File size: 11,759 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 |
import json
import os
import traceback
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Optional
import numpy as np
import safetensors
from transformers import AutoModelForCausalLM
from ..._utils import pad_vocab_size
from ...functional import PositionEmbeddingType, Tensor
from ...layers import (MLP, Attention, AttentionMaskType, BlockSparseAttnParams,
Embedding, LayerNorm, ParallelLMHead, RmsNorm)
from ...lora_manager import LoraConfig, use_lora
from ...module import Module
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
PretrainedConfig)
from .convert import convert_hf_config, convert_hf_weights
class Phi3DecoderLayer(Module):
def __init__(self, config: PretrainedConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
tp_group = config.mapping.tp_group
tp_size = config.mapping.tp_size
attention_mask_type = AttentionMaskType.causal
block_sparse_attn_params = BlockSparseAttnParams()
q_scaling = 1.0
self.gegelu_limit = None
self.small_variant = config.architecture == "Phi3SmallForCausalLM"
if self.small_variant:
self.gegelu_limit = config.gegelu_limit
# MuP uses norm_factor=attention_head_size (rather than sqrt(attention_head_size))
# We achieve this using q_scaling = sqrt(attention_head_size)
hidden_size = config.hidden_size
num_attention_heads = config.num_attention_heads
attention_head_size = hidden_size / num_attention_heads
q_scaling = attention_head_size**.5
block_sparse = (
(layer_idx + 1) % config.dense_attention_every_n_layers) != 0
attention_mask_type = AttentionMaskType.blocksparse if block_sparse else AttentionMaskType.causal
block_sparse_attn_params = BlockSparseAttnParams(
config.blocksparse_block_size,
config.blocksparse_homo_head_pattern,
config.blocksparse_num_local_blocks,
config.blocksparse_vertical_stride)
self.input_layernorm = LayerNorm(
normalized_shape=config.hidden_size, dtype=config.dtype)
self.post_layernorm = LayerNorm(normalized_shape=config.hidden_size,
dtype=config.dtype)
else:
self.input_layernorm = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
self.post_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]
position_embedding_type = PositionEmbeddingType.rope_gpt_neox
rope_scaling_short_factors, rope_scaling_long_factors = None, None
rope_scaling_short_mscale, rope_scaling_long_mscale = None, None
original_max_position_embeddings = config.max_position_embeddings
if hasattr(config, "longrope_scaling_short_factors"):
rope_scaling_short_factors = np.asarray(
config.longrope_scaling_short_factors).astype(np.float32)
rope_scaling_long_factors = np.asarray(
config.longrope_scaling_long_factors).astype(np.float32)
original_max_position_embeddings = config.original_max_position_embeddings
position_embedding_type = PositionEmbeddingType.long_rope
if self.small_variant:
rope_scaling_short_mscale = config.longrope_short_mscale
rope_scaling_long_mscale = config.longrope_long_mscale
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,
position_embedding_type=position_embedding_type,
rotary_embedding_base=config.rotary_base,
max_position_embeddings=config.max_position_embeddings,
dtype=config.dtype,
attention_mask_type=attention_mask_type,
bias=self.small_variant,
q_scaling=q_scaling,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=config.quant_mode,
rope_scaling_short_factors=rope_scaling_short_factors,
rope_scaling_long_factors=rope_scaling_long_factors,
rope_scaling_short_mscale=rope_scaling_short_mscale,
rope_scaling_long_mscale=rope_scaling_long_mscale,
original_max_position_embeddings=original_max_position_embeddings,
block_sparse_params=block_sparse_attn_params)
self.mlp = MLP(hidden_size=config.hidden_size,
ffn_hidden_size=config.intermediate_size,
hidden_act=config.hidden_act,
dtype=config.dtype,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=config.quant_mode,
bias=self.small_variant)
def forward(
self,
hidden_states: Tensor,
attention_mask=None,
use_cache=False,
kv_cache_params=None,
attention_params=None,
lora_layer_params=None,
):
input_layernorm_output = self.input_layernorm(hidden_states)
attention_output = self.attention(
input_layernorm_output,
attention_mask=attention_mask,
use_cache=use_cache,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
norm_before_bmm1=not self.small_variant,
lora_layer_params=lora_layer_params,
)
if use_cache:
attention_output, presents = attention_output
post_attention_input = hidden_states + attention_output
post_attention_output = self.post_layernorm(post_attention_input)
feed_forward_hidden_states = self.mlp(
post_attention_output,
gegelu_limit=self.gegelu_limit,
lora_layer_params=lora_layer_params)
hidden_states = post_attention_input + feed_forward_hidden_states
if use_cache:
return (hidden_states, presents)
return hidden_states
class Phi3Model(Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.vocab_embedding = Embedding(num_embeddings=config.vocab_size,
embedding_dim=config.hidden_size,
dtype=config.dtype)
self.layers = DecoderLayerList(Phi3DecoderLayer, config)
self.small_variant = config.architecture == "Phi3SmallForCausalLM"
if self.small_variant:
self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
dtype=config.dtype)
self.mup_embedding_multiplier = config.mup_embedding_multiplier
else:
self.ln_f = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
def forward(
self,
input_ids: Tensor,
position_ids=None,
use_cache=False,
attention_mask=None,
kv_cache_params=None,
attention_params=None,
prompt_embedding_table=None,
prompt_tasks=None,
prompt_vocab_size=None,
lora_params=None,
):
args = [prompt_embedding_table, prompt_tasks, prompt_vocab_size
] if prompt_embedding_table is not None else []
hidden_states = self.vocab_embedding(input_ids, *args)
if self.small_variant and self.mup_embedding_multiplier > 0.0:
hidden_states = hidden_states * self.mup_embedding_multiplier
hidden_states = self.layers(
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
hidden_states = self.ln_f(hidden_states)
if use_cache:
return (hidden_states, tuple(presents))
return hidden_states
class Phi3ForCausalLM(DecoderModelForCausalLM):
def __init__(self, config: PretrainedConfig):
transformer = Phi3Model(config)
vocab_size_padded = pad_vocab_size(config.vocab_size,
config.mapping.tp_size)
lm_head = ParallelLMHead(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)
self.trtllm_modules_to_hf_modules = {
"attn_qkv": ["qkv_proj", "query_key_value"],
"attn_dense": ["o_proj", "dense"],
"mlp_h_to_4h": ["gate_up_proj", "up_proj"],
"mlp_4h_to_h": "down_proj",
}
super().__init__(config, transformer, lm_head)
@classmethod
def convert_hf_checkpoint(cls,
hf_model_dir: str,
dtype: Optional[str] = "float16",
output_dir: Optional[str] = None,
args=None):
'''
Convert Huggingface checkpoint to TRT-LLM checkpoint
'''
hf_model = AutoModelForCausalLM.from_pretrained(hf_model_dir,
torch_dtype="auto",
trust_remote_code=True)
config = convert_hf_config(hf_model.config, dtype, args)
with open(os.path.join(output_dir, 'config.json'), 'w') as f:
json.dump(config, f, indent=4)
small_variant = config['architecture'] == "Phi3SmallForCausalLM"
def covert_and_save(rank):
weights = convert_hf_weights(hf_model, dtype, config, small_variant,
args, rank)
safetensors.torch.save_file(
weights, os.path.join(output_dir, f'rank{rank}.safetensors'))
world_size = args.tp_size * args.pp_size
if args.workers == 1:
for rank in range(world_size):
covert_and_save(rank)
else:
with ThreadPoolExecutor(max_workers=args.workers) as p:
futures = [
p.submit(covert_and_save, rank)
for rank in range(world_size)
]
exceptions = []
for future in as_completed(futures):
try:
future.result()
except Exception as e:
traceback.print_exc()
exceptions.append(e)
assert len(
exceptions
) == 0, "Checkpoint conversion failed, please check error log."
def use_lora(self, lora_config: LoraConfig):
use_lora(self, lora_config, self.trtllm_modules_to_hf_modules)
|