feat: bump accelerate to 1.0.0

.github/workflows/preset-image-build.yml

@@ -23,7 +23,10 @@ on:
         type: boolean
         default: false
         description: "Run all models for build"
-
+      force-run-all-public:
+        type: boolean
+        default: false
+        description: "Run all public models for build"
 env:
     GO_VERSION: "1.22"
     BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
@@ -49,14 +52,17 @@ jobs:
 
       - name: Set FORCE_RUN_ALL Flag
         id: set_force_run_all
-        run: echo "FORCE_RUN_ALL=${{ github.event_name == 'workflow_dispatch' && github.event.inputs.force-run-all == 'true' }}" >> $GITHUB_OUTPUT
-
+        run: |
+          echo "FORCE_RUN_ALL=${{ github.event_name == 'workflow_dispatch' && github.event.inputs.force-run-all == 'true' }}" >> $GITHUB_OUTPUT
+          echo "FORCE_RUN_ALL_PUBLIC=${{ github.event_name == 'workflow_dispatch' && github.event.inputs.force-run-all-public == 'true' }}" >> $GITHUB_OUTPUT
+
       # This script should output a JSON array of model names
       - name: Determine Affected Models
         id: affected_models
         run: |
           PR_BRANCH=${{ env.BRANCH_NAME }} \
           FORCE_RUN_ALL=${{ steps.set_force_run_all.outputs.FORCE_RUN_ALL }} \
+          FORCE_RUN_ALL_PUBLIC=${{ steps.set_force_run_all.outputs.FORCE_RUN_ALL_PUBLIC }} \
           python3 .github/workflows/kind-cluster/determine_models.py
 
       - name: Print Determined Models

docker/presets/models/tfs/Dockerfile

@@ -7,6 +7,9 @@ ARG VERSION
 # Set the working directory
 WORKDIR /workspace
 
+# Model weights
+COPY ${WEIGHTS_PATH} /workspace/weights
+
 COPY kaito/presets/workspace/dependencies/requirements.txt /workspace/requirements.txt
 
 RUN pip install --no-cache-dir -r /workspace/requirements.txt
@@ -26,8 +29,6 @@ COPY kaito/presets/workspace/inference/vllm/inference_api.py /workspace/vllm/inf
 # Chat template
 ADD kaito/presets/workspace/inference/chat_templates /workspace/chat_templates
 
-# Model weights
-COPY ${WEIGHTS_PATH} /workspace/weights
 RUN echo $VERSION > /workspace/version.txt && \
     ln -s /workspace/weights /workspace/tfs/weights && \
     ln -s /workspace/weights /workspace/vllm/weights

presets/workspace/dependencies/requirements.txt

@@ -2,9 +2,9 @@
 
 # Core Dependencies
 vllm==0.6.3
-transformers >= 4.45.0
+transformers == 4.45.0
 torch==2.4.0
-accelerate==0.30.1
+accelerate==1.0.0
 fastapi>=0.111.0,<0.112.0  # Allow patch updates
 pydantic>=2.9
 uvicorn[standard]>=0.29.0,<0.30.0  # Allow patch updates

presets/workspace/inference/text-generation/inference_api.py

@@ -103,7 +103,7 @@ def load_chat_template(chat_template: Optional[str]) -> Optional[str]:
         resolved_chat_template = Path(chat_template).read_text()
 
     logger.info("Chat template loaded successfully")
-    logger.info("Chat template: %s", resolved_chat_template)
+    logger.info("Chat template:\n%s", resolved_chat_template)
     return resolved_chat_template
 
 

presets/workspace/tuning/text-generation/cli.py

@@ -2,8 +2,6 @@
 # Licensed under the MIT license.
 import os
 from dataclasses import dataclass, field
-from datetime import datetime
-from enum import Enum, auto
 from typing import Any, Dict, List, Optional
 
 import torch
@@ -78,6 +76,7 @@ class ModelConfig:
     load_in_8bit: bool = field(default=False, metadata={"help": "Load model in 8-bit mode"})
     torch_dtype: Optional[str] = field(default=None, metadata={"help": "The torch dtype for the pre-trained model"})
     device_map: str = field(default="auto", metadata={"help": "The device map for the pre-trained model"})
+    chat_template: Optional[str] = field(default=None, metadata={"help": "The file path to the chat template, or the template in single-line form for the specified model"})
 
     def __post_init__(self):
         """
@@ -89,6 +88,12 @@ def __post_init__(self):
             elif not isinstance(self.torch_dtype, torch.dtype):
                 raise ValueError(f"Invalid torch dtype: {self.torch_dtype}")
 
+    def get_tokenizer_args(self):
+        return {k: v for k, v in self.__dict__.items() if k not in ["torch_dtype", "chat_template"]}
+
+    def get_model_args(self):
+        return {k: v for k, v in self.__dict__.items() if k not in ["chat_template"]}
+
 @dataclass
 class QuantizationConfig(BitsAndBytesConfig):
     """

presets/workspace/tuning/text-generation/fine_tuning.py

@@ -2,26 +2,26 @@
 # Licensed under the MIT license.
 import logging
 import os
-import sys
 from dataclasses import asdict
 from datetime import datetime
-from parser import parse_configs
+from parser import parse_configs, load_chat_template
 
 import torch
-import transformers
 from accelerate import Accelerator
 from dataset import DatasetManager
 from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
-                          BitsAndBytesConfig, HfArgumentParser, Trainer,
-                          TrainerCallback, TrainerControl, TrainerState,
-                          TrainingArguments)
+                          BitsAndBytesConfig,
+                          TrainerCallback, TrainerControl, TrainerState)
 from trl import SFTTrainer
 
 # Initialize logger
 logger = logging.getLogger(__name__)
 debug_mode = os.environ.get('DEBUG_MODE', 'false').lower() == 'true'
-logging.basicConfig(level=logging.DEBUG if debug_mode else logging.INFO)
+logging.basicConfig(
+    level=logging.DEBUG if debug_mode else logging.INFO,
+    format='%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s',
+    datefmt='%m-%d %H:%M:%S')
 
 CONFIG_YAML = os.environ.get('YAML_FILE_PATH', '/mnt/config/training_config.yaml')
 parsed_configs = parse_configs(CONFIG_YAML)
@@ -36,7 +36,7 @@
 accelerator = Accelerator()
 
 # Load Model Args
-model_args = asdict(model_config)
+model_args = model_config.get_model_args()
 if accelerator.distributed_type != "NO":  # Meaning we require distributed training
     logger.debug("Setting device map for distributed training")
     model_args["device_map"] = {"": accelerator.process_index}
@@ -47,10 +47,13 @@
 enable_qlora = bnb_config.is_quantizable()
 
 # Load the Pre-Trained Tokenizer
-tokenizer_args = {key: value for key, value in model_args.items() if key != "torch_dtype"}
+tokenizer_args = model_config.get_tokenizer_args()
+resovled_chat_template = load_chat_template(model_config.chat_template)
 tokenizer = AutoTokenizer.from_pretrained(**tokenizer_args)
 if not tokenizer.pad_token:
     tokenizer.pad_token = tokenizer.eos_token
+if resovled_chat_template is not None:
+    tokenizer.chat_template = resovled_chat_template
 if dc_args.mlm and tokenizer.mask_token is None:
     logger.warning(
         "This tokenizer does not have a mask token which is necessary for masked language modeling. "

presets/workspace/tuning/text-generation/parser.py

@@ -1,14 +1,17 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-import os
-import sys
-from collections import defaultdict
+import logging
 from dataclasses import asdict, fields
+import codecs
+from pathlib import Path
+from typing import Optional
 
 import yaml
 from cli import (DatasetConfig, ExtDataCollator, ExtLoraConfig, ModelConfig, QuantizationConfig)
 from transformers import HfArgumentParser, TrainingArguments
 
+logger = logging.getLogger(__name__)
+
 # Mapping from config section names to data classes
 CONFIG_CLASS_MAP = {
     'ModelConfig': ModelConfig,
@@ -69,3 +72,18 @@ def parse_configs(config_yaml):
         parsed_configs[section_name] = CONFIG_CLASS_MAP[section_name](**filtered_config)
 
     return parsed_configs
+
+def load_chat_template(chat_template: Optional[str]) -> Optional[str]:
+    logger.info(chat_template)
+    if chat_template is None:
+        return None
+
+    JINJA_CHARS = "{}\n"
+    if any(c in chat_template for c in JINJA_CHARS):
+        resolved_chat_template = codecs.decode(chat_template, "unicode_escape")
+    else:
+        resolved_chat_template = Path(chat_template).read_text()
+
+    logger.info("Chat template loaded successfully")
+    logger.info("Chat template:\n%s", resolved_chat_template)
+    return resolved_chat_template