File size: 9,245 Bytes
ce587a5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
import os
import json
import logging
from torch.cuda import device_count
from vllm import AsyncEngineArgs
from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
RENAME_ARGS_MAP = {
"MODEL_NAME": "model",
"MODEL_REVISION": "revision",
"TOKENIZER_NAME": "tokenizer",
"MAX_CONTEXT_LEN_TO_CAPTURE": "max_seq_len_to_capture"
}
DEFAULT_ARGS = {
"disable_log_stats": os.getenv('DISABLE_LOG_STATS', 'False').lower() == 'true',
"disable_log_requests": os.getenv('DISABLE_LOG_REQUESTS', 'False').lower() == 'true',
"gpu_memory_utilization": float(os.getenv('GPU_MEMORY_UTILIZATION', 0.95)),
"pipeline_parallel_size": int(os.getenv('PIPELINE_PARALLEL_SIZE', 1)),
"tensor_parallel_size": int(os.getenv('TENSOR_PARALLEL_SIZE', 1)),
"served_model_name": os.getenv('SERVED_MODEL_NAME', None),
"tokenizer": os.getenv('TOKENIZER', None),
"skip_tokenizer_init": os.getenv('SKIP_TOKENIZER_INIT', 'False').lower() == 'true',
"tokenizer_mode": os.getenv('TOKENIZER_MODE', 'auto'),
"trust_remote_code": os.getenv('TRUST_REMOTE_CODE', 'False').lower() == 'true',
"download_dir": os.getenv('DOWNLOAD_DIR', None),
"load_format": os.getenv('LOAD_FORMAT', 'auto'),
"dtype": os.getenv('DTYPE', 'auto'),
"kv_cache_dtype": os.getenv('KV_CACHE_DTYPE', 'auto'),
"quantization_param_path": os.getenv('QUANTIZATION_PARAM_PATH', None),
"seed": int(os.getenv('SEED', 0)),
"max_model_len": int(os.getenv('MAX_MODEL_LEN', 0)) or None,
"worker_use_ray": os.getenv('WORKER_USE_RAY', 'False').lower() == 'true',
"distributed_executor_backend": os.getenv('DISTRIBUTED_EXECUTOR_BACKEND', None),
"max_parallel_loading_workers": int(os.getenv('MAX_PARALLEL_LOADING_WORKERS', 0)) or None,
"block_size": int(os.getenv('BLOCK_SIZE', 16)),
"enable_prefix_caching": os.getenv('ENABLE_PREFIX_CACHING', 'False').lower() == 'true',
"disable_sliding_window": os.getenv('DISABLE_SLIDING_WINDOW', 'False').lower() == 'true',
"use_v2_block_manager": os.getenv('USE_V2_BLOCK_MANAGER', 'False').lower() == 'true',
"swap_space": int(os.getenv('SWAP_SPACE', 4)), # GiB
"cpu_offload_gb": int(os.getenv('CPU_OFFLOAD_GB', 0)), # GiB
"max_num_batched_tokens": int(os.getenv('MAX_NUM_BATCHED_TOKENS', 0)) or None,
"max_num_seqs": int(os.getenv('MAX_NUM_SEQS', 256)),
"max_logprobs": int(os.getenv('MAX_LOGPROBS', 20)), # Default value for OpenAI Chat Completions API
"revision": os.getenv('REVISION', None),
"code_revision": os.getenv('CODE_REVISION', None),
"rope_scaling": os.getenv('ROPE_SCALING', None),
"rope_theta": float(os.getenv('ROPE_THETA', 0)) or None,
"tokenizer_revision": os.getenv('TOKENIZER_REVISION', None),
"quantization": os.getenv('QUANTIZATION', None),
"enforce_eager": os.getenv('ENFORCE_EAGER', 'False').lower() == 'true',
"max_context_len_to_capture": int(os.getenv('MAX_CONTEXT_LEN_TO_CAPTURE', 0)) or None,
"max_seq_len_to_capture": int(os.getenv('MAX_SEQ_LEN_TO_CAPTURE', 8192)),
"disable_custom_all_reduce": os.getenv('DISABLE_CUSTOM_ALL_REDUCE', 'False').lower() == 'true',
"tokenizer_pool_size": int(os.getenv('TOKENIZER_POOL_SIZE', 0)),
"tokenizer_pool_type": os.getenv('TOKENIZER_POOL_TYPE', 'ray'),
"tokenizer_pool_extra_config": os.getenv('TOKENIZER_POOL_EXTRA_CONFIG', None),
"enable_lora": os.getenv('ENABLE_LORA', 'False').lower() == 'true',
"max_loras": int(os.getenv('MAX_LORAS', 1)),
"max_lora_rank": int(os.getenv('MAX_LORA_RANK', 16)),
"enable_prompt_adapter": os.getenv('ENABLE_PROMPT_ADAPTER', 'False').lower() == 'true',
"max_prompt_adapters": int(os.getenv('MAX_PROMPT_ADAPTERS', 1)),
"max_prompt_adapter_token": int(os.getenv('MAX_PROMPT_ADAPTER_TOKEN', 0)),
"fully_sharded_loras": os.getenv('FULLY_SHARDED_LORAS', 'False').lower() == 'true',
"lora_extra_vocab_size": int(os.getenv('LORA_EXTRA_VOCAB_SIZE', 256)),
"long_lora_scaling_factors": tuple(map(float, os.getenv('LONG_LORA_SCALING_FACTORS', '').split(','))) if os.getenv('LONG_LORA_SCALING_FACTORS') else None,
"lora_dtype": os.getenv('LORA_DTYPE', 'auto'),
"max_cpu_loras": int(os.getenv('MAX_CPU_LORAS', 0)) or None,
"device": os.getenv('DEVICE', 'auto'),
"ray_workers_use_nsight": os.getenv('RAY_WORKERS_USE_NSIGHT', 'False').lower() == 'true',
"num_gpu_blocks_override": int(os.getenv('NUM_GPU_BLOCKS_OVERRIDE', 0)) or None,
"num_lookahead_slots": int(os.getenv('NUM_LOOKAHEAD_SLOTS', 0)),
"model_loader_extra_config": os.getenv('MODEL_LOADER_EXTRA_CONFIG', None),
"ignore_patterns": os.getenv('IGNORE_PATTERNS', None),
"preemption_mode": os.getenv('PREEMPTION_MODE', None),
"scheduler_delay_factor": float(os.getenv('SCHEDULER_DELAY_FACTOR', 0.0)),
"enable_chunked_prefill": os.getenv('ENABLE_CHUNKED_PREFILL', None),
"guided_decoding_backend": os.getenv('GUIDED_DECODING_BACKEND', 'outlines'),
"speculative_model": os.getenv('SPECULATIVE_MODEL', None),
"speculative_draft_tensor_parallel_size": int(os.getenv('SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE', 0)) or None,
"num_speculative_tokens": int(os.getenv('NUM_SPECULATIVE_TOKENS', 0)) or None,
"speculative_max_model_len": int(os.getenv('SPECULATIVE_MAX_MODEL_LEN', 0)) or None,
"speculative_disable_by_batch_size": int(os.getenv('SPECULATIVE_DISABLE_BY_BATCH_SIZE', 0)) or None,
"ngram_prompt_lookup_max": int(os.getenv('NGRAM_PROMPT_LOOKUP_MAX', 0)) or None,
"ngram_prompt_lookup_min": int(os.getenv('NGRAM_PROMPT_LOOKUP_MIN', 0)) or None,
"spec_decoding_acceptance_method": os.getenv('SPEC_DECODING_ACCEPTANCE_METHOD', 'rejection_sampler'),
"typical_acceptance_sampler_posterior_threshold": float(os.getenv('TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD', 0)) or None,
"typical_acceptance_sampler_posterior_alpha": float(os.getenv('TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA', 0)) or None,
"qlora_adapter_name_or_path": os.getenv('QLORA_ADAPTER_NAME_OR_PATH', None),
"disable_logprobs_during_spec_decoding": os.getenv('DISABLE_LOGPROBS_DURING_SPEC_DECODING', None),
"otlp_traces_endpoint": os.getenv('OTLP_TRACES_ENDPOINT', None),
"use_v2_block_manager": os.getenv('USE_V2_BLOCK_MANAGER', 'true')
}
def match_vllm_args(args):
"""Rename args to match vllm by:
1. Renaming keys to lower case
2. Renaming keys to match vllm
3. Filtering args to match vllm's AsyncEngineArgs
Args:
args (dict): Dictionary of args
Returns:
dict: Dictionary of args with renamed keys
"""
renamed_args = {RENAME_ARGS_MAP.get(k, k): v for k, v in args.items()}
matched_args = {k: v for k, v in renamed_args.items() if k in AsyncEngineArgs.__dataclass_fields__}
return {k: v for k, v in matched_args.items() if v not in [None, ""]}
def get_local_args():
"""
Retrieve local arguments from a JSON file.
Returns:
dict: Local arguments.
"""
if not os.path.exists("/local_model_args.json"):
return {}
with open("/local_model_args.json", "r") as f:
local_args = json.load(f)
if local_args.get("MODEL_NAME") is None:
raise ValueError("Model name not found in /local_model_args.json. There was a problem when baking the model in.")
logging.info(f"Using baked in model with args: {local_args}")
os.environ["TRANSFORMERS_OFFLINE"] = "1"
os.environ["HF_HUB_OFFLINE"] = "1"
return local_args
def get_engine_args():
# Start with default args
args = DEFAULT_ARGS
# Get env args that match keys in AsyncEngineArgs
args.update(os.environ)
# Get local args if model is baked in and overwrite env args
args.update(get_local_args())
# if args.get("TENSORIZER_URI"): TODO: add back once tensorizer is ready
# args["load_format"] = "tensorizer"
# args["model_loader_extra_config"] = TensorizerConfig(tensorizer_uri=args["TENSORIZER_URI"], num_readers=None)
# logging.info(f"Using tensorized model from {args['TENSORIZER_URI']}")
# Rename and match to vllm args
args = match_vllm_args(args)
# Set tensor parallel size and max parallel loading workers if more than 1 GPU is available
num_gpus = device_count()
if num_gpus > 1:
args["tensor_parallel_size"] = num_gpus
args["max_parallel_loading_workers"] = None
if os.getenv("MAX_PARALLEL_LOADING_WORKERS"):
logging.warning("Overriding MAX_PARALLEL_LOADING_WORKERS with None because more than 1 GPU is available.")
# Deprecated env args backwards compatibility
if args.get("kv_cache_dtype") == "fp8_e5m2":
args["kv_cache_dtype"] = "fp8"
logging.warning("Using fp8_e5m2 is deprecated. Please use fp8 instead.")
if os.getenv("MAX_CONTEXT_LEN_TO_CAPTURE"):
args["max_seq_len_to_capture"] = int(os.getenv("MAX_CONTEXT_LEN_TO_CAPTURE"))
logging.warning("Using MAX_CONTEXT_LEN_TO_CAPTURE is deprecated. Please use MAX_SEQ_LEN_TO_CAPTURE instead.")
# if "gemma-2" in args.get("model", "").lower():
# os.environ["VLLM_ATTENTION_BACKEND"] = "FLASHINFER"
# logging.info("Using FLASHINFER for gemma-2 model.")
return AsyncEngineArgs(**args)
|