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https://github.com/NVIDIA/TensorRT-LLM.git
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728cc0044b
* Update TensorRT-LLM --------- Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
599 lines
24 KiB
Python
599 lines
24 KiB
Python
import copy
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import dataclasses
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import json
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import os
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from typing import List, Optional, Union
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import numpy as np
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import safetensors
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import torch
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from .._common import default_net
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from .._utils import (numpy_to_torch, str_dtype_to_torch, str_dtype_to_trt,
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trt_dtype_to_torch)
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from ..functional import PositionEmbeddingType, Tensor, gather_last_token_logits
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from ..layers import (AttentionParams, FusedGatedMLP, GatedMLP,
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KeyValueCacheParams, LoraParams)
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from ..mapping import Mapping
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from ..module import Module, ModuleList
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from ..quantization import QuantMode
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from ..quantization.quantize import quantize
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from .generation_mixin import GenerationMixin
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@dataclasses.dataclass
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class QuantizationConfig:
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'''Serializable quantization configuration class, part of the PretrainedConfig
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'''
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quant_algo: Optional[str] = None
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kv_cache_quant_algo: Optional[str] = None
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group_size: Optional[int] = 128
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has_zero_point: Optional[bool] = False
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pre_quant_scale: Optional[bool] = False
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exclude_modules: Optional[List[str]] = None
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sq_use_plugin: Optional[bool] = False
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class PretrainedConfig:
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def __init__(self,
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architecture: str,
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dtype: str,
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logits_dtype: str,
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vocab_size: int,
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max_position_embeddings: int,
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hidden_size: int,
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num_hidden_layers: int,
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num_attention_heads: int,
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num_key_value_heads: int,
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hidden_act: str,
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intermediate_size: int,
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norm_epsilon: float,
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position_embedding_type: str,
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world_size: int,
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tp_size: int,
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pp_size: int,
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quantization: Union[QuantizationConfig, dict],
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use_prompt_tuning: bool = False,
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use_parallel_embedding: bool = False,
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embedding_sharding_dim: int = 0,
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share_embedding_table: bool = False,
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max_lora_rank: int = 64,
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head_size: int = None,
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**kwargs):
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self.architecture = architecture
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self.dtype = dtype
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self.logits_dtype = logits_dtype
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.head_size = hidden_size // num_attention_heads if head_size is None else head_size
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.norm_epsilon = norm_epsilon
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self.position_embedding_type = PositionEmbeddingType.from_string(
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position_embedding_type)
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self.use_prompt_tuning = use_prompt_tuning
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self.use_parallel_embedding = use_parallel_embedding
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self.embedding_sharding_dim = embedding_sharding_dim
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self.share_embedding_table = share_embedding_table
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self.mapping = Mapping(world_size=world_size,
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tp_size=tp_size,
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pp_size=pp_size)
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# Ideally shall only keep one of quant_mode and quant_config to make sure single source of truth
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# keep them now since many code path is still using the quant_mode
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self.quant_mode = QuantMode.from_quant_algo(
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quantization.quant_algo, quantization.kv_cache_quant_algo)
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if isinstance(quantization, dict):
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self.quantization = dataclasses.replace(QuantizationConfig(),
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**quantization)
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else:
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assert isinstance(
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quantization, QuantizationConfig
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), f"Expecting type of QuantizationConfig, found {type(quantization)}"
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self.quantization = quantization
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self.kv_dtype = self.dtype
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self.max_lora_rank = max_lora_rank
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if self.quant_mode.has_int8_kv_cache():
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self.kv_dtype = 'int8'
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elif self.quant_mode.has_fp8_kv_cache():
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self.kv_dtype = 'fp8'
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for key, value in kwargs.items():
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try:
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setattr(self, key, value)
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except AttributeError as err:
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raise err
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def set_if_not_exist(self, key, value):
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if not hasattr(self, key):
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setattr(self, key, value)
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@classmethod
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def from_dict(cls, config):
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config = copy.deepcopy(
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config
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) # many config.pop calls inside, make one local copy of the config dict such that the function has no side effects
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architecture = config.pop('architecture')
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dtype = config.pop('dtype')
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vocab_size = config.pop('vocab_size')
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hidden_size = config.pop('hidden_size')
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num_hidden_layers = config.pop('num_hidden_layers')
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num_attention_heads = config.pop('num_attention_heads')
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hidden_act = config.pop('hidden_act')
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norm_epsilon = config.pop('norm_epsilon', 1e-5)
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position_embedding_type = config.pop('position_embedding_type',
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'learned_absolute')
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logits_dtype = config.pop('logits_dtype', 'float32')
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num_key_value_heads = config.pop('num_key_value_heads',
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num_attention_heads)
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intermediate_size = config.pop('intermediate_size', None)
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max_position_embeddings = config.pop('max_position_embeddings', None)
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use_prompt_tuning = config.pop('use_prompt_tuning', False)
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use_parallel_embedding = config.pop('use_parallel_embedding', False)
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embedding_sharding_dim = config.pop('embedding_sharding_dim', 0)
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share_embedding_table = config.pop('share_embedding_table', False)
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mapping = config.pop('mapping', {
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'world_size': 1,
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'tp_size': 1,
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'pp_size': 1
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})
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world_size = mapping.get('world_size', 1)
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tp_size = mapping.get('tp_size', 1)
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pp_size = mapping.get('pp_size', 1)
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if share_embedding_table and tp_size > 1:
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if (not use_parallel_embedding) or (use_parallel_embedding and
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embedding_sharding_dim == 1):
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raise NotImplementedError(
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"For multiple-processes cases, sharing the embedding table must set" \
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"use_parallel_embedding=True and embedding_sharding_dim=0"
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)
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quant_config = QuantizationConfig()
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if 'quantization' in config:
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# override the default quantization object from the given dict, allows user to specify partial set of the fields
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quant_config_from_user = config.pop('quantization')
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if isinstance(quant_config_from_user, dict):
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quant_config = dataclasses.replace(quant_config,
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**quant_config_from_user)
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# allow user to directly pass one QuantizationConfig object
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else:
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assert isinstance(quant_config_from_user, QuantizationConfig)
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quant_config = quant_config_from_user
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max_lora_rank = config.pop('max_lora_rank', 64)
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return cls(architecture, dtype, logits_dtype, vocab_size,
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max_position_embeddings, hidden_size, num_hidden_layers,
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num_attention_heads, num_key_value_heads, hidden_act,
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intermediate_size, norm_epsilon, position_embedding_type,
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world_size, tp_size, pp_size, quant_config,
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use_prompt_tuning, use_parallel_embedding,
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embedding_sharding_dim, share_embedding_table, max_lora_rank,
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**config)
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@classmethod
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def from_json_file(cls, config_file: str):
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with open(config_file) as f:
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config = json.load(f)
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return PretrainedConfig.from_dict(config)
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def to_dict(self):
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output = copy.deepcopy(self.__dict__)
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output['position_embedding_type'] = str(self.position_embedding_type)
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output['mapping'] = {
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'world_size': self.mapping.world_size,
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'tp_size': self.mapping.tp_size,
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'pp_size': self.mapping.pp_size,
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}
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output.pop('quant_mode')
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output['quantization'] = dataclasses.asdict(self.quantization)
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return output
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def set_rank(self, rank):
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self.mapping = Mapping(self.mapping.world_size,
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rank=rank,
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tp_size=self.mapping.tp_size,
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pp_size=self.mapping.pp_size)
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class DecoderLayerList(ModuleList):
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def __init__(self, cls, config):
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self.layer_list = config.mapping.pp_layers(config.num_hidden_layers)
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super().__init__([cls(config, idx) for idx in self.layer_list])
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def forward(self,
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hidden_states,
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use_cache=False,
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attention_mask=None,
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kv_cache_params=None,
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attention_params=None,
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lora_params=None,
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medusa_position_offsets=None,
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medusa_packed_mask=None):
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kv_cache_params.fill_none_tensor_list(len(self.layer_list))
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if use_cache:
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presents = []
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for layer_idx, (layer, past) in enumerate(
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zip(self, kv_cache_params.past_key_value)):
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lora_layer_params = None
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if lora_params is not None and lora_params.lora_ranks is not None:
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lora_layer_params = lora_params.get_layer_params(layer_idx)
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kwargs = {}
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if lora_layer_params is not None:
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kwargs['lora_layer_params'] = lora_layer_params
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if medusa_position_offsets is not None:
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kwargs['medusa_position_offsets'] = medusa_position_offsets
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if medusa_packed_mask is not None:
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kwargs['medusa_packed_mask'] = medusa_packed_mask
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hidden_states = layer(
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hidden_states,
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use_cache=use_cache,
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attention_mask=attention_mask,
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kv_cache_params=KeyValueCacheParams(
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past_key_value=[past],
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host_past_key_value_lengths=kv_cache_params.
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host_past_key_value_lengths,
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host_max_attention_window_sizes=kv_cache_params.
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host_max_attention_window_sizes,
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host_sink_token_length=kv_cache_params.
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host_sink_token_length,
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kv_cache_block_pointers=kv_cache_params.
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kv_cache_block_pointers,
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host_kv_cache_block_pointers=kv_cache_params.
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host_kv_cache_block_pointers,
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cache_indirection=kv_cache_params.cache_indirection),
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attention_params=attention_params,
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**kwargs)
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if use_cache:
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presents.append(hidden_states[1])
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hidden_states = hidden_states[0]
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if use_cache:
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return hidden_states, presents
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return hidden_states
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class PostInitCaller(type):
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def __call__(cls, *args, **kwargs):
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obj = type.__call__(cls, *args, **kwargs)
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obj.__post_init__()
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return obj
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class PretrainedModel(Module, GenerationMixin, metaclass=PostInitCaller):
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def __init__(self, config: PretrainedConfig):
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super().__init__()
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self.config = config
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def __post_init__(self):
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quantize(self, self.config.quant_mode,
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**dataclasses.asdict(self.config.quantization))
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def check_config(self, config):
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raise NotImplementedError(
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f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
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)
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@classmethod
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def from_config(cls, config: PretrainedConfig):
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return cls(config)
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@classmethod
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def from_checkpoint(cls,
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ckpt_dir: str,
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rank: int = 0,
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config: PretrainedConfig = None):
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if config is None:
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config = PretrainedConfig.from_json_file(
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os.path.join(ckpt_dir, 'config.json'))
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config.set_rank(rank)
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model = cls.from_config(config)
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weights = {}
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with safetensors.safe_open(os.path.join(ckpt_dir,
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f'rank{rank}.safetensors'),
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framework='pt',
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device='cpu') as f:
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for key in f.keys():
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weights[key] = f.get_tensor(key)
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model.load(weights)
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return model
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def load(self, weights):
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expected_names = set([name for name, param in self.named_parameters()])
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provided_names = set(weights.keys())
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if provided_names != expected_names:
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err_msg = "Provided tensor names are different from those expected by the engine."
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if expected_names.difference(provided_names):
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err_msg += f"\nExpected but not provided tensors: {expected_names.difference(provided_names)}"
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if provided_names.difference(expected_names):
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err_msg += f"\nProvided but not expected tensors: {provided_names.difference(expected_names)}"
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raise RuntimeError(err_msg)
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for name, param in self.named_parameters():
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param.value = weights[name]
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def prepare_inputs(self,
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max_batch_size,
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max_input_len,
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max_seq_len,
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use_cache,
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max_beam_width: int = 1,
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max_num_tokens: int = None,
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prompt_embedding_table_size: int = 0,
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position_encoding_2d: bool = False,
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max_draft_len: int = 0,
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gather_context_logits: bool = False,
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gather_generation_logits: bool = False,
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lora_target_modules: List[str] = None):
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'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
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ranges of the dimensions of when using TRT dynamic shapes.
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@return: a list contains values which can be fed into the self.forward()
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'''
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# Prepare inputs
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remove_input_padding = default_net().plugin_config.remove_input_padding
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use_gpt_attention_plugin = default_net(
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).plugin_config.gpt_attention_plugin
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use_gemm_plugin = default_net().plugin_config.gemm_plugin
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paged_kv_cache = default_net().plugin_config.paged_kv_cache
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tokens_per_block = default_net().plugin_config.tokens_per_block
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use_custom_all_reduce = default_net(
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).plugin_config.use_custom_all_reduce
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use_lora_plugin = default_net().plugin_config.lora_plugin
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model_inputs = self.prepare_basic_inputs(
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max_batch_size=max_batch_size,
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max_beam_width=max_beam_width,
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max_input_len=max_input_len,
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max_seq_len=max_seq_len,
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num_kv_heads=self.config.num_key_value_heads,
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head_size=self.config.head_size,
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num_layers=self.config.num_hidden_layers,
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kv_dtype=str_dtype_to_trt(self.config.kv_dtype),
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remove_input_padding=remove_input_padding,
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use_gpt_attention_plugin=use_gpt_attention_plugin,
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use_gemm_plugin=use_gemm_plugin,
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paged_kv_cache=paged_kv_cache,
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tokens_per_block=tokens_per_block,
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num_heads=self.config.num_attention_heads,
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max_num_tokens=max_num_tokens,
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dtype=str_dtype_to_trt(self.config.dtype),
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prompt_embedding_table_size=prompt_embedding_table_size,
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position_encoding_2d=position_encoding_2d,
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mapping=self.config.mapping,
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gather_context_logits=gather_context_logits,
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gather_generation_logits=gather_generation_logits,
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use_custom_all_reduce=use_custom_all_reduce,
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use_lora_plugin=use_lora_plugin,
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max_draft_len=max_draft_len,
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lora_target_modules=lora_target_modules)
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result = {
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'input_ids':
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model_inputs['input_ids'],
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'position_ids':
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model_inputs['position_ids'],
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'use_cache':
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True,
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'last_token_ids':
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model_inputs['last_token_ids'],
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'attention_mask':
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model_inputs['attention_mask'],
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'kv_cache_params':
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KeyValueCacheParams(
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past_key_value=model_inputs['past_key_value'],
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host_past_key_value_lengths=model_inputs[
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'host_past_key_value_lengths'],
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host_max_attention_window_sizes=model_inputs[
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'host_max_attention_window_sizes'],
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host_sink_token_length=model_inputs['host_sink_token_length'],
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kv_cache_block_pointers=model_inputs['kv_cache_block_pointers'],
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host_kv_cache_block_pointers=model_inputs[
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'host_kv_cache_block_pointers'],
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cache_indirection=model_inputs['cache_indirection'],
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),
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'attention_params':
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AttentionParams(
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sequence_length=model_inputs['sequence_length'],
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context_lengths=model_inputs['context_lengths'],
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host_context_lengths=model_inputs['host_context_lengths'],
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max_context_length=max_input_len,
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host_request_types=model_inputs['host_request_types'])
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}
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if prompt_embedding_table_size > 0:
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result['prompt_embedding_table'] = model_inputs[
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'prompt_embedding_table']
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result['prompt_tasks'] = model_inputs['tasks']
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result['prompt_vocab_size'] = model_inputs['prompt_vocab_size']
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if model_inputs['hidden_states_input'] is not None:
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result['hidden_states'] = model_inputs['hidden_states_input']
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if use_lora_plugin:
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result['lora_params'] = LoraParams(
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model_inputs['lora_ranks'],
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model_inputs['lora_weights_pointers'],
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host_context_lengths=model_inputs['host_context_lengths'],
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max_context_length=max_input_len,
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host_request_types=model_inputs['host_request_types'])
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return result
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class DecoderModelForCausalLM(PretrainedModel):
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def __init__(self, config, transformer, lm_head):
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super().__init__(config)
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self.transformer = transformer
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self.lm_head = lm_head
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def forward(self,
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input_ids: Tensor,
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position_ids=None,
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use_cache=False,
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last_token_ids=None,
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attention_mask=None,
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kv_cache_params=None,
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attention_params=None,
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hidden_states=None,
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prompt_embedding_table: Optional[Tensor] = None,
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prompt_tasks: Optional[Tensor] = None,
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prompt_vocab_size: Optional[Tensor] = None,
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lora_params=None,
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medusa_position_offsets=None,
|
|
medusa_packed_mask=None):
|
|
kwargs = {
|
|
'input_ids': input_ids,
|
|
'position_ids': position_ids,
|
|
'use_cache': use_cache,
|
|
'attention_mask': attention_mask,
|
|
'kv_cache_params': kv_cache_params,
|
|
'attention_params': attention_params,
|
|
}
|
|
if lora_params is not None:
|
|
kwargs['lora_params'] = lora_params
|
|
if hidden_states is not None:
|
|
kwargs['hidden_states'] = hidden_states
|
|
if prompt_embedding_table is not None:
|
|
kwargs['prompt_embedding_table'] = prompt_embedding_table
|
|
if prompt_tasks is not None:
|
|
kwargs['prompt_tasks'] = prompt_tasks
|
|
if prompt_vocab_size is not None:
|
|
kwargs['prompt_vocab_size'] = prompt_vocab_size
|
|
|
|
if medusa_position_offsets is not None:
|
|
kwargs['medusa_position_offsets'] = medusa_position_offsets
|
|
if medusa_packed_mask is not None:
|
|
kwargs['medusa_packed_mask'] = medusa_packed_mask
|
|
|
|
hidden_states = self.transformer.forward(**kwargs)
|
|
|
|
if use_cache:
|
|
hidden_states, presents = hidden_states
|
|
|
|
if self.config.mapping.is_last_pp_rank():
|
|
hidden_states = gather_last_token_logits(
|
|
hidden_states, last_token_ids,
|
|
default_net().plugin_config.remove_input_padding)
|
|
|
|
# [batch_size, hidden_size] -> [batch_size, vocab_size]
|
|
lm_logits = self.lm_head(hidden_states)
|
|
lm_logits.mark_output('logits', self.config.logits_dtype)
|
|
else:
|
|
hidden_states.mark_output('hidden_states_output', self.config.dtype)
|
|
|
|
if use_cache and not default_net().plugin_config.paged_kv_cache:
|
|
for i, present in zip(
|
|
self.config.mapping.pp_layers(
|
|
self.config.num_hidden_layers), presents):
|
|
present.mark_output(f'present_key_value_{i}',
|
|
self.config.kv_dtype)
|
|
if self.config.mapping.is_last_pp_rank():
|
|
return (lm_logits, presents, hidden_states)
|
|
return (hidden_states, presents)
|
|
else:
|
|
if self.config.mapping.is_last_pp_rank():
|
|
return lm_logits, hidden_states
|
|
return hidden_states
|
|
|
|
|
|
def fuse_gate_mlp(model):
|
|
for layer in model.transformer.layers:
|
|
if not hasattr(layer, 'mlp'):
|
|
continue
|
|
|
|
quant_algo = model.config.quantization.quant_algo
|
|
if isinstance(layer.mlp, GatedMLP):
|
|
fused_layer = FusedGatedMLP(
|
|
hidden_size=layer.mlp.hidden_size,
|
|
ffn_hidden_size=layer.mlp.ffn_hidden_size,
|
|
hidden_act=layer.mlp.hidden_act,
|
|
bias=layer.mlp.bias,
|
|
dtype=layer.mlp.dtype,
|
|
tp_group=layer.mlp.tp_group,
|
|
tp_size=layer.mlp.tp_size,
|
|
quant_mode=layer.mlp.quant_mode,
|
|
max_lora_rank=layer.mlp.max_lora_rank)
|
|
|
|
if quant_algo == 'FP8':
|
|
if isinstance(layer.mlp.dtype, str):
|
|
dtype = str_dtype_to_torch(layer.mlp.dtype)
|
|
else:
|
|
dtype = trt_dtype_to_torch(layer.mlp.dtype)
|
|
|
|
# dequantize
|
|
gate_weight = numpy_to_torch(
|
|
layer.mlp.gate.weight.raw_value).to(dtype) * numpy_to_torch(
|
|
layer.mlp.gate.weights_scaling_factor.raw_value)
|
|
fc_weight = numpy_to_torch(
|
|
layer.mlp.fc.weight.raw_value).to(dtype) * numpy_to_torch(
|
|
layer.mlp.fc.weights_scaling_factor.raw_value)
|
|
|
|
# concat
|
|
fused_weight = torch.cat([gate_weight, fc_weight], dim=0)
|
|
|
|
# quantize
|
|
fused_weight_scaling_factor = numpy_to_torch(
|
|
max(
|
|
layer.mlp.gate.weights_scaling_factor.raw_value,
|
|
layer.mlp.fc.weights_scaling_factor.raw_value,
|
|
))
|
|
fused_weight = (fused_weight / fused_weight_scaling_factor).to(
|
|
torch.float8_e4m3fn)
|
|
|
|
fused_layer.fused_fc.weight.value = fused_weight
|
|
fused_layer.fused_fc.weights_scaling_factor.value = fused_weight_scaling_factor
|
|
|
|
fused_layer.fused_fc.activation_scaling_factor.value = \
|
|
max(layer.mlp.gate.activation_scaling_factor.raw_value,
|
|
layer.mlp.fc.activation_scaling_factor.raw_value
|
|
)
|
|
elif quant_algo is None:
|
|
fused_layer.fused_fc.weight.value = np.concatenate([
|
|
layer.mlp.gate.weight.raw_value,
|
|
layer.mlp.fc.weight.raw_value
|
|
],
|
|
axis=0)
|
|
if layer.mlp.bias:
|
|
fused_layer.fused_fc.bias.value = np.concatenate([
|
|
layer.mlp.gate.bias.raw_value,
|
|
layer.mlp.fc.bias.raw_value
|
|
],
|
|
axis=0)
|
|
else:
|
|
raise ValueError(f'Unsupported quant algo: {quant_algo}')
|
|
|
|
fused_layer.proj = layer.mlp.proj
|
|
|
|
layer.mlp = fused_layer
|
|
|
|
return model
|
|
|
|
|
|
def optimize_model(model, use_fused_mlp=False):
|
|
if use_fused_mlp:
|
|
model = fuse_gate_mlp(model)
|
|
return model
|