pixtral.py 47.5 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import math
5
from collections.abc import Iterable, Mapping, Sequence
Patrick von Platen's avatar
Patrick von Platen committed
6
from dataclasses import dataclass, fields
7
from functools import cached_property
8
from typing import Annotated, Literal, Optional, Union
Patrick von Platen's avatar
Patrick von Platen committed
9
10
11
12

import torch
import torch.nn as nn
import torch.nn.functional as F
13
14
15
from mistral_common.protocol.instruct.messages import (ImageChunk, TextChunk,
                                                       UserMessage)
from mistral_common.protocol.instruct.request import ChatCompletionRequest
16
from mistral_common.tokens.tokenizers.multimodal import ImageEncoder
Patrick von Platen's avatar
Patrick von Platen committed
17
from PIL import Image
18
from transformers import BatchFeature, PixtralVisionConfig, TensorType
19
from transformers.image_utils import ImageInput
20
from transformers.models.pixtral.image_processing_pixtral import (
21
    _num_image_tokens as _get_pixtral_hf_num_image_tokens)
22
from transformers.models.pixtral.modeling_pixtral import (
23
    PixtralRotaryEmbedding, apply_rotary_pos_emb, position_ids_in_meshgrid)
24
from transformers.tokenization_utils_base import TextInput
Patrick von Platen's avatar
Patrick von Platen committed
25

26
from vllm.config import VllmConfig
27
from vllm.distributed import divide, get_tensor_model_parallel_world_size
28
from vllm.model_executor.layers.activation import get_act_and_mul_fn
Patrick von Platen's avatar
Patrick von Platen committed
29
from vllm.model_executor.layers.layernorm import RMSNorm
30
31
32
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
Patrick von Platen's avatar
Patrick von Platen committed
33
34
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
35
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems
36
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
37
                                    MultiModalUUIDDict, NestedTensors)
38
39
40
from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
                                   MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
41
42
                                        BaseProcessingInfo,
                                        MultiModalProcessingInfo,
43
44
                                        PromptReplacement, PromptUpdate,
                                        PromptUpdateDetails)
45
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
46
from vllm.platforms import current_platform
47
48
49
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.tokenizer import (MistralTokenizer,
                                               cached_tokenizer_from_config)
50
from vllm.utils.tensor_schema import TensorSchema, TensorShape
Patrick von Platen's avatar
Patrick von Platen committed
51

52
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
53
from .utils import flatten_bn, init_vllm_registered_model, maybe_prefix
54
55
from .vision import (VisionEncoderInfo, VisionFeatureSelectStrategy,
                     resolve_visual_encoder_outputs)
Patrick von Platen's avatar
Patrick von Platen committed
56

57
58
try:
    from xformers import ops as xops
59
60
61
62
63
64
    if (current_platform.is_cuda()
            and current_platform.has_device_capability(100)):
        # Xformers FA is not compatible with B200
        USE_XFORMERS_OPS = False
    else:
        USE_XFORMERS_OPS = True
65
66
67
except ImportError:
    USE_XFORMERS_OPS = False

Patrick von Platen's avatar
Patrick von Platen committed
68
69
PATCH_MERGE = "patch_merge"

Patrick von Platen's avatar
Patrick von Platen committed
70

71
class PixtralImagePixelInputs(TensorSchema):
72
    """
73
74
75
76
77
78
    Dimensions:
        - bn: Batch size * number of images
        - c: Number of channels (3)
        - h: Height of each image
        - w: Width of each image
    
79
    The result of stacking `ImageEncoding.tokens` from each prompt.
80
    """
81
82
83
84
    type: Literal["pixel_values"] = "pixel_values"

    images: Annotated[Union[torch.Tensor, list[torch.Tensor]],
                      TensorShape("bn", 3, "h", "w", dynamic_dims={"h", "w"})]
Patrick von Platen's avatar
Patrick von Platen committed
85
86


87
88
89
class PixtralProcessorAdapter:
    """
    Provide a HF-compatible interface for
90
    `mistral_common.tokens.tokenizers.multimodal.ImageEncoder`.
91
    """
Patrick von Platen's avatar
Patrick von Platen committed
92

93
94
    def __init__(self, tokenizer: MistralTokenizer) -> None:
        super().__init__()
Patrick von Platen's avatar
Patrick von Platen committed
95

96
        self.tokenizer = tokenizer
Patrick von Platen's avatar
Patrick von Platen committed
97

98
99
100
101
102
    @property
    def image_processor(self) -> ImageEncoder:
        image_encoder = self.tokenizer.instruct.mm_encoder
        assert isinstance(image_encoder, ImageEncoder)
        return image_encoder
103

104
105
106
    @cached_property
    def image_break_id(self) -> int:
        return self.image_processor.special_ids.img_break
Patrick von Platen's avatar
Patrick von Platen committed
107

108
109
110
    @cached_property
    def image_token_id(self) -> int:
        return self.image_processor.special_ids.img
Patrick von Platen's avatar
Patrick von Platen committed
111

112
113
114
    @cached_property
    def image_end_id(self) -> int:
        return self.image_processor.special_ids.img_end
Patrick von Platen's avatar
Patrick von Platen committed
115

116
117
118
    @cached_property
    def image_size(self) -> int:
        return self.image_processor.mm_config.max_image_size
Patrick von Platen's avatar
Patrick von Platen committed
119

120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
    @cached_property
    def patch_size(self) -> int:
        return self.image_processor.mm_config.image_patch_size

    def __call__(
        self,
        text: Optional[Union[TextInput, list[TextInput]]] = None,
        images: Optional[Union[ImageInput, list[ImageInput]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> Mapping[str, NestedTensors]:
        if text is None:
            text = []
        if not isinstance(text, list):
            text = [text]
        if images is None:
            images = []
        if not isinstance(images, list):
            images = [images]

        if not images:
            input_ids = self.tokenizer(text).input_ids

            return {"input_ids": torch.tensor(input_ids)}

        # Allow dummy text, which is used for profiling as well as token inputs
        if any(len(t) > 0 for t in text):
            raise ValueError(
                "You've passed text inputs instead of token inputs. "
                "Make sure to process your input via `mistral_common`'s "
                "tokenizer or pass a chat completion request. "
                "For more info, see: "
                "https://github.com/vllm-project/vllm/issues/8411.")

        images_processed = list[torch.Tensor]()
        images_tokens = list[torch.Tensor]()

        for image in images:
            image_inputs = self.image_processor(ImageChunk(image=image))
            image_processed = torch.tensor(image_inputs.image)
            image_tokens = torch.tensor(image_inputs.tokens)

            images_processed.append(image_processed)
            images_tokens.append(image_tokens)

165
166
167
168
169
170
        return BatchFeature({
            "input_ids":
            torch.cat(images_tokens)[None].expand(len(text), -1),
            "images":
            images_processed,
        })
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212


class PixtralProcessingInfo(BaseProcessingInfo):

    def get_tokenizer(self) -> MistralTokenizer:
        tokenizer = cached_tokenizer_from_config(self.ctx.model_config)
        if not isinstance(tokenizer, MistralTokenizer):
            raise ValueError("This model requires `--tokenizer-mode mistral`")

        return tokenizer

    def get_hf_processor(self) -> PixtralProcessorAdapter:
        return PixtralProcessorAdapter(self.get_tokenizer())

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": None}

    def get_vision_config(
        self,
        processor: Optional[PixtralProcessorAdapter] = None,
    ):
        if processor is None:
            processor = self.get_hf_processor()

        return PixtralVisionConfig(
            image_size=processor.image_size,
            patch_size=processor.patch_size,
        )

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        processor: Optional[PixtralProcessorAdapter] = None,
    ) -> int:
        if processor is None:
            processor = self.get_hf_processor()

        ncols, nrows = processor.image_processor._image_to_num_tokens(
            Image.new("RGB", (image_width, image_height)))

213
        return ncols * nrows
214
215
216
217
218
219
220
221
222
223

    def get_image_size_with_most_features(self) -> ImageSize:
        image_processor = self.get_hf_processor().image_processor
        max_image_size = image_processor.mm_config.max_image_size

        return ImageSize(width=max_image_size, height=max_image_size)


class PixtralDummyInputsBuilder(BaseDummyInputsBuilder[PixtralProcessingInfo]):

224
225
226
227
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        return ""

    def get_dummy_mm_data(
228
229
230
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
231
    ) -> MultiModalDataDict:
232
233
234
235
236
        num_images = mm_counts.get("image", 0)

        target_width, target_height = \
            self.info.get_image_size_with_most_features()

237
        return {
238
239
240
241
242
243
            "image":
            self._get_dummy_images(width=target_width,
                                   height=target_height,
                                   num_images=num_images)
        }

244
245
246
247
248
249
250
251
252
253
    def get_dummy_processor_inputs(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> ProcessorInputs:
        tokenizer = self.info.get_tokenizer()

        dummy_text = self.get_dummy_text(mm_counts)
        dummy_mm_data = self.get_dummy_mm_data(seq_len, mm_counts)
        dummy_images = dummy_mm_data.get("image", [])
254
        tokenization_kwargs = {"truncation": False}
255
256
257
258
259
260
261
262
263
264

        request = ChatCompletionRequest(messages=[
            UserMessage(content=[
                TextChunk(text=dummy_text),
                *(ImageChunk(image=image) for image in dummy_images),
            ]),
        ])
        res = tokenizer.mistral.encode_chat_completion(request)
        dummy_tokens = res.tokens

265
266
267
        return ProcessorInputs(prompt=dummy_tokens,
                               mm_data=dummy_mm_data,
                               tokenization_kwargs=tokenization_kwargs)
268

269
270
271

class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]
                                 ):
Patrick von Platen's avatar
Patrick von Platen committed
272

273
274
275
276
277
    def _get_mm_fields_config(
        self,
        hf_inputs: Mapping[str, NestedTensors],
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
278
        return dict(images=MultiModalFieldConfig.batched("image"))
279
280
281
282
283

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
284
        out_mm_kwargs: MultiModalKwargsItems,
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
    ) -> Sequence[PromptUpdate]:
        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        image_break_id = processor.image_break_id
        image_token_id = processor.image_token_id
        image_end_id = processor.image_end_id

        def get_replacement(item_idx: int):
            images = mm_items.get_items("image", ImageProcessorItems)
            image_size = images.get_image_size(item_idx)

            ncols, nrows = processor.image_processor._image_to_num_tokens(
                Image.new("RGB", (image_size.width, image_size.height)))

            tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
            tokens[-1] = image_end_id

302
            return PromptUpdateDetails.select_token_id(tokens, image_token_id)
303
304
305
306
307
308
309
310
311
312
313
314
315
316

        return [
            PromptReplacement(
                modality="image",
                target="",  # Never match the prompt (see below note)
                replacement=get_replacement,
            ),
        ]

    def _cached_apply_hf_processor(
        self,
        prompt: Union[str, list[int]],
        mm_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
317
        tokenization_kwargs: Mapping[str, object],
318
        mm_uuids: Optional[MultiModalUUIDDict] = None,
319
320
    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
        prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(
321
322
323
            prompt=prompt,
            mm_data_items=mm_data_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
324
            tokenization_kwargs=tokenization_kwargs,
325
            mm_uuids=mm_uuids,
326
327
328
        )

        # NOTE: The tokens are already inserted by the chat template
329
        return prompt_ids, mm_info, True
Patrick von Platen's avatar
Patrick von Platen committed
330

331
332
333
334

@MULTIMODAL_REGISTRY.register_processor(PixtralMultiModalProcessor,
                                        info=PixtralProcessingInfo,
                                        dummy_inputs=PixtralDummyInputsBuilder)
335
336
class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
                                      SupportsPP):
Patrick von Platen's avatar
Patrick von Platen committed
337

338
339
340
341
342
343
344
    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return None

        raise ValueError("Only image modality is supported")

345
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
Patrick von Platen's avatar
Patrick von Platen committed
346
        super().__init__()
347
348
        config = vllm_config.model_config.hf_config
        multimodal_config = vllm_config.model_config.multimodal_config
Patrick von Platen's avatar
Patrick von Platen committed
349
350
351
352
353
354
355
356
357
358
359
360
361
362
        self.config = config
        self.multimodal_config = multimodal_config

        dataclass_fields = {field.name for field in fields(VisionEncoderArgs)}
        vision_args = {
            key: value
            for key, value in self.config.vision_config.to_dict().items()
            if key in dataclass_fields
        }

        self.vision_args = VisionEncoderArgs(**vision_args)

        # init MistralForCausalLM
        self.language_model = init_vllm_registered_model(
363
            vllm_config=vllm_config,
364
365
366
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
        )
Patrick von Platen's avatar
Patrick von Platen committed
367
368

        self.vision_encoder = VisionTransformer(self.vision_args)
Patrick von Platen's avatar
Patrick von Platen committed
369
370
371
372
373
374
375
376
377
378
379
380

        if self.vision_args.add_pre_mm_projector_layer_norm:
            self.pre_mm_projector_norm = RMSNorm(self.vision_args.hidden_size,
                                                 eps=1e-5)

        if self.vision_args.mm_projector_id == PATCH_MERGE:
            self.patch_merger = PatchMerger(
                vision_encoder_dim=self.vision_args.hidden_size,
                spatial_merge_size=self.vision_args.spatial_merge_size,
                use_mlp_bias=False,
            )

Patrick von Platen's avatar
Patrick von Platen committed
381
382
383
        self.vision_language_adapter = VisionLanguageAdapter(
            self.vision_args, dim=config.text_config.hidden_size)

384
385
386
        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[PixtralImagePixelInputs]:
        images = kwargs.pop("images", None)
        if images is None:
            return None

        return PixtralImagePixelInputs(
            type="pixel_values",
            images=flatten_bn(images),
        )

    def _process_image_input(
        self,
        image_input: PixtralImagePixelInputs,
    ) -> tuple[torch.Tensor, ...]:
        images = image_input["images"]
        image_features = self.vision_encoder(images)
        feature_sizes = [
            image_feature.shape[0] for image_feature in image_features
        ]
Patrick von Platen's avatar
Patrick von Platen committed
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
        image_features = torch.cat(image_features)
        if self.vision_args.add_pre_mm_projector_layer_norm:
            image_features = self.pre_mm_projector_norm(image_features)
        if self.vision_args.mm_projector_id == PATCH_MERGE:
            patch_size = self.vision_args.patch_size
            spatial_merge_size_square = self.vision_args.spatial_merge_size**2
            img_patch_dims = [(img.shape[1] // patch_size,
                               img.shape[2] // patch_size) for img in images]
            feature_sizes = [
                feature_size // spatial_merge_size_square
                for feature_size in feature_sizes
            ]
            image_features = self.patch_merger(image_features,
                                               image_sizes=img_patch_dims)
        image_embeds = self.vision_language_adapter(image_features)
422
423
424
        image_embeds = torch.split(image_embeds, feature_sizes)
        return image_embeds

425
426
427
    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

428
429
    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:
430
        image_input = self._parse_and_validate_image_input(**kwargs)
431
        if image_input is None:
432
            return []
433

434
        return self._process_image_input(image_input)
435

Patrick von Platen's avatar
Patrick von Platen committed
436
437
438
439
440
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
441
        inputs_embeds: Optional[torch.Tensor] = None,
Patrick von Platen's avatar
Patrick von Platen committed
442
        **kwargs: object,
443
    ) -> Union[torch.Tensor, IntermediateTensors]:
444
        """Run forward pass for pixtral."""
445
446
        if intermediate_tensors is not None:
            inputs_embeds = None
Patrick von Platen's avatar
Patrick von Platen committed
447
448
449

        hidden_states = self.language_model.model(input_ids,
                                                  positions,
450
                                                  intermediate_tensors,
Patrick von Platen's avatar
Patrick von Platen committed
451
452
453
454
455
456
457
458
                                                  inputs_embeds=inputs_embeds)

        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> Optional[torch.Tensor]:
459
        return self.language_model.compute_logits(hidden_states)
Patrick von Platen's avatar
Patrick von Platen committed
460

461
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
Patrick von Platen's avatar
Patrick von Platen committed
462

463
        def is_vision_encoder_weights(weight: tuple[str, torch.Tensor]):
Patrick von Platen's avatar
Patrick von Platen committed
464
465
            return weight[0].startswith("vision_encoder")

466
        def is_vision_lang_adapter_weights(weight: tuple[str, torch.Tensor]):
Patrick von Platen's avatar
Patrick von Platen committed
467
468
            return weight[0].startswith("vision_language_adapter")

469
        def is_patch_merger(weight: tuple[str, torch.Tensor]):
Patrick von Platen's avatar
Patrick von Platen committed
470
471
            return weight[0].startswith("patch_merger")

472
        def is_pre_mm_projector_norm(weight: tuple[str, torch.Tensor]):
Patrick von Platen's avatar
Patrick von Platen committed
473
474
            return weight[0].startswith("pre_mm_projector_norm")

475
        # Get references to parameters for direct loading
Patrick von Platen's avatar
Patrick von Platen committed
476
        vision_encoder_dict = dict(self.vision_encoder.named_parameters())
Patrick von Platen's avatar
Patrick von Platen committed
477
478
479
480
481
        patch_merger_dict = dict(self.patch_merger.named_parameters(
        )) if self.vision_args.mm_projector_id == PATCH_MERGE else dict()
        pre_mm_projector_norm_dict = dict(
            self.pre_mm_projector_norm.named_parameters(
            )) if self.vision_args.add_pre_mm_projector_layer_norm else dict()
482
        vision_lang_adapter_dict = dict(
Patrick von Platen's avatar
Patrick von Platen committed
483
            self.vision_language_adapter.named_parameters())
484
485
486
487
488
489
490
491
492
493

        def llm_weights_generator():
            # Single pass over weights
            for name, w in weights:
                if is_vision_encoder_weights((name, w)):
                    # Load vision encoder weights directly
                    trimmed_name = '.'.join(name.split(".")[1:])
                    param = vision_encoder_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
Patrick von Platen's avatar
Patrick von Platen committed
494
495
496
497
498
499
500
501
502
503
504
505
                elif is_patch_merger((name, w)):
                    # Load vision patch merger weights directly
                    trimmed_name = '.'.join(name.split(".")[1:])
                    param = patch_merger_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                elif is_pre_mm_projector_norm((name, w)):
                    # Load vision pre_mm_projector_norm weights directly
                    trimmed_name = '.'.join(name.split(".")[1:])
                    param = pre_mm_projector_norm_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
506
507
508
509
510
511
512
513
514
515
516
517
518
                elif is_vision_lang_adapter_weights((name, w)):
                    # Load vision-language adapter weights directly
                    trimmed_name = '.'.join(name.split(".")[1:])
                    param = vision_lang_adapter_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                else:
                    # LLM weights: yield them to be loaded
                    # by language_model.load_weights
                    yield (name, w)

        # Now we call the language model load with the generator
        self.language_model.load_weights(llm_weights_generator())
Patrick von Platen's avatar
Patrick von Platen committed
519
520
521
522
523
524
525
526
527
528
529
530
531
532


# Vision encoder
@dataclass
class VisionEncoderArgs:
    hidden_size: int
    num_channels: int
    image_size: int
    patch_size: int
    intermediate_size: int
    num_hidden_layers: int
    num_attention_heads: int
    rope_theta: float  # for rope-2D
    image_token_id: int
533
    adapter_bias: bool = True
Patrick von Platen's avatar
Patrick von Platen committed
534
535
536
    spatial_merge_size: int = 1
    add_pre_mm_projector_layer_norm: bool = False
    mm_projector_id: str = ""
Patrick von Platen's avatar
Patrick von Platen committed
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588


def _reshape_for_broadcast(freqs_cis: torch.Tensor,
                           x: torch.Tensor) -> torch.Tensor:
    """
    freqs_cis: complex - (seq_len, head_dim / 2)
    x: complex - (bsz, seq_len, head_dim / 2)
    """
    ndim = x.ndim
    assert ndim > 1
    assert freqs_cis.shape == (x.shape[1], x.shape[-1]), (
        freqs_cis.shape,
        (x.shape[1], x.shape[-1]),
    )
    shape = [
        d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)
    ]
    return freqs_cis.view(*shape)


def precompute_freqs_cis_2d(
    dim: int,
    height: int,
    width: int,
    theta: float,
) -> torch.Tensor:
    """
    freqs_cis: 2D complex tensor of shape (height, width, dim // 2)
        to be indexed by (height, width) position tuples
    """
    # (dim / 2) frequency bases
    freqs = 1.0 / (theta**(torch.arange(0, dim, 2).float() / dim))

    h = torch.arange(height, device=freqs.device)
    w = torch.arange(width, device=freqs.device)

    freqs_h = torch.outer(h, freqs[::2]).float()
    freqs_w = torch.outer(w, freqs[1::2]).float()
    freqs_2d = torch.cat(
        [
            freqs_h[:, None, :].repeat(1, width, 1),
            freqs_w[None, :, :].repeat(height, 1, 1),
        ],
        dim=-1,
    )
    return torch.polar(torch.ones_like(freqs_2d), freqs_2d)


def apply_rotary_emb_vit(
    xq: torch.Tensor,
    xk: torch.Tensor,
    freqs_cis: torch.Tensor,
589
) -> tuple[torch.Tensor, torch.Tensor]:
Patrick von Platen's avatar
Patrick von Platen committed
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
    assert freqs_cis.dtype == torch.complex64
    freqs_cis = _reshape_for_broadcast(freqs_cis, xq_)
    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
    return xq_out.type_as(xq), xk_out.type_as(xk)


class FeedForward(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        assert args.intermediate_size is not None
        self.w1 = nn.Linear(args.hidden_size,
                            args.intermediate_size,
                            bias=False)
        self.w2 = nn.Linear(args.intermediate_size,
                            args.hidden_size,
                            bias=False)
        self.w3 = nn.Linear(args.hidden_size,
                            args.intermediate_size,
                            bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w2(F.silu(self.w1(x)) * self.w3(x))


class Attention(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.args = args
        assert not args.hidden_size % args.num_attention_heads
        self.n_heads = args.num_attention_heads
        self.head_dim = args.hidden_size // args.num_attention_heads

        self.wq = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
        self.wk = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
        self.wv = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
        self.wo = nn.Linear(args.hidden_size, args.hidden_size, bias=False)

    def forward(
        self,
        x: torch.Tensor,
635
        mask: torch.Tensor,
Patrick von Platen's avatar
Patrick von Platen committed
636
637
638
639
640
641
642
643
644
645
        freqs_cis: torch.Tensor,
    ) -> torch.Tensor:
        batch, patches, _ = x.shape

        q, k, v = self.wq(x), self.wk(x), self.wv(x)
        q = q.reshape(batch, patches, self.n_heads, self.head_dim)
        k = k.reshape(batch, patches, self.n_heads, self.head_dim)
        v = v.reshape(batch, patches, self.n_heads, self.head_dim)

        q, k = apply_rotary_emb_vit(q, k, freqs_cis=freqs_cis)
646
647
648
649
650
651
652
653
654
655
656
657
658

        if USE_XFORMERS_OPS:
            out = xops.memory_efficient_attention(q, k, v, attn_bias=mask)
        else:
            q = q.transpose(1, 2)
            k = k.transpose(1, 2)
            v = v.transpose(1, 2)
            out = nn.functional.scaled_dot_product_attention(q,
                                                             k,
                                                             v,
                                                             attn_mask=mask)
            out = out.transpose(1, 2)

Patrick von Platen's avatar
Patrick von Platen committed
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
        out = out.reshape(batch, patches, self.n_heads * self.head_dim)
        return self.wo(out)


class TransformerBlock(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.attention = Attention(args)
        self.feed_forward = FeedForward(args)
        self.attention_norm = RMSNorm(args.hidden_size, eps=1e-5)
        self.ffn_norm = RMSNorm(args.hidden_size, eps=1e-5)

    def forward(
        self,
        x: torch.Tensor,
675
        mask: torch.Tensor,
Patrick von Platen's avatar
Patrick von Platen committed
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
        freqs_cis: torch.Tensor,
    ) -> torch.Tensor:
        r = self.attention.forward(self.attention_norm(x),
                                   mask=mask,
                                   freqs_cis=freqs_cis)
        h = x + r
        r = self.feed_forward.forward(self.ffn_norm(h))
        out = h + r
        return out


class Transformer(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.layers = torch.nn.ModuleList()
        for _ in range(args.num_hidden_layers):
            self.layers.append(TransformerBlock(args))

    def forward(
        self,
        x: torch.Tensor,
698
        mask: torch.Tensor,
Patrick von Platen's avatar
Patrick von Platen committed
699
700
701
702
703
704
705
        freqs_cis: Optional[torch.Tensor],
    ) -> torch.Tensor:
        for layer in self.layers:
            x = layer(x, mask=mask, freqs_cis=freqs_cis)
        return x


706
def position_meshgrid(patch_embeds_list: list[torch.Tensor], ) -> torch.Tensor:
Patrick von Platen's avatar
Patrick von Platen committed
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
    positions = torch.cat([
        torch.stack(
            torch.meshgrid(
                torch.arange(p.shape[-2]),
                torch.arange(p.shape[-1]),
                indexing="ij",
            ),
            dim=-1,
        ).reshape(-1, 2) for p in patch_embeds_list
    ])
    return positions


class VisionTransformer(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.args = args
        self.patch_conv = nn.Conv2d(
            in_channels=args.num_channels,
            out_channels=args.hidden_size,
            kernel_size=args.patch_size,
            stride=args.patch_size,
            bias=False,
        )
        self.ln_pre = RMSNorm(args.hidden_size, eps=1e-5)
        self.transformer = Transformer(args)

        head_dim = self.args.hidden_size // self.args.num_attention_heads
        assert head_dim % 2 == 0, "ROPE requires even head_dim"
        self._freqs_cis: Optional[torch.Tensor] = None

    @property
    def max_patches_per_side(self) -> int:
        return self.args.image_size // self.args.patch_size

    @property
744
    def device(self) -> torch.types.Device:
Patrick von Platen's avatar
Patrick von Platen committed
745
746
747
        return next(self.parameters()).device

    @property
748
    def dtype(self) -> torch.dtype:
Patrick von Platen's avatar
Patrick von Platen committed
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
        return next(self.parameters()).dtype

    @property
    def freqs_cis(self) -> torch.Tensor:
        if self._freqs_cis is None:
            self._freqs_cis = precompute_freqs_cis_2d(
                dim=self.args.hidden_size // self.args.num_attention_heads,
                height=self.max_patches_per_side,
                width=self.max_patches_per_side,
                theta=self.args.rope_theta,
            )

        if self._freqs_cis.device != self.device:
            self._freqs_cis = self._freqs_cis.to(device=self.device)

        return self._freqs_cis

    def forward(
        self,
768
        images: list[torch.Tensor],
Patrick von Platen's avatar
Patrick von Platen committed
769
770
771
    ) -> torch.Tensor:
        """
        Args:
772
            images: list of N_img images of variable sizes,
Patrick von Platen's avatar
Patrick von Platen committed
773
774
                each of shape (C, H, W)
        Returns:
775
            image_features: tensor of token features for
Patrick von Platen's avatar
Patrick von Platen committed
776
777
778
779
780
781
782
                all tokens of all images of shape (N_toks, D)
        """
        # pass images through initial convolution independently
        patch_embeds_list = [
            self.patch_conv(img.unsqueeze(0).to(self.dtype)) for img in images
        ]

783
784
785
786
787
        patch_embeds = [
            p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list
        ]
        embed_sizes = [p.shape[1] for p in patch_embeds]

Patrick von Platen's avatar
Patrick von Platen committed
788
        # flatten to a single sequence
789
        patch_embeds = torch.cat(patch_embeds, dim=1)
Patrick von Platen's avatar
Patrick von Platen committed
790
791
792
793
794
795
796
        patch_embeds = self.ln_pre(patch_embeds)

        # positional embeddings
        positions = position_meshgrid(patch_embeds_list).to(self.device)
        freqs_cis = self.freqs_cis[positions[:, 0], positions[:, 1]]

        # pass through Transformer with a block diagonal mask delimiting images
797
798
799
800
        if USE_XFORMERS_OPS:
            mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], )
        else:
801
802
803
804
805
            from transformers.models.pixtral.modeling_pixtral import (
                generate_block_attention_mask)
            mask = generate_block_attention_mask(
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
                patch_embeds)
Patrick von Platen's avatar
Patrick von Platen committed
806
807
        out = self.transformer(patch_embeds, mask=mask, freqs_cis=freqs_cis)

808
809
        # squeeze dim 0 and split into separate tensors for each image
        return torch.split(out.squeeze(0), embed_sizes)
Patrick von Platen's avatar
Patrick von Platen committed
810
811
812
813
814
815
816
817
818
819


class VisionLanguageAdapter(nn.Module):

    def __init__(self, args: VisionEncoderArgs, dim: int):
        super().__init__()
        assert isinstance(args, VisionEncoderArgs)
        self.w_in = nn.Linear(
            args.hidden_size,
            dim,
820
            bias=args.adapter_bias,
Patrick von Platen's avatar
Patrick von Platen committed
821
822
        )
        self.gelu = nn.GELU()
823
        self.w_out = nn.Linear(dim, dim, bias=args.adapter_bias)
Patrick von Platen's avatar
Patrick von Platen committed
824
825
826

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w_out(self.gelu(self.w_in(x)))
827
828


Patrick von Platen's avatar
Patrick von Platen committed
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
class PatchMerger(nn.Module):
    """
    Learned merging of spatial_merge_size ** 2 patches
    """

    def __init__(
        self,
        vision_encoder_dim: int,
        spatial_merge_size: int,
        use_mlp_bias: bool = False,
    ) -> None:
        super().__init__()

        mlp_input_dim = vision_encoder_dim * (spatial_merge_size**2)

        self.spatial_merge_size = spatial_merge_size
        self.mlp_input_dim = mlp_input_dim

        self.merging_layer = nn.Linear(
            mlp_input_dim,
            vision_encoder_dim,
            bias=use_mlp_bias,
        )

    def forward(self, x: torch.Tensor,
                image_sizes: list[tuple[int, int]]) -> torch.Tensor:
        # image_sizes specified in tokens
        assert sum([h * w for h, w in image_sizes]) == len(x)

        # x is (N, vision_encoder_dim)
        x = self.permute(x, image_sizes)

861
862
        # x is (N / spatial_merge_size ** 2,
        #       vision_encoder_dim * spatial_merge_size ** 2)
Patrick von Platen's avatar
Patrick von Platen committed
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
        x = self.merging_layer(x)

        # x is (N / spatial_merge_size ** 2, vision_encoder_dim)
        return x

    def permute(
        self,
        x: torch.Tensor,
        image_sizes: list[tuple[int, int]],
    ) -> torch.Tensor:
        """
        Args:
            x: (N, D) where N is flattened and concatenated patch tokens
                for all images
            image_sizes: list of tuple of (height, width) in tokens for
                each image
        Returns:
            image_features: reorders patch tokens so each grid of
                (spatial_merge_size, spatial_merge_size) is contiguous.
                now (N / spatial_merge_size ** 2, D * spatial_merge_size ** 2)
        """

        sub_grids = get_sub_grids(
            x=x,
            image_sizes=image_sizes,
            spatial_merge_size=self.spatial_merge_size
        )  # list of [d x sub_grid_size x sub_grid_size x n_patches]
        permuted_tensor: list[torch.Tensor] = []
        for grid in sub_grids:
            n_patches = grid.shape[-1]
            permuted_tensor.append(grid.view(-1, n_patches).t(
            ))  # n_patches x d * sub_grid_size * sub_grid_size
        return torch.cat(
            permuted_tensor, dim=0
        )  # (N / spatial_merge_size ** 2, d * spatial_merge_size ** 2)


def get_sub_grids(
    x: torch.Tensor,
    image_sizes: list[tuple[int, int]],
    spatial_merge_size: int,
) -> list[torch.Tensor]:
    # image_sizes specified in tokens
    tokens_per_image = [h * w for h, w in image_sizes]
    d = x.shape[-1]
    all_img_sub_grids: list[torch.Tensor] = []
    sub_grid_size = spatial_merge_size

    for image_index, image_tokens in enumerate(x.split(tokens_per_image)):
        # Reshape image_tokens into a 2D grid
        h, w = image_sizes[image_index]
        image_grid = image_tokens.view(h, w, d).permute(
            2, 0, 1)[None, :, :, :]  # 1 x d x h x w
        sub_grids = torch.nn.functional.unfold(image_grid,
                                               kernel_size=sub_grid_size,
                                               stride=sub_grid_size)
        sub_grids = sub_grids.view(
            1, d, sub_grid_size, sub_grid_size,
            -1)  # 1 x d x sub_grid_size x sub_grid_size x n_patches

        all_img_sub_grids.append(sub_grids[0])

    return all_img_sub_grids


928
929
930
931
932
933
934
935
#### HF Transformers version of Pixtral ####
# Based off https://github.com/huggingface/transformers/blob/d7950bff82b18c823193d17d72188c5e46d06c83/src/transformers/models/pixtral/modeling_pixtral.py
# This model follows the Llava family, meaning image embeddings are placed
# instead of the `[IMG]` token placeholders.
# The model uses [`PixtralVisionModel`] for its vision encoder,
# and [`MistralForCausalLM`] for its language decoder.


936
937
938
939
940
941
942
943
class PixtralHFEncoderInfo(VisionEncoderInfo[PixtralVisionConfig]):

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
944
945
946
        ncols, nrows = self.get_patch_grid_size(
            image_width=image_width,
            image_height=image_height,
947
        )
948
        return ncols * nrows
949

950
951
952
953
    def get_image_size(self) -> int:
        return self.vision_config.image_size

    def get_patch_size(self) -> int:
954
955
956
        # spatial_merge_size is needed for Mistral3
        spatial_merge_size = getattr(self.hf_config, "spatial_merge_size", 1)
        return self.vision_config.patch_size * spatial_merge_size
957
958

    def get_patch_grid_length(self) -> int:
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
        image_size, patch_size = self.get_image_size(), self.get_patch_size()

        # Since interpolation is applied, the image size need not be divisible
        # assert image_size % patch_size == 0
        return image_size // patch_size

    # Adapted from: https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/pixtral/image_processing_pixtral.py#L99
    def get_patch_grid_size(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> tuple[int, int]:
        max_width = max_height = self.get_image_size()
        patch_width = patch_height = self.get_patch_size()

        ratio = max(image_width / max_width, image_height / max_height)

        if ratio > 1:
978
979
            image_width = int(math.floor(image_width / ratio))
            image_height = int(math.floor(image_height / ratio))
980
981
982
983
984
985
986

        nrows, ncols = _get_pixtral_hf_num_image_tokens(
            (image_height, image_width),
            (patch_height, patch_width),
        )  # type: ignore

        return ncols, nrows
987
988
989
990


class PixtralHFMLP(nn.Module):

991
992
993
994
995
996
997
    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        prefix: str = "",
    ) -> None:
998
        super().__init__()
999

1000
        assert config.intermediate_size is not None
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=config.hidden_size,
            output_sizes=[config.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj")
        self.down_proj = RowParallelLinear(input_size=config.intermediate_size,
                                           output_size=config.hidden_size,
                                           bias=False,
                                           quant_config=quant_config,
                                           prefix=f"{prefix}.down_proj")
        self.act_and_mul = get_act_and_mul_fn(config.hidden_act)
1013
1014

    def forward(self, x: torch.Tensor) -> torch.Tensor:
1015
1016
1017
1018
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_and_mul(gate_up)
        x, _ = self.down_proj(x)
        return x
1019
1020
1021
1022


class PixtralHFAttention(nn.Module):

1023
1024
1025
1026
1027
1028
1029
    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        prefix: str = "",
    ) -> None:
1030
        super().__init__()
1031

1032
1033
        self.config = config
        assert not config.hidden_size % config.num_attention_heads
1034
1035
1036
        self.total_num_heads = config.num_attention_heads
        tp_size = get_tensor_model_parallel_world_size()
        self.n_heads = divide(config.num_attention_heads, tp_size)
1037
1038
        self.head_dim = config.hidden_size // config.num_attention_heads

1039
1040
1041
        self.qkv_proj = QKVParallelLinear(
            hidden_size=config.hidden_size,
            head_size=self.head_dim,
1042
            total_num_heads=self.total_num_heads,
1043
1044
1045
1046
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
1047
        assert self.total_num_heads * self.head_dim == config.hidden_size
1048
1049
1050
1051
1052
1053
1054
        self.o_proj = RowParallelLinear(
            input_size=config.hidden_size,
            output_size=config.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )
1055
1056
1057
1058

    def forward(
        self,
        hidden_states: torch.Tensor,
1059
        attention_mask: torch.Tensor,
1060
        position_embeddings: torch.Tensor,
1061
    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
1062
        batch, patches, _ = hidden_states.size()
1063

1064
1065
        qkv_states, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv_states.chunk(3, dim=-1)
1066

1067
1068
1069
        # Transpose q and k to apply HF's Rotary Position Embedding
        q = q.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
        k = k.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
1070
        v = v.view(batch, patches, self.n_heads, self.head_dim)
1071
        cos, sin = position_embeddings
1072
        q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0)
1073

1074
1075
1076
1077
1078
1079
1080
1081
1082
        if USE_XFORMERS_OPS:
            # Transpose q and k back for attention
            q = q.transpose(1, 2).contiguous()
            k = k.transpose(1, 2).contiguous()
            out = xops.memory_efficient_attention(q,
                                                  k,
                                                  v,
                                                  attn_bias=attention_mask)
        else:
1083
            v = v.transpose(1, 2)
1084
1085
1086
            out = nn.functional.scaled_dot_product_attention(
                q, k, v, attn_mask=attention_mask)
            out = out.transpose(1, 2)
1087

1088
1089
        out = out.view(batch, patches, self.n_heads * self.head_dim)
        attn_output, _ = self.o_proj(out)
1090

1091
        return attn_output, None
1092
1093
1094
1095


class PixtralHFTransformerBlock(nn.Module):

1096
1097
1098
1099
1100
1101
1102
    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        prefix: str = "",
    ) -> None:
1103
        super().__init__()
1104

1105
        self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5)
1106
1107
1108
1109
1110
1111
        self.attention = PixtralHFAttention(config,
                                            quant_config=quant_config,
                                            prefix=f"{prefix}.attention")
        self.feed_forward = PixtralHFMLP(config,
                                         quant_config=quant_config,
                                         prefix=f"{prefix}.feed_forward")
1112
1113
1114
1115
1116
        self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5)

    def forward(
        self,
        hidden_states: torch.Tensor,
1117
        attention_mask: torch.Tensor,
1118
1119
        position_embeddings: torch.Tensor,
    ) -> torch.Tensor:
1120
1121
1122
        r, _ = self.attention.forward(self.attention_norm(hidden_states),
                                      attention_mask=attention_mask,
                                      position_embeddings=position_embeddings)
1123
1124
1125
1126
1127
1128
1129
1130
        h = hidden_states + r
        r = self.feed_forward.forward(self.ffn_norm(h))
        out = h + r
        return out


class PixtralHFTransformer(nn.Module):

1131
1132
1133
1134
1135
1136
1137
1138
    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        num_hidden_layers_override: Optional[int] = None,
        prefix: str = "",
    ) -> None:
1139
        super().__init__()
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151

        if num_hidden_layers_override is None:
            num_hidden_layers = config.num_hidden_layers
        else:
            num_hidden_layers = num_hidden_layers_override

        self.layers = nn.ModuleList([
            PixtralHFTransformerBlock(config=config,
                                      quant_config=quant_config,
                                      prefix=f"{prefix}.layers.{layer_idx}")
            for layer_idx in range(num_hidden_layers)
        ])
1152
1153
1154
1155

    def forward(
        self,
        x: torch.Tensor,
1156
        attention_mask: torch.Tensor,
1157
        position_embeddings: torch.Tensor,
1158
        return_all_hidden_states: bool,
1159
    ) -> torch.Tensor:
1160
        hidden_states_pool = [x]
1161

1162
1163
        for layer in self.layers:
            x = layer(x, attention_mask, position_embeddings)
1164
1165
1166
1167
1168
1169
            if return_all_hidden_states:
                hidden_states_pool.append(x)
        # If we have multiple feature sample layers, we return all hidden
        # states in order and grab the ones we need by index.
        if return_all_hidden_states:
            return hidden_states_pool
1170
1171
1172
1173
1174
        return x


class PixtralHFVisionModel(nn.Module):

1175
1176
1177
1178
1179
1180
1181
1182
1183
    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        num_hidden_layers_override: Optional[int] = None,
        require_post_norm: Optional[bool] = None,
        prefix: str = "",
    ) -> None:
1184
1185
1186
        super().__init__()

        self.config = config
1187

1188
1189
1190
1191
1192
1193
1194
1195
        self.patch_conv = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=config.hidden_size,
            kernel_size=config.patch_size,
            stride=config.patch_size,
            bias=False,
        )
        self.ln_pre = RMSNorm(config.hidden_size, eps=1e-5)
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
        self.transformer = PixtralHFTransformer(
            config,
            quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            prefix=f"{prefix}.transformer",
        )

        num_hidden_layers = config.num_hidden_layers
        if len(self.transformer.layers) > config.num_hidden_layers:
            raise ValueError(
                f"The original encoder only has {num_hidden_layers} "
                f"layers, but you requested {len(self.transformer.layers)} "
                "layers.")

        if require_post_norm is True:
            msg = "PixtralHFVisionModel does not have post-layernorm"
            raise ValueError(msg)

1214
1215
1216
1217
1218
1219
1220
        self.dtype = next(self.parameters()).dtype
        self.device = next(self.parameters()).device
        self.patch_positional_embedding = PixtralRotaryEmbedding(
            config, self.device)

    def forward(
        self,
1221
        pixel_values: list[torch.Tensor],
1222
1223
1224
        *,
        select_layers: Optional[list[int]] = None,
        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
1225
    ) -> tuple[torch.Tensor, ...]:
1226
1227
        """
        Args:
1228
1229
1230
1231
            pixel_values: Each image to be processed will be a separate tensor
                in pixel_values. This means it will be a list of tensors
                because multiple requests batched can have multiple images,
                each with their own shape potentially
1232
            select_layers: Layer indices whose features should be
1233
1234
                concatenated and used as the visual encoder output. If none
                are provided, the last layer is used.
1235

1236
1237
1238
1239
1240
1241
        Returns:
            image_features: tensor of token features for
                all tokens of all images of shape (N_toks, D)
        """
        # pass images through initial convolution independently
        patch_embeds_list = [
1242
            self.patch_conv(img.unsqueeze(0).to(self.dtype))
1243
1244
1245
            for img in pixel_values
        ]

1246
1247
1248
1249
1250
        patch_embeds = [
            p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list
        ]
        embed_sizes = [p.shape[1] for p in patch_embeds]

1251
        # flatten to a single sequence
1252
        patch_embeds = torch.cat(patch_embeds, dim=1)
1253
1254
1255
1256
1257
1258
1259
1260
1261
        patch_embeds = self.ln_pre(patch_embeds)

        # positional embeddings
        position_ids = position_ids_in_meshgrid(
            patch_embeds_list,
            max_width=self.config.image_size // self.config.patch_size).to(
                self.device)
        position_embedding = self.patch_positional_embedding(
            patch_embeds, position_ids)
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272

        if USE_XFORMERS_OPS:
            attention_mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], )
        else:
            from transformers.models.pixtral.modeling_pixtral import (
                generate_block_attention_mask)
            attention_mask = generate_block_attention_mask(
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
                patch_embeds)

1273
1274
1275
1276
        out = self.transformer(
            patch_embeds,
            attention_mask,
            position_embedding,
1277
1278
            return_all_hidden_states=select_layers is not None,
        )
1279

1280
1281
1282
1283
1284
1285
1286
        out = resolve_visual_encoder_outputs(
            out,
            None,
            select_layers=select_layers,
            max_possible_layers=self.config.num_hidden_layers,
            feature_select_strategy=feature_select_strategy,
        )
1287

1288
        # squeeze dim 0 and split into separate tensors for each image
1289
        return torch.split(out.squeeze(0), embed_sizes)
1290
1291
1292

    # (TODO) Add prefix argument for filtering out weights to be loaded
    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
1293
1294
    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
1295
1296
1297
1298
1299
1300
1301
1302
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
1303
        params_dict = dict(self.named_parameters())
1304
        loaded_params: set[str] = set()
1305
        layer_count = len(self.transformer.layers)
1306
1307

        for name, loaded_weight in weights:
1308
1309
1310
1311
1312
1313
            # omit layers when num_hidden_layers_override is set
            if name.startswith("transformer.layers"):
                layer_idx = int(name.split(".")[2])
                if layer_idx >= layer_count:
                    continue

1314
1315
1316
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
1317
1318
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
1319
1320
1321
1322
1323
1324
1325
1326
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
1327
1328
            loaded_params.add(name)
        return loaded_params