modeling_deepseekv2.py
| 1 | # coding=utf-8 |
| 2 | # Copyright 2023 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved. |
| 3 | # |
| 4 | # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX |
| 5 | # and OPT implementations in this library. It has been modified from its |
| 6 | # original forms to accommodate minor architectural differences compared |
| 7 | # to GPT-NeoX and OPT used by the Meta AI team that trained the model. |
| 8 | # |
| 9 | # Licensed under the Apache License, Version 2.0 (the "License"); |
| 10 | # you may not use this file except in compliance with the License. |
| 11 | # You may obtain a copy of the License at |
| 12 | # |
| 13 | # http://www.apache.org/licenses/LICENSE-2.0 |
| 14 | # |
| 15 | # Unless required by applicable law or agreed to in writing, software |
| 16 | # distributed under the License is distributed on an "AS IS" BASIS, |
| 17 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 18 | # See the License for the specific language governing permissions and |
| 19 | # limitations under the License. |
| 20 | """ PyTorch DeepSeek model and compatible with both DeepSeekV2 and DeepSeekV3""" |
| 21 | import math |
| 22 | import warnings |
| 23 | from typing import List, Optional, Tuple, Union |
| 24 | import numpy as np |
| 25 | |
| 26 | import torch |
| 27 | import torch.nn.functional as F |
| 28 | import torch.utils.checkpoint |
| 29 | import torch.distributed as dist |
| 30 | from einops import repeat |
| 31 | from torch import nn |
| 32 | from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
| 33 | |
| 34 | from transformers.activations import ACT2FN |
| 35 | from transformers.cache_utils import Cache, DynamicCache |
| 36 | from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask |
| 37 | from transformers.models.llama.modeling_llama import ( |
| 38 | LlamaAttention, |
| 39 | apply_rotary_pos_emb as _llama_apply_rotary_pos_emb, |
| 40 | repeat_kv as _llama_repeat_kv, |
| 41 | # LlamaFlashAttention2 |
| 42 | ) |
| 43 | from transformers.modeling_outputs import ( |
| 44 | BaseModelOutputWithPast, |
| 45 | CausalLMOutputWithPast, |
| 46 | SequenceClassifierOutputWithPast, |
| 47 | ) |
| 48 | from transformers.modeling_utils import PreTrainedModel |
| 49 | from transformers.pytorch_utils import ( |
| 50 | ALL_LAYERNORM_LAYERS, |
| 51 | is_torch_greater_or_equal_than_1_13, |
| 52 | ) |
| 53 | from transformers.utils import ( |
| 54 | add_start_docstrings, |
| 55 | add_start_docstrings_to_model_forward, |
| 56 | is_flash_attn_2_available, |
| 57 | is_flash_attn_greater_or_equal_2_10, |
| 58 | logging, |
| 59 | replace_return_docstrings, |
| 60 | ) |
| 61 | from transformers.utils.import_utils import is_torch_fx_available |
| 62 | |
| 63 | from .configuration_deepseek_v2 import DeepseekV2Config |
| 64 | |
| 65 | if is_flash_attn_2_available(): |
| 66 | from flash_attn import flash_attn_func, flash_attn_varlen_func |
| 67 | from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa |
| 68 | |
| 69 | # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. |
| 70 | # It means that the function will not be traced through and simply appear as a node in the graph. |
| 71 | if is_torch_fx_available(): |
| 72 | if not is_torch_greater_or_equal_than_1_13: |
| 73 | import torch.fx |
| 74 | |
| 75 | _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) |
| 76 | |
| 77 | logger = logging.get_logger(__name__) |
| 78 | |
| 79 | _CONFIG_FOR_DOC = "DeepseekV2Config" |
| 80 | |
| 81 | |
| 82 | def _get_unpad_data(attention_mask): |
| 83 | seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) |
| 84 | indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() |
| 85 | max_seqlen_in_batch = seqlens_in_batch.max().item() |
| 86 | cu_seqlens = F.pad( |
| 87 | torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0) |
| 88 | ) |
| 89 | return ( |
| 90 | indices, |
| 91 | cu_seqlens, |
| 92 | max_seqlen_in_batch, |
| 93 | ) |
| 94 | |
| 95 | |
| 96 | class DeepseekV2RMSNorm(nn.Module): |
| 97 | def __init__(self, hidden_size, eps=1e-6): |
| 98 | """ |
| 99 | DeepseekV2RMSNorm is equivalent to T5LayerNorm |
| 100 | """ |
| 101 | super().__init__() |
| 102 | self.weight = nn.Parameter(torch.ones(hidden_size)) |
| 103 | self.variance_epsilon = eps |
| 104 | |
| 105 | def forward(self, hidden_states): |
| 106 | input_dtype = hidden_states.dtype |
| 107 | hidden_states = hidden_states.to(torch.float32) |
| 108 | variance = hidden_states.pow(2).mean(-1, keepdim=True) |
| 109 | hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) |
| 110 | return self.weight * hidden_states.to(input_dtype) |
| 111 | |
| 112 | |
| 113 | ALL_LAYERNORM_LAYERS.append(DeepseekV2RMSNorm) |
| 114 | |
| 115 | |
| 116 | |
| 117 | |
| 118 | class DeepseekV2RotaryEmbedding(nn.Module): |
| 119 | def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): |
| 120 | super().__init__() |
| 121 | |
| 122 | self.dim = dim |
| 123 | self.max_position_embeddings = max_position_embeddings |
| 124 | self.base = base |
| 125 | inv_freq = 1.0 / ( |
| 126 | self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) |
| 127 | ) |
| 128 | self.register_buffer("inv_freq", inv_freq, persistent=False) |
| 129 | |
| 130 | # Build here to make `torch.jit.trace` work. |
| 131 | self._set_cos_sin_cache( |
| 132 | seq_len=max_position_embeddings, |
| 133 | device=self.inv_freq.device, |
| 134 | dtype=torch.get_default_dtype(), |
| 135 | ) |
| 136 | self.max_seq_len_cached = None |
| 137 | |
| 138 | def _set_cos_sin_cache(self, seq_len, device, dtype): |
| 139 | self.max_seq_len_cached = seq_len |
| 140 | t = torch.arange( |
| 141 | self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype |
| 142 | ) |
| 143 | |
| 144 | freqs = torch.outer(t, self.inv_freq.to(t.device)) |
| 145 | # Different from paper, but it uses a different permutation in order to obtain the same calculation |
| 146 | emb = torch.cat((freqs, freqs), dim=-1) |
| 147 | self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) |
| 148 | self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) |
| 149 | |
| 150 | def forward(self, x, seq_len=None): |
| 151 | # x: [bs, num_attention_heads, seq_len, head_size] |
| 152 | if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached: |
| 153 | self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) |
| 154 | |
| 155 | return ( |
| 156 | self.cos_cached[:seq_len].to(dtype=x.dtype), |
| 157 | self.sin_cached[:seq_len].to(dtype=x.dtype), |
| 158 | ) |
| 159 | |
| 160 | |
| 161 | # Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV2 |
| 162 | class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding): |
| 163 | """DeepseekV2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" |
| 164 | |
| 165 | def __init__( |
| 166 | self, |
| 167 | dim, |
| 168 | max_position_embeddings=2048, |
| 169 | base=10000, |
| 170 | device=None, |
| 171 | scaling_factor=1.0, |
| 172 | ): |
| 173 | self.scaling_factor = scaling_factor |
| 174 | super().__init__(dim, max_position_embeddings, base, device) |
| 175 | |
| 176 | def _set_cos_sin_cache(self, seq_len, device, dtype): |
| 177 | self.max_seq_len_cached = seq_len |
| 178 | t = torch.arange( |
| 179 | self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype |
| 180 | ) |
| 181 | t = t / self.scaling_factor |
| 182 | |
| 183 | freqs = torch.outer(t, self.inv_freq) |
| 184 | # Different from paper, but it uses a different permutation in order to obtain the same calculation |
| 185 | emb = torch.cat((freqs, freqs), dim=-1) |
| 186 | self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) |
| 187 | self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) |
| 188 | |
| 189 | |
| 190 | # Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV2 |
| 191 | class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding): |
| 192 | """DeepseekV2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" |
| 193 | |
| 194 | def __init__( |
| 195 | self, |
| 196 | dim, |
| 197 | max_position_embeddings=2048, |
| 198 | base=10000, |
| 199 | device=None, |
| 200 | scaling_factor=1.0, |
| 201 | ): |
| 202 | self.scaling_factor = scaling_factor |
| 203 | super().__init__(dim, max_position_embeddings, base, device) |
| 204 | |
| 205 | def _set_cos_sin_cache(self, seq_len, device, dtype): |
| 206 | self.max_seq_len_cached = seq_len |
| 207 | |
| 208 | if seq_len > self.max_position_embeddings: |
| 209 | base = self.base * ( |
| 210 | (self.scaling_factor * seq_len / self.max_position_embeddings) |
| 211 | - (self.scaling_factor - 1) |
| 212 | ) ** (self.dim / (self.dim - 2)) |
| 213 | inv_freq = 1.0 / ( |
| 214 | base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) |
| 215 | ) |
| 216 | self.register_buffer("inv_freq", inv_freq, persistent=False) |
| 217 | |
| 218 | t = torch.arange( |
| 219 | self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype |
| 220 | ) |
| 221 | |
| 222 | freqs = torch.outer(t, self.inv_freq) |
| 223 | # Different from paper, but it uses a different permutation in order to obtain the same calculation |
| 224 | emb = torch.cat((freqs, freqs), dim=-1) |
| 225 | self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) |
| 226 | self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) |
| 227 | |
| 228 | |
| 229 | # Inverse dim formula to find dim based on number of rotations |
| 230 | def yarn_find_correction_dim( |
| 231 | num_rotations, dim, base=10000, max_position_embeddings=2048 |
| 232 | ): |
| 233 | return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / ( |
| 234 | 2 * math.log(base) |
| 235 | ) |
| 236 | |
| 237 | |
| 238 | # Find dim range bounds based on rotations |
| 239 | def yarn_find_correction_range( |
| 240 | low_rot, high_rot, dim, base=10000, max_position_embeddings=2048 |
| 241 | ): |
| 242 | low = math.floor( |
| 243 | yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings) |
| 244 | ) |
| 245 | high = math.ceil( |
| 246 | yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings) |
| 247 | ) |
| 248 | return max(low, 0), min(high, dim - 1) # Clamp values just in case |
| 249 | |
| 250 | |
| 251 | def yarn_get_mscale(scale=1, mscale=1): |
| 252 | if scale <= 1: |
| 253 | return 1.0 |
| 254 | return 0.1 * mscale * math.log(scale) + 1.0 |
| 255 | |
| 256 | |
| 257 | def yarn_linear_ramp_mask(min, max, dim): |
| 258 | if min == max: |
| 259 | max += 0.001 # Prevent singularity |
| 260 | |
| 261 | linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min) |
| 262 | ramp_func = torch.clamp(linear_func, 0, 1) |
| 263 | return ramp_func |
| 264 | |
| 265 | |
| 266 | class DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding): |
| 267 | |
| 268 | def __init__( |
| 269 | self, |
| 270 | dim, |
| 271 | max_position_embeddings=2048, |
| 272 | base=10000, |
| 273 | device=None, |
| 274 | scaling_factor=1.0, |
| 275 | original_max_position_embeddings=4096, |
| 276 | beta_fast=32, |
| 277 | beta_slow=1, |
| 278 | mscale=1, |
| 279 | mscale_all_dim=0, |
| 280 | ): |
| 281 | self.scaling_factor = scaling_factor |
| 282 | self.original_max_position_embeddings = original_max_position_embeddings |
| 283 | self.beta_fast = beta_fast |
| 284 | self.beta_slow = beta_slow |
| 285 | self.mscale = mscale |
| 286 | self.mscale_all_dim = mscale_all_dim |
| 287 | super().__init__(dim, max_position_embeddings, base, device) |
| 288 | |
| 289 | def _set_cos_sin_cache(self, seq_len, device, dtype): |
| 290 | self.max_seq_len_cached = seq_len |
| 291 | dim = self.dim |
| 292 | |
| 293 | freq_extra = 1.0 / ( |
| 294 | self.base |
| 295 | ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim) |
| 296 | ) |
| 297 | freq_inter = 1.0 / ( |
| 298 | self.scaling_factor |
| 299 | * self.base |
| 300 | ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim) |
| 301 | ) |
| 302 | |
| 303 | low, high = yarn_find_correction_range( |
| 304 | self.beta_fast, |
| 305 | self.beta_slow, |
| 306 | dim, |
| 307 | self.base, |
| 308 | self.original_max_position_embeddings, |
| 309 | ) |
| 310 | inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to( |
| 311 | device=device, dtype=torch.float32 |
| 312 | ) |
| 313 | inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask |
| 314 | self.register_buffer("inv_freq", inv_freq, persistent=False) |
| 315 | |
| 316 | t = torch.arange(seq_len, device=device, dtype=torch.float32) |
| 317 | |
| 318 | freqs = torch.outer(t, inv_freq) |
| 319 | |
| 320 | _mscale = float( |
| 321 | yarn_get_mscale(self.scaling_factor, self.mscale) |
| 322 | / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim) |
| 323 | ) |
| 324 | |
| 325 | emb = torch.cat((freqs, freqs), dim=-1) |
| 326 | self.register_buffer( |
| 327 | "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False |
| 328 | ) |
| 329 | self.register_buffer( |
| 330 | "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False |
| 331 | ) |
| 332 | |
| 333 | |
| 334 | # Copied from transformers.models.llama.modeling_llama.rotate_half |
| 335 | def rotate_half(x): |
| 336 | """Rotates half the hidden dims of the input.""" |
| 337 | x1 = x[..., : x.shape[-1] // 2] |
| 338 | x2 = x[..., x.shape[-1] // 2 :] |
| 339 | return torch.cat((-x2, x1), dim=-1) |
| 340 | |
| 341 | |
| 342 | # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb |
| 343 | def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): |
| 344 | """Applies Rotary Position Embedding to the query and key tensors. |
| 345 | |
| 346 | Args: |
| 347 | q (`torch.Tensor`): The query tensor. |
| 348 | k (`torch.Tensor`): The key tensor. |
| 349 | cos (`torch.Tensor`): The cosine part of the rotary embedding. |
| 350 | sin (`torch.Tensor`): The sine part of the rotary embedding. |
| 351 | position_ids (`torch.Tensor`): |
| 352 | The position indices of the tokens corresponding to the query and key tensors. For example, this can be |
| 353 | used to pass offsetted position ids when working with a KV-cache. |
| 354 | unsqueeze_dim (`int`, *optional*, defaults to 1): |
| 355 | The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and |
| 356 | sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note |
| 357 | that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and |
| 358 | k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes |
| 359 | cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have |
| 360 | the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. |
| 361 | Returns: |
| 362 | `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. |
| 363 | """ |
| 364 | cos = cos[position_ids].unsqueeze(unsqueeze_dim) |
| 365 | sin = sin[position_ids].unsqueeze(unsqueeze_dim) |
| 366 | |
| 367 | |
| 368 | # print() |
| 369 | |
| 370 | b, h, s, d = q.shape |
| 371 | q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) |
| 372 | |
| 373 | b, h, s, d = k.shape |
| 374 | k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) |
| 375 | |
| 376 | q_embed = (q * cos) + (rotate_half(q) * sin) |
| 377 | k_embed = (k * cos) + (rotate_half(k) * sin) |
| 378 | |
| 379 | |
| 380 | return q_embed, k_embed |
| 381 | |
| 382 | |
| 383 | class DeepseekV2MLP(nn.Module): |
| 384 | def __init__(self, config, hidden_size=None, intermediate_size=None): |
| 385 | super().__init__() |
| 386 | self.config = config |
| 387 | self.hidden_size = config.hidden_size if hidden_size is None else hidden_size |
| 388 | self.intermediate_size = ( |
| 389 | config.intermediate_size if intermediate_size is None else intermediate_size |
| 390 | ) |
| 391 | |
| 392 | self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) |
| 393 | self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) |
| 394 | self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) |
| 395 | self.act_fn = ACT2FN[config.hidden_act] |
| 396 | |
| 397 | def forward(self, x): |
| 398 | down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) |
| 399 | return down_proj |
| 400 | |
| 401 | |
| 402 | class MoEGate(nn.Module): |
| 403 | def __init__(self, config): |
| 404 | super().__init__() |
| 405 | self.config = config |
| 406 | self.top_k = config.num_experts_per_tok |
| 407 | self.n_routed_experts = config.n_routed_experts |
| 408 | self.routed_scaling_factor = config.routed_scaling_factor |
| 409 | self.scoring_func = config.scoring_func |
| 410 | self.alpha = config.aux_loss_alpha |
| 411 | self.seq_aux = config.seq_aux |
| 412 | self.topk_method = config.topk_method |
| 413 | self.n_group = config.n_group |
| 414 | self.topk_group = config.topk_group |
| 415 | |
| 416 | # topk selection algorithm |
| 417 | self.norm_topk_prob = config.norm_topk_prob |
| 418 | self.gating_dim = config.hidden_size |
| 419 | self.weight = nn.Parameter( |
| 420 | torch.empty((self.n_routed_experts, self.gating_dim)) |
| 421 | ) |
| 422 | if self.topk_method == "noaux_tc": |
| 423 | self.e_score_correction_bias = nn.Parameter( |
| 424 | torch.empty((self.n_routed_experts)) |
| 425 | ) |
| 426 | self.reset_parameters() |
| 427 | |
| 428 | def reset_parameters(self) -> None: |
| 429 | import torch.nn.init as init |
| 430 | |
| 431 | init.kaiming_uniform_(self.weight, a=math.sqrt(5)) |
| 432 | |
| 433 | def forward(self, hidden_states): |
| 434 | bsz, seq_len, h = hidden_states.shape |
| 435 | ### compute gating score |
| 436 | hidden_states = hidden_states.view(-1, h) |
| 437 | logits = F.linear( |
| 438 | hidden_states.type(torch.float32), self.weight.type(torch.float32), None |
| 439 | ) |
| 440 | if self.scoring_func == "softmax": |
| 441 | scores = logits.softmax(dim=-1, dtype=torch.float32) |
| 442 | elif self.scoring_func == "sigmoid": |
| 443 | scores = logits.sigmoid() |
| 444 | else: |
| 445 | raise NotImplementedError( |
| 446 | f"insupportable scoring function for MoE gating: {self.scoring_func}" |
| 447 | ) |
| 448 | |
| 449 | ### select top-k experts |
| 450 | if self.topk_method == "greedy": |
| 451 | topk_weight, topk_idx = torch.topk( |
| 452 | scores, k=self.top_k, dim=-1, sorted=False |
| 453 | ) |
| 454 | elif self.topk_method == "group_limited_greedy": |
| 455 | group_scores = ( |
| 456 | scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values |
| 457 | ) # [n, n_group] |
| 458 | group_idx = torch.topk( |
| 459 | group_scores, k=self.topk_group, dim=-1, sorted=False |
| 460 | )[ |
| 461 | 1 |
| 462 | ] # [n, top_k_group] |
| 463 | group_mask = torch.zeros_like(group_scores) # [n, n_group] |
| 464 | group_mask.scatter_(1, group_idx, 1) # [n, n_group] |
| 465 | score_mask = ( |
| 466 | group_mask.unsqueeze(-1) |
| 467 | .expand( |
| 468 | bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group |
| 469 | ) |
| 470 | .reshape(bsz * seq_len, -1) |
| 471 | ) # [n, e] |
| 472 | tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0) # [n, e] |
| 473 | topk_weight, topk_idx = torch.topk( |
| 474 | tmp_scores, k=self.top_k, dim=-1, sorted=False |
| 475 | ) |
| 476 | elif self.topk_method == "noaux_tc": |
| 477 | assert not self.training |
| 478 | scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0) |
| 479 | group_scores = ( |
| 480 | scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim = -1) |
| 481 | ) # [n, n_group] |
| 482 | group_idx = torch.topk( |
| 483 | group_scores, k=self.topk_group, dim=-1, sorted=False |
| 484 | )[ |
| 485 | 1 |
| 486 | ] # [n, top_k_group] |
| 487 | group_mask = torch.zeros_like(group_scores) # [n, n_group] |
| 488 | group_mask.scatter_(1, group_idx, 1) # [n, n_group] |
| 489 | score_mask = ( |
| 490 | group_mask.unsqueeze(-1) |
| 491 | .expand( |
| 492 | bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group |
| 493 | ) |
| 494 | .reshape(bsz * seq_len, -1) |
| 495 | ) # [n, e] |
| 496 | tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), 0.0) # [n, e] |
| 497 | _, topk_idx = torch.topk( |
| 498 | tmp_scores, k=self.top_k, dim=-1, sorted=False |
| 499 | ) |
| 500 | topk_weight = scores.gather(1, topk_idx) |
| 501 | |
| 502 | ### norm gate to sum 1 |
| 503 | if self.top_k > 1 and self.norm_topk_prob: |
| 504 | denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 |
| 505 | topk_weight = topk_weight / denominator * self.routed_scaling_factor |
| 506 | else: |
| 507 | topk_weight = topk_weight * self.routed_scaling_factor |
| 508 | ### expert-level computation auxiliary loss |
| 509 | if self.training and self.alpha > 0.0: |
| 510 | scores_for_aux = scores |
| 511 | aux_topk = self.top_k |
| 512 | # always compute aux loss based on the naive greedy topk method |
| 513 | topk_idx_for_aux_loss = topk_idx.view(bsz, -1) |
| 514 | if self.seq_aux: |
| 515 | scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1) |
| 516 | ce = torch.zeros( |
| 517 | bsz, self.n_routed_experts, device=hidden_states.device |
| 518 | ) |
| 519 | ce.scatter_add_( |
| 520 | 1, |
| 521 | topk_idx_for_aux_loss, |
| 522 | torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device), |
| 523 | ).div_(seq_len * aux_topk / self.n_routed_experts) |
| 524 | aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum( |
| 525 | dim=1 |
| 526 | ).mean() * self.alpha |
| 527 | else: |
| 528 | mask_ce = F.one_hot( |
| 529 | topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts |
| 530 | ) |
| 531 | ce = mask_ce.float().mean(0) |
| 532 | Pi = scores_for_aux.mean(0) |
| 533 | fi = ce * self.n_routed_experts |
| 534 | aux_loss = (Pi * fi).sum() * self.alpha |
| 535 | else: |
| 536 | aux_loss = None |
| 537 | return topk_idx, topk_weight, aux_loss |
| 538 | |
| 539 | |
| 540 | class AddAuxiliaryLoss(torch.autograd.Function): |
| 541 | """ |
| 542 | The trick function of adding auxiliary (aux) loss, |
| 543 | which includes the gradient of the aux loss during backpropagation. |
| 544 | """ |
| 545 | |
| 546 | @staticmethod |
| 547 | def forward(ctx, x, loss): |
| 548 | assert loss.numel() == 1 |
| 549 | ctx.dtype = loss.dtype |
| 550 | ctx.required_aux_loss = loss.requires_grad |
| 551 | return x |
| 552 | |
| 553 | @staticmethod |
| 554 | def backward(ctx, grad_output): |
| 555 | grad_loss = None |
| 556 | if ctx.required_aux_loss: |
| 557 | grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device) |
| 558 | return grad_output, grad_loss |
| 559 | |
| 560 | |
| 561 | class DeepseekV2MoE(nn.Module): |
| 562 | """ |
| 563 | A mixed expert module containing shared experts. |
| 564 | """ |
| 565 | |
| 566 | def __init__(self, config): |
| 567 | super().__init__() |
| 568 | self.config = config |
| 569 | self.num_experts_per_tok = config.num_experts_per_tok |
| 570 | |
| 571 | if hasattr(config, "ep_size") and config.ep_size > 1: |
| 572 | assert config.ep_size == dist.get_world_size() |
| 573 | self.ep_size = config.ep_size |
| 574 | self.experts_per_rank = config.n_routed_experts // config.ep_size |
| 575 | self.ep_rank = dist.get_rank() |
| 576 | self.experts = nn.ModuleList( |
| 577 | [ |
| 578 | ( |
| 579 | DeepseekV2MLP( |
| 580 | config, intermediate_size=config.moe_intermediate_size |
| 581 | ) |
| 582 | if i >= self.ep_rank * self.experts_per_rank |
| 583 | and i < (self.ep_rank + 1) * self.experts_per_rank |
| 584 | else None |
| 585 | ) |
| 586 | for i in range(config.n_routed_experts) |
| 587 | ] |
| 588 | ) |
| 589 | else: |
| 590 | self.ep_size = 1 |
| 591 | self.experts_per_rank = config.n_routed_experts |
| 592 | self.ep_rank = 0 |
| 593 | self.experts = nn.ModuleList( |
| 594 | [ |
| 595 | DeepseekV2MLP( |
| 596 | config, intermediate_size=config.moe_intermediate_size |
| 597 | ) |
| 598 | for i in range(config.n_routed_experts) |
| 599 | ] |
| 600 | ) |
| 601 | self.gate = MoEGate(config) |
| 602 | if config.n_shared_experts is not None: |
| 603 | intermediate_size = config.moe_intermediate_size * config.n_shared_experts |
| 604 | self.shared_experts = DeepseekV2MLP( |
| 605 | config=config, intermediate_size=intermediate_size |
| 606 | ) |
| 607 | |
| 608 | def forward(self, hidden_states): |
| 609 | identity = hidden_states |
| 610 | orig_shape = hidden_states.shape |
| 611 | topk_idx, topk_weight, aux_loss = self.gate(hidden_states) |
| 612 | hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) |
| 613 | flat_topk_idx = topk_idx.view(-1) |
| 614 | if self.training: |
| 615 | hidden_states = hidden_states.repeat_interleave( |
| 616 | self.num_experts_per_tok, dim=0 |
| 617 | ) |
| 618 | y = torch.empty_like(hidden_states) |
| 619 | for i, expert in enumerate(self.experts): |
| 620 | y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i]) |
| 621 | y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) |
| 622 | y = y.to(hidden_states.dtype).view(*orig_shape) |
| 623 | y = AddAuxiliaryLoss.apply(y, aux_loss) |
| 624 | else: |
| 625 | y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape) |
| 626 | if self.config.n_shared_experts is not None: |
| 627 | y = y + self.shared_experts(identity) |
| 628 | return y |
| 629 | |
| 630 | @torch.no_grad() |
| 631 | def moe_infer(self, x, topk_ids, topk_weight): |
| 632 | cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts))) |
| 633 | cnts.scatter_(1, topk_ids, 1) |
| 634 | tokens_per_expert = cnts.sum(dim=0) |
| 635 | idxs = topk_ids.view(-1).argsort() |
| 636 | sorted_tokens = x[idxs // topk_ids.shape[1]] |
| 637 | sorted_tokens_shape = sorted_tokens.shape |
| 638 | if self.ep_size > 1: |
| 639 | tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1) |
| 640 | tokens_per_expert_group = tokens_per_expert.new_empty( |
| 641 | tokens_per_expert.shape[0] |
| 642 | ) |
| 643 | dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert) |
| 644 | output_splits = ( |
| 645 | tokens_per_expert_group.view(self.ep_size, -1) |
| 646 | .sum(1) |
| 647 | .cpu() |
| 648 | .numpy() |
| 649 | .tolist() |
| 650 | ) |
| 651 | gathered_tokens = sorted_tokens.new_empty( |
| 652 | tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1] |
| 653 | ) |
| 654 | input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist() |
| 655 | dist.all_to_all( |
| 656 | list(gathered_tokens.split(output_splits)), |
| 657 | list(sorted_tokens.split(input_split_sizes)), |
| 658 | ) |
| 659 | tokens_per_expert_post_gather = tokens_per_expert_group.view( |
| 660 | self.ep_size, self.experts_per_rank |
| 661 | ).sum(dim=0) |
| 662 | gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32) |
| 663 | s = 0 |
| 664 | for i, k in enumerate(tokens_per_expert_group.cpu().numpy()): |
| 665 | gatherd_idxs[s : s + k] = i % self.experts_per_rank |
| 666 | s += k |
| 667 | gatherd_idxs = gatherd_idxs.argsort() |
| 668 | sorted_tokens = gathered_tokens[gatherd_idxs] |
| 669 | tokens_per_expert = tokens_per_expert_post_gather |
| 670 | tokens_per_expert = tokens_per_expert.cpu().numpy() |
| 671 | |
| 672 | outputs = [] |
| 673 | start_idx = 0 |
| 674 | for i, num_tokens in enumerate(tokens_per_expert): |
| 675 | end_idx = start_idx + num_tokens |
| 676 | if num_tokens == 0: |
| 677 | continue |
| 678 | expert = self.experts[i + self.ep_rank * self.experts_per_rank] |
| 679 | tokens_for_this_expert = sorted_tokens[start_idx:end_idx] |
| 680 | expert_out = expert(tokens_for_this_expert) |
| 681 | outputs.append(expert_out) |
| 682 | start_idx = end_idx |
| 683 | |
| 684 | outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0) |
| 685 | if self.ep_size > 1: |
| 686 | new_x = torch.empty_like(outs) |
| 687 | new_x[gatherd_idxs] = outs |
| 688 | gathered_tokens = new_x.new_empty(*sorted_tokens_shape) |
| 689 | dist.all_to_all( |
| 690 | list(gathered_tokens.split(input_split_sizes)), |
| 691 | list(new_x.split(output_splits)), |
| 692 | ) |
| 693 | outs = gathered_tokens |
| 694 | |
| 695 | new_x = torch.empty_like(outs) |
| 696 | new_x[idxs] = outs |
| 697 | final_out = ( |
| 698 | new_x.view(*topk_ids.shape, -1) |
| 699 | .type(topk_weight.dtype) |
| 700 | .mul_(topk_weight.unsqueeze(dim=-1)) |
| 701 | .sum(dim=1) |
| 702 | .type(new_x.dtype) |
| 703 | ) |
| 704 | return final_out |
| 705 | |
| 706 | |
| 707 | # Copied from transformers.models.llama.modeling_llama.repeat_kv |
| 708 | def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: |
| 709 | """ |
| 710 | This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, |
| 711 | num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) |
| 712 | """ |
| 713 | batch, num_key_value_heads, slen, head_dim = hidden_states.shape |
| 714 | if n_rep == 1: |
| 715 | return hidden_states |
| 716 | hidden_states = hidden_states[:, :, None, :, :].expand( |
| 717 | batch, num_key_value_heads, n_rep, slen, head_dim |
| 718 | ) |
| 719 | return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) |
| 720 | |
| 721 | |
| 722 | # Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2 |
| 723 | class DeepseekV2Attention(nn.Module): |
| 724 | """Multi-headed attention from 'Attention Is All You Need' paper""" |
| 725 | |
| 726 | def __init__(self, config: DeepseekV2Config, layer_idx: Optional[int] = None): |
| 727 | super().__init__() |
| 728 | self.config = config |
| 729 | self.layer_idx = layer_idx |
| 730 | if layer_idx is None: |
| 731 | logger.warning_once( |
| 732 | f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " |
| 733 | "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " |
| 734 | "when creating this class." |
| 735 | ) |
| 736 | |
| 737 | self.attention_dropout = config.attention_dropout |
| 738 | self.hidden_size = config.hidden_size |
| 739 | self.num_heads = config.num_attention_heads |
| 740 | |
| 741 | self.max_position_embeddings = config.max_position_embeddings |
| 742 | self.rope_theta = config.rope_theta |
| 743 | self.q_lora_rank = config.q_lora_rank |
| 744 | self.qk_rope_head_dim = config.qk_rope_head_dim |
| 745 | self.kv_lora_rank = config.kv_lora_rank |
| 746 | self.v_head_dim = config.v_head_dim |
| 747 | self.qk_nope_head_dim = config.qk_nope_head_dim |
| 748 | self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim |
| 749 | |
| 750 | self.is_causal = True |
| 751 | |
| 752 | if self.q_lora_rank is None: |
| 753 | self.q_proj = nn.Linear( |
| 754 | self.hidden_size, self.num_heads * self.q_head_dim, bias=False |
| 755 | ) |
| 756 | else: |
| 757 | self.q_a_proj = nn.Linear( |
| 758 | self.hidden_size, config.q_lora_rank, bias=config.attention_bias |
| 759 | ) |
| 760 | self.q_a_layernorm = DeepseekV2RMSNorm(config.q_lora_rank) |
| 761 | self.q_b_proj = nn.Linear( |
| 762 | config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False |
| 763 | ) |
| 764 | # config.kv_lora_rank + config.qk_rope_head_dim, |
| 765 | self.kv_a_proj_with_mqa = nn.Linear( |
| 766 | self.hidden_size, |
| 767 | config.kv_lora_rank + config.qk_rope_head_dim, |
| 768 | bias=config.attention_bias, |
| 769 | ) |
| 770 | self.kv_a_layernorm = DeepseekV2RMSNorm(config.kv_lora_rank) |
| 771 | self.kv_b_proj = nn.Linear( |
| 772 | config.kv_lora_rank, |
| 773 | self.num_heads |
| 774 | * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim), |
| 775 | bias=False, |
| 776 | ) |
| 777 | |
| 778 | self.o_proj = nn.Linear( |
| 779 | self.num_heads * self.v_head_dim, |
| 780 | self.hidden_size, |
| 781 | bias=config.attention_bias, |
| 782 | ) |
| 783 | self._init_rope() |
| 784 | |
| 785 | self.softmax_scale = self.q_head_dim ** (-0.5) |
| 786 | if self.config.rope_scaling is not None: |
| 787 | mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0) |
| 788 | scaling_factor = self.config.rope_scaling["factor"] |
| 789 | if mscale_all_dim: |
| 790 | mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) |
| 791 | self.softmax_scale = self.softmax_scale * mscale * mscale |
| 792 | |
| 793 | def _init_rope(self): |
| 794 | if self.config.rope_scaling is None: |
| 795 | self.rotary_emb = DeepseekV2RotaryEmbedding( |
| 796 | self.qk_rope_head_dim, |
| 797 | max_position_embeddings=self.max_position_embeddings, |
| 798 | base=self.rope_theta, |
| 799 | ) |
| 800 | # self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding( |
| 801 | # self.qk_rope_head_dim, |
| 802 | # max_position_embeddings=self.max_position_embeddings, |
| 803 | # scaling_factor=scaling_factor, |
| 804 | # base=self.rope_theta, |
| 805 | # ) |
| 806 | else: |
| 807 | scaling_type = self.config.rope_scaling["type"] |
| 808 | scaling_factor = self.config.rope_scaling["factor"] |
| 809 | if scaling_type == "linear": |
| 810 | self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding( |
| 811 | self.qk_rope_head_dim, |
| 812 | max_position_embeddings=self.max_position_embeddings, |
| 813 | scaling_factor=scaling_factor, |
| 814 | base=self.rope_theta, |
| 815 | ) |
| 816 | elif scaling_type == "dynamic": |
| 817 | self.rotary_emb = DeepseekV2DynamicNTKScalingRotaryEmbedding( |
| 818 | self.qk_rope_head_dim, |
| 819 | max_position_embeddings=self.max_position_embeddings, |
| 820 | scaling_factor=scaling_factor, |
| 821 | base=self.rope_theta, |
| 822 | ) |
| 823 | elif scaling_type == "yarn": |
| 824 | kwargs = { |
| 825 | key: self.config.rope_scaling[key] |
| 826 | for key in [ |
| 827 | "original_max_position_embeddings", |
| 828 | "beta_fast", |
| 829 | "beta_slow", |
| 830 | "mscale", |
| 831 | "mscale_all_dim", |
| 832 | ] |
| 833 | if key in self.config.rope_scaling |
| 834 | } |
| 835 | self.rotary_emb = DeepseekV2YarnRotaryEmbedding( |
| 836 | self.qk_rope_head_dim, |
| 837 | max_position_embeddings=self.max_position_embeddings, |
| 838 | scaling_factor=scaling_factor, |
| 839 | base=self.rope_theta, |
| 840 | **kwargs, |
| 841 | ) |
| 842 | else: |
| 843 | raise ValueError(f"Unknown RoPE scaling type {scaling_type}") |
| 844 | |
| 845 | def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): |
| 846 | return ( |
| 847 | tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim) |
| 848 | .transpose(1, 2) |
| 849 | .contiguous() |
| 850 | ) |
| 851 | |
| 852 | def forward( |
| 853 | self, |
| 854 | hidden_states: torch.Tensor, |
| 855 | attention_mask: Optional[torch.Tensor] = None, |
| 856 | position_ids: Optional[torch.LongTensor] = None, |
| 857 | past_key_value: Optional[Cache] = None, |
| 858 | output_attentions: bool = False, |
| 859 | use_cache: bool = False, |
| 860 | **kwargs, |
| 861 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
| 862 | if "padding_mask" in kwargs: |
| 863 | warnings.warn( |
| 864 | "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" |
| 865 | ) |
| 866 | bsz, q_len, _ = hidden_states.size() |
| 867 | |
| 868 | if self.q_lora_rank is None: |
| 869 | q = self.q_proj(hidden_states) |
| 870 | else: |
| 871 | q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))) |
| 872 | q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2) |
| 873 | |
| 874 | |
| 875 | q_nope, q_pe = torch.split( |
| 876 | q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1 |
| 877 | ) |
| 878 | |
| 879 | compressed_kv = self.kv_a_proj_with_mqa(hidden_states) |
| 880 | compressed_kv, k_pe = torch.split( |
| 881 | compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1 |
| 882 | ) |
| 883 | compressed_kv = self.kv_a_layernorm(compressed_kv) |
| 884 | k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2) |
| 885 | |
| 886 | kv_seq_len = k_pe.shape[-2] |
| 887 | if past_key_value is not None: |
| 888 | if self.layer_idx is None: |
| 889 | raise ValueError( |
| 890 | f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " |
| 891 | "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " |
| 892 | "with a layer index." |
| 893 | ) |
| 894 | kv_seq_len += past_key_value.get_seq_length(self.layer_idx) |
| 895 | |
| 896 | cos, sin = self.rotary_emb(q_pe, seq_len=kv_seq_len) |
| 897 | q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) |
| 898 | |
| 899 | if past_key_value is not None: |
| 900 | cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models |
| 901 | compressed_kv = compressed_kv.unsqueeze(1) |
| 902 | k_pe, compressed_kv = past_key_value.update(k_pe, compressed_kv, self.layer_idx, cache_kwargs) |
| 903 | compressed_kv = compressed_kv.squeeze(1) |
| 904 | |
| 905 | kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank) |
| 906 | q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :] |
| 907 | out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :] |
| 908 | |
| 909 | q_nope = torch.matmul(q_nope, q_absorb) |
| 910 | attn_weights = (torch.matmul(q_pe, k_pe.mT) + |
| 911 | torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)) * self.softmax_scale |
| 912 | if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): |
| 913 | raise ValueError( |
| 914 | f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" |
| 915 | f" {attn_weights.size()}" |
| 916 | ) |
| 917 | assert attention_mask is not None |
| 918 | if attention_mask is not None: |
| 919 | if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): |
| 920 | raise ValueError( |
| 921 | f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" |
| 922 | ) |
| 923 | attn_weights = attn_weights + attention_mask |
| 924 | |
| 925 | # upcast attention to fp32 |
| 926 | attn_weights = nn.functional.softmax( |
| 927 | attn_weights, dim=-1, dtype=torch.float32 |
| 928 | ).to(q_pe.dtype) |
| 929 | attn_weights = nn.functional.dropout( |
| 930 | attn_weights, p=self.attention_dropout, training=self.training |
| 931 | ) |
| 932 | attn_output = torch.einsum('bhql,blc->bhqc', attn_weights, compressed_kv) |
| 933 | |
| 934 | attn_output = torch.matmul(attn_output, out_absorb.mT) |
| 935 | |
| 936 | if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim): |
| 937 | raise ValueError( |
| 938 | f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_head_dim)}, but is" |
| 939 | f" {attn_output.size()}" |
| 940 | ) |
| 941 | |
| 942 | attn_output = attn_output.transpose(1, 2).contiguous() |
| 943 | |
| 944 | attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim) |
| 945 | |
| 946 | attn_output = self.o_proj(attn_output) |
| 947 | |
| 948 | if not output_attentions: |
| 949 | attn_weights = None |
| 950 | |
| 951 | return attn_output, attn_weights, past_key_value |
| 952 | |
| 953 | |
| 954 | # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV2 |
| 955 | class DeepseekV2FlashAttention2(DeepseekV2Attention): |
| 956 | """ |
| 957 | DeepseekV2 flash attention module. This module inherits from `DeepseekV2Attention` as the weights of the module stays |
| 958 | untouched. The only required change would be on the forward pass where it needs to correctly call the public API of |
| 959 | flash attention and deal with padding tokens in case the input contains any of them. |
| 960 | """ |
| 961 | |
| 962 | def __init__(self, *args, **kwargs): |
| 963 | super().__init__(*args, **kwargs) |
| 964 | |
| 965 | # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. |
| 966 | # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. |
| 967 | # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). |
| 968 | self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() |
| 969 | |
| 970 | def forward( |
| 971 | self, |
| 972 | hidden_states: torch.Tensor, |
| 973 | attention_mask: Optional[torch.LongTensor] = None, |
| 974 | position_ids: Optional[torch.LongTensor] = None, |
| 975 | past_key_value: Optional[Cache] = None, |
| 976 | output_attentions: bool = False, |
| 977 | use_cache: bool = False, |
| 978 | **kwargs, |
| 979 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
| 980 | # DeepseekV2FlashAttention2 attention does not support output_attentions |
| 981 | if "padding_mask" in kwargs: |
| 982 | warnings.warn( |
| 983 | "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" |
| 984 | ) |
| 985 | |
| 986 | # overwrite attention_mask with padding_mask |
| 987 | attention_mask = kwargs.pop("padding_mask") |
| 988 | |
| 989 | output_attentions = False |
| 990 | |
| 991 | bsz, q_len, _ = hidden_states.size() |
| 992 | |
| 993 | if self.q_lora_rank is None: |
| 994 | q = self.q_proj(hidden_states) |
| 995 | else: |
| 996 | q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))) |
| 997 | q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2) |
| 998 | q_nope, q_pe = torch.split( |
| 999 | q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1 |
| 1000 | ) |
| 1001 | |
| 1002 | # Flash attention requires the input to have the shape |
| 1003 | # batch_size x seq_length x head_dim x hidden_dim |
| 1004 | # therefore we just need to keep the original shape |
| 1005 | compressed_kv = self.kv_a_proj_with_mqa(hidden_states) |
| 1006 | compressed_kv, k_pe = torch.split( |
| 1007 | compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1 |
| 1008 | ) |
| 1009 | k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2) |
| 1010 | kv = ( |
| 1011 | self.kv_b_proj(self.kv_a_layernorm(compressed_kv)) |
| 1012 | .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim) |
| 1013 | .transpose(1, 2) |
| 1014 | ) |
| 1015 | |
| 1016 | k_nope, value_states = torch.split( |
| 1017 | kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1 |
| 1018 | ) |
| 1019 | kv_seq_len = value_states.shape[-2] |
| 1020 | |
| 1021 | kv_seq_len = value_states.shape[-2] |
| 1022 | if past_key_value is not None: |
| 1023 | kv_seq_len += past_key_value.get_seq_length(self.layer_idx) |
| 1024 | |
| 1025 | cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) |
| 1026 | q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) |
| 1027 | |
| 1028 | query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) |
| 1029 | query_states[:, :, :, : self.qk_nope_head_dim] = q_nope |
| 1030 | query_states[:, :, :, self.qk_nope_head_dim :] = q_pe |
| 1031 | |
| 1032 | key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) |
| 1033 | key_states[:, :, :, : self.qk_nope_head_dim] = k_nope |
| 1034 | key_states[:, :, :, self.qk_nope_head_dim :] = k_pe |
| 1035 | |
| 1036 | if self.q_head_dim != self.v_head_dim: |
| 1037 | value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim]) |
| 1038 | |
| 1039 | # TODO: support compressed_kv for kv_cache (instead of key_states, value_states) in flash_attention version |
| 1040 | if past_key_value is not None: |
| 1041 | cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models |
| 1042 | key_states, value_states = past_key_value.update( |
| 1043 | key_states, value_states, self.layer_idx, cache_kwargs |
| 1044 | ) |
| 1045 | |
| 1046 | # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache |
| 1047 | # to be able to avoid many of these transpose/reshape/view. |
| 1048 | query_states = query_states.transpose(1, 2) |
| 1049 | key_states = key_states.transpose(1, 2) |
| 1050 | value_states = value_states.transpose(1, 2) |
| 1051 | |
| 1052 | dropout_rate = self.attention_dropout if self.training else 0.0 |
| 1053 | |
| 1054 | # In PEFT, usually we cast the layer norms in float32 for training stability reasons |
| 1055 | # therefore the input hidden states gets silently casted in float32. Hence, we need |
| 1056 | # cast them back in the correct dtype just to be sure everything works as expected. |
| 1057 | # This might slowdown training & inference so it is recommended to not cast the LayerNorms |
| 1058 | # in fp32. (DeepseekV2RMSNorm handles it correctly) |
| 1059 | |
| 1060 | input_dtype = query_states.dtype |
| 1061 | if input_dtype == torch.float32: |
| 1062 | # Handle the case where the model is quantized |
| 1063 | if hasattr(self.config, "_pre_quantization_dtype"): |
| 1064 | target_dtype = self.config._pre_quantization_dtype |
| 1065 | elif torch.is_autocast_enabled(): |
| 1066 | target_dtype = torch.get_autocast_gpu_dtype() |
| 1067 | else: |
| 1068 | target_dtype = ( |
| 1069 | self.q_proj.weight.dtype |
| 1070 | if self.q_lora_rank is None |
| 1071 | else self.q_a_proj.weight.dtype |
| 1072 | ) |
| 1073 | |
| 1074 | logger.warning_once( |
| 1075 | f"The input hidden states seems to be silently casted in float32, this might be related to" |
| 1076 | f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" |
| 1077 | f" {target_dtype}." |
| 1078 | ) |
| 1079 | |
| 1080 | query_states = query_states.to(target_dtype) |
| 1081 | key_states = key_states.to(target_dtype) |
| 1082 | value_states = value_states.to(target_dtype) |
| 1083 | |
| 1084 | attn_output = self._flash_attention_forward( |
| 1085 | query_states, |
| 1086 | key_states, |
| 1087 | value_states, |
| 1088 | attention_mask, |
| 1089 | q_len, |
| 1090 | dropout=dropout_rate, |
| 1091 | softmax_scale=self.softmax_scale, |
| 1092 | ) |
| 1093 | if self.q_head_dim != self.v_head_dim: |
| 1094 | attn_output = attn_output[:, :, :, : self.v_head_dim] |
| 1095 | |
| 1096 | attn_output = attn_output.reshape( |
| 1097 | bsz, q_len, self.num_heads * self.v_head_dim |
| 1098 | ).contiguous() |
| 1099 | attn_output = self.o_proj(attn_output) |
| 1100 | |
| 1101 | if not output_attentions: |
| 1102 | attn_weights = None |
| 1103 | |
| 1104 | return attn_output, attn_weights, past_key_value |
| 1105 | |
| 1106 | def _flash_attention_forward( |
| 1107 | self, |
| 1108 | query_states, |
| 1109 | key_states, |
| 1110 | value_states, |
| 1111 | attention_mask, |
| 1112 | query_length, |
| 1113 | dropout=0.0, |
| 1114 | softmax_scale=None, |
| 1115 | ): |
| 1116 | """ |
| 1117 | Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token |
| 1118 | first unpad the input, then computes the attention scores and pad the final attention scores. |
| 1119 | |
| 1120 | Args: |
| 1121 | query_states (`torch.Tensor`): |
| 1122 | Input query states to be passed to Flash Attention API |
| 1123 | key_states (`torch.Tensor`): |
| 1124 | Input key states to be passed to Flash Attention API |
| 1125 | value_states (`torch.Tensor`): |
| 1126 | Input value states to be passed to Flash Attention API |
| 1127 | attention_mask (`torch.Tensor`): |
| 1128 | The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the |
| 1129 | position of padding tokens and 1 for the position of non-padding tokens. |
| 1130 | dropout (`int`, *optional*): |
| 1131 | Attention dropout |
| 1132 | softmax_scale (`float`, *optional*): |
| 1133 | The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) |
| 1134 | """ |
| 1135 | if not self._flash_attn_uses_top_left_mask: |
| 1136 | causal = self.is_causal |
| 1137 | else: |
| 1138 | # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV2FlashAttention2 __init__. |
| 1139 | causal = self.is_causal and query_length != 1 |
| 1140 | |
| 1141 | # Contains at least one padding token in the sequence |
| 1142 | if attention_mask is not None: |
| 1143 | batch_size = query_states.shape[0] |
| 1144 | ( |
| 1145 | query_states, |
| 1146 | key_states, |
| 1147 | value_states, |
| 1148 | indices_q, |
| 1149 | cu_seq_lens, |
| 1150 | max_seq_lens, |
| 1151 | ) = self._upad_input( |
| 1152 | query_states, key_states, value_states, attention_mask, query_length |
| 1153 | ) |
| 1154 | |
| 1155 | cu_seqlens_q, cu_seqlens_k = cu_seq_lens |
| 1156 | max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens |
| 1157 | |
| 1158 | attn_output_unpad = flash_attn_varlen_func( |
| 1159 | query_states, |
| 1160 | key_states, |
| 1161 | value_states, |
| 1162 | cu_seqlens_q=cu_seqlens_q, |
| 1163 | cu_seqlens_k=cu_seqlens_k, |
| 1164 | max_seqlen_q=max_seqlen_in_batch_q, |
| 1165 | max_seqlen_k=max_seqlen_in_batch_k, |
| 1166 | dropout_p=dropout, |
| 1167 | softmax_scale=softmax_scale, |
| 1168 | causal=causal, |
| 1169 | ) |
| 1170 | |
| 1171 | attn_output = pad_input( |
| 1172 | attn_output_unpad, indices_q, batch_size, query_length |
| 1173 | ) |
| 1174 | else: |
| 1175 | attn_output = flash_attn_func( |
| 1176 | query_states, |
| 1177 | key_states, |
| 1178 | value_states, |
| 1179 | dropout, |
| 1180 | softmax_scale=softmax_scale, |
| 1181 | causal=causal, |
| 1182 | ) |
| 1183 | |
| 1184 | return attn_output |
| 1185 | |
| 1186 | def _upad_input( |
| 1187 | self, query_layer, key_layer, value_layer, attention_mask, query_length |
| 1188 | ): |
| 1189 | indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) |
| 1190 | batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape |
| 1191 | |
| 1192 | key_layer = index_first_axis( |
| 1193 | key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), |
| 1194 | indices_k, |
| 1195 | ) |
| 1196 | value_layer = index_first_axis( |
| 1197 | value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), |
| 1198 | indices_k, |
| 1199 | ) |
| 1200 | if query_length == kv_seq_len: |
| 1201 | query_layer = index_first_axis( |
| 1202 | query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), |
| 1203 | indices_k, |
| 1204 | ) |
| 1205 | cu_seqlens_q = cu_seqlens_k |
| 1206 | max_seqlen_in_batch_q = max_seqlen_in_batch_k |
| 1207 | indices_q = indices_k |
| 1208 | elif query_length == 1: |
| 1209 | max_seqlen_in_batch_q = 1 |
| 1210 | cu_seqlens_q = torch.arange( |
| 1211 | batch_size + 1, dtype=torch.int32, device=query_layer.device |
| 1212 | ) # There is a memcpy here, that is very bad. |
| 1213 | indices_q = cu_seqlens_q[:-1] |
| 1214 | query_layer = query_layer.squeeze(1) |
| 1215 | else: |
| 1216 | # The -q_len: slice assumes left padding. |
| 1217 | attention_mask = attention_mask[:, -query_length:] |
| 1218 | query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( |
| 1219 | query_layer, attention_mask |
| 1220 | ) |
| 1221 | |
| 1222 | return ( |
| 1223 | query_layer, |
| 1224 | key_layer, |
| 1225 | value_layer, |
| 1226 | indices_q, |
| 1227 | (cu_seqlens_q, cu_seqlens_k), |
| 1228 | (max_seqlen_in_batch_q, max_seqlen_in_batch_k), |
| 1229 | ) |
| 1230 | |
| 1231 | |
| 1232 | class SlidingWindowLlamaAttention(LlamaAttention): |
| 1233 | """LlamaAttention with sliding window KV cache using a ring buffer during decode.""" |
| 1234 | |
| 1235 | def __init__(self, config, layer_idx): |
| 1236 | super().__init__(config, layer_idx) |
| 1237 | # New transformers moved rotary_emb to model level; create our own |
| 1238 | if not hasattr(self, 'rotary_emb'): |
| 1239 | from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding |
| 1240 | self.rotary_emb = LlamaRotaryEmbedding(config=config) |
| 1241 | # Save sliding_window separately so we can disable it in config to prevent |
| 1242 | # DynamicCache from truncating prefill tokens |
| 1243 | self._sliding_window = getattr(config, 'sliding_window', None) |
| 1244 | |
| 1245 | def forward(self, *args, **kwargs): |
| 1246 | import math |
| 1247 | |
| 1248 | # Compatibility: new DynamicCache uses .layers[i].keys/.values instead of .key_cache[i]/.value_cache[i] |
| 1249 | def _get_kcache(cache, layer_idx): |
| 1250 | if hasattr(cache, 'key_cache'): |
| 1251 | return cache.key_cache[layer_idx] |
| 1252 | return cache.layers[layer_idx].keys |
| 1253 | |
| 1254 | def _get_vcache(cache, layer_idx): |
| 1255 | if hasattr(cache, 'value_cache'): |
| 1256 | return cache.value_cache[layer_idx] |
| 1257 | return cache.layers[layer_idx].values |
| 1258 | |
| 1259 | # Extract args |
| 1260 | def _get(name, idx, default=None): |
| 1261 | if name in kwargs: |
| 1262 | return kwargs[name] |
| 1263 | if len(args) > idx: |
| 1264 | return args[idx] |
| 1265 | return default |
| 1266 | |
| 1267 | hidden_states = _get('hidden_states', 0) |
| 1268 | attention_mask = _get('attention_mask', 1) |
| 1269 | position_ids = _get('position_ids', 2) |
| 1270 | past_kv = _get('past_key_value', 3) |
| 1271 | if past_kv is None: |
| 1272 | past_kv = kwargs.get('past_key_values', None) |
| 1273 | output_attentions = _get('output_attentions', 4, False) |
| 1274 | |
| 1275 | # Dimensions from config (new transformers removed self.num_heads) |
| 1276 | num_heads = self.config.num_attention_heads |
| 1277 | num_kv_heads = self.config.num_key_value_heads |
| 1278 | head_dim = self.head_dim |
| 1279 | num_kv_groups = self.num_key_value_groups |
| 1280 | |
| 1281 | bsz, q_len, _ = hidden_states.size() |
| 1282 | W = getattr(self.config, '_ring_window', None) # Read from config (set before generate) |
| 1283 | |
| 1284 | # --- Helper: standard QKV attention --- |
| 1285 | def _attn_forward(use_cache_update=True): |
| 1286 | query_states = self.q_proj(hidden_states).view(bsz, q_len, num_heads, head_dim).transpose(1, 2) |
| 1287 | key_states = self.k_proj(hidden_states).view(bsz, q_len, num_kv_heads, head_dim).transpose(1, 2) |
| 1288 | value_states = self.v_proj(hidden_states).view(bsz, q_len, num_kv_heads, head_dim).transpose(1, 2) |
| 1289 | |
| 1290 | cos, sin = self.rotary_emb(value_states, position_ids) |
| 1291 | query_states, key_states = _llama_apply_rotary_pos_emb(query_states, key_states, cos, sin) |
| 1292 | |
| 1293 | if past_kv is not None and use_cache_update: |
| 1294 | key_states, value_states = past_kv.update(key_states, value_states, self.layer_idx) |
| 1295 | |
| 1296 | k = _llama_repeat_kv(key_states, num_kv_groups) |
| 1297 | v = _llama_repeat_kv(value_states, num_kv_groups) |
| 1298 | |
| 1299 | attn_weights = torch.matmul(query_states, k.transpose(2, 3)) / math.sqrt(head_dim) |
| 1300 | if attention_mask is not None: |
| 1301 | causal_mask = attention_mask[:, :, :, :k.shape[-2]] |
| 1302 | attn_weights = attn_weights + causal_mask |
| 1303 | attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) |
| 1304 | attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) |
| 1305 | attn_output = torch.matmul(attn_weights, v) |
| 1306 | attn_output = attn_output.transpose(1, 2).contiguous().reshape(bsz, q_len, -1) |
| 1307 | attn_output = self.o_proj(attn_output) |
| 1308 | return attn_output, None, past_kv |
| 1309 | |
| 1310 | # Prefill or no sliding window |
| 1311 | # True prefill: W disabled, no cache, or first forward (prefill_length not yet recorded) |
| 1312 | _is_true_prefill = (W is None or past_kv is None or |
| 1313 | (q_len > 1 and (not hasattr(past_kv, '_prefill_length') or |
| 1314 | self.layer_idx not in past_kv._prefill_length))) |
| 1315 | if _is_true_prefill: |
| 1316 | result = _attn_forward() |
| 1317 | if W is not None and past_kv is not None and q_len > 1: |
| 1318 | # Only record prefill_length the FIRST time (don't overwrite on subsequent q_len>1 calls) |
| 1319 | if not hasattr(past_kv, '_prefill_length'): |
| 1320 | past_kv._prefill_length = {} |
| 1321 | if self.layer_idx not in past_kv._prefill_length: |
| 1322 | past_kv._prefill_length[self.layer_idx] = _get_kcache(past_kv, self.layer_idx).shape[-2] |
| 1323 | return result |
| 1324 | |
| 1325 | # Decode path: first decode step -> record prefill_length (only once!) |
| 1326 | if not hasattr(past_kv, '_prefill_length') or self.layer_idx not in past_kv._prefill_length: |
| 1327 | if not hasattr(past_kv, '_prefill_length'): |
| 1328 | past_kv._prefill_length = {} |
| 1329 | past_kv._prefill_length[self.layer_idx] = _get_kcache(past_kv, self.layer_idx).shape[-2] |
| 1330 | |
| 1331 | prefill_len = past_kv._prefill_length[self.layer_idx] |
| 1332 | cur_len = _get_kcache(past_kv, self.layer_idx).shape[-2] |
| 1333 | |
| 1334 | # Warmup: cat-append until ring region is full |
| 1335 | if cur_len < prefill_len + W: |
| 1336 | result = _attn_forward() |
| 1337 | new_len = _get_kcache(past_kv, self.layer_idx).shape[-2] |
| 1338 | if new_len >= prefill_len + W: |
| 1339 | if not hasattr(past_kv, '_ring_pos'): |
| 1340 | past_kv._ring_pos = {} |
| 1341 | past_kv._ring_pos[self.layer_idx] = 0 |
| 1342 | return result |
| 1343 | |
| 1344 | # Steady state: ring in-place overwrite |
| 1345 | if not hasattr(past_kv, '_ring_pos') or self.layer_idx not in past_kv._ring_pos: |
| 1346 | past_kv._ring_pos = getattr(past_kv, '_ring_pos', {}) or {} |
| 1347 | past_kv._ring_pos[self.layer_idx] = 0 |
| 1348 | |
| 1349 | # Ring decode: overwrite ring slots, then attention over full cache |
| 1350 | ring_pos = past_kv._ring_pos[self.layer_idx] |
| 1351 | kcache = _get_kcache(past_kv, self.layer_idx) |
| 1352 | vcache = _get_vcache(past_kv, self.layer_idx) |
| 1353 | |
| 1354 | # Compute new K, V and apply RoPE, then overwrite ring slots |
| 1355 | query_states = self.q_proj(hidden_states).view(bsz, q_len, num_heads, head_dim).transpose(1, 2) |
| 1356 | key_states = self.k_proj(hidden_states).view(bsz, q_len, num_kv_heads, head_dim).transpose(1, 2) |
| 1357 | value_states = self.v_proj(hidden_states).view(bsz, q_len, num_kv_heads, head_dim).transpose(1, 2) |
| 1358 | cos, sin = self.rotary_emb(value_states, position_ids) |
| 1359 | query_states, key_states = _llama_apply_rotary_pos_emb(query_states, key_states, cos, sin) |
| 1360 | |
| 1361 | # Overwrite ring slots in-place |
| 1362 | for t in range(q_len): |
| 1363 | slot = prefill_len + ring_pos |
| 1364 | kcache[:, :, slot:slot + 1, :] = key_states[:, :, t:t + 1, :] |
| 1365 | vcache[:, :, slot:slot + 1, :] = value_states[:, :, t:t + 1, :] |
| 1366 | ring_pos = (ring_pos + 1) % W |
| 1367 | past_kv._ring_pos[self.layer_idx] = ring_pos |
| 1368 | |
| 1369 | # Attention over full cache (no causal mask needed for decode q_len=1) |
| 1370 | k = _llama_repeat_kv(kcache, num_kv_groups) |
| 1371 | v = _llama_repeat_kv(vcache, num_kv_groups) |
| 1372 | attn_weights = torch.matmul(query_states, k.transpose(2, 3)) / math.sqrt(head_dim) |
| 1373 | attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) |
| 1374 | attn_output = torch.matmul(attn_weights, v) |
| 1375 | attn_output = attn_output.transpose(1, 2).contiguous().reshape(bsz, q_len, -1) |
| 1376 | attn_output = self.o_proj(attn_output) |
| 1377 | return attn_output, None, past_kv |
| 1378 | |
| 1379 | |
| 1380 | ATTENTION_CLASSES = { |
| 1381 | "eager": DeepseekV2Attention, |
| 1382 | "flash_attention_2": DeepseekV2FlashAttention2, |
| 1383 | |
| 1384 | "mla_eager": DeepseekV2Attention, |
| 1385 | "mla_flash_attention_2": DeepseekV2FlashAttention2, |
| 1386 | |
| 1387 | "mha_eager": SlidingWindowLlamaAttention, |
| 1388 | # "mha_flash_attention_2": LlamaFlashAttention2 |
| 1389 | } |
| 1390 | |
| 1391 | |
| 1392 | class DeepseekV2DecoderLayer(nn.Module): |
| 1393 | def __init__(self, config: DeepseekV2Config, layer_idx: int): |
| 1394 | super().__init__() |
| 1395 | self.hidden_size = config.hidden_size |
| 1396 | |
| 1397 | |
| 1398 | if config.use_mla: |
| 1399 | attn_implementation = "mla_" + config._attn_implementation |
| 1400 | else: |
| 1401 | attn_implementation = "mha_" + config._attn_implementation |
| 1402 | |
| 1403 | self.self_attn = ATTENTION_CLASSES[attn_implementation]( |
| 1404 | config=config, layer_idx=layer_idx |
| 1405 | ) |
| 1406 | |
| 1407 | self.mlp = ( |
| 1408 | DeepseekV2MoE(config) |
| 1409 | if ( |
| 1410 | config.n_routed_experts is not None |
| 1411 | and layer_idx >= config.first_k_dense_replace |
| 1412 | and layer_idx % config.moe_layer_freq == 0 |
| 1413 | ) |
| 1414 | else DeepseekV2MLP(config) |
| 1415 | ) |
| 1416 | self.input_layernorm = DeepseekV2RMSNorm( |
| 1417 | config.hidden_size, eps=config.rms_norm_eps |
| 1418 | ) |
| 1419 | self.post_attention_layernorm = DeepseekV2RMSNorm( |
| 1420 | config.hidden_size, eps=config.rms_norm_eps |
| 1421 | ) |
| 1422 | |
| 1423 | def forward( |
| 1424 | self, |
| 1425 | hidden_states: torch.Tensor, |
| 1426 | attention_mask: Optional[torch.Tensor] = None, |
| 1427 | position_ids: Optional[torch.LongTensor] = None, |
| 1428 | past_key_value: Optional[Tuple[torch.Tensor]] = None, |
| 1429 | output_attentions: Optional[bool] = False, |
| 1430 | use_cache: Optional[bool] = False, |
| 1431 | **kwargs, |
| 1432 | ) -> Tuple[ |
| 1433 | torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] |
| 1434 | ]: |
| 1435 | """ |
| 1436 | Args: |
| 1437 | hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` |
| 1438 | attention_mask (`torch.FloatTensor`, *optional*): |
| 1439 | attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, |
| 1440 | query_sequence_length, key_sequence_length)` if default attention is used. |
| 1441 | output_attentions (`bool`, *optional*): |
| 1442 | Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
| 1443 | returned tensors for more detail. |
| 1444 | use_cache (`bool`, *optional*): |
| 1445 | If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding |
| 1446 | (see `past_key_values`). |
| 1447 | past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states |
| 1448 | """ |
| 1449 | if "padding_mask" in kwargs: |
| 1450 | warnings.warn( |
| 1451 | "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" |
| 1452 | ) |
| 1453 | residual = hidden_states |
| 1454 | |
| 1455 | hidden_states = self.input_layernorm(hidden_states) |
| 1456 | |
| 1457 | # Self Attention |
| 1458 | hidden_states, self_attn_weights, present_key_value = self.self_attn( |
| 1459 | hidden_states=hidden_states, |
| 1460 | attention_mask=attention_mask, |
| 1461 | position_ids=position_ids, |
| 1462 | past_key_value=past_key_value, |
| 1463 | output_attentions=output_attentions, |
| 1464 | use_cache=use_cache, |
| 1465 | **kwargs, |
| 1466 | ) |
| 1467 | hidden_states = residual + hidden_states |
| 1468 | |
| 1469 | # Fully Connected |
| 1470 | residual = hidden_states |
| 1471 | hidden_states = self.post_attention_layernorm(hidden_states) |
| 1472 | hidden_states = self.mlp(hidden_states) |
| 1473 | hidden_states = residual + hidden_states |
| 1474 | |
| 1475 | outputs = (hidden_states,) |
| 1476 | |
| 1477 | if output_attentions: |
| 1478 | outputs += (self_attn_weights,) |
| 1479 | |
| 1480 | if use_cache: |
| 1481 | outputs += (present_key_value,) |
| 1482 | |
| 1483 | return outputs |
| 1484 | |
| 1485 | |
| 1486 | DeepseekV2_START_DOCSTRING = r""" |
| 1487 | This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the |
| 1488 | library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads |
| 1489 | etc.) |
| 1490 | |
| 1491 | This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. |
| 1492 | Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage |
| 1493 | and behavior. |
| 1494 | |
| 1495 | Parameters: |
| 1496 | config ([`DeepseekV2Config`]): |
| 1497 | Model configuration class with all the parameters of the model. Initializing with a config file does not |
| 1498 | load the weights associated with the model, only the configuration. Check out the |
| 1499 | [`~PreTrainedModel.from_pretrained`] method to load the model weights. |
| 1500 | """ |
| 1501 | |
| 1502 | |
| 1503 | @add_start_docstrings( |
| 1504 | "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.", |
| 1505 | DeepseekV2_START_DOCSTRING, |
| 1506 | ) |
| 1507 | class DeepseekV2PreTrainedModel(PreTrainedModel): |
| 1508 | config_class = DeepseekV2Config |
| 1509 | base_model_prefix = "model" |
| 1510 | supports_gradient_checkpointing = True |
| 1511 | _no_split_modules = ["DeepseekV2DecoderLayer"] |
| 1512 | _skip_keys_device_placement = "past_key_values" |
| 1513 | _supports_flash_attn_2 = True |
| 1514 | _supports_cache_class = True |
| 1515 | |
| 1516 | def _init_weights(self, module): |
| 1517 | std = self.config.initializer_range |
| 1518 | if isinstance(module, nn.Linear): |
| 1519 | module.weight.data.normal_(mean=0.0, std=std) |
| 1520 | if module.bias is not None: |
| 1521 | module.bias.data.zero_() |
| 1522 | elif isinstance(module, nn.Embedding): |
| 1523 | module.weight.data.normal_(mean=0.0, std=std) |
| 1524 | if module.padding_idx is not None: |
| 1525 | module.weight.data[module.padding_idx].zero_() |
| 1526 | |
| 1527 | |
| 1528 | DeepseekV2_INPUTS_DOCSTRING = r""" |
| 1529 | Args: |
| 1530 | input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
| 1531 | Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide |
| 1532 | it. |
| 1533 | |
| 1534 | Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
| 1535 | [`PreTrainedTokenizer.__call__`] for details. |
| 1536 | |
| 1537 | [What are input IDs?](../glossary#input-ids) |
| 1538 | attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
| 1539 | Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
| 1540 | |
| 1541 | - 1 for tokens that are **not masked**, |
| 1542 | - 0 for tokens that are **masked**. |
| 1543 | |
| 1544 | [What are attention masks?](../glossary#attention-mask) |
| 1545 | |
| 1546 | Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
| 1547 | [`PreTrainedTokenizer.__call__`] for details. |
| 1548 | |
| 1549 | If `past_key_values` is used, optionally only the last `input_ids` have to be input (see |
| 1550 | `past_key_values`). |
| 1551 | |
| 1552 | If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] |
| 1553 | and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more |
| 1554 | information on the default strategy. |
| 1555 | |
| 1556 | - 1 indicates the head is **not masked**, |
| 1557 | - 0 indicates the head is **masked**. |
| 1558 | position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
| 1559 | Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, |
| 1560 | config.n_positions - 1]`. |
| 1561 | |
| 1562 | [What are position IDs?](../glossary#position-ids) |
| 1563 | past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): |
| 1564 | Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention |
| 1565 | blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` |
| 1566 | returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. |
| 1567 | |
| 1568 | Two formats are allowed: |
| 1569 | - a [`~cache_utils.Cache`] instance; |
| 1570 | - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of |
| 1571 | shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy |
| 1572 | cache format. |
| 1573 | |
| 1574 | The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the |
| 1575 | legacy cache format will be returned. |
| 1576 | |
| 1577 | If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't |
| 1578 | have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` |
| 1579 | of shape `(batch_size, sequence_length)`. |
| 1580 | inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): |
| 1581 | Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This |
| 1582 | is useful if you want more control over how to convert `input_ids` indices into associated vectors than the |
| 1583 | model's internal embedding lookup matrix. |
| 1584 | use_cache (`bool`, *optional*): |
| 1585 | If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see |
| 1586 | `past_key_values`). |
| 1587 | output_attentions (`bool`, *optional*): |
| 1588 | Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
| 1589 | tensors for more detail. |
| 1590 | output_hidden_states (`bool`, *optional*): |
| 1591 | Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
| 1592 | more detail. |
| 1593 | return_dict (`bool`, *optional*): |
| 1594 | Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
| 1595 | """ |
| 1596 | |
| 1597 | |
| 1598 | @add_start_docstrings( |
| 1599 | "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.", |
| 1600 | DeepseekV2_START_DOCSTRING, |
| 1601 | ) |
| 1602 | class DeepseekV2Model(DeepseekV2PreTrainedModel): |
| 1603 | """ |
| 1604 | Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`] |
| 1605 | |
| 1606 | Args: |
| 1607 | config: DeepseekV2Config |
| 1608 | """ |
| 1609 | |
| 1610 | def __init__(self, config: DeepseekV2Config): |
| 1611 | super().__init__(config) |
| 1612 | self.padding_idx = config.pad_token_id |
| 1613 | self.vocab_size = config.vocab_size |
| 1614 | |
| 1615 | self.embed_tokens = nn.Embedding( |
| 1616 | config.vocab_size, config.hidden_size, self.padding_idx |
| 1617 | ) |
| 1618 | self.layers = nn.ModuleList( |
| 1619 | [ |
| 1620 | DeepseekV2DecoderLayer(config, layer_idx) |
| 1621 | for layer_idx in range(config.num_hidden_layers) |
| 1622 | ] |
| 1623 | ) |
| 1624 | # print(config._attn_implementation) |
| 1625 | self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" |
| 1626 | self.norm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
| 1627 | |
| 1628 | self.gradient_checkpointing = False |
| 1629 | # Initialize weights and apply final processing |
| 1630 | self.post_init() |
| 1631 | |
| 1632 | def get_input_embeddings(self): |
| 1633 | return self.embed_tokens |
| 1634 | |
| 1635 | def set_input_embeddings(self, value): |
| 1636 | self.embed_tokens = value |
| 1637 | |
| 1638 | @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING) |
| 1639 | def forward( |
| 1640 | self, |
| 1641 | input_ids: torch.LongTensor = None, |
| 1642 | attention_mask: Optional[torch.Tensor] = None, |
| 1643 | position_ids: Optional[torch.LongTensor] = None, |
| 1644 | past_key_values: Optional[List[torch.FloatTensor]] = None, |
| 1645 | inputs_embeds: Optional[torch.FloatTensor] = None, |
| 1646 | use_cache: Optional[bool] = None, |
| 1647 | output_attentions: Optional[bool] = None, |
| 1648 | output_hidden_states: Optional[bool] = None, |
| 1649 | return_dict: Optional[bool] = None, |
| 1650 | cache_position: Optional[torch.LongTensor] = None |
| 1651 | ) -> Union[Tuple, BaseModelOutputWithPast]: |
| 1652 | output_attentions = ( |
| 1653 | output_attentions |
| 1654 | if output_attentions is not None |
| 1655 | else self.config.output_attentions |
| 1656 | ) |
| 1657 | output_hidden_states = ( |
| 1658 | output_hidden_states |
| 1659 | if output_hidden_states is not None |
| 1660 | else self.config.output_hidden_states |
| 1661 | ) |
| 1662 | use_cache = use_cache if use_cache is not None else self.config.use_cache |
| 1663 | |
| 1664 | return_dict = ( |
| 1665 | return_dict if return_dict is not None else self.config.use_return_dict |
| 1666 | ) |
| 1667 | |
| 1668 | # retrieve input_ids and inputs_embeds |
| 1669 | if input_ids is not None and inputs_embeds is not None: |
| 1670 | raise ValueError( |
| 1671 | "You cannot specify both input_ids and inputs_embeds at the same time" |
| 1672 | ) |
| 1673 | elif input_ids is not None: |
| 1674 | batch_size, seq_length = input_ids.shape[:2] |
| 1675 | elif inputs_embeds is not None: |
| 1676 | batch_size, seq_length = inputs_embeds.shape[:2] |
| 1677 | else: |
| 1678 | raise ValueError("You have to specify either input_ids or inputs_embeds") |
| 1679 | |
| 1680 | if self.gradient_checkpointing and self.training: |
| 1681 | if use_cache: |
| 1682 | logger.warning_once( |
| 1683 | "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers." |
| 1684 | ) |
| 1685 | use_cache = False |
| 1686 | |
| 1687 | past_key_values_length = 0 |
| 1688 | if use_cache: |
| 1689 | use_legacy_cache = not isinstance(past_key_values, Cache) |
| 1690 | if use_legacy_cache: |
| 1691 | past_key_values = DynamicCache.from_legacy_cache(past_key_values) |
| 1692 | past_key_values_length = past_key_values.get_seq_length() |
| 1693 | |
| 1694 | if position_ids is None: |
| 1695 | device = input_ids.device if input_ids is not None else inputs_embeds.device |
| 1696 | position_ids = torch.arange( |
| 1697 | past_key_values_length, |
| 1698 | seq_length + past_key_values_length, |
| 1699 | dtype=torch.long, |
| 1700 | device=device, |
| 1701 | ) |
| 1702 | position_ids = position_ids.unsqueeze(0) |
| 1703 | |
| 1704 | if inputs_embeds is None: |
| 1705 | inputs_embeds = self.embed_tokens(input_ids) |
| 1706 | |
| 1707 | # Skip 4D causal mask for decode (q_len=1 with KV cache doesn't need it) |
| 1708 | if seq_length == 1 and past_key_values_length > 0: |
| 1709 | attention_mask = None |
| 1710 | elif self._use_flash_attention_2: |
| 1711 | # 2d mask is passed through the layers |
| 1712 | attention_mask = ( |
| 1713 | attention_mask |
| 1714 | if (attention_mask is not None and 0 in attention_mask) |
| 1715 | else None |
| 1716 | ) |
| 1717 | else: |
| 1718 | # 4d mask is passed through the layers |
| 1719 | attention_mask = _prepare_4d_causal_attention_mask( |
| 1720 | attention_mask, |
| 1721 | (batch_size, seq_length), |
| 1722 | inputs_embeds, |
| 1723 | past_key_values_length, |
| 1724 | ) |
| 1725 | |
| 1726 | # embed positions |
| 1727 | hidden_states = inputs_embeds |
| 1728 | |
| 1729 | # decoder layers |
| 1730 | all_hidden_states = () if output_hidden_states else None |
| 1731 | all_self_attns = () if output_attentions else None |
| 1732 | next_decoder_cache = None |
| 1733 | |
| 1734 | for decoder_layer in self.layers: |
| 1735 | if output_hidden_states: |
| 1736 | all_hidden_states += (hidden_states,) |
| 1737 | |
| 1738 | if self.gradient_checkpointing and self.training: |
| 1739 | layer_outputs = self._gradient_checkpointing_func( |
| 1740 | decoder_layer.__call__, |
| 1741 | hidden_states, |
| 1742 | attention_mask, |
| 1743 | position_ids, |
| 1744 | past_key_values, |
| 1745 | output_attentions, |
| 1746 | use_cache, |
| 1747 | ) |
| 1748 | else: |
| 1749 | layer_outputs = decoder_layer( |
| 1750 | hidden_states, |
| 1751 | attention_mask=attention_mask, |
| 1752 | position_ids=position_ids, |
| 1753 | past_key_value=past_key_values, |
| 1754 | output_attentions=output_attentions, |
| 1755 | use_cache=use_cache, |
| 1756 | ) |
| 1757 | |
| 1758 | hidden_states = layer_outputs[0] |
| 1759 | |
| 1760 | if use_cache: |
| 1761 | next_decoder_cache = layer_outputs[2 if output_attentions else 1] |
| 1762 | |
| 1763 | if output_attentions: |
| 1764 | all_self_attns += (layer_outputs[1],) |
| 1765 | |
| 1766 | hidden_states = self.norm(hidden_states) |
| 1767 | |
| 1768 | # add hidden states from the last decoder layer |
| 1769 | if output_hidden_states: |
| 1770 | all_hidden_states += (hidden_states,) |
| 1771 | |
| 1772 | next_cache = None |
| 1773 | if use_cache: |
| 1774 | next_cache = next_decoder_cache # Always return DynamicCache to preserve custom attributes |
| 1775 | if not return_dict: |
| 1776 | return tuple( |
| 1777 | v |
| 1778 | for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] |
| 1779 | if v is not None |
| 1780 | ) |
| 1781 | return BaseModelOutputWithPast( |
| 1782 | last_hidden_state=hidden_states, |
| 1783 | past_key_values=next_cache, |
| 1784 | hidden_states=all_hidden_states, |
| 1785 | attentions=all_self_attns, |
| 1786 | ) |
| 1787 | |
| 1788 | |
| 1789 | class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel): |
| 1790 | _tied_weights_keys = ["lm_head.weight"] |
| 1791 | |
| 1792 | def __init__(self, config): |
| 1793 | super().__init__(config) |
| 1794 | self.model = DeepseekV2Model(config) |
| 1795 | self.vocab_size = config.vocab_size |
| 1796 | self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) |
| 1797 | |
| 1798 | # Initialize weights and apply final processing |
| 1799 | self.post_init() |
| 1800 | |
| 1801 | def get_input_embeddings(self): |
| 1802 | return self.model.embed_tokens |
| 1803 | |
| 1804 | def set_input_embeddings(self, value): |
| 1805 | self.model.embed_tokens = value |
| 1806 | |
| 1807 | def get_output_embeddings(self): |
| 1808 | return self.lm_head |
| 1809 | |
| 1810 | def set_output_embeddings(self, new_embeddings): |
| 1811 | self.lm_head = new_embeddings |
| 1812 | |
| 1813 | def set_decoder(self, decoder): |
| 1814 | self.model = decoder |
| 1815 | |
| 1816 | def get_decoder(self): |
| 1817 | return self.model |
| 1818 | |
| 1819 | @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING) |
| 1820 | @replace_return_docstrings( |
| 1821 | output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC |
| 1822 | ) |
| 1823 | def forward( |
| 1824 | self, |
| 1825 | input_ids: torch.LongTensor = None, |
| 1826 | attention_mask: Optional[torch.Tensor] = None, |
| 1827 | position_ids: Optional[torch.LongTensor] = None, |
| 1828 | past_key_values: Optional[List[torch.FloatTensor]] = None, |
| 1829 | inputs_embeds: Optional[torch.FloatTensor] = None, |
| 1830 | labels: Optional[torch.LongTensor] = None, |
| 1831 | use_cache: Optional[bool] = None, |
| 1832 | output_attentions: Optional[bool] = None, |
| 1833 | output_hidden_states: Optional[bool] = None, |
| 1834 | return_dict: Optional[bool] = None, |
| 1835 | cache_position: Optional[torch.LongTensor] = None |
| 1836 | ) -> Union[Tuple, CausalLMOutputWithPast]: |
| 1837 | r""" |
| 1838 | Args: |
| 1839 | labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
| 1840 | Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers., |
| 1841 | config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored |
| 1842 | (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`. |
| 1843 | |
| 1844 | Returns: |
| 1845 | |
| 1846 | Example: |
| 1847 | |
| 1848 | ```python |
| 1849 | >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM |
| 1850 | |
| 1851 | >>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) |
| 1852 | >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) |
| 1853 | |
| 1854 | >>> prompt = "Hey, are you conscious? Can you talk to me?" |
| 1855 | >>> inputs = tokenizer(prompt, return_tensors="pt") |
| 1856 | |
| 1857 | >>> # Generate |
| 1858 | >>> generate_ids = model.generate(inputs.input_ids, max_length=30) |
| 1859 | >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
| 1860 | "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." |
| 1861 | ```""" |
| 1862 | output_attentions = ( |
| 1863 | output_attentions |
| 1864 | if output_attentions is not None |
| 1865 | else self.config.output_attentions |
| 1866 | ) |
| 1867 | output_hidden_states = ( |
| 1868 | output_hidden_states |
| 1869 | if output_hidden_states is not None |
| 1870 | else self.config.output_hidden_states |
| 1871 | ) |
| 1872 | return_dict = ( |
| 1873 | return_dict if return_dict is not None else self.config.use_return_dict |
| 1874 | ) |
| 1875 | |
| 1876 | # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) |
| 1877 | outputs = self.model( |
| 1878 | input_ids=input_ids, |
| 1879 | attention_mask=attention_mask, |
| 1880 | position_ids=position_ids, |
| 1881 | past_key_values=past_key_values, |
| 1882 | inputs_embeds=inputs_embeds, |
| 1883 | use_cache=use_cache, |
| 1884 | output_attentions=output_attentions, |
| 1885 | output_hidden_states=output_hidden_states, |
| 1886 | return_dict=return_dict, |
| 1887 | cache_position=cache_position |
| 1888 | ) |
| 1889 | |
| 1890 | hidden_states = outputs[0] |
| 1891 | logits = self.lm_head(hidden_states) |
| 1892 | logits = logits.float() |
| 1893 | |
| 1894 | loss = None |
| 1895 | if labels is not None: |
| 1896 | # Shift so that tokens < n predict n |
| 1897 | shift_logits = logits[..., :-1, :].contiguous() |
| 1898 | shift_labels = labels[..., 1:].contiguous() |
| 1899 | # Flatten the tokens |
| 1900 | loss_fct = CrossEntropyLoss() |
| 1901 | shift_logits = shift_logits.view(-1, self.config.vocab_size) |
| 1902 | shift_labels = shift_labels.view(-1) |
| 1903 | # Enable model parallelism |
| 1904 | shift_labels = shift_labels.to(shift_logits.device) |
| 1905 | loss = loss_fct(shift_logits, shift_labels) |
| 1906 | |
| 1907 | if not return_dict: |
| 1908 | output = (logits,) + outputs[1:] |
| 1909 | return (loss,) + output if loss is not None else output |
| 1910 | |
| 1911 | return CausalLMOutputWithPast( |
| 1912 | loss=loss, |
| 1913 | logits=logits, |
| 1914 | past_key_values=outputs.past_key_values, |
| 1915 | hidden_states=outputs.hidden_states, |
| 1916 | attentions=outputs.attentions, |
| 1917 | ) |
| 1918 | |
| 1919 | def prepare_inputs_for_generation( |
| 1920 | self, |
| 1921 | input_ids, |
| 1922 | past_key_values=None, |
| 1923 | attention_mask=None, |
| 1924 | inputs_embeds=None, |
| 1925 | **kwargs, |
| 1926 | ): |
| 1927 | past_length = 0 |
| 1928 | if past_key_values is not None: |
| 1929 | if isinstance(past_key_values, Cache): |
| 1930 | cache_length = past_key_values.get_seq_length() |
| 1931 | past_length = past_key_values.get_seq_length() |
| 1932 | max_cache_length = getattr(past_key_values, 'get_max_length', lambda: None)() |
| 1933 | else: |
| 1934 | cache_length = past_length = past_key_values[0][0].shape[2] |
| 1935 | max_cache_length = None |
| 1936 | |
| 1937 | # Keep only the unprocessed tokens: |
| 1938 | # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where |
| 1939 | # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as |
| 1940 | # input) |
| 1941 | if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: |
| 1942 | input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):] |
| 1943 | # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard |
| 1944 | # input_ids based on the past_length. |
| 1945 | elif past_length < input_ids.shape[1]: |
| 1946 | input_ids = input_ids[:, past_length:] |
| 1947 | # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. |
| 1948 | |
| 1949 | # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. |
| 1950 | if ( |
| 1951 | max_cache_length is not None |
| 1952 | and attention_mask is not None |
| 1953 | and cache_length + input_ids.shape[1] > max_cache_length |
| 1954 | ): |
| 1955 | attention_mask = attention_mask[:, -max_cache_length:] |
| 1956 | |
| 1957 | position_ids = kwargs.get("position_ids", None) |
| 1958 | if attention_mask is not None and position_ids is None: |
| 1959 | # create position_ids on the fly for batch generation |
| 1960 | position_ids = attention_mask.long().cumsum(-1) - 1 |
| 1961 | position_ids.masked_fill_(attention_mask == 0, 1) |
| 1962 | if past_key_values: |
| 1963 | position_ids = position_ids[:, -input_ids.shape[1]:] |
| 1964 | |
| 1965 | if self.generation_config.cache_implementation == "static": |
| 1966 | # generation with static cache |
| 1967 | cache_position = kwargs.get("cache_position", None) |
| 1968 | if cache_position is None: |
| 1969 | past_length = 0 |
| 1970 | else: |
| 1971 | past_length = cache_position[-1] + 1 |
| 1972 | input_ids = input_ids[:, past_length:] |
| 1973 | position_ids = position_ids[:, past_length:] |
| 1974 | |
| 1975 | # TODO @gante we should only keep a `cache_position` in generate, and do +=1. |
| 1976 | # same goes for position ids. Could also help with continued generation. |
| 1977 | cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device) |
| 1978 | |
| 1979 | # if `inputs_embeds` are passed, we only want to use them in the 1st generation step |
| 1980 | if inputs_embeds is not None and past_key_values is None: |
| 1981 | model_inputs = {"inputs_embeds": inputs_embeds} |
| 1982 | else: |
| 1983 | # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise |
| 1984 | # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114 |
| 1985 | # TODO: use `next_tokens` directly instead. |
| 1986 | model_inputs = {"input_ids": input_ids.contiguous()} |
| 1987 | |
| 1988 | model_inputs.update( |
| 1989 | { |
| 1990 | "position_ids": position_ids.contiguous(), |
| 1991 | "cache_position": cache_position, |
| 1992 | "past_key_values": past_key_values, |
| 1993 | "use_cache": kwargs.get("use_cache"), |
| 1994 | "attention_mask": attention_mask, |
| 1995 | } |
| 1996 | ) |
| 1997 | return model_inputs |
| 1998 | |
| 1999 | @staticmethod |
| 2000 | def _reorder_cache(past_key_values, beam_idx): |
| 2001 | reordered_past = () |
| 2002 | for layer_past in past_key_values: |
| 2003 | reordered_past += ( |
| 2004 | tuple( |
| 2005 | past_state.index_select(0, beam_idx.to(past_state.device)) |
| 2006 | for past_state in layer_past |
| 2007 | ), |
| 2008 | ) |
| 2009 | return reordered_past |
| 2010 | |
| 2011 | |
| 2012 | @add_start_docstrings( |
| 2013 | """ |
| 2014 | The DeepseekV2 Model transformer with a sequence classification head on top (linear layer). |
| 2015 | |
| 2016 | [`DeepseekV2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models |
| 2017 | (e.g. GPT-2) do. |
| 2018 | |
| 2019 | Since it does classification on the last token, it requires to know the position of the last token. If a |
| 2020 | `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If |
| 2021 | no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the |
| 2022 | padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in |
| 2023 | each row of the batch). |
| 2024 | """, |
| 2025 | DeepseekV2_START_DOCSTRING, |
| 2026 | ) |
| 2027 | class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel): |
| 2028 | def __init__(self, config): |
| 2029 | super().__init__(config) |
| 2030 | self.num_labels = config.num_labels |
| 2031 | self.model = DeepseekV2Model(config) |
| 2032 | self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) |
| 2033 | |
| 2034 | # Initialize weights and apply final processing |
| 2035 | self.post_init() |
| 2036 | |
| 2037 | def get_input_embeddings(self): |
| 2038 | return self.model.embed_tokens |
| 2039 | |
| 2040 | def set_input_embeddings(self, value): |
| 2041 | self.model.embed_tokens = value |
| 2042 | |
| 2043 | @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING) |
| 2044 | def forward( |
| 2045 | self, |
| 2046 | input_ids: torch.LongTensor = None, |
| 2047 | attention_mask: Optional[torch.Tensor] = None, |
| 2048 | position_ids: Optional[torch.LongTensor] = None, |
| 2049 | past_key_values: Optional[List[torch.FloatTensor]] = None, |
| 2050 | inputs_embeds: Optional[torch.FloatTensor] = None, |
| 2051 | labels: Optional[torch.LongTensor] = None, |
| 2052 | use_cache: Optional[bool] = None, |
| 2053 | output_attentions: Optional[bool] = None, |
| 2054 | output_hidden_states: Optional[bool] = None, |
| 2055 | return_dict: Optional[bool] = None, |
| 2056 | ) -> Union[Tuple, SequenceClassifierOutputWithPast]: |
| 2057 | r""" |
| 2058 | labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
| 2059 | Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers., |
| 2060 | config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
| 2061 | `config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
| 2062 | """ |
| 2063 | return_dict = ( |
| 2064 | return_dict if return_dict is not None else self.config.use_return_dict |
| 2065 | ) |
| 2066 | |
| 2067 | transformer_outputs = self.model( |
| 2068 | input_ids, |
| 2069 | attention_mask=attention_mask, |
| 2070 | position_ids=position_ids, |
| 2071 | past_key_values=past_key_values, |
| 2072 | inputs_embeds=inputs_embeds, |
| 2073 | use_cache=use_cache, |
| 2074 | output_attentions=output_attentions, |
| 2075 | output_hidden_states=output_hidden_states, |
| 2076 | return_dict=return_dict, |
| 2077 | ) |
| 2078 | hidden_states = transformer_outputs[0] |
| 2079 | logits = self.score(hidden_states) |
| 2080 | |
| 2081 | if input_ids is not None: |
| 2082 | batch_size = input_ids.shape[0] |
| 2083 | else: |
| 2084 | batch_size = inputs_embeds.shape[0] |
| 2085 | |
| 2086 | if self.config.pad_token_id is None and batch_size != 1: |
| 2087 | raise ValueError( |
| 2088 | "Cannot handle batch sizes > 1 if no padding token is defined." |
| 2089 | ) |
| 2090 | if self.config.pad_token_id is None: |
| 2091 | sequence_lengths = -1 |
| 2092 | else: |
| 2093 | if input_ids is not None: |
| 2094 | sequence_lengths = ( |
| 2095 | torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 |
| 2096 | ).to(logits.device) |
| 2097 | else: |
| 2098 | sequence_lengths = -1 |
| 2099 | |
| 2100 | pooled_logits = logits[ |
| 2101 | torch.arange(batch_size, device=logits.device), sequence_lengths |
| 2102 | ] |
| 2103 | |
| 2104 | loss = None |
| 2105 | if labels is not None: |
| 2106 | labels = labels.to(logits.device) |
| 2107 | if self.config.problem_type is None: |
| 2108 | if self.num_labels == 1: |
| 2109 | self.config.problem_type = "regression" |
| 2110 | elif self.num_labels > 1 and ( |
| 2111 | labels.dtype == torch.long or labels.dtype == torch.int |
| 2112 | ): |
| 2113 | self.config.problem_type = "single_label_classification" |
| 2114 | else: |
| 2115 | self.config.problem_type = "multi_label_classification" |
| 2116 | |
| 2117 | if self.config.problem_type == "regression": |
| 2118 | loss_fct = MSELoss() |
| 2119 | if self.num_labels == 1: |
| 2120 | loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) |
| 2121 | else: |
| 2122 | loss = loss_fct(pooled_logits, labels) |
| 2123 | elif self.config.problem_type == "single_label_classification": |
| 2124 | loss_fct = CrossEntropyLoss() |
| 2125 | loss = loss_fct( |
| 2126 | pooled_logits.view(-1, self.num_labels), labels.view(-1) |
| 2127 | ) |
| 2128 | elif self.config.problem_type == "multi_label_classification": |
| 2129 | loss_fct = BCEWithLogitsLoss() |
| 2130 | loss = loss_fct(pooled_logits, labels) |
| 2131 | if not return_dict: |
| 2132 | output = (pooled_logits,) + transformer_outputs[1:] |
| 2133 | return ((loss,) + output) if loss is not None else output |
| 2134 | |
| 2135 | return SequenceClassifierOutputWithPast( |
| 2136 | loss=loss, |
| 2137 | logits=pooled_logits, |
| 2138 | past_key_values=transformer_outputs.past_key_values, |
| 2139 | hidden_states=transformer_outputs.hidden_states, |
| 2140 | attentions=transformer_outputs.attentions, |
| 2141 | ) |
| 2142 | |