs[0] for desc in descs]) + return descs[0] + phases = traces_df['phase'].unique() + phase_descs = [get_phase_description(traces_df, p) for p in phases] traces_df = traces_df.pivot_table(index="phase", columns="name", values=plot_metric, @@ -324,7 +378,8 @@ def plot_trace_df(traces_df: pd.DataFrame, traces_df = group_trace_by_operations(traces_df) # Make the figure - fig, ax = plt.subplots(1, figsize=(5, 8), sharex=True) + fig_size_x = max(5, len(phases)) + fig, ax = plt.subplots(1, figsize=(fig_size_x, 8), sharex=True) # Draw the stacked bars ops = list(traces_df) @@ -332,7 +387,7 @@ def plot_trace_df(traces_df: pd.DataFrame, for op in ops: values = [traces_df[op][phase] for phase in phases] values = list(map(lambda x: 0.0 if math.isnan(x) else x, values)) - ax.bar(phases, values, label=op, bottom=bottom) + ax.bar(phase_descs, values, label=op, bottom=bottom) bottom = [bottom[j] + values[j] for j in range(len(phases))] # Write the values as text on the bars @@ -390,6 +445,14 @@ def keep_only_top_entries(df: pd.DataFrame, ["name"]] = "others" return df + def get_phase_description(key: str) -> str: + num_running_seqs = profile_json[key]['metadata'][ + 'num_running_seqs'] + if num_running_seqs is not None: + return f"{key}-seqs-{num_running_seqs}" + else: + return key + # Get data for each key traces = list(map(lambda x: get_entries_and_traces(x), step_keys)) @@ -413,6 +476,7 @@ def keep_only_top_entries(df: pd.DataFrame, # Fill in information about the step-keys for trace_df, step_key in zip(trace_dfs, step_keys): trace_df['phase'] = step_key + trace_df['phase_desc'] = get_phase_description(step_key) # Combine all data frames so they can be put in a single plot traces_df = pd.concat(trace_dfs) @@ -426,12 +490,16 @@ def keep_only_top_entries(df: pd.DataFrame, def make_plot_title_suffix(profile_json: dict) -> str: context = profile_json["context"] sparsity = context.get('sparsity', None) - return (f"{context['model']}\n" + run_type = \ + f'Run {context["num_steps"]} steps' if context['num_steps'] else \ + (f'Complete {context["complete_num_requests_per_step"]} per ' + f'step; Run till completion') + return (f"{context['engine_args']['model']}\n" f"Batch={context['batch_size']}, " f"PromptLen={context['prompt_len']}, " - f"OutputLen={context['output_len']}," - f"NumGpus={context['tensor_parallel_size']}" - f"{', Sparsity ' + sparsity if sparsity else ''}") + f"NumGpus={context['engine_args']['tensor_parallel_size']}" + f"{', Sparsity ' + sparsity if sparsity else ''}\n" + f"Run Type: {run_type}") profile_json = None with open(json_trace) as f: diff --git a/vllm/profiler/layerwise_profile.py b/vllm/profiler/layerwise_profile.py index 9d9f427e807f6..33babfebdca1e 100644 --- a/vllm/profiler/layerwise_profile.py +++ b/vllm/profiler/layerwise_profile.py @@ -72,6 +72,9 @@ class LayerwiseProfileResults(profile): _model_stats_tree: List[_StatsTreeNode] = field(init=False) _summary_stats_tree: List[_StatsTreeNode] = field(init=False) + # profile metadata + num_running_seqs: Optional[int] = None + def __post_init__(self): self._build_correlation_map() self._build_module_tree() @@ -127,6 +130,9 @@ def export_summary_stats_table_csv(self, filename: str): def convert_stats_to_dict(self) -> str: return { + "metadata": { + "num_running_seqs": self.num_running_seqs + }, "summary_stats": self._convert_stats_tree_to_dict(self._summary_stats_tree), "model_stats": @@ -338,7 +344,15 @@ def df_traversal(node: _StatsTreeNode, curr_json_list: List[Dict]): class layerwise_profile(profile): - def __init__(self): + def __init__(self, num_running_seqs: Optional[int] = None): + """ + layerwise profile constructor. + + Args: + num_running_seqs (Optional[int], optional): When given, + num_running_seqs will be passed to LayerProfileResults for metadata + update. Defaults to None. + """ super().__init__( activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True, @@ -346,9 +360,13 @@ def __init__(self): with_modules=True, experimental_config=_ExperimentalConfig(verbose=True)) + self.num_running_seqs = num_running_seqs + def __enter__(self): return super().__enter__() def __exit__(self, exc_type, exc_val, exc_tb): super().__exit__(exc_type, exc_val, exc_tb) - self.results = LayerwiseProfileResults(self.profiler.kineto_results) + self.results = LayerwiseProfileResults( + self.profiler.kineto_results, + num_running_seqs=self.num_running_seqs) From 551603feffd9b4ba98ccdd34e02e403e04db88c1 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 16 Dec 2024 13:32:25 -0800 Subject: [PATCH 097/155] [core] overhaul memory profiling and fix backward compatibility (#10511) Signed-off-by: youkaichao --- tests/entrypoints/llm/test_gpu_utilization.py | 25 ++++ tests/entrypoints/llm/test_lazy_outlines.py | 2 +- tests/test_utils.py | 44 +++++- tests/worker/test_profile.py | 18 +-- vllm/engine/arg_utils.py | 11 +- vllm/utils.py | 125 +++++++++++++++++- vllm/worker/multi_step_model_runner.py | 3 +- vllm/worker/worker.py | 68 ++++------ 8 files changed, 236 insertions(+), 60 deletions(-) create mode 100644 tests/entrypoints/llm/test_gpu_utilization.py diff --git a/tests/entrypoints/llm/test_gpu_utilization.py b/tests/entrypoints/llm/test_gpu_utilization.py new file mode 100644 index 0000000000000..c2dab300ecefb --- /dev/null +++ b/tests/entrypoints/llm/test_gpu_utilization.py @@ -0,0 +1,25 @@ +from vllm import LLM, SamplingParams + + +def test_gpu_memory_utilization(): + prompts = [ + "Hello, my name is", + "The president of the United States is", + "The capital of France is", + "The future of AI is", + ] + sampling_params = SamplingParams(temperature=0.8, top_p=0.95) + + # makes sure gpu_memory_utilization is per-instance limit, + # not a global limit + llms = [ + LLM(model="facebook/opt-125m", + gpu_memory_utilization=0.3, + enforce_eager=True) for i in range(3) + ] + for llm in llms: + outputs = llm.generate(prompts, sampling_params) + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") diff --git a/tests/entrypoints/llm/test_lazy_outlines.py b/tests/entrypoints/llm/test_lazy_outlines.py index 2c53676c5f5dd..bf609b38a94f5 100644 --- a/tests/entrypoints/llm/test_lazy_outlines.py +++ b/tests/entrypoints/llm/test_lazy_outlines.py @@ -36,7 +36,7 @@ def run_lmfe(sample_regex): llm = LLM(model="facebook/opt-125m", enforce_eager=True, guided_decoding_backend="lm-format-enforcer", - gpu_memory_utilization=0.6) + gpu_memory_utilization=0.3) sampling_params = SamplingParams(temperature=0.8, top_p=0.95) outputs = llm.generate( prompts=[ diff --git a/tests/test_utils.py b/tests/test_utils.py index a731b11eae81c..0bc9e5bc32a46 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -5,11 +5,13 @@ from typing import AsyncIterator, Tuple import pytest +import torch from vllm.utils import (FlexibleArgumentParser, StoreBoolean, deprecate_kwargs, - get_open_port, merge_async_iterators, supports_kw) + get_open_port, memory_profiling, merge_async_iterators, + supports_kw) -from .utils import error_on_warning +from .utils import error_on_warning, fork_new_process_for_each_test @pytest.mark.asyncio @@ -270,3 +272,41 @@ def test_supports_kw(callable,kw_name,requires_kw_only, requires_kw_only=requires_kw_only, allow_var_kwargs=allow_var_kwargs ) == is_supported + + +@fork_new_process_for_each_test +def test_memory_profiling(): + # Fake out some model loading + inference memory usage to test profiling + # Memory used by other processes will show up as cuda usage outside of torch + from vllm.distributed.device_communicators.cuda_wrapper import ( + CudaRTLibrary) + lib = CudaRTLibrary() + # 512 MiB allocation outside of this instance + handle1 = lib.cudaMalloc(512 * 1024 * 1024) + + baseline_memory_in_bytes = \ + torch.cuda.mem_get_info()[1] - torch.cuda.mem_get_info()[0] + + # load weights + + weights = torch.randn(128, 1024, 1024, device='cuda', dtype=torch.float32) + + weights_memory_in_bytes = 128 * 1024 * 1024 * 4 # 512 MiB + + with memory_profiling(baseline_memory_in_bytes=baseline_memory_in_bytes, + weights_memory_in_bytes=weights_memory_in_bytes) as result: + # make a memory spike, 1 GiB + spike = torch.randn(256, 1024, 1024, device='cuda', dtype=torch.float32) + del spike + + # Add some extra non-torch memory 256 MiB (simulate NCCL) + handle2 = lib.cudaMalloc(256 * 1024 * 1024) + + # Check that the memory usage is within 5% of the expected values + non_torch_ratio = result.non_torch_increase_in_bytes / (256 * 1024 * 1024) # noqa + torch_peak_ratio = result.torch_peak_increase_in_bytes / (1024 * 1024 * 1024) # noqa + assert abs(non_torch_ratio - 1) <= 0.05 + assert abs(torch_peak_ratio - 1) <= 0.05 + del weights + lib.cudaFree(handle1) + lib.cudaFree(handle2) diff --git a/tests/worker/test_profile.py b/tests/worker/test_profile.py index 194ea2aa506f4..79233c75714de 100644 --- a/tests/worker/test_profile.py +++ b/tests/worker/test_profile.py @@ -31,10 +31,6 @@ def test_gpu_memory_profiling(): is_driver_worker=True, ) - # Load the model so we can profile it - worker.init_device() - worker.load_model() - # Set 10GiB as the total gpu ram to be device-agnostic def mock_mem_info(): current_usage = torch.cuda.memory_stats( @@ -46,20 +42,24 @@ def mock_mem_info(): from unittest.mock import patch with patch("torch.cuda.mem_get_info", side_effect=mock_mem_info): + # Load the model so we can profile it + worker.init_device() + worker.load_model() gpu_blocks, _ = worker.determine_num_available_blocks() - # Peak vram usage by torch should be 0.7077 GiB + # Peak vram usage by torch should be 0.47 GiB + # Model weights take 0.25 GiB # No memory should be allocated outside of torch # 9.0 GiB should be the utilization target - # 8.2923 GiB should be available for the KV cache + # 8.28 GiB should be available for the KV cache block_size = CacheEngine.get_cache_block_size( engine_config.cache_config, engine_config.model_config, engine_config.parallel_config) - expected_blocks = (8.2923 * 1024**3) // block_size + expected_blocks = (8.28 * 1024**3) // block_size # Check within a small tolerance for portability # Hardware, kernel, or dependency changes could all affect memory # utilization. - # A 10 block tolerance here should be about 6MB of wiggle room. - assert abs(gpu_blocks - expected_blocks) < 10 + # A 100 block tolerance here should be about 60MB of wiggle room. + assert abs(gpu_blocks - expected_blocks) < 100 diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 0aa367a173b6c..06b8542779dc0 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -487,11 +487,12 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: help='The fraction of GPU memory to be used for the model ' 'executor, which can range from 0 to 1. For example, a value of ' '0.5 would imply 50%% GPU memory utilization. If unspecified, ' - 'will use the default value of 0.9. This is a global gpu memory ' - 'utilization limit, for example if 50%% of the gpu memory is ' - 'already used before vLLM starts and --gpu-memory-utilization is ' - 'set to 0.9, then only 40%% of the gpu memory will be allocated ' - 'to the model executor.') + 'will use the default value of 0.9. This is a per-instance ' + 'limit, and only applies to the current vLLM instance.' + 'It does not matter if you have another vLLM instance running ' + 'on the same GPU. For example, if you have two vLLM instances ' + 'running on the same GPU, you can set the GPU memory utilization ' + 'to 0.5 for each instance.') parser.add_argument( '--num-gpu-blocks-override', type=int, diff --git a/vllm/utils.py b/vllm/utils.py index 45e682ac15782..73d2ae25f15ca 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -23,10 +23,12 @@ from asyncio import FIRST_COMPLETED, AbstractEventLoop, Future, Task from collections import UserDict, defaultdict from collections.abc import Iterable, Mapping +from dataclasses import dataclass, field from functools import lru_cache, partial, wraps from typing import (TYPE_CHECKING, Any, AsyncGenerator, Awaitable, Callable, - Dict, Generic, Hashable, List, Literal, Optional, - OrderedDict, Set, Tuple, Type, TypeVar, Union, overload) + Dict, Generator, Generic, Hashable, List, Literal, + Optional, OrderedDict, Set, Tuple, Type, TypeVar, Union, + overload) from uuid import uuid4 import numpy as np @@ -1664,3 +1666,122 @@ def kill_process_tree(pid: int): # Finally kill the parent with contextlib.suppress(ProcessLookupError): os.kill(pid, signal.SIGKILL) + + +@dataclass +class MemorySnapshot: + """Memory snapshot.""" + torch_peak_in_bytes: int = 0 + torch_memory_in_bytes: int = 0 + timestamp: float = 0.0 + + def measure(self): + self.torch_peak_in_bytes = torch.cuda.memory_stats( + )["allocated_bytes.all.peak"] + self.torch_memory_in_bytes = torch.cuda.memory_stats( + )["allocated_bytes.all.current"] + self.timestamp = time.time() + + def __sub__(self, other: "MemorySnapshot") -> "MemorySnapshot": + """support a - b""" + return MemorySnapshot( + torch_peak_in_bytes=self.torch_peak_in_bytes - + other.torch_peak_in_bytes, + torch_memory_in_bytes=self.torch_memory_in_bytes - + other.torch_memory_in_bytes, + timestamp=self.timestamp - other.timestamp) + + +@dataclass +class MemoryProfilingResult: + """Memory profiling result. + """ # noqa + baseline_memory_in_bytes: int = 0 + non_kv_cache_memory_in_bytes: int = 0 + torch_peak_increase_in_bytes: int = 0 + non_torch_increase_in_bytes: int = 0 + weights_memory_in_bytes: float = 0 + before_profile: MemorySnapshot = field(default_factory=MemorySnapshot) + after_profile: MemorySnapshot = field(default_factory=MemorySnapshot) + profile_time: float = 0.0 + + +@contextlib.contextmanager +def memory_profiling( + baseline_memory_in_bytes: int, weights_memory_in_bytes: int +) -> Generator[MemoryProfilingResult, None, None]: + """Memory profiling context manager. + baseline_memory_in_bytes: memory used by all the components other than + the current vLLM instance. It contains: memory used by other processes, memory + used by another vLLM instance in the same process, etc. It is usually measured + before the current vLLM instance initialize the device. And we assume it is + constant during the profiling of the current vLLM instance. + weights_memory_in_bytes: memory used by PyTorch when loading the model weights. + Note that, before loading the model weights, we also initialize the device + and distributed environment, which may consume some memory. This part is not + included in the weights_memory_in_bytes because PyTorch does not control it. + + The memory in one GPU can be classified into 3 categories: + 1. memory used by anything other than the current vLLM instance. + 2. memory used by torch in the current vLLM instance. + 3. memory used in the current vLLM instance, but not by torch. + + A quantitive example: + + Before creating the current vLLM instance: + category 1: 1 GiB + category 2: 0 GiB + category 3: 0 GiB + + After creating the current vLLM instance and loading the model, + (i.e. before profiling): + category 1: 1 GiB + category 2: 2 GiB (model weights take 2 GiB) + category 3: 0.5 GiB (memory used by NCCL) + + During profiling (peak): + category 1: 1 GiB + category 2: 4 GiB (peak activation tensors take 2 GiB) + category 3: 1 GiB (memory used by NCCL + buffers for some attention backends) + + After profiling: + category 1: 1 GiB + category 2: 3 GiB (after garbage-collecting activation tensors) + category 3: 1 GiB (memory used by NCCL + buffers for some attention backends) + + In this case, non-kv cache takes 5 GiB in total, including: + a. 2 GiB used by the model weights (category 2) + b. 2 GiB reserved for the peak activation tensors (category 2) + c. 1 GiB used by non-torch components (category 3) + + The memory used for loading weights (a.) is directly given from the argument `weights_memory_in_bytes`. + + The increase of ``torch.cuda.memory_stats()["allocated_bytes.all.peak"]` after profiling gives (b.). + + (c.) is tricky. We measure the total memory used in this GPU (`torch.cuda.mem_get_info()[1] - torch.cuda.mem_get_info()[0]`), + subtract the baseline memory, the memory used by the model weights, and diff of `torch.cuda.memory_stats()["allocated_bytes.all.current"]`. + """ # noqa + torch.cuda.reset_peak_memory_stats() + + result = MemoryProfilingResult() + + result.baseline_memory_in_bytes = baseline_memory_in_bytes + # the part of memory used for holding the model weights + result.weights_memory_in_bytes = weights_memory_in_bytes + + result.before_profile.measure() + + yield result + + gc.collect() + torch.cuda.empty_cache() + + result.after_profile.measure() + + diff = result.after_profile - result.before_profile + result.torch_peak_increase_in_bytes = diff.torch_peak_in_bytes + current_cuda_memory_bytes = torch.cuda.mem_get_info( + )[1] - torch.cuda.mem_get_info()[0] + result.non_torch_increase_in_bytes = current_cuda_memory_bytes - baseline_memory_in_bytes - weights_memory_in_bytes - diff.torch_memory_in_bytes # noqa + result.profile_time = diff.timestamp + result.non_kv_cache_memory_in_bytes = result.non_torch_increase_in_bytes + result.torch_peak_increase_in_bytes + result.weights_memory_in_bytes # noqa diff --git a/vllm/worker/multi_step_model_runner.py b/vllm/worker/multi_step_model_runner.py index e08a61e31fe42..18b03bf1bfb56 100644 --- a/vllm/worker/multi_step_model_runner.py +++ b/vllm/worker/multi_step_model_runner.py @@ -645,7 +645,8 @@ def _advance_step(self, model_input: StatefulModelInput, return model_input def load_model(self) -> None: - return self._base_model_runner.load_model() + self._base_model_runner.load_model() + self.model_memory_usage = self._base_model_runner.model_memory_usage def save_sharded_state( self, diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py index a368bb9ee9a5b..f51b51d433d3d 100644 --- a/vllm/worker/worker.py +++ b/vllm/worker/worker.py @@ -1,7 +1,6 @@ """A GPU worker class.""" import gc import os -import time from typing import Dict, List, Optional, Set, Tuple, Type, Union import torch @@ -22,6 +21,7 @@ from vllm.prompt_adapter.request import PromptAdapterRequest from vllm.sequence import (ExecuteModelRequest, IntermediateTensors, SequenceGroupMetadata, SequenceGroupMetadataDelta) +from vllm.utils import GiB_bytes, memory_profiling from vllm.worker.cache_engine import CacheEngine from vllm.worker.enc_dec_model_runner import EncoderDecoderModelRunner from vllm.worker.model_runner import GPUModelRunnerBase, ModelRunner @@ -192,33 +192,22 @@ def determine_num_available_blocks(self) -> Tuple[int, int]: torch.cuda.reset_peak_memory_stats() free_memory_pre_profile, total_gpu_memory = torch.cuda.mem_get_info() - start_time = time.time() # Execute a forward pass with dummy inputs to profile the memory usage # of the model. - self.model_runner.profile_run() - torch.cuda.synchronize() + with memory_profiling(baseline_memory_in_bytes=total_gpu_memory - + self.init_gpu_memory, + weights_memory_in_bytes=self.model_runner. + model_memory_usage) as result: + self.model_runner.profile_run() + torch.cuda.synchronize() self._assert_memory_footprint_increased_during_profiling() - # Get the peak memory allocation recorded by torch - peak_memory = torch.cuda.memory_stats()["allocated_bytes.all.peak"] - - # Check for any memory left around that may have been allocated on the - # gpu outside of `torch`. NCCL operations, for example, can use a few - # GB during a forward pass - torch.cuda.empty_cache() - torch_allocated_bytes = torch.cuda.memory_stats( - )["allocated_bytes.all.current"] - total_allocated_bytes = torch.cuda.mem_get_info( - )[1] - torch.cuda.mem_get_info()[0] - non_torch_allocations = total_allocated_bytes - torch_allocated_bytes - if non_torch_allocations > 0: - peak_memory += non_torch_allocations - - available_kv_cache_memory = ( - total_gpu_memory * self.cache_config.gpu_memory_utilization - - peak_memory) + memory_for_current_instance = total_gpu_memory * \ + self.cache_config.gpu_memory_utilization + available_kv_cache_memory = (memory_for_current_instance - + result.non_kv_cache_memory_in_bytes) # Calculate the number of blocks that can be allocated with the # profiled peak memory. @@ -233,24 +222,23 @@ def determine_num_available_blocks(self) -> Tuple[int, int]: num_gpu_blocks = max(num_gpu_blocks, 0) num_cpu_blocks = max(num_cpu_blocks, 0) - end_time = time.time() - logger.info( - "Memory profiling results: " - "duration=%.2f seconds, " - "total_gpu_memory=%.2fGiB, " - "initial_memory_usage=%.2fGiB, " - "peak_torch_memory=%.2fGiB, " - "memory_usage_post_profile=%.2fGiB, " - "non_torch_memory=%.2fGiB, " - "kv_cache_size=%.2fGiB, " - "gpu_memory_utilization=%.2f.", end_time - start_time, - total_gpu_memory / (1024**3), - (total_gpu_memory - free_memory_pre_profile) / (1024**3), - (peak_memory - non_torch_allocations) / (1024**3), - total_allocated_bytes / (1024**3), - non_torch_allocations / (1024**3), - available_kv_cache_memory / (1024**3), - self.cache_config.gpu_memory_utilization) + msg = (f"Memory profiling takes {result.profile_time:.2f} seconds\n" + "the current vLLM instance can use " + "total_gpu_memory " + f"({(total_gpu_memory / GiB_bytes):.2f}GiB)" + " x gpu_memory_utilization " + f"({self.cache_config.gpu_memory_utilization:.2f})" + f" = {(memory_for_current_instance / GiB_bytes):.2f}GiB\n" + "model weights take " + f"{(result.weights_memory_in_bytes / GiB_bytes):.2f}GiB;" + " non_torch_memory takes " + f"{(result.non_torch_increase_in_bytes / GiB_bytes):.2f}GiB;" + " PyTorch activation peak memory takes " + f"{(result.torch_peak_increase_in_bytes / GiB_bytes):.2f}GiB;" + " the rest of the memory reserved for KV Cache is " + f"{(available_kv_cache_memory / GiB_bytes):.2f}GiB.") + + logger.info(msg) # Final cleanup if self.model_runner.lora_manager: From 35ffa682b1cd3f47eb6cda586a16dab5c0401477 Mon Sep 17 00:00:00 2001 From: bk-TurbaAI Date: Mon, 16 Dec 2024 23:20:39 +0100 Subject: [PATCH 098/155] [Docs] hint to enable use of GPU performance counters in profiling tools for multi-node distributed serving (#11235) Co-authored-by: Michael Goin --- docs/source/serving/distributed_serving.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/serving/distributed_serving.rst b/docs/source/serving/distributed_serving.rst index 4d57206e53a05..b24ba53e59694 100644 --- a/docs/source/serving/distributed_serving.rst +++ b/docs/source/serving/distributed_serving.rst @@ -54,7 +54,7 @@ Multi-Node Inference and Serving If a single node does not have enough GPUs to hold the model, you can run the model using multiple nodes. It is important to make sure the execution environment is the same on all nodes, including the model path, the Python environment. The recommended way is to use docker images to ensure the same environment, and hide the heterogeneity of the host machines via mapping them into the same docker configuration. -The first step, is to start containers and organize them into a cluster. We have provided a helper `script `_ to start the cluster. +The first step, is to start containers and organize them into a cluster. We have provided a helper `script `_ to start the cluster. Please note, this script launches docker without administrative privileges that would be required to access GPU performance counters when running profiling and tracing tools. For that purpose, the script can have ``CAP_SYS_ADMIN`` to the docker container by using the ``--cap-add`` option in the docker run command. Pick a node as the head node, and run the following command: From c301616ed23fef433db1a49df332b9d61d3178ad Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 16 Dec 2024 15:53:18 -0800 Subject: [PATCH 099/155] [ci][tests] add gh200 tests (#11244) Signed-off-by: youkaichao --- .buildkite/run-gh200-test.sh | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 .buildkite/run-gh200-test.sh diff --git a/.buildkite/run-gh200-test.sh b/.buildkite/run-gh200-test.sh new file mode 100644 index 0000000000000..d25510c47fe6b --- /dev/null +++ b/.buildkite/run-gh200-test.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +# This script build the GH200 docker image and run the offline inference inside the container. +# It serves a sanity check for compilation and basic model usage. +set -ex + +# Try building the docker image +DOCKER_BUILDKIT=1 docker build . \ + --target test \ + -platform "linux/arm64" \ + -t gh200-test \ + --build-arg max_jobs=66 \ + --build-arg nvcc_threads=2 \ + --build-arg torch_cuda_arch_list="9.0+PTX" \ + --build-arg vllm_fa_cmake_gpu_arches="90-real" + +# Setup cleanup +remove_docker_container() { docker rm -f gh200-test || true; } +trap remove_docker_container EXIT +remove_docker_container + +# Run the image and test offline inference +docker run --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c ' + python3 examples/offline_inference.py +' From 88a412ed3d964de3443c42a6a35108115ee0ad25 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 16 Dec 2024 16:15:22 -0800 Subject: [PATCH 100/155] [torch.compile] fast inductor (#11108) Signed-off-by: youkaichao Co-authored-by: Tyler Michael Smith --- vllm/compilation/backends.py | 213 +++++++++++++++++- vllm/config.py | 415 ++++++++++++++++++++++++++++++++++- vllm/envs.py | 3 + 3 files changed, 624 insertions(+), 7 deletions(-) diff --git a/vllm/compilation/backends.py b/vllm/compilation/backends.py index 4a5dc337d01b8..0c7bbfe599b02 100644 --- a/vllm/compilation/backends.py +++ b/vllm/compilation/backends.py @@ -1,6 +1,10 @@ +import ast import copy import dataclasses +import os +import pprint import time +from collections import defaultdict from contextlib import ExitStack from typing import Any, Callable, Dict, List, Optional, Sequence, Set, Tuple from unittest.mock import patch @@ -21,6 +25,122 @@ logger = init_logger(__name__) +class InductorHashCache: + """ + Disk format: a Python list of tuples, each tuple is + (runtime_shape, graph_index, hash_str) + We use list of tuple for readability. + + In-memory format: a defaultdict of dict, where the key is + runtime_shape, and the value is a dict of graph_index to hash_str. + + The data is essentially `Dict[Optional[int], Dict[int, str]]`, + we don't use json here because json doesn't support int as key. + + TODO: better off-the-shelf solution to serialize the data? + """ + + def __init__(self, cache_dir: str, disabled: bool = False): + self.cache: defaultdict = defaultdict(dict) + self.disabled = disabled + self.cache_dir = cache_dir + self.cache_file_path = os.path.join(cache_dir, + "inductor_hash_cache.py") + if disabled: + return + # set flags so that Inductor and Triton store their cache + # in the cache_dir, then users only need to copy the cache_dir + # to another machine to reuse the cache. + inductor_cache = os.path.join(cache_dir, "inductor_cache") + os.makedirs(inductor_cache, exist_ok=True) + os.environ["TORCHINDUCTOR_CACHE_DIR"] = inductor_cache + triton_cache = os.path.join(cache_dir, "triton_cache") + os.makedirs(triton_cache, exist_ok=True) + os.environ["TRITON_CACHE_DIR"] = triton_cache + if os.path.exists(self.cache_file_path): + with open(self.cache_file_path) as f: + self.deserialize(f.read()) + + def deserialize(self, data: str): + # we use ast.literal_eval to parse the data + # because it is a safe way to parse Python literals. + # do not use eval(), it is unsafe. + list_data = ast.literal_eval(data) + for runtime_shape, graph_index, hash_str in list_data: + self.cache[runtime_shape][graph_index] = hash_str + + def serialize(self) -> str: + data = [] + for runtime_shape, graph_index_to_hash_str in self.cache.items(): + for graph_index, hash_str in graph_index_to_hash_str.items(): + data.append((runtime_shape, graph_index, hash_str)) + printer = pprint.PrettyPrinter(indent=4) + return printer.pformat(data) + + def save_to_file(self): + if self.disabled: + return + with open(self.cache_file_path, "w") as f: + f.write(self.serialize()) + + def __contains__(self, key: Tuple[Optional[int], int]) -> bool: + if self.disabled: + return False + runtime_shape, graph_index = key + return runtime_shape in self.cache and graph_index in self.cache[ + runtime_shape] + + def __getitem__(self, key: Tuple[Optional[int], int]) -> str: + if self.disabled: + raise KeyError("cannot read from disabled cache") + runtime_shape, graph_index = key + return self.cache[runtime_shape][graph_index] + + def __setitem__(self, key: Tuple[Optional[int], int], value: str): + # setitem for disabled cache is fine, because we + # don't actually write to the disk + runtime_shape, graph_index = key + self.cache[runtime_shape][graph_index] = value + + +class AlwaysHitShapeEnv: + """ + Why do we need this class: + + For normal `torch.compile` usage, every compilation will have + one Dynamo bytecode compilation and one Inductor compilation. + The Inductor compilation happens under the context of the + Dynamo bytecode compilation, and that context is used to + determine the dynamic shape information, etc. + + For our use case, we only run Dynamo bytecode compilation once, + and run Inductor compilation multiple times with different shapes + plus a general shape. The compilation for specific shapes happens + outside of the context of the Dynamo bytecode compilation. At that + time, we don't have shape environment to provide to Inductor, and + it will fail the Inductor code cache lookup. + + By providing a dummy shape environment that always hits, we can + make the Inductor code cache lookup always hit, and we can + compile the graph for different shapes as needed. + + The following dummy methods are obtained by trial-and-error + until it works. + """ + + def __init__(self) -> None: + self.guards: List[Any] = [] + + def evaluate_guards_expression(self, *args, **kwargs): + return True + + def get_pruned_guards(self, *args, **kwargs): + return [] + + def produce_guards_expression(self, *args, **kwargs): + return "" + + def wrap_inductor(graph, example_inputs, additional_inductor_config, @@ -55,9 +175,93 @@ def wrap_inductor(graph, # inductor can inplace modify the graph, so we need to copy it # see https://github.com/pytorch/pytorch/issues/138980 graph = copy.deepcopy(graph) - compiled_graph = compile_fx(graph, - example_inputs, - config_patches=current_config) + + cache_data = compilation_config.inductor_hash_cache + if (runtime_shape, graph_index) in cache_data: + # we compiled this graph before + # so we can directly lookup the compiled graph via hash + hash_str = cache_data[(runtime_shape, graph_index)] + if graph_index == 0: + # adds some info logging for the first graph + logger.info( + "Directly lookup the graph for shape %s from the cache", + str(runtime_shape)) # noqa + logger.debug( + "directly lookup the %s-th graph for shape %s via hash %s", + graph_index, str(runtime_shape), hash_str) + from torch._inductor.codecache import FxGraphCache + with patch("torch._inductor.codecache.FxGraphCache._get_shape_env", + lambda *args, **kwargs: AlwaysHitShapeEnv()): + inductor_compiled_graph = FxGraphCache._lookup_graph( + hash_str, example_inputs, True, False) + assert inductor_compiled_graph is not None, ( + "Inductor cache lookup failed. Please remove" + f"the cache file {compilation_config.inductor_hash_cache.cache_file_path} and try again." # noqa + ) + + # Inductor calling convention (function signature): + # f(list) -> tuple + # Dynamo calling convention (function signature): + # f(*args) -> Any + + # need to know if the graph returns a tuple + from torch._inductor.compile_fx import graph_returns_tuple + returns_tuple = graph_returns_tuple(graph) + + # this is the graph we return to Dynamo to run + def compiled_graph(*args): + # convert args to list + list_args = list(args) + graph_output = inductor_compiled_graph(list_args) + # unpack the tuple if needed + if returns_tuple: + return graph_output + else: + return graph_output[0] + else: + # it's the first time we compile this graph + # the assumption is that we don't have nested Inductor compilation. + # compiled_fx_graph_hash will only be called once, and we can hook + # it to get the hash of the compiled graph directly. + from torch._inductor.codecache import compiled_fx_graph_hash + + def hijack_compiled_fx_graph_hash(*args, **kwargs): + out = compiled_fx_graph_hash(*args, **kwargs) + # store the hash in the cache + nonlocal cache_data + cache_data[(runtime_shape, graph_index)] = out[0] + if graph_index == 0: + # adds some info logging for the first graph + logger.info("Cache the graph of shape %s for later use", + str(runtime_shape)) + logger.debug("store the %s-th graph for shape %s via hash %s", + graph_index, str(runtime_shape), out[0]) + return out + + def _check_can_cache(*args, **kwargs): + # no error means it can be cached. + # Inductor refuses to cache the graph outside of Dynamo + # tracing context, and also disables caching for graphs + # with high-order ops. + # For vLLM, in either case, we want to cache the graph. + # see https://github.com/pytorch/pytorch/blob/9f5ebf3fc609105a74eab4ccc24932d6353ff566/torch/_inductor/codecache.py#L1221 # noqa + return + + def _get_shape_env(): + return AlwaysHitShapeEnv() + + with patch(# for hijacking the hash of the compiled graph + "torch._inductor.codecache.compiled_fx_graph_hash", + hijack_compiled_fx_graph_hash), \ + patch(# for providing a dummy shape environment + "torch._inductor.codecache.FxGraphCache._get_shape_env", + _get_shape_env), \ + patch(# for forcing the graph to be cached + "torch._inductor.codecache.FxGraphCache._check_can_cache", + _check_can_cache): + compiled_graph = compile_fx(graph, + example_inputs, + config_patches=current_config) # after compiling the last graph, record the end time if graph_index == num_graphs - 1: @@ -457,6 +661,9 @@ def __call__(self, *args) -> Any: # finished compilations for all required shapes if self.is_last_graph and not self.to_be_compiled_sizes: + + # save the hash of the inductor graph for the next run + self.compilation_config.inductor_hash_cache.save_to_file() end_monitoring_torch_compile(self.vllm_config) if not entry.use_cudagraph: diff --git a/vllm/config.py b/vllm/config.py index fce8011be4015..9cfd08024ea7b 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -3,6 +3,7 @@ import enum import hashlib import json +import os import warnings from contextlib import contextmanager from dataclasses import dataclass, field, replace @@ -162,6 +163,30 @@ class ModelConfig: which allows no processors. """ + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + factors: List[Any] = [] + factors.append(self.model) + factors.append(self.dtype) + factors.append(self.quantization) + factors.append(self.quantization_param_path) + factors.append(self.revision) + factors.append(self.code_revision) + factors.append(self.trust_remote_code) + factors.append(self.rope_scaling) + factors.append(self.rope_theta) + return hashlib.sha256(str(factors).encode()).hexdigest() + def __init__(self, model: str, task: Union[TaskOption, Literal["draft"]], @@ -203,6 +228,8 @@ def __init__(self, self.seed = seed self.revision = revision self.code_revision = code_revision + self.rope_scaling = rope_scaling + self.rope_theta = rope_theta if hf_overrides is None: hf_overrides = {} @@ -832,6 +859,24 @@ class CacheConfig: cpu_offload_gb: Size of the CPU offload buffer in GiB. """ + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + factors: List[Any] = [] + factors.append(self.cache_dtype) + # `cpu_offload_gb` does not use `torch.compile` yet. + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + def __init__( self, block_size: int, @@ -928,6 +973,24 @@ class TokenizerPoolConfig: pool_type: Union[str, Type["BaseTokenizerGroup"]] extra_config: dict + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # this config will not affect the computation graph. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + def __post_init__(self): if self.pool_type not in ("ray", ) and not isinstance( self.pool_type, type): @@ -1010,6 +1073,24 @@ class LoadConfig: default_factory=dict) ignore_patterns: Optional[Union[List[str], str]] = None + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # this config will not affect the computation graph. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + def __post_init__(self): model_loader_extra_config = self.model_loader_extra_config or {} if isinstance(model_loader_extra_config, str): @@ -1073,6 +1154,19 @@ class ParallelConfig: rank: int = 0 + def compute_hash(self): + """ + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + factors: List[Any] = [] + factors.append(self.pipeline_parallel_size) + factors.append(self.tensor_parallel_size) + return hashlib.sha256(str(factors).encode()).hexdigest() + def __post_init__(self) -> None: self.world_size = self.pipeline_parallel_size * \ self.tensor_parallel_size @@ -1209,6 +1303,24 @@ class SchedulerConfig: chunked_prefill_enabled: bool = field(init=False) + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # this config will not affect the computation graph. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + def __post_init__(self) -> None: if self.max_num_batched_tokens is None: if self.enable_chunked_prefill: @@ -1286,6 +1398,25 @@ class DeviceConfig: device: Optional[torch.device] device_type: str + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # the device/platform information will be summarized + # by torch/vllm automatically. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + def __init__(self, device: str = "auto") -> None: if device == "auto": # Automated device type detection @@ -1313,6 +1444,24 @@ class SpeculativeConfig: decoding with top-1 proposals. """ + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # spec decode does not use `torch.compile` yet. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + @staticmethod def maybe_create_spec_config( target_model_config: ModelConfig, @@ -1753,6 +1902,24 @@ class LoRAConfig: long_lora_scaling_factors: Optional[Tuple[float]] = None bias_enabled: bool = False + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # LoRA is not compatible with `torch.compile` . + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + def __post_init__(self): # Setting the maximum rank to 256 should be able to satisfy the vast # majority of applications. @@ -1802,6 +1969,24 @@ class PromptAdapterConfig: max_cpu_prompt_adapters: Optional[int] = None prompt_adapter_dtype: Optional[torch.dtype] = None + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # this config will not affect the computation graph. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + def __post_init__(self): if self.max_prompt_adapters < 1: @@ -1830,6 +2015,24 @@ class MultiModalConfig: for each :class:`~vllm.multimodal.MultiModalPlugin`. """ + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # this config will not affect the computation graph. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + # TODO: Add configs to init vision tower or not. @@ -1869,6 +2072,24 @@ class PoolerConfig: ``math-shepherd-mistral-7b-prm`` model. """ + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # this config will not affect the computation graph. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + @staticmethod def from_json(json_str: str) -> "PoolerConfig": return PoolerConfig(**json.loads(json_str)) @@ -2103,6 +2324,24 @@ class DecodingConfig: # 'outlines' / 'lm-format-enforcer' / 'xgrammar' guided_decoding_backend: str = 'xgrammar' + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # this config will not affect the computation graph. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + def __post_init__(self): valid_guided_backends = ['outlines', 'lm-format-enforcer', 'xgrammar'] backend = self.guided_decoding_backend @@ -2124,6 +2363,24 @@ class ObservabilityConfig: # If set, collects the model execute time for the request. collect_model_execute_time: bool = False + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # this config will not affect the computation graph. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + def __post_init__(self): if not is_otel_available() and self.otlp_traces_endpoint is not None: raise ValueError( @@ -2165,6 +2422,24 @@ class KVTransferConfig(BaseModel): # The KV connector port, used to build distributed connection kv_port: int = 14579 + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + # no factors to consider. + # this config will not affect the computation graph. + factors: List[Any] = [] + hash_str = hashlib.md5(str(factors).encode()).hexdigest() + return hash_str + @classmethod def from_cli(cls, cli_value: str) -> "KVTransferConfig": """Parse the CLI value for the kv cache transfer config.""" @@ -2234,6 +2509,9 @@ class CompilationConfig(BaseModel): - 2: dynamo once. - 3: piecewise compilation. - debug_dump_path: the path to dump the debug information. + - cache_dir: the directory to store the compiled graph, to + accelerate Inductor compilation. By default, it will use + model-related information to generate a cache directory. - backend: the backend for compilation. It needs to be a string. - "" (empty string): use the default backend. - "eager"/"openxla"/...: use the specified backend registered in PyTorch. @@ -2302,12 +2580,10 @@ class CompilationConfig(BaseModel): """ # noqa level: int = 0 debug_dump_path: str = "" + cache_dir: str = "" backend: str = "" custom_ops: List[str] = Field(default_factory=list) - splitting_ops: List[str] = Field(default_factory=lambda: [ - "vllm.unified_attention", - "vllm.unified_attention_with_output", - ]) + splitting_ops: List[str] = Field(default=None) # type: ignore use_inductor: bool = True candidate_compile_sizes: Optional[List[int]] = Field(default=None) @@ -2371,12 +2647,37 @@ def model_post_init(self, __context: Any) -> None: enabled_custom_ops: Counter[str] = PrivateAttr disabled_custom_ops: Counter[str] = PrivateAttr compilation_time: float = PrivateAttr + # should be InductorHashCache, but Pydantic does not support it + inductor_hash_cache: Any = PrivateAttr # Per-model forward context # Mainly used to store attention cls # Map from layer name to the attention cls static_forward_context: Dict[str, Any] = PrivateAttr + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + factors: List[Any] = [] + factors.append(self.level) + factors.append(self.backend) + factors.append(self.custom_ops) + factors.append(self.splitting_ops) + factors.append(self.use_inductor) + factors.append(self.inductor_compile_config) + factors.append(self.inductor_passes) + factors.append(self.pass_config.uuid()) + return hashlib.sha256(str(factors).encode()).hexdigest() + def __repr__(self) -> str: exclude = { "static_forward_context", @@ -2405,6 +2706,27 @@ def model_post_init(self, __context: Any) -> None: count_all = self.custom_ops.count("all") assert count_none + count_all <= 1, "Can only specify 'none' or 'all'" + if self.splitting_ops is None: + if envs.VLLM_USE_V1: + # v1 must split the graph on attention ops + # for piecewise cudagraph + self.splitting_ops = [ + "vllm.unified_attention", + "vllm.unified_attention_with_output", + ] + else: + # v0 can use full graph compilation without splitting, + # splitting is optional. + # right now we still need it. kv cache shape + # will be included in the graph if we don't split + # the graph. + # TODO: hide kv cache in static forward context + # so that inductor does not see it. + self.splitting_ops = [ + "vllm.unified_attention", + "vllm.unified_attention_with_output", + ] + for k, v in self.inductor_passes.items(): if not isinstance(v, str): assert callable(v), ( @@ -2444,6 +2766,30 @@ def init_backend(self, vllm_config: "VllmConfig") -> Union[str, Callable]: # TODO: pass user-specified backend to piecewise compilation # merge with the config use_inductor assert self.level == CompilationLevel.PIECEWISE + + if not self.cache_dir: + # no provided cache dir, generate one based on the known factors + # that affects the compilation. if none of the factors change, + # the cache dir will be the same so that we can reuse the compiled + # graph. + hash_key = vllm_config.compute_hash() + cache_dir = os.path.join( + envs.VLLM_CACHE_ROOT, "torch_compile_cache", hash_key, + f"rank_{vllm_config.parallel_config.rank}") + os.makedirs(cache_dir, exist_ok=True) + self.cache_dir = cache_dir + + disabled = envs.VLLM_DISABLE_COMPILE_CACHE + from vllm.compilation.backends import InductorHashCache + self.inductor_hash_cache: InductorHashCache = InductorHashCache( + self.cache_dir, disabled=disabled) + if disabled: + logger.info("vLLM's torch.compile cache is disabled.") + else: + logger.info( + "Using cache directory: %s for vLLM's torch.compile", + self.cache_dir) + from vllm.compilation.backends import VllmBackend return VllmBackend(vllm_config) @@ -2520,6 +2866,67 @@ class VllmConfig: init=True) # type: ignore instance_id: str = "" + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + factors: List[Any] = [] + # summarize system state + from torch._inductor.codecache import CacheBase + system_factors = CacheBase.get_system() + factors.append(system_factors) + + # summarize pytorch state + from torch._inductor.codecache import torch_key + torch_factors = torch_key() + factors.append(torch_factors) + + # summarize vllm config + vllm_factors: List[Any] = [] + from vllm import __version__ + vllm_factors.append(__version__) + if self.model_config: + vllm_factors.append(self.model_config.compute_hash()) + if self.cache_config: + vllm_factors.append(self.cache_config.compute_hash()) + if self.parallel_config: + vllm_factors.append(self.parallel_config.compute_hash()) + if self.scheduler_config: + vllm_factors.append(self.scheduler_config.compute_hash()) + if self.device_config: + vllm_factors.append(self.device_config.compute_hash()) + if self.load_config: + vllm_factors.append(self.load_config.compute_hash()) + if self.lora_config: + vllm_factors.append(self.lora_config.compute_hash()) + if self.speculative_config: + vllm_factors.append(self.speculative_config.compute_hash()) + if self.decoding_config: + vllm_factors.append(self.decoding_config.compute_hash()) + if self.observability_config: + vllm_factors.append(self.observability_config.compute_hash()) + if self.prompt_adapter_config: + vllm_factors.append(self.prompt_adapter_config.compute_hash()) + if self.quant_config: + pass # should be captured by model_config.quantization + if self.compilation_config: + vllm_factors.append(self.compilation_config.compute_hash()) + if self.kv_transfer_config: + vllm_factors.append(self.kv_transfer_config.compute_hash()) + + factors.append(vllm_factors) + + hash_str = hashlib.md5(str(factors).encode()).hexdigest()[:10] + return hash_str + def pad_for_cudagraph(self, batch_size: int) -> int: # if batch_size > self.compilation_config.max_capture_size, # it should raise an IndexError. diff --git a/vllm/envs.py b/vllm/envs.py index da17b747ea215..18870c1c6b51a 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -71,6 +71,7 @@ VLLM_USE_V1: bool = False VLLM_ENABLE_V1_MULTIPROCESSING: bool = True VLLM_LOG_BATCHSIZE_INTERVAL: float = -1 + VLLM_DISABLE_COMPILE_CACHE: bool = False def get_default_cache_root(): @@ -463,6 +464,8 @@ def get_default_config_root(): lambda: bool(int(os.getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1"))), "VLLM_LOG_BATCHSIZE_INTERVAL": lambda: float(os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")), + "VLLM_DISABLE_COMPILE_CACHE": + lambda: bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0"))), } # end-env-vars-definition From 35bae114a89e03e3dc6a6d2f758378e58938bffa Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 16 Dec 2024 17:22:38 -0800 Subject: [PATCH 101/155] fix gh200 tests on main (#11246) Signed-off-by: youkaichao --- .buildkite/run-gh200-test.sh | 4 ++-- docs/source/serving/deploying_with_docker.rst | 5 +---- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/.buildkite/run-gh200-test.sh b/.buildkite/run-gh200-test.sh index d25510c47fe6b..d06604f96f2b8 100644 --- a/.buildkite/run-gh200-test.sh +++ b/.buildkite/run-gh200-test.sh @@ -6,8 +6,8 @@ set -ex # Try building the docker image DOCKER_BUILDKIT=1 docker build . \ - --target test \ - -platform "linux/arm64" \ + --target vllm-openai \ + --platform "linux/arm64" \ -t gh200-test \ --build-arg max_jobs=66 \ --build-arg nvcc_threads=2 \ diff --git a/docs/source/serving/deploying_with_docker.rst b/docs/source/serving/deploying_with_docker.rst index 11a9f12fd17cd..56f0020a1011a 100644 --- a/docs/source/serving/deploying_with_docker.rst +++ b/docs/source/serving/deploying_with_docker.rst @@ -54,16 +54,13 @@ of PyTorch Nightly and should be considered **experimental**. Using the flag `-- # Example of building on Nvidia GH200 server. (Memory usage: ~12GB, Build time: ~1475s / ~25 min, Image size: 7.26GB) $ DOCKER_BUILDKIT=1 sudo docker build . \ --target vllm-openai \ - -platform "linux/arm64" \ + --platform "linux/arm64" \ -t vllm/vllm-gh200-openai:latest \ --build-arg max_jobs=66 \ --build-arg nvcc_threads=2 \ --build-arg torch_cuda_arch_list="9.0+PTX" \ --build-arg vllm_fa_cmake_gpu_arches="90-real" - - - To run vLLM: .. code-block:: console From 0064f697d318a2ce38342f7c20754cf229311b8b Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Mon, 16 Dec 2024 22:39:58 -0500 Subject: [PATCH 102/155] [CI] Add test case with JSON schema using references + use xgrammar by default with OpenAI parse (#10935) Signed-off-by: mgoin --- tests/entrypoints/conftest.py | 39 +++++++++++++++++++ tests/entrypoints/llm/test_guided_generate.py | 28 +++++++++++++ vllm/entrypoints/openai/protocol.py | 2 +- 3 files changed, 68 insertions(+), 1 deletion(-) diff --git a/tests/entrypoints/conftest.py b/tests/entrypoints/conftest.py index 0f7d15e1d85aa..ef74062ce4b41 100644 --- a/tests/entrypoints/conftest.py +++ b/tests/entrypoints/conftest.py @@ -100,6 +100,45 @@ def sample_complex_json_schema(): } +@pytest.fixture +def sample_definition_json_schema(): + return { + '$defs': { + 'Step': { + 'properties': { + 'explanation': { + 'title': 'Explanation', + 'type': 'string' + }, + 'output': { + 'title': 'Output', + 'type': 'string' + } + }, + 'required': ['explanation', 'output'], + 'title': 'Step', + 'type': 'object' + } + }, + 'properties': { + 'steps': { + 'items': { + '$ref': '#/$defs/Step' + }, + 'title': 'Steps', + 'type': 'array' + }, + 'final_answer': { + 'title': 'Final Answer', + 'type': 'string' + } + }, + 'required': ['steps', 'final_answer'], + 'title': 'MathReasoning', + 'type': 'object' + } + + @pytest.fixture def sample_guided_choice(): return [ diff --git a/tests/entrypoints/llm/test_guided_generate.py b/tests/entrypoints/llm/test_guided_generate.py index de6257cfc551c..ed50ec6bbc9eb 100644 --- a/tests/entrypoints/llm/test_guided_generate.py +++ b/tests/entrypoints/llm/test_guided_generate.py @@ -104,6 +104,34 @@ def test_guided_complex_json_completion(sample_complex_json_schema, llm): schema=sample_complex_json_schema) +@pytest.mark.skip_global_cleanup +def test_guided_definition_json_completion(sample_definition_json_schema, llm): + sampling_params = SamplingParams(temperature=1.0, + max_tokens=1000, + guided_decoding=GuidedDecodingParams( + json=sample_definition_json_schema)) + outputs = llm.generate(prompts=[ + f"Give an example JSON for solving 8x + 7 = -23 " + f"that fits this schema: {sample_definition_json_schema}" + ] * 2, + sampling_params=sampling_params, + use_tqdm=True) + + assert outputs is not None + + for output in outputs: + assert output is not None + assert isinstance(output, RequestOutput) + prompt = output.prompt + + generated_text = output.outputs[0].text + assert generated_text is not None + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + output_json = json.loads(generated_text) + jsonschema.validate(instance=output_json, + schema=sample_definition_json_schema) + + @pytest.mark.skip_global_cleanup def test_guided_choice_completion(sample_guided_choice, llm): sampling_params = SamplingParams( diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py index 6ed7c2e9dcd6b..5a70e0952666b 100644 --- a/vllm/entrypoints/openai/protocol.py +++ b/vllm/entrypoints/openai/protocol.py @@ -387,7 +387,7 @@ def to_sampling_params( assert json_schema is not None self.guided_json = json_schema.json_schema if self.guided_decoding_backend is None: - self.guided_decoding_backend = "lm-format-enforcer" + self.guided_decoding_backend = "xgrammar" guided_decoding = GuidedDecodingParams.from_optional( json=self._get_guided_json_from_tool() or self.guided_json, From 66d4b16724226e9f377551198cc7425c12ddafae Mon Sep 17 00:00:00 2001 From: kYLe Date: Tue, 17 Dec 2024 00:09:58 -0600 Subject: [PATCH 103/155] [Frontend] Add OpenAI API support for input_audio (#11027) Signed-off-by: DarkLight1337 Co-authored-by: DarkLight1337 --- .../serving/openai_compatible_server.md | 10 +- docs/source/usage/multimodal_inputs.rst | 90 ++++++++++++- ...i_chat_completion_client_for_multimodal.py | 34 ++++- tests/entrypoints/openai/test_audio.py | 125 +++++++++++++++++- vllm/entrypoints/chat_utils.py | 65 +++++++-- 5 files changed, 301 insertions(+), 23 deletions(-) diff --git a/docs/source/serving/openai_compatible_server.md b/docs/source/serving/openai_compatible_server.md index 14a5b02d72aa5..1bc8d32d2d161 100644 --- a/docs/source/serving/openai_compatible_server.md +++ b/docs/source/serving/openai_compatible_server.md @@ -34,11 +34,6 @@ We currently support the following OpenAI APIs: - *Note: `suffix` parameter is not supported.* - [Chat Completions API](#chat-api) (`/v1/chat/completions`) - Only applicable to [text generation models](../models/generative_models.rst) (`--task generate`) with a [chat template](#chat-template). - - [Vision](https://platform.openai.com/docs/guides/vision)-related parameters are supported; see [Multimodal Inputs](../usage/multimodal_inputs.rst). - - *Note: `image_url.detail` parameter is not supported.* - - We also support `audio_url` content type for audio files. - - Refer to [vllm.entrypoints.chat_utils](https://github.com/vllm-project/vllm/tree/main/vllm/entrypoints/chat_utils.py) for the exact schema. - - *TODO: Support `input_audio` content type as defined [here](https://github.com/openai/openai-python/blob/v1.52.2/src/openai/types/chat/chat_completion_content_part_input_audio_param.py).* - *Note: `parallel_tool_calls` and `user` parameters are ignored.* - [Embeddings API](#embeddings-api) (`/v1/embeddings`) - Only applicable to [embedding models](../models/pooling_models.rst) (`--task embed`). @@ -209,6 +204,11 @@ The following extra parameters are supported: Refer to [OpenAI's API reference](https://platform.openai.com/docs/api-reference/chat) for more details. +We support both [Vision](https://platform.openai.com/docs/guides/vision)- and +[Audio](https://platform.openai.com/docs/guides/audio?audio-generation-quickstart-example=audio-in)-related parameters; +see our [Multimodal Inputs](../usage/multimodal_inputs.rst) guide for more information. +- *Note: `image_url.detail` parameter is not supported.* + #### Extra parameters The following [sampling parameters (click through to see documentation)](../dev/sampling_params.rst) are supported. diff --git a/docs/source/usage/multimodal_inputs.rst b/docs/source/usage/multimodal_inputs.rst index 1e00f26f9a3ba..680382e457cc5 100644 --- a/docs/source/usage/multimodal_inputs.rst +++ b/docs/source/usage/multimodal_inputs.rst @@ -315,7 +315,95 @@ You can use `these tests `_. +Here is a simple example using Ultravox-v0.3. + +First, launch the OpenAI-compatible server: + +.. code-block:: bash + + vllm serve fixie-ai/ultravox-v0_3 + +Then, you can use the OpenAI client as follows: + +.. code-block:: python + + import base64 + import requests + from openai import OpenAI + from vllm.assets.audio import AudioAsset + + def encode_base64_content_from_url(content_url: str) -> str: + """Encode a content retrieved from a remote url to base64 format.""" + + with requests.get(content_url) as response: + response.raise_for_status() + result = base64.b64encode(response.content).decode('utf-8') + + return result + + openai_api_key = "EMPTY" + openai_api_base = "http://localhost:8000/v1" + + client = OpenAI( + api_key=openai_api_key, + base_url=openai_api_base, + ) + + # Any format supported by librosa is supported + audio_url = AudioAsset("winning_call").url + audio_base64 = encode_base64_content_from_url(audio_url) + + chat_completion_from_base64 = client.chat.completions.create( + messages=[{ + "role": "user", + "content": [ + { + "type": "text", + "text": "What's in this audio?" + }, + { + "type": "input_audio", + "input_audio": { + "data": audio_base64, + "format": "wav" + }, + }, + ], + }], + model=model, + max_completion_tokens=64, + ) + + result = chat_completion_from_base64.choices[0].message.content + print("Chat completion output from input audio:", result) + +Alternatively, you can pass :code:`audio_url`, which is the audio counterpart of :code:`image_url` for image input: + +.. code-block:: python + + chat_completion_from_url = client.chat.completions.create( + messages=[{ + "role": "user", + "content": [ + { + "type": "text", + "text": "What's in this audio?" + }, + { + "type": "audio_url", + "audio_url": { + "url": audio_url + }, + }, + ], + }], + model=model, + max_completion_tokens=64, + ) + + result = chat_completion_from_url.choices[0].message.content + print("Chat completion output from audio url:", result) A full code example can be found in `examples/openai_chat_completion_client_for_multimodal.py `_. diff --git a/examples/openai_chat_completion_client_for_multimodal.py b/examples/openai_chat_completion_client_for_multimodal.py index 0ec4f71dddf93..6a160fd70423f 100644 --- a/examples/openai_chat_completion_client_for_multimodal.py +++ b/examples/openai_chat_completion_client_for_multimodal.py @@ -153,10 +153,37 @@ def run_multi_image() -> None: # Audio input inference def run_audio() -> None: - # Any format supported by librosa is supported audio_url = AudioAsset("winning_call").url + audio_base64 = encode_base64_content_from_url(audio_url) + + # OpenAI-compatible schema (`input_audio`) + chat_completion_from_base64 = client.chat.completions.create( + messages=[{ + "role": + "user", + "content": [ + { + "type": "text", + "text": "What's in this audio?" + }, + { + "type": "input_audio", + "input_audio": { + # Any format supported by librosa is supported + "data": audio_base64, + "format": "wav" + }, + }, + ], + }], + model=model, + max_completion_tokens=64, + ) + + result = chat_completion_from_base64.choices[0].message.content + print("Chat completion output from input audio:", result) - # Use audio url in the payload + # HTTP URL chat_completion_from_url = client.chat.completions.create( messages=[{ "role": @@ -169,6 +196,7 @@ def run_audio() -> None: { "type": "audio_url", "audio_url": { + # Any format supported by librosa is supported "url": audio_url }, }, @@ -181,7 +209,7 @@ def run_audio() -> None: result = chat_completion_from_url.choices[0].message.content print("Chat completion output from audio url:", result) - audio_base64 = encode_base64_content_from_url(audio_url) + # base64 URL chat_completion_from_base64 = client.chat.completions.create( messages=[{ "role": diff --git a/tests/entrypoints/openai/test_audio.py b/tests/entrypoints/openai/test_audio.py index b579dcbb5c402..0a29d77e73abc 100644 --- a/tests/entrypoints/openai/test_audio.py +++ b/tests/entrypoints/openai/test_audio.py @@ -155,6 +155,61 @@ async def test_single_chat_session_audio_base64encoded( assert message.content is not None and len(message.content) >= 0 +@pytest.mark.asyncio +@pytest.mark.parametrize("model_name", [MODEL_NAME]) +@pytest.mark.parametrize("audio_url", TEST_AUDIO_URLS) +async def test_single_chat_session_input_audio( + client: openai.AsyncOpenAI, model_name: str, audio_url: str, + base64_encoded_audio: Dict[str, str]): + messages = [{ + "role": + "user", + "content": [ + { + "type": "input_audio", + "input_audio": { + "data": base64_encoded_audio[audio_url], + "format": "wav" + } + }, + { + "type": "text", + "text": "What's happening in this audio?" + }, + ], + }] + + # test single completion + chat_completion = await client.chat.completions.create( + model=model_name, + messages=messages, + max_completion_tokens=10, + logprobs=True, + top_logprobs=5) + assert len(chat_completion.choices) == 1 + + choice = chat_completion.choices[0] + assert choice.finish_reason == "length" + assert chat_completion.usage == openai.types.CompletionUsage( + completion_tokens=10, prompt_tokens=202, total_tokens=212) + + message = choice.message + message = chat_completion.choices[0].message + assert message.content is not None and len(message.content) >= 10 + assert message.role == "assistant" + messages.append({"role": "assistant", "content": message.content}) + + # test multi-turn dialogue + messages.append({"role": "user", "content": "express your result in json"}) + chat_completion = await client.chat.completions.create( + model=model_name, + messages=messages, + max_completion_tokens=10, + ) + message = chat_completion.choices[0].message + assert message.content is not None and len(message.content) >= 0 + + @pytest.mark.asyncio @pytest.mark.parametrize("model_name", [MODEL_NAME]) @pytest.mark.parametrize("audio_url", TEST_AUDIO_URLS) @@ -212,11 +267,72 @@ async def test_chat_streaming_audio(client: openai.AsyncOpenAI, assert "".join(chunks) == output +@pytest.mark.asyncio +@pytest.mark.parametrize("model_name", [MODEL_NAME]) +@pytest.mark.parametrize("audio_url", TEST_AUDIO_URLS) +async def test_chat_streaming_input_audio(client: openai.AsyncOpenAI, + model_name: str, audio_url: str, + base64_encoded_audio: Dict[str, + str]): + messages = [{ + "role": + "user", + "content": [ + { + "type": "input_audio", + "input_audio": { + "data": base64_encoded_audio[audio_url], + "format": "wav" + } + }, + { + "type": "text", + "text": "What's happening in this audio?" + }, + ], + }] + + # test single completion + chat_completion = await client.chat.completions.create( + model=model_name, + messages=messages, + max_completion_tokens=10, + temperature=0.0, + ) + output = chat_completion.choices[0].message.content + stop_reason = chat_completion.choices[0].finish_reason + + # test streaming + stream = await client.chat.completions.create( + model=model_name, + messages=messages, + max_completion_tokens=10, + temperature=0.0, + stream=True, + ) + chunks: List[str] = [] + finish_reason_count = 0 + async for chunk in stream: + delta = chunk.choices[0].delta + if delta.role: + assert delta.role == "assistant" + if delta.content: + chunks.append(delta.content) + if chunk.choices[0].finish_reason is not None: + finish_reason_count += 1 + # finish reason should only return in last block + assert finish_reason_count == 1 + assert chunk.choices[0].finish_reason == stop_reason + assert delta.content + assert "".join(chunks) == output + + @pytest.mark.asyncio @pytest.mark.parametrize("model_name", [MODEL_NAME]) @pytest.mark.parametrize("audio_url", TEST_AUDIO_URLS) async def test_multi_audio_input(client: openai.AsyncOpenAI, model_name: str, - audio_url: str): + audio_url: str, + base64_encoded_audio: Dict[str, str]): messages = [{ "role": @@ -229,9 +345,10 @@ async def test_multi_audio_input(client: openai.AsyncOpenAI, model_name: str, } }, { - "type": "audio_url", - "audio_url": { - "url": audio_url + "type": "input_audio", + "input_audio": { + "data": base64_encoded_audio[audio_url], + "format": "wav" } }, { diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index aaa5cd759366a..3df08c740d65b 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -13,7 +13,8 @@ # yapf conflicts with isort for this block # yapf: disable from openai.types.chat import (ChatCompletionAssistantMessageParam, - ChatCompletionContentPartImageParam) + ChatCompletionContentPartImageParam, + ChatCompletionContentPartInputAudioParam) from openai.types.chat import ( ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam) from openai.types.chat import (ChatCompletionContentPartRefusalParam, @@ -105,6 +106,7 @@ class CustomChatCompletionContentSimpleVideoParam(TypedDict, total=False): ChatCompletionContentPartParam: TypeAlias = Union[ OpenAIChatCompletionContentPartParam, ChatCompletionContentPartAudioParam, + ChatCompletionContentPartInputAudioParam, ChatCompletionContentPartVideoParam, ChatCompletionContentPartRefusalParam, CustomChatCompletionContentSimpleImageParam, CustomChatCompletionContentSimpleAudioParam, @@ -519,6 +521,10 @@ def parse_image(self, image_url: str) -> None: def parse_audio(self, audio_url: str) -> None: raise NotImplementedError + @abstractmethod + def parse_input_audio(self, input_audio: Dict[str, str]) -> None: + raise NotImplementedError + @abstractmethod def parse_video(self, video_url: str) -> None: raise NotImplementedError @@ -545,6 +551,15 @@ def parse_audio(self, audio_url: str) -> None: placeholder = self._tracker.add("audio", audio) self._add_placeholder(placeholder) + def parse_input_audio(self, input_audio: Dict[str, str]) -> None: + input_audio_data = input_audio.get("data","") + input_audio_format = input_audio.get("format","") + audio_url = f"data:audio/{input_audio_format};base64,{input_audio_data}" + audio = get_and_parse_audio(audio_url) + + placeholder = self._tracker.add("audio", audio) + self._add_placeholder(placeholder) + def parse_video(self, video_url: str) -> None: video = get_and_parse_video(video_url) @@ -574,6 +589,15 @@ def parse_audio(self, audio_url: str) -> None: placeholder = self._tracker.add("audio", audio_coro) self._add_placeholder(placeholder) + def parse_input_audio(self, input_audio: Dict[str, str]) -> None: + input_audio_data = input_audio.get("data","") + input_audio_format = input_audio.get("format","") + audio_url = f"data:audio/{input_audio_format};base64,{input_audio_data}" + audio_coro = async_get_and_parse_audio(audio_url) + + placeholder = self._tracker.add("audio", audio_coro) + self._add_placeholder(placeholder) + def parse_video(self, video_url: str) -> None: video = async_get_and_parse_video(video_url) @@ -667,17 +691,22 @@ def _get_full_multimodal_text_prompt(placeholder_counts: Dict[str, int], _TextParser = partial(cast, ChatCompletionContentPartTextParam) _ImageParser = partial(cast, ChatCompletionContentPartImageParam) _AudioParser = partial(cast, ChatCompletionContentPartAudioParam) +_InputAudioParser = partial(cast, ChatCompletionContentPartInputAudioParam) _RefusalParser = partial(cast, ChatCompletionContentPartRefusalParam) _VideoParser = partial(cast, ChatCompletionContentPartVideoParam) # Define a mapping from part types to their corresponding parsing functions. -MM_PARSER_MAP: Dict[str, Callable[[ChatCompletionContentPartParam], str]] = { +MM_PARSER_MAP: Dict[str, + Callable[[ChatCompletionContentPartParam], + Union[str, Dict[str,str]]]] = { "text": lambda part: _TextParser(part).get("text", ""), "image_url": lambda part: _ImageParser(part).get("image_url", {}).get("url", ""), "audio_url": lambda part: _AudioParser(part).get("audio_url", {}).get("url", ""), + "input_audio": + lambda part: _InputAudioParser(part).get("input_audio", {}), "refusal": lambda part: _RefusalParser(part).get("refusal", ""), "video_url": @@ -686,7 +715,8 @@ def _get_full_multimodal_text_prompt(placeholder_counts: Dict[str, int], def _parse_chat_message_content_mm_part( - part: ChatCompletionContentPartParam) -> Tuple[str, str]: + part: ChatCompletionContentPartParam) -> Tuple[str, + Union[str, Dict[str, str]]]: """ Parses a given multi-modal content part based on its type. @@ -717,6 +747,7 @@ def _parse_chat_message_content_mm_part( return part_type, content # Handle missing 'type' but provided direct URL fields. + # 'type' is required field by pydantic if part_type is None: if part.get("image_url") is not None: image_params = cast(CustomChatCompletionContentSimpleImageParam, @@ -726,6 +757,9 @@ def _parse_chat_message_content_mm_part( audio_params = cast(CustomChatCompletionContentSimpleAudioParam, part) return "audio_url", audio_params.get("audio_url", "") + if part.get("input_audio") is not None: + input_audio_params = cast(Dict[str, str], part) + return "input_audio", input_audio_params if part.get("video_url") is not None: video_params = cast(CustomChatCompletionContentSimpleVideoParam, part) @@ -739,7 +773,7 @@ def _parse_chat_message_content_mm_part( VALID_MESSAGE_CONTENT_MM_PART_TYPES = ("text", "refusal", "image_url", - "audio_url", "video_url") + "audio_url", "input_audio", "video_url") def _parse_chat_message_content_parts( @@ -795,7 +829,7 @@ def _parse_chat_message_content_part( # Handle structured dictionary parts part_type, content = _parse_chat_message_content_mm_part(part) - # if part_type is text/refusal/image_url/audio_url/video_url but + # if part_type is text/refusal/image_url/audio_url/video_url/input_audio but # content is empty, log a warning and skip if part_type in VALID_MESSAGE_CONTENT_MM_PART_TYPES and not content: logger.warning( @@ -804,18 +838,30 @@ def _parse_chat_message_content_part( return None if part_type in ("text", "refusal"): - return {'type': 'text', 'text': content} if wrap_dicts else content + str_content = cast(str, content) + if wrap_dicts: + return {'type': 'text', 'text': str_content} + else: + return str_content if part_type == "image_url": - mm_parser.parse_image(content) + str_content = cast(str, content) + mm_parser.parse_image(str_content) return {'type': 'image'} if wrap_dicts else None if part_type == "audio_url": - mm_parser.parse_audio(content) + str_content = cast(str, content) + mm_parser.parse_audio(str_content) + return {'type': 'audio'} if wrap_dicts else None + + if part_type == "input_audio": + dict_content = cast(Dict[str, str], content) + mm_parser.parse_input_audio(dict_content) return {'type': 'audio'} if wrap_dicts else None if part_type == "video_url": - mm_parser.parse_video(content) + str_content = cast(str, content) + mm_parser.parse_video(str_content) return {'type': 'video'} if wrap_dicts else None raise NotImplementedError(f"Unknown part type: {part_type}") @@ -840,7 +886,6 @@ def _parse_chat_message_content( content = [ ChatCompletionContentPartTextParam(type="text", text=content) ] - result = _parse_chat_message_content_parts( role, content, # type: ignore From 59c9b6ebeba79b2d744eec86734a7e13b03dcab7 Mon Sep 17 00:00:00 2001 From: Roger Wang <136131678+ywang96@users.noreply.github.com> Date: Mon, 16 Dec 2024 22:10:57 -0800 Subject: [PATCH 104/155] [V1][VLM] Proper memory profiling for image language models (#11210) Signed-off-by: Roger Wang Co-authored-by: ywang96 --- vllm/config.py | 8 ++++ vllm/model_executor/models/pixtral.py | 5 ++ vllm/multimodal/registry.py | 23 +++++++-- vllm/v1/core/scheduler.py | 7 ++- vllm/v1/engine/mm_input_mapper.py | 1 + vllm/v1/worker/gpu_model_runner.py | 67 ++++++++++++++++++++++++--- 6 files changed, 98 insertions(+), 13 deletions(-) diff --git a/vllm/config.py b/vllm/config.py index 9cfd08024ea7b..9ecd3e72afa9f 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -1280,6 +1280,14 @@ class SchedulerConfig: is_multimodal_model: bool = False + # FIXME(woosuk & ywang96): Below are placeholder values. We need to + # calculate the actual values from the configurations. + # Multimodal encoder run compute budget, only used in V1 + max_num_encoder_input_tokens = 16384 + + # Multimodal encoder cache size, only used in V1 + encoder_cache_size = 16384 + # Whether to perform preemption by swapping or # recomputation. If not specified, we determine the mode as follows: # We use recomputation by default since it incurs lower overhead than diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index 161d6b41bfa5f..f05ea195e043d 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -245,6 +245,11 @@ def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]: # Do not split, return as tensor of shape [1, fs, hs] return image_embeds.unsqueeze(0) + # If the last split index is the last index in image_tokens, we + # ignore it to avoid empty split tensor + if split_indices[-1] == len(image_tokens): + split_indices = split_indices[:-1] + image_embeds = image_embeds.tensor_split(split_indices.cpu()) return image_embeds diff --git a/vllm/multimodal/registry.py b/vllm/multimodal/registry.py index 03f8814a95356..6cd79d414c978 100644 --- a/vllm/multimodal/registry.py +++ b/vllm/multimodal/registry.py @@ -200,6 +200,23 @@ def register_max_image_tokens( """ return self.register_max_multimodal_tokens("image", max_mm_tokens) + def get_max_tokens_per_item_by_modality( + self, + model_config: "ModelConfig", + ) -> Mapping[str, int]: + """ + Get the maximum number of tokens per data item from each modality + for profiling the memory usage of a model. + + Note: + This is currently directly used only in V1. + """ + + return { + key: plugin.get_max_multimodal_tokens(model_config) + for key, plugin in self._plugins.items() + } + def get_max_tokens_by_modality( self, model_config: "ModelConfig", @@ -216,9 +233,9 @@ def get_max_tokens_by_modality( limits_per_plugin = self._limits_by_model[model_config] return { - key: (limits_per_plugin[key] * - plugin.get_max_multimodal_tokens(model_config)) - for key, plugin in self._plugins.items() + key: limits_per_plugin[key] * max_tokens_per_mm_item + for key, max_tokens_per_mm_item in + self.get_max_tokens_per_item_by_modality(model_config).items() } def get_max_multimodal_tokens(self, model_config: "ModelConfig") -> int: diff --git a/vllm/v1/core/scheduler.py b/vllm/v1/core/scheduler.py index f76364f64033d..178532e477dae 100644 --- a/vllm/v1/core/scheduler.py +++ b/vllm/v1/core/scheduler.py @@ -73,14 +73,13 @@ def __init__( # NOTE(woosuk): Here, "encoder" includes the vision encoder (and # projector if needed). Currently, we assume that the encoder also # has the Transformer architecture (e.g., ViT). - # FIXME(woosuk): Below are placeholder values. We need to calculate the - # actual values from the configurations. - self.max_num_encoder_input_tokens = 16384 + self.max_num_encoder_input_tokens = self.scheduler_config.max_num_encoder_input_tokens #noqa: E501 # NOTE(woosuk): For the models without encoder (e.g., text-only models), # the encoder cache will not be initialized and used, regardless of # the cache size. This is because the memory space for the encoder cache # is preallocated in the profiling run. - self.encoder_cache_manager = EncoderCacheManager(cache_size=16384) + self.encoder_cache_manager = EncoderCacheManager( + cache_size=self.scheduler_config.encoder_cache_size) def schedule(self) -> "SchedulerOutput": # NOTE(woosuk) on the scheduling algorithm: diff --git a/vllm/v1/engine/mm_input_mapper.py b/vllm/v1/engine/mm_input_mapper.py index cca27c2218af7..6cdeba6f3f71e 100644 --- a/vllm/v1/engine/mm_input_mapper.py +++ b/vllm/v1/engine/mm_input_mapper.py @@ -54,6 +54,7 @@ def cache_hit_ratio(self, steps): logger.debug("MMInputMapper: cache_hit_ratio = %.2f ", self.mm_cache_hits / self.mm_cache_total) + # TODO: Support modalities beyond image. def process_inputs( self, mm_data: MultiModalDataDict, diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 67166fb05085c..c6fab5f05fcb3 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -10,15 +10,16 @@ from vllm.config import CompilationLevel, VllmConfig from vllm.distributed.parallel_state import graph_capture from vllm.forward_context import set_forward_context -from vllm.inputs import INPUT_REGISTRY, InputRegistry +from vllm.inputs import INPUT_REGISTRY from vllm.logger import init_logger from vllm.model_executor.model_loader import get_model -from vllm.multimodal import MultiModalKwargs +from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs from vllm.sampling_params import SamplingType from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler, LayerBlockType, cdiv, is_pin_memory_available) from vllm.v1.attention.backends.flash_attn import (FlashAttentionBackend, FlashAttentionMetadata) +from vllm.v1.engine.mm_input_mapper import MMInputMapperClient from vllm.v1.outputs import ModelRunnerOutput from vllm.v1.sample.metadata import SamplingMetadata from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch @@ -35,7 +36,6 @@ def __init__( self, vllm_config: VllmConfig, device: torch.device, - input_registry: InputRegistry = INPUT_REGISTRY, ): self.vllm_config = vllm_config self.model_config = vllm_config.model_config @@ -77,7 +77,12 @@ def __init__( self.hidden_size = model_config.get_hidden_size() # Multi-modal data support - self.input_registry = input_registry + self.input_registry = INPUT_REGISTRY + self.mm_registry = MULTIMODAL_REGISTRY + # NOTE: mm_input_mapper is only used for memory profiling. + self.mm_input_mapper = MMInputMapperClient(self.model_config) + self.max_num_encoder_input_tokens = self.scheduler_config.max_num_encoder_input_tokens # noqa: E501 + self.encoder_cache_size = self.scheduler_config.encoder_cache_size # Lazy initialization # self.model: nn.Module # Set after load_model @@ -599,8 +604,6 @@ def _dummy_run( return hidden_states def profile_run(self) -> None: - # TODO(woosuk): Profile the max memory usage of the encoder and - # the encoder cache. # use an empty tensor instead of `None`` to force Dynamo to pass # it by reference, rather by specializing on the value `None`. # the `dtype` argument does not matter, and we use `float32` as @@ -612,6 +615,57 @@ def profile_run(self) -> None: torch.tensor([], dtype=torch.float32, device=self.device) for _ in range(self.num_attn_layers) ] + + # Profile with multimodal encoder & encoder cache. + # TODO (ywang96): generalize this beyond image modality since + # mm_input_mapper only supports image inputs. + if self.is_multimodal_model: + + # Create dummy batch of multimodal inputs. + dummy_request_data = self.input_registry.dummy_data_for_profiling( + model_config=self.model_config, + seq_len=self.max_num_tokens, + mm_registry=self.mm_registry, + ) + dummy_mm_data = dummy_request_data.multi_modal_data + dummy_mm_kwargs, _ = self.mm_input_mapper.process_inputs( + mm_data=dummy_mm_data, + mm_hashes=None, + mm_processor_kwargs=None, + precomputed_mm_inputs=None) + + # NOTE: Currently model is profiled with a single non-text + # modality even when it supports multiple. + max_tokens_per_mm_item = max( + self.mm_registry.get_max_tokens_per_item_by_modality( + self.model_config).values()) + + max_num_mm_items = min( + self.max_num_encoder_input_tokens, + self.encoder_cache_size) // max_tokens_per_mm_item + + # Dummy data definition in V0 may contain multiple multimodal items + # (e.g, multiple images) for a single request, therefore here we + # always replicate first item by max_num_mm_items times since in V1 + # they are scheduled to be processed separately. + batched_dummy_mm_inputs = MultiModalKwargs.batch( + [dummy_mm_kwargs[0]] * max_num_mm_items) + batched_dummy_mm_inputs = MultiModalKwargs.as_kwargs( + batched_dummy_mm_inputs, device=self.device) + + # Run multimodal encoder. + dummy_encoder_outputs = self.model.get_multimodal_embeddings( + **batched_dummy_mm_inputs) + assert len(dummy_encoder_outputs) == max_num_mm_items, ( + "Expected dimension 0 of encoder outputs to match the number " + f"of multimodal data items: {max_num_mm_items}, got " + f"{len(dummy_encoder_outputs)=} instead. This is most likely " + "due to the 'get_multimodal_embeddings' method of the model " + "not implemented correctly.") + + # Cache the dummy encoder outputs. + self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs)) + # Trigger compilation for general shape. hidden_states = self._dummy_run(self.model, self.max_num_tokens, dummy_kv_caches) @@ -620,6 +674,7 @@ def profile_run(self) -> None: # TODO(woosuk): Consider the memory usage of the sampler. torch.cuda.synchronize() del hidden_states, logits + self.encoder_cache.clear() gc.collect() def capture_model(self) -> None: From e88db68cf5712956f36e77c288699592327b15bd Mon Sep 17 00:00:00 2001 From: wangxiyuan Date: Tue, 17 Dec 2024 14:11:06 +0800 Subject: [PATCH 105/155] [Platform] platform agnostic for EngineArgs initialization (#11225) Signed-off-by: wangxiyuan --- vllm/engine/arg_utils.py | 8 ++------ vllm/platforms/cpu.py | 3 +++ vllm/platforms/cuda.py | 4 ++++ vllm/platforms/hpu.py | 6 ++++++ vllm/platforms/neuron.py | 6 ++++++ vllm/platforms/openvino.py | 3 +++ vllm/platforms/rocm.py | 4 ++++ vllm/platforms/tpu.py | 5 +++++ vllm/platforms/xpu.py | 4 ++++ 9 files changed, 37 insertions(+), 6 deletions(-) diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 06b8542779dc0..f6d276fe7c0c8 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -112,9 +112,7 @@ class EngineArgs: pipeline_parallel_size: int = 1 tensor_parallel_size: int = 1 max_parallel_loading_workers: Optional[int] = None - # NOTE(kzawora): default block size for Gaudi should be 128 - # smaller sizes still work, but very inefficiently - block_size: int = 16 if not current_platform.is_hpu() else 128 + block_size: Optional[int] = None enable_prefix_caching: Optional[bool] = None disable_sliding_window: bool = False use_v2_block_manager: bool = True @@ -1036,9 +1034,7 @@ def create_engine_config(self, self.enable_prefix_caching = False cache_config = CacheConfig( - # neuron needs block_size = max_model_len - block_size=self.block_size if self.device != "neuron" else - (self.max_model_len if self.max_model_len is not None else 0), + block_size=self.block_size, gpu_memory_utilization=self.gpu_memory_utilization, swap_space=self.swap_space, cache_dtype=self.kv_cache_dtype, diff --git a/vllm/platforms/cpu.py b/vllm/platforms/cpu.py index aad8755d9fcd8..d95a2b4cd5565 100644 --- a/vllm/platforms/cpu.py +++ b/vllm/platforms/cpu.py @@ -60,6 +60,9 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None: cache_config = vllm_config.cache_config + if cache_config and cache_config.block_size is None: + cache_config.block_size = 16 + kv_cache_space = envs.VLLM_CPU_KVCACHE_SPACE if kv_cache_space >= 0: diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index ae1fd6d5ce068..3c5350b778345 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -137,6 +137,10 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None: else: parallel_config.worker_cls = "vllm.worker.worker.Worker" + cache_config = vllm_config.cache_config + if cache_config and cache_config.block_size is None: + cache_config.block_size = 16 + # NVML utils # Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`, diff --git a/vllm/platforms/hpu.py b/vllm/platforms/hpu.py index 2b947d280f9f8..0a44f2b74163a 100644 --- a/vllm/platforms/hpu.py +++ b/vllm/platforms/hpu.py @@ -48,6 +48,12 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None: if parallel_config.worker_cls == "auto": parallel_config.worker_cls = "vllm.worker.hpu_worker.HPUWorker" + # NOTE(kzawora): default block size for Gaudi should be 128 + # smaller sizes still work, but very inefficiently + cache_config = vllm_config.cache_config + if cache_config and cache_config.block_size is None: + cache_config.block_size = 128 + @classmethod def is_pin_memory_available(cls): logger.warning("Pin memory is not supported on HPU.") diff --git a/vllm/platforms/neuron.py b/vllm/platforms/neuron.py index 86113523385f6..a4bbbd27c8a89 100644 --- a/vllm/platforms/neuron.py +++ b/vllm/platforms/neuron.py @@ -33,6 +33,12 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None: parallel_config.worker_cls = \ "vllm.worker.neuron_worker.NeuronWorker" + cache_config = vllm_config.cache_config + if cache_config: + # neuron needs block_size = max_model_len + vllm_config.cache_config.block_size = \ + vllm_config.model_config.max_model_len + @classmethod def is_pin_memory_available(cls) -> bool: logger.warning("Pin memory is not supported on Neuron.") diff --git a/vllm/platforms/openvino.py b/vllm/platforms/openvino.py index ccd94e8adb3b1..16eb8dc81efc2 100644 --- a/vllm/platforms/openvino.py +++ b/vllm/platforms/openvino.py @@ -87,6 +87,9 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None: # check and update cache config ov_core = ov.Core() cache_config = vllm_config.cache_config + if cache_config and cache_config.block_size is None: + cache_config.block_size = 16 + if envs.VLLM_OPENVINO_CPU_KV_CACHE_PRECISION == "u8": if not OpenVinoPlatform.is_openvino_cpu(): logger.info("VLLM_OPENVINO_CPU_KV_CACHE_PRECISION is" diff --git a/vllm/platforms/rocm.py b/vllm/platforms/rocm.py index 0133f26a0b1bc..7778b565372cb 100644 --- a/vllm/platforms/rocm.py +++ b/vllm/platforms/rocm.py @@ -84,6 +84,10 @@ def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool: @classmethod def check_and_update_config(cls, vllm_config: VllmConfig) -> None: + cache_config = vllm_config.cache_config + if cache_config and cache_config.block_size is None: + cache_config.block_size = 16 + parallel_config = vllm_config.parallel_config scheduler_config = vllm_config.scheduler_config if parallel_config.worker_cls == "auto": diff --git a/vllm/platforms/tpu.py b/vllm/platforms/tpu.py index 10d874349f36b..77f5c8401424b 100644 --- a/vllm/platforms/tpu.py +++ b/vllm/platforms/tpu.py @@ -46,6 +46,11 @@ def inference_mode(cls): @classmethod def check_and_update_config(cls, vllm_config: VllmConfig) -> None: from vllm.config import CompilationLevel + + cache_config = vllm_config.cache_config + if cache_config and cache_config.block_size is None: + cache_config.block_size = 16 + compilation_config = vllm_config.compilation_config if compilation_config.level == CompilationLevel.NO_COMPILATION: # TPU does not support NO_COMPILATION diff --git a/vllm/platforms/xpu.py b/vllm/platforms/xpu.py index c20190e789d7e..78e17c2afec65 100644 --- a/vllm/platforms/xpu.py +++ b/vllm/platforms/xpu.py @@ -51,6 +51,10 @@ def inference_mode(): @classmethod def check_and_update_config(cls, vllm_config: VllmConfig) -> None: + cache_config = vllm_config.cache_config + if cache_config and cache_config.block_size is None: + cache_config.block_size = 16 + # check and update model config model_config = vllm_config.model_config if model_config.dtype == torch.bfloat16: From 2bfdbf2a36256bb08547cea3d4ef83b5d27c4b04 Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Tue, 17 Dec 2024 01:11:33 -0500 Subject: [PATCH 106/155] [V1][Core] Use weakref.finalize instead of atexit (#11242) Signed-off-by: Tyler Michael Smith --- vllm/v1/engine/core_client.py | 13 ++----------- vllm/v1/executor/multiproc_executor.py | 10 +++------- 2 files changed, 5 insertions(+), 18 deletions(-) diff --git a/vllm/v1/engine/core_client.py b/vllm/v1/engine/core_client.py index ff25a9b2e9cac..d56fcbdb1e7c4 100644 --- a/vllm/v1/engine/core_client.py +++ b/vllm/v1/engine/core_client.py @@ -1,5 +1,5 @@ -import atexit import os +import weakref from typing import List, Optional import msgspec @@ -165,15 +165,9 @@ def __init__( ready_path=ready_path, # type: ignore[misc] **kwargs, ) - atexit.register(self.shutdown) + self._finalizer = weakref.finalize(self, self.shutdown) def shutdown(self): - # During final garbage collection in process shutdown, atexit may be - # None. - if atexit: - # in case shutdown gets called via __del__ first - atexit.unregister(self.shutdown) - # Shut down the zmq context. self.ctx.destroy(linger=0) @@ -197,9 +191,6 @@ def shutdown(self): os.remove(socket_file) self.proc_handle = None - def __del__(self): - self.shutdown() - class SyncMPClient(MPClient): """Synchronous client for multi-proc EngineCore.""" diff --git a/vllm/v1/executor/multiproc_executor.py b/vllm/v1/executor/multiproc_executor.py index 17441dacdc5cf..128101aa6956d 100644 --- a/vllm/v1/executor/multiproc_executor.py +++ b/vllm/v1/executor/multiproc_executor.py @@ -1,9 +1,9 @@ -import atexit import os import pickle import signal import sys import time +import weakref from dataclasses import dataclass from enum import Enum, auto from multiprocessing.process import BaseProcess @@ -37,7 +37,7 @@ class MultiprocExecutor(Executor): def __init__(self, vllm_config: VllmConfig) -> None: # Call self.shutdown at exit to clean up # and ensure workers will be terminated. - atexit.register(self.shutdown) + self._finalizer = weakref.finalize(self, self.shutdown) self.vllm_config = vllm_config self.parallel_config = vllm_config.parallel_config @@ -195,14 +195,10 @@ def _cleanup_sockets(self): os.remove(socket_path) def shutdown(self): - if atexit: - # in case shutdown was called explicitly, we don't need to call it - # again - atexit.unregister(self.shutdown) """Properly shut down the executor and its workers""" if getattr(self, 'shutting_down', False): self.shutting_down = True - for w in self.workers: #TODO: not sure if needed + for w in self.workers: w.worker_response_mq = None self._ensure_worker_termination() From 02222a0256f60319f5bcd56d1d036a943d6334f8 Mon Sep 17 00:00:00 2001 From: Roger Wang <136131678+ywang96@users.noreply.github.com> Date: Mon, 16 Dec 2024 22:57:02 -0800 Subject: [PATCH 107/155] [Misc] Kernel Benchmark for `RMSNorm` (#11241) Signed-off-by: Roger Wang Co-authored-by: Xiaoyu Zhang --- benchmarks/kernels/benchmark_rmsnorm.py | 262 ++++++++++++++++++++++++ 1 file changed, 262 insertions(+) create mode 100644 benchmarks/kernels/benchmark_rmsnorm.py diff --git a/benchmarks/kernels/benchmark_rmsnorm.py b/benchmarks/kernels/benchmark_rmsnorm.py new file mode 100644 index 0000000000000..baa5de0fff1bd --- /dev/null +++ b/benchmarks/kernels/benchmark_rmsnorm.py @@ -0,0 +1,262 @@ +import itertools +from typing import Optional, Tuple, Union + +import torch +import triton +from flashinfer.norm import fused_add_rmsnorm, rmsnorm +from torch import nn + +from vllm import _custom_ops as vllm_ops + + +class HuggingFaceRMSNorm(nn.Module): + + def __init__(self, hidden_size: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward( + self, + x: torch.Tensor, + residual: Optional[torch.Tensor] = None, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + orig_dtype = x.dtype + x = x.to(torch.float32) + if residual is not None: + x = x + residual.to(torch.float32) + residual = x.to(orig_dtype) + + variance = x.pow(2).mean(dim=-1, keepdim=True) + x = x * torch.rsqrt(variance + self.variance_epsilon) + x = x.to(orig_dtype) * self.weight + if residual is None: + return x + else: + return x, residual + + +def rmsnorm_naive( + x: torch.Tensor, + weight: torch.Tensor, + residual: Optional[torch.Tensor] = None, + eps: float = 1e-6, +): + naive_norm = HuggingFaceRMSNorm(x.shape[-1], eps=eps) + naive_norm.weight = nn.Parameter(weight) + naive_norm = naive_norm.to(x.device) + + orig_shape = x.shape + x = x.view(-1, x.shape[-1]) + if residual is not None: + residual = residual.view(-1, residual.shape[-1]) + + output = naive_norm(x, residual) + + if isinstance(output, tuple): + output = (output[0].view(orig_shape), output[1].view(orig_shape)) + else: + output = output.view(orig_shape) + return output + + +def rmsnorm_flashinfer( + x: torch.Tensor, + weight: torch.Tensor, + residual: Optional[torch.Tensor] = None, + eps: float = 1e-6, +): + orig_shape = x.shape + x = x.view(-1, x.shape[-1]) + if residual is not None: + residual = residual.view(-1, residual.shape[-1]) + + if residual is not None: + fused_add_rmsnorm(x, residual, weight, eps) + output = (x, residual) + else: + output = rmsnorm(x, weight, eps) + + if isinstance(output, tuple): + output = (output[0].view(orig_shape), output[1].view(orig_shape)) + else: + output = output.view(orig_shape) + return output + + +def rmsnorm_vllm( + x: torch.Tensor, + weight: torch.Tensor, + residual: Optional[torch.Tensor] = None, + eps: float = 1e-6, +): + orig_shape = x.shape + x = x.view(-1, x.shape[-1]) + if residual is not None: + residual = residual.view(-1, residual.shape[-1]) + + if residual is not None: + vllm_ops.fused_add_rms_norm(x, residual, weight, eps) + output = (x, residual) + else: + out = torch.empty_like(x) + vllm_ops.rms_norm(out, x, weight, eps) + output = out + + if isinstance(output, tuple): + output = (output[0].view(orig_shape), output[1].view(orig_shape)) + else: + output = output.view(orig_shape) + return output + + +def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True): + dtype = torch.bfloat16 + x = torch.randn(batch_size, + seq_len, + hidden_size, + dtype=dtype, + device="cuda") + weight = torch.ones(hidden_size, dtype=dtype, device="cuda") + residual = torch.randn_like(x) if use_residual else None + + output_naive = rmsnorm_naive( + x.clone(), weight, + residual.clone() if residual is not None else None) + output_flashinfer = rmsnorm_flashinfer( + x.clone(), weight, + residual.clone() if residual is not None else None) + output_vllm = rmsnorm_vllm( + x.clone(), weight, + residual.clone() if residual is not None else None) + + if use_residual: + output_naive = output_naive[0] + output_flashinfer = output_flashinfer[0] + output_vllm = output_vllm[0] + + print(f"Naive output={output_naive}") + print(f"FlashInfer output={output_flashinfer}") + print(f"VLLM output={output_vllm}") + + if torch.allclose(output_naive, output_flashinfer, atol=1e-2, + rtol=1e-2) and torch.allclose( + output_naive, output_vllm, atol=1e-2, rtol=1e-2): + print("✅ All implementations match") + else: + print("❌ Implementations differ") + + +batch_size_range = [2**i for i in range(0, 7, 2)] +seq_length_range = [2**i for i in range(6, 11, 1)] +head_num_range = [32, 48] +configs = list( + itertools.product(head_num_range, batch_size_range, seq_length_range)) + + +def get_benchmark(use_residual): + + @triton.testing.perf_report( + triton.testing.Benchmark( + x_names=["head_num", "batch_size", "seq_len"], + x_vals=[list(_) for _ in configs], + line_arg="provider", + line_vals=["huggingface", "flashinfer", "vllm"], + line_names=["HuggingFace", "FlashInfer", "vLLM"], + styles=[("blue", "-"), ("green", "-"), ("red", "-")], + ylabel="us", + plot_name= + f"rmsnorm-perf-{'with' if use_residual else 'without'}-residual", + args={}, + )) + def benchmark(head_num, batch_size, seq_len, provider): + dtype = torch.bfloat16 + hidden_size = head_num * 128 # assuming head_dim = 128 + + x = torch.randn(batch_size, + seq_len, + hidden_size, + dtype=dtype, + device="cuda") + weight = torch.ones(hidden_size, dtype=dtype, device="cuda") + residual = torch.randn_like(x) if use_residual else None + + quantiles = [0.5, 0.2, 0.8] + + if provider == "huggingface": + ms, min_ms, max_ms = triton.testing.do_bench( + lambda: rmsnorm_naive( + x.clone(), + weight, + residual.clone() if residual is not None else None, + ), + quantiles=quantiles, + ) + elif provider == "flashinfer": + ms, min_ms, max_ms = triton.testing.do_bench( + lambda: rmsnorm_flashinfer( + x.clone(), + weight, + residual.clone() if residual is not None else None, + ), + quantiles=quantiles, + ) + else: + ms, min_ms, max_ms = triton.testing.do_bench( + lambda: rmsnorm_vllm( + x.clone(), + weight, + residual.clone() if residual is not None else None, + ), + quantiles=quantiles, + ) + + return 1000 * ms, 1000 * max_ms, 1000 * min_ms + + return benchmark + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument( + "--batch-size", + type=int, + default=4, + help="Batch size", + ) + parser.add_argument( + "--seq-len", + type=int, + default=128, + help="Sequence length", + ) + parser.add_argument( + "--hidden-size", + type=int, + default=4096, + help="Hidden size (2nd dimension) of the sequence", + ) + parser.add_argument("--use-residual", + action="store_true", + help="Whether to use residual connection") + parser.add_argument( + "--save-path", + type=str, + default="./configs/rmsnorm/", + help="Path to save rmsnorm benchmark results", + ) + + args = parser.parse_args() + + # Run correctness test + calculate_diff(batch_size=args.batch_size, + seq_len=args.seq_len, + hidden_size=args.hidden_size, + use_residual=args.use_residual) + + # Get the benchmark function with proper use_residual setting + benchmark = get_benchmark(args.use_residual) + # Run performance benchmark + benchmark.run(print_data=True, save_path=args.save_path) From f9ecbb18bf03338a4272c933a49a87021363b048 Mon Sep 17 00:00:00 2001 From: Isotr0py Date: Tue, 17 Dec 2024 16:37:04 +0800 Subject: [PATCH 108/155] [Misc] Allow passing logits_soft_cap for xformers backend (#11252) Signed-off-by: Isotr0py <2037008807@qq.com> --- vllm/attention/backends/xformers.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/vllm/attention/backends/xformers.py b/vllm/attention/backends/xformers.py index e2e989efb020c..3e59b3603d2c6 100644 --- a/vllm/attention/backends/xformers.py +++ b/vllm/attention/backends/xformers.py @@ -17,9 +17,7 @@ is_all_cross_attn_metadata_set, is_all_encoder_attn_metadata_set) from vllm.attention.ops.paged_attn import (PagedAttention, PagedAttentionMetadata) -from vllm.logger import init_logger - -logger = init_logger(__name__) +from vllm.utils import print_warning_once class XFormersBackend(AttentionBackend): @@ -386,8 +384,8 @@ def __init__( raise ValueError( "XFormers does not support block-sparse attention.") if logits_soft_cap is not None: - raise ValueError( - "XFormers does not support attention logits soft capping.") + print_warning_once("XFormers does not support logits soft cap. " + "Outputs may be slightly off.") self.num_heads = num_heads self.head_size = head_size self.scale = float(scale) From 2d1b9baa8f57fc59912c7bcd07fd630fb9d72c9d Mon Sep 17 00:00:00 2001 From: Joe Runde Date: Tue, 17 Dec 2024 13:26:32 -0700 Subject: [PATCH 109/155] [Bugfix] Fix request cancellation without polling (#11190) --- tests/entrypoints/openai/test_basic.py | 51 ++++++++++++++++ tests/test_utils.py | 6 +- tests/utils.py | 11 ++-- vllm/engine/async_llm_engine.py | 46 +++++++++------ vllm/entrypoints/api_server.py | 11 ++-- vllm/entrypoints/openai/api_server.py | 8 +++ vllm/entrypoints/openai/serving_chat.py | 5 -- vllm/entrypoints/openai/serving_completion.py | 3 +- vllm/entrypoints/openai/serving_embedding.py | 5 +- vllm/entrypoints/openai/serving_score.py | 5 +- vllm/entrypoints/utils.py | 57 ++++++++++++++++++ vllm/utils.py | 59 ++----------------- 12 files changed, 164 insertions(+), 103 deletions(-) create mode 100644 vllm/entrypoints/utils.py diff --git a/tests/entrypoints/openai/test_basic.py b/tests/entrypoints/openai/test_basic.py index 4616f363cc04a..547c1fd020928 100644 --- a/tests/entrypoints/openai/test_basic.py +++ b/tests/entrypoints/openai/test_basic.py @@ -1,6 +1,8 @@ +import asyncio from http import HTTPStatus from typing import List +import openai import pytest import pytest_asyncio import requests @@ -103,3 +105,52 @@ async def test_check_health(server: RemoteOpenAIServer): response = requests.get(server.url_for("health")) assert response.status_code == HTTPStatus.OK + + +@pytest.mark.parametrize( + "server_args", + [ + pytest.param(["--max-model-len", "10100"], + id="default-frontend-multiprocessing"), + pytest.param( + ["--disable-frontend-multiprocessing", "--max-model-len", "10100"], + id="disable-frontend-multiprocessing") + ], + indirect=True, +) +@pytest.mark.asyncio +async def test_request_cancellation(server: RemoteOpenAIServer): + # clunky test: send an ungodly amount of load in with short timeouts + # then ensure that it still responds quickly afterwards + + chat_input = [{"role": "user", "content": "Write a long story"}] + client = server.get_async_client(timeout=0.5) + tasks = [] + # Request about 2 million tokens + for _ in range(200): + task = asyncio.create_task( + client.chat.completions.create(messages=chat_input, + model=MODEL_NAME, + max_tokens=10000, + extra_body={"min_tokens": 10000})) + tasks.append(task) + + done, pending = await asyncio.wait(tasks, + return_when=asyncio.ALL_COMPLETED) + + # Make sure all requests were sent to the server and timed out + # (We don't want to hide other errors like 400s that would invalidate this + # test) + assert len(pending) == 0 + for d in done: + with pytest.raises(openai.APITimeoutError): + d.result() + + # If the server had not cancelled all the other requests, then it would not + # be able to respond to this one within the timeout + client = server.get_async_client(timeout=5) + response = await client.chat.completions.create(messages=chat_input, + model=MODEL_NAME, + max_tokens=10) + + assert len(response.choices) == 1 diff --git a/tests/test_utils.py b/tests/test_utils.py index 0bc9e5bc32a46..32a6b0aed66aa 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,7 +1,6 @@ import asyncio import os import socket -from functools import partial from typing import AsyncIterator, Tuple import pytest @@ -26,10 +25,7 @@ async def mock_async_iterator(idx: int): print(f"iterator {idx} cancelled") iterators = [mock_async_iterator(i) for i in range(3)] - merged_iterator = merge_async_iterators(*iterators, - is_cancelled=partial(asyncio.sleep, - 0, - result=False)) + merged_iterator = merge_async_iterators(*iterators) async def stream_output(generator: AsyncIterator[Tuple[int, str]]): async for idx, output in generator: diff --git a/tests/utils.py b/tests/utils.py index afeb708f3bcdc..bf3d88194e4ca 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -163,12 +163,11 @@ def get_client(self): api_key=self.DUMMY_API_KEY, ) - def get_async_client(self): - return openai.AsyncOpenAI( - base_url=self.url_for("v1"), - api_key=self.DUMMY_API_KEY, - max_retries=0, - ) + def get_async_client(self, **kwargs): + return openai.AsyncOpenAI(base_url=self.url_for("v1"), + api_key=self.DUMMY_API_KEY, + max_retries=0, + **kwargs) def _test_completion( diff --git a/vllm/engine/async_llm_engine.py b/vllm/engine/async_llm_engine.py index 32396fd10188d..f50e20cf70323 100644 --- a/vllm/engine/async_llm_engine.py +++ b/vllm/engine/async_llm_engine.py @@ -1065,16 +1065,20 @@ async def generate( >>> # Process and return the final output >>> ... """ - async for output in await self.add_request( - request_id, - prompt, - sampling_params, - lora_request=lora_request, - trace_headers=trace_headers, - prompt_adapter_request=prompt_adapter_request, - priority=priority, - ): - yield LLMEngine.validate_output(output, RequestOutput) + try: + async for output in await self.add_request( + request_id, + prompt, + sampling_params, + lora_request=lora_request, + trace_headers=trace_headers, + prompt_adapter_request=prompt_adapter_request, + priority=priority, + ): + yield LLMEngine.validate_output(output, RequestOutput) + except asyncio.CancelledError: + await self.abort(request_id) + raise async def encode( self, @@ -1147,15 +1151,19 @@ async def encode( >>> # Process and return the final output >>> ... """ - async for output in await self.add_request( - request_id, - prompt, - pooling_params, - lora_request=lora_request, - trace_headers=trace_headers, - priority=priority, - ): - yield LLMEngine.validate_output(output, PoolingRequestOutput) + try: + async for output in await self.add_request( + request_id, + prompt, + pooling_params, + lora_request=lora_request, + trace_headers=trace_headers, + priority=priority, + ): + yield LLMEngine.validate_output(output, PoolingRequestOutput) + except asyncio.CancelledError: + await self.abort(request_id) + raise async def abort(self, request_id: str) -> None: """Abort a request. diff --git a/vllm/entrypoints/api_server.py b/vllm/entrypoints/api_server.py index ea3c93f733038..95da1c6e7b9bf 100644 --- a/vllm/entrypoints/api_server.py +++ b/vllm/entrypoints/api_server.py @@ -17,11 +17,11 @@ from vllm.engine.arg_utils import AsyncEngineArgs from vllm.engine.async_llm_engine import AsyncLLMEngine from vllm.entrypoints.launcher import serve_http +from vllm.entrypoints.utils import with_cancellation from vllm.logger import init_logger from vllm.sampling_params import SamplingParams from vllm.usage.usage_lib import UsageContext -from vllm.utils import (FlexibleArgumentParser, iterate_with_cancellation, - random_uuid) +from vllm.utils import FlexibleArgumentParser, random_uuid from vllm.version import __version__ as VLLM_VERSION logger = init_logger("vllm.entrypoints.api_server") @@ -47,6 +47,11 @@ async def generate(request: Request) -> Response: - other fields: the sampling parameters (See `SamplingParams` for details). """ request_dict = await request.json() + return await _generate(request_dict, raw_request=request) + + +@with_cancellation +async def _generate(request_dict: dict, raw_request: Request) -> Response: prompt = request_dict.pop("prompt") stream = request_dict.pop("stream", False) sampling_params = SamplingParams(**request_dict) @@ -54,8 +59,6 @@ async def generate(request: Request) -> Response: assert engine is not None results_generator = engine.generate(prompt, sampling_params, request_id) - results_generator = iterate_with_cancellation( - results_generator, is_cancelled=request.is_disconnected) # Streaming case async def stream_results() -> AsyncGenerator[bytes, None]: diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py index 14e3a34ce141c..00e2d1a56f160 100644 --- a/vllm/entrypoints/openai/api_server.py +++ b/vllm/entrypoints/openai/api_server.py @@ -59,6 +59,7 @@ from vllm.entrypoints.openai.serving_tokenization import ( OpenAIServingTokenization) from vllm.entrypoints.openai.tool_parsers import ToolParserManager +from vllm.entrypoints.utils import with_cancellation from vllm.logger import init_logger from vllm.usage.usage_lib import UsageContext from vllm.utils import (FlexibleArgumentParser, get_open_zmq_ipc_path, @@ -311,6 +312,7 @@ async def health(raw_request: Request) -> Response: @router.post("/tokenize") +@with_cancellation async def tokenize(request: TokenizeRequest, raw_request: Request): handler = tokenization(raw_request) @@ -325,6 +327,7 @@ async def tokenize(request: TokenizeRequest, raw_request: Request): @router.post("/detokenize") +@with_cancellation async def detokenize(request: DetokenizeRequest, raw_request: Request): handler = tokenization(raw_request) @@ -353,6 +356,7 @@ async def show_version(): @router.post("/v1/chat/completions") +@with_cancellation async def create_chat_completion(request: ChatCompletionRequest, raw_request: Request): handler = chat(raw_request) @@ -373,6 +377,7 @@ async def create_chat_completion(request: ChatCompletionRequest, @router.post("/v1/completions") +@with_cancellation async def create_completion(request: CompletionRequest, raw_request: Request): handler = completion(raw_request) if handler is None: @@ -390,6 +395,7 @@ async def create_completion(request: CompletionRequest, raw_request: Request): @router.post("/v1/embeddings") +@with_cancellation async def create_embedding(request: EmbeddingRequest, raw_request: Request): handler = embedding(raw_request) if handler is None: @@ -407,6 +413,7 @@ async def create_embedding(request: EmbeddingRequest, raw_request: Request): @router.post("/score") +@with_cancellation async def create_score(request: ScoreRequest, raw_request: Request): handler = score(raw_request) if handler is None: @@ -424,6 +431,7 @@ async def create_score(request: ScoreRequest, raw_request: Request): @router.post("/v1/score") +@with_cancellation async def create_score_v1(request: ScoreRequest, raw_request: Request): logger.warning( "To indicate that Score API is not part of standard OpenAI API, we " diff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py index 527418c635093..81bce0dd370bb 100644 --- a/vllm/entrypoints/openai/serving_chat.py +++ b/vllm/entrypoints/openai/serving_chat.py @@ -32,7 +32,6 @@ from vllm.sequence import Logprob from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer from vllm.transformers_utils.tokenizers import maybe_serialize_tool_calls -from vllm.utils import iterate_with_cancellation logger = init_logger(__name__) @@ -234,10 +233,6 @@ async def create_chat_completion( assert len(generators) == 1 result_generator, = generators - if raw_request: - result_generator = iterate_with_cancellation( - result_generator, raw_request.is_disconnected) - # Streaming response if request.stream: return self.chat_completion_stream_generator( diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py index bd39a4c42e938..5cf9df92e296e 100644 --- a/vllm/entrypoints/openai/serving_completion.py +++ b/vllm/entrypoints/openai/serving_completion.py @@ -159,8 +159,7 @@ async def create_completion( # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) - result_generator = merge_async_iterators( - *generators, is_cancelled=raw_request.is_disconnected) + result_generator = merge_async_iterators(*generators) model_name = self._get_model_name(lora_request) num_prompts = len(engine_prompts) diff --git a/vllm/entrypoints/openai/serving_embedding.py b/vllm/entrypoints/openai/serving_embedding.py index fd501ad4f833e..879276646d2ba 100644 --- a/vllm/entrypoints/openai/serving_embedding.py +++ b/vllm/entrypoints/openai/serving_embedding.py @@ -202,10 +202,7 @@ async def create_embedding( # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) - result_generator = merge_async_iterators( - *generators, - is_cancelled=raw_request.is_disconnected if raw_request else None, - ) + result_generator = merge_async_iterators(*generators) num_prompts = len(engine_prompts) diff --git a/vllm/entrypoints/openai/serving_score.py b/vllm/entrypoints/openai/serving_score.py index 6f5cc14ac37cc..101d170bee4d6 100644 --- a/vllm/entrypoints/openai/serving_score.py +++ b/vllm/entrypoints/openai/serving_score.py @@ -186,10 +186,7 @@ async def create_score( # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) - result_generator = merge_async_iterators( - *generators, - is_cancelled=raw_request.is_disconnected if raw_request else None, - ) + result_generator = merge_async_iterators(*generators) num_prompts = len(engine_prompts) diff --git a/vllm/entrypoints/utils.py b/vllm/entrypoints/utils.py new file mode 100644 index 0000000000000..e8a78d216d0f0 --- /dev/null +++ b/vllm/entrypoints/utils.py @@ -0,0 +1,57 @@ +import asyncio +import functools + +from fastapi import Request + + +async def listen_for_disconnect(request: Request) -> None: + """Returns if a disconnect message is received""" + while True: + message = await request.receive() + if message["type"] == "http.disconnect": + break + + +def with_cancellation(handler_func): + """Decorator that allows a route handler to be cancelled by client + disconnections. + + This does _not_ use request.is_disconnected, which does not work with + middleware. Instead this follows the pattern from + starlette.StreamingResponse, which simultaneously awaits on two tasks- one + to wait for an http disconnect message, and the other to do the work that we + want done. When the first task finishes, the other is cancelled. + + A core assumption of this method is that the body of the request has already + been read. This is a safe assumption to make for fastapi handlers that have + already parsed the body of the request into a pydantic model for us. + This decorator is unsafe to use elsewhere, as it will consume and throw away + all incoming messages for the request while it looks for a disconnect + message. + + In the case where a `StreamingResponse` is returned by the handler, this + wrapper will stop listening for disconnects and instead the response object + will start listening for disconnects. + """ + + # Functools.wraps is required for this wrapper to appear to fastapi as a + # normal route handler, with the correct request type hinting. + @functools.wraps(handler_func) + async def wrapper(*args, **kwargs): + + # The request is either the second positional arg or `raw_request` + request = args[1] if len(args) > 1 else kwargs["raw_request"] + + handler_task = asyncio.create_task(handler_func(*args, **kwargs)) + cancellation_task = asyncio.create_task(listen_for_disconnect(request)) + + done, pending = await asyncio.wait([handler_task, cancellation_task], + return_when=asyncio.FIRST_COMPLETED) + for task in pending: + task.cancel() + + if handler_task in done: + return handler_task.result() + return None + + return wrapper diff --git a/vllm/utils.py b/vllm/utils.py index 73d2ae25f15ca..38c7dea6d2d3d 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -20,7 +20,7 @@ import uuid import warnings import weakref -from asyncio import FIRST_COMPLETED, AbstractEventLoop, Future, Task +from asyncio import FIRST_COMPLETED, AbstractEventLoop, Task from collections import UserDict, defaultdict from collections.abc import Iterable, Mapping from dataclasses import dataclass, field @@ -370,72 +370,23 @@ def _next_task(iterator: AsyncGenerator[T, None], return loop.create_task(iterator.__anext__()) # type: ignore[arg-type] -async def iterate_with_cancellation( - iterator: AsyncGenerator[T, None], - is_cancelled: Callable[[], Awaitable[bool]], -) -> AsyncGenerator[T, None]: - """Convert async iterator into one that polls the provided function - at least once per second to check for client cancellation. - """ - - loop = asyncio.get_running_loop() - - awaits: List[Future[T]] = [_next_task(iterator, loop)] - next_cancel_check: float = 0 - while True: - done, pending = await asyncio.wait(awaits, timeout=1.5) - - # Check for cancellation at most once per second - time_now = time.time() - if time_now >= next_cancel_check: - if await is_cancelled(): - with contextlib.suppress(BaseException): - awaits[0].cancel() - await iterator.aclose() - raise asyncio.CancelledError("client cancelled") - next_cancel_check = time_now + 1 - - if done: - try: - item = await awaits[0] - awaits[0] = _next_task(iterator, loop) - yield item - except StopAsyncIteration: - # we are done - return - - async def merge_async_iterators( - *iterators: AsyncGenerator[T, None], - is_cancelled: Optional[Callable[[], Awaitable[bool]]] = None, -) -> AsyncGenerator[Tuple[int, T], None]: + *iterators: AsyncGenerator[T, + None], ) -> AsyncGenerator[Tuple[int, T], None]: """Merge multiple asynchronous iterators into a single iterator. This method handle the case where some iterators finish before others. When it yields, it yields a tuple (i, item) where i is the index of the iterator that yields the item. - - It also optionally polls a provided function at least once per second - to check for client cancellation. """ loop = asyncio.get_running_loop() awaits = {_next_task(pair[1], loop): pair for pair in enumerate(iterators)} - timeout = None if is_cancelled is None else 1.5 - next_cancel_check: float = 0 try: while awaits: - done, pending = await asyncio.wait(awaits.keys(), - return_when=FIRST_COMPLETED, - timeout=timeout) - if is_cancelled is not None: - # Check for cancellation at most once per second - time_now = time.time() - if time_now >= next_cancel_check: - if await is_cancelled(): - raise asyncio.CancelledError("client cancelled") - next_cancel_check = time_now + 1 + done, _ = await asyncio.wait(awaits.keys(), + return_when=FIRST_COMPLETED) for d in done: pair = awaits.pop(d) try: From c77eb8a33ceb62858d951ffef87ae626a0d09973 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Tue, 17 Dec 2024 19:34:06 -0500 Subject: [PATCH 110/155] [Bugfix] Set temperature=0.7 in test_guided_choice_chat (#11264) --- tests/entrypoints/openai/test_chat.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/entrypoints/openai/test_chat.py b/tests/entrypoints/openai/test_chat.py index 8d23a2be6f9bb..47c521a9b5eb5 100644 --- a/tests/entrypoints/openai/test_chat.py +++ b/tests/entrypoints/openai/test_chat.py @@ -482,6 +482,7 @@ async def test_guided_choice_chat(client: openai.AsyncOpenAI, model=MODEL_NAME, messages=messages, max_completion_tokens=10, + temperature=0.7, extra_body=dict(guided_choice=sample_guided_choice, guided_decoding_backend=guided_decoding_backend)) choice1 = chat_completion.choices[0].message.content @@ -496,6 +497,7 @@ async def test_guided_choice_chat(client: openai.AsyncOpenAI, model=MODEL_NAME, messages=messages, max_completion_tokens=10, + temperature=0.7, extra_body=dict(guided_choice=sample_guided_choice, guided_decoding_backend=guided_decoding_backend)) choice2 = chat_completion.choices[0].message.content From bf8717ebaea8d74279df84fbe127ad22cf62e219 Mon Sep 17 00:00:00 2001 From: Cody Yu Date: Tue, 17 Dec 2024 16:37:59 -0800 Subject: [PATCH 111/155] [V1] Prefix caching for vision language models (#11187) Signed-off-by: Cody Yu --- tests/v1/core/test_prefix_caching.py | 88 +++++++++++++++++++- tests/v1/engine/test_engine_args.py | 15 ---- vllm/engine/arg_utils.py | 27 ++++--- vllm/inputs/data.py | 20 +++++ vllm/multimodal/inputs.py | 3 + vllm/v1/core/kv_cache_manager.py | 74 +++++++++++------ vllm/v1/core/kv_cache_utils.py | 115 ++++++++++++++++++++++++--- vllm/v1/core/scheduler.py | 2 + vllm/v1/engine/async_llm.py | 10 ++- vllm/v1/engine/core.py | 8 +- vllm/v1/engine/llm_engine.py | 9 ++- vllm/v1/engine/mm_input_mapper.py | 33 ++++---- vllm/v1/engine/processor.py | 12 +-- vllm/v1/request.py | 24 +++++- 14 files changed, 342 insertions(+), 98 deletions(-) diff --git a/tests/v1/core/test_prefix_caching.py b/tests/v1/core/test_prefix_caching.py index 00f7b0fcfe1dc..ed04f0a373c51 100644 --- a/tests/v1/core/test_prefix_caching.py +++ b/tests/v1/core/test_prefix_caching.py @@ -2,16 +2,23 @@ import pytest from vllm.inputs import token_inputs +from vllm.multimodal.inputs import PlaceholderRange from vllm.sampling_params import SamplingParams from vllm.utils import cdiv from vllm.v1.core.kv_cache_manager import KVCacheManager, Request from vllm.v1.core.kv_cache_utils import KVCacheBlock, hash_block_tokens -def make_request(request_id, prompt_token_ids): +def make_request(request_id, + prompt_token_ids, + mm_positions=None, + mm_hashes=None): return Request( request_id=request_id, - inputs=token_inputs(prompt_token_ids=prompt_token_ids), + inputs=token_inputs(prompt_token_ids=prompt_token_ids, + multi_modal_placeholders={"image": mm_positions} + if mm_positions else None, + multi_modal_hashes=mm_hashes), sampling_params=SamplingParams(max_tokens=17), eos_token_id=100, arrival_time=0, @@ -38,6 +45,7 @@ def test_prefill(): all_token_ids = common_token_ids + unique_token_ids req0 = make_request("0", all_token_ids) computed_blocks = manager.get_computed_blocks(req0) + assert len(req0.kv_block_hashes) == 3 assert not computed_blocks blocks = manager.allocate_slots(req0, 55, computed_blocks) assert [b.block_id for b in blocks] == [0, 1, 2, 3, 4] @@ -61,6 +69,7 @@ def test_prefill(): unique_token_ids = [3] * 5 req1 = make_request("1", common_token_ids + unique_token_ids) computed_blocks = manager.get_computed_blocks(req1) + assert len(req1.kv_block_hashes) == 3 assert [b.block_id for b in computed_blocks] == [0, 1, 2] num_new_tokens = 53 - 3 * 16 blocks = manager.allocate_slots(req1, num_new_tokens, computed_blocks) @@ -90,6 +99,7 @@ def test_prefill(): unique_token_ids = [3] * 6 req2 = make_request("2", common_token_ids + unique_token_ids) computed_block = manager.get_computed_blocks(req2) + assert len(req2.kv_block_hashes) == 3 assert [b.block_id for b in computed_block] == [0, 1, 2] num_new_tokens = 53 - 3 * 16 blocks = manager.allocate_slots(req2, num_new_tokens, computed_blocks) @@ -416,3 +426,77 @@ def test_cache_blocks(): ) assert len(manager.cached_block_hash_to_block) == 3 assert blocks[0].block_hash is not None + + +def test_mm_prefix_caching(): + """ + This tests that the multi-modal prefix caching is correct. + """ + manager = KVCacheManager( + block_size=16, + num_gpu_blocks=10, + max_model_len=8192, + sliding_window=None, + enable_caching=True, + num_preallocate_tokens=16, + ) + + # Common prompt tokens (T is text tokens and P is image placeholder tokens) + # [T,...,T, P0,...,P0], [P0,...,P0,T,...,T,P1,...,P1], [P1,...,P1] + common_token_ids = list(range(10)) + [-1] * 6 + common_token_ids += [-1] * 4 + list(range(10, 20)) + [-1] * 2 + common_token_ids += [-1] * 16 + + common_mm_positions = [ + PlaceholderRange(offset=11, length=10), + PlaceholderRange(offset=30, length=18), + ] + common_mm_hashes = ["aaa", "bbb"] + + # A unique image plus some text tokens. + unique_token_ids = [-1] * 7 + [100] * 4 + all_token_ids = common_token_ids + unique_token_ids + mm_positions = common_mm_positions + [ + PlaceholderRange(offset=48, length=7) + ] + mm_hashes = common_mm_hashes + ["ccc"] + req0 = make_request("0", + all_token_ids, + mm_positions=mm_positions, + mm_hashes=mm_hashes) + computed_blocks = manager.get_computed_blocks(req0) + + # Completed block should have hashes with extra keys. + assert not computed_blocks + assert len(req0.kv_block_hashes) == 3 + assert req0.kv_block_hashes[0].extra_keys == (("aaa", 0), ) + assert req0.kv_block_hashes[1].extra_keys == (("aaa", 5), ("bbb", 0)) + assert req0.kv_block_hashes[2].extra_keys == (("bbb", 2), ) + + blocks = manager.allocate_slots(req0, 59, computed_blocks) + assert [b.block_id for b in blocks] == [0, 1, 2, 3, 4] + req0.num_computed_tokens = 59 + + # Append slots without allocating a new block. + for _ in range(5): + req0.append_output_token_ids(8) + new_blocks = manager.append_slots(req0, 5) + assert new_blocks is not None and len(new_blocks) == 0 + + # The just completed block should have hashes with extra keys. + assert len(req0.kv_block_hashes) == 4 + assert req0.kv_block_hashes[3].extra_keys == (("ccc", 0), ) + + # Cache hit. + unique_token_ids = [-1] * 7 + [200] * 5 + all_token_ids = common_token_ids + unique_token_ids + mm_positions = common_mm_positions + [ + PlaceholderRange(offset=48, length=7) + ] + mm_hashes = common_mm_hashes + ["ccc"] + req1 = make_request("1", + all_token_ids, + mm_positions=mm_positions, + mm_hashes=mm_hashes) + computed_blocks = manager.get_computed_blocks(req1) + assert len(computed_blocks) == 3 diff --git a/tests/v1/engine/test_engine_args.py b/tests/v1/engine/test_engine_args.py index ac5e7dde525a7..ff38a4568ecb1 100644 --- a/tests/v1/engine/test_engine_args.py +++ b/tests/v1/engine/test_engine_args.py @@ -31,14 +31,6 @@ def test_prefix_caching_from_cli(): assert engine_args.enable_prefix_caching -def test_defaults(): - engine_args = EngineArgs(model="facebook/opt-125m") - - # Assert V1 defaults - assert (engine_args.enable_prefix_caching - ), "V1 turns on prefix caching by default" - - def test_defaults_with_usage_context(): engine_args = EngineArgs(model="facebook/opt-125m") vllm_config: VllmConfig = engine_args.create_engine_config( @@ -52,10 +44,3 @@ def test_defaults_with_usage_context(): UsageContext.OPENAI_API_SERVER) assert vllm_config.scheduler_config.max_num_seqs == 1024 assert vllm_config.scheduler_config.max_num_batched_tokens == 2048 - - -def test_prefix_cache_disabled_with_multimodel(): - engine_args = EngineArgs(model="llava-hf/llava-1.5-7b-hf") - - vllm_config = engine_args.create_engine_config(UsageContext.LLM_CLASS) - assert not vllm_config.cache_config.enable_prefix_caching diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index f6d276fe7c0c8..674577f23eba6 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -205,6 +205,7 @@ def __post_init__(self): # by user. if self.enable_prefix_caching is None: self.enable_prefix_caching = bool(envs.VLLM_USE_V1) + # Override max_num_seqs if it's not set by user. if self.max_num_seqs is None: self.max_num_seqs = 256 if not envs.VLLM_USE_V1 else 1024 @@ -1026,11 +1027,11 @@ def create_engine_config(self, device_config = DeviceConfig(device=self.device) model_config = self.create_model_config() - if model_config.is_multimodal_model: - if self.enable_prefix_caching: - logger.warning( - "--enable-prefix-caching is currently not " - "supported for multimodal models and has been disabled.") + if (model_config.is_multimodal_model and not envs.VLLM_USE_V1 + and self.enable_prefix_caching): + logger.warning("--enable-prefix-caching is currently not " + "supported for multimodal models in v0 and " + "has been disabled.") self.enable_prefix_caching = False cache_config = CacheConfig( @@ -1249,11 +1250,14 @@ def _override_v1_engine_args(self, usage_context: UsageContext) -> None: # When no user override, set the default values based on the usage # context. # TODO(woosuk): Tune the default values for different hardware. - if self.max_num_batched_tokens is None: - if usage_context == UsageContext.LLM_CLASS: - self.max_num_batched_tokens = 8192 - elif usage_context == UsageContext.OPENAI_API_SERVER: - self.max_num_batched_tokens = 2048 + default_max_num_batched_tokens = { + UsageContext.LLM_CLASS: 8192, + UsageContext.OPENAI_API_SERVER: 2048, + } + if (self.max_num_batched_tokens is None + and usage_context in default_max_num_batched_tokens): + self.max_num_batched_tokens = default_max_num_batched_tokens[ + usage_context] logger.warning( "Setting max_num_batched_tokens to %d for %s usage context.", self.max_num_batched_tokens, usage_context.value) @@ -1263,9 +1267,6 @@ def _override_v1_engine_config(self, engine_config: VllmConfig) -> None: Override the EngineConfig's configs based on the usage context for V1. """ assert envs.VLLM_USE_V1, "V1 is not enabled" - if engine_config.model_config.is_multimodal_model: - # TODO (ywang96): Enable APC by default when VLM supports it. - assert not engine_config.cache_config.enable_prefix_caching @dataclass diff --git a/vllm/inputs/data.py b/vllm/inputs/data.py index 85aaaa776907f..d54cbb5c37819 100644 --- a/vllm/inputs/data.py +++ b/vllm/inputs/data.py @@ -162,6 +162,11 @@ class TokenInputs(TypedDict): Placeholder ranges for the multi-modal data. """ + multi_modal_hashes: NotRequired[List[str]] + """ + The hashes of the multi-modal data. + """ + mm_processor_kwargs: NotRequired[Dict[str, Any]] """ Optional multi-modal processor kwargs to be forwarded to the @@ -177,6 +182,7 @@ def token_inputs( prompt: Optional[str] = None, multi_modal_data: Optional["MultiModalDataDict"] = None, multi_modal_inputs: Optional["MultiModalKwargs"] = None, + multi_modal_hashes: Optional[List[str]] = None, multi_modal_placeholders: Optional["MultiModalPlaceholderDict"] = None, mm_processor_kwargs: Optional[Dict[str, Any]] = None, ) -> TokenInputs: @@ -191,6 +197,8 @@ def token_inputs( inputs["multi_modal_data"] = multi_modal_data if multi_modal_inputs is not None: inputs["multi_modal_inputs"] = multi_modal_inputs + if multi_modal_hashes is not None: + inputs["multi_modal_hashes"] = multi_modal_hashes if multi_modal_placeholders is not None: inputs["multi_modal_placeholders"] = multi_modal_placeholders if mm_processor_kwargs is not None: @@ -295,6 +303,18 @@ def multi_modal_inputs(self) -> Union[Dict, "MultiModalKwargs"]: assert_never(inputs) + @cached_property + def multi_modal_hashes(self) -> List[str]: + inputs = self.inputs + + if inputs["type"] == "token": + return inputs.get("multi_modal_hashes", []) + + if inputs["type"] == "multimodal": + return inputs.get("mm_hashes", []) + + assert_never(inputs) + @cached_property def multi_modal_placeholders(self) -> "MultiModalPlaceholderDict": inputs = self.inputs diff --git a/vllm/multimodal/inputs.py b/vllm/multimodal/inputs.py index 229a8fbdf5831..c00943a5f26d9 100644 --- a/vllm/multimodal/inputs.py +++ b/vllm/multimodal/inputs.py @@ -215,6 +215,9 @@ class MultiModalInputsV2(TypedDict): mm_kwargs: MultiModalKwargs """Keyword arguments to be directly passed to the model after batching.""" + mm_hashes: NotRequired[List[str]] + """The hashes of the multi-modal data.""" + mm_placeholders: MultiModalPlaceholderDict """ For each modality, information about the placeholder tokens in diff --git a/vllm/v1/core/kv_cache_manager.py b/vllm/v1/core/kv_cache_manager.py index aaa44c930e324..61a3f5fd6d841 100644 --- a/vllm/v1/core/kv_cache_manager.py +++ b/vllm/v1/core/kv_cache_manager.py @@ -4,7 +4,9 @@ from vllm.logger import init_logger from vllm.utils import cdiv from vllm.v1.core.kv_cache_utils import (BlockHashType, FreeKVCacheBlockQueue, - KVCacheBlock, hash_block_tokens, + KVCacheBlock, + generate_block_hash_extra_keys, + hash_block_tokens, hash_request_tokens) from vllm.v1.request import Request @@ -83,10 +85,12 @@ def get_computed_blocks(self, request: Request) -> List[KVCacheBlock]: computed_blocks = [] - # TODO(rickyx): potentially we could cache this so we don't have to - # recompute it every time. - block_hashes = hash_request_tokens(self.block_size, - request.all_token_ids) + # The block hashes for the request may already be computed + # if the request was preempted and resumed. + if not request.kv_block_hashes: + request.set_kv_block_hashes( + hash_request_tokens(self.block_size, request)) + block_hashes = request.kv_block_hashes for block_hash in block_hashes: # block_hashes is a chain of block hashes. If a block hash is not @@ -242,14 +246,16 @@ def allocate_slots( num_computed_tokens = len(computed_blocks) * self.block_size num_full_blocks = (num_computed_tokens + num_tokens) // self.block_size - self._cache_full_blocks( - request=request, - blk_start_idx=len(computed_blocks), - # The new full blocks are the full blocks that are not computed. - full_blocks=self.req_to_blocks[request.request_id] - [len(computed_blocks):num_full_blocks], - prev_block=computed_blocks[-1] if computed_blocks else None, - ) + new_full_blocks = self.req_to_blocks[ + request.request_id][len(computed_blocks):num_full_blocks] + if new_full_blocks: + self._cache_full_blocks( + request=request, + blk_start_idx=len(computed_blocks), + # The new full blocks are the full blocks that are not computed. + full_blocks=new_full_blocks, + prev_block=computed_blocks[-1] if computed_blocks else None, + ) return new_blocks @@ -376,6 +382,8 @@ def _cache_full_blocks( full_blocks: The list of blocks to update hash metadata. prev_block: The previous block in the chain. """ + num_cached_block_hashes = len(request.kv_block_hashes) + # Update the new blocks with the block hashes through the chain. prev_block_hash_value = None if prev_block is not None: @@ -387,17 +395,35 @@ def _cache_full_blocks( for i, blk in enumerate(full_blocks): blk_idx = blk_start_idx + i - block_tokens = request.all_token_ids[blk_idx * - self.block_size:(blk_idx + - 1) * - self.block_size] - assert len(block_tokens) == self.block_size, ( - f"Expected {self.block_size} tokens, got {len(block_tokens)} " - f"at {blk_idx}th block for request " - f"{request.request_id}({request})") - - # Compute the hash of the current block. - block_hash = hash_block_tokens(prev_block_hash_value, block_tokens) + if blk_idx < num_cached_block_hashes: + # The block hash may already be computed in + # "get_computed_blocks" if the tokens are not generated by + # this request (either the prompt tokens or the previously + # generated tokens with preemption). In this case we simply + # reuse the block hash. + block_hash = request.kv_block_hashes[blk_idx] + else: + # Otherwise compute the block hash and cache it in the request + # in case it will be preempted in the future. + start_token_idx = blk_idx * self.block_size + end_token_idx = (blk_idx + 1) * self.block_size + block_tokens = request.all_token_ids[ + start_token_idx:end_token_idx] + assert len(block_tokens) == self.block_size, ( + f"Expected {self.block_size} tokens, got " + f"{len(block_tokens)} at {blk_idx}th block for request " + f"{request.request_id}({request})") + + # Generate extra keys for multi-modal inputs. Note that since + # we reach to this branch only when the block is completed with + # generated tokens, we only need to consider the last mm input. + extra_keys, _ = generate_block_hash_extra_keys( + request, start_token_idx, end_token_idx, -1) + + # Compute the hash of the current block. + block_hash = hash_block_tokens(prev_block_hash_value, + block_tokens, extra_keys) + request.append_kv_block_hashes(block_hash) # Update and added the full block to the cache. blk.block_hash = block_hash diff --git a/vllm/v1/core/kv_cache_utils.py b/vllm/v1/core/kv_cache_utils.py index 0ba338aa5a3d2..d80ea128c7749 100644 --- a/vllm/v1/core/kv_cache_utils.py +++ b/vllm/v1/core/kv_cache_utils.py @@ -1,20 +1,25 @@ """KV-Cache Utilities.""" from collections.abc import Sequence from dataclasses import dataclass -from typing import List, NamedTuple, Optional, Tuple +from typing import Any, List, NamedTuple, Optional, Tuple from vllm.logger import init_logger +from vllm.v1.request import Request logger = init_logger(__name__) class BlockHashType(NamedTuple): - """Hash value of a block and the token IDs in the block. - The reason we keep a tuple of token IDs is to make sure no hash - collision happens when the hash value is the same. + """Hash value of a block (int), the token IDs in the block, and extra keys. + The reason we keep a tuple of token IDs and extra keys is to make sure + no hash collision happens when the hash value is the same. """ + # Hash value of the block in an integer. hash_value: int + # Token IDs in the block. token_ids: Tuple[int, ...] + # Extra keys for the block. + extra_keys: Optional[Any] = None @dataclass @@ -159,8 +164,80 @@ def get_all_free_blocks(self) -> List[KVCacheBlock]: return ret -def hash_block_tokens(parent_block_hash: Optional[int], - curr_block_token_ids: Sequence[int]) -> BlockHashType: +def generate_block_hash_extra_keys( + request: Request, start_token_idx: int, end_token_idx: int, + start_mm_idx: int) -> Tuple[Optional[Tuple[Any, ...]], int]: + """Generate extra keys for the block hash. The extra keys can come from + the multi-modal inputs and request specific metadata (e.g., LoRA ID). + For multi-modal inputs, the extra keys are (mm_hash, start_offset) that + indicate a mm input contained in the block and its starting offset in + the block tokens. + + Args: + request: The request object. + start_token_idx: The start token index of the block. + end_token_idx: The end token index of the block. + start_mm_idx: The start multi-modal index of the block. + + Returns: + A tuple of extra keys and the next multi-modal index. + """ + + mm_positions, mm_hashes = request.mm_positions, request.mm_hashes + if not mm_positions: + return None, start_mm_idx + + if mm_positions and len(mm_positions) != len(mm_hashes): + raise ValueError( + "The number of multi-modal positions and hashes must match. This " + "is likely because you do not enable MM preprocessor hashing. " + "Please set mm_cache_preprocessor=True.") + + # Note that we assume mm_positions is sorted by offset. + # We do not need to check all mm inputs if the start token index is out of + # range. This usually happens in the late prefill phase and decoding phase. + if mm_positions[-1]["offset"] + mm_positions[-1][ + "length"] < start_token_idx: + return None, start_mm_idx + + # Support start_mm_idx == -1 to indicate the last mm input. + if start_mm_idx < 0: + assert -start_mm_idx <= len(mm_positions) + start_mm_idx = len(mm_positions) + start_mm_idx + + extra_keys = [] + curr_mm_idx = start_mm_idx + while mm_positions and curr_mm_idx < len(mm_positions): + assert mm_hashes[curr_mm_idx] is not None + offset = mm_positions[curr_mm_idx]["offset"] + length = mm_positions[curr_mm_idx]["length"] + if end_token_idx > offset: + if start_token_idx > offset + length: + # This block has passed the current mm input. + curr_mm_idx += 1 + continue + + # The block contains the current mm input. + mm_start = max(0, start_token_idx - offset) + extra_keys.append((mm_hashes[curr_mm_idx], mm_start)) + if end_token_idx >= offset + length: + # If this block contains the end of the current mm input, + # move to the next mm input as this block may also contain + # the next mm input. + curr_mm_idx += 1 + else: + # Otherwise this block is done with mm inputs. + break + else: + # This block has not reached the current mm input. + break + return tuple(extra_keys), curr_mm_idx + + +def hash_block_tokens( + parent_block_hash: Optional[int], + curr_block_token_ids: Sequence[int], + extra_keys: Optional[Tuple[Any, ...]] = None) -> BlockHashType: """Computes a hash value corresponding to the contents of a block and the contents of the preceding block(s). The hash value is used for prefix caching. We use LRU cache for this function to avoid recomputing @@ -174,27 +251,39 @@ def hash_block_tokens(parent_block_hash: Optional[int], if this is the first block. curr_block_token_ids: A list of token ids in the current block. The current block is assumed to be full. + extra_keys: Extra keys for the block. Returns: The hash value of the block and the token ids in the block. The entire tuple is used as the hash key of the block. """ return BlockHashType(hash((parent_block_hash, *curr_block_token_ids)), - tuple(curr_block_token_ids)) + tuple(curr_block_token_ids), extra_keys) def hash_request_tokens(block_size: int, - token_ids: Sequence[int]) -> List[BlockHashType]: + request: Request) -> List[BlockHashType]: """Computes hash values of a chain of blocks given a sequence of token IDs. The hash value is used for prefix caching. Args: block_size: The size of each block. - token_ids: A sequence of token ids in the request. + request: The request object. Returns: The list of computed hash values. """ + token_ids = request.all_token_ids + mm_positions, mm_hashes = request.mm_positions, request.mm_hashes + if mm_positions and len(mm_positions) != len(mm_hashes): + raise ValueError( + "The number of multi-modal positions and hashes must match.") + + # TODO: Extend this to support other features such as LoRA. + need_extra_keys = bool(mm_positions) + extra_keys = None + curr_mm_idx = 0 + ret = [] parent_block_hash_value = None for start in range(0, len(token_ids), block_size): @@ -203,8 +292,14 @@ def hash_request_tokens(block_size: int, # Do not hash the block if it is not full. if len(block_token_ids) < block_size: break + + # Add extra keys if the block is a multi-modal block. + if need_extra_keys: + extra_keys, curr_mm_idx = generate_block_hash_extra_keys( + request, start, end, curr_mm_idx) + block_hash = hash_block_tokens(parent_block_hash_value, - block_token_ids) + block_token_ids, extra_keys) ret.append(block_hash) parent_block_hash_value = block_hash.hash_value return ret diff --git a/vllm/v1/core/scheduler.py b/vllm/v1/core/scheduler.py index 178532e477dae..08e7c0fd4dc9b 100644 --- a/vllm/v1/core/scheduler.py +++ b/vllm/v1/core/scheduler.py @@ -516,6 +516,7 @@ class NewRequestData: prompt_token_ids: List[int] prompt: Optional[str] mm_inputs: List["MultiModalKwargs"] + mm_hashes: List[str] mm_positions: List["PlaceholderRange"] sampling_params: SamplingParams block_ids: List[int] @@ -533,6 +534,7 @@ def from_request( prompt_token_ids=request.prompt_token_ids, prompt=request.prompt, mm_inputs=request.mm_inputs, + mm_hashes=request.mm_hashes, mm_positions=request.mm_positions, sampling_params=request.sampling_params, block_ids=block_ids, diff --git a/vllm/v1/engine/async_llm.py b/vllm/v1/engine/async_llm.py index b36de5f66917c..41fb4b25d45bb 100644 --- a/vllm/v1/engine/async_llm.py +++ b/vllm/v1/engine/async_llm.py @@ -60,9 +60,13 @@ def __init__( self.client_aborted_requests: List[str] = [] # Processor (converts Inputs --> EngineCoreRequests). - self.processor = Processor(vllm_config.model_config, - vllm_config.lora_config, self.tokenizer, - input_registry) + self.processor = Processor( + model_config=vllm_config.model_config, + cache_config=vllm_config.cache_config, + lora_config=vllm_config.lora_config, + tokenizer=self.tokenizer, + input_registry=input_registry, + ) # Detokenizer (converts EngineCoreOutputs --> RequestOutput). self.detokenizer = Detokenizer( diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py index 56d4dc67e4a0e..497d5db5b4c99 100644 --- a/vllm/v1/engine/core.py +++ b/vllm/v1/engine/core.py @@ -65,7 +65,8 @@ def __init__( self._last_logging_time = time.time() - self.mm_input_mapper_server = MMInputMapperServer() + self.mm_input_mapper_server = MMInputMapperServer( + vllm_config.model_config) def _initialize_kv_caches(self, cache_config: CacheConfig) -> Tuple[int, int]: @@ -98,9 +99,8 @@ def add_request(self, request: EngineCoreRequest): # MM mapper, so anything that has a hash must have a HIT cache # entry here as well. assert request.mm_inputs is not None - request.mm_inputs, request.mm_hashes = ( - self.mm_input_mapper_server.process_inputs( - request.mm_inputs, request.mm_hashes)) + request.mm_inputs = self.mm_input_mapper_server.process_inputs( + request.mm_inputs, request.mm_hashes) req = Request.from_engine_core_request(request) diff --git a/vllm/v1/engine/llm_engine.py b/vllm/v1/engine/llm_engine.py index 15dedbd0f9529..bea8c5502f612 100644 --- a/vllm/v1/engine/llm_engine.py +++ b/vllm/v1/engine/llm_engine.py @@ -55,9 +55,12 @@ def __init__( self.tokenizer.ping() # Processor (convert Inputs --> EngineCoreRequests) - self.processor = Processor(vllm_config.model_config, - vllm_config.lora_config, self.tokenizer, - input_registry, mm_registry) + self.processor = Processor(model_config=vllm_config.model_config, + cache_config=vllm_config.cache_config, + lora_config=vllm_config.lora_config, + tokenizer=self.tokenizer, + input_registry=input_registry, + mm_registry=mm_registry) # Detokenizer (converts EngineCoreOutputs --> RequestOutput) self.detokenizer = Detokenizer( diff --git a/vllm/v1/engine/mm_input_mapper.py b/vllm/v1/engine/mm_input_mapper.py index 6cdeba6f3f71e..e53ba092ede04 100644 --- a/vllm/v1/engine/mm_input_mapper.py +++ b/vllm/v1/engine/mm_input_mapper.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional, Tuple +from typing import Any, Dict, List, Optional import PIL from blake3 import blake3 @@ -42,6 +42,8 @@ def __init__( model_config) self.mm_registry.init_mm_limits_per_prompt(model_config) + # Init cache + self.use_cache = model_config.mm_cache_preprocessor self.mm_cache = LRUDictCache[str, MultiModalKwargs](MM_CACHE_SIZE) # DEBUG: Set to None to disable @@ -61,7 +63,7 @@ def process_inputs( mm_hashes: Optional[List[str]], mm_processor_kwargs: Optional[Dict[str, Any]], precomputed_mm_inputs: Optional[List[MultiModalKwargs]], - ) -> Tuple[List[MultiModalKwargs], Optional[List[str]]]: + ) -> List[MultiModalKwargs]: if precomputed_mm_inputs is None: image_inputs = mm_data["image"] if not isinstance(image_inputs, list): @@ -70,26 +72,21 @@ def process_inputs( else: num_inputs = len(precomputed_mm_inputs) - # Check if hash is enabled - use_hash = mm_hashes is not None - if use_hash: + # Sanity + if self.use_cache: assert mm_hashes is not None - assert num_inputs == len( - mm_hashes), "num_inputs = {} len(mm_hashes) = {}".format( - num_inputs, len(mm_hashes)) + assert num_inputs == len(mm_hashes) # Process each image input separately, so that later we can schedule # them in a fine-grained manner. # Apply caching (if enabled) and reuse precomputed inputs (if provided) - ret_hashes: Optional[List[str]] = [] if use_hash else None ret_inputs: List[MultiModalKwargs] = [] for input_id in range(num_inputs): if self.mm_debug_cache_hit_ratio_steps is not None: self.cache_hit_ratio(self.mm_debug_cache_hit_ratio_steps) - mm_hash = None mm_input = None - if use_hash: + if self.use_cache: assert mm_hashes is not None mm_hash = mm_hashes[input_id] mm_input = self.mm_cache.get(mm_hash) @@ -106,7 +103,7 @@ def process_inputs( mm_processor_kwargs=mm_processor_kwargs, ) - if use_hash: + if self.use_cache: # Add to cache assert mm_hash is not None self.mm_cache.put(mm_hash, mm_input) @@ -114,18 +111,15 @@ def process_inputs( self.mm_cache_hits += 1 mm_input = None # Avoids sending mm_input to Server - if use_hash: - assert mm_hash is not None - assert ret_hashes is not None - ret_hashes.append(mm_hash) ret_inputs.append(mm_input) - return ret_inputs, ret_hashes + return ret_inputs class MMInputMapperServer: - def __init__(self, ): + def __init__(self, model_config): + self.use_cache = model_config.mm_cache_preprocessor self.mm_cache = LRUDictCache[str, MultiModalKwargs](MM_CACHE_SIZE) def process_inputs( @@ -135,6 +129,9 @@ def process_inputs( ) -> List[MultiModalKwargs]: assert len(mm_inputs) == len(mm_hashes) + if not self.use_cache: + return mm_inputs + full_mm_inputs = [] for mm_input, mm_hash in zip(mm_inputs, mm_hashes): assert mm_hash is not None diff --git a/vllm/v1/engine/processor.py b/vllm/v1/engine/processor.py index 679bf8e25e9ca..732757d6b0ac2 100644 --- a/vllm/v1/engine/processor.py +++ b/vllm/v1/engine/processor.py @@ -1,7 +1,7 @@ import time from typing import Any, Dict, Mapping, Optional, Tuple, Union -from vllm.config import LoRAConfig, ModelConfig +from vllm.config import CacheConfig, LoRAConfig, ModelConfig from vllm.inputs import (INPUT_REGISTRY, InputRegistry, ProcessorInputs, PromptType, SingletonInputsAdapter) from vllm.inputs.parse import is_encoder_decoder_inputs @@ -23,6 +23,7 @@ class Processor: def __init__( self, model_config: ModelConfig, + cache_config: CacheConfig, lora_config: Optional[LoRAConfig], tokenizer: BaseTokenizerGroup, input_registry: InputRegistry = INPUT_REGISTRY, @@ -45,8 +46,9 @@ def __init__( self.mm_input_mapper_client = MMInputMapperClient(model_config) # Multi-modal hasher (for images) - self.mm_hasher = MMHasher( - ) if model_config.mm_cache_preprocessor else None + self.use_hash = model_config.mm_cache_preprocessor or \ + cache_config.enable_prefix_caching + self.mm_hasher = MMHasher() # TODO: run in an ThreadpoolExecutor or BackgroundProcess. # This ideally should releases the GIL, so we should not block the @@ -77,7 +79,7 @@ def process_inputs( # Compute MM hashes (if enabled) mm_hashes = None - if self.mm_hasher is not None: + if self.use_hash: mm_hashes = self.mm_hasher.hash(prompt) # Process inputs. @@ -118,7 +120,7 @@ def process_inputs( # Apply MM mapper mm_inputs = None if len(decoder_inputs.multi_modal_data) > 0: - mm_inputs, mm_hashes = self.mm_input_mapper_client.process_inputs( + mm_inputs = self.mm_input_mapper_client.process_inputs( decoder_inputs.multi_modal_data, mm_hashes, decoder_inputs.mm_processor_kwargs, precomputed_mm_inputs) diff --git a/vllm/v1/request.py b/vllm/v1/request.py index 1737d096e811d..f4783ae366ef0 100644 --- a/vllm/v1/request.py +++ b/vllm/v1/request.py @@ -1,5 +1,5 @@ import enum -from typing import List, Optional, Union +from typing import TYPE_CHECKING, List, Optional, Union from vllm.inputs import DecoderOnlyInputs, SingletonInputsAdapter, token_inputs from vllm.lora.request import LoRARequest @@ -9,6 +9,9 @@ from vllm.v1.engine import EngineCoreRequest from vllm.v1.utils import ConstantList +if TYPE_CHECKING: + from vllm.v1.core.kv_cache_utils import BlockHashType + class Request: @@ -45,6 +48,7 @@ def __init__( self._all_token_ids: List[int] = self.prompt_token_ids.copy() self.num_computed_tokens = 0 + # Multi-modal input metadata. mm_positions = self.inputs.multi_modal_placeholders if mm_positions: # FIXME(woosuk): Support other modalities. @@ -56,6 +60,12 @@ def __init__( if self.inputs.multi_modal_inputs: self.mm_inputs = self.inputs.multi_modal_inputs + self.mm_hashes: List[str] = self.inputs.multi_modal_hashes + + # Cache the computed kv block hashes of the request to avoid + # recomputing. + self._kv_block_hashes: List[BlockHashType] = [] + @classmethod def from_engine_core_request(cls, request: EngineCoreRequest) -> "Request": return cls( @@ -65,6 +75,7 @@ def from_engine_core_request(cls, request: EngineCoreRequest) -> "Request": prompt=request.prompt, multi_modal_data=None, multi_modal_inputs=request.mm_inputs, + multi_modal_hashes=request.mm_hashes, multi_modal_placeholders=request.mm_placeholders, mm_processor_kwargs=None, ), @@ -121,6 +132,17 @@ def get_num_encoder_tokens(self, input_id: int) -> int: num_tokens = self.mm_positions[input_id]["length"] return num_tokens + @property + def kv_block_hashes(self) -> ConstantList["BlockHashType"]: + # Prevent directly appending to the kv_block_hashes. + return ConstantList(self._kv_block_hashes) + + def set_kv_block_hashes(self, value: List["BlockHashType"]) -> None: + self._kv_block_hashes = value + + def append_kv_block_hashes(self, block_hash: "BlockHashType") -> None: + self._kv_block_hashes.append(block_hash) + class RequestStatus(enum.IntEnum): """Status of a request.""" From 866fa4550d572f4ff3521ccf503e0df2e76591a1 Mon Sep 17 00:00:00 2001 From: Konrad Zawora Date: Wed, 18 Dec 2024 01:39:07 +0100 Subject: [PATCH 112/155] [Bugfix] Restore support for larger block sizes (#11259) Signed-off-by: Konrad Zawora --- vllm/config.py | 4 ++++ vllm/engine/arg_utils.py | 6 ++++-- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/vllm/config.py b/vllm/config.py index 9ecd3e72afa9f..307cf9c8d5b2a 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -917,6 +917,10 @@ def _verify_args(self) -> None: raise ValueError( "GPU memory utilization must be less than 1.0. Got " f"{self.gpu_memory_utilization}.") + if (current_platform.is_cuda() and self.block_size is not None + and self.block_size > 32): + raise ValueError("CUDA Paged Attention kernel only supports " + f"block sizes up to 32. Got {self.block_size}.") def _verify_cache_dtype(self) -> None: if self.cache_dtype == "auto": diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 674577f23eba6..64cc4592c2861 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -424,10 +424,12 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: parser.add_argument('--block-size', type=int, default=EngineArgs.block_size, - choices=[8, 16, 32], + choices=[8, 16, 32, 64, 128], help='Token block size for contiguous chunks of ' 'tokens. This is ignored on neuron devices and ' - 'set to max-model-len') + 'set to max-model-len. On CUDA devices, ' + 'only block sizes up to 32 are supported. ' + 'On HPU devices, block size defaults to 128.') parser.add_argument( "--enable-prefix-caching", From 8b79f9e107fd4214187bf65485b3ea1bb3191a46 Mon Sep 17 00:00:00 2001 From: Wallas Henrique Date: Wed, 18 Dec 2024 03:34:08 -0300 Subject: [PATCH 113/155] [Bugfix] Fix guided decoding with tokenizer mode mistral (#11046) --- .buildkite/test-pipeline.yaml | 6 +- requirements-common.txt | 3 +- .../model_executor/test_guided_processors.py | 54 ++++++++- .../decoder_only/language/test_mistral.py | 86 ++++++++++++- .../guided_decoding/xgrammar_decoding.py | 113 +++++++++++------- vllm/transformers_utils/tokenizer.py | 2 +- vllm/transformers_utils/tokenizers/mistral.py | 5 +- 7 files changed, 217 insertions(+), 52 deletions(-) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 44f47fac1c1b3..b563c96343f92 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -224,8 +224,12 @@ steps: mirror_hardwares: [amd] source_file_dependencies: - vllm/model_executor/layers + - vllm/model_executor/guided_decoding - tests/test_logits_processor - command: pytest -v -s test_logits_processor.py + - tests/model_executor/test_guided_processors + commands: + - pytest -v -s test_logits_processor.py + - pytest -v -s model_executor/test_guided_processors.py - label: Speculative decoding tests # 30min source_file_dependencies: diff --git a/requirements-common.txt b/requirements-common.txt index bd2b4b7a01668..1c935303c8d79 100644 --- a/requirements-common.txt +++ b/requirements-common.txt @@ -14,12 +14,13 @@ aiohttp openai >= 1.45.0 # Ensure modern openai package (ensure types module present and max_completion_tokens field support) uvicorn[standard] pydantic >= 2.9 # Required for fastapi >= 0.113.0 -pillow # Required for image processing prometheus_client >= 0.18.0 +pillow # Required for image processing prometheus-fastapi-instrumentator >= 7.0.0 tiktoken >= 0.6.0 # Required for DBRX tokenizer lm-format-enforcer >= 0.10.9, < 0.11 outlines == 0.1.11 +lark == 1.2.2 xgrammar >= 0.1.6; platform_machine == "x86_64" typing_extensions >= 4.10 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317 diff --git a/tests/model_executor/test_guided_processors.py b/tests/model_executor/test_guided_processors.py index 9f4d81b583141..3334c0df149b5 100644 --- a/tests/model_executor/test_guided_processors.py +++ b/tests/model_executor/test_guided_processors.py @@ -1,13 +1,19 @@ +import pickle + import pytest import torch from transformers import AutoTokenizer +from vllm.config import ModelConfig from vllm.model_executor.guided_decoding import ( - get_guided_decoding_logits_processor) + get_guided_decoding_logits_processor, + get_local_guided_decoding_logits_processor) from vllm.model_executor.guided_decoding.outlines_logits_processors import ( JSONLogitsProcessor, RegexLogitsProcessor) from vllm.sampling_params import GuidedDecodingParams +MODEL_NAME = 'HuggingFaceH4/zephyr-7b-beta' + def test_guided_logits_processors(sample_regex, sample_json_schema): """Basic unit test for RegexLogitsProcessor and JSONLogitsProcessor.""" @@ -38,14 +44,29 @@ def test_guided_logits_processors(sample_regex, sample_json_schema): @pytest.mark.asyncio @pytest.mark.parametrize("backend", ["outlines", "lm-format-enforcer", "xgrammar"]) -async def test_guided_logits_processor_black_box(backend: str, sample_regex, +@pytest.mark.parametrize("is_local", [True, False]) +async def test_guided_logits_processor_black_box(backend: str, is_local: bool, + sample_regex, sample_json_schema): - tokenizer = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-beta') + + config = ModelConfig( + MODEL_NAME, + task="generate", + tokenizer=MODEL_NAME, + tokenizer_mode="auto", + trust_remote_code=False, + seed=0, + dtype="bfloat16", + ) + tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) token_ids = tokenizer.encode( f"Give an example IPv4 address with this regex: {sample_regex}") regex_request = GuidedDecodingParams(regex=sample_regex, backend=backend) - regex_lp = await get_guided_decoding_logits_processor( - regex_request, tokenizer) + + regex_lp = get_local_guided_decoding_logits_processor( + regex_request, tokenizer, config) if is_local else \ + await get_guided_decoding_logits_processor( + regex_request, tokenizer, config) assert regex_lp is not None tensor = torch.rand(32000) original_tensor = torch.clone(tensor) @@ -59,7 +80,7 @@ async def test_guided_logits_processor_black_box(backend: str, sample_regex, json_request = GuidedDecodingParams(json=sample_json_schema, backend=backend) json_lp = await get_guided_decoding_logits_processor( - json_request, tokenizer) + json_request, tokenizer, config) assert json_lp is not None tensor = torch.rand(32000) original_tensor = torch.clone(tensor) @@ -84,3 +105,24 @@ def test_multiple_guided_options_not_allowed(sample_json_schema, sample_regex): with pytest.raises(ValueError, match="You can only use one kind of guided"): GuidedDecodingParams(json=sample_json_schema, grammar="test grammar") + + +def test_pickle_xgrammar_tokenizer_data(): + + # TODO: move to another test file for xgrammar + try: + import xgrammar as xgr + except ImportError: + pytest.skip("Could not import xgrammar to run test") + + from vllm.model_executor.guided_decoding.xgrammar_decoding import ( + TokenizerData) + tokenizer_data = TokenizerData(vocab_type=xgr.VocabType.RAW) + pickled = pickle.dumps(tokenizer_data) + + assert pickled is not None + + depickled: TokenizerData = pickle.loads(pickled) + + assert depickled is not None + assert depickled.vocab_type == xgr.VocabType.RAW diff --git a/tests/models/decoder_only/language/test_mistral.py b/tests/models/decoder_only/language/test_mistral.py index 99b5d5694f9f7..bdc1571784b5d 100644 --- a/tests/models/decoder_only/language/test_mistral.py +++ b/tests/models/decoder_only/language/test_mistral.py @@ -3,17 +3,20 @@ Run `pytest tests/models/test_mistral.py`. """ import copy +import json +import jsonschema +import jsonschema.exceptions import pytest -from vllm import SamplingParams from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import ( # noqa MistralToolParser) +from vllm.sampling_params import GuidedDecodingParams, SamplingParams from ...utils import check_logprobs_close MODELS = [ - "mistralai/Mistral-7B-Instruct-v0.1", + "mistralai/Mistral-7B-Instruct-v0.3", ] MISTRAL_FORMAT_MODELS = [ @@ -126,6 +129,45 @@ } ] +SAMPLE_JSON_SCHEMA = { + "type": "object", + "properties": { + "name": { + "type": "string" + }, + "age": { + "type": "integer" + }, + "skills": { + "type": "array", + "items": { + "type": "string", + "maxLength": 10 + }, + "minItems": 3 + }, + "work_history": { + "type": "array", + "items": { + "type": "object", + "properties": { + "company": { + "type": "string" + }, + "duration": { + "type": "number" + }, + "position": { + "type": "string" + } + }, + "required": ["company", "position"] + } + } + }, + "required": ["name", "age", "skills", "work_history"] +} + @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("dtype", ["bfloat16"]) @@ -251,3 +293,43 @@ def test_mistral_function_calling( assert parsed_message.tool_calls[ 0].function.arguments == '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}' # noqa assert parsed_message.content is None + + +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.parametrize("guided_backend", + ["outlines", "lm-format-enforcer", "xgrammar"]) +def test_mistral_guided_decoding( + vllm_runner, + model: str, + guided_backend: str, +) -> None: + with vllm_runner(model, dtype='bfloat16', + tokenizer_mode="mistral") as vllm_model: + + guided_decoding = GuidedDecodingParams(json=SAMPLE_JSON_SCHEMA, + backend=guided_backend) + params = SamplingParams(max_tokens=512, + temperature=0.7, + guided_decoding=guided_decoding) + + messages = [{ + "role": "system", + "content": "you are a helpful assistant" + }, { + "role": + "user", + "content": + f"Give an example JSON for an employee profile that " + f"fits this schema: {SAMPLE_JSON_SCHEMA}" + }] + outputs = vllm_model.model.chat(messages, sampling_params=params) + + generated_text = outputs[0].outputs[0].text + json_response = json.loads(generated_text) + assert outputs is not None + + try: + jsonschema.validate(instance=json_response, + schema=SAMPLE_JSON_SCHEMA) + except jsonschema.exceptions.ValidationError: + pytest.fail("Generated response is not valid with JSON schema") diff --git a/vllm/model_executor/guided_decoding/xgrammar_decoding.py b/vllm/model_executor/guided_decoding/xgrammar_decoding.py index fc45e37cf6f06..5b97f03257502 100644 --- a/vllm/model_executor/guided_decoding/xgrammar_decoding.py +++ b/vllm/model_executor/guided_decoding/xgrammar_decoding.py @@ -3,7 +3,7 @@ import json from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any, NamedTuple +from typing import TYPE_CHECKING, Any import torch from transformers import PreTrainedTokenizerFast @@ -16,6 +16,7 @@ from vllm.model_executor.guided_decoding.xgrammar_utils import ( convert_lark_to_gbnf, grammar_is_likely_lark) +from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer if TYPE_CHECKING: from transformers import PreTrainedTokenizer @@ -37,11 +38,21 @@ def get_local_xgrammar_guided_decoding_logits_processor( return XGrammarLogitsProcessor(config) -class TokenizerData(NamedTuple): +@dataclass(frozen=True) +class TokenizerData: """Immutable container for cached tokenizer data.""" - encoded_vocab: list[str] - stop_token_ids: list[int] | None - backend_str: str + encoded_vocab: list[str] = field(default_factory=list) + stop_token_ids: list[int] | None = None + # These fields are mutually exclusive: `backend_str` is used to create a + # TokenizeInfo with `TokenizerInfo.from_huggingface` while `vocab_type` is + # used within the constructor of TokenizeInfo + backend_str: str | None = None + vocab_type: xgr.VocabType | None = None + + def __post_init__(self): + # Check for mutual exclusive + assert not (self.backend_str and self.vocab_type), \ + "backend_str and vocab_type are mutual exclusive" class TokenizerDataCache: @@ -68,18 +79,27 @@ def get_tokenizer_data(cls, "get_vocab method.") from e stop_token_ids = None - backend_str = xgr.VocabType.RAW + backend_str = "" + vocab_type = xgr.VocabType.RAW + + if stop_token_ids is None and hasattr( + tokenizer, + "eos_token_id") and tokenizer.eos_token_id is not None: + stop_token_ids = [tokenizer.eos_token_id] + if isinstance(tokenizer, PreTrainedTokenizerFast): backend_str = tokenizer.backend_tokenizer.to_str() - if stop_token_ids is None and hasattr( - tokenizer, - "eos_token_id") and tokenizer.eos_token_id is not None: - stop_token_ids = [tokenizer.eos_token_id] + vocab_type = None + + elif isinstance(tokenizer, MistralTokenizer): + # REF: https://github.com/mlc-ai/xgrammar/blob/5e141f6ff1ca02bc31f9e512e68b61f2a8ae88e5/tests/python/test_tokenizer_info.py#L43 # noqa: E501 + vocab_type = xgr.VocabType.BYTE_FALLBACK cls._cache[tokenizer_hash] = TokenizerData( encoded_vocab=encoded_vocab, stop_token_ids=stop_token_ids, - backend_str=backend_str) + backend_str=backend_str, + vocab_type=vocab_type) return cls._cache[tokenizer_hash] @@ -98,11 +118,30 @@ def get_compiler(cls, config: GrammarConfig) -> xgr.GrammarCompiler: cache_key = str(config.tokenizer_hash) if cache_key not in cls._cache: - assert config.encoded_vocab is not None - tokenizer_info = xgr.TokenizerInfo._create_from_handle( - xgr_core.TokenizerInfo.from_huggingface( - config.encoded_vocab, config.backend_str, - config.vocab_size, config.stop_token_ids)) + assert config.tokenizer_data is not None + assert config.tokenizer_data.encoded_vocab is not None + + config_data = config.tokenizer_data + + # In TokenizerDataCache.get_tokenizer_data, a serializable + # tokenizer_data is created and cached. This data is used to build + # a tokenizer_info and create an xgrammar compiler. + # - If tokenizer_data has backend_str set, use + # xgr_core.TokenizerInfo.from_huggingface (a C++ bind). + # - Otherwise, use the default constructor with vocab_type. + # - xgr_core.TokenizerInfo.from_huggingface != + # xgr.TokenizerInfo.from_huggingface. + if config_data.backend_str: + tokenizer_info = xgr.TokenizerInfo._create_from_handle( + xgr_core.TokenizerInfo.from_huggingface( + config_data.encoded_vocab, config_data.backend_str, + config.vocab_size, config_data.stop_token_ids)) + else: + tokenizer_info = xgr.TokenizerInfo( + config_data.encoded_vocab, + config_data.vocab_type, + vocab_size=config.vocab_size, + stop_token_ids=config_data.stop_token_ids) cls._cache[cache_key] = xgr.GrammarCompiler( tokenizer_info, max_threads=config.max_threads) @@ -118,10 +157,7 @@ class GrammarConfig: grammar_str: str | None = None json_object: bool | None = None max_threads: int = 8 - # Only populated if tokenizer_hash not in cache - encoded_vocab: list[str] | None = None - stop_token_ids: list[int] | None = None - backend_str: str | None = None + tokenizer_data: TokenizerData | None = None @classmethod def from_guided_params(cls, @@ -132,9 +168,6 @@ def from_guided_params(cls, tokenizer_hash = hash(tokenizer) tokenizer_data = TokenizerDataCache.get_tokenizer_data(tokenizer) - encoded_vocab = tokenizer_data.encoded_vocab - stop_token_ids = tokenizer_data.stop_token_ids - backend_str = tokenizer_data.backend_str if guided_params.json: if not isinstance(guided_params.json, str): @@ -152,11 +185,9 @@ def from_guided_params(cls, return cls(json_str=json_str, vocab_size=model_config.hf_text_config.vocab_size, - encoded_vocab=encoded_vocab, - stop_token_ids=stop_token_ids, - backend_str=backend_str, tokenizer_hash=tokenizer_hash, - max_threads=max_threads) + max_threads=max_threads, + tokenizer_data=tokenizer_data) elif guided_params.grammar: # XGrammar only supports GBNF grammars, so we must convert Lark if grammar_is_likely_lark(guided_params.grammar): @@ -181,19 +212,17 @@ def from_guided_params(cls, return cls(grammar_str=grammar_str, vocab_size=model_config.hf_text_config.vocab_size, - encoded_vocab=encoded_vocab, - stop_token_ids=stop_token_ids, - backend_str=backend_str, tokenizer_hash=tokenizer_hash, - max_threads=max_threads) + max_threads=max_threads, + tokenizer_data=tokenizer_data) elif guided_params.json_object: - return cls(json_object=True, - vocab_size=model_config.hf_text_config.vocab_size, - encoded_vocab=encoded_vocab, - stop_token_ids=stop_token_ids, - backend_str=backend_str, - tokenizer_hash=tokenizer_hash, - max_threads=max_threads) + return cls( + json_object=True, + vocab_size=model_config.hf_text_config.vocab_size, + tokenizer_hash=tokenizer_hash, + max_threads=max_threads, + tokenizer_data=tokenizer_data, + ) else: raise ValueError( "Currently only support JSON and EBNF grammar mode for xgrammar" @@ -269,10 +298,14 @@ def __call__(self, input_ids: list[int], # fill_next_token_bitmask so we move it to the device of scores device_type = scores.device.type if device_type != "cuda": - scores = scores.to("cpu") + scores = scores.to("cpu").unsqueeze(0) + + # Note: In this method, if the tensors have different dimensions + # on CPU device fails, but on GPU it runs without error. Hence the + # unsqueeze above for scores, to match the token bitmask shape xgr.apply_token_bitmask_inplace(scores, self.token_bitmask.to(scores.device)) if device_type != "cuda": - scores = scores.to(device_type) + scores = scores.to(device_type).squeeze() return scores diff --git a/vllm/transformers_utils/tokenizer.py b/vllm/transformers_utils/tokenizer.py index 54f9f895fe541..e6701f4c4b835 100644 --- a/vllm/transformers_utils/tokenizer.py +++ b/vllm/transformers_utils/tokenizer.py @@ -132,7 +132,7 @@ def get_tokenizer( if is_from_mistral_org and tokenizer_mode != "mistral": warnings.warn( 'It is strongly recommended to run mistral models with ' - '`--tokenizer_mode "mistral"` to ensure correct ' + '`--tokenizer-mode "mistral"` to ensure correct ' 'encoding and decoding.', FutureWarning, stacklevel=2) diff --git a/vllm/transformers_utils/tokenizers/mistral.py b/vllm/transformers_utils/tokenizers/mistral.py index 83b3c37d6f04c..17d722e3d88fe 100644 --- a/vllm/transformers_utils/tokenizers/mistral.py +++ b/vllm/transformers_utils/tokenizers/mistral.py @@ -314,12 +314,15 @@ def _token_to_id(t: str): if regular_tokens: decoded_list.append( - self.decode(regular_tokens)) # type: ignore + self.tokenizer.decode(regular_tokens)) # type: ignore decoded = ''.join(decoded_list) return decoded + # WARN: Outlines logits processors can overwrite this method. + # See: guided_decoding/outlines_logits_processors.py::_adapt_tokenizer + # for more. def decode(self, ids: Union[List[int], int], skip_special_tokens: bool = True) -> str: From f04e407e6b6b9ce65c16cffda836f05c2ad32682 Mon Sep 17 00:00:00 2001 From: Yan Ma Date: Wed, 18 Dec 2024 14:34:23 +0800 Subject: [PATCH 114/155] [MISC][XPU]update ipex link for CI fix (#11278) --- requirements-xpu.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements-xpu.txt b/requirements-xpu.txt index e41295792283f..42c6c321d040c 100644 --- a/requirements-xpu.txt +++ b/requirements-xpu.txt @@ -9,8 +9,8 @@ setuptools-scm>=8 wheel jinja2 -torch @ https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/torch-2.5.0a0%2Bgite84e33f-cp310-cp310-linux_x86_64.whl -intel-extension-for-pytorch @ https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/intel_extension_for_pytorch-2.5.10%2Bgit9d489a8-cp310-cp310-linux_x86_64.whl -oneccl_bind_pt @ https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/oneccl_bind_pt-2.5.0%2Bxpu-cp310-cp310-linux_x86_64.whl +torch @ https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/torch-2.5.0a0%2Bgite84e33f-cp310-cp310-linux_x86_64.whl +intel-extension-for-pytorch @ https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/intel_extension_for_pytorch-2.5.10%2Bgit9d489a8-cp310-cp310-linux_x86_64.whl +oneccl_bind_pt @ https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/oneccl_bind_pt-2.5.0%2Bxpu-cp310-cp310-linux_x86_64.whl triton-xpu == 3.0.0b1 From 60508ffda91c22e4cde3b18f149d222211db8886 Mon Sep 17 00:00:00 2001 From: Dipika Sikka Date: Wed, 18 Dec 2024 09:57:16 -0500 Subject: [PATCH 115/155] [Kernel]: Cutlass 2:4 Sparsity + FP8/Int8 Quant Support (#10995) Co-authored-by: Faraz Shahsavan Co-authored-by: ilmarkov Co-authored-by: Rahul Tuli Co-authored-by: rshaw@neuralmagic.com --- CMakeLists.txt | 26 +- .../cutlass_benchmarks/sparse_benchmarks.py | 384 ++++++++++++++ benchmarks/cutlass_benchmarks/utils.py | 96 ++++ .../cutlass_benchmarks/w8a8_benchmarks.py | 28 +- .../cutlass_benchmarks/weight_shapes.py | 2 +- csrc/core/math.hpp | 7 + csrc/cutlass_extensions/common.cpp | 11 + csrc/cutlass_extensions/common.hpp | 35 ++ .../epilogue/scaled_mm_epilogues_c3x.hpp | 4 +- csrc/ops.h | 9 + csrc/quantization/cutlass_w8a8/common.hpp | 27 - .../cutlass_w8a8/scaled_mm_c2x.cuh | 3 +- .../cutlass_w8a8/scaled_mm_c3x.cu | 3 +- .../cutlass_w8a8/scaled_mm_entry.cu | 12 +- csrc/sparse/cutlass/sparse_compressor_c3x.cu | 163 ++++++ .../sparse/cutlass/sparse_compressor_entry.cu | 42 ++ csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu | 303 +++++++++++ csrc/sparse/cutlass/sparse_scaled_mm_c3x.cuh | 496 ++++++++++++++++++ csrc/sparse/cutlass/sparse_scaled_mm_entry.cu | 59 +++ csrc/torch_bindings.cpp | 15 + pyproject.toml | 2 +- tests/kernels/test_semi_structured.py | 131 +++++ tests/quantization/test_compressed_tensors.py | 103 +++- tests/weight_loading/models.txt | 2 + .../run_model_weight_loading_test.sh | 4 + tests/weight_loading/test_weight_loading.py | 7 + vllm/_custom_ops.py | 103 ++++ .../compressed_tensors/compressed_tensors.py | 187 ++++++- .../compressed_tensors/schemes/__init__.py | 15 +- .../schemes/compressed_tensors_24.py | 203 +++++++ 30 files changed, 2365 insertions(+), 117 deletions(-) create mode 100644 benchmarks/cutlass_benchmarks/sparse_benchmarks.py create mode 100644 benchmarks/cutlass_benchmarks/utils.py create mode 100644 csrc/core/math.hpp create mode 100644 csrc/cutlass_extensions/common.cpp create mode 100644 csrc/cutlass_extensions/common.hpp delete mode 100644 csrc/quantization/cutlass_w8a8/common.hpp create mode 100644 csrc/sparse/cutlass/sparse_compressor_c3x.cu create mode 100644 csrc/sparse/cutlass/sparse_compressor_entry.cu create mode 100644 csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu create mode 100644 csrc/sparse/cutlass/sparse_scaled_mm_c3x.cuh create mode 100644 csrc/sparse/cutlass/sparse_scaled_mm_entry.cu create mode 100644 tests/kernels/test_semi_structured.py create mode 100644 vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_24.py diff --git a/CMakeLists.txt b/CMakeLists.txt index bf19b3d227171..51b49a18dddf2 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -206,7 +206,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library") # Set CUTLASS_REVISION manually -- its revision detection doesn't work in this case. - set(CUTLASS_REVISION "v3.5.1" CACHE STRING "CUTLASS revision to use") + set(CUTLASS_REVISION "v3.6.0" CACHE STRING "CUTLASS revision to use") # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR}) @@ -223,13 +223,13 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") FetchContent_Declare( cutlass GIT_REPOSITORY https://github.com/nvidia/cutlass.git - GIT_TAG v3.5.1 + GIT_TAG 8aa95dbb888be6d81c6fbf7169718c5244b53227 GIT_PROGRESS TRUE # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history. # Important: If GIT_SHALLOW is enabled then GIT_TAG works only with branch names and tags. # So if the GIT_TAG above is updated to a commit hash, GIT_SHALLOW must be set to FALSE - GIT_SHALLOW TRUE + GIT_SHALLOW FALSE ) endif() FetchContent_MakeAvailable(cutlass) @@ -241,7 +241,10 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") "csrc/quantization/awq/gemm_kernels.cu" "csrc/custom_all_reduce.cu" "csrc/permute_cols.cu" - "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu") + "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu" + "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu" + "csrc/sparse/cutlass/sparse_compressor_entry.cu" + "csrc/cutlass_extensions/common.cpp") set_gencode_flags_for_srcs( SRCS "${VLLM_EXT_SRC}" @@ -271,11 +274,14 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") endif() # - # The cutlass_scaled_mm kernels for Hopper (c3x, i.e. CUTLASS 3.x) require + # The cutlass_scaled_mm cutlass_scaled_sparse_mm, and cutlass_compressor kernels + # For Hopper (c3x, i.e. CUTLASS 3.x) require # CUDA 12.0 or later (and only work on Hopper, 9.0/9.0a for now). cuda_archs_loose_intersection(SCALED_MM_3X_ARCHS "9.0;9.0a" "${CUDA_ARCHS}") if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_3X_ARCHS) - set(SRCS "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu") + set(SRCS "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu" + "csrc/sparse/cutlass/sparse_compressor_c3x.cu" + "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu") set_gencode_flags_for_srcs( SRCS "${SRCS}" CUDA_ARCHS "${SCALED_MM_3X_ARCHS}") @@ -284,12 +290,12 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") message(STATUS "Building scaled_mm_c3x for archs: ${SCALED_MM_3X_ARCHS}") else() if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_3X_ARCHS) - message(STATUS "Not building scaled_mm_c3x as CUDA Compiler version is " + message(STATUS "Not building cutlass_c3x kernels as CUDA Compiler version is " "not >= 12.0, we recommend upgrading to CUDA 12.0 or " - "later if you intend on running FP8 quantized models on " + "later if you intend on running FP8 sparse or quantized models on " "Hopper.") else() - message(STATUS "Not building scaled_mm_c3x as no compatible archs found " + message(STATUS "Not building cutlass_c3x as no compatible archs found " "in CUDA target architectures") endif() @@ -404,7 +410,7 @@ define_gpu_extension_target( SOURCES ${VLLM_EXT_SRC} COMPILE_FLAGS ${VLLM_GPU_FLAGS} ARCHITECTURES ${VLLM_GPU_ARCHES} - INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR} + INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR};${CUTLASS_TOOLS_UTIL_INCLUDE_DIR} USE_SABI 3 WITH_SOABI) diff --git a/benchmarks/cutlass_benchmarks/sparse_benchmarks.py b/benchmarks/cutlass_benchmarks/sparse_benchmarks.py new file mode 100644 index 0000000000000..3d1c5e392f9e2 --- /dev/null +++ b/benchmarks/cutlass_benchmarks/sparse_benchmarks.py @@ -0,0 +1,384 @@ +import argparse +import copy +import itertools +import pickle as pkl +import time +from typing import Callable, Iterable, List, Tuple + +import torch +import torch.utils.benchmark as TBenchmark +from torch.utils.benchmark import Measurement as TMeasurement +from utils import make_rand_sparse_tensors +from weight_shapes import WEIGHT_SHAPES + +from vllm import _custom_ops as ops +from vllm.utils import FlexibleArgumentParser + +DEFAULT_MODELS = list(WEIGHT_SHAPES.keys()) +DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512] +DEFAULT_TP_SIZES = [1] + + +# bench +def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args, + **kwargs) -> TMeasurement: + min_run_time = 1 + + globals = { + "args": args, + "kwargs": kwargs, + "fn": fn, + } + return TBenchmark.Timer( + stmt="fn(*args, **kwargs)", + globals=globals, + label=label, + sub_label=sub_label, + description=description, + ).blocked_autorange(min_run_time=min_run_time) + + +def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str, + sub_label: str) -> Iterable[TMeasurement]: + assert dtype == torch.int8 + b_compressed, e, a, b = make_rand_sparse_tensors(torch.int8, m, n, k) + scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32) + scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32) + bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16) + + out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b, + torch.bfloat16) + out_ref = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16) + + if not torch.allclose(out, out_ref): + print("Incorrect results") + print(out) + print(out_ref) + else: + print("Correct results") + + timers = [] + # pytorch impl - bfloat16 + timers.append( + bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales", + torch.mm, a.to(dtype=torch.bfloat16), + b.to(dtype=torch.bfloat16))) + + # pytorch impl - float16 + timers.append( + bench_fn(label, sub_label, + "pytorch_fp16_fp16_fp16_matmul-no-scales", torch.mm, + a.to(dtype=torch.float16), b.to(dtype=torch.float16))) + + # cutlass impl + timers.append( + bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm", + ops.cutlass_scaled_mm, a, b, scale_a, scale_b, + torch.bfloat16)) + + # cutlass with bias + timers.append( + bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_bias", + ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16, + bias)) + + # cutlass sparse impl + timers.append( + bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm", + ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a, + scale_b, torch.bfloat16)) + + # cutlass sparse with bias + timers.append( + bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm_bias", + ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a, + scale_b, torch.bfloat16, bias)) + + return timers + + +def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str, + sub_label: str) -> Iterable[TMeasurement]: + assert dtype == torch.float8_e4m3fn + b_compressed, e, a, b = make_rand_sparse_tensors(torch.float8_e4m3fn, m, n, + k) + scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32) + scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32) + bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16) + + out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b, + torch.bfloat16) + out_ref = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16) + + if not torch.allclose(out, out_ref): + print("Incorrect results") + print(out) + print(out_ref) + else: + print("Correct results") + + timers = [] + + # pytorch impl w. bf16 + timers.append( + bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales", + torch.mm, a.to(dtype=torch.bfloat16, device="cuda"), + b.to(dtype=torch.bfloat16, device="cuda"))) + + # pytorch impl: bf16 output, without fp8 fast accum + timers.append( + bench_fn(label, + sub_label, + "pytorch_fp8_fp8_bf16_scaled_mm", + torch._scaled_mm, + a, + b, + scale_a=scale_a, + scale_b=scale_b, + out_dtype=torch.bfloat16)) + + # pytorch impl: bf16 output, with fp8 fast accum + timers.append( + bench_fn(label, + sub_label, + "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum", + torch._scaled_mm, + a, + b, + scale_a=scale_a, + scale_b=scale_b, + out_dtype=torch.bfloat16, + use_fast_accum=True)) + + # pytorch impl: fp16 output, without fp8 fast accum + timers.append( + bench_fn(label, + sub_label, + "pytorch_fp8_fp8_fp16_scaled_mm", + torch._scaled_mm, + a, + b, + scale_a=scale_a, + scale_b=scale_b, + out_dtype=torch.float16)) + + # pytorch impl: fp16 output, with fp8 fast accum + timers.append( + bench_fn(label, + sub_label, + "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum", + torch._scaled_mm, + a, + b, + scale_a=scale_a, + scale_b=scale_b, + out_dtype=torch.float16, + use_fast_accum=True)) + + # cutlass impl: bf16 output + timers.append( + bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm", + ops.cutlass_scaled_mm, a, b, scale_a, scale_b, + torch.bfloat16)) + + # cutlass impl: bf16 output + timers.append( + bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_sparse_mm", + ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a, + scale_b, torch.bfloat16)) + + # cutlass impl: fp16 output + timers.append( + bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_sparse_mm", + ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a, + scale_b, torch.float16)) + + # cutlass impl: bf16 output, with bias + timers.append( + bench_fn(label, sub_label, + "cutlass_fp8_fp8_bf16_scaled_sparse_mm_bias", + ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a, + scale_b, torch.bfloat16, bias)) + + # cutlass impl: fp16 output, with bias + timers.append( + bench_fn(label, sub_label, + "cutlass_fp8_fp8_fp16_scaled_sparse_mm_bias", + ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a, + scale_b, torch.float16, bias.to(dtype=torch.float16))) + + return timers + + +def bench(dtype: torch.dtype, m: int, k: int, n: int, label: str, + sub_label: str) -> Iterable[TMeasurement]: + if dtype == torch.int8: + return bench_int8(dtype, m, k, n, label, sub_label) + if dtype == torch.float8_e4m3fn: + return bench_fp8(dtype, m, k, n, label, sub_label) + raise ValueError("unsupported type") + + +# runner +def print_timers(timers: Iterable[TMeasurement]): + compare = TBenchmark.Compare(timers) + compare.print() + + +def run(dtype: torch.dtype, + MKNs: Iterable[Tuple[int, int, int]]) -> Iterable[TMeasurement]: + results = [] + for m, k, n in MKNs: + timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm", + f"MKN=({m}x{k}x{n})") + print_timers(timers) + results.extend(timers) + + return results + + +# output makers +def make_output(data: Iterable[TMeasurement], + MKNs: Iterable[Tuple[int, int, int]], + base_description: str, + timestamp=None): + print(f"== All Results {base_description} ====") + print_timers(data) + + # pickle all the results + timestamp = int(time.time()) if timestamp is None else timestamp + with open(f"{base_description}-{timestamp}.pkl", "wb") as f: + pkl.dump(data, f) + + +# argparse runners + + +def run_square_bench(args): + dim_sizes = list( + range(args.dim_start, args.dim_end + 1, args.dim_increment)) + MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes)) + data = run(args.dtype, MKNs) + + make_output(data, MKNs, f"square_bench-{args.dtype}") + + +def run_range_bench(args): + dim_sizes = list(range(args.dim_start, args.dim_end, args.dim_increment)) + n = len(dim_sizes) + Ms = [args.m_constant] * n if args.m_constant is not None else dim_sizes + Ks = [args.k_constant] * n if args.k_constant is not None else dim_sizes + Ns = [args.n_constant] * n if args.n_constant is not None else dim_sizes + MKNs = list(zip(Ms, Ks, Ns)) + data = run(args.dtype, MKNs) + + make_output(data, MKNs, f"range_bench-{args.dtype}") + + +def run_model_bench(args): + print("Benchmarking models:") + for i, model in enumerate(args.models): + print(f"[{i}] {model}") + + def model_shapes(model_name: str, tp_size: int) -> List[Tuple[int, int]]: + KNs = [] + for KN, tp_split_dim in copy.deepcopy(WEIGHT_SHAPES[model_name]): + KN[tp_split_dim] = KN[tp_split_dim] // tp_size + KNs.append(KN) + return KNs + + model_bench_data = [] + models_tps = list(itertools.product(args.models, args.tp_sizes)) + for model, tp_size in models_tps: + Ms = args.batch_sizes + KNs = model_shapes(model, tp_size) + MKNs = [] + for m in Ms: + for k, n in KNs: + MKNs.append((m, k, n)) + + data = run(args.dtype, MKNs) + model_bench_data.append(data) + + # Print all results + for data, model_tp in zip(model_bench_data, models_tps): + model, tp_size = model_tp + print(f"== Results {args.dtype} {model}-TP{tp_size} ====") + print_timers(data) + + timestamp = int(time.time()) + + all_data = [] + for d in model_bench_data: + all_data.extend(d) + # pickle all data + with open(f"model_bench-{args.dtype}-{timestamp}.pkl", "wb") as f: + pkl.dump(all_data, f) + + +if __name__ == '__main__': + + def to_torch_dtype(dt): + if dt == "int8": + return torch.int8 + if dt == "fp8": + return torch.float8_e4m3fn + raise ValueError("unsupported dtype") + + parser = FlexibleArgumentParser( + description=""" +Benchmark Cutlass GEMM. + + To run square GEMMs: + python3 ./benchmarks/cutlass_benchmarks/sparse_benchmarks.py --dtype fp8 square_bench --dim-start 128 --dim-end 512 --dim-increment 64 + + To run constant N and K and sweep M: + python3 ./benchmarks/cutlass_benchmarks/sparse_benchmarks.py --dtype fp8 range_bench --dim-start 128 --dim-end 512 --dim-increment 64 --n-constant 16384 --k-constant 16384 + + To run dimensions from a model: + python3 ./benchmarks/cutlass_benchmarks/sparse_benchmarks.py --dtype fp8 model_bench --models meta-llama/Llama-2-7b-hf --batch-sizes 16 --tp-sizes 1 + + Output: + - a .pkl file, that is a list of raw torch.benchmark.utils.Measurements for the pytorch and cutlass implementations for the various GEMMs. + """, # noqa: E501 + formatter_class=argparse.RawTextHelpFormatter) + + parser.add_argument("--dtype", + type=to_torch_dtype, + required=True, + help="Available options are ['int8', 'fp8']") + subparsers = parser.add_subparsers(dest="cmd") + + square_parser = subparsers.add_parser("square_bench") + square_parser.add_argument("--dim-start", type=int, required=True) + square_parser.add_argument("--dim-end", type=int, required=True) + square_parser.add_argument("--dim-increment", type=int, required=True) + square_parser.set_defaults(func=run_square_bench) + + range_parser = subparsers.add_parser("range_bench") + range_parser.add_argument("--dim-start", type=int, required=True) + range_parser.add_argument("--dim-end", type=int, required=True) + range_parser.add_argument("--dim-increment", type=int, required=True) + range_parser.add_argument("--m-constant", type=int, default=None) + range_parser.add_argument("--n-constant", type=int, default=None) + range_parser.add_argument("--k-constant", type=int, default=None) + range_parser.set_defaults(func=run_range_bench) + + model_parser = subparsers.add_parser("model_bench") + model_parser.add_argument("--models", + nargs="+", + type=str, + default=DEFAULT_MODELS, + choices=WEIGHT_SHAPES.keys()) + model_parser.add_argument("--tp-sizes", + nargs="+", + type=int, + default=DEFAULT_TP_SIZES) + model_parser.add_argument("--batch-sizes", + nargs="+", + type=int, + default=DEFAULT_BATCH_SIZES) + model_parser.set_defaults(func=run_model_bench) + + args = parser.parse_args() + args.func(args) diff --git a/benchmarks/cutlass_benchmarks/utils.py b/benchmarks/cutlass_benchmarks/utils.py new file mode 100644 index 0000000000000..ef06fcd6604dd --- /dev/null +++ b/benchmarks/cutlass_benchmarks/utils.py @@ -0,0 +1,96 @@ +# Cutlass bench utils +from typing import Iterable, Tuple + +import torch + +import vllm._custom_ops as ops + + +def to_fp8(tensor: torch.Tensor) -> torch.Tensor: + finfo = torch.finfo(torch.float8_e4m3fn) + return torch.round(tensor.clamp( + min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn) + + +def to_int8(tensor: torch.Tensor) -> torch.Tensor: + return torch.round(tensor.clamp(min=-128, max=127)).to(dtype=torch.int8) + + +def to_bf16(tensor: torch.Tensor) -> torch.Tensor: + return tensor.to(dtype=torch.bfloat16) + + +def to_fp16(tensor: torch.Tensor) -> torch.Tensor: + return tensor.to(dtype=torch.float16) + + +def make_rand_tensors(dtype: torch.dtype, m: int, n: int, + k: int) -> Tuple[torch.Tensor, torch.Tensor]: + a = torch.randn((m, k), device='cuda') * 5 + b = torch.randn((n, k), device='cuda').t() * 5 + + if dtype == torch.int8: + return to_int8(a), to_int8(b) + if dtype == torch.float8_e4m3fn: + return to_fp8(a), to_fp8(b) + + raise ValueError("unsupported dtype") + + +def prune_to_2_4(tensor): + # Reshape tensor to [N, 4] where N is number of groups of 4 + original_shape = tensor.shape + reshaped = tensor.reshape(-1, 4) + + # Get indices of top 2 absolute values in each group of 4 + _, indices = torch.topk(torch.abs(reshaped), k=2, dim=1) + + # Create binary mask + mask = torch.zeros_like(reshaped) + mask.scatter_(dim=1, + index=indices, + src=torch.ones_like(indices, dtype=mask.dtype)) + + # Apply mask and reshape back + pruned = reshaped * mask + + # Turn all -0.0 to 0.0 + pruned[pruned == -0.0] = 0.0 + + return pruned.reshape(original_shape) + + +def make_rand_sparse_tensors(dtype: torch.dtype, m: int, n: int, + k: int) -> Tuple[torch.Tensor, torch.Tensor]: + a = torch.randn((m, k), device='cuda') * 5 + b = torch.randn((n, k), device='cuda').t() * 5 + + b = prune_to_2_4(b.t()).t() + + if dtype == torch.int8: + a, b = to_int8(a), to_int8(b) + elif dtype == torch.float8_e4m3fn: + a, b = to_fp8(a), to_fp8(b) + elif dtype == torch.float16: + a, b = to_fp16(a), to_fp16(b) + elif dtype == torch.bfloat16: + a, b = to_bf16(a), to_bf16(b) + else: + raise ValueError("unsupported dtype") + + b_compressed, e = ops.cutlass_sparse_compress(b.t()) + + # Compressed B, Metadata, Original A, B + return b_compressed, e, a, b + + +def make_n_rand_sparse_tensors(num_tensors: int, dtype: torch.dtype, + m: int, n: int, k: int) -> \ + Tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]: + ABs = [] + for _ in range(num_tensors): + b_comp, e, a, b = make_rand_sparse_tensors(dtype, m, n, k) + if b_comp is not None: + ABs.append(make_rand_sparse_tensors(dtype, m, n, k)) + BComps, Es, As, Bs = zip(*ABs) + return list(BComps), list(Es), list(As), list(Bs) diff --git a/benchmarks/cutlass_benchmarks/w8a8_benchmarks.py b/benchmarks/cutlass_benchmarks/w8a8_benchmarks.py index 63cf5d50cac75..d0353bc8cb42a 100644 --- a/benchmarks/cutlass_benchmarks/w8a8_benchmarks.py +++ b/benchmarks/cutlass_benchmarks/w8a8_benchmarks.py @@ -8,6 +8,7 @@ import torch import torch.utils.benchmark as TBenchmark from torch.utils.benchmark import Measurement as TMeasurement +from utils import make_rand_tensors from weight_shapes import WEIGHT_SHAPES from vllm import _custom_ops as ops @@ -17,31 +18,6 @@ DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512] DEFAULT_TP_SIZES = [1] -# helpers - - -def to_fp8(tensor: torch.Tensor) -> torch.Tensor: - finfo = torch.finfo(torch.float8_e4m3fn) - return torch.round(tensor.clamp( - min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn) - - -def to_int8(tensor: torch.Tensor) -> torch.Tensor: - return torch.round(tensor.clamp(min=-128, max=127)).to(dtype=torch.int8) - - -def make_rand_tensors(dtype: torch.dtype, m: int, n: int, - k: int) -> Tuple[torch.Tensor, torch.Tensor]: - a = torch.randn((m, k), device='cuda') * 5 - b = torch.randn((n, k), device='cuda').t() * 5 - - if dtype == torch.int8: - return to_int8(a), to_int8(b) - if dtype == torch.float8_e4m3fn: - return to_fp8(a), to_fp8(b) - - raise ValueError("unsupported dtype") - # bench def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args, @@ -386,4 +362,4 @@ def to_torch_dtype(dt): model_parser.set_defaults(func=run_model_bench) args = parser.parse_args() - args.func(args) + args.func(args) \ No newline at end of file diff --git a/benchmarks/cutlass_benchmarks/weight_shapes.py b/benchmarks/cutlass_benchmarks/weight_shapes.py index 25ec9d6028627..d58fb0bf86374 100644 --- a/benchmarks/cutlass_benchmarks/weight_shapes.py +++ b/benchmarks/cutlass_benchmarks/weight_shapes.py @@ -40,4 +40,4 @@ ([8192, 57344], 1), ([28672, 8192], 0), ], -} +} \ No newline at end of file diff --git a/csrc/core/math.hpp b/csrc/core/math.hpp new file mode 100644 index 0000000000000..ba9f40a230c8e --- /dev/null +++ b/csrc/core/math.hpp @@ -0,0 +1,7 @@ +#include +#include + +inline uint32_t next_pow_2(uint32_t const num) { + if (num <= 1) return num; + return 1 << (CHAR_BIT * sizeof(num) - __builtin_clz(num - 1)); +} \ No newline at end of file diff --git a/csrc/cutlass_extensions/common.cpp b/csrc/cutlass_extensions/common.cpp new file mode 100644 index 0000000000000..3d2093ab94297 --- /dev/null +++ b/csrc/cutlass_extensions/common.cpp @@ -0,0 +1,11 @@ +#include "cutlass_extensions/common.hpp" + +int32_t get_sm_version_num() { + int32_t major_capability, minor_capability; + cudaDeviceGetAttribute(&major_capability, cudaDevAttrComputeCapabilityMajor, + 0); + cudaDeviceGetAttribute(&minor_capability, cudaDevAttrComputeCapabilityMinor, + 0); + int32_t version_num = major_capability * 10 + minor_capability; + return version_num; +} \ No newline at end of file diff --git a/csrc/cutlass_extensions/common.hpp b/csrc/cutlass_extensions/common.hpp new file mode 100644 index 0000000000000..85e359aa57113 --- /dev/null +++ b/csrc/cutlass_extensions/common.hpp @@ -0,0 +1,35 @@ +#pragma once + +#include "cutlass/cutlass.h" +#include +#include "cuda_runtime.h" +#include + +/** + * Helper function for checking CUTLASS errors + */ +#define CUTLASS_CHECK(status) \ + { \ + cutlass::Status error = status; \ + TORCH_CHECK(error == cutlass::Status::kSuccess, \ + cutlassGetStatusString(error)); \ + } + +/** + * Panic wrapper for unwinding CUDA runtime errors + */ +#define CUDA_CHECK(status) \ + { \ + cudaError_t error = status; \ + TORCH_CHECK(error == cudaSuccess, cudaGetErrorString(error)); \ + } + +inline int get_cuda_max_shared_memory_per_block_opt_in(int const device) { + int max_shared_mem_per_block_opt_in = 0; + cudaDeviceGetAttribute(&max_shared_mem_per_block_opt_in, + cudaDevAttrMaxSharedMemoryPerBlockOptin, + device); + return max_shared_mem_per_block_opt_in; +} + +int32_t get_sm_version_num(); diff --git a/csrc/cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp b/csrc/cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp index 95764ecddc79f..fcc17c7727f94 100644 --- a/csrc/cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp +++ b/csrc/cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp @@ -36,13 +36,13 @@ struct ScaledEpilogueBase { // Don't want to support nullptr by default template using ColLoad = cutlass::epilogue::fusion::Sm90ColBroadcast< - 0 /*Stages*/, typename EpilogueDescriptor::TileShape, T, + 0 /*Stages*/, typename EpilogueDescriptor::TileShape, T, T, Stride, Int<0>, Int<0>>, 128 / sizeof_bits_v, EnableNullPtr>; // Don't want to support nullptr by default template using RowLoad = cutlass::epilogue::fusion::Sm90RowBroadcast< - 0 /*Stages*/, typename EpilogueDescriptor::TileShape, T, + 0 /*Stages*/, typename EpilogueDescriptor::TileShape, T, T, Stride, Int<1>, Int<0>>, 128 / sizeof_bits_v, EnableNullPtr>; // This utility function constructs the arguments for the load descriptors diff --git a/csrc/ops.h b/csrc/ops.h index 816b471d062d2..c145e4eda0845 100644 --- a/csrc/ops.h +++ b/csrc/ops.h @@ -162,6 +162,15 @@ void cutlass_scaled_mm_azp(torch::Tensor& out, torch::Tensor const& a, torch::Tensor const& azp_adj, c10::optional const& azp, c10::optional const& bias); + +void cutlass_scaled_sparse_mm(torch::Tensor& out, torch::Tensor const& a, + torch::Tensor const& b, torch::Tensor const& e, + torch::Tensor const& a_scales, + torch::Tensor const& b_scales, + c10::optional const& bias); + +bool cutlass_sparse_compress_entry(torch::Tensor& a_compressed, + torch::Tensor& e, torch::Tensor const& a); #endif void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input, diff --git a/csrc/quantization/cutlass_w8a8/common.hpp b/csrc/quantization/cutlass_w8a8/common.hpp deleted file mode 100644 index bf04bb400790f..0000000000000 --- a/csrc/quantization/cutlass_w8a8/common.hpp +++ /dev/null @@ -1,27 +0,0 @@ -#pragma once - -#include "cutlass/cutlass.h" -#include - -/** - * Helper function for checking CUTLASS errors - */ -#define CUTLASS_CHECK(status) \ - { \ - TORCH_CHECK(status == cutlass::Status::kSuccess, \ - cutlassGetStatusString(status)) \ - } - -inline uint32_t next_pow_2(uint32_t const num) { - if (num <= 1) return num; - return 1 << (CHAR_BIT * sizeof(num) - __builtin_clz(num - 1)); -} - -inline int get_cuda_max_shared_memory_per_block_opt_in(int const device) { - int max_shared_mem_per_block_opt_in = 0; - cudaDeviceGetAttribute(&max_shared_mem_per_block_opt_in, - cudaDevAttrMaxSharedMemoryPerBlockOptin, - device); - return max_shared_mem_per_block_opt_in; -} - diff --git a/csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cuh b/csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cuh index d03242f44ab1d..75681f7f37820 100644 --- a/csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cuh +++ b/csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cuh @@ -21,7 +21,8 @@ #include "cutlass/epilogue/threadblock/fusion/visitors.hpp" #include "cutlass/gemm/kernel/default_gemm_universal_with_visitor.h" -#include "common.hpp" +#include "core/math.hpp" +#include "cutlass_extensions/common.hpp" // clang-format on using namespace cute; diff --git a/csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu b/csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu index 33581a63d4c3d..8190277997161 100644 --- a/csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu +++ b/csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu @@ -24,7 +24,8 @@ #include "cutlass/gemm/collective/collective_builder.hpp" #include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp" -#include "common.hpp" +#include "core/math.hpp" +#include "cutlass_extensions/common.hpp" // clang-format on using namespace cute; diff --git a/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu b/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu index 97a969cf5e3e0..4f7b6588ef3f7 100644 --- a/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu +++ b/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu @@ -3,6 +3,8 @@ #include #include +#include "cutlass_extensions/common.hpp" + void cutlass_scaled_mm_sm75(torch::Tensor& c, torch::Tensor const& a, torch::Tensor const& b, torch::Tensor const& a_scales, @@ -79,16 +81,6 @@ bool cutlass_scaled_mm_supports_fp8(int64_t cuda_device_capability) { return false; } -int32_t get_sm_version_num() { - int32_t major_capability, minor_capability; - cudaDeviceGetAttribute(&major_capability, cudaDevAttrComputeCapabilityMajor, - 0); - cudaDeviceGetAttribute(&minor_capability, cudaDevAttrComputeCapabilityMinor, - 0); - int32_t version_num = major_capability * 10 + minor_capability; - return version_num; -} - void cutlass_scaled_mm(torch::Tensor& c, torch::Tensor const& a, torch::Tensor const& b, torch::Tensor const& a_scales, torch::Tensor const& b_scales, diff --git a/csrc/sparse/cutlass/sparse_compressor_c3x.cu b/csrc/sparse/cutlass/sparse_compressor_c3x.cu new file mode 100644 index 0000000000000..218c5317b4de6 --- /dev/null +++ b/csrc/sparse/cutlass/sparse_compressor_c3x.cu @@ -0,0 +1,163 @@ +// clang-format will break include orders +// clang-format off +#include + +#include "sparse_scaled_mm_c3x.cuh" + +#include "cutlass/numeric_conversion.h" +#include "cutlass/transform/device/transform_universal_adapter.hpp" +#include "cutlass/transform/kernel/sparse_gemm_compressor.hpp" +#include "cutlass/epilogue/collective/default_epilogue.hpp" + +#include "cutlass/util/host_tensor.h" +#include "cutlass/util/packed_stride.hpp" +// clang-format on + +using namespace cute; +using namespace vllm; + +/// Make A structured sparse by replacing elements with 0 and compress it +template +bool cutlass_sparse_compress(torch::Tensor& a_nzs, torch::Tensor& a_meta, + torch::Tensor const& a) { + // Checks for conformality + TORCH_CHECK(a.dtype() == torch::kInt8 || a.dtype() == torch::kFloat8_e4m3fn || + a.dtype() == torch::kFloat16 || a.dtype() == torch::kBFloat16); + TORCH_CHECK(a.dim() == 2) + // Check for strides and alignment + TORCH_CHECK(a.stride(0) % 4 == 0) // Required for semi-structured sparsity + TORCH_CHECK(a.stride(1) == 1) + + int m = a.size(0); + int k = a.size(1); + + // Sparse kernel setup; this kernel is not used for matmul, + // but just for setting up the compressor utility + // A matrix configuration + using ElementA = ElementA_; + using LayoutTagA = cutlass::layout::RowMajor; + constexpr int AlignmentA = 128 / cutlass::sizeof_bits::value; + // B matrix configuration + using ElementB = ElementA; + using LayoutTagB = cutlass::layout::ColumnMajor; + constexpr int AlignmentB = 128 / cutlass::sizeof_bits::value; + // C/D matrix configuration + using ElementC = float; + using LayoutTagC = cutlass::layout::ColumnMajor; + constexpr int AlignmentC = 128 / cutlass::sizeof_bits::value; + // Core kernel configurations + using ElementAccumulator = ElementAcc_; + using TileShape = Shape<_128, _128, _128>; + using TileShapeRef = Shape<_128, _128, _64>; + using ClusterShape = Shape<_1, _2, _1>; + using KernelSchedule = typename std::conditional< + std::is_same_v, + cutlass::gemm::KernelTmaWarpSpecializedFP8FastAccum, + cutlass::gemm::KernelTmaWarpSpecialized>::type; + + using EpilogueSchedule = cutlass::epilogue::TmaWarpSpecialized; + using ProblemShape = Shape; + + using CollectiveEpilogue = + typename cutlass::epilogue::collective::CollectiveBuilder< + cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape, + ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto, + ElementAccumulator, ElementAccumulator, ElementC, LayoutTagC, + AlignmentC, ElementC, LayoutTagC, AlignmentC, + EpilogueSchedule>::CollectiveOp; + + using CollectiveMainloop = + typename cutlass::gemm::collective::CollectiveBuilder< + cutlass::arch::Sm90, cutlass::arch::OpClassSparseTensorOp, ElementA, + LayoutTagA, AlignmentA, ElementB, LayoutTagB, AlignmentB, + ElementAccumulator, TileShape, ClusterShape, + cutlass::gemm::collective::StageCountAutoCarveout( + sizeof(typename CollectiveEpilogue::SharedStorage))>, + KernelSchedule>::CollectiveOp; + + using GemmKernel = + cutlass::gemm::kernel::GemmUniversal; + + using Gemm = cutlass::gemm::device::GemmUniversalAdapter; + + using StrideA = cutlass::gemm::TagToStrideA_t; + using StrideE = StrideA; + + using StrideA = Stride, int64_t>; + + // The n (=1) dimension does not matter for the compressor + typename GemmKernel::ProblemShape prob_shape{m, 1, k, 1}; + + using LayoutA = typename GemmKernel::CollectiveMainloop::LayoutA; + using LayoutE = typename GemmKernel::CollectiveMainloop::LayoutE; + + using ElementE = typename GemmKernel::CollectiveMainloop::ElementE; + using SparseConfig = typename GemmKernel::CollectiveMainloop::SparseConfig; + + // Offline compressor kernel + using CompressorUtility = + cutlass::transform::kernel::StructuredSparseCompressorUtility< + ProblemShape, ElementA, LayoutTagA, SparseConfig>; + + using CompressorKernel = + cutlass::transform::kernel::StructuredSparseCompressor< + ProblemShape, ElementA, LayoutTagA, SparseConfig, + cutlass::arch::Sm90>; + + using Compressor = + cutlass::transform::device::TransformUniversalAdapter; + + auto [M, N, K, L] = prob_shape; + + StrideA stride_A; + stride_A = + cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, L)); + + CompressorUtility compressor_utility(prob_shape, stride_A); + + int ME = compressor_utility.get_metadata_m_physical(); + int KE = compressor_utility.get_metadata_k_physical(); + int KC = compressor_utility.get_tensorA_k_physical(); + + auto a_ptr = static_cast(a.data_ptr()); + + auto a_nzs_ptr = static_cast(a_nzs.data_ptr()); + auto a_meta_ptr = static_cast( + a_meta.data_ptr()); + + cutlass::KernelHardwareInfo hw_info; + hw_info.device_id = 0; + hw_info.sm_count = + cutlass::KernelHardwareInfo::query_device_multiprocessor_count( + hw_info.device_id); + typename Compressor::Arguments arguments{ + prob_shape, {a_ptr, stride_A, a_nzs_ptr, a_meta_ptr}, {hw_info}}; + + Compressor compressor_op; + size_t workspace_size = Compressor::get_workspace_size(arguments); + cutlass::device_memory::allocation workspace(workspace_size); + + CUTLASS_CHECK(compressor_op.can_implement(arguments)); + CUTLASS_CHECK(compressor_op.initialize(arguments, workspace.get())); + CUTLASS_CHECK(compressor_op.run()); + CUDA_CHECK(cudaDeviceSynchronize()); + + return true; +} + +bool cutlass_sparse_compress_sm90(torch::Tensor& a_nzs, torch::Tensor& a_meta, + torch::Tensor const& a) { + if (a.dtype() == torch::kBFloat16) { + return cutlass_sparse_compress(a_nzs, a_meta, + a); + } else if (a.dtype() == torch::kFloat16) { + return cutlass_sparse_compress(a_nzs, a_meta, a); + } else if (a.dtype() == torch::kFloat8_e4m3fn) { + return cutlass_sparse_compress(a_nzs, a_meta, + a); + } else if (a.dtype() == torch::kInt8) { + return cutlass_sparse_compress(a_nzs, a_meta, a); + } + return false; +} \ No newline at end of file diff --git a/csrc/sparse/cutlass/sparse_compressor_entry.cu b/csrc/sparse/cutlass/sparse_compressor_entry.cu new file mode 100644 index 0000000000000..d23d937b6ac28 --- /dev/null +++ b/csrc/sparse/cutlass/sparse_compressor_entry.cu @@ -0,0 +1,42 @@ +#include + +#include +#include + +#include "cutlass_extensions/common.hpp" + +#if defined ENABLE_SCALED_MM_C3X && ENABLE_SCALED_MM_C3X +bool cutlass_sparse_compress_sm90(torch::Tensor& a_nzs, torch::Tensor& a_meta, + torch::Tensor const& a); +#endif + +bool cutlass_sparse_compress_entry(torch::Tensor& a_nzs, torch::Tensor& a_meta, + torch::Tensor const& a) { + // Checks for conformality + TORCH_CHECK(a.dim() == 2 && a_meta.dim() == 2 && a_nzs.dim() == 2); + TORCH_CHECK(a.size(0) == a_nzs.size(0) && a.size(0) == a_meta.size(0) && + a_nzs.size(1) * 2 == a.size(1) && + a_meta.size(1) * 2 * 4 == a.size(1)); + // Considering elemsPerMetaElem = 8b / 2b_per_nz = 4 + + // Check for strides and alignment + TORCH_CHECK(a.stride(1) == 1 && a_nzs.stride(1) == 1 && + a_meta.stride(1) == 1); // Row-major + TORCH_CHECK(a.stride(0) % 8 == 0); // 8 Byte Alignment for Compression + + at::cuda::OptionalCUDAGuard const device_guard(device_of(a)); + int32_t version_num = get_sm_version_num(); + + // Guard against compilation issues for sm90 kernels +#if defined ENABLE_SCALED_MM_C3X && ENABLE_SCALED_MM_C3X + if (version_num >= 90) { + return cutlass_sparse_compress_sm90(a_nzs, a_meta, a); + } +#endif + + TORCH_CHECK_NOT_IMPLEMENTED( + false, + "No compiled cutlass_scaled_sparse_mm for a compute capability less than " + "CUDA device capability: ", + version_num); +} diff --git a/csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu b/csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu new file mode 100644 index 0000000000000..b50e9a3a2c240 --- /dev/null +++ b/csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu @@ -0,0 +1,303 @@ +// clang-format will break include orders +// clang-format off +#include + +#if defined CUDA_VERSION && CUDA_VERSION >= 12000 +#include "sparse_scaled_mm_c3x.cuh" +// clang-format on + +using namespace cute; +using namespace vllm; + +template typename Epilogue, + typename... EpilogueArgs> +void cutlass_gemm_sm90_fp8_dispatch(torch::Tensor& out, torch::Tensor const& a, + torch::Tensor const& bt_nzs, + torch::Tensor const& bt_meta, + EpilogueArgs&&... args) { + static_assert(std::is_same()); + TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn); + TORCH_CHECK(bt_meta.dtype() == torch::kUInt8); + TORCH_CHECK(bt_nzs.dtype() == torch::kFloat8_e4m3fn); + + using Cutlass3xGemmDefault = + typename sm90_config_default::Cutlass3xGemm; + using Cutlass3xGemmM64 = + typename sm90_fp8_config_M64::Cutlass3xGemm; + using Cutlass3xGemmM128 = + typename sm90_fp8_config_M128::Cutlass3xGemm; + using Cutlass3xGemmM256 = + typename sm90_fp8_config_M256::Cutlass3xGemm; + using Cutlass3xGemmM512 = + typename sm90_fp8_config_M512::Cutlass3xGemm; + + using Cutlass3xGemm1 = + typename sm90_fp8_config_1::Cutlass3xGemm; + using Cutlass3xGemm2 = + typename sm90_fp8_config_2::Cutlass3xGemm; + using Cutlass3xGemm3 = + typename sm90_fp8_config_3::Cutlass3xGemm; + using Cutlass3xGemm4 = + typename sm90_fp8_config_4::Cutlass3xGemm; + using Cutlass3xGemm5 = + typename sm90_fp8_config_5::Cutlass3xGemm; + using Cutlass3xGemm6 = + typename sm90_fp8_config_6::Cutlass3xGemm; + using Cutlass3xGemm7 = + typename sm90_fp8_config_7::Cutlass3xGemm; + using Cutlass3xGemm8 = + typename sm90_fp8_config_8::Cutlass3xGemm; + + uint32_t const n = bt_nzs.size(0); + uint32_t const m = a.size(0); // Batch size + uint32_t const mp2 = + std::max(static_cast(64), next_pow_2(m)); // next power of 2 + + if (mp2 <= 64) { + if (n == 28672) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else if (n == 4096 || n == 6144) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } + } else if (mp2 <= 128) { + if (n == 4096) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else if (n == 28672) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else if (n == 6144) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } + } else if (mp2 <= 256) { + if (n == 4096) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else if (n == 28672) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else if (n == 6144) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } + } else { + if (n == 6144 || n == 28672) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else if (n == 4096) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } + } + + // Otherwise the default heuristic + if (mp2 <= 64) { + // n in [1, 64] + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else if (mp2 <= 128) { + // n in (64, 128] + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else if (mp2 <= 256) { + // n in (128, 256] + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else { + // n in (256, inf) + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } +} + +template typename Epilogue, + typename... EpilogueArgs> +void cutlass_gemm_sm90_fp16_dispatch(torch::Tensor& out, torch::Tensor const& a, + torch::Tensor const& bt_nzs, + torch::Tensor const& bt_meta, + EpilogueArgs&&... args) { + static_assert(std::is_same()); + TORCH_CHECK(a.dtype() == torch::kFloat16); + TORCH_CHECK(bt_meta.dtype() == torch::kUInt8); + TORCH_CHECK(bt_nzs.dtype() == torch::kFloat16); + + using Cutlass3xGemmDefault = + typename sm90_config_default::Cutlass3xGemm; + + // m in (128, inf) + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); +} + +template typename Epilogue, + typename... EpilogueArgs> +void cutlass_gemm_sm90_bf16_dispatch(torch::Tensor& out, torch::Tensor const& a, + torch::Tensor const& bt_nzs, + torch::Tensor const& bt_meta, + EpilogueArgs&&... args) { + static_assert(std::is_same()); + TORCH_CHECK(a.dtype() == torch::kBFloat16); + TORCH_CHECK(bt_meta.dtype() == torch::kUInt8); + TORCH_CHECK(bt_nzs.dtype() == torch::kBFloat16); + + using Cutlass3xGemmDefault = + typename sm90_config_default::Cutlass3xGemm; + + // m in (128, inf) + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); +} + +template typename Epilogue, + typename... EpilogueArgs> +void cutlass_gemm_sm90_int8_dispatch(torch::Tensor& out, torch::Tensor const& a, + torch::Tensor const& bt_nzs, + torch::Tensor const& bt_meta, + EpilogueArgs&&... args) { + static_assert(std::is_same()); + TORCH_CHECK(a.dtype() == torch::kInt8); + TORCH_CHECK(bt_meta.dtype() == torch::kUInt8); + TORCH_CHECK(bt_nzs.dtype() == torch::kInt8); + + using Cutlass3xGemmDefault = + typename sm90_config_default::Cutlass3xGemm; + using Cutlass3xGemmM128 = + typename sm90_int8_config_M128::Cutlass3xGemm; + using Cutlass3xGemmM64 = + typename sm90_int8_config_M64::Cutlass3xGemm; + using Cutlass3xGemmM32NBig = + typename sm90_int8_config_M32_NBig::Cutlass3xGemm; + using Cutlass3xGemmM32NSmall = + typename sm90_int8_config_M32_NSmall::Cutlass3xGemm; + + uint32_t const n = out.size(1); + bool const is_small_n = n < 8192; + + uint32_t const m = a.size(0); + uint32_t const mp2 = + std::max(static_cast(32), next_pow_2(m)); // next power of 2 + + if (mp2 <= 32) { + // m in [1, 32] + if (is_small_n) { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else { + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } + } else if (mp2 <= 64) { + // m in (32, 64] + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else if (mp2 <= 128) { + // m in (64, 128] + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } else { + // m in (128, inf) + return cutlass_sparse_gemm_caller( + out, a, bt_nzs, bt_meta, std::forward(args)...); + } +} + +template