File size: 17,982 Bytes
5000658 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 |
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Union
from ..._common import default_net
from ..._utils import pad_vocab_size
from ...functional import (AllReduceFusionOp, AllReduceFusionParams, Tensor,
non_gated_version, recv, send)
from ...layers import (MOE, Attention, AttentionMaskType, ColumnLinear,
Embedding, GatedMLP, PositionEmbeddingType, RmsNorm)
from ...lora_manager import LoraConfig, use_lora
from ...mapping import Mapping
from ...module import Module
from ...quantization import W8A8_SQ_PLUGIN_LIST, QuantAlgo
from ..convert_utils import has_safetensors
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
QuantConfig, check_share_embedding)
from .config import LLaMAConfig
from .convert import (load_hf_llama, load_weights_from_hf_by_shard,
load_weights_from_hf_model,
load_weights_from_hf_safetensors,
load_weights_from_meta_ckpt)
class LLaMADecoderLayer(Module):
def __init__(self, config: LLaMAConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.config = config
self.input_layernorm = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
layers_range = config.mapping.pp_layers(config.num_hidden_layers)
self.local_layer_idx = layer_idx - layers_range[0]
self.attention = Attention(
local_layer_idx=self.local_layer_idx,
hidden_size=config.hidden_size,
attention_head_size=config.head_size,
num_attention_heads=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
max_position_embeddings=config.max_position_embeddings,
dtype=config.dtype,
attention_mask_type=AttentionMaskType.causal,
bias=config.attn_bias,
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
rotary_embedding_base=config.rotary_base,
rotary_embedding_scaling=config.rotary_scaling,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
tp_rank=config.mapping.tp_rank,
quant_mode=config.quant_mode)
mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
ClsMLP = GatedMLP
mlp_kwargs = {}
if config.moe.has_moe():
ClsMLP = MOE
mlp_kwargs = {
"moe_config": config.moe,
"mapping": config.mapping,
}
self.mlp = ClsMLP(hidden_size=config.hidden_size,
ffn_hidden_size=mlp_hidden_size,
hidden_act=config.hidden_act,
dtype=config.dtype,
bias=config.mlp_bias,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
quant_mode=config.quant_mode,
**mlp_kwargs)
self.post_layernorm = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
# Residual MLP that applies on pre-attention input
# TODO: change to self.has_residual_mlp = self.config.residual_mlp after ModelOpt quantize config is updated
self.has_residual_mlp = False
if hasattr(self.config,
"residual_mlp") and self.config.residual_mlp is True:
self.has_residual_mlp = True
if self.has_residual_mlp:
self.residual_layernorm = RmsNorm(
normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
ClsMLP = GatedMLP # TODO: may use FusedGatedMLP to further speedup
self.residual_mlp = ClsMLP(
hidden_size=config.hidden_size,
ffn_hidden_size=config.
hidden_size, # residual mlp uses hidden_size
hidden_act=non_gated_version(
config.hidden_act), # back to non-gated
dtype=config.dtype,
bias=config.mlp_bias,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
quant_mode=config.quant_mode)
def forward(self,
hidden_states,
attention_mask=None,
use_cache=False,
spec_decoding_params=None,
kv_cache_params=None,
attention_params=None,
lora_layer_params=None,
next_layer_input_layernorm_args=None):
assert not (
default_net().plugin_config.reduce_fusion and self.has_residual_mlp
), "Custom all reduce and residual mlp can't be enabled at the same time."
if default_net(
).plugin_config.reduce_fusion and self.local_layer_idx > 0:
hidden_states, residual = hidden_states
else:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
attention_output = self.attention(
hidden_states,
attention_mask=attention_mask,
use_cache=use_cache,
spec_decoding_params=spec_decoding_params,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
lora_layer_params=lora_layer_params,
reduce_fusion_params=AllReduceFusionParams(
fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM
if default_net().plugin_config.reduce_fusion else
AllReduceFusionOp.NONE,
residual=residual,
norm_weight=self.post_layernorm.weight.value,
eps=self.post_layernorm.eps))
if use_cache:
attention_output, presents = attention_output
if self.has_residual_mlp:
hidden_states = residual + attention_output
residual_attn = hidden_states
# arctic layer w/ residual mlp
# residual mlp
hidden_states = self.residual_layernorm(hidden_states)
hidden_states = self.residual_mlp(hidden_states)
residual_mlp = residual_attn + hidden_states
# parallel moe
# parallel moe layers applies on PRE-ATTENTION input residual, therefore achieving pre-fetching and better parallelism
hidden_states = self.post_layernorm(residual)
hidden_states = self.mlp(hidden_states,
lora_layer_params=lora_layer_params)
hidden_states = residual_mlp + hidden_states
else:
if default_net().plugin_config.reduce_fusion:
hidden_states, residual = attention_output
else:
hidden_states = residual + attention_output
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
if next_layer_input_layernorm_args is not None:
hidden_states = self.mlp(
hidden_states,
lora_layer_params=lora_layer_params,
reduce_fusion_params=AllReduceFusionParams(
fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM
if default_net().plugin_config.reduce_fusion else
AllReduceFusionOp.NONE,
residual=residual,
norm_weight=next_layer_input_layernorm_args[0],
eps=next_layer_input_layernorm_args[1]))
else:
hidden_states = self.mlp(hidden_states,
lora_layer_params=lora_layer_params)
hidden_states = residual + hidden_states
if use_cache:
return (hidden_states, presents)
return hidden_states
class LLaMAModel(Module):
def __init__(self, config: LLaMAConfig) -> None:
super().__init__()
self.mapping = config.mapping
if self.mapping.is_first_pp_rank():
self.vocab_embedding = Embedding(config.vocab_size,
config.hidden_size,
dtype=config.dtype)
self.layers = DecoderLayerList(LLaMADecoderLayer, config)
if self.mapping.is_last_pp_rank():
self.ln_f = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
def forward(self,
input_ids,
position_ids=None,
use_cache=False,
attention_mask=None,
spec_decoding_params=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):
ptuning_args = [
prompt_embedding_table, prompt_tasks, prompt_vocab_size
] if prompt_embedding_table is not None else []
if self.mapping.is_first_pp_rank():
hidden_states = self.vocab_embedding(input_ids, *ptuning_args)
else:
hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
hidden_states = self.layers.forward(
hidden_states,
use_cache=use_cache,
attention_mask=attention_mask,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
lora_params=lora_params,
spec_decoding_params=spec_decoding_params)
if use_cache:
hidden_states, presents = hidden_states
if self.mapping.is_last_pp_rank():
hidden_states = self.ln_f(hidden_states)
else:
hidden_states = send(hidden_states, self.mapping.next_pp_rank())
if use_cache:
return (hidden_states, tuple(presents))
return hidden_states
class LLaMAForCausalLM(DecoderModelForCausalLM):
config_class = LLaMAConfig
def __init__(self, config: LLaMAConfig):
transformer = LLaMAModel(config)
vocab_size_padded = pad_vocab_size(config.vocab_size,
config.mapping.tp_size)
if config.mapping.is_last_pp_rank():
lm_head = ColumnLinear(config.hidden_size,
vocab_size_padded,
bias=False,
dtype=config.dtype,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
gather_output=True)
else:
lm_head = None
self.quant_mode = config.quant_mode
self.mapping = config.mapping
super().__init__(config, transformer, lm_head)
@classmethod
def from_hugging_face(
cls,
hf_model_or_dir: Union[str, 'transformers.PreTrainedModel'],
dtype: str = 'auto',
mapping: Optional[Mapping] = None,
quant_config: Optional[QuantConfig] = None,
**kwargs):
''' Create a LLaMAForCausalLM object from give parameters
'''
import transformers
load_by_shard = kwargs.pop('load_by_shard', False)
load_model_on_cpu = kwargs.pop('load_model_on_cpu', False)
assert hf_model_or_dir is not None
use_preloading = isinstance(hf_model_or_dir,
transformers.PreTrainedModel)
if use_preloading:
hf_model = hf_model_or_dir
hf_config_or_dir = hf_model.config
else:
hf_model_dir = hf_model_or_dir
hf_config_or_dir = hf_model_or_dir
config = LLaMAConfig.from_hugging_face(hf_config_or_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
if use_preloading:
assert not load_by_shard
weights = load_weights_from_hf_model(hf_model, config)
elif load_by_shard:
weights = load_weights_from_hf_by_shard(hf_model_dir, config)
elif has_safetensors(
hf_model_dir) and not config.quant_mode.has_any_quant():
weights = load_weights_from_hf_safetensors(hf_model_dir, config)
else:
hf_model = load_hf_llama(hf_model_dir, load_model_on_cpu)
weights = load_weights_from_hf_model(hf_model, config)
check_share_embedding(weights, config)
model = LLaMAForCausalLM(config)
model.load(weights)
return model
def default_plugin_config(self, **kwargs):
plugin_config = super().default_plugin_config(**kwargs)
if self.quant_mode.is_int4_weight_only_per_group():
plugin_config.weight_only_groupwise_quant_matmul_plugin = 'auto'
return plugin_config
@classmethod
def from_meta_ckpt(cls,
meta_ckpt_dir: str,
dtype: str = 'auto',
mapping: Optional[Mapping] = None,
quant_config: Optional[QuantConfig] = None,
**kwargs):
config = LLaMAConfig.from_meta_ckpt(meta_ckpt_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
weights = load_weights_from_meta_ckpt(meta_ckpt_dir, config)
check_share_embedding(weights, config)
model = LLaMAForCausalLM(config)
model.load(weights)
return model
@classmethod
def quantize(
cls,
hf_model_dir: str,
output_dir: str,
dtype: str = 'auto',
mapping: Optional[Mapping] = None,
quant_config: Optional[QuantConfig] = None,
*,
device: str = 'cuda',
calib_dataset: str = 'cnn_dailymail',
calib_batches: int = 512,
calib_batch_size: int = 1,
calib_max_seq_length: int = 512,
random_seed: int = 1234,
tokenizer_max_seq_length: int = 2048,
**kwargs,
):
DEFAULT_MODELOPT_FLOW = [
QuantAlgo.W4A16_AWQ, QuantAlgo.FP8, QuantAlgo.W8A8_SQ_PER_CHANNEL,
QuantAlgo.W4A8_AWQ
]
config = LLaMAConfig.from_hugging_face(hf_model_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
if quant_config.quant_algo in DEFAULT_MODELOPT_FLOW:
super().quantize(hf_model_dir,
output_dir,
dtype=config.dtype,
mapping=config.mapping,
quant_config=config.quantization,
device=device,
calib_dataset=calib_dataset,
calib_batches=calib_batches,
calib_batch_size=calib_batch_size,
calib_max_seq_length=calib_max_seq_length,
random_seed=random_seed,
tokenizer_max_seq_length=tokenizer_max_seq_length)
else:
# non-modelopt, the legacy TRT-LLM native quantization algorithm:
# sq, int4/int8 weights only, int8 kv cache
NATIVE_QUANT_FLOW = [
QuantAlgo.W4A16, QuantAlgo.W8A16,
QuantAlgo.FP8_PER_CHANNEL_PER_TOKEN, None
] + W8A8_SQ_PLUGIN_LIST
is_valid_native_quant = (quant_config.quant_algo in NATIVE_QUANT_FLOW) and \
(quant_config.kv_cache_quant_algo in [QuantAlgo.INT8, None])
assert quant_config.quant_algo is not None or quant_config.kv_cache_quant_algo is not None, \
"There is no point to call the quantize function if both quant_algo and kv_cache_quant_algo is None"
assert is_valid_native_quant, f"Internal error: shall call Modelopt for this quantization {quant_config}"
from . import convert
convert.quantize(hf_model_dir,
output_dir,
config=config,
device=device,
calib_dataset=calib_dataset)
def use_lora(self, lora_config: LoraConfig):
use_lora(self, lora_config)
|