mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-26 05:32:57 +08:00
949 lines
39 KiB
Python
949 lines
39 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 Dict, 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, Embedding, FusedGatedMLP, GatedMLP,
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KeyValueCacheParams, LoraParams, PromptTuningEmbedding)
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from ..layers.attention import Attention, BertAttention
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from ..layers.linear import ColumnLinear, Linear, RowLinear
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from ..layers.lora import Lora
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from ..logger import logger
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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.layers import FP8Linear
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from ..quantization.mode import (FP8, W4A8_AWQ, W4A16, W4A16_AWQ,
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W8A8_SQ_PLUGIN_LIST, W8A16)
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from ..quantization.quantize import quantize
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from .generation_mixin import GenerationMixin
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WEIGHT_LOADER_MODELS = {"PhiForCausalLM"}
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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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@property
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def use_plugin_sq(self):
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return self.quant_algo in W8A8_SQ_PLUGIN_LIST
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def quant_algo_to_ammo_qformat(self):
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from ..quantization import mode as quant_algo
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#"fp8", "int8_sq", "int4_awq", "w4a8_awq", "int8_wo", "int4_wo",
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algo_to_ammo_map = {
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quant_algo.W8A16: "int8_wo",
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quant_algo.W4A16: "int4_wo",
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quant_algo.W4A16_AWQ: "int4_awq",
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quant_algo.W4A8_AWQ: 'w4a8_awq',
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quant_algo.FP8: 'fp8',
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quant_algo.W4A16_GPTQ: None,
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quant_algo.W8A8_SQ_PER_CHANNEL: 'int8_sq',
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quant_algo.W8A8_SQ_PER_TENSOR_PLUGIN: None,
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quant_algo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN: None,
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quant_algo.W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN: None,
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quant_algo.W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN: None,
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None: 'full_prec'
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}
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assert self.quant_algo in algo_to_ammo_map
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qformat = algo_to_ammo_map[self.quant_algo]
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assert qformat is not None, "None means we don't use AMMO for this kind of quantization algorithm, you probably shall not call this"
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return qformat
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def asdict(self):
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return dataclasses.asdict(self)
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def default_weight_loader(mapping: Mapping, param: torch.Tensor,
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loaded_weight: torch.Tensor) -> None:
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"""Default weight loader."""
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param.value = loaded_weight
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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_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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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_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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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_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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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_parallel_embedding, embedding_sharding_dim,
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share_embedding_table, **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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preprocess_weights(weights, config)
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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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assert expected_names.issubset(
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provided_names
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), f"Expected but not provided tensors:{expected_names.difference(provided_names)}"
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if self.config.architecture in WEIGHT_LOADER_MODELS:
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mapping = self.config.mapping
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for name, param in self.named_parameters():
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(mapping, param, weights[name])
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else:
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for name, param in self.named_parameters():
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try:
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param.value = weights[name]
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except Exception as e:
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raise RuntimeError(
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f"Encounter error '{e}' for parameter '{name}'")
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def load_partial_weights(self, weights: dict):
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params = {name: param for name, param in self.named_parameters()}
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mapping = self.config.mapping
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for k, v in weights.items():
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if k in params.keys():
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param = params[k]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(mapping, param, v)
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elif mapping.pp_size == 1:
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logger.warning(f"Provided but not expected tensors: {k}")
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def save_checkpoint(self, output_dir, save_config=True):
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# multiple ranks could share same config.json, so adding a save_config parameter to let user avoiding writing config.json in all ranks
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rank = self.config.mapping.rank
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weights = {
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name: numpy_to_torch(param.raw_value)
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for name, param in self.named_parameters()
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}
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from safetensors.torch import save_file
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save_file(weights, os.path.join(output_dir, f'rank{rank}.safetensors'))
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if save_config:
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with open(os.path.join(output_dir, 'config.json'), 'w') as f:
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json.dump(self.config.to_dict(), f, indent=4)
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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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multiple_profiles = default_net().plugin_config.multiple_profiles
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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,
|
|
use_lora_plugin=use_lora_plugin,
|
|
max_draft_len=max_draft_len,
|
|
lora_target_modules=lora_target_modules,
|
|
multiple_profiles=multiple_profiles)
|
|
|
|
result = {
|
|
'input_ids':
|
|
model_inputs['input_ids'],
|
|
'position_ids':
|
|
model_inputs['position_ids'],
|
|
'use_cache':
|
|
True,
|
|
'last_token_ids':
|
|
model_inputs['last_token_ids'],
|
|
'attention_mask':
|
|
model_inputs['attention_mask'],
|
|
'kv_cache_params':
|
|
KeyValueCacheParams(
|
|
past_key_value=model_inputs['past_key_value'],
|
|
host_past_key_value_lengths=model_inputs[
|
|
'host_past_key_value_lengths'],
|
|
host_max_attention_window_sizes=model_inputs[
|
|
'host_max_attention_window_sizes'],
|
|
host_sink_token_length=model_inputs['host_sink_token_length'],
|
|
kv_cache_block_pointers=model_inputs['kv_cache_block_pointers'],
|
|
host_kv_cache_block_pointers=model_inputs[
|
|
'host_kv_cache_block_pointers'],
|
|
cache_indirection=model_inputs['cache_indirection'],
|
|
),
|
|
'attention_params':
|
|
AttentionParams(
|
|
sequence_length=model_inputs['sequence_length'],
|
|
context_lengths=model_inputs['context_lengths'],
|
|
host_context_lengths=model_inputs['host_context_lengths'],
|
|
max_context_length=max_input_len,
|
|
host_request_types=model_inputs['host_request_types'])
|
|
}
|
|
|
|
if prompt_embedding_table_size > 0:
|
|
result['prompt_embedding_table'] = model_inputs[
|
|
'prompt_embedding_table']
|
|
result['prompt_tasks'] = model_inputs['tasks']
|
|
result['prompt_vocab_size'] = model_inputs['prompt_vocab_size']
|
|
if model_inputs['hidden_states_input'] is not None:
|
|
result['hidden_states'] = model_inputs['hidden_states_input']
|
|
if use_lora_plugin:
|
|
result['lora_params'] = LoraParams(
|
|
model_inputs['lora_ranks'],
|
|
model_inputs['lora_weights_pointers'],
|
|
host_context_lengths=model_inputs['host_context_lengths'],
|
|
max_context_length=max_input_len,
|
|
host_request_types=model_inputs['host_request_types'])
|
|
|
|
return result
|
|
|
|
@classmethod
|
|
def quantize(
|
|
cls,
|
|
hf_model_dir,
|
|
output_dir,
|
|
quant_config: QuantizationConfig,
|
|
*,
|
|
dtype='float16',
|
|
mapping: Optional[Mapping] = None,
|
|
calib_batches=512,
|
|
calib_batch_size=1,
|
|
random_seed=1234,
|
|
tokenizer_max_seq_length=2048,
|
|
):
|
|
if mapping is None: # single gpu
|
|
mapping = Mapping()
|
|
ammo_qformat = quant_config.quant_algo_to_ammo_qformat()
|
|
kv_cache_dtype = quant_config.kv_cache_quant_algo
|
|
assert ammo_qformat is not None
|
|
from ..quantization import quantize_and_export
|
|
hf_model_dir = str(
|
|
hf_model_dir) # quantize_and_export has some code can not take Path
|
|
quantize_and_export(
|
|
model_dir=hf_model_dir,
|
|
dtype=dtype,
|
|
device='cuda',
|
|
qformat=ammo_qformat,
|
|
kv_cache_dtype=kv_cache_dtype,
|
|
calib_size=calib_batches,
|
|
batch_size=calib_batch_size,
|
|
output_dir=output_dir,
|
|
tp_size=mapping.tp_size,
|
|
pp_size=mapping.pp_size,
|
|
seed=random_seed,
|
|
max_seq_length=tokenizer_max_seq_length,
|
|
awq_block_size=quant_config.group_size,
|
|
)
|
|
|
|
|
|
class DecoderModelForCausalLM(PretrainedModel):
|
|
|
|
def __init__(self, config, transformer, lm_head):
|
|
super().__init__(config)
|
|
self.transformer = transformer
|
|
self.lm_head = lm_head
|
|
|
|
def forward(self,
|
|
input_ids: Tensor,
|
|
position_ids=None,
|
|
use_cache=False,
|
|
last_token_ids=None,
|
|
attention_mask=None,
|
|
kv_cache_params=None,
|
|
attention_params=None,
|
|
hidden_states=None,
|
|
prompt_embedding_table: Optional[Tensor] = None,
|
|
prompt_tasks: Optional[Tensor] = None,
|
|
prompt_vocab_size: Optional[Tensor] = None,
|
|
lora_params=None,
|
|
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)
|
|
|
|
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 unfuse_qkv_gemm(model):
|
|
for name, layer in model.named_modules(remove_duplicate=True):
|
|
if isinstance(layer, Attention) and not layer.cross_attention:
|
|
assert layer.tp_size == 1, "please disable manual tp when enable auto parallel"
|
|
if layer.unfuse_qkv_gemm:
|
|
continue
|
|
layer.unfuse_qkv_gemm = True
|
|
linear_class = FP8Linear if layer.use_fp8_qdq else ColumnLinear
|
|
q = linear_class(layer.hidden_size,
|
|
layer.attention_hidden_size,
|
|
bias=layer.bias,
|
|
dtype=layer.dtype,
|
|
gather_output=False)
|
|
k = linear_class(layer.hidden_size,
|
|
layer.num_attention_kv_heads *
|
|
layer.attention_head_size,
|
|
bias=layer.bias,
|
|
dtype=layer.dtype,
|
|
gather_output=False)
|
|
v = linear_class(layer.hidden_size,
|
|
layer.num_attention_kv_heads *
|
|
layer.attention_head_size,
|
|
bias=layer.bias,
|
|
dtype=layer.dtype,
|
|
gather_output=False)
|
|
if layer.qkv.weight.is_inited():
|
|
qkv_weight = layer.qkv.weight.raw_value
|
|
weights = np.split(qkv_weight, [
|
|
q.out_features,
|
|
q.out_features + k.out_features,
|
|
])
|
|
for gemm, weight in zip([q, k, v], weights):
|
|
gemm.weight.value = weight
|
|
if layer.qkv.bias is not None and layer.qkv.bias.is_inited():
|
|
qkv_bias = layer.qkv.bias.raw_value
|
|
biases = np.split(qkv_bias, [
|
|
q.out_features,
|
|
q.out_features + k.out_features,
|
|
])
|
|
for gemm, bias in zip([q, k, v], biases):
|
|
gemm.bias.value = bias
|
|
for name, parameter in layer.qkv._parameters.items():
|
|
if name not in ["weight", "bias"]:
|
|
for gemm in [q, k, v]:
|
|
setattr(gemm, name, parameter)
|
|
layer.q = q
|
|
layer.k = k
|
|
layer.v = v
|
|
layer.qkv = None
|
|
return model
|
|
|
|
|
|
def set_prompt_tuning(model):
|
|
if isinstance(model.transformer.vocab_embedding, Embedding):
|
|
embedding = model.transformer.vocab_embedding
|
|
model.transformer.vocab_embedding = PromptTuningEmbedding(
|
|
num_embeddings=embedding.num_embeddings,
|
|
embedding_dim=embedding.embedding_dim,
|
|
dtype=embedding.dtype,
|
|
tp_size=embedding.tp_size,
|
|
tp_group=embedding.tp_group,
|
|
sharding_dim=embedding.sharding_dim,
|
|
tp_rank=embedding.tp_rank)
|
|
|
|
model.transformer.vocab_embedding.weight.value = embedding.weight.raw_value
|
|
return model
|
|
|
|
|
|
def add_lora(model, max_lora_rank: Optional[int]):
|
|
for name, layer in model.named_modules(remove_duplicate=True):
|
|
max_rank = max_lora_rank
|
|
if isinstance(layer, (Attention, BertAttention)):
|
|
if max_rank is None:
|
|
max_rank = min(
|
|
layer.hidden_size,
|
|
layer.num_attention_heads * layer.attention_head_size,
|
|
layer.num_attention_kv_heads * layer.attention_head_size)
|
|
layer.qkv_lora = Lora(
|
|
in_hidden_size=layer.hidden_size,
|
|
out_hidden_sizes=[
|
|
layer.num_attention_heads * layer.attention_head_size,
|
|
layer.num_attention_kv_heads * layer.attention_head_size,
|
|
layer.num_attention_kv_heads * layer.attention_head_size
|
|
],
|
|
max_low_rank=max_rank,
|
|
)
|
|
if isinstance(layer, (Linear, RowLinear)):
|
|
if max_rank is None:
|
|
max_rank = min(layer.in_features, layer.out_features)
|
|
layer.lora = Lora(
|
|
in_hidden_size=layer.in_features,
|
|
out_hidden_sizes=[layer.out_features],
|
|
max_low_rank=max_rank,
|
|
)
|
|
if isinstance(layer, FusedGatedMLP):
|
|
if max_rank is None:
|
|
max_rank = min(layer.hidden_size,
|
|
layer.ffn_hidden_size // layer.tp_size)
|
|
layer.mlp_in_lora = Lora(
|
|
in_hidden_size=layer.hidden_size,
|
|
out_hidden_sizes=[
|
|
layer.ffn_hidden_size // layer.tp_size,
|
|
layer.ffn_hidden_size // layer.tp_size
|
|
],
|
|
max_low_rank=max_rank,
|
|
)
|
|
return model
|
|
|
|
|
|
def optimize_model(
|
|
model,
|
|
use_fused_mlp=False,
|
|
use_unfused_qkv_gemm=False,
|
|
use_prompt_tuning=False,
|
|
use_lora=False,
|
|
max_lora_rank=None,
|
|
):
|
|
if use_fused_mlp:
|
|
model = fuse_gate_mlp(model)
|
|
if use_unfused_qkv_gemm:
|
|
model = unfuse_qkv_gemm(model)
|
|
if use_prompt_tuning:
|
|
model = set_prompt_tuning(model)
|
|
if use_lora:
|
|
model = add_lora(model, max_lora_rank)
|
|
return model
|
|
|
|
|
|
def preprocess_weights(
|
|
weights: Dict[str, torch.Tensor],
|
|
model_config: PretrainedConfig) -> Dict[str, torch.Tensor]:
|
|
quant_algo = model_config.quantization.quant_algo
|
|
kv_cache_quant_algo = model_config.quantization.kv_cache_quant_algo
|
|
|
|
# INT4_AWQ
|
|
if quant_algo == W4A8_AWQ or quant_algo == W4A16_AWQ:
|
|
preprocessor = torch.ops.trtllm.preprocess_weights_for_mixed_gemm
|
|
for name, param in weights.items():
|
|
if name.endswith('weight') and param.dtype == torch.int8:
|
|
dtype = torch.float16
|
|
if model_config.dtype == "bfloat16":
|
|
dtype = torch.bfloat16
|
|
weights[name] = preprocessor(param.T.contiguous(),
|
|
torch.quint4x2).view(dtype)
|
|
if name.endswith('weights_scaling_factor'):
|
|
weights[name] = param.T.contiguous().to(
|
|
str_dtype_to_torch(model_config.dtype))
|
|
if name.endswith('prequant_scaling_factor'):
|
|
weights[name] = param.reshape(1, -1)
|
|
if model_config.mapping.tp_rank > 0:
|
|
if name.endswith('attention.dense.bias') or name.endswith(
|
|
'mlp.proj.bias'):
|
|
weights[name] = torch.zeros_like(param)
|
|
|
|
if quant_algo == W4A8_AWQ:
|
|
for name in list(weights):
|
|
if name.endswith('weights_scaling_factor'):
|
|
activation_scaling_factor = weights.pop(
|
|
name.replace('weights_scaling_factor',
|
|
'activation_scaling_factor'))
|
|
weights_scaling_factor_2 = weights.pop(
|
|
name.replace('weights_scaling_factor',
|
|
'weights_scaling_factor_2'))
|
|
weights[name] /= weights_scaling_factor_2
|
|
weights[name.replace(
|
|
'weights_scaling_factor',
|
|
'prequant_scaling_factor')] /= activation_scaling_factor
|
|
weights[name.replace(
|
|
'weights_scaling_factor', 'alpha'
|
|
)] = activation_scaling_factor * weights_scaling_factor_2
|
|
|
|
# FP8
|
|
elif quant_algo == FP8:
|
|
for name, param in weights.items():
|
|
if name.endswith('weight') and param.dtype == torch.int8:
|
|
weights[name] = param.view(torch.float8_e4m3fn)
|
|
# lm_head is not quantized to FP8
|
|
if "lm_head.weight" in weights:
|
|
assert weights['lm_head.weight'].dtype == str_dtype_to_torch(
|
|
model_config.dtype)
|
|
weights.pop('lm_head.weights_scaling_factor', None)
|
|
weights.pop('lm_head.activation_scaling_factor', None)
|
|
|
|
# Weight only 4bit
|
|
elif quant_algo == W4A16:
|
|
for name in list(weights):
|
|
if any([
|
|
_name in name for _name in [
|
|
'qkv.weight', 'dense.weight', 'fc.weight',
|
|
'proj.weight', 'gate.weight'
|
|
]
|
|
]) and weights[name].dtype != torch.int8:
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
weights[name].t().contiguous(), torch.quint4x2)
|
|
weights[name] = processed_torch_weights
|
|
weights[name.replace(
|
|
'.weight', '.per_channel_scale')] = torch_weight_scales
|
|
|
|
# Weight only 8bit
|
|
elif quant_algo == W8A16:
|
|
for name in list(weights):
|
|
if any([
|
|
_name in name for _name in [
|
|
'qkv.weight', 'dense.weight', 'fc.weight',
|
|
'proj.weight', 'gate.weight'
|
|
]
|
|
]) and weights[name].dtype != torch.int8:
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
weights[name].t().contiguous(), torch.int8)
|
|
weights[name] = processed_torch_weights
|
|
weights[name.replace(
|
|
'.weight', '.per_channel_scale')] = torch_weight_scales
|
|
|
|
# FP8 kv_cache_scaling_factor is always 1.0
|
|
if kv_cache_quant_algo == FP8:
|
|
for name, param in weights.items():
|
|
if name.endswith('kv_cache_scaling_factor'):
|
|
weights[name] = torch.tensor([1.0], dtype=torch.float32)
|
|
|
|
# If layer_norm bias is None. (For MPT)
|
|
if model_config.architecture == 'MPTForCausalLM':
|
|
update_dict = {}
|
|
for name, param in weights.items():
|
|
if 'input_layernorm.weight' in name and name.replace(
|
|
'weight', 'bias') not in weights:
|
|
update_dict[name.replace('weight',
|
|
'bias')] = torch.zeros_like(param)
|
|
if 'post_layernorm.weight' in name and name.replace(
|
|
'weight', 'bias') not in weights:
|
|
update_dict[name.replace('weight',
|
|
'bias')] = torch.zeros_like(param)
|
|
if 'ln_f.weight' in name and name.replace('weight',
|
|
'bias') not in weights:
|
|
update_dict[name.replace('weight',
|
|
'bias')] = torch.zeros_like(param)
|
|
weights.update(update_dict)
|
|
|
|
# Parallel block rowlinear should not have duplicate bias.
|
|
elif model_config.architecture == 'GPTJForCausalLM':
|
|
if model_config.mapping.tp_rank > 0:
|
|
for name, param in weights.items():
|
|
if 'attention.dense.bias' in name or 'mlp.proj.bias' in name:
|
|
weights[name] = torch.zeros_like(param)
|