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[LLM] First commit the llm deployment code
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jiangjiajun
279
fastdeploy/worker/utils.py
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279
fastdeploy/worker/utils.py
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"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import os
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from dataclasses import dataclass
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from dataclasses import field
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from typing import List
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@dataclass
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class PredictorArgument:
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model_name_or_path: str = field(
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default=None, metadata={"help": "The directory of model."})
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model_prefix: str = field(
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default="model", metadata={"help": "the prefix name of static model"})
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src_length: int = field(
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default=None, metadata={"help": "The max length of source text."})
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min_length: int = field(default=1,
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metadata={"help": "the min length for decoding."})
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max_length: int = field(default=1024,
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metadata={"help": "the max length for decoding."})
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top_k: int = field(default=0,
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metadata={"help": "top_k parameter for generation"})
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top_p: float = field(default=0.7,
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metadata={"help": "top_p parameter for generation"})
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temperature: float = field(
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default=0.95,
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metadata={"help": "temperature parameter for generation"})
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repetition_penalty: float = field(
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default=1.0,
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metadata={"help": "repetition penalty parameter for generation"})
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device: str = field(default="gpu", metadata={"help": "Device"})
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dtype: str = field(default=None, metadata={"help": "Model dtype"})
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lora_path: str = field(
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default=None,
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metadata={"help": "The directory of LoRA parameters. Default to None"})
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export_precache: bool = field(
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default=False,
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metadata={"help": "whether use prefix weight to do infer"})
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prefix_path: str = field(
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default=None,
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metadata={
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"help":
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"The directory of Prefix Tuning parameters. Default to None"
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})
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decode_strategy: str = field(
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default="sampling",
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metadata={
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"help":
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"the decoding strategy of generation, which should be one of "
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"['sampling', 'greedy_search', 'beam_search']. Default to sampling"
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},
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)
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use_flash_attention: bool = field(
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default=False,
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metadata={"help": "Whether to use flash attention"},
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)
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mode: str = field(
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default="dynamic",
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metadata={
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"help":
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"the type of predictor, it should be one of [dynamic, static]"
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})
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inference_model: bool = field(
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default=False,
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metadata={"help": "whether use InferenceModel to do generation"})
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quant_type: str = field(
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default="",
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metadata={
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"help":
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"Quantization type. Supported values: a8w8, a8w8c8, a8w8_fp8, "
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"a8w8c8_fp8, weight_only_int4, weight_only_int8"
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},
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)
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avx_model: bool = field(
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default=False,
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metadata={
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"help":
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"whether use AvxModel to do generation when using cpu inference"
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})
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avx_type: str = field(
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default=None,
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metadata={
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"help":
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"avx compute type. Supported values: fp16, bf16,fp16_int8\
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fp16: first_token and next_token run in fp16\
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fp16_int8 : first_token run in fp16, next token run in int8"
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},
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)
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avx_cachekv_type: str = field(
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default="fp16",
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metadata={"help": "avx cachekv type. Supported values: fp16,int8"},
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)
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batch_size: int = field(default=1,
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metadata={"help": "The batch size of data."})
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benchmark: bool = field(
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default=False,
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metadata={
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"help":
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"If benchmark set as `True`, we will force model decode to max_length, "
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"which is helpful to compute throughput. "
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},
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)
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use_fake_parameter: bool = field(
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default=False,
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metadata={"help": "use fake parameter, for ptq scales now."})
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block_attn: bool = field(default=False,
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metadata={"help": "whether use block attention"})
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block_size: int = field(default=64,
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metadata={"help": "the block size for cache_kvs."})
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cachekv_int8_type: str = field(
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default=None,
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metadata={
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"help":
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"If cachekv_int8_type set as `dynamic`, cache kv would be quantized to "
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"int8 dynamically. If cachekv_int8_type set as `static`, cache kv would "
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"be quantized to int8 Statically."
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},
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)
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append_attn: bool = field(
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default=False, metadata={"help": "whether use append attention"})
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chat_template: str = field(
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default=None,
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metadata={
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"help":
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"the path of `chat_template.json` file to handle multi-rounds conversation. "
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"If is None(do not set --chat_template argument), it will use the default `chat_template.json`;"
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"If is equal with `model_name_or_path`, it will use the default loading; "
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"If is directory, it will find the `chat_template.json` under the directory; If is file, it will load it."
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"If is none string, it will not use chat_template.json."
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},
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)
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total_max_length: int = field(
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default=4096,
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metadata={
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"help":
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"Super parameter. Maximum sequence length(encoder+decoder)."
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})
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speculate_method: str = field(
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default=None,
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metadata={
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"help":
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"speculate method, it should be one of ['None', 'inference_with_reference', 'eagle', 'mtp']"
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},
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)
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speculate_max_draft_token_num: int = field(
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default=1,
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metadata={
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"help": "the max length of draft tokens for speculate method."
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},
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)
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speculate_max_ngram_size: int = field(
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default=1,
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metadata={"help": "the max ngram size of speculate method."})
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speculate_verify_window: int = field(
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default=2,
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metadata={
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"help": "the max length of verify window for speculate method."
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})
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speculate_max_candidate_len: int = field(
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default=5, metadata={"help": "the max length of candidate tokens."})
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draft_model_name_or_path: str = field(
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default=None,
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metadata={"help": "The directory of eagle or draft model"})
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draft_model_quant_type: str = field(
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default="",
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metadata={
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"help": "Draft model quantization type. Reserved for future"
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},
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)
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return_full_hidden_states: bool = field(
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default=False, metadata={"help": "whether return full hidden_states"})
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mla_use_matrix_absorption: bool = field(
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default=False,
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metadata={"help": "implement mla with matrix-absorption."})
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weightonly_group_size: int = field(
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default=-1, metadata={"help": "the max length of candidate tokens."})
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weight_block_size: List[int] = field(
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default_factory=lambda: [128, 128],
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metadata={
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"help":
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"Quantitative granularity of weights. Supported values: [128 128]"
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},
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)
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moe_quant_type: str = field(
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default="",
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metadata={
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"help":
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"Quantization type of moe. Supported values: weight_only_int4, weight_only_int8"
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},
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)
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output_via_mq: bool = field(
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default=True,
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metadata={
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"help": "Controls whether the message queue is enabled for output"
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},
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)
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dynamic_insert: bool = field(
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default=False, metadata={"help": "whether use dynamic insert"})
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total_request_num: int = field(
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default=None, metadata={"help": "The total number of request data"})
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def __post_init__(self):
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if self.speculate_method is not None:
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self.append_attn = True
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if self.append_attn:
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self.block_attn = True
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if self.block_attn:
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self.inference_model = True
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assert self.max_length < self.total_max_length, "max_length should smaller than total_max_length."
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if self.src_length is None:
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self.src_length = self.total_max_length - self.max_length
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# update config parameter for inference predictor
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if self.decode_strategy == "greedy_search":
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self.top_p = 0.0
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self.temperature = 1.0
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@dataclass
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class ModelArgument:
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model_type: str = field(
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default=None,
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metadata={
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"help":
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"the type of the model, which can be one of ['gpt-3', 'ernie-3.5-se', 'llama-img2txt']"
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},
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)
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data_file: str = field(default=None,
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metadata={"help": "data file directory"})
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output_file: str = field(
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default="output.json",
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metadata={"help": "predict result file directory"})
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def check_safetensors_model(model_dir):
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"""Check whther the model is safetensors format"""
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model_files = list()
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all_files = os.listdir(model_dir)
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for x in all_files:
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if x.startswith("model") and x.endswith(".safetensors"):
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model_files.append(x)
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is_safetensors = len(model_files) > 0
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if not is_safetensors:
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return False
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if len(model_files) == 1 and model_files[0] == "model.safetensors":
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return True
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try:
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# check all the file exists
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safetensors_num = int(
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model_files[0].strip(".safetensors").split("-")[-1])
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flags = [0] * safetensors_num
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for x in model_files:
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current_index = int(x.strip(".safetensors").split("-")[1])
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flags[current_index - 1] = 1
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assert (
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sum(flags) == safetensors_num
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), f"Number of safetensor files should be {len(model_files)}, but now it's {sum(flags)}"
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except Exception as e:
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raise Exception(f"Failed to check unified checkpoint, details: {e}.")
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return is_safetensors
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