modeling_phi3_v.py
| 1 | # coding=utf-8 |
| 2 | # Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. |
| 3 | # |
| 4 | # Licensed under the Apache License, Version 2.0 (the "License"); |
| 5 | # you may not use this file except in compliance with the License. |
| 6 | # You may obtain a copy of the License at |
| 7 | # |
| 8 | # http://www.apache.org/licenses/LICENSE-2.0 |
| 9 | # |
| 10 | # Unless required by applicable law or agreed to in writing, software |
| 11 | # distributed under the License is distributed on an "AS IS" BASIS, |
| 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 13 | # See the License for the specific language governing permissions and |
| 14 | # limitations under the License. |
| 15 | |
| 16 | """ PyTorch Phi-3-V model.""" |
| 17 | |
| 18 | import inspect |
| 19 | import math |
| 20 | import warnings |
| 21 | from typing import List, Optional, Tuple, Union |
| 22 | |
| 23 | import torch |
| 24 | import torch.nn.functional as F |
| 25 | import torch.utils.checkpoint |
| 26 | from torch import nn |
| 27 | from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
| 28 | |
| 29 | from transformers.activations import ACT2FN |
| 30 | from transformers.cache_utils import Cache, DynamicCache |
| 31 | from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask |
| 32 | from transformers.modeling_outputs import ( |
| 33 | BaseModelOutputWithPast, |
| 34 | CausalLMOutputWithPast, |
| 35 | SequenceClassifierOutputWithPast, |
| 36 | TokenClassifierOutput, |
| 37 | ) |
| 38 | from transformers.modeling_utils import PreTrainedModel |
| 39 | from transformers.utils import ( |
| 40 | add_code_sample_docstrings, |
| 41 | add_start_docstrings, |
| 42 | add_start_docstrings_to_model_forward, |
| 43 | is_flash_attn_greater_or_equal_2_10, |
| 44 | logging, |
| 45 | replace_return_docstrings, |
| 46 | ) |
| 47 | from .configuration_phi3_v import Phi3VConfig |
| 48 | |
| 49 | try: |
| 50 | from flash_attn import flash_attn_func, flash_attn_varlen_func |
| 51 | from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa |
| 52 | |
| 53 | _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) |
| 54 | except ImportError: |
| 55 | pass |
| 56 | |
| 57 | import torch |
| 58 | from torch import nn |
| 59 | from transformers import CLIPVisionConfig, CLIPVisionModel, PretrainedConfig |
| 60 | from transformers.models.clip.modeling_clip import CLIPAttention |
| 61 | from transformers.utils import logging |
| 62 | |
| 63 | logger = logging.get_logger(__name__) |
| 64 | |
| 65 | |
| 66 | MAX_INPUT_ID = int(1e9) |
| 67 | |
| 68 | CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig( |
| 69 | attention_dropout=0.0, |
| 70 | dropout=0.0, |
| 71 | hidden_act="quick_gelu", |
| 72 | hidden_size=1024, |
| 73 | image_size=336, |
| 74 | initializer_factor=1.0, |
| 75 | initializer_range=0.02, |
| 76 | intermediate_size=4096, |
| 77 | layer_norm_eps=1e-05, |
| 78 | num_attention_heads=16, |
| 79 | num_channels=3, |
| 80 | num_hidden_layers=24, |
| 81 | patch_size=14, |
| 82 | projection_dim=768 |
| 83 | ) |
| 84 | |
| 85 | class CLIPAttentionFA2(CLIPAttention): |
| 86 | """Add flash attention 2 to CLIPAttention. (This is only used in the vision encoder)""" |
| 87 | |
| 88 | def forward(self, |
| 89 | hidden_states, |
| 90 | attention_mask=None, |
| 91 | causal_attention_mask=None, |
| 92 | output_attentions=False, |
| 93 | ): |
| 94 | """Input shape: Batch x Time x Channel""" |
| 95 | |
| 96 | assert attention_mask is None, "CLIPAttentionFA2 does not support attention_mask" |
| 97 | assert causal_attention_mask is None, "CLIPAttentionFA2 does not support causal_attention_mask" |
| 98 | assert output_attentions is False, "CLIPAttentionFA2 does not support output_attentions" |
| 99 | |
| 100 | bsz, tgt_len, embed_dim = hidden_states.size() |
| 101 | query_states = self.q_proj(hidden_states).reshape(bsz, tgt_len, self.num_heads, self.head_dim) |
| 102 | key_states = self.k_proj(hidden_states).reshape(bsz, tgt_len, self.num_heads, self.head_dim) |
| 103 | value_states = self.v_proj(hidden_states).reshape(bsz, tgt_len, self.num_heads, self.head_dim) |
| 104 | |
| 105 | attn_output = flash_attn_func( |
| 106 | query_states, |
| 107 | key_states, |
| 108 | value_states, |
| 109 | dropout_p=self.dropout if self.training else 0.0, |
| 110 | softmax_scale=self.scale, |
| 111 | causal=False, |
| 112 | ).reshape(bsz, tgt_len, embed_dim) |
| 113 | |
| 114 | attn_output = self.out_proj(attn_output) |
| 115 | return attn_output, None |
| 116 | |
| 117 | |
| 118 | class Phi3ImageEmbedding(nn.Module): |
| 119 | """Phi3 Image embedding.""" |
| 120 | |
| 121 | def __init__(self, config: PretrainedConfig, wte=None, **kwargs) -> None: |
| 122 | super().__init__() |
| 123 | |
| 124 | # n_embed or hidden_size |
| 125 | hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size |
| 126 | if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'): |
| 127 | embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop |
| 128 | self.drop = nn.Dropout(embd_drop) |
| 129 | else: |
| 130 | self.drop = None |
| 131 | |
| 132 | self.wte = wte |
| 133 | |
| 134 | if isinstance(config.img_processor, dict) and config.img_processor.get('name', None) == 'clip_vision_model': |
| 135 | assert 'model_name' in config.img_processor, 'model_name must be provided for CLIPVisionModel' |
| 136 | assert 'image_dim_out' in config.img_processor, 'image_dim_out must be provided for CLIPVisionModel' |
| 137 | assert 'num_img_tokens' in config.img_processor, 'num_img_tokens must be provided for CLIPVisionModel' |
| 138 | assert config.img_processor['model_name'] == 'openai/clip-vit-large-patch14-336' |
| 139 | clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG |
| 140 | self.img_processor = CLIPVisionModel(clip_config) |
| 141 | image_dim_out = config.img_processor['image_dim_out'] |
| 142 | self.num_img_tokens = config.img_processor['num_img_tokens'] |
| 143 | |
| 144 | # FA2 in CLIP |
| 145 | if config._attn_implementation == 'flash_attention_2': |
| 146 | for layer in self.img_processor.vision_model.encoder.layers: |
| 147 | clip_fa2 = CLIPAttentionFA2(clip_config) |
| 148 | del layer.self_attn |
| 149 | layer.self_attn = clip_fa2 |
| 150 | else: |
| 151 | raise NotImplementedError(f'img_processor = {config.img_processor}, not implemented') |
| 152 | |
| 153 | self.image_dim_out = image_dim_out |
| 154 | self.img_sizes = None |
| 155 | |
| 156 | # global_gn and sub_gn for hd transform, serves as line separator |
| 157 | self.use_hd_transform = kwargs.get('use_hd_transform', False) |
| 158 | self.with_learnable_separator = kwargs.get('with_learnable_separator', False) |
| 159 | self.hd_transform_order = kwargs.get('hd_transform_order', 'glb_sub') |
| 160 | # with_hd_transform and with_learnable_separator should have same value |
| 161 | assert self.use_hd_transform == self.with_learnable_separator, 'use_hd_transform and with_learnable_separator should have same value' |
| 162 | if self.with_learnable_separator: |
| 163 | assert self.use_hd_transform, 'learnable separator is only for hd transform' |
| 164 | # 1024 * 4, merge spatial to channel dimension |
| 165 | self.glb_GN = nn.Parameter(torch.zeros([1, 1, self.image_dim_out * 4])) |
| 166 | self.sub_GN = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out * 4])) |
| 167 | logger.info(f'learnable separator enabled for hd transform, hd_transform_order = {self.hd_transform_order}') |
| 168 | |
| 169 | projection_cls = kwargs.get('projection_cls', 'linear') |
| 170 | if projection_cls == 'linear': |
| 171 | self.img_projection = nn.Linear(image_dim_out, hidden_size) |
| 172 | elif projection_cls == 'mlp' and self.use_hd_transform: |
| 173 | dim_projection = hidden_size |
| 174 | depth = 2 |
| 175 | layers = [nn.Linear(image_dim_out * 4, dim_projection)] |
| 176 | for _ in range(1, depth): |
| 177 | layers.extend([nn.GELU(), |
| 178 | nn.Linear(dim_projection, dim_projection)]) |
| 179 | self.img_projection = nn.Sequential(*layers) |
| 180 | elif projection_cls == 'mlp': |
| 181 | dim_projection = hidden_size |
| 182 | depth = 2 |
| 183 | layers = [nn.Linear(image_dim_out, dim_projection)] |
| 184 | for _ in range(1, depth): |
| 185 | layers.extend([nn.GELU(), |
| 186 | nn.Linear(dim_projection, dim_projection)]) |
| 187 | self.img_projection = nn.Sequential(*layers) |
| 188 | else: |
| 189 | raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented') |
| 190 | |
| 191 | self.vocab_size = config.vocab_size |
| 192 | self.img_features = None |
| 193 | |
| 194 | if isinstance(config.img_processor, dict): |
| 195 | self.layer_idx = config.img_processor.get('layer_idx', -2) |
| 196 | self.type_feature = config.img_processor.get('type_feature', 'patch') |
| 197 | else: |
| 198 | self.layer_idx = -2 |
| 199 | self.type_feature = 'patch' |
| 200 | |
| 201 | |
| 202 | def set_img_features(self, img_features: torch.FloatTensor) -> None: |
| 203 | self.img_features = img_features |
| 204 | |
| 205 | def set_img_sizes(self, img_sizes: torch.LongTensor) -> None: |
| 206 | self.img_sizes = img_sizes |
| 207 | |
| 208 | def get_img_features(self, img_embeds: torch.FloatTensor) -> torch.FloatTensor: |
| 209 | LAYER_IDX = self.layer_idx |
| 210 | TYPE_FEATURE = self.type_feature |
| 211 | |
| 212 | img_processor_output = self.img_processor(img_embeds, output_hidden_states=True) |
| 213 | img_feature = img_processor_output.hidden_states[LAYER_IDX] |
| 214 | |
| 215 | if TYPE_FEATURE == "patch": |
| 216 | patch_feature = img_feature[:, 1:] |
| 217 | return patch_feature |
| 218 | |
| 219 | raise NotImplementedError |
| 220 | |
| 221 | def forward( |
| 222 | self, input_ids: torch.LongTensor, pixel_values: torch.FloatTensor, image_sizes=None |
| 223 | ) -> torch.FloatTensor: |
| 224 | input_shape = input_ids.size() |
| 225 | input_ids = input_ids.view(-1, input_shape[-1]) |
| 226 | |
| 227 | # positions for image tokens |
| 228 | positions = torch.nonzero((input_ids < 0) & (input_ids > -MAX_INPUT_ID), as_tuple=True) |
| 229 | has_image = len(positions[0].tolist()) > 0 |
| 230 | input_ids = input_ids.clamp_min(0).clamp_max(self.vocab_size).detach() |
| 231 | hidden_states = self.wte(input_ids) |
| 232 | |
| 233 | if has_image: |
| 234 | assert self.use_hd_transform |
| 235 | num_images, num_crops, c, h, w = pixel_values.shape |
| 236 | assert c == 3 and h == w == 336 |
| 237 | img_features = self.get_img_features(pixel_values.flatten(0, 1)).reshape( |
| 238 | num_images, num_crops, -1, self.image_dim_out |
| 239 | ) |
| 240 | image_features_proj = self.hd_feature_transform(img_features, image_sizes) |
| 241 | hidden_states = hidden_states.index_put( |
| 242 | positions, image_features_proj, accumulate=False |
| 243 | ) |
| 244 | |
| 245 | if self.drop is not None: |
| 246 | hidden_states = self.drop(hidden_states) |
| 247 | |
| 248 | return hidden_states |
| 249 | |
| 250 | def hd_feature_transform(self, image_features, image_sizes): |
| 251 | """ |
| 252 | image_features: (num_images, num_crops+1, 24*24, 1024) |
| 253 | """ |
| 254 | assert ( |
| 255 | self.hd_transform_order == 'sub_glb' |
| 256 | ), f'hd_transform_order `{self.hd_transform_order}` not implemented' |
| 257 | if isinstance(self.img_projection, nn.Sequential): |
| 258 | target_device = self.img_projection[0].bias.device |
| 259 | target_dtype = self.img_projection[0].bias.dtype |
| 260 | else: # It's a single nn.Linear layer |
| 261 | target_device = self.img_projection.bias.device |
| 262 | target_dtype = self.img_projection.bias.dtype |
| 263 | |
| 264 | global_image_features = image_features[:, 0] # (num_images, 24*24, 1024) |
| 265 | # global feature can be viewed as a special HD case with num_crops 1x1 |
| 266 | global_image_features_hd = self.reshape_hd_patches_2x2merge(global_image_features, 1, 1) |
| 267 | global_image_features_hd_newline = self.add_image_newline(global_image_features_hd) |
| 268 | |
| 269 | all_image_embeddings = [] |
| 270 | # need a for loop to process each image because of different image sizes |
| 271 | # (patch arrangement is different for each image) |
| 272 | for i, img_size in enumerate(image_sizes): |
| 273 | h, w = img_size |
| 274 | h_crop = h // 336 |
| 275 | w_crop = w // 336 |
| 276 | num_crops = h_crop * w_crop |
| 277 | |
| 278 | # NOTE: real num_crops is padded |
| 279 | # (num_crops, 24*24, 1024) |
| 280 | sub_image_features = image_features[i, 1 : 1 + num_crops] |
| 281 | sub_image_features_hd = self.reshape_hd_patches_2x2merge( |
| 282 | sub_image_features, h_crop, w_crop |
| 283 | ) |
| 284 | sub_image_features_hd_newline = self.add_image_newline(sub_image_features_hd) |
| 285 | |
| 286 | # [sub features, separator, global features] |
| 287 | all_image_embeddings.extend( |
| 288 | [ |
| 289 | sub_image_features_hd_newline.squeeze(0), # (h_crop*12*(w_crop*12+1), 4096) |
| 290 | self.glb_GN.squeeze(0), |
| 291 | global_image_features_hd_newline[i], |
| 292 | ] |
| 293 | ) |
| 294 | |
| 295 | image_features_proj = self.img_projection( |
| 296 | torch.cat(all_image_embeddings, dim=0).to(target_device).to(target_dtype) |
| 297 | ) |
| 298 | |
| 299 | return image_features_proj |
| 300 | |
| 301 | def reshape_hd_patches_2x2merge(self, image_features, h_crop, w_crop): |
| 302 | """ |
| 303 | image_features: (num_images*num_crops, 24*24, 1024) |
| 304 | output: (num_images, h_crop*12, w_crop*12, 4096), h_crop*w_crop == num_crops |
| 305 | """ |
| 306 | N, L, C = image_features.shape |
| 307 | assert L == 24 * 24 and C == 1024 and N % (h_crop * w_crop) == 0 |
| 308 | num_images = N // (h_crop * w_crop) |
| 309 | H = int(L**0.5) |
| 310 | image_features_hd = ( |
| 311 | image_features.reshape(N, H, H, C) # N, 24, 24, 1024 |
| 312 | .reshape(N, H // 2, 2, H // 2, 2, C) # N, 12, 2, 12, 2, 1024 |
| 313 | .permute(0, 1, 3, 2, 4, 5) # N, 12, 12, 2, 2, 1024 |
| 314 | .reshape(N, -1, 4 * C) # N, 144, 4096 |
| 315 | .reshape( |
| 316 | num_images, h_crop, w_crop, H // 2, H // 2, -1 |
| 317 | ) # n_img, h_crop, w_crop, 12, 12, 4096 |
| 318 | .permute(0, 1, 3, 2, 4, 5) # n_img, h_crop, 12, w_crop, 12, 4096 |
| 319 | .reshape( |
| 320 | num_images, h_crop * H // 2, w_crop * H // 2, 4 * C |
| 321 | ) # n_img, h_crop*12, w_crop*12, 4096 |
| 322 | ) |
| 323 | |
| 324 | # alternative implementation using einops |
| 325 | # from einops import rearrange |
| 326 | # image_features_nhwc = rearrange( |
| 327 | # image_features, |
| 328 | # 'N (H W) c -> N H W c', |
| 329 | # H=H, |
| 330 | # W=H, |
| 331 | # ) |
| 332 | # image_features_2x2merge = rearrange( |
| 333 | # image_features_nhwc, |
| 334 | # 'N (h h_pool) (w w_pool) c -> N h w (h_pool w_pool c)', |
| 335 | # h_pool=2, |
| 336 | # w_pool=2, |
| 337 | # ) |
| 338 | # image_features_hd = rearrange( |
| 339 | # image_features_2x2merge, |
| 340 | # '(n_img h_crop w_crop) h w C -> n_img (h_crop h) (w_crop w) C', |
| 341 | # h_crop=h_crop, |
| 342 | # w_crop=w_crop, |
| 343 | # ) |
| 344 | |
| 345 | return image_features_hd |
| 346 | |
| 347 | def add_image_newline(self, image_features_hd): |
| 348 | """ |
| 349 | image_features_hd: (num_images, h_crop*12, w_crop*12, 4096) |
| 350 | output: (num_images, (h_crop*12) * (w_crop*12+1), 4096) |
| 351 | """ |
| 352 | num_images, h, w, hid_dim = image_features_hd.shape |
| 353 | # add the newline token to the HD image feature patches |
| 354 | newline_embeddings = self.sub_GN.expand(num_images, h, -1, -1) # (n_img, h, 1, hid_dim) |
| 355 | image_features_hd_newline = torch.cat( |
| 356 | [image_features_hd, newline_embeddings], dim=2 |
| 357 | ).reshape(num_images, -1, hid_dim) |
| 358 | return image_features_hd_newline |
| 359 | |
| 360 | |
| 361 | logger = logging.get_logger(__name__) |
| 362 | |
| 363 | _CHECKPOINT_FOR_DOC = "microsoft/Phi-3-vision-128k-instruct" |
| 364 | _CONFIG_FOR_DOC = "Phi3VConfig" |
| 365 | |
| 366 | PHI3V_PRETRAINED_MODEL_ARCHIVE_LIST = [ |
| 367 | "microsoft/Phi-3-vision-128k-instruct", |
| 368 | # See all Phi-3 models at https://huggingface.co/models?filter=Phi-3 |
| 369 | ] |
| 370 | |
| 371 | |
| 372 | # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Phi3 |
| 373 | class Phi3RMSNorm(nn.Module): |
| 374 | def __init__(self, hidden_size, eps=1e-6): |
| 375 | """ |
| 376 | Phi3RMSNorm is equivalent to T5LayerNorm |
| 377 | """ |
| 378 | super().__init__() |
| 379 | self.weight = nn.Parameter(torch.ones(hidden_size)) |
| 380 | self.variance_epsilon = eps |
| 381 | |
| 382 | def forward(self, hidden_states): |
| 383 | input_dtype = hidden_states.dtype |
| 384 | hidden_states = hidden_states.to(torch.float32) |
| 385 | variance = hidden_states.pow(2).mean(-1, keepdim=True) |
| 386 | hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) |
| 387 | return self.weight * hidden_states.to(input_dtype) |
| 388 | |
| 389 | |
| 390 | # Copied from transformers.models.llama.modeling_llama._get_unpad_data |
| 391 | def _get_unpad_data(attention_mask): |
| 392 | seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) |
| 393 | indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() |
| 394 | max_seqlen_in_batch = seqlens_in_batch.max().item() |
| 395 | cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) |
| 396 | return ( |
| 397 | indices, |
| 398 | cu_seqlens, |
| 399 | max_seqlen_in_batch, |
| 400 | ) |
| 401 | |
| 402 | |
| 403 | # Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with gemma->phi3, Gemma->Phi3 |
| 404 | class Phi3RotaryEmbedding(nn.Module): |
| 405 | def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): |
| 406 | super().__init__() |
| 407 | |
| 408 | self.dim = dim |
| 409 | self.max_position_embeddings = max_position_embeddings |
| 410 | self.base = base |
| 411 | self.register_buffer("inv_freq", None, persistent=False) |
| 412 | |
| 413 | @torch.no_grad() |
| 414 | def forward(self, x, position_ids, seq_len=None): |
| 415 | # x: [bs, num_attention_heads, seq_len, head_size] |
| 416 | if self.inv_freq is None: |
| 417 | self.inv_freq = 1.0 / ( |
| 418 | self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim) |
| 419 | ) |
| 420 | inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) |
| 421 | position_ids_expanded = position_ids[:, None, :].float() |
| 422 | # Force float32 since bfloat16 loses precision on long contexts |
| 423 | # See https://github.com/huggingface/transformers/pull/29285 |
| 424 | device_type = x.device.type |
| 425 | device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" |
| 426 | with torch.autocast(device_type=device_type, enabled=False): |
| 427 | freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) |
| 428 | emb = torch.cat((freqs, freqs), dim=-1) |
| 429 | cos = emb.cos() |
| 430 | sin = emb.sin() |
| 431 | return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) |
| 432 | |
| 433 | |
| 434 | class Phi3SuScaledRotaryEmbedding(Phi3RotaryEmbedding): |
| 435 | def __init__(self, dim, config, device=None): |
| 436 | super().__init__(dim, config.max_position_embeddings, config.rope_theta, device) |
| 437 | |
| 438 | self.short_factor = config.rope_scaling["short_factor"] |
| 439 | self.long_factor = config.rope_scaling["long_factor"] |
| 440 | self.original_max_position_embeddings = config.original_max_position_embeddings |
| 441 | |
| 442 | @torch.no_grad() |
| 443 | def forward(self, x, position_ids, seq_len=None): |
| 444 | seq_len = seq_len or torch.max(position_ids) + 1 |
| 445 | if seq_len > self.original_max_position_embeddings: |
| 446 | ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device) |
| 447 | else: |
| 448 | ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device) |
| 449 | |
| 450 | inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim |
| 451 | self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape) |
| 452 | |
| 453 | inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) |
| 454 | position_ids_expanded = position_ids[:, None, :].float() |
| 455 | |
| 456 | # Force float32 since bfloat16 loses precision on long contexts |
| 457 | # See https://github.com/huggingface/transformers/pull/29285 |
| 458 | device_type = x.device.type |
| 459 | device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" |
| 460 | with torch.autocast(device_type=device_type, enabled=False): |
| 461 | freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) |
| 462 | emb = torch.cat((freqs, freqs), dim=-1) |
| 463 | |
| 464 | scale = self.max_position_embeddings / self.original_max_position_embeddings |
| 465 | if scale <= 1.0: |
| 466 | scaling_factor = 1.0 |
| 467 | else: |
| 468 | scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings)) |
| 469 | |
| 470 | cos = emb.cos() * scaling_factor |
| 471 | sin = emb.sin() * scaling_factor |
| 472 | return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) |
| 473 | |
| 474 | |
| 475 | class Phi3YarnScaledRotaryEmbedding(Phi3RotaryEmbedding): |
| 476 | def __init__(self, dim, config, device=None): |
| 477 | super().__init__(dim, config.max_position_embeddings, config.rope_theta, device) |
| 478 | |
| 479 | self.short_factor = config.rope_scaling["short_factor"] |
| 480 | self.long_factor = config.rope_scaling["long_factor"] |
| 481 | self.original_max_position_embeddings = config.original_max_position_embeddings |
| 482 | |
| 483 | @torch.no_grad() |
| 484 | def forward(self, x, position_ids, seq_len=None): |
| 485 | seq_len = torch.max(position_ids) + 1 |
| 486 | if seq_len > self.original_max_position_embeddings: |
| 487 | ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device) |
| 488 | else: |
| 489 | ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device) |
| 490 | |
| 491 | inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim |
| 492 | self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape) |
| 493 | |
| 494 | inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) |
| 495 | position_ids_expanded = position_ids[:, None, :].float() |
| 496 | |
| 497 | # Force float32 since bfloat16 loses precision on long contexts |
| 498 | # See https://github.com/huggingface/transformers/pull/29285 |
| 499 | device_type = x.device.type |
| 500 | device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" |
| 501 | with torch.autocast(device_type=device_type, enabled=False): |
| 502 | freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) |
| 503 | emb = torch.cat((freqs, freqs), dim=-1) |
| 504 | |
| 505 | scale = self.max_position_embeddings / self.original_max_position_embeddings |
| 506 | if scale <= 1.0: |
| 507 | scaling_factor = 1.0 |
| 508 | else: |
| 509 | scaling_factor = 0.1 * math.log(scale) + 1.0 |
| 510 | |
| 511 | cos = emb.cos() * scaling_factor |
| 512 | sin = emb.sin() * scaling_factor |
| 513 | return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) |
| 514 | |
| 515 | |
| 516 | # Copied from transformers.models.llama.modeling_llama.rotate_half |
| 517 | def rotate_half(x): |
| 518 | """Rotates half the hidden dims of the input.""" |
| 519 | x1 = x[..., : x.shape[-1] // 2] |
| 520 | x2 = x[..., x.shape[-1] // 2 :] |
| 521 | return torch.cat((-x2, x1), dim=-1) |
| 522 | |
| 523 | |
| 524 | # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb |
| 525 | def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): |
| 526 | """Applies Rotary Position Embedding to the query and key tensors. |
| 527 | |
| 528 | Args: |
| 529 | q (`torch.Tensor`): The query tensor. |
| 530 | k (`torch.Tensor`): The key tensor. |
| 531 | cos (`torch.Tensor`): The cosine part of the rotary embedding. |
| 532 | sin (`torch.Tensor`): The sine part of the rotary embedding. |
| 533 | position_ids (`torch.Tensor`, *optional*): |
| 534 | Deprecated and unused. |
| 535 | unsqueeze_dim (`int`, *optional*, defaults to 1): |
| 536 | The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and |
| 537 | sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note |
| 538 | that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and |
| 539 | k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes |
| 540 | cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have |
| 541 | the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. |
| 542 | Returns: |
| 543 | `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. |
| 544 | """ |
| 545 | cos = cos.unsqueeze(unsqueeze_dim) |
| 546 | sin = sin.unsqueeze(unsqueeze_dim) |
| 547 | q_embed = (q * cos) + (rotate_half(q) * sin) |
| 548 | k_embed = (k * cos) + (rotate_half(k) * sin) |
| 549 | return q_embed, k_embed |
| 550 | |
| 551 | |
| 552 | class Phi3MLP(nn.Module): |
| 553 | def __init__(self, config): |
| 554 | super().__init__() |
| 555 | |
| 556 | self.config = config |
| 557 | self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False) |
| 558 | self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) |
| 559 | |
| 560 | self.activation_fn = ACT2FN[config.hidden_act] |
| 561 | |
| 562 | def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: |
| 563 | up_states = self.gate_up_proj(hidden_states) |
| 564 | |
| 565 | gate, up_states = up_states.chunk(2, dim=-1) |
| 566 | up_states = up_states * self.activation_fn(gate) |
| 567 | |
| 568 | return self.down_proj(up_states) |
| 569 | |
| 570 | |
| 571 | # Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi |
| 572 | def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: |
| 573 | """ |
| 574 | This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, |
| 575 | num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) |
| 576 | """ |
| 577 | batch, num_key_value_heads, slen, head_dim = hidden_states.shape |
| 578 | if n_rep == 1: |
| 579 | return hidden_states |
| 580 | hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) |
| 581 | return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) |
| 582 | |
| 583 | |
| 584 | class Phi3Attention(nn.Module): |
| 585 | """Multi-headed attention from 'Attention Is All You Need' paper""" |
| 586 | |
| 587 | def __init__(self, config: Phi3VConfig, layer_idx: Optional[int] = None): |
| 588 | super().__init__() |
| 589 | self.config = config |
| 590 | self.layer_idx = layer_idx |
| 591 | if layer_idx is None: |
| 592 | logger.warning_once( |
| 593 | f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " |
| 594 | "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " |
| 595 | "when creating this class." |
| 596 | ) |
| 597 | |
| 598 | self.attention_dropout = config.attention_dropout |
| 599 | self.hidden_size = config.hidden_size |
| 600 | self.num_heads = config.num_attention_heads |
| 601 | self.head_dim = self.hidden_size // self.num_heads |
| 602 | self.num_key_value_heads = config.num_key_value_heads |
| 603 | self.num_key_value_groups = self.num_heads // self.num_key_value_heads |
| 604 | self.max_position_embeddings = config.max_position_embeddings |
| 605 | self.original_max_position_embeddings = config.original_max_position_embeddings |
| 606 | self.rope_theta = config.rope_theta |
| 607 | self.rope_scaling = config.rope_scaling |
| 608 | self.is_causal = True |
| 609 | |
| 610 | if (self.head_dim * self.num_heads) != self.hidden_size: |
| 611 | raise ValueError( |
| 612 | f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" |
| 613 | f" and `num_heads`: {self.num_heads})." |
| 614 | ) |
| 615 | |
| 616 | op_size = self.num_heads * self.head_dim + 2 * (self.num_key_value_heads * self.head_dim) |
| 617 | self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) |
| 618 | self.qkv_proj = nn.Linear(self.hidden_size, op_size, bias=False) |
| 619 | self._init_rope() |
| 620 | |
| 621 | def _init_rope(self): |
| 622 | if self.rope_scaling is None: |
| 623 | self.rotary_emb = Phi3RotaryEmbedding( |
| 624 | self.head_dim, |
| 625 | max_position_embeddings=self.max_position_embeddings, |
| 626 | base=self.rope_theta, |
| 627 | ) |
| 628 | else: |
| 629 | scaling_type = self.config.rope_scaling["type"] |
| 630 | if scaling_type == "su": |
| 631 | self.rotary_emb = Phi3SuScaledRotaryEmbedding(self.head_dim, self.config) |
| 632 | elif scaling_type == "yarn": |
| 633 | self.rotary_emb = Phi3YarnScaledRotaryEmbedding(self.head_dim, self.config) |
| 634 | else: |
| 635 | raise ValueError(f"Unknown RoPE scaling type {scaling_type}") |
| 636 | |
| 637 | def forward( |
| 638 | self, |
| 639 | hidden_states: torch.Tensor, |
| 640 | attention_mask: Optional[torch.Tensor] = None, |
| 641 | position_ids: Optional[torch.LongTensor] = None, |
| 642 | past_key_value: Optional[Cache] = None, |
| 643 | output_attentions: bool = False, |
| 644 | use_cache: bool = False, |
| 645 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
| 646 | logger.warning_once("You are not running the flash-attention implementation, expect numerical differences.") |
| 647 | |
| 648 | bsz, q_len, _ = hidden_states.size() |
| 649 | |
| 650 | qkv = self.qkv_proj(hidden_states) |
| 651 | query_pos = self.num_heads * self.head_dim |
| 652 | query_states = qkv[..., :query_pos] |
| 653 | key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] |
| 654 | value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] |
| 655 | |
| 656 | query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
| 657 | key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
| 658 | value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
| 659 | |
| 660 | kv_seq_len = key_states.shape[-2] |
| 661 | if past_key_value is not None: |
| 662 | if self.layer_idx is None: |
| 663 | raise ValueError( |
| 664 | f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " |
| 665 | "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " |
| 666 | "with a layer index." |
| 667 | ) |
| 668 | kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) |
| 669 | cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len) |
| 670 | |
| 671 | query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) |
| 672 | |
| 673 | if past_key_value is not None: |
| 674 | cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models |
| 675 | key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) |
| 676 | |
| 677 | # repeat k/v heads if n_kv_heads < n_heads |
| 678 | key_states = repeat_kv(key_states, self.num_key_value_groups) |
| 679 | value_states = repeat_kv(value_states, self.num_key_value_groups) |
| 680 | |
| 681 | attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) |
| 682 | |
| 683 | if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): |
| 684 | raise ValueError( |
| 685 | f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" |
| 686 | f" {attn_weights.size()}" |
| 687 | ) |
| 688 | |
| 689 | if attention_mask is not None: |
| 690 | if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): |
| 691 | raise ValueError( |
| 692 | f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" |
| 693 | ) |
| 694 | attn_weights = attn_weights + attention_mask |
| 695 | |
| 696 | # upcast attention to fp32 |
| 697 | attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype) |
| 698 | attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) |
| 699 | |
| 700 | attn_output = torch.matmul(attn_weights, value_states) |
| 701 | |
| 702 | if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): |
| 703 | raise ValueError( |
| 704 | f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" |
| 705 | f" {attn_output.size()}" |
| 706 | ) |
| 707 | |
| 708 | attn_output = attn_output.transpose(1, 2).contiguous() |
| 709 | attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) |
| 710 | |
| 711 | attn_output = self.o_proj(attn_output) |
| 712 | |
| 713 | if not output_attentions: |
| 714 | attn_weights = None |
| 715 | |
| 716 | return attn_output, attn_weights, past_key_value |
| 717 | |
| 718 | |
| 719 | class Phi3FlashAttention2(Phi3Attention): |
| 720 | """ |
| 721 | Phi-3 flash attention module. This module inherits from `Phi3Attention` as the weights of the module stays |
| 722 | untouched. The only required change would be on the forward pass where it needs to correctly call the public API of |
| 723 | flash attention and deal with padding tokens in case the input contains any of them. |
| 724 | """ |
| 725 | |
| 726 | # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ |
| 727 | def __init__(self, *args, **kwargs): |
| 728 | super().__init__(*args, **kwargs) |
| 729 | |
| 730 | # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. |
| 731 | # 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. |
| 732 | # 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). |
| 733 | self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() |
| 734 | |
| 735 | def forward( |
| 736 | self, |
| 737 | hidden_states: torch.Tensor, |
| 738 | attention_mask: Optional[torch.LongTensor] = None, |
| 739 | position_ids: Optional[torch.LongTensor] = None, |
| 740 | past_key_value: Optional[Cache] = None, |
| 741 | output_attentions: bool = False, |
| 742 | use_cache: bool = False, |
| 743 | **kwargs, |
| 744 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
| 745 | # Phi3FlashAttention2 attention does not support output_attentions |
| 746 | |
| 747 | if not _flash_supports_window_size: |
| 748 | logger.warning_once( |
| 749 | "The current flash attention version does not support sliding window attention. Please use `attn_implementation='eager'` or upgrade flash-attn library." |
| 750 | ) |
| 751 | raise ValueError("The current flash attention version does not support sliding window attention.") |
| 752 | |
| 753 | output_attentions = False |
| 754 | |
| 755 | if "padding_mask" in kwargs: |
| 756 | warnings.warn( |
| 757 | "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" |
| 758 | ) |
| 759 | |
| 760 | # overwrite attention_mask with padding_mask |
| 761 | attention_mask = kwargs.pop("padding_mask") |
| 762 | |
| 763 | bsz, q_len, _ = hidden_states.size() |
| 764 | |
| 765 | qkv = self.qkv_proj(hidden_states) |
| 766 | query_pos = self.num_heads * self.head_dim |
| 767 | query_states = qkv[..., :query_pos] |
| 768 | key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] |
| 769 | value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] |
| 770 | |
| 771 | # Flash attention requires the input to have the shape |
| 772 | # batch_size x seq_length x head_dim x hidden_dim |
| 773 | # therefore we just need to keep the original shape |
| 774 | query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
| 775 | key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
| 776 | value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
| 777 | |
| 778 | kv_seq_len = key_states.shape[-2] |
| 779 | if past_key_value is not None: |
| 780 | if self.layer_idx is None: |
| 781 | raise ValueError( |
| 782 | f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " |
| 783 | "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " |
| 784 | "with a layer index." |
| 785 | ) |
| 786 | kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) |
| 787 | |
| 788 | # Because the input can be padded, the absolute sequence length depends on the max position id. |
| 789 | rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 |
| 790 | cos, sin = self.rotary_emb(value_states, position_ids, seq_len=rotary_seq_len) |
| 791 | |
| 792 | query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) |
| 793 | |
| 794 | use_sliding_windows = ( |
| 795 | _flash_supports_window_size |
| 796 | and getattr(self.config, "sliding_window", None) is not None |
| 797 | and kv_seq_len > self.config.sliding_window |
| 798 | ) |
| 799 | |
| 800 | if past_key_value is not None: |
| 801 | # Activate slicing cache only if the config has a value `sliding_windows` attribute |
| 802 | cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0 |
| 803 | if ( |
| 804 | getattr(self.config, "sliding_window", None) is not None |
| 805 | and kv_seq_len > self.config.sliding_window |
| 806 | and cache_has_contents |
| 807 | ): |
| 808 | slicing_tokens = 1 - self.config.sliding_window |
| 809 | |
| 810 | past_key = past_key_value[self.layer_idx][0] |
| 811 | past_value = past_key_value[self.layer_idx][1] |
| 812 | |
| 813 | past_key = past_key[:, :, slicing_tokens:, :].contiguous() |
| 814 | past_value = past_value[:, :, slicing_tokens:, :].contiguous() |
| 815 | |
| 816 | if past_key.shape[-2] != self.config.sliding_window - 1: |
| 817 | raise ValueError( |
| 818 | f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got" |
| 819 | f" {past_key.shape}" |
| 820 | ) |
| 821 | |
| 822 | if attention_mask is not None: |
| 823 | attention_mask = attention_mask[:, slicing_tokens:] |
| 824 | attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) |
| 825 | |
| 826 | cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models |
| 827 | key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) |
| 828 | |
| 829 | # repeat k/v heads if n_kv_heads < n_heads |
| 830 | key_states = repeat_kv(key_states, self.num_key_value_groups) |
| 831 | value_states = repeat_kv(value_states, self.num_key_value_groups) |
| 832 | |
| 833 | attn_dropout = self.attention_dropout if self.training else 0.0 |
| 834 | |
| 835 | # In PEFT, usually we cast the layer norms in float32 for training stability reasons |
| 836 | # therefore the input hidden states gets silently casted in float32. Hence, we need |
| 837 | # cast them back in the correct dtype just to be sure everything works as expected. |
| 838 | # This might slowdown training & inference so it is recommended to not cast the LayerNorms |
| 839 | # in fp32. |
| 840 | |
| 841 | if query_states.dtype == torch.float32: |
| 842 | if torch.is_autocast_enabled(): |
| 843 | target_dtype = torch.get_autocast_gpu_dtype() |
| 844 | # Handle the case where the model is quantized |
| 845 | elif hasattr(self.config, "_pre_quantization_dtype"): |
| 846 | target_dtype = self.config._pre_quantization_dtype |
| 847 | else: |
| 848 | target_dtype = self.qkv_proj.weight.dtype |
| 849 | |
| 850 | logger.warning_once( |
| 851 | f"The input hidden states seems to be silently casted in float32, this might be related to" |
| 852 | f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" |
| 853 | f" {target_dtype}." |
| 854 | ) |
| 855 | |
| 856 | query_states = query_states.to(target_dtype) |
| 857 | key_states = key_states.to(target_dtype) |
| 858 | value_states = value_states.to(target_dtype) |
| 859 | |
| 860 | # Reashape to the expected shape for Flash Attention |
| 861 | query_states = query_states.transpose(1, 2) |
| 862 | key_states = key_states.transpose(1, 2) |
| 863 | value_states = value_states.transpose(1, 2) |
| 864 | |
| 865 | attn_output = self._flash_attention_forward( |
| 866 | query_states, |
| 867 | key_states, |
| 868 | value_states, |
| 869 | attention_mask, |
| 870 | q_len, |
| 871 | dropout=attn_dropout, |
| 872 | use_sliding_windows=use_sliding_windows, |
| 873 | ) |
| 874 | |
| 875 | attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() |
| 876 | attn_output = self.o_proj(attn_output) |
| 877 | |
| 878 | if not output_attentions: |
| 879 | attn_weights = None |
| 880 | |
| 881 | return attn_output, attn_weights, past_key_value |
| 882 | |
| 883 | # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._flash_attention_forward |
| 884 | def _flash_attention_forward( |
| 885 | self, |
| 886 | query_states, |
| 887 | key_states, |
| 888 | value_states, |
| 889 | attention_mask, |
| 890 | query_length, |
| 891 | dropout=0.0, |
| 892 | softmax_scale=None, |
| 893 | use_sliding_windows=False, |
| 894 | ): |
| 895 | """ |
| 896 | Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token |
| 897 | first unpad the input, then computes the attention scores and pad the final attention scores. |
| 898 | |
| 899 | Args: |
| 900 | query_states (`torch.Tensor`): |
| 901 | Input query states to be passed to Flash Attention API |
| 902 | key_states (`torch.Tensor`): |
| 903 | Input key states to be passed to Flash Attention API |
| 904 | value_states (`torch.Tensor`): |
| 905 | Input value states to be passed to Flash Attention API |
| 906 | attention_mask (`torch.Tensor`): |
| 907 | The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the |
| 908 | position of padding tokens and 1 for the position of non-padding tokens. |
| 909 | dropout (`float`): |
| 910 | Attention dropout |
| 911 | softmax_scale (`float`, *optional*): |
| 912 | The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) |
| 913 | use_sliding_windows (`bool`, *optional*): |
| 914 | Whether to activate sliding window attention. |
| 915 | """ |
| 916 | if not self._flash_attn_uses_top_left_mask: |
| 917 | causal = self.is_causal |
| 918 | else: |
| 919 | # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. |
| 920 | causal = self.is_causal and query_length != 1 |
| 921 | |
| 922 | # Contains at least one padding token in the sequence |
| 923 | if attention_mask is not None: |
| 924 | batch_size = query_states.shape[0] |
| 925 | query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( |
| 926 | query_states, key_states, value_states, attention_mask, query_length |
| 927 | ) |
| 928 | |
| 929 | cu_seqlens_q, cu_seqlens_k = cu_seq_lens |
| 930 | max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens |
| 931 | |
| 932 | if not use_sliding_windows: |
| 933 | attn_output_unpad = flash_attn_varlen_func( |
| 934 | query_states, |
| 935 | key_states, |
| 936 | value_states, |
| 937 | cu_seqlens_q=cu_seqlens_q, |
| 938 | cu_seqlens_k=cu_seqlens_k, |
| 939 | max_seqlen_q=max_seqlen_in_batch_q, |
| 940 | max_seqlen_k=max_seqlen_in_batch_k, |
| 941 | dropout_p=dropout, |
| 942 | softmax_scale=softmax_scale, |
| 943 | causal=causal, |
| 944 | ) |
| 945 | else: |
| 946 | attn_output_unpad = flash_attn_varlen_func( |
| 947 | query_states, |
| 948 | key_states, |
| 949 | value_states, |
| 950 | cu_seqlens_q=cu_seqlens_q, |
| 951 | cu_seqlens_k=cu_seqlens_k, |
| 952 | max_seqlen_q=max_seqlen_in_batch_q, |
| 953 | max_seqlen_k=max_seqlen_in_batch_k, |
| 954 | dropout_p=dropout, |
| 955 | softmax_scale=softmax_scale, |
| 956 | causal=causal, |
| 957 | window_size=(self.config.sliding_window, self.config.sliding_window), |
| 958 | ) |
| 959 | |
| 960 | attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) |
| 961 | else: |
| 962 | if not use_sliding_windows: |
| 963 | attn_output = flash_attn_func( |
| 964 | query_states, |
| 965 | key_states, |
| 966 | value_states, |
| 967 | dropout, |
| 968 | softmax_scale=softmax_scale, |
| 969 | causal=causal, |
| 970 | ) |
| 971 | else: |
| 972 | attn_output = flash_attn_func( |
| 973 | query_states, |
| 974 | key_states, |
| 975 | value_states, |
| 976 | dropout, |
| 977 | softmax_scale=softmax_scale, |
| 978 | causal=causal, |
| 979 | window_size=(self.config.sliding_window, self.config.sliding_window), |
| 980 | ) |
| 981 | |
| 982 | return attn_output |
| 983 | |
| 984 | # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input |
| 985 | def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): |
| 986 | batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape |
| 987 | |
| 988 | # On the first iteration we need to properly re-create the padding mask |
| 989 | # by slicing it on the proper place |
| 990 | if kv_seq_len != attention_mask.shape[-1]: |
| 991 | attention_mask_num_tokens = attention_mask.shape[-1] |
| 992 | attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :] |
| 993 | |
| 994 | indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) |
| 995 | |
| 996 | key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) |
| 997 | value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) |
| 998 | |
| 999 | if query_length == kv_seq_len: |
| 1000 | query_layer = index_first_axis( |
| 1001 | query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k |
| 1002 | ) |
| 1003 | cu_seqlens_q = cu_seqlens_k |
| 1004 | max_seqlen_in_batch_q = max_seqlen_in_batch_k |
| 1005 | indices_q = indices_k |
| 1006 | elif query_length == 1: |
| 1007 | max_seqlen_in_batch_q = 1 |
| 1008 | cu_seqlens_q = torch.arange( |
| 1009 | batch_size + 1, dtype=torch.int32, device=query_layer.device |
| 1010 | ) # There is a memcpy here, that is very bad. |
| 1011 | indices_q = cu_seqlens_q[:-1] |
| 1012 | query_layer = query_layer.squeeze(1) |
| 1013 | else: |
| 1014 | # The -q_len: slice assumes left padding. |
| 1015 | attention_mask = attention_mask[:, -query_length:] |
| 1016 | query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) |
| 1017 | |
| 1018 | return ( |
| 1019 | query_layer, |
| 1020 | key_layer, |
| 1021 | value_layer, |
| 1022 | indices_q, |
| 1023 | (cu_seqlens_q, cu_seqlens_k), |
| 1024 | (max_seqlen_in_batch_q, max_seqlen_in_batch_k), |
| 1025 | ) |
| 1026 | |
| 1027 | |
| 1028 | # copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Phi3 |
| 1029 | # TODO @Arthur no longer copied from LLama after static cache |
| 1030 | class Phi3SdpaAttention(Phi3Attention): |
| 1031 | """ |
| 1032 | Phi3 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from |
| 1033 | `Phi3Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to |
| 1034 | SDPA API. |
| 1035 | """ |
| 1036 | |
| 1037 | # Adapted from Phi3Attention.forward |
| 1038 | def forward( |
| 1039 | self, |
| 1040 | hidden_states: torch.Tensor, |
| 1041 | attention_mask: Optional[torch.Tensor] = None, |
| 1042 | position_ids: Optional[torch.LongTensor] = None, |
| 1043 | past_key_value: Optional[Cache] = None, |
| 1044 | output_attentions: bool = False, |
| 1045 | use_cache: bool = False, |
| 1046 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
| 1047 | if output_attentions: |
| 1048 | # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. |
| 1049 | logger.warning_once( |
| 1050 | "Phi3Model is using Phi3SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " |
| 1051 | 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' |
| 1052 | ) |
| 1053 | return super().forward( |
| 1054 | hidden_states=hidden_states, |
| 1055 | attention_mask=attention_mask, |
| 1056 | position_ids=position_ids, |
| 1057 | past_key_value=past_key_value, |
| 1058 | output_attentions=output_attentions, |
| 1059 | use_cache=use_cache, |
| 1060 | ) |
| 1061 | |
| 1062 | bsz, q_len, _ = hidden_states.size() |
| 1063 | |
| 1064 | qkv = self.qkv_proj(hidden_states) |
| 1065 | query_pos = self.num_heads * self.head_dim |
| 1066 | query_states = qkv[..., :query_pos] |
| 1067 | key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] |
| 1068 | value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] |
| 1069 | |
| 1070 | query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
| 1071 | key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
| 1072 | value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
| 1073 | |
| 1074 | kv_seq_len = key_states.shape[-2] |
| 1075 | if past_key_value is not None: |
| 1076 | kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) |
| 1077 | cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len) |
| 1078 | |
| 1079 | query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) |
| 1080 | |
| 1081 | if past_key_value is not None: |
| 1082 | cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models |
| 1083 | key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) |
| 1084 | |
| 1085 | key_states = repeat_kv(key_states, self.num_key_value_groups) |
| 1086 | value_states = repeat_kv(value_states, self.num_key_value_groups) |
| 1087 | |
| 1088 | if attention_mask is not None: |
| 1089 | if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): |
| 1090 | raise ValueError( |
| 1091 | f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" |
| 1092 | ) |
| 1093 | |
| 1094 | # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, |
| 1095 | # Reference: https://github.com/pytorch/pytorch/issues/112577. |
| 1096 | if query_states.device.type == "cuda" and attention_mask is not None: |
| 1097 | query_states = query_states.contiguous() |
| 1098 | key_states = key_states.contiguous() |
| 1099 | value_states = value_states.contiguous() |
| 1100 | |
| 1101 | attn_output = torch.nn.functional.scaled_dot_product_attention( |
| 1102 | query_states, |
| 1103 | key_states, |
| 1104 | value_states, |
| 1105 | attn_mask=attention_mask, |
| 1106 | dropout_p=self.attention_dropout if self.training else 0.0, |
| 1107 | # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. |
| 1108 | is_causal=self.is_causal and attention_mask is None and q_len > 1, |
| 1109 | ) |
| 1110 | |
| 1111 | attn_output = attn_output.transpose(1, 2).contiguous() |
| 1112 | attn_output = attn_output.view(bsz, q_len, self.hidden_size) |
| 1113 | |
| 1114 | attn_output = self.o_proj(attn_output) |
| 1115 | |
| 1116 | return attn_output, None, past_key_value |
| 1117 | |
| 1118 | |
| 1119 | PHI3_ATTENTION_CLASSES = { |
| 1120 | "eager": Phi3Attention, |
| 1121 | "flash_attention_2": Phi3FlashAttention2, |
| 1122 | "sdpa": Phi3SdpaAttention, |
| 1123 | } |
| 1124 | |
| 1125 | |
| 1126 | class Phi3DecoderLayer(nn.Module): |
| 1127 | def __init__(self, config: Phi3VConfig, layer_idx: int): |
| 1128 | super().__init__() |
| 1129 | |
| 1130 | self.config = config |
| 1131 | self.self_attn = PHI3_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx) |
| 1132 | |
| 1133 | self.mlp = Phi3MLP(config) |
| 1134 | self.input_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
| 1135 | |
| 1136 | self.resid_attn_dropout = nn.Dropout(config.resid_pdrop) |
| 1137 | self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop) |
| 1138 | self.post_attention_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
| 1139 | |
| 1140 | def forward( |
| 1141 | self, |
| 1142 | hidden_states: torch.Tensor, |
| 1143 | attention_mask: Optional[torch.Tensor] = None, |
| 1144 | position_ids: Optional[torch.LongTensor] = None, |
| 1145 | past_key_value: Optional[Tuple[torch.Tensor]] = None, |
| 1146 | output_attentions: Optional[bool] = False, |
| 1147 | use_cache: Optional[bool] = False, |
| 1148 | **kwargs, |
| 1149 | ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: |
| 1150 | if "padding_mask" in kwargs: |
| 1151 | warnings.warn( |
| 1152 | "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" |
| 1153 | ) |
| 1154 | """ |
| 1155 | Args: |
| 1156 | hidden_states (`torch.FloatTensor`): |
| 1157 | input to the layer of shape `(batch, seq_len, embed_dim)` |
| 1158 | attention_mask (`torch.FloatTensor`, *optional*): attention mask of size |
| 1159 | `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. |
| 1160 | position_ids (`torch.LongTensor` of shape `({0})`, *optional*): |
| 1161 | Indices of positions of each input sequence tokens in the position embeddings. Selected in the range |
| 1162 | `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) |
| 1163 | output_attentions (`bool`, *optional*): |
| 1164 | Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
| 1165 | returned tensors for more detail. |
| 1166 | use_cache (`bool`, *optional*): |
| 1167 | If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding |
| 1168 | (see `past_key_values`). |
| 1169 | past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states |
| 1170 | """ |
| 1171 | |
| 1172 | residual = hidden_states |
| 1173 | |
| 1174 | hidden_states = self.input_layernorm(hidden_states) |
| 1175 | |
| 1176 | # Self Attention |
| 1177 | attn_outputs, self_attn_weights, present_key_value = self.self_attn( |
| 1178 | hidden_states=hidden_states, |
| 1179 | attention_mask=attention_mask, |
| 1180 | position_ids=position_ids, |
| 1181 | past_key_value=past_key_value, |
| 1182 | output_attentions=output_attentions, |
| 1183 | use_cache=use_cache, |
| 1184 | ) |
| 1185 | |
| 1186 | hidden_states = residual + self.resid_attn_dropout(attn_outputs) |
| 1187 | |
| 1188 | residual = hidden_states |
| 1189 | hidden_states = self.post_attention_layernorm(hidden_states) |
| 1190 | hidden_states = self.mlp(hidden_states) |
| 1191 | hidden_states = residual + self.resid_mlp_dropout(hidden_states) |
| 1192 | |
| 1193 | outputs = (hidden_states,) |
| 1194 | |
| 1195 | if output_attentions: |
| 1196 | outputs += (self_attn_weights,) |
| 1197 | |
| 1198 | if use_cache: |
| 1199 | outputs += (present_key_value,) |
| 1200 | |
| 1201 | return outputs |
| 1202 | |
| 1203 | |
| 1204 | PHI3V_START_DOCSTRING = r""" |
| 1205 | This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the |
| 1206 | library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads |
| 1207 | etc.) |
| 1208 | |
| 1209 | This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. |
| 1210 | Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage |
| 1211 | and behavior. |
| 1212 | |
| 1213 | Parameters: |
| 1214 | config ([`Phi3VConfig`]): |
| 1215 | Model configuration class with all the parameters of the model. Initializing with a config file does not |
| 1216 | load the weights associated with the model, only the configuration. Check out the |
| 1217 | [`~PreTrainedModel.from_pretrained`] method to load the model weights. |
| 1218 | """ |
| 1219 | |
| 1220 | |
| 1221 | @add_start_docstrings( |
| 1222 | "The bare Phi-3-V model outputting raw hidden-states without any specific head on top.", |
| 1223 | PHI3V_START_DOCSTRING, |
| 1224 | ) |
| 1225 | class Phi3VPreTrainedModel(PreTrainedModel): |
| 1226 | config_class = Phi3VConfig |
| 1227 | base_model_prefix = "model" |
| 1228 | supports_gradient_checkpointing = True |
| 1229 | _no_split_modules = ["Phi3DecoderLayer"] |
| 1230 | _skip_keys_device_placement = "past_key_values" |
| 1231 | _supports_flash_attn_2 = True |
| 1232 | _supports_sdpa = False |
| 1233 | _supports_cache_class = True |
| 1234 | |
| 1235 | _version = "0.0.5" |
| 1236 | |
| 1237 | def _init_weights(self, module): |
| 1238 | std = self.config.initializer_range |
| 1239 | if isinstance(module, nn.Linear): |
| 1240 | module.weight.data.normal_(mean=0.0, std=std) |
| 1241 | if module.bias is not None: |
| 1242 | module.bias.data.zero_() |
| 1243 | elif isinstance(module, nn.Embedding): |
| 1244 | module.weight.data.normal_(mean=0.0, std=std) |
| 1245 | if module.padding_idx is not None: |
| 1246 | module.weight.data[module.padding_idx].zero_() |
| 1247 | |
| 1248 | |
| 1249 | PHI3V_INPUTS_DOCSTRING = r""" |
| 1250 | Args: |
| 1251 | input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
| 1252 | Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide |
| 1253 | it. |
| 1254 | |
| 1255 | Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
| 1256 | [`PreTrainedTokenizer.__call__`] for details. |
| 1257 | |
| 1258 | [What are input IDs?](../glossary#input-ids) |
| 1259 | attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
| 1260 | Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
| 1261 | |
| 1262 | - 1 for tokens that are **not masked**, |
| 1263 | - 0 for tokens that are **masked**. |
| 1264 | |
| 1265 | [What are attention masks?](../glossary#attention-mask) |
| 1266 | |
| 1267 | Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
| 1268 | [`PreTrainedTokenizer.__call__`] for details. |
| 1269 | |
| 1270 | If `past_key_values` is used, optionally only the last `input_ids` have to be input (see |
| 1271 | `past_key_values`). |
| 1272 | |
| 1273 | If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] |
| 1274 | and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more |
| 1275 | information on the default strategy. |
| 1276 | |
| 1277 | - 1 indicates the head is **not masked**, |
| 1278 | - 0 indicates the head is **masked**. |
| 1279 | position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
| 1280 | Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, |
| 1281 | config.n_positions - 1]`. |
| 1282 | |
| 1283 | [What are position IDs?](../glossary#position-ids) |
| 1284 | past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): |
| 1285 | Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention |
| 1286 | blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` |
| 1287 | returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. |
| 1288 | |
| 1289 | Two formats are allowed: |
| 1290 | - a [`~cache_utils.Cache`] instance; |
| 1291 | - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of |
| 1292 | shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy |
| 1293 | cache format. |
| 1294 | |
| 1295 | The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the |
| 1296 | legacy cache format will be returned. |
| 1297 | |
| 1298 | If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't |
| 1299 | have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` |
| 1300 | of shape `(batch_size, sequence_length)`. |
| 1301 | inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): |
| 1302 | Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This |
| 1303 | is useful if you want more control over how to convert `input_ids` indices into associated vectors than the |
| 1304 | model's internal embedding lookup matrix. |
| 1305 | pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): |
| 1306 | The tensors corresponding to the input images. Pixel values can be obtained using [`AutoImageProcessor`]. |
| 1307 | See [`Phi3ImageProcessor.__call__`] for details. |
| 1308 | image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, *optional*): |
| 1309 | The sizes of the images in the batch, being (height, width) for each image. |
| 1310 | use_cache (`bool`, *optional*): |
| 1311 | If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see |
| 1312 | `past_key_values`). |
| 1313 | output_attentions (`bool`, *optional*): |
| 1314 | Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
| 1315 | tensors for more detail. |
| 1316 | output_hidden_states (`bool`, *optional*): |
| 1317 | Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
| 1318 | more detail. |
| 1319 | return_dict (`bool`, *optional*): |
| 1320 | Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
| 1321 | """ |
| 1322 | |
| 1323 | |
| 1324 | @add_start_docstrings( |
| 1325 | "The bare Phi-3-V model outputting raw hidden-states without any specific head on top.", |
| 1326 | PHI3V_START_DOCSTRING, |
| 1327 | ) |
| 1328 | class Phi3VModel(Phi3VPreTrainedModel): |
| 1329 | """ |
| 1330 | Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`] |
| 1331 | |
| 1332 | Args: |
| 1333 | config: Phi3Config |
| 1334 | """ |
| 1335 | |
| 1336 | def __init__(self, config: Phi3VConfig): |
| 1337 | super().__init__(config) |
| 1338 | self.padding_idx = config.pad_token_id |
| 1339 | self.vocab_size = config.vocab_size |
| 1340 | |
| 1341 | self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) |
| 1342 | self.embed_dropout = nn.Dropout(config.embd_pdrop) |
| 1343 | |
| 1344 | self.vision_embed_tokens = None |
| 1345 | if isinstance(config.embd_layer, dict): |
| 1346 | # vision embedding layer |
| 1347 | embedding_config = { |
| 1348 | 'embedding_cls': config.embd_layer['embedding_cls'], |
| 1349 | **config.embd_layer |
| 1350 | } |
| 1351 | self.vision_embed_tokens = Phi3ImageEmbedding(config, wte=self.embed_tokens, **embedding_config) |
| 1352 | # # set wte the same for vision embedding |
| 1353 | # self.vision_embed_tokens.wte.weight = self.embed_tokens.weight |
| 1354 | |
| 1355 | self.layers = nn.ModuleList( |
| 1356 | [Phi3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] |
| 1357 | ) |
| 1358 | self._attn_implementation = config._attn_implementation |
| 1359 | self.norm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
| 1360 | |
| 1361 | self.gradient_checkpointing = False |
| 1362 | # Initialize weights and apply final processing |
| 1363 | self.post_init() |
| 1364 | |
| 1365 | def get_input_embeddings(self): |
| 1366 | return self.embed_tokens |
| 1367 | |
| 1368 | def set_input_embeddings(self, value): |
| 1369 | self.embed_tokens = value |
| 1370 | |
| 1371 | @add_start_docstrings_to_model_forward(PHI3V_INPUTS_DOCSTRING) |
| 1372 | def forward( |
| 1373 | self, |
| 1374 | input_ids: torch.LongTensor = None, |
| 1375 | attention_mask: Optional[torch.Tensor] = None, |
| 1376 | position_ids: Optional[torch.LongTensor] = None, |
| 1377 | past_key_values: Optional[List[torch.FloatTensor]] = None, |
| 1378 | inputs_embeds: Optional[torch.FloatTensor] = None, |
| 1379 | pixel_values: Optional[torch.FloatTensor] = None, |
| 1380 | image_sizes: Optional[torch.LongTensor] = None, |
| 1381 | use_cache: Optional[bool] = None, |
| 1382 | output_attentions: Optional[bool] = None, |
| 1383 | output_hidden_states: Optional[bool] = None, |
| 1384 | return_dict: Optional[bool] = None, |
| 1385 | ) -> Union[Tuple, BaseModelOutputWithPast]: |
| 1386 | output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
| 1387 | output_hidden_states = ( |
| 1388 | output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
| 1389 | ) |
| 1390 | use_cache = use_cache if use_cache is not None else self.config.use_cache |
| 1391 | |
| 1392 | return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
| 1393 | |
| 1394 | # retrieve input_ids and inputs_embeds |
| 1395 | if input_ids is not None and inputs_embeds is not None: |
| 1396 | raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") |
| 1397 | elif input_ids is not None: |
| 1398 | batch_size, seq_length = input_ids.shape[:2] |
| 1399 | elif inputs_embeds is not None: |
| 1400 | batch_size, seq_length = inputs_embeds.shape[:2] |
| 1401 | else: |
| 1402 | raise ValueError("You have to specify either input_ids or inputs_embeds") |
| 1403 | |
| 1404 | past_key_values_length = 0 |
| 1405 | |
| 1406 | if self.gradient_checkpointing and self.training: |
| 1407 | if use_cache: |
| 1408 | logger.warning_once( |
| 1409 | "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." |
| 1410 | ) |
| 1411 | use_cache = False |
| 1412 | |
| 1413 | if use_cache: |
| 1414 | use_legacy_cache = not isinstance(past_key_values, Cache) |
| 1415 | if use_legacy_cache: |
| 1416 | past_key_values = DynamicCache.from_legacy_cache(past_key_values) |
| 1417 | past_key_values_length = past_key_values.get_usable_length(seq_length) |
| 1418 | |
| 1419 | if position_ids is None: |
| 1420 | device = input_ids.device if input_ids is not None else inputs_embeds.device |
| 1421 | position_ids = torch.arange( |
| 1422 | past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device |
| 1423 | ) |
| 1424 | position_ids = position_ids.unsqueeze(0).view(-1, seq_length) |
| 1425 | else: |
| 1426 | position_ids = position_ids.view(-1, seq_length).long() |
| 1427 | |
| 1428 | if inputs_embeds is None: |
| 1429 | if pixel_values is not None and image_sizes is not None: |
| 1430 | assert self.vision_embed_tokens is not None, "Vision embedding layer is not defined" |
| 1431 | inputs_embeds = self.vision_embed_tokens(input_ids, pixel_values=pixel_values, image_sizes=image_sizes) |
| 1432 | else: |
| 1433 | inputs_embeds = self.embed_tokens(input_ids) |
| 1434 | |
| 1435 | if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: |
| 1436 | is_padding_right = attention_mask[:, -1].sum().item() != batch_size |
| 1437 | if is_padding_right: |
| 1438 | raise ValueError( |
| 1439 | "You are attempting to perform batched generation with padding_side='right'" |
| 1440 | " this may lead to unexpected behaviour for Flash Attention version of Phi3. Make sure to " |
| 1441 | " call `tokenizer.padding_side = 'left'` before tokenizing the input. " |
| 1442 | ) |
| 1443 | |
| 1444 | if self._attn_implementation == "flash_attention_2": |
| 1445 | # 2d mask is passed through the layers |
| 1446 | attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None |
| 1447 | else: |
| 1448 | # 4d mask is passed through the layers |
| 1449 | attention_mask = _prepare_4d_causal_attention_mask( |
| 1450 | attention_mask, |
| 1451 | (batch_size, seq_length), |
| 1452 | inputs_embeds, |
| 1453 | past_key_values_length, |
| 1454 | sliding_window=self.config.sliding_window, |
| 1455 | ) |
| 1456 | |
| 1457 | hidden_states = inputs_embeds |
| 1458 | |
| 1459 | # decoder layers |
| 1460 | all_hidden_states = () if output_hidden_states else None |
| 1461 | all_self_attns = () if output_attentions else None |
| 1462 | next_decoder_cache = None |
| 1463 | |
| 1464 | for decoder_layer in self.layers: |
| 1465 | if output_hidden_states: |
| 1466 | all_hidden_states += (hidden_states,) |
| 1467 | |
| 1468 | if self.gradient_checkpointing and self.training: |
| 1469 | layer_outputs = self._gradient_checkpointing_func( |
| 1470 | decoder_layer.__call__, |
| 1471 | hidden_states, |
| 1472 | attention_mask, |
| 1473 | position_ids, |
| 1474 | past_key_values, |
| 1475 | output_attentions, |
| 1476 | use_cache, |
| 1477 | ) |
| 1478 | else: |
| 1479 | layer_outputs = decoder_layer( |
| 1480 | hidden_states, |
| 1481 | attention_mask=attention_mask, |
| 1482 | position_ids=position_ids, |
| 1483 | past_key_value=past_key_values, |
| 1484 | output_attentions=output_attentions, |
| 1485 | use_cache=use_cache, |
| 1486 | ) |
| 1487 | |
| 1488 | hidden_states = layer_outputs[0] |
| 1489 | |
| 1490 | if use_cache: |
| 1491 | next_decoder_cache = layer_outputs[2 if output_attentions else 1] |
| 1492 | |
| 1493 | if output_attentions: |
| 1494 | all_self_attns += (layer_outputs[1],) |
| 1495 | |
| 1496 | hidden_states = self.norm(hidden_states) |
| 1497 | |
| 1498 | # add hidden states from the last decoder layer |
| 1499 | if output_hidden_states: |
| 1500 | all_hidden_states += (hidden_states,) |
| 1501 | |
| 1502 | next_cache = None |
| 1503 | if use_cache: |
| 1504 | next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache |
| 1505 | if not return_dict: |
| 1506 | return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) |
| 1507 | return BaseModelOutputWithPast( |
| 1508 | last_hidden_state=hidden_states, |
| 1509 | past_key_values=next_cache, |
| 1510 | hidden_states=all_hidden_states, |
| 1511 | attentions=all_self_attns, |
| 1512 | ) |
| 1513 | |
| 1514 | |
| 1515 | class Phi3VForCausalLM(Phi3VPreTrainedModel): |
| 1516 | _tied_weights_keys = ["lm_head.weight"] |
| 1517 | |
| 1518 | # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi3 |
| 1519 | def __init__(self, config): |
| 1520 | super().__init__(config) |
| 1521 | self.model = Phi3VModel(config) |
| 1522 | self.vocab_size = config.vocab_size |
| 1523 | self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) |
| 1524 | |
| 1525 | # Initialize weights and apply final processing |
| 1526 | self.post_init() |
| 1527 | |
| 1528 | # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings |
| 1529 | def get_input_embeddings(self): |
| 1530 | return self.model.embed_tokens |
| 1531 | |
| 1532 | # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings |
| 1533 | def set_input_embeddings(self, value): |
| 1534 | self.model.embed_tokens = value |
| 1535 | |
| 1536 | # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings |
| 1537 | def get_output_embeddings(self): |
| 1538 | return self.lm_head |
| 1539 | |
| 1540 | # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings |
| 1541 | def set_output_embeddings(self, new_embeddings): |
| 1542 | self.lm_head = new_embeddings |
| 1543 | |
| 1544 | # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder |
| 1545 | def set_decoder(self, decoder): |
| 1546 | self.model = decoder |
| 1547 | |
| 1548 | # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder |
| 1549 | def get_decoder(self): |
| 1550 | return self.model |
| 1551 | |
| 1552 | # Ignore copy |
| 1553 | @add_start_docstrings_to_model_forward(PHI3V_INPUTS_DOCSTRING) |
| 1554 | @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) |
| 1555 | def forward( |
| 1556 | self, |
| 1557 | input_ids: torch.LongTensor = None, |
| 1558 | attention_mask: Optional[torch.Tensor] = None, |
| 1559 | position_ids: Optional[torch.LongTensor] = None, |
| 1560 | past_key_values: Optional[List[torch.FloatTensor]] = None, |
| 1561 | inputs_embeds: Optional[torch.FloatTensor] = None, |
| 1562 | pixel_values: Optional[torch.FloatTensor] = None, |
| 1563 | image_sizes: Optional[torch.LongTensor] = None, |
| 1564 | labels: Optional[torch.LongTensor] = None, |
| 1565 | use_cache: Optional[bool] = None, |
| 1566 | output_attentions: Optional[bool] = None, |
| 1567 | output_hidden_states: Optional[bool] = None, |
| 1568 | return_dict: Optional[bool] = None, |
| 1569 | ) -> Union[Tuple, CausalLMOutputWithPast]: |
| 1570 | r""" |
| 1571 | Args: |
| 1572 | labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
| 1573 | Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., |
| 1574 | config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored |
| 1575 | (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. |
| 1576 | |
| 1577 | Returns: |
| 1578 | |
| 1579 | Example: |
| 1580 | |
| 1581 | ```python |
| 1582 | >>> from transformers import AutoTokenizer, Phi3ForCausalLM |
| 1583 | |
| 1584 | >>> model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct") |
| 1585 | >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3-mini-4k-instruct") |
| 1586 | |
| 1587 | >>> prompt = "This is an example script ." |
| 1588 | >>> inputs = tokenizer(prompt, return_tensors="pt") |
| 1589 | |
| 1590 | >>> # Generate |
| 1591 | >>> generate_ids = model.generate(inputs.input_ids, max_length=30) |
| 1592 | >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
| 1593 | 'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum' |
| 1594 | ```""" |
| 1595 | |
| 1596 | output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
| 1597 | output_hidden_states = ( |
| 1598 | output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
| 1599 | ) |
| 1600 | return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
| 1601 | |
| 1602 | # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) |
| 1603 | outputs = self.model( |
| 1604 | input_ids=input_ids, |
| 1605 | attention_mask=attention_mask, |
| 1606 | position_ids=position_ids, |
| 1607 | past_key_values=past_key_values, |
| 1608 | inputs_embeds=inputs_embeds, |
| 1609 | pixel_values=pixel_values, |
| 1610 | image_sizes=image_sizes, |
| 1611 | use_cache=use_cache, |
| 1612 | output_attentions=output_attentions, |
| 1613 | output_hidden_states=output_hidden_states, |
| 1614 | return_dict=return_dict, |
| 1615 | ) |
| 1616 | |
| 1617 | hidden_states = outputs[0] |
| 1618 | logits = self.lm_head(hidden_states) |
| 1619 | logits = logits.float() |
| 1620 | |
| 1621 | loss = None |
| 1622 | if labels is not None: |
| 1623 | # Shift so that tokens < n predict n |
| 1624 | shift_logits = logits[..., :-1, :].contiguous() |
| 1625 | shift_labels = labels[..., 1:].contiguous() |
| 1626 | # Flatten the tokens |
| 1627 | loss_fct = CrossEntropyLoss() |
| 1628 | shift_logits = shift_logits.view(-1, self.config.vocab_size) |
| 1629 | shift_labels = shift_labels.view(-1) |
| 1630 | # Enable model parallelism |
| 1631 | shift_labels = shift_labels.to(shift_logits.device) |
| 1632 | loss = loss_fct(shift_logits, shift_labels) |
| 1633 | |
| 1634 | if not return_dict: |
| 1635 | output = (logits,) + outputs[1:] |
| 1636 | return (loss,) + output if loss is not None else output |
| 1637 | |
| 1638 | return CausalLMOutputWithPast( |
| 1639 | loss=loss, |
| 1640 | logits=logits, |
| 1641 | past_key_values=outputs.past_key_values, |
| 1642 | hidden_states=outputs.hidden_states, |
| 1643 | attentions=outputs.attentions, |
| 1644 | ) |
| 1645 | |
| 1646 | # Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM.prepare_inputs_for_generation |
| 1647 | def prepare_inputs_for_generation( |
| 1648 | self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, pixel_values=None, image_sizes=None, **kwargs |
| 1649 | ): |
| 1650 | # When the first time input length reached long and short factor switching point, enforce re-compute cache |
| 1651 | # It will cause downside of slower at this single token position, however, better than current failure. |
| 1652 | if past_key_values and self.config.rope_scaling and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1: |
| 1653 | past_length = past_key_values.seen_tokens if isinstance(past_key_values, Cache) else past_key_values[0][0].shape[2] |
| 1654 | if past_length <= self.config.original_max_position_embeddings: |
| 1655 | past_key_values = None |
| 1656 | |
| 1657 | if past_key_values is not None: |
| 1658 | if isinstance(past_key_values, Cache): |
| 1659 | cache_length = past_key_values.get_seq_length() |
| 1660 | past_length = past_key_values.seen_tokens |
| 1661 | max_cache_length = past_key_values.get_max_length() |
| 1662 | else: |
| 1663 | cache_length = past_length = past_key_values[0][0].shape[2] |
| 1664 | max_cache_length = None |
| 1665 | |
| 1666 | # Keep only the unprocessed tokens: |
| 1667 | # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where |
| 1668 | # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as |
| 1669 | # input) |
| 1670 | if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: |
| 1671 | input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] |
| 1672 | # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard |
| 1673 | # input_ids based on the past_length. |
| 1674 | elif past_length < input_ids.shape[1]: |
| 1675 | input_ids = input_ids[:, past_length:] |
| 1676 | # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. |
| 1677 | |
| 1678 | # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. |
| 1679 | if ( |
| 1680 | max_cache_length is not None |
| 1681 | and attention_mask is not None |
| 1682 | and cache_length + input_ids.shape[1] > max_cache_length |
| 1683 | ): |
| 1684 | attention_mask = attention_mask[:, -max_cache_length:] |
| 1685 | |
| 1686 | position_ids = kwargs.get("position_ids", None) |
| 1687 | if attention_mask is not None and position_ids is None: |
| 1688 | # create position_ids on the fly for batch generation |
| 1689 | position_ids = attention_mask.long().cumsum(-1) - 1 |
| 1690 | position_ids.masked_fill_(attention_mask == 0, 1) |
| 1691 | if past_key_values: |
| 1692 | position_ids = position_ids[:, -input_ids.shape[1] :] |
| 1693 | |
| 1694 | # if `inputs_embeds` are passed, we only want to use them in the 1st generation step |
| 1695 | if inputs_embeds is not None and past_key_values is None: |
| 1696 | model_inputs = {"inputs_embeds": inputs_embeds} |
| 1697 | else: |
| 1698 | model_inputs = {"input_ids": input_ids} |
| 1699 | |
| 1700 | model_inputs.update( |
| 1701 | { |
| 1702 | "position_ids": position_ids, |
| 1703 | "past_key_values": past_key_values, |
| 1704 | "use_cache": kwargs.get("use_cache"), |
| 1705 | "attention_mask": attention_mask, |
| 1706 | "pixel_values": pixel_values, |
| 1707 | "image_sizes": image_sizes, |
| 1708 | } |
| 1709 | ) |
| 1710 | return model_inputs |
| 1711 | |
| 1712 | @staticmethod |
| 1713 | # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache |
| 1714 | def _reorder_cache(past_key_values, beam_idx): |
| 1715 | reordered_past = () |
| 1716 | for layer_past in past_key_values: |
| 1717 | reordered_past += ( |
| 1718 | tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), |
| 1719 | ) |
| 1720 | return reordered_past |
| 1721 | |
| 1722 | |
| 1723 | @add_start_docstrings( |
| 1724 | """ |
| 1725 | The [`Phi3VModel`] with a sequence classification head on top (linear layer). |
| 1726 | |
| 1727 | [`Phi3VForSequenceClassification`] uses the last token in order to do the classification, as other causal models |
| 1728 | (e.g. GPT-2) do. |
| 1729 | |
| 1730 | Since it does classification on the last token, it requires to know the position of the last token. If a |
| 1731 | `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If |
| 1732 | no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the |
| 1733 | padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in |
| 1734 | each row of the batch). |
| 1735 | """, |
| 1736 | PHI3V_START_DOCSTRING, |
| 1737 | ) |
| 1738 | # Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Phi3, LLAMA->PHI3, self.transformer->self.model, transformer_outputs->model_outputs |
| 1739 | class Phi3VForSequenceClassification(Phi3VPreTrainedModel): |
| 1740 | def __init__(self, config): |
| 1741 | super().__init__(config) |
| 1742 | self.num_labels = config.num_labels |
| 1743 | self.model = Phi3VModel(config) |
| 1744 | self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) |
| 1745 | |
| 1746 | # Initialize weights and apply final processing |
| 1747 | self.post_init() |
| 1748 | |
| 1749 | def get_input_embeddings(self): |
| 1750 | return self.model.embed_tokens |
| 1751 | |
| 1752 | def set_input_embeddings(self, value): |
| 1753 | self.model.embed_tokens = value |
| 1754 | |
| 1755 | @add_start_docstrings_to_model_forward(PHI3V_INPUTS_DOCSTRING) |
| 1756 | def forward( |
| 1757 | self, |
| 1758 | input_ids: torch.LongTensor = None, |
| 1759 | attention_mask: Optional[torch.Tensor] = None, |
| 1760 | position_ids: Optional[torch.LongTensor] = None, |
| 1761 | past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, |
| 1762 | inputs_embeds: Optional[torch.FloatTensor] = None, |
| 1763 | pixel_values: Optional[torch.FloatTensor] = None, |
| 1764 | image_sizes: Optional[torch.LongTensor] = None, |
| 1765 | labels: Optional[torch.LongTensor] = None, |
| 1766 | use_cache: Optional[bool] = None, |
| 1767 | output_attentions: Optional[bool] = None, |
| 1768 | output_hidden_states: Optional[bool] = None, |
| 1769 | return_dict: Optional[bool] = None, |
| 1770 | ) -> Union[Tuple, SequenceClassifierOutputWithPast]: |
| 1771 | r""" |
| 1772 | labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
| 1773 | Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., |
| 1774 | config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
| 1775 | `config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
| 1776 | """ |
| 1777 | return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
| 1778 | |
| 1779 | model_outputs = self.model( |
| 1780 | input_ids, |
| 1781 | attention_mask=attention_mask, |
| 1782 | position_ids=position_ids, |
| 1783 | past_key_values=past_key_values, |
| 1784 | inputs_embeds=inputs_embeds, |
| 1785 | pixel_values=pixel_values, |
| 1786 | image_sizes=image_sizes, |
| 1787 | use_cache=use_cache, |
| 1788 | output_attentions=output_attentions, |
| 1789 | output_hidden_states=output_hidden_states, |
| 1790 | return_dict=return_dict, |
| 1791 | ) |
| 1792 | hidden_states = model_outputs[0] |
| 1793 | logits = self.score(hidden_states) |
| 1794 | |
| 1795 | if input_ids is not None: |
| 1796 | batch_size = input_ids.shape[0] |
| 1797 | else: |
| 1798 | batch_size = inputs_embeds.shape[0] |
| 1799 | |
| 1800 | if self.config.pad_token_id is None and batch_size != 1: |
| 1801 | raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") |
| 1802 | if self.config.pad_token_id is None: |
| 1803 | sequence_lengths = -1 |
| 1804 | else: |
| 1805 | if input_ids is not None: |
| 1806 | # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility |
| 1807 | sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 |
| 1808 | sequence_lengths = sequence_lengths % input_ids.shape[-1] |
| 1809 | sequence_lengths = sequence_lengths.to(logits.device) |
| 1810 | else: |
| 1811 | sequence_lengths = -1 |
| 1812 | |
| 1813 | pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] |
| 1814 | |
| 1815 | loss = None |
| 1816 | if labels is not None: |
| 1817 | labels = labels.to(logits.device) |
| 1818 | if self.config.problem_type is None: |
| 1819 | if self.num_labels == 1: |
| 1820 | self.config.problem_type = "regression" |
| 1821 | elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): |
| 1822 | self.config.problem_type = "single_label_classification" |
| 1823 | else: |
| 1824 | self.config.problem_type = "multi_label_classification" |
| 1825 | |
| 1826 | if self.config.problem_type == "regression": |
| 1827 | loss_fct = MSELoss() |
| 1828 | if self.num_labels == 1: |
| 1829 | loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) |
| 1830 | else: |
| 1831 | loss = loss_fct(pooled_logits, labels) |
| 1832 | elif self.config.problem_type == "single_label_classification": |
| 1833 | loss_fct = CrossEntropyLoss() |
| 1834 | loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) |
| 1835 | elif self.config.problem_type == "multi_label_classification": |
| 1836 | loss_fct = BCEWithLogitsLoss() |
| 1837 | loss = loss_fct(pooled_logits, labels) |
| 1838 | if not return_dict: |
| 1839 | output = (pooled_logits,) + model_outputs[1:] |
| 1840 | return ((loss,) + output) if loss is not None else output |
| 1841 | |
| 1842 | return SequenceClassifierOutputWithPast( |
| 1843 | loss=loss, |
| 1844 | logits=pooled_logits, |
| 1845 | past_key_values=model_outputs.past_key_values, |
| 1846 | hidden_states=model_outputs.hidden_states, |
| 1847 | attentions=model_outputs.attentions, |
| 1848 | ) |
| 1849 | |
| 1850 | |
| 1851 | @add_start_docstrings( |
| 1852 | """ |
| 1853 | [`Phi3VModel`] with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for |
| 1854 | Named-Entity-Recognition (NER) tasks. |
| 1855 | """, |
| 1856 | PHI3V_START_DOCSTRING, |
| 1857 | ) |
| 1858 | # Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with Mpt->Phi3,MPT->PHI3,self.transformer->self.model,transformer_outputs->model_outputs |
| 1859 | class Phi3VForTokenClassification(Phi3VPreTrainedModel): |
| 1860 | def __init__(self, config: Phi3VConfig): |
| 1861 | super().__init__(config) |
| 1862 | self.num_labels = config.num_labels |
| 1863 | |
| 1864 | self.model = Phi3VModel(config) |
| 1865 | if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: |
| 1866 | classifier_dropout = config.classifier_dropout |
| 1867 | elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None: |
| 1868 | classifier_dropout = config.hidden_dropout |
| 1869 | else: |
| 1870 | classifier_dropout = 0.1 |
| 1871 | self.dropout = nn.Dropout(classifier_dropout) |
| 1872 | self.classifier = nn.Linear(config.hidden_size, config.num_labels) |
| 1873 | |
| 1874 | # Initialize weights and apply final processing |
| 1875 | self.post_init() |
| 1876 | |
| 1877 | @add_start_docstrings_to_model_forward(PHI3V_INPUTS_DOCSTRING) |
| 1878 | @add_code_sample_docstrings( |
| 1879 | checkpoint=_CHECKPOINT_FOR_DOC, |
| 1880 | output_type=TokenClassifierOutput, |
| 1881 | config_class=_CONFIG_FOR_DOC, |
| 1882 | ) |
| 1883 | def forward( |
| 1884 | self, |
| 1885 | input_ids: Optional[torch.LongTensor] = None, |
| 1886 | past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, |
| 1887 | attention_mask: Optional[torch.Tensor] = None, |
| 1888 | inputs_embeds: Optional[torch.Tensor] = None, |
| 1889 | pixel_values: Optional[torch.FloatTensor] = None, |
| 1890 | image_sizes: Optional[torch.LongTensor] = None, |
| 1891 | labels: Optional[torch.Tensor] = None, |
| 1892 | use_cache: Optional[bool] = None, |
| 1893 | output_attentions: Optional[bool] = None, |
| 1894 | output_hidden_states: Optional[bool] = None, |
| 1895 | return_dict: Optional[bool] = None, |
| 1896 | **deprecated_arguments, |
| 1897 | ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: |
| 1898 | r""" |
| 1899 | labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
| 1900 | Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., |
| 1901 | config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
| 1902 | `config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
| 1903 | """ |
| 1904 | return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
| 1905 | |
| 1906 | model_outputs = self.model( |
| 1907 | input_ids, |
| 1908 | past_key_values=past_key_values, |
| 1909 | attention_mask=attention_mask, |
| 1910 | inputs_embeds=inputs_embeds, |
| 1911 | pixel_values=pixel_values, |
| 1912 | image_sizes=image_sizes, |
| 1913 | use_cache=use_cache, |
| 1914 | output_attentions=output_attentions, |
| 1915 | output_hidden_states=output_hidden_states, |
| 1916 | return_dict=return_dict, |
| 1917 | ) |
| 1918 | |
| 1919 | hidden_states = model_outputs[0] |
| 1920 | hidden_states = self.dropout(hidden_states) |
| 1921 | logits = self.classifier(hidden_states) |
| 1922 | |
| 1923 | loss = None |
| 1924 | if labels is not None: |
| 1925 | # move labels to correct device to enable model parallelism |
| 1926 | labels = labels.to(logits.device) |
| 1927 | batch_size, seq_length = labels.shape |
| 1928 | loss_fct = CrossEntropyLoss() |
| 1929 | loss = loss_fct( |
| 1930 | logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length) |
| 1931 | ) |
| 1932 | |
| 1933 | if not return_dict: |
| 1934 | output = (logits,) + model_outputs[2:] |
| 1935 | return ((loss,) + output) if loss is not None else output |
| 1936 | |
| 1937 | return TokenClassifierOutput( |
| 1938 | loss=loss, |
| 1939 | logits=logits, |
| 1940 | hidden_states=model_outputs.hidden_states, |
| 1941 | attentions=model_outputs.attentions, |
| 1942 | ) |
| 1943 | |