File size: 12,319 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 |
# 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.
import math
from typing import Optional
from ..._utils import pad_vocab_size
from ...functional import Tensor, cast, recv, send
from ...layers import (Attention, AttentionMaskType, AttentionParams,
ColumnLinear, Embedding, GatedMLP, KeyValueCacheParams,
LoraParams, PositionEmbeddingType, RmsNorm)
from ...mapping import Mapping
from ...module import Module
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
PretrainedConfig, QuantConfig)
from .weight import load_from_hf_gemma
class GemmaDecoderLayer(Module):
def __init__(self, config: PretrainedConfig, 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)
local_layer_idx = layer_idx - layers_range[0]
self.attention = Attention(
local_layer_idx=local_layer_idx,
hidden_size=config.hidden_size,
num_attention_heads=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
attention_head_size=config.head_size,
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,
quant_mode=config.quant_mode,
)
mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
self.mlp = GatedMLP(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)
self.post_layernorm = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
def forward(self,
hidden_states: Tensor,
attention_mask: Optional[Tensor] = None,
use_cache: bool = False,
kv_cache_params: Optional[KeyValueCacheParams] = None,
attention_params: Optional[AttentionParams] = None,
lora_layer_params: Optional[LoraParams] = None):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
attention_output = self.attention(hidden_states,
attention_mask=attention_mask,
use_cache=use_cache,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
lora_layer_params=lora_layer_params)
if use_cache:
attention_output, presents = attention_output
hidden_states = residual + attention_output
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
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 GemmaModel(Module):
def __init__(self, config: PretrainedConfig) -> 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(GemmaDecoderLayer, config)
if self.mapping.is_last_pp_rank():
self.ln_f = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
self.hidden_size = config.hidden_size
def forward(self,
input_ids,
position_ids=None,
use_cache=False,
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):
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)
hidden_states = cast(hidden_states * math.sqrt(self.hidden_size),
hidden_states.dtype)
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,
)
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 GemmaForCausalLM(DecoderModelForCausalLM):
def __init__(self, config: PretrainedConfig):
self.check_config(config)
transformer = GemmaModel(config)
vocab_size_padded = pad_vocab_size(config.vocab_size,
config.mapping.tp_size)
try:
import modelopt
major, minor, patch = modelopt.__version__.split(".")
major = int(major)
minor = int(minor)
patch = int(patch)
if major == 0 and minor == 11 and patch < 1:
# modelopt=0.11.0 won't force this field to True, this is a hot fix
# TODO: can remove after modelop=0.11.1 is out
# TRT LLM forces the embedding table to be shared for gemma.
config.share_embedding_table = True
assert config.share_embedding_table, "Gemma only supports share_embedding_table"
except:
# Not find modelopt, assume not use modelopt quantized model
assert config.share_embedding_table, "Gemma only supports share_embedding_table"
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_dir,
dtype='float16',
mapping: Optional[Mapping] = None,
**kwargs):
import transformers
from transformers import GemmaConfig
from ...models.modeling_utils import PretrainedConfig
cfg = GemmaConfig.from_pretrained(hf_model_dir)
num_kv_heads = cfg.num_key_value_heads if hasattr(cfg, "num_key_value_heads") \
else cfg.num_attention_heads
quantization = kwargs.get('quantization', QuantConfig())
if mapping is None:
mapping = Mapping()
cfg.mapping = mapping
cfg.dtype = dtype
cfg.norm_epsilon = cfg.rms_norm_eps
config = {
'architecture': cfg.architectures[0],
'dtype': cfg.dtype,
'logits_dtype': 'float32',
'num_hidden_layers': cfg.num_hidden_layers,
'num_attention_heads': cfg.num_attention_heads,
'head_size': cfg.head_dim,
'hidden_size': cfg.hidden_size,
'intermediate_size': cfg.intermediate_size,
'num_key_value_heads': num_kv_heads,
'vocab_size': cfg.vocab_size,
'position_embedding_type': 'rope_gpt_neox',
'max_position_embeddings': cfg.max_position_embeddings,
'hidden_act': cfg.hidden_act,
'rotary_base': getattr(cfg, 'rotary_base', 10000.0),
'rotary_scaling': getattr(cfg, 'rotary_scaling', None),
'norm_epsilon': cfg.rms_norm_eps,
'quantization': quantization.to_dict(),
'mapping': {
'world_size': mapping.world_size,
'tp_size': mapping.world_size,
},
'use_parallel_embedding': kwargs.get("use_parallel_embedding",
False),
'embedding_sharding_dim': kwargs.get("embedding_sharding_dim", 0),
'use_fused_mlp': kwargs.get("use_fused_mlp", False),
}
assert not quantization.quant_mode.has_any_quant()
tllm_llama = GemmaForCausalLM(PretrainedConfig.from_dict(config))
hf_model = transformers.GemmaForCausalLM
hf_llama = hf_model.from_pretrained(
hf_model_dir,
device_map={
"model": "cpu",
"lm_head": "cpu",
"embed_tokens": "cpu",
"layers": "cpu",
"norm": "cpu",
}, # Load to CPU memory
torch_dtype='auto',
)
weights = load_from_hf_gemma(
tllm_llama,
hf_llama,
mapping=mapping,
dtype=dtype,
# TODO: these shall be outside from_hugging_face too.
use_gemm_woq_plugin=kwargs.get("use_gemm_woq_plugin", False),
)
del hf_llama
tllm_llama.load(weights)
return tllm_llama
def check_config(self, config):
config.set_if_not_exist("share_embedding_table", True)
config.set_if_not_exist('use_parallel_embedding', False)
config.set_if_not_exist('embedding_sharding_dim', 0)
config.set_if_not_exist('mlp_bias', False)
config.set_if_not_exist('attn_bias', False)
config.set_if_not_exist('rotary_base', 10000.0)
config.set_if_not_exist('rotary_scaling', None)
config.set_if_not_exist('use_fused_mlp', False)
|