Grok-1 optimization #164
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| """Benchmark the latency of processing a single batch of requests.""" | ||
| import argparse | ||
| import json | ||
| import time | ||
| from pathlib import Path | ||
| from typing import List, Optional | ||
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| import numpy as np | ||
| import torch | ||
| from tqdm import tqdm | ||
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| from vllm import LLM, SamplingParams | ||
| from vllm.inputs import PromptStrictInputs | ||
| from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS | ||
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| from data_loader import * | ||
| from rpd_handler import * | ||
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| def main(args: argparse.Namespace): | ||
| print(args) | ||
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| # NOTE(woosuk): If the request cannot be processed in a single batch, | ||
| # the engine will automatically process the request in multiple batches. | ||
| llm = LLM(model=args.model, | ||
| speculative_model=args.speculative_model, | ||
| num_speculative_tokens=args.num_speculative_tokens, | ||
| tokenizer=args.tokenizer, | ||
| quantization=args.quantization, | ||
| quantized_weights_path=args.quantized_weights_path, | ||
| tensor_parallel_size=args.tensor_parallel_size, | ||
| trust_remote_code=args.trust_remote_code, | ||
| dtype=args.dtype, | ||
| enforce_eager=args.enforce_eager, | ||
| kv_cache_dtype=args.kv_cache_dtype, | ||
| quantization_param_path=args.quantization_param_path, | ||
| device=args.device, | ||
| ray_workers_use_nsight=args.ray_workers_use_nsight, | ||
| worker_use_ray=args.worker_use_ray, | ||
| use_v2_block_manager=args.use_v2_block_manager, | ||
| enable_chunked_prefill=args.enable_chunked_prefill, | ||
| download_dir=args.download_dir, | ||
| block_size=args.block_size, | ||
| disable_custom_all_reduce=args.disable_custom_all_reduce, | ||
| gpu_memory_utilization=args.gpu_memory_utilization) | ||
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| sampling_params = SamplingParams( | ||
| n=args.n, | ||
| temperature=0.0 if args.use_beam_search else 1.0, | ||
| top_p=1.0, | ||
| use_beam_search=args.use_beam_search, | ||
| ignore_eos=True, | ||
| max_tokens=args.output_len, | ||
| ) | ||
| print(sampling_params) | ||
| #dummy_prompt_token_ids = np.random.randint(10000, | ||
| # size=(args.batch_size, | ||
| # args.input_len)) | ||
| #dummy_inputs: List[PromptStrictInputs] = [{ | ||
| # "prompt_token_ids": batch | ||
| #} for batch in dummy_prompt_token_ids.tolist()] | ||
| #from transformers import AutoTokenizer | ||
| #tokenizer = AutoTokenizer.from_pretrained("hpcai-tech/grok-1", trust_remote_code=True) | ||
| #input_tokens = get_input_sentences(args.batch_size, args.input_len, "wikitext", 'wikitext-2-raw-v1', tokenizer) | ||
| #prompts = [tokenizer.decode(sample) for sample in input_tokens] | ||
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| prompts = ["Chronicles III . Development work took approximately one year . After the release of Valkyria Chronicles II"] | ||
| #prompts = ["Replace this with your text."] | ||
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| #dummy_inputs: List[PromptStrictInputs] = [{ | ||
| # "prompt_token_ids": batch | ||
| #} for batch in input_tokens] | ||
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| def run_to_completion(profile_dir: Optional[str] = None): | ||
| if profile_dir: | ||
| with torch.profiler.profile( | ||
| activities=[ | ||
| torch.profiler.ProfilerActivity.CPU, | ||
| torch.profiler.ProfilerActivity.CUDA, | ||
| ], | ||
| on_trace_ready=torch.profiler.tensorboard_trace_handler( | ||
| str(profile_dir))) as p: | ||
| llm.generate(prompts, | ||
| sampling_params=sampling_params, | ||
| use_tqdm=False) | ||
| print(p.key_averages()) | ||
| else: | ||
| start_time = time.perf_counter() | ||
| outputs = llm.generate(prompts, | ||
| sampling_params=sampling_params, | ||
| use_tqdm=False) | ||
| end_time = time.perf_counter() | ||
| for output in outputs: | ||
| prompt = output.prompt | ||
| generated_text = output.outputs[0].text | ||
| print(f"Prompt: {prompt!r}") | ||
| print(f"Generated text: {generated_text!r}") | ||
| latency = end_time - start_time | ||
| return latency | ||
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| print("Warming up...") | ||
| for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"): | ||
| run_to_completion(profile_dir=None) | ||
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| if args.profile: | ||
| profile_dir = args.profile_result_dir | ||
| if not profile_dir: | ||
| profile_dir = Path( | ||
| "." | ||
| ) / "vllm_benchmark_result" / f"latency_result_{time.time()}" | ||
| print(f"Profiling (results will be saved to '{profile_dir}')...") | ||
| run_to_completion(profile_dir=profile_dir) | ||
| return | ||
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| # Benchmark. | ||
| latencies = [] | ||
| if args.enable_prof: | ||
| profiler = HipTx() | ||
| profiler.start_profiling() | ||
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| for _ in tqdm(range(args.num_iters), desc="Profiling iterations"): | ||
| latencies.append(run_to_completion(profile_dir=None)) | ||
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| if args.enable_prof: | ||
| profiler.stop_profiling() | ||
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| latencies = np.array(latencies) | ||
| percentages = [10, 25, 50, 75, 90] | ||
| percentiles = np.percentile(latencies, percentages) | ||
| print(f'Avg latency: {np.mean(latencies)} seconds') | ||
| for percentage, percentile in zip(percentages, percentiles): | ||
| print(f'{percentage}% percentile latency: {percentile} seconds') | ||
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| # Output JSON results if specified | ||
| if args.output_json: | ||
| results = { | ||
| "avg_latency": np.mean(latencies), | ||
| "latencies": latencies.tolist(), | ||
| "percentiles": dict(zip(percentages, percentiles.tolist())), | ||
| } | ||
| with open(args.output_json, "w") as f: | ||
| json.dump(results, f, indent=4) | ||
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| if __name__ == '__main__': | ||
| parser = argparse.ArgumentParser( | ||
| description='Benchmark the latency of processing a single batch of ' | ||
| 'requests till completion.') | ||
| parser.add_argument('--model', type=str, default='facebook/opt-125m') | ||
| parser.add_argument('--speculative-model', type=str, default=None) | ||
| parser.add_argument('--num-speculative-tokens', type=int, default=None) | ||
| parser.add_argument('--tokenizer', type=str, default=None) | ||
| parser.add_argument('--quantization', | ||
| '-q', | ||
| choices=[*QUANTIZATION_METHODS, None], | ||
| default=None) | ||
| parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1) | ||
| parser.add_argument('--input-len', type=int, default=32) | ||
| parser.add_argument('--output-len', type=int, default=128) | ||
| parser.add_argument('--batch-size', type=int, default=8) | ||
| parser.add_argument('--n', | ||
| type=int, | ||
| default=1, | ||
| help='Number of generated sequences per prompt.') | ||
| parser.add_argument('--use-beam-search', action='store_true') | ||
| parser.add_argument('--num-iters-warmup', | ||
| type=int, | ||
| default=0, | ||
| help='Number of iterations to run for warmup.') | ||
| parser.add_argument('--num-iters', | ||
| type=int, | ||
| default=1, | ||
| help='Number of iterations to run.') | ||
| parser.add_argument('--trust-remote-code', | ||
| action='store_true', | ||
| help='trust remote code from huggingface') | ||
| parser.add_argument( | ||
| '--dtype', | ||
| type=str, | ||
| default='auto', | ||
| choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'], | ||
| help='data type for model weights and activations. ' | ||
| 'The "auto" option will use FP16 precision ' | ||
| 'for FP32 and FP16 models, and BF16 precision ' | ||
| 'for BF16 models.') | ||
| parser.add_argument('--enforce-eager', | ||
| action='store_true', | ||
| help='enforce eager mode and disable CUDA graph') | ||
| parser.add_argument( | ||
| '--kv-cache-dtype', | ||
| type=str, | ||
| choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'], | ||
| default="auto", | ||
| help='Data type for kv cache storage. If "auto", will use model ' | ||
| 'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ' | ||
| 'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)') | ||
| parser.add_argument( | ||
| '--quantization-param-path', | ||
| type=str, | ||
| default=None, | ||
| help='Path to the JSON file containing the KV cache scaling factors. ' | ||
| 'This should generally be supplied, when KV cache dtype is FP8. ' | ||
| 'Otherwise, KV cache scaling factors default to 1.0, which may cause ' | ||
| 'accuracy issues. FP8_E5M2 (without scaling) is only supported on ' | ||
| 'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is ' | ||
| 'instead supported for common inference criteria.') | ||
| parser.add_argument( | ||
| '--quantized-weights-path', | ||
| type=str, | ||
| default=None, | ||
| help='Path to the safetensor file containing the quantized weights ' | ||
| 'and scaling factors. This should generally be supplied, when ' | ||
| 'quantization is FP8.') | ||
| parser.add_argument( | ||
| '--profile', | ||
| action='store_true', | ||
| help='profile the generation process of a single batch') | ||
| parser.add_argument( | ||
| '--profile-result-dir', | ||
| type=str, | ||
| default=None, | ||
| help=('path to save the pytorch profiler output. Can be visualized ' | ||
| 'with ui.perfetto.dev or Tensorboard.')) | ||
| parser.add_argument( | ||
| "--device", | ||
| type=str, | ||
| default="cuda", | ||
| choices=["cuda", "cpu"], | ||
| help='device type for vLLM execution, supporting CUDA and CPU.') | ||
| parser.add_argument('--block-size', | ||
| type=int, | ||
| default=16, | ||
| help='block size of key/value cache') | ||
| parser.add_argument( | ||
| '--enable-chunked-prefill', | ||
| action='store_true', | ||
| help='If True, the prefill requests can be chunked based on the ' | ||
| 'max_num_batched_tokens') | ||
| parser.add_argument('--use-v2-block-manager', action='store_true') | ||
| parser.add_argument( | ||
| "--ray-workers-use-nsight", | ||
| action='store_true', | ||
| help="If specified, use nsight to profile ray workers", | ||
| ) | ||
| parser.add_argument('--worker-use-ray', | ||
| action='store_true', | ||
| help='use Ray for distributed serving, will be ' | ||
| 'automatically set when using more than 1 GPU ' | ||
| 'unless on ROCm where the default is torchrun') | ||
| parser.add_argument('--download-dir', | ||
| type=str, | ||
| default=None, | ||
| help='directory to download and load the weights, ' | ||
| 'default to the default cache dir of huggingface') | ||
| parser.add_argument( | ||
| '--output-json', | ||
| type=str, | ||
| default=None, | ||
| help='Path to save the latency results in JSON format.') | ||
| parser.add_argument('--disable_custom_all_reduce', action='store_true') | ||
| parser.add_argument('--gpu-memory-utilization', | ||
| type=float, | ||
| default=0.9, | ||
| help='the fraction of GPU memory to be used for ' | ||
| 'the model executor, which can range from 0 to 1.' | ||
| 'If unspecified, will use the default value of 0.9.') | ||
| parser.add_argument( | ||
| "--enable-prof", | ||
| action='store_true', | ||
| help="enable profiler.") | ||
| args = parser.parse_args() | ||
| main(args) |
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This looks to be a typo, probably does not build.