| 1 | # SPDX-License-Identifier: Apache-2.0 |
| 2 | # SPDX-FileCopyrightText: Copyright contributors to the vLLM project |
| 3 | |
| 4 | # Adapted from |
| 5 | # https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/blob/main/modeling_exaone.py |
| 6 | # Copyright 2024 The LG U+ CTO AI Tech Lab. |
| 7 | # Copyright 2021 The LG AI Research EXAONE Lab |
| 8 | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. |
| 9 | # |
| 10 | # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX |
| 11 | # and OPT implementations in this library. It has been modified from its |
| 12 | # original forms to accommodate minor architectural differences compared |
| 13 | # to GPT-NeoX and OPT used by the Meta AI team that trained the model. |
| 14 | # |
| 15 | # Licensed under the Apache License, Version 2.0 (the "License"); |
| 16 | # you may not use this file except in compliance with the License. |
| 17 | # You may obtain a copy of the License at |
| 18 | # |
| 19 | # http://www.apache.org/licenses/LICENSE-2.0 |
| 20 | # |
| 21 | # Unless required by applicable law or agreed to in writing, software |
| 22 | # distributed under the License is distributed on an "AS IS" BASIS, |
| 23 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 24 | # See the License for the specific language governing permissions and |
| 25 | # limitations under the License. |
| 26 | """Inference-only Exaone model compatible with HuggingFace weights.""" |
| 27 | |
| 28 | from collections.abc import Iterable |
| 29 | from itertools import islice |
| 30 | |
| 31 | import torch |
| 32 | from torch import nn |
| 33 | from transformers import PreTrainedConfig |
| 34 | |
| 35 | from vllm.compilation.decorators import support_torch_compile |
| 36 | from vllm.config import CacheConfig, VllmConfig |
| 37 | from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size |
| 38 | from vllm.model_executor.layers.activation import SiluAndMul |
| 39 | from vllm.model_executor.layers.attention import Attention |
| 40 | from vllm.model_executor.layers.layernorm import RMSNorm |
| 41 | from vllm.model_executor.layers.linear import ( |
| 42 | MergedColumnParallelLinear, |
| 43 | QKVParallelLinear, |
| 44 | RowParallelLinear, |
| 45 | ) |
| 46 | from vllm.model_executor.layers.logits_processor import LogitsProcessor |
| 47 | from vllm.model_executor.layers.quantization import QuantizationConfig |
| 48 | from vllm.model_executor.layers.rotary_embedding import get_rope |
| 49 | from vllm.model_executor.layers.vocab_parallel_embedding import ( |
| 50 | ParallelLMHead, |
| 51 | VocabParallelEmbedding, |
| 52 | ) |
| 53 | from vllm.sequence import IntermediateTensors |
| 54 | |
| 55 | from .interfaces import SupportsLoRA, SupportsPP |
| 56 | from .utils import ( |
| 57 | AutoWeightsLoader, |
| 58 | PPMissingLayer, |
| 59 | WeightsMapper, |
| 60 | make_empty_intermediate_tensors_factory, |
| 61 | make_layers, |
| 62 | maybe_prefix, |
| 63 | ) |
| 64 | |
| 65 | |
| 66 | class ExaoneGatedMLP(nn.Module): |
| 67 | def __init__( |
| 68 | self, |
| 69 | hidden_size: int, |
| 70 | intermediate_size: int, |
| 71 | hidden_act: str, |
| 72 | quant_config: QuantizationConfig | None = None, |
| 73 | bias: bool = False, |
| 74 | prefix: str = "", |
| 75 | ) -> None: |
| 76 | super().__init__() |
| 77 | self.gate_up_proj = MergedColumnParallelLinear( |
| 78 | input_size=hidden_size, |
| 79 | output_sizes=[intermediate_size] * 2, |
| 80 | bias=bias, |
| 81 | quant_config=quant_config, |
| 82 | prefix=f"{prefix}.gate_up_proj", |
| 83 | ) |
| 84 | self.c_proj = RowParallelLinear( |
| 85 | input_size=intermediate_size, |
| 86 | output_size=hidden_size, |
| 87 | bias=bias, |
| 88 | quant_config=quant_config, |
| 89 | prefix=f"{prefix}.c_proj", |
| 90 | ) |
| 91 | if hidden_act != "silu": |
| 92 | raise ValueError( |
| 93 | f"Unsupported activation: {hidden_act}. Only silu is supported for now." |
| 94 | ) |
| 95 | self.act_fn = SiluAndMul() |
| 96 | |
| 97 | def forward(self, x): |
| 98 | gate_up, _ = self.gate_up_proj(x) |
| 99 | x = self.act_fn(gate_up) |
| 100 | x, _ = self.c_proj(x) |
| 101 | return x |
| 102 | |
| 103 | |
| 104 | class ExaoneAttention(nn.Module): |
| 105 | def __init__( |
| 106 | self, |
| 107 | config: PreTrainedConfig, |
| 108 | hidden_size: int, |
| 109 | num_heads: int, |
| 110 | num_kv_heads: int, |
| 111 | max_position_embeddings: int = 8192, |
| 112 | quant_config: QuantizationConfig | None = None, |
| 113 | bias: bool = False, |
| 114 | cache_config: CacheConfig | None = None, |
| 115 | prefix: str = "", |
| 116 | ) -> None: |
| 117 | super().__init__() |
| 118 | self.hidden_size = hidden_size |
| 119 | tp_size = get_tensor_model_parallel_world_size() |
| 120 | self.total_num_heads = num_heads |
| 121 | assert self.total_num_heads % tp_size == 0 |
| 122 | self.num_heads = self.total_num_heads // tp_size |
| 123 | self.total_num_kv_heads = num_kv_heads |
| 124 | if self.total_num_kv_heads >= tp_size: |
| 125 | # Number of KV heads is greater than TP size, so we partition |
| 126 | # the KV heads across multiple tensor parallel GPUs. |
| 127 | assert self.total_num_kv_heads % tp_size == 0 |
| 128 | else: |
| 129 | # Number of KV heads is less than TP size, so we replicate |
| 130 | # the KV heads across multiple tensor parallel GPUs. |
| 131 | assert tp_size % self.total_num_kv_heads == 0 |
| 132 | self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) |
| 133 | # MistralConfig has an optional head_dim introduced by Mistral-Nemo |
| 134 | self.head_dim = getattr(config, "head_dim", None) |
| 135 | if self.head_dim is None: |
| 136 | self.head_dim = self.hidden_size // self.total_num_heads |
| 137 | self.q_size = self.num_heads * self.head_dim |
| 138 | self.kv_size = self.num_kv_heads * self.head_dim |
| 139 | self.scaling = self.head_dim**-0.5 |
| 140 | self.max_position_embeddings = max_position_embeddings |
| 141 | |
| 142 | self.qkv_proj = QKVParallelLinear( |
| 143 | hidden_size=hidden_size, |
| 144 | head_size=self.head_dim, |
| 145 | total_num_heads=self.total_num_heads, |
| 146 | total_num_kv_heads=self.total_num_kv_heads, |
| 147 | bias=bias, |
| 148 | quant_config=quant_config, |
| 149 | prefix=f"{prefix}.qkv_proj", |
| 150 | ) |
| 151 | |
| 152 | self.out_proj = RowParallelLinear( |
| 153 | input_size=self.total_num_heads * self.head_dim, |
| 154 | output_size=hidden_size, |
| 155 | bias=bias, |
| 156 | quant_config=quant_config, |
| 157 | prefix=f"{prefix}.out_proj", |
| 158 | ) |
| 159 | |
| 160 | is_neox_style = True |
| 161 | |
| 162 | self.rotary_emb = get_rope( |
| 163 | self.head_dim, |
| 164 | max_position=max_position_embeddings, |
| 165 | rope_parameters=config.rope_parameters, |
| 166 | is_neox_style=is_neox_style, |
| 167 | ) |
| 168 | self.attn = Attention( |
| 169 | self.num_heads, |
| 170 | self.head_dim, |
| 171 | self.scaling, |
| 172 | num_kv_heads=self.num_kv_heads, |
| 173 | cache_config=cache_config, |
| 174 | quant_config=quant_config, |
| 175 | prefix=f"{prefix}.attn", |
| 176 | ) |
| 177 | |
| 178 | def forward( |
| 179 | self, |
| 180 | positions: torch.Tensor, |
| 181 | hidden_states: torch.Tensor, |
| 182 | ) -> torch.Tensor: |
| 183 | qkv, _ = self.qkv_proj(hidden_states) |
| 184 | q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) |
| 185 | q, k = self.rotary_emb(positions, q, k) |
| 186 | attn_output = self.attn(q, k, v) |
| 187 | output, _ = self.out_proj(attn_output) |
| 188 | return output |
| 189 | |
| 190 | |
| 191 | class ExaoneBlockAttention(nn.Module): |
| 192 | def __init__( |
| 193 | self, |
| 194 | config: PreTrainedConfig, |
| 195 | hidden_size: int, |
| 196 | num_heads: int, |
| 197 | num_kv_heads: int, |
| 198 | max_position_embeddings: int = 8192, |
| 199 | quant_config: QuantizationConfig | None = None, |
| 200 | bias: bool = False, |
| 201 | cache_config: CacheConfig | None = None, |
| 202 | prefix: str = "", |
| 203 | ) -> None: |
| 204 | super().__init__() |
| 205 | self.attention = ExaoneAttention( |
| 206 | config=config, |
| 207 | hidden_size=hidden_size, |
| 208 | num_heads=num_heads, |
| 209 | num_kv_heads=num_kv_heads, |
| 210 | max_position_embeddings=max_position_embeddings, |
| 211 | quant_config=quant_config, |
| 212 | bias=bias, |
| 213 | cache_config=cache_config, |
| 214 | prefix=f"{prefix}.attention", |
| 215 | ) |
| 216 | |
| 217 | def forward( |
| 218 | self, |
| 219 | positions: torch.Tensor, |
| 220 | hidden_states: torch.Tensor, |
| 221 | ) -> torch.Tensor: |
| 222 | return self.attention( |
| 223 | positions=positions, |
| 224 | hidden_states=hidden_states, |
| 225 | ) |
| 226 | |
| 227 | |
| 228 | class ExaoneDecoderLayer(nn.Module): |
| 229 | def __init__( |
| 230 | self, |
| 231 | config: PreTrainedConfig, |
| 232 | cache_config: CacheConfig | None = None, |
| 233 | quant_config: QuantizationConfig | None = None, |
| 234 | prefix: str = "", |
| 235 | ) -> None: |
| 236 | super().__init__() |
| 237 | self.hidden_size = config.hidden_size |
| 238 | max_position_embeddings = getattr(config, "max_position_embeddings", 8192) |
| 239 | # Support abacusai/Smaug-72B-v0.1 with attention_bias |
| 240 | attention_bias = getattr(config, "attention_bias", False) or getattr( |
| 241 | config, "bias", False |
| 242 | ) |
| 243 | self.attn = ExaoneBlockAttention( |
| 244 | config=config, |
| 245 | hidden_size=self.hidden_size, |
| 246 | num_heads=config.num_attention_heads, |
| 247 | num_kv_heads=getattr( |
| 248 | config, "num_key_value_heads", config.num_attention_heads |
| 249 | ), |
| 250 | max_position_embeddings=max_position_embeddings, |
| 251 | quant_config=quant_config, |
| 252 | bias=attention_bias, |
| 253 | cache_config=cache_config, |
| 254 | prefix=f"{prefix}.attn", |
| 255 | ) |
| 256 | self.mlp = ExaoneGatedMLP( |
| 257 | hidden_size=self.hidden_size, |
| 258 | intermediate_size=config.intermediate_size, |
| 259 | hidden_act=config.activation_function, |
| 260 | quant_config=quant_config, |
| 261 | bias=getattr(config, "mlp_bias", False), |
| 262 | prefix=f"{prefix}.mlp", |
| 263 | ) |
| 264 | self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) |
| 265 | self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) |
| 266 | |
| 267 | def forward( |
| 268 | self, |
| 269 | positions: torch.Tensor, |
| 270 | hidden_states: torch.Tensor, |
| 271 | residual: torch.Tensor | None, |
| 272 | ) -> tuple[torch.Tensor, torch.Tensor]: |
| 273 | # Self Attention |
| 274 | if residual is None: |
| 275 | residual = hidden_states |
| 276 | hidden_states = self.ln_1(hidden_states) |
| 277 | else: |
| 278 | hidden_states, residual = self.ln_1(hidden_states, residual) |
| 279 | hidden_states = self.attn( |
| 280 | positions=positions, |
| 281 | hidden_states=hidden_states, |
| 282 | ) |
| 283 | |
| 284 | # Fully Connected |
| 285 | hidden_states, residual = self.ln_2(hidden_states, residual) |
| 286 | hidden_states = self.mlp(hidden_states) |
| 287 | return hidden_states, residual |
| 288 | |
| 289 | |
| 290 | @support_torch_compile |
| 291 | class ExaoneModel(nn.Module): |
| 292 | def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): |
| 293 | super().__init__() |
| 294 | |
| 295 | config = vllm_config.model_config.hf_config |
| 296 | cache_config = vllm_config.cache_config |
| 297 | quant_config = vllm_config.quant_config |
| 298 | |
| 299 | self.config = config |
| 300 | self.quant_config = quant_config |
| 301 | |
| 302 | self.vocab_size = config.vocab_size |
| 303 | self.wte = config.vocab_size |
| 304 | if get_pp_group().is_first_rank or ( |
| 305 | config.tie_word_embeddings and get_pp_group().is_last_rank |
| 306 | ): |
| 307 | self.wte = VocabParallelEmbedding( |
| 308 | self.vocab_size, |
| 309 | config.hidden_size, |
| 310 | quant_config=quant_config, |
| 311 | ) |
| 312 | else: |
| 313 | self.wte = PPMissingLayer() |
| 314 | self.start_layer, self.end_layer, self.h = make_layers( |
| 315 | config.num_hidden_layers, |
| 316 | lambda prefix: ExaoneDecoderLayer( |
| 317 | config=config, |
| 318 | cache_config=cache_config, |
| 319 | quant_config=quant_config, |
| 320 | prefix=prefix, |
| 321 | ), |
| 322 | prefix=f"{prefix}.h", |
| 323 | ) |
| 324 | if get_pp_group().is_last_rank: |
| 325 | self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) |
| 326 | else: |
| 327 | self.ln_f = PPMissingLayer() |
| 328 | |
| 329 | self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( |
| 330 | ["hidden_states", "residual"], config.hidden_size |
| 331 | ) |
| 332 | |
| 333 | def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: |
| 334 | return self.wte(input_ids) |
| 335 | |
| 336 | def forward( |
| 337 | self, |
| 338 | input_ids: torch.Tensor | None, |
| 339 | positions: torch.Tensor, |
| 340 | intermediate_tensors: IntermediateTensors | None, |
| 341 | inputs_embeds: torch.Tensor | None = None, |
| 342 | ) -> torch.Tensor | IntermediateTensors: |
| 343 | if get_pp_group().is_first_rank: |
| 344 | if inputs_embeds is not None: |
| 345 | hidden_states = inputs_embeds |
| 346 | else: |
| 347 | hidden_states = self.embed_input_ids(input_ids) |
| 348 | residual = None |
| 349 | else: |
| 350 | assert intermediate_tensors is not None |
| 351 | hidden_states = intermediate_tensors["hidden_states"] |
| 352 | residual = intermediate_tensors["residual"] |
| 353 | |
| 354 | for layer in islice(self.h, self.start_layer, self.end_layer): |
| 355 | hidden_states, residual = layer( |
| 356 | positions, |
| 357 | hidden_states, |
| 358 | residual, |
| 359 | ) |
| 360 | |
| 361 | if not get_pp_group().is_last_rank: |
| 362 | return IntermediateTensors( |
| 363 | {"hidden_states": hidden_states, "residual": residual} |
| 364 | ) |
| 365 | |
| 366 | hidden_states, _ = self.ln_f(hidden_states, residual) |
| 367 | return hidden_states |
| 368 | |
| 369 | |
| 370 | class ExaoneForCausalLM(nn.Module, SupportsLoRA, SupportsPP): |
| 371 | hf_to_vllm_mapper = WeightsMapper( |
| 372 | orig_to_new_stacked={ |
| 373 | # weight_name: (param_name, shard_id) |
| 374 | ".q_proj": (".qkv_proj", "q"), |
| 375 | ".k_proj": (".qkv_proj", "k"), |
| 376 | ".v_proj": (".qkv_proj", "v"), |
| 377 | ".c_fc_0": (".gate_up_proj", 0), |
| 378 | ".c_fc_1": (".gate_up_proj", 1), |
| 379 | } |
| 380 | ) |
| 381 | packed_modules_mapping = { |
| 382 | "qkv_proj": ["q_proj", "k_proj", "v_proj"], |
| 383 | "gate_up_proj": ["c_fc_0", "c_fc_1"], |
| 384 | } |
| 385 | |
| 386 | # LoRA specific attributes |
| 387 | embedding_modules = { |
| 388 | "wte": "input_embeddings", |
| 389 | "lm_head": "output_embeddings", |
| 390 | } |
| 391 | |
| 392 | def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): |
| 393 | super().__init__() |
| 394 | config = vllm_config.model_config.hf_config |
| 395 | quant_config = vllm_config.quant_config |
| 396 | |
| 397 | self.config = config |
| 398 | |
| 399 | self.quant_config = quant_config |
| 400 | |
| 401 | self.transformer = ExaoneModel( |
| 402 | vllm_config=vllm_config, |
| 403 | prefix=maybe_prefix(prefix, "model"), |
| 404 | ) |
| 405 | if get_pp_group().is_last_rank: |
| 406 | self.lm_head = ParallelLMHead( |
| 407 | config.vocab_size, |
| 408 | config.hidden_size, |
| 409 | quant_config=quant_config, |
| 410 | prefix=maybe_prefix(prefix, "lm_head"), |
| 411 | ) |
| 412 | if config.tie_word_embeddings: |
| 413 | self.lm_head = self.lm_head.tie_weights(self.transformer.wte) |
| 414 | |
| 415 | logit_scale = getattr(config, "logit_scale", 1.0) |
| 416 | self.logits_processor = LogitsProcessor( |
| 417 | config.vocab_size, scale=logit_scale |
| 418 | ) |
| 419 | else: |
| 420 | self.lm_head = PPMissingLayer() |
| 421 | |
| 422 | self.make_empty_intermediate_tensors = ( |
| 423 | self.transformer.make_empty_intermediate_tensors |
| 424 | ) |
| 425 | |
| 426 | def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: |
| 427 | return self.model.embed_input_ids(input_ids) |
| 428 | |
| 429 | def forward( |
| 430 | self, |
| 431 | input_ids: torch.Tensor | None, |
| 432 | positions: torch.Tensor, |
| 433 | intermediate_tensors: IntermediateTensors | None = None, |
| 434 | inputs_embeds: torch.Tensor | None = None, |
| 435 | ) -> torch.Tensor | IntermediateTensors: |
| 436 | model_output = self.transformer( |
| 437 | input_ids, positions, intermediate_tensors, inputs_embeds |
| 438 | ) |
| 439 | return model_output |
| 440 | |
| 441 | def compute_logits( |
| 442 | self, |
| 443 | hidden_states: torch.Tensor, |
| 444 | ) -> torch.Tensor | None: |
| 445 | logits = self.logits_processor(self.lm_head, hidden_states) |
| 446 | return logits |
| 447 | |
| 448 | def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: |
| 449 | loader = AutoWeightsLoader(self) |
| 450 | return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper) |