mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
9dbc5b38ba
* Update TensorRT-LLM --------- Co-authored-by: Marks101 <markus.schnoes@gmx.de> Co-authored-by: lkm2835 <lkm2835@gmail.com>
1195 lines
49 KiB
Python
1195 lines
49 KiB
Python
import argparse
|
|
import copy
|
|
import dataclasses
|
|
import json
|
|
import os
|
|
from enum import IntFlag, auto
|
|
from functools import cached_property
|
|
from typing import Dict, List, Optional, Union
|
|
|
|
import numpy as np
|
|
import safetensors
|
|
import torch
|
|
|
|
from .._common import default_net
|
|
from .._utils import (get_init_params, numpy_to_torch, release_gc,
|
|
str_dtype_to_torch, str_dtype_to_trt, trt_dtype_to_torch)
|
|
from ..functional import PositionEmbeddingType, Tensor, gather_last_token_logits
|
|
from ..layers import (AttentionParams, Embedding, FusedGatedMLP, FusedRgLru,
|
|
GatedMLP, KeyValueCacheParams, LoraParams,
|
|
PromptTuningEmbedding, RgLru)
|
|
from ..layers.attention import Attention, BertAttention
|
|
from ..layers.linear import ColumnLinear, Linear, RowLinear
|
|
from ..layers.lora import Lora
|
|
from ..layers.moe import MOE, MoeOOTB
|
|
from ..logger import logger
|
|
from ..mapping import Mapping
|
|
from ..module import Module, ModuleList
|
|
from ..parameter import Parameter
|
|
from ..quantization import QuantMode
|
|
from ..quantization.layers import (WeightOnlyGroupwiseQuantLinear,
|
|
WeightOnlyGroupwiseQuantRowLinear,
|
|
WeightOnlyQuantLinear,
|
|
WeightOnlyQuantRowLinear)
|
|
from ..quantization.mode import W8A8_SQ_PLUGIN_LIST, QuantAlgo
|
|
from ..top_model_mixin import TopModelMixin
|
|
from .convert_utils import weight_only_quantize_dict
|
|
from .generation_mixin import GenerationMixin
|
|
|
|
WEIGHT_LOADER_MODELS = {"PhiForCausalLM"}
|
|
|
|
|
|
class SpeculativeDecodingMode(IntFlag):
|
|
# [WARNING] KEEP BELOW DEFINITION IN SYNC WITH cpp/tensorrt_llm/runtime/speculativeDecodingMode.h
|
|
NONE = auto()
|
|
DRAFT_TOKENS_EXTERNAL = auto()
|
|
MEDUSA = auto()
|
|
LOOKAHEAD_DECODING = auto()
|
|
EXPLICIT_DRAFT_TOKENS = auto()
|
|
|
|
@staticmethod
|
|
def from_arguments(args: argparse.Namespace):
|
|
if args.speculative_decoding_mode is None:
|
|
return SpeculativeDecodingMode.NONE
|
|
elif args.speculative_decoding_mode == "draft_tokens_external":
|
|
return SpeculativeDecodingMode.DRAFT_TOKENS_EXTERNAL
|
|
elif args.speculative_decoding_mode == "medusa":
|
|
return SpeculativeDecodingMode.MEDUSA
|
|
elif args.speculative_decoding_mode == "lookahead_decoding":
|
|
return SpeculativeDecodingMode.LOOKAHEAD_DECODING
|
|
elif args.speculative_decoding_mode == "explicit_draft_tokens":
|
|
return SpeculativeDecodingMode.EXPLICIT_DRAFT_TOKENS
|
|
else:
|
|
assert False, "Unknown speculative_decoding_mode " + args.speculative_decoding_mode
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class QuantConfig:
|
|
'''Serializable quantization configuration class, part of the PretrainedConfig
|
|
'''
|
|
|
|
quant_algo: Optional[QuantAlgo] = None
|
|
kv_cache_quant_algo: Optional[QuantAlgo] = None
|
|
group_size: Optional[int] = 128
|
|
smoothquant_val: Optional[float] = None
|
|
has_zero_point: Optional[bool] = False
|
|
pre_quant_scale: Optional[bool] = False
|
|
exclude_modules: Optional[List[str]] = None
|
|
|
|
@property
|
|
def use_plugin_sq(self):
|
|
return self.quant_algo in W8A8_SQ_PLUGIN_LIST
|
|
|
|
@cached_property
|
|
def quant_mode(self) -> QuantMode:
|
|
return QuantMode.from_quant_algo(
|
|
self.quant_algo,
|
|
self.kv_cache_quant_algo,
|
|
)
|
|
|
|
def quant_algo_to_modelopt_qformat(self):
|
|
algo_to_modelopt_map = {
|
|
QuantAlgo.W8A16: "int8_wo",
|
|
QuantAlgo.W4A16: "int4_wo",
|
|
QuantAlgo.W4A16_AWQ: "int4_awq",
|
|
QuantAlgo.W4A8_AWQ: 'w4a8_awq',
|
|
QuantAlgo.FP8: 'fp8',
|
|
QuantAlgo.W8A8_SQ_PER_CHANNEL: 'int8_sq',
|
|
}
|
|
if self.quant_algo is not None:
|
|
assert self.quant_algo in algo_to_modelopt_map, f"We don't use Modelopt for quantization algorithm {self.quant_algo}, you probably shall not call this"
|
|
qformat = algo_to_modelopt_map[self.quant_algo]
|
|
else:
|
|
qformat = 'full_prec'
|
|
return qformat
|
|
|
|
@classmethod
|
|
def from_dict(cls, config: dict):
|
|
return cls(**config)
|
|
|
|
def to_dict(self):
|
|
return dataclasses.asdict(self)
|
|
|
|
|
|
def default_weight_loader(mapping: Mapping, param: torch.Tensor,
|
|
loaded_weight: torch.Tensor) -> None:
|
|
"""Default weight loader."""
|
|
param.value = loaded_weight
|
|
|
|
|
|
def save_checkpoint(output_dir: str, config: dict, weights: dict) -> None:
|
|
""" Checkpoint saver for weight loader."""
|
|
with open(os.path.join(output_dir, 'config.json'), 'w') as f:
|
|
json.dump(config, f, indent=4)
|
|
safetensors.torch.save_file(weights,
|
|
os.path.join(output_dir, 'rank0.safetensors'))
|
|
|
|
|
|
class PretrainedConfig:
|
|
|
|
def __init__(self,
|
|
*,
|
|
architecture: str,
|
|
dtype: str,
|
|
hidden_size: int,
|
|
num_hidden_layers: int,
|
|
num_attention_heads: int,
|
|
vocab_size: Optional[int] = None,
|
|
hidden_act: str = 'gelu',
|
|
logits_dtype: str = 'float32',
|
|
norm_epsilon: float = 1e-5,
|
|
position_embedding_type: Union[
|
|
PositionEmbeddingType,
|
|
str] = PositionEmbeddingType.learned_absolute,
|
|
max_position_embeddings: Optional[int] = None,
|
|
num_key_value_heads: Optional[int] = None,
|
|
intermediate_size: Optional[int] = None,
|
|
mapping: Optional[Union[Mapping, dict]] = None,
|
|
quantization: Optional[Union[QuantConfig, dict]] = None,
|
|
use_parallel_embedding: bool = False,
|
|
embedding_sharding_dim: int = 0,
|
|
share_embedding_table: bool = False,
|
|
head_size: Optional[int] = None,
|
|
qk_layernorm: bool = False,
|
|
**kwargs):
|
|
self.architecture = architecture
|
|
self.dtype = dtype
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.hidden_act = hidden_act
|
|
|
|
self.logits_dtype = logits_dtype
|
|
self.norm_epsilon = norm_epsilon
|
|
|
|
if isinstance(position_embedding_type, str):
|
|
position_embedding_type = PositionEmbeddingType.from_string(
|
|
position_embedding_type)
|
|
assert isinstance(position_embedding_type, PositionEmbeddingType)
|
|
self.position_embedding_type = position_embedding_type
|
|
|
|
self.max_position_embeddings = max_position_embeddings
|
|
|
|
if num_key_value_heads is None:
|
|
num_key_value_heads = num_attention_heads
|
|
self.num_key_value_heads = num_key_value_heads
|
|
|
|
if intermediate_size is None:
|
|
intermediate_size = hidden_size * 4
|
|
self.intermediate_size = intermediate_size
|
|
|
|
if mapping is None:
|
|
mapping = Mapping()
|
|
elif isinstance(mapping, dict):
|
|
mapping = Mapping.from_dict(mapping)
|
|
assert isinstance(mapping, Mapping)
|
|
self.mapping = mapping
|
|
|
|
if quantization is None:
|
|
quantization = QuantConfig()
|
|
elif isinstance(quantization, dict):
|
|
quantization = QuantConfig.from_dict(quantization)
|
|
assert isinstance(quantization, QuantConfig)
|
|
self.quantization = quantization
|
|
|
|
self.use_parallel_embedding = use_parallel_embedding
|
|
self.embedding_sharding_dim = embedding_sharding_dim
|
|
self.share_embedding_table = share_embedding_table
|
|
|
|
if share_embedding_table and mapping.tp_size > 1:
|
|
if (not use_parallel_embedding) or (use_parallel_embedding and
|
|
embedding_sharding_dim == 1):
|
|
raise NotImplementedError(
|
|
"For tensor parallelism, sharing the embedding table must set" \
|
|
"use_parallel_embedding=True and embedding_sharding_dim=0"
|
|
)
|
|
if share_embedding_table and mapping.pp_size > 1:
|
|
raise NotImplementedError(
|
|
"Embedding table cannot be shared for pipeline parallelism")
|
|
|
|
if head_size is None:
|
|
head_size = hidden_size // num_attention_heads
|
|
self.head_size = head_size
|
|
self.qk_layernorm = qk_layernorm
|
|
|
|
for key, value in kwargs.items():
|
|
try:
|
|
setattr(self, key, value)
|
|
logger.warning(
|
|
f"Implicitly setting {self.__class__.__name__}.{key} = {value}"
|
|
)
|
|
except AttributeError as err:
|
|
raise err
|
|
|
|
@property
|
|
def kv_dtype(self):
|
|
if self.quant_mode.has_int8_kv_cache():
|
|
return 'int8'
|
|
elif self.quant_mode.has_fp8_kv_cache():
|
|
return 'fp8'
|
|
else:
|
|
return self.dtype
|
|
|
|
def set_if_not_exist(self, key, value):
|
|
if not hasattr(self, key):
|
|
setattr(self, key, value)
|
|
|
|
@classmethod
|
|
def from_dict(cls, config: dict):
|
|
# Maybe we need AutoConfig for this
|
|
from . import MODEL_MAP
|
|
model_cls = MODEL_MAP[config['architecture']]
|
|
config_cls = getattr(model_cls, 'config_class', cls)
|
|
return config_cls(**config)
|
|
|
|
def to_dict(self):
|
|
output = copy.deepcopy(self.__dict__)
|
|
|
|
output['position_embedding_type'] = str(self.position_embedding_type)
|
|
output['mapping'] = self.mapping.to_dict()
|
|
output['mapping'].pop('rank')
|
|
output['quantization'] = self.quantization.to_dict()
|
|
|
|
return output
|
|
|
|
@classmethod
|
|
def from_json_file(cls, config_file: str):
|
|
with open(config_file) as f:
|
|
config = json.load(f)
|
|
return cls.from_dict(config)
|
|
|
|
@classmethod
|
|
def from_checkpoint(cls, ckpt_dir: str):
|
|
return cls.from_json_file(os.path.join(ckpt_dir, 'config.json'))
|
|
|
|
def to_json_file(self, config_file: str):
|
|
with open(config_file, 'w') as f:
|
|
json.dump(self.to_dict(), f, indent=4)
|
|
|
|
@property
|
|
def quant_mode(self):
|
|
return self.quantization.quant_mode
|
|
|
|
def set_rank(self, rank):
|
|
self.mapping = Mapping(self.mapping.world_size,
|
|
rank=rank,
|
|
tp_size=self.mapping.tp_size,
|
|
pp_size=self.mapping.pp_size,
|
|
moe_tp_size=self.mapping.moe_tp_size,
|
|
moe_ep_size=self.mapping.moe_ep_size,
|
|
gpus_per_node=self.mapping.gpus_per_node)
|
|
|
|
|
|
class DecoderLayerList(ModuleList):
|
|
|
|
def __init__(self, cls, config):
|
|
self.num_hidden_layers = config.num_hidden_layers
|
|
self.layer_list = config.mapping.pp_layers(config.num_hidden_layers)
|
|
super().__init__([cls(config, idx) for idx in self.layer_list])
|
|
|
|
def forward(self,
|
|
hidden_states,
|
|
use_cache=False,
|
|
attention_mask=None,
|
|
kv_cache_params=None,
|
|
attention_params=None,
|
|
position_ids=None,
|
|
lora_params=None,
|
|
spec_decoding_params=None):
|
|
kv_cache_params.fill_none_tensor_list(len(self.layer_list))
|
|
|
|
if use_cache:
|
|
presents = []
|
|
|
|
for layer_idx, (layer, past) in enumerate(
|
|
zip(self, kv_cache_params.past_key_value)):
|
|
|
|
lora_layer_params = None
|
|
if lora_params is not None and lora_params.lora_ranks is not None:
|
|
lora_layer_params = lora_params.get_layer_params(layer_idx)
|
|
|
|
kwargs = {}
|
|
if position_ids is not None:
|
|
kwargs['position_ids'] = position_ids
|
|
if lora_layer_params is not None:
|
|
kwargs['lora_layer_params'] = lora_layer_params
|
|
if spec_decoding_params is not None:
|
|
kwargs['spec_decoding_params'] = spec_decoding_params
|
|
if default_net().plugin_config.reduce_fusion:
|
|
if layer_idx < self.layer_list[-1]:
|
|
kwargs['next_layer_input_layernorm_args'] = (
|
|
self[layer_idx + 1].input_layernorm.weight.value,
|
|
self[layer_idx + 1].input_layernorm.eps)
|
|
else:
|
|
kwargs['next_layer_input_layernorm_args'] = None
|
|
|
|
hidden_states = layer(
|
|
hidden_states,
|
|
use_cache=use_cache,
|
|
attention_mask=attention_mask,
|
|
kv_cache_params=KeyValueCacheParams(
|
|
past_key_value=[past],
|
|
host_past_key_value_lengths=kv_cache_params.
|
|
host_past_key_value_lengths,
|
|
host_max_attention_window_sizes=kv_cache_params.
|
|
host_max_attention_window_sizes,
|
|
host_sink_token_length=kv_cache_params.
|
|
host_sink_token_length,
|
|
kv_cache_block_offsets=kv_cache_params.
|
|
kv_cache_block_offsets,
|
|
host_kv_cache_block_offsets=kv_cache_params.
|
|
host_kv_cache_block_offsets,
|
|
host_kv_cache_pool_pointers=kv_cache_params.
|
|
host_kv_cache_pool_pointers,
|
|
cache_indirection=kv_cache_params.cache_indirection),
|
|
attention_params=attention_params,
|
|
**kwargs)
|
|
|
|
if use_cache:
|
|
presents.append(hidden_states[1])
|
|
hidden_states = hidden_states[0]
|
|
|
|
if use_cache:
|
|
return hidden_states, presents
|
|
return hidden_states
|
|
|
|
|
|
class PostInitCaller(type):
|
|
|
|
def __call__(cls, *args, **kwargs):
|
|
obj = type.__call__(cls, *args, **kwargs)
|
|
obj.__post_init__()
|
|
return obj
|
|
|
|
|
|
class PretrainedModel(Module,
|
|
GenerationMixin,
|
|
TopModelMixin,
|
|
metaclass=PostInitCaller):
|
|
|
|
def __init__(self, config: PretrainedConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
def __post_init__(self):
|
|
from ..quantization.quantize import quantize
|
|
quantize(self, self.config.quantization)
|
|
|
|
# Currently, use_parallel_embedding and share_embedding_table must be enabled before weight loading;
|
|
# otherwise, the model will be inconsistent with the weights loaded from checkpoint.
|
|
optimize_model(
|
|
self,
|
|
use_parallel_embedding=self.config.use_parallel_embedding,
|
|
share_embedding_table=self.config.share_embedding_table,
|
|
)
|
|
|
|
def release(self):
|
|
release_gc()
|
|
|
|
def __del__(self):
|
|
self.release()
|
|
|
|
def check_config(self, config):
|
|
raise NotImplementedError(
|
|
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
|
|
)
|
|
|
|
@classmethod
|
|
def from_config(cls, config: PretrainedConfig):
|
|
return cls(config)
|
|
|
|
@classmethod
|
|
def from_checkpoint(cls,
|
|
ckpt_dir: str,
|
|
rank: Optional[int] = None,
|
|
config: Optional[PretrainedConfig] = None):
|
|
if config is None:
|
|
config = PretrainedConfig.from_json_file(
|
|
os.path.join(ckpt_dir, 'config.json'))
|
|
|
|
if rank is not None:
|
|
config.set_rank(rank)
|
|
|
|
if config.architecture in WEIGHT_LOADER_MODELS:
|
|
weights_path = os.path.join(ckpt_dir, 'rank0.safetensors')
|
|
else:
|
|
rank = config.mapping.rank
|
|
weights_path = os.path.join(ckpt_dir, f'rank{rank}.safetensors')
|
|
|
|
assert os.path.isfile(weights_path)
|
|
weights = safetensors.torch.load_file(weights_path)
|
|
|
|
is_checkpoint_pruned = getattr(config, 'is_pruned', False)
|
|
preprocess_weights(weights, config, from_pruned=is_checkpoint_pruned)
|
|
model = cls(config)
|
|
model.load(weights, from_pruned=is_checkpoint_pruned)
|
|
return model
|
|
|
|
def load(self, weights, from_pruned=False):
|
|
expected_names = set()
|
|
required_names = set()
|
|
for name, param in self.named_parameters():
|
|
expected_names.add(name)
|
|
if not param.is_inited():
|
|
required_names.add(name)
|
|
|
|
provided_names = set(weights.keys())
|
|
if not required_names.issubset(provided_names):
|
|
raise RuntimeError(
|
|
f"Required but not provided tensors:{required_names.difference(provided_names)}"
|
|
)
|
|
if not provided_names.issubset(expected_names):
|
|
logger.warning(
|
|
f"Provided but not expected tensors: {provided_names.difference(expected_names)}"
|
|
)
|
|
|
|
if self.config.architecture in WEIGHT_LOADER_MODELS:
|
|
mapping = self.config.mapping
|
|
for name, param in self.named_parameters():
|
|
if name in provided_names:
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
if from_pruned and param._shape != weights[name].shape:
|
|
dummy_weight = torch.empty(param._shape,
|
|
dtype=trt_dtype_to_torch(
|
|
param._dtype))
|
|
weight_loader(mapping, param, dummy_weight)
|
|
else:
|
|
weight_loader(mapping, param, weights[name])
|
|
else:
|
|
for name, param in self.named_parameters():
|
|
if name in provided_names:
|
|
if not from_pruned:
|
|
try:
|
|
param.value = weights[name]
|
|
except Exception as e:
|
|
raise RuntimeError(
|
|
f"Encounter error '{e}' for parameter '{name}'")
|
|
else:
|
|
param.set_value_or_dummy(weights[name])
|
|
|
|
def load_partial_weights(self, weights: dict):
|
|
params = {name: param for name, param in self.named_parameters()}
|
|
mapping = self.config.mapping
|
|
|
|
for k, v in weights.items():
|
|
if k in params.keys():
|
|
param = params[k]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(mapping, param, v)
|
|
elif mapping.pp_size == 1:
|
|
logger.warning(f"Provided but not expected tensors: {k}")
|
|
|
|
def save_checkpoint(self, output_dir, save_config=True):
|
|
# multiple ranks could share same config.json, so adding a save_config parameter to let user avoiding writing config.json in all ranks
|
|
rank = self.config.mapping.rank
|
|
weights = {
|
|
name: numpy_to_torch(param.raw_value)
|
|
for name, param in self.named_parameters()
|
|
}
|
|
safetensors.torch.save_file(
|
|
weights, os.path.join(output_dir, f'rank{rank}.safetensors'))
|
|
if save_config:
|
|
self.config.to_json_file(os.path.join(output_dir, 'config.json'))
|
|
|
|
def prepare_inputs(self,
|
|
max_batch_size,
|
|
max_input_len,
|
|
max_seq_len,
|
|
max_num_tokens,
|
|
use_cache,
|
|
max_beam_width: int = 1,
|
|
opt_num_tokens: int = None,
|
|
prompt_embedding_table_size: int = 0,
|
|
position_encoding_2d: bool = False,
|
|
max_draft_len: int = 0,
|
|
speculative_decoding_draft_tokens_external: bool = False,
|
|
gather_context_logits: bool = False,
|
|
gather_generation_logits: bool = False,
|
|
lora_target_modules: List[str] = None,
|
|
opt_batch_size: int = 0):
|
|
'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
|
|
ranges of the dimensions of when using TRT dynamic shapes.
|
|
|
|
@return: a list contains values which can be fed into the self.forward()
|
|
'''
|
|
|
|
# Prepare inputs
|
|
remove_input_padding = default_net().plugin_config.remove_input_padding
|
|
use_gpt_attention_plugin = default_net(
|
|
).plugin_config.gpt_attention_plugin
|
|
use_gemm_plugin = default_net().plugin_config.gemm_plugin
|
|
paged_kv_cache = default_net().plugin_config.paged_kv_cache
|
|
tokens_per_block = default_net().plugin_config.tokens_per_block
|
|
use_custom_all_reduce = default_net(
|
|
).plugin_config.use_custom_all_reduce
|
|
use_lora_plugin = default_net().plugin_config.lora_plugin
|
|
multiple_profiles = default_net().plugin_config.multiple_profiles
|
|
streamingllm = default_net().plugin_config.streamingllm
|
|
|
|
model_inputs = self.prepare_basic_inputs(
|
|
max_batch_size=max_batch_size,
|
|
max_beam_width=max_beam_width,
|
|
max_input_len=max_input_len,
|
|
max_seq_len=max_seq_len,
|
|
hidden_size=self.config.hidden_size,
|
|
num_kv_heads=self.config.num_key_value_heads,
|
|
head_size=self.config.head_size,
|
|
num_layers=self.config.num_hidden_layers,
|
|
kv_dtype=str_dtype_to_trt(self.config.kv_dtype),
|
|
remove_input_padding=remove_input_padding,
|
|
use_gpt_attention_plugin=use_gpt_attention_plugin,
|
|
use_gemm_plugin=use_gemm_plugin,
|
|
paged_kv_cache=paged_kv_cache,
|
|
tokens_per_block=tokens_per_block,
|
|
num_heads=self.config.num_attention_heads,
|
|
max_num_tokens=max_num_tokens,
|
|
opt_num_tokens=opt_num_tokens,
|
|
dtype=str_dtype_to_trt(self.config.dtype),
|
|
prompt_embedding_table_size=prompt_embedding_table_size,
|
|
position_encoding_2d=position_encoding_2d,
|
|
mapping=self.config.mapping,
|
|
gather_context_logits=gather_context_logits,
|
|
gather_generation_logits=gather_generation_logits,
|
|
use_custom_all_reduce=use_custom_all_reduce,
|
|
use_lora_plugin=use_lora_plugin,
|
|
max_draft_len=max_draft_len,
|
|
speculative_decoding_draft_tokens_external=
|
|
speculative_decoding_draft_tokens_external,
|
|
lora_target_modules=lora_target_modules,
|
|
multiple_profiles=multiple_profiles,
|
|
streamingllm=streamingllm,
|
|
opt_batch_size=opt_batch_size)
|
|
|
|
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_offsets=model_inputs['kv_cache_block_offsets'],
|
|
host_kv_cache_block_offsets=model_inputs[
|
|
'host_kv_cache_block_offsets'],
|
|
host_kv_cache_pool_pointers=model_inputs[
|
|
'host_kv_cache_pool_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'])
|
|
if model_inputs['spec_decoding_params'] is not None:
|
|
result['spec_decoding_params'] = model_inputs[
|
|
'spec_decoding_params']
|
|
|
|
return result
|
|
|
|
@classmethod
|
|
def quantize(
|
|
cls,
|
|
hf_model_dir: str,
|
|
output_dir: str,
|
|
dtype: str = 'float16',
|
|
mapping: Optional[Mapping] = None,
|
|
quant_config: Optional[QuantConfig] = None,
|
|
*,
|
|
calib_dataset='cnn_dailymail',
|
|
calib_batches=512,
|
|
calib_batch_size=1,
|
|
calib_max_seq_length=512,
|
|
random_seed=1234,
|
|
tokenizer_max_seq_length=2048,
|
|
):
|
|
if mapping is None: # single gpu
|
|
mapping = Mapping()
|
|
if mapping.moe_ep_size > 1:
|
|
raise NotImplementedError(
|
|
"Quantization for expert parallelism is not supported")
|
|
modelopt_qformat = quant_config.quant_algo_to_modelopt_qformat()
|
|
kv_cache_dtype = quant_config.kv_cache_quant_algo
|
|
assert modelopt_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,
|
|
device='cuda',
|
|
calib_dataset=calib_dataset,
|
|
dtype=dtype,
|
|
qformat=modelopt_qformat,
|
|
kv_cache_dtype=kv_cache_dtype,
|
|
calib_size=calib_batches,
|
|
batch_size=calib_batch_size,
|
|
calib_max_seq_length=calib_max_seq_length,
|
|
awq_block_size=quant_config.group_size,
|
|
output_dir=output_dir,
|
|
tp_size=mapping.tp_size,
|
|
pp_size=mapping.pp_size,
|
|
seed=random_seed,
|
|
tokenizer_max_seq_length=tokenizer_max_seq_length,
|
|
)
|
|
|
|
|
|
class DecoderModelForCausalLM(PretrainedModel):
|
|
|
|
def __init__(self, config: PretrainedConfig, transformer, lm_head):
|
|
super().__init__(config)
|
|
self.transformer = transformer
|
|
self.lm_head = lm_head
|
|
self.mup_width_multiplier = getattr(config, 'mup_width_multiplier',
|
|
None)
|
|
|
|
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,
|
|
spec_decoding_params=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 spec_decoding_params is not None:
|
|
kwargs['spec_decoding_params'] = spec_decoding_params
|
|
|
|
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)
|
|
if hasattr(self.config, 'output_multiplier_scale'):
|
|
lm_logits *= getattr(self.config, 'output_multiplier_scale', 1)
|
|
if self.mup_width_multiplier is not None:
|
|
lm_logits = lm_logits / self.mup_width_multiplier
|
|
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: PretrainedModel,
|
|
gemm_swiglu_plugin_dtype: Optional[str] = None,
|
|
) -> PretrainedModel:
|
|
from ..quantization.quantize import fp8_quantize
|
|
|
|
quant_algo = model.config.quantization.quant_algo
|
|
for name, mlp, layer in model.named_modules_with_parent():
|
|
if isinstance(mlp, GatedMLP):
|
|
init_params = get_init_params(mlp)
|
|
init_params["inner_layernorm"] = mlp.inner_layernorm is not None
|
|
fused_layer = FusedGatedMLP(**init_params)
|
|
|
|
if quant_algo == QuantAlgo.FP8:
|
|
fused_layer = fp8_quantize(fused_layer,
|
|
model.config.quantization)
|
|
|
|
if isinstance(mlp.dtype, str):
|
|
dtype = str_dtype_to_torch(mlp.dtype)
|
|
else:
|
|
dtype = trt_dtype_to_torch(mlp.dtype)
|
|
|
|
# dequantize
|
|
gate_weight = numpy_to_torch(
|
|
mlp.gate.weight.raw_value).to(dtype) * numpy_to_torch(
|
|
mlp.gate.weights_scaling_factor.raw_value)
|
|
fc_weight = numpy_to_torch(
|
|
mlp.fc.weight.raw_value).to(dtype) * numpy_to_torch(
|
|
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(
|
|
mlp.gate.weights_scaling_factor.raw_value,
|
|
mlp.fc.weights_scaling_factor.raw_value,
|
|
))
|
|
fused_weight = (fused_weight / fused_weight_scaling_factor).to(
|
|
torch.float8_e4m3fn)
|
|
|
|
if gemm_swiglu_plugin_dtype == 'fp8':
|
|
# gemm_swiglu_plugin needs (k, n) weights
|
|
# but weights should still be k-major for fp8
|
|
fused_layer.fused_fc.weight = Parameter(
|
|
shape=(fused_layer.fused_fc.in_features,
|
|
fused_layer.fused_fc.out_features),
|
|
dtype='fp8')
|
|
fused_layer.fused_fc.weight.value = fused_weight.view(
|
|
fused_layer.fused_fc.in_features,
|
|
fused_layer.fused_fc.out_features)
|
|
else:
|
|
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(
|
|
mlp.gate.activation_scaling_factor.raw_value,
|
|
mlp.fc.activation_scaling_factor.raw_value,
|
|
)
|
|
elif quant_algo is None:
|
|
fused_layer.fused_fc.weight.value = np.concatenate(
|
|
[
|
|
mlp.gate.weight.raw_value,
|
|
mlp.fc.weight.raw_value,
|
|
],
|
|
axis=0,
|
|
)
|
|
if mlp.bias:
|
|
fused_layer.fused_fc.bias.value = np.concatenate(
|
|
[mlp.gate.bias.raw_value, mlp.fc.bias.raw_value],
|
|
axis=0)
|
|
else:
|
|
raise ValueError(f'Unsupported quant algo: {quant_algo}')
|
|
|
|
fused_layer.proj = mlp.proj
|
|
fused_layer.inner_layernorm = mlp.inner_layernorm
|
|
|
|
mlp_name = name.rsplit('.', 1)[-1]
|
|
setattr(layer, mlp_name, fused_layer)
|
|
|
|
return model
|
|
|
|
|
|
def unfuse_qkv_gemm(model: PretrainedModel) -> PretrainedModel:
|
|
'''Split all the models' Attention layer's QKV GEMM into 3 GEMMs layer.q layer.k, layer.v and return the changed model
|
|
'''
|
|
from ..quantization.quantize import quantize
|
|
|
|
for name, layer in model.named_modules():
|
|
if isinstance(layer, Attention) and not layer.cross_attention:
|
|
assert layer.tp_size == 1, "please disable manual tp when enable auto parallel"
|
|
if layer.qkv is None:
|
|
continue
|
|
qkv_params = get_init_params(layer.qkv, ColumnLinear)
|
|
qkv_params["bias"] = qkv_params["bias"] is not None
|
|
qkv_params["strict_dtype"] = qkv_params.get(
|
|
"strict_dtype") is not None
|
|
q = ColumnLinear(
|
|
**{
|
|
**qkv_params,
|
|
"out_features":
|
|
layer.tp_size * layer.num_attention_heads *
|
|
layer.attention_head_size,
|
|
})
|
|
k = ColumnLinear(
|
|
**{
|
|
**qkv_params,
|
|
"out_features":
|
|
layer.tp_size * layer.num_attention_kv_heads *
|
|
layer.attention_head_size,
|
|
})
|
|
v = ColumnLinear(
|
|
**{
|
|
**qkv_params,
|
|
"out_features":
|
|
layer.tp_size * layer.num_attention_kv_heads *
|
|
layer.attention_head_size,
|
|
})
|
|
q = quantize(q, model.config.quantization)
|
|
k = quantize(k, model.config.quantization)
|
|
v = quantize(v, model.config.quantization)
|
|
out_features = q.out_features + k.out_features + v.out_features
|
|
if isinstance(layer.qkv, (
|
|
WeightOnlyQuantLinear,
|
|
WeightOnlyQuantRowLinear,
|
|
WeightOnlyGroupwiseQuantLinear,
|
|
WeightOnlyGroupwiseQuantRowLinear,
|
|
)):
|
|
out_dim = 1
|
|
else:
|
|
out_dim = 0
|
|
if layer.qkv.weight.is_inited():
|
|
qkv_weight = layer.qkv.weight.raw_value
|
|
weights = np.split(qkv_weight, [
|
|
qkv_weight.shape[out_dim] * q.out_features // out_features,
|
|
qkv_weight.shape[out_dim] *
|
|
(q.out_features + k.out_features) // out_features,
|
|
],
|
|
axis=out_dim)
|
|
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, [
|
|
qkv_bias.shape[out_dim] * q.out_features // out_features,
|
|
qkv_bias.shape[out_dim] *
|
|
(q.out_features + k.out_features) // out_features,
|
|
],
|
|
axis=out_dim)
|
|
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 fuse_rg_lru(model: PretrainedModel) -> PretrainedModel:
|
|
for name, rg_lru, parent in model.named_modules_with_parent():
|
|
if isinstance(rg_lru, RgLru):
|
|
fused_layer = FusedRgLru(**get_init_params(rg_lru))
|
|
fused_layer.gate.weight.value = np.concatenate(
|
|
[
|
|
rg_lru.input_gate.weight.raw_value,
|
|
rg_lru.recurrent_gate.weight.raw_value,
|
|
],
|
|
axis=-1,
|
|
)
|
|
fused_layer.gate.bias.value = np.concatenate(
|
|
[
|
|
rg_lru.input_gate.bias.raw_value,
|
|
rg_lru.recurrent_gate.bias.raw_value,
|
|
],
|
|
axis=-1,
|
|
)
|
|
fused_layer.recurrent_param.value = rg_lru.recurrent_param.raw_value
|
|
rg_lru_name = name.rsplit('.', 1)[-1]
|
|
setattr(parent, rg_lru_name, fused_layer)
|
|
return model
|
|
|
|
|
|
def set_prompt_tuning(model: PretrainedModel) -> PretrainedModel:
|
|
'''Replace the given models embedding layer with a PromptTuningEmbedding layer in-place, return the changed model
|
|
Pre-conditions: vocab_embedding exists
|
|
Post-conditions: isinstance(vocab_embedding, PromptTuningEmbedding)
|
|
|
|
'''
|
|
for name, embedding, parent in model.named_modules_with_parent():
|
|
layer_name = name.rsplit('.', 1)[-1]
|
|
if layer_name == "vocab_embedding" and isinstance(embedding, Embedding):
|
|
ptuning_embedding = PromptTuningEmbedding(
|
|
**get_init_params(embedding))
|
|
ptuning_embedding.weight.value = embedding.weight.raw_value
|
|
parent.vocab_embedding = ptuning_embedding
|
|
return model
|
|
|
|
|
|
def add_lora(model: PretrainedModel,
|
|
max_lora_rank: Optional[int]) -> PretrainedModel:
|
|
''' Add lora layers to the Attention/BertAttention/Linear/RowLinear/FusedGatedMLP layers to the given model, return the changed model
|
|
'''
|
|
for name, layer in model.named_modules():
|
|
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.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 to_ootb_moe(model: PretrainedModel) -> PretrainedModel:
|
|
''' Use OOTB MoE instead of MoE plugin, return the changed model
|
|
'''
|
|
for name, layer, parent in model.named_modules_with_parent():
|
|
if isinstance(layer, MOE):
|
|
layer_name = name.rsplit('.', 1)[-1]
|
|
ootb_layer = layer.to(MoeOOTB, model.config.quantization)
|
|
setattr(parent, layer_name, ootb_layer)
|
|
return model
|
|
|
|
|
|
def parallelize_embedding(model: PretrainedModel) -> PretrainedModel:
|
|
for name, embedding, parent in model.named_modules_with_parent():
|
|
layer_name = name.rsplit('.', 1)[-1]
|
|
if isinstance(embedding, Embedding) and embedding.tp_group is None:
|
|
init_params = get_init_params(embedding)
|
|
init_params["tp_group"] = model.config.mapping.tp_group
|
|
init_params["tp_size"] = model.config.mapping.tp_size
|
|
init_params["tp_rank"] = model.config.mapping.tp_rank
|
|
init_params["sharding_dim"] = model.config.embedding_sharding_dim
|
|
new_embedding = embedding.__class__(**init_params)
|
|
setattr(parent, layer_name, new_embedding)
|
|
return model
|
|
|
|
|
|
def share_embedding(model: PretrainedModel) -> PretrainedModel:
|
|
lm_head = None
|
|
vocab_embedding = None
|
|
for name, layer in model.named_modules():
|
|
layer_name = name.rsplit('.', 1)[-1]
|
|
if layer_name == "lm_head":
|
|
lm_head = layer
|
|
if layer_name == "vocab_embedding":
|
|
vocab_embedding = layer
|
|
if lm_head is not None and vocab_embedding is not None:
|
|
break
|
|
|
|
if lm_head is not None and vocab_embedding is not None:
|
|
lm_head.weight = vocab_embedding.weight
|
|
if (hasattr(vocab_embedding, "per_token_scale")
|
|
and vocab_embedding.per_token_scale is not None):
|
|
lm_head.per_channel_scale = vocab_embedding.per_token_scale
|
|
return model
|
|
|
|
|
|
def set_fp8_context_fhma(model: PretrainedModel) -> PretrainedModel:
|
|
for name, layer in model.named_modules():
|
|
if isinstance(layer, Attention):
|
|
scale = [1.0] / layer.dense.activation_scaling_factor.raw_value
|
|
layer.attention_output_orig_quant_scale = Parameter(
|
|
value=scale.astype(np.float32))
|
|
return model
|
|
|
|
|
|
def optimize_model(
|
|
model: PretrainedModel,
|
|
use_parallel_embedding: bool = False,
|
|
share_embedding_table: bool = False,
|
|
use_ootb_moe: bool = False,
|
|
use_fused_mlp: bool = False,
|
|
gemm_swiglu_plugin_dtype: Optional[str] = None,
|
|
use_fused_rg_lru: bool = False,
|
|
use_unfused_qkv_gemm: bool = False,
|
|
use_prompt_tuning: bool = False,
|
|
use_lora: bool = False,
|
|
max_lora_rank: Optional[int] = None,
|
|
use_fp8_context_fmha: bool = False,
|
|
) -> PretrainedModel:
|
|
"""
|
|
Run optimization passes on model.
|
|
There are dependencies between some passes,
|
|
so we always run passes in the order of arguments to guarantee the execution order.
|
|
"""
|
|
# before weight loading
|
|
if use_parallel_embedding:
|
|
model = parallelize_embedding(model)
|
|
if share_embedding_table:
|
|
model = share_embedding(model)
|
|
|
|
# After weight loading
|
|
if use_ootb_moe:
|
|
model = to_ootb_moe(model)
|
|
if use_fused_mlp:
|
|
model = fuse_gate_mlp(model, gemm_swiglu_plugin_dtype)
|
|
if use_fused_rg_lru:
|
|
model = fuse_rg_lru(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)
|
|
if use_fp8_context_fmha:
|
|
model = set_fp8_context_fhma(model)
|
|
return model
|
|
|
|
|
|
def preprocess_weights(weights: Dict[str, torch.Tensor],
|
|
model_config: PretrainedConfig,
|
|
from_pruned=False) -> None:
|
|
"""This function in-place modifies weights and model_config, making them compatible with each other.
|
|
|
|
Note: Typically, it should be called before model creation and weight loading. For example,
|
|
preprocess_weights(weights, model_config)
|
|
model = XXXForCausalLM(model_config)
|
|
model.load(weights)
|
|
"""
|
|
quant_algo = model_config.quantization.quant_algo
|
|
kv_cache_quant_algo = model_config.quantization.kv_cache_quant_algo
|
|
|
|
# INT4_AWQ
|
|
if quant_algo == QuantAlgo.W4A8_AWQ or quant_algo == QuantAlgo.W4A16_AWQ:
|
|
preprocessor = torch.ops.trtllm.preprocess_weights_for_mixed_gemm
|
|
if quant_algo == QuantAlgo.W4A8_AWQ:
|
|
activation_type = torch.float8_e4m3fn
|
|
elif quant_algo == QuantAlgo.W4A16_AWQ:
|
|
activation_type = torch.float16
|
|
for name, param in weights.items():
|
|
if from_pruned and param.numel() == 0:
|
|
continue
|
|
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,
|
|
activation_type).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 == QuantAlgo.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 == QuantAlgo.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)
|
|
|
|
elif quant_algo in [QuantAlgo.W4A16, QuantAlgo.W8A16]:
|
|
weights = weight_only_quantize_dict(weights=weights,
|
|
quant_algo=quant_algo,
|
|
plugin=True)
|
|
|
|
# FP8 kv_cache_scaling_factor is always 1.0
|
|
if kv_cache_quant_algo == QuantAlgo.FP8:
|
|
for name, param in weights.items():
|
|
if name.endswith('kv_cache_scaling_factor'):
|
|
weights[name] = torch.tensor([1.0], dtype=torch.float32)
|
|
|
|
# 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)
|
|
|
|
# For share_embedding_table
|
|
check_share_embedding(weights, model_config)
|
|
|
|
|
|
def check_share_embedding(weights: Dict[str, torch.Tensor],
|
|
model_config: PretrainedConfig):
|
|
if model_config.share_embedding_table:
|
|
if "lm_head.weight" in weights and "transformer.vocab_embedding.weight" in weights:
|
|
if (weights["lm_head.weight"] -
|
|
weights["transformer.vocab_embedding.weight"]).any():
|
|
logger.warning(
|
|
"lm_head.weight and transformer.vocab_embedding.weight are not identical, "
|
|
"share_embedding_table cannot be enabled; setting share_embedding_table=False."
|
|
)
|
|
model_config.share_embedding_table = False
|
|
else:
|
|
weights.pop("lm_head.weight")
|