pixtral.py 46.5 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 typing import Annotated, Literal
Patrick von Platen's avatar
Patrick von Platen committed
8
9
10
11

import torch
import torch.nn as nn
import torch.nn.functional as F
12
13
from mistral_common.protocol.instruct.chunk import ImageChunk, TextChunk
from mistral_common.protocol.instruct.messages import UserMessage
14
from mistral_common.protocol.instruct.request import ChatCompletionRequest
15
from transformers import BatchFeature, PixtralVisionConfig
16
from transformers.models.pixtral.image_processing_pixtral import (
17
18
    _num_image_tokens as _get_pixtral_hf_num_image_tokens,
)
19
from transformers.models.pixtral.modeling_pixtral import (
20
21
22
23
    PixtralRotaryEmbedding,
    apply_rotary_pos_emb,
    position_ids_in_meshgrid,
)
Patrick von Platen's avatar
Patrick von Platen committed
24

25
from vllm.config import VllmConfig
26
from vllm.config.multimodal import BaseDummyOptions
27
from vllm.distributed import divide, get_tensor_model_parallel_world_size
28
from vllm.inputs import MultiModalDataDict
29
from vllm.model_executor.layers.activation import get_act_and_mul_fn
30
from vllm.model_executor.layers.conv import Conv2dLayer
Patrick von Platen's avatar
Patrick von Platen committed
31
from vllm.model_executor.layers.layernorm import RMSNorm
32
33
34
35
36
from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
Patrick von Platen's avatar
Patrick von Platen committed
37
38
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
39
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems
40
41
42
43
from vllm.multimodal.inputs import (
    MultiModalFieldConfig,
    NestedTensors,
)
44
45
46
47
48
from vllm.multimodal.parse import (
    ImageProcessorItems,
    ImageSize,
    MultiModalDataItems,
)
49
from vllm.multimodal.processing import BaseDummyInputsBuilder
50
from vllm.multimodal.processing.processor import (
51
52
53
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    MultiModalProcessingInfo,
54
    ProcessorInputs,
55
56
57
    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
58
    TimingContext,
59
)
60
from vllm.platforms import current_platform
61
from vllm.sequence import IntermediateTensors
62
63
from vllm.tokenizers import cached_tokenizer_from_config
from vllm.tokenizers.mistral import MistralTokenizer
64
from vllm.transformers_utils.processors.pixtral import MistralCommonPixtralProcessor
65
from vllm.utils.collection_utils import is_list_of
66
from vllm.utils.tensor_schema import TensorSchema, TensorShape
Patrick von Platen's avatar
Patrick von Platen committed
67

68
69
from .interfaces import (
    MultiModalEmbeddings,
70
    SupportsEagle3,
71
72
73
    SupportsLoRA,
    SupportsMultiModal,
    SupportsPP,
74
    supports_eagle3,
75
76
)
from .module_mapping import MultiModelKeys
77
from .utils import StageMissingLayer, init_vllm_registered_model, maybe_prefix
78
79
80
from .vision import (
    VisionEncoderInfo,
    VisionFeatureSelectStrategy,
81
    is_vit_use_data_parallel,
82
83
    resolve_visual_encoder_outputs,
)
Patrick von Platen's avatar
Patrick von Platen committed
84

85
try:
86
    # Note: vLLM does not install xformers by default.
87
    from xformers import ops as xops
88
89

    if current_platform.is_cuda() and current_platform.has_device_capability(100):
90
91
92
93
        # Xformers FA is not compatible with B200
        USE_XFORMERS_OPS = False
    else:
        USE_XFORMERS_OPS = True
94
95
96
except ImportError:
    USE_XFORMERS_OPS = False

Patrick von Platen's avatar
Patrick von Platen committed
97
98
PATCH_MERGE = "patch_merge"

Patrick von Platen's avatar
Patrick von Platen committed
99

100
101
102
103
def _is_layer_none_or_staged(layer: nn.Module) -> bool:
    return layer is None or isinstance(layer, StageMissingLayer)


104
class PixtralImagePixelInputs(TensorSchema):
105
    """
106
107
108
109
110
    Dimensions:
        - bn: Batch size * number of images
        - c: Number of channels (3)
        - h: Height of each image
        - w: Width of each image
111

112
    The result of stacking `ImageEncoding.tokens` from each prompt.
113
    """
114

115
116
    type: Literal["pixel_values"] = "pixel_values"

117
    images: Annotated[
118
        torch.Tensor | list[torch.Tensor],
119
120
        TensorShape("bn", 3, "h", "w", dynamic_dims={"h", "w"}),
    ]
Patrick von Platen's avatar
Patrick von Platen committed
121
122


123
124
class PixtralProcessingInfo(BaseProcessingInfo):
    def get_tokenizer(self) -> MistralTokenizer:
125
        tokenizer = cached_tokenizer_from_config(self.ctx.model_config)
126
127
128
129
130
        if not isinstance(tokenizer, MistralTokenizer):
            raise ValueError("This model requires `--tokenizer-mode mistral`")

        return tokenizer

131
132
133
134
135
136
    def get_hf_processor(self, **kwargs) -> MistralCommonPixtralProcessor:
        return self.ctx.init_processor(
            MistralCommonPixtralProcessor,
            tokenizer=self.get_tokenizer(),
            **kwargs,
        )
137

138
    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
139
140
141
142
        return {"image": None}

    def get_image_size_with_most_features(self) -> ImageSize:
        image_processor = self.get_hf_processor().image_processor
143
        max_image_size = image_processor.mm_encoder.mm_config.max_image_size
144
145
146
147
148

        return ImageSize(width=max_image_size, height=max_image_size)


class PixtralDummyInputsBuilder(BaseDummyInputsBuilder[PixtralProcessingInfo]):
149
150
151
152
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        return ""

    def get_dummy_mm_data(
153
154
155
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
156
        mm_options: Mapping[str, BaseDummyOptions],
157
    ) -> MultiModalDataDict:
158
159
        num_images = mm_counts.get("image", 0)

160
        target_width, target_height = self.info.get_image_size_with_most_features()
161

162
        image_overrides = mm_options.get("image")
163

164
        return {
165
166
167
168
169
170
            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            )
171
172
        }

173
174
175
176
    def get_dummy_processor_inputs(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
177
        mm_options: Mapping[str, BaseDummyOptions],
178
        mm_data: MultiModalDataDict | None = None,
179
180
181
182
    ) -> ProcessorInputs:
        tokenizer = self.info.get_tokenizer()

        dummy_text = self.get_dummy_text(mm_counts)
183
184
185
186
187
188
189
190
191
        dummy_mm_data = (
            self.get_dummy_mm_data(seq_len, mm_counts, mm_options)
            if mm_data is None
            else mm_data
        )
        dummy_mm_items = self.info.parse_mm_data(dummy_mm_data)
        dummy_images = (
            [] if "image" not in dummy_mm_data else dummy_mm_items["image"].get_all()
        )
192

193
194
195
196
197
198
199
200
201
202
        request = ChatCompletionRequest(
            messages=[
                UserMessage(
                    content=[
                        TextChunk(text=dummy_text),
                        *(ImageChunk(image=image) for image in dummy_images),
                    ]
                ),
            ]
        )
203
204
205
        res = tokenizer.mistral.encode_chat_completion(request)
        dummy_tokens = res.tokens

206
        return ProcessorInputs(prompt=dummy_tokens, mm_data_items=dummy_mm_items)
207

Patrick von Platen's avatar
Patrick von Platen committed
208

209
class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]):
210
211
212
213
214
    def _get_mm_fields_config(
        self,
        hf_inputs: Mapping[str, NestedTensors],
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
215
        return dict(images=MultiModalFieldConfig.batched("image"))
216

217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            # Avoid padding issue
            tok_kwargs={**tok_kwargs, "return_tensors": None},
        )

        # Missing batch dimension
        if is_list_of(outputs["input_ids"], int):
            outputs["input_ids"] = [outputs["input_ids"]]

        return outputs

238
239
240
241
    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
242
        out_mm_kwargs: MultiModalKwargsItems,
243
244
245
246
247
248
249
250
251
252
253
    ) -> 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)

254
255
256
            _, nrows, ncols = processor.image_processor.get_number_of_image_patches(
                image_size.height,
                image_size.width,
257
            )
258
259
260
261

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

262
            return PromptUpdateDetails.select_token_id(tokens, image_token_id)
263
264
265
266
267
268
269
270
271
272
273

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

    def _cached_apply_hf_processor(
        self,
274
275
        inputs: ProcessorInputs,
        timing_ctx: TimingContext,
276
    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
277
        prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(inputs, timing_ctx)
278
279

        # NOTE: The tokens are already inserted by the chat template
280
        return prompt_ids, mm_info, True
Patrick von Platen's avatar
Patrick von Platen committed
281

282

283
284
285
286
287
@MULTIMODAL_REGISTRY.register_processor(
    PixtralMultiModalProcessor,
    info=PixtralProcessingInfo,
    dummy_inputs=PixtralDummyInputsBuilder,
)
288
class PixtralForConditionalGeneration(
289
    nn.Module, SupportsLoRA, SupportsEagle3, SupportsMultiModal, SupportsPP
290
):
291
    @classmethod
292
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
293
294
295
296
297
        if modality.startswith("image"):
            return None

        raise ValueError("Only image modality is supported")

298
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
Patrick von Platen's avatar
Patrick von Platen committed
299
        super().__init__()
300
301
        config = vllm_config.model_config.hf_config
        multimodal_config = vllm_config.model_config.multimodal_config
Patrick von Platen's avatar
Patrick von Platen committed
302
303
304
305
306
307
308
309
310
311
312
313
314
        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
315
316
317
318
319
320
        with self._mark_language_model(vllm_config):
            self.language_model = init_vllm_registered_model(
                vllm_config=vllm_config,
                hf_config=config.text_config,
                prefix=maybe_prefix(prefix, "language_model"),
            )
Patrick von Platen's avatar
Patrick von Platen committed
321

322
        with self._mark_tower_model(vllm_config, "image"):
323
324
325
326
327
            self.vision_encoder = VisionTransformer(self.vision_args)
            self.pre_mm_projector_norm = (
                RMSNorm(self.vision_args.hidden_size, eps=1e-5)
                if self.vision_args.add_pre_mm_projector_layer_norm
                else None
Patrick von Platen's avatar
Patrick von Platen committed
328
            )
329
330
331
332
333
334
335
336
337
338
339
340
            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,
                )
                if self.vision_args.mm_projector_id == PATCH_MERGE
                else None
            )
            self.vision_language_adapter = VisionLanguageAdapter(
                self.vision_args, dim=config.text_config.hidden_size
            )
Patrick von Platen's avatar
Patrick von Platen committed
341

342
        self.make_empty_intermediate_tensors = (
343
344
            self.language_model.make_empty_intermediate_tensors
        )
345

346
    def _parse_and_validate_image_input(
347
        self, **kwargs: object
348
    ) -> PixtralImagePixelInputs | None:
349
350
351
352
353
354
        images = kwargs.pop("images", None)
        if images is None:
            return None

        return PixtralImagePixelInputs(
            type="pixel_values",
355
            images=images,
356
357
358
359
360
361
362
363
        )

    def _process_image_input(
        self,
        image_input: PixtralImagePixelInputs,
    ) -> tuple[torch.Tensor, ...]:
        images = image_input["images"]
        image_features = self.vision_encoder(images)
364
        feature_sizes = [image_feature.shape[0] for image_feature in image_features]
Patrick von Platen's avatar
Patrick von Platen committed
365
        image_features = torch.cat(image_features)
366
        if self.pre_mm_projector_norm is not None:
Patrick von Platen's avatar
Patrick von Platen committed
367
            image_features = self.pre_mm_projector_norm(image_features)
368
        if self.patch_merger is not None:
Patrick von Platen's avatar
Patrick von Platen committed
369
370
            patch_size = self.vision_args.patch_size
            spatial_merge_size_square = self.vision_args.spatial_merge_size**2
371
372
373
374
            img_patch_dims = [
                (img.shape[1] // patch_size, img.shape[2] // patch_size)
                for img in images
            ]
Patrick von Platen's avatar
Patrick von Platen committed
375
376
377
378
            feature_sizes = [
                feature_size // spatial_merge_size_square
                for feature_size in feature_sizes
            ]
379
380
381
            image_features = self.patch_merger(
                image_features, image_sizes=img_patch_dims
            )
Patrick von Platen's avatar
Patrick von Platen committed
382
        image_embeds = self.vision_language_adapter(image_features)
383
384
385
        image_embeds = torch.split(image_embeds, feature_sizes)
        return image_embeds

386
    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
387
        image_input = self._parse_and_validate_image_input(**kwargs)
388
        if image_input is None:
389
            return []
390

391
        return self._process_image_input(image_input)
392

Patrick von Platen's avatar
Patrick von Platen committed
393
394
    def forward(
        self,
395
        input_ids: torch.Tensor | None,
Patrick von Platen's avatar
Patrick von Platen committed
396
        positions: torch.Tensor,
397
398
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
Patrick von Platen's avatar
Patrick von Platen committed
399
        **kwargs: object,
400
    ) -> torch.Tensor | IntermediateTensors:
401
        """Run forward pass for pixtral."""
402
403
        if intermediate_tensors is not None:
            inputs_embeds = None
Patrick von Platen's avatar
Patrick von Platen committed
404

405
406
407
        hidden_states = self.language_model.model(
            input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
        )
Patrick von Platen's avatar
Patrick von Platen committed
408
409
410
411
412
413

        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
414
    ) -> torch.Tensor | None:
415
        return self.language_model.compute_logits(hidden_states)
Patrick von Platen's avatar
Patrick von Platen committed
416

417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
    def _require_language_model_eagle3(self) -> None:
        if not supports_eagle3(self.language_model):
            raise RuntimeError(
                f"EAGLE-3 speculative decoding requires the language model to "
                f"support EAGLE-3, but {type(self.language_model).__name__} does not."
            )

    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
        self._require_language_model_eagle3()
        self.language_model.set_aux_hidden_state_layers(layers)

    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
        self._require_language_model_eagle3()
        return self.language_model.get_eagle3_aux_hidden_state_layers()

432
433
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
        def is_vision_encoder_weights(weight: tuple[str, torch.Tensor]):
434
            return weight[0].startswith(("vision_encoder", "vision_tower"))
Patrick von Platen's avatar
Patrick von Platen committed
435

436
        def is_vision_lang_adapter_weights(weight: tuple[str, torch.Tensor]):
437
438
439
            return weight[0].startswith(
                ("vision_language_adapter", "multi_modal_projector")
            )
Patrick von Platen's avatar
Patrick von Platen committed
440

441
        def is_patch_merger(weight: tuple[str, torch.Tensor]):
Patrick von Platen's avatar
Patrick von Platen committed
442
443
            return weight[0].startswith("patch_merger")

444
        def is_pre_mm_projector_norm(weight: tuple[str, torch.Tensor]):
Patrick von Platen's avatar
Patrick von Platen committed
445
446
            return weight[0].startswith("pre_mm_projector_norm")

447
        # Get references to parameters for direct loading
448
449
450
451
452
        vision_encoder_dict = (
            dict(self.vision_encoder.named_parameters())
            if self.vision_encoder is not None
            else {}
        )
453
454
        patch_merger_dict = (
            dict(self.patch_merger.named_parameters())
455
456
            if self.patch_merger is not None
            else {}
457
458
459
        )
        pre_mm_projector_norm_dict = (
            dict(self.pre_mm_projector_norm.named_parameters())
460
461
462
463
464
465
466
            if self.pre_mm_projector_norm is not None
            else {}
        )
        vision_lang_adapter_dict = (
            dict(self.vision_language_adapter.named_parameters())
            if self.vision_language_adapter is not None
            else {}
467
        )
468
469
470
471
472

        def llm_weights_generator():
            # Single pass over weights
            for name, w in weights:
                if is_vision_encoder_weights((name, w)):
473
                    if _is_layer_none_or_staged(self.vision_encoder):
474
                        continue
475
                    # Load vision encoder weights directly
476
                    trimmed_name = ".".join(name.split(".")[1:])
477
478
479
480
                    param = vision_encoder_dict.get(trimmed_name)
                    if param is not None:
                        with torch.no_grad():
                            default_weight_loader(param, w)
Patrick von Platen's avatar
Patrick von Platen committed
481
                elif is_patch_merger((name, w)):
482
                    if _is_layer_none_or_staged(self.patch_merger):
483
                        continue
Patrick von Platen's avatar
Patrick von Platen committed
484
                    # Load vision patch merger weights directly
485
                    trimmed_name = ".".join(name.split(".")[1:])
Patrick von Platen's avatar
Patrick von Platen committed
486
487
488
489
                    param = patch_merger_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                elif is_pre_mm_projector_norm((name, w)):
490
                    if _is_layer_none_or_staged(self.pre_mm_projector_norm):
491
                        continue
Patrick von Platen's avatar
Patrick von Platen committed
492
                    # Load vision pre_mm_projector_norm weights directly
493
                    trimmed_name = ".".join(name.split(".")[1:])
Patrick von Platen's avatar
Patrick von Platen committed
494
495
496
                    param = pre_mm_projector_norm_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
497
                elif is_vision_lang_adapter_weights((name, w)):
498
                    if _is_layer_none_or_staged(self.vision_language_adapter):
499
                        continue
500
                    # Load vision-language adapter weights directly
501
                    trimmed_name = ".".join(name.split(".")[1:])
502
503
504
505
                    param = vision_lang_adapter_dict.get(trimmed_name)
                    if param is not None:
                        with torch.no_grad():
                            default_weight_loader(param, w)
506
507
508
                else:
                    # LLM weights: yield them to be loaded
                    # by language_model.load_weights
509
510
                    # Strip "language_model." prefix if present (HF sharded format)
                    name = name.removeprefix("language_model.")
511
512
513
514
                    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
515

516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
    def get_mm_mapping(self) -> MultiModelKeys:
        return MultiModelKeys.from_string_field(
            language_model="language_model",
            connector="vision_language_adapter",
            tower_model="vision_encoder",
        )

    def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int:
        if getattr(self, "patch_merger", None) is None:
            return num_image_tokens
        merge_size = self.vision_args.spatial_merge_size
        return num_image_tokens * (merge_size**2)

    def get_num_mm_connector_tokens(self, num_vision_tokens: int) -> int:
        if getattr(self, "patch_merger", None) is None:
            return num_vision_tokens
        merge_size = self.vision_args.spatial_merge_size
        return num_vision_tokens // (merge_size**2)

Patrick von Platen's avatar
Patrick von Platen committed
535
536
537
538
539
540
541
542
543
544
545
546
547

# 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
548
    adapter_bias: bool = True
Patrick von Platen's avatar
Patrick von Platen committed
549
550
551
    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
552
553


554
def _reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
Patrick von Platen's avatar
Patrick von Platen committed
555
556
557
558
559
560
561
562
563
564
    """
    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]),
    )
565
    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
Patrick von Platen's avatar
Patrick von Platen committed
566
567
568
569
570
571
572
573
574
575
576
577
578
579
    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
580
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
Patrick von Platen's avatar
Patrick von Platen committed
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600

    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,
601
) -> tuple[torch.Tensor, torch.Tensor]:
Patrick von Platen's avatar
Patrick von Platen committed
602
603
604
605
606
607
608
609
610
611
612
613
614
    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
615
616
617
        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)
Patrick von Platen's avatar
Patrick von Platen committed
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638

    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,
639
        mask: torch.Tensor,
Patrick von Platen's avatar
Patrick von Platen committed
640
641
642
643
644
645
646
647
648
649
        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)
650
651
652
653
654
655
656

        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)
657
            out = nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask)
658
659
            out = out.transpose(1, 2)

Patrick von Platen's avatar
Patrick von Platen committed
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
        freqs_cis: torch.Tensor,
    ) -> torch.Tensor:
678
679
680
        r = self.attention.forward(
            self.attention_norm(x), mask=mask, freqs_cis=freqs_cis
        )
Patrick von Platen's avatar
Patrick von Platen committed
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
        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,
697
        mask: torch.Tensor,
698
        freqs_cis: torch.Tensor | None,
Patrick von Platen's avatar
Patrick von Platen committed
699
700
701
702
703
704
    ) -> torch.Tensor:
        for layer in self.layers:
            x = layer(x, mask=mask, freqs_cis=freqs_cis)
        return x


705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
def position_meshgrid(
    patch_embeds_list: list[torch.Tensor],
) -> torch.Tensor:
    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
        ]
    )
Patrick von Platen's avatar
Patrick von Platen committed
721
722
723
724
725
726
727
    return positions


class VisionTransformer(nn.Module):
    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.args = args
728
        self.patch_conv = Conv2dLayer(
Patrick von Platen's avatar
Patrick von Platen committed
729
730
731
732
733
734
735
736
737
738
739
            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"
740
        self._freqs_cis: torch.Tensor | None = None
Patrick von Platen's avatar
Patrick von Platen committed
741
742
743
744
745
746

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

    @property
747
    def device(self) -> torch.types.Device:
Patrick von Platen's avatar
Patrick von Platen committed
748
749
750
        return next(self.parameters()).device

    @property
751
    def dtype(self) -> torch.dtype:
Patrick von Platen's avatar
Patrick von Platen committed
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
        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,
771
        images: list[torch.Tensor],
Patrick von Platen's avatar
Patrick von Platen committed
772
773
774
    ) -> torch.Tensor:
        """
        Args:
775
            images: list of N_img images of variable sizes,
Patrick von Platen's avatar
Patrick von Platen committed
776
777
                each of shape (C, H, W)
        Returns:
778
            image_features: tensor of token features for
Patrick von Platen's avatar
Patrick von Platen committed
779
780
781
782
783
784
785
                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
        ]

786
        patch_embeds = [p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list]
787
788
        embed_sizes = [p.shape[1] for p in patch_embeds]

Patrick von Platen's avatar
Patrick von Platen committed
789
        # flatten to a single sequence
790
        patch_embeds = torch.cat(patch_embeds, dim=1)
Patrick von Platen's avatar
Patrick von Platen committed
791
792
793
794
795
796
797
        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
798
799
        if USE_XFORMERS_OPS:
            mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
800
801
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
            )
802
        else:
803
            from transformers.models.pixtral.modeling_pixtral import (
804
805
806
                generate_block_attention_mask,
            )

807
            mask = generate_block_attention_mask(
808
809
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], patch_embeds
            )
Patrick von Platen's avatar
Patrick von Platen committed
810
811
        out = self.transformer(patch_embeds, mask=mask, freqs_cis=freqs_cis)

812
813
        # 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
814
815
816
817
818
819
820
821
822


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,
823
            bias=args.adapter_bias,
Patrick von Platen's avatar
Patrick von Platen committed
824
825
        )
        self.gelu = nn.GELU()
826
        self.w_out = nn.Linear(dim, dim, bias=args.adapter_bias)
Patrick von Platen's avatar
Patrick von Platen committed
827
828
829

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w_out(self.gelu(self.w_in(x)))
830
831


Patrick von Platen's avatar
Patrick von Platen committed
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
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,
        )

856
857
858
    def forward(
        self, x: torch.Tensor, image_sizes: list[tuple[int, int]]
    ) -> torch.Tensor:
Patrick von Platen's avatar
Patrick von Platen committed
859
860
861
862
863
864
        # 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)

865
866
        # x is (N / spatial_merge_size ** 2,
        #       vision_encoder_dim * spatial_merge_size ** 2)
Patrick von Platen's avatar
Patrick von Platen committed
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
        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(
890
            x=x, image_sizes=image_sizes, spatial_merge_size=self.spatial_merge_size
Patrick von Platen's avatar
Patrick von Platen committed
891
892
893
894
        )  # 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]
895
896
897
            permuted_tensor.append(
                grid.view(-1, n_patches).t()
            )  # n_patches x d * sub_grid_size * sub_grid_size
Patrick von Platen's avatar
Patrick von Platen committed
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
        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]
917
918
919
920
921
922
        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
        )
Patrick von Platen's avatar
Patrick von Platen committed
923
        sub_grids = sub_grids.view(
924
925
            1, d, sub_grid_size, sub_grid_size, -1
        )  # 1 x d x sub_grid_size x sub_grid_size x n_patches
Patrick von Platen's avatar
Patrick von Platen committed
926
927
928
929
930
931

        all_img_sub_grids.append(sub_grids[0])

    return all_img_sub_grids


932
933
934
935
936
937
938
939
#### 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.


940
941
942
943
944
945
946
class PixtralHFEncoderInfo(VisionEncoderInfo[PixtralVisionConfig]):
    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
947
948
949
        ncols, nrows = self.get_patch_grid_size(
            image_width=image_width,
            image_height=image_height,
950
        )
951
        return ncols * nrows
952

953
954
955
956
    def get_image_size(self) -> int:
        return self.vision_config.image_size

    def get_patch_size(self) -> int:
957
958
959
        # 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
960
961

    def get_patch_grid_length(self) -> int:
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
        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:
981
982
            image_width = int(math.floor(image_width / ratio))
            image_height = int(math.floor(image_height / ratio))
983
984
985
986
987
988
989

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

        return ncols, nrows
990
991
992


class PixtralHFMLP(nn.Module):
993
994
995
    def __init__(
        self,
        config: PixtralVisionConfig,
996
        quant_config: QuantizationConfig | None = None,
997
998
999
        *,
        prefix: str = "",
    ) -> None:
1000
        super().__init__()
1001

1002
        use_data_parallel = is_vit_use_data_parallel()
1003

1004
        assert config.intermediate_size is not None
1005
1006
1007
1008
1009
        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=config.hidden_size,
            output_sizes=[config.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
1010
            prefix=f"{prefix}.gate_up_proj",
1011
            disable_tp=use_data_parallel,
1012
1013
1014
1015
1016
1017
1018
        )
        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",
1019
            disable_tp=use_data_parallel,
1020
        )
1021
        self.act_and_mul = get_act_and_mul_fn(config.hidden_act)
1022
1023

    def forward(self, x: torch.Tensor) -> torch.Tensor:
1024
1025
1026
1027
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_and_mul(gate_up)
        x, _ = self.down_proj(x)
        return x
1028
1029
1030


class PixtralHFAttention(nn.Module):
1031
1032
1033
    def __init__(
        self,
        config: PixtralVisionConfig,
1034
        quant_config: QuantizationConfig | None = None,
1035
1036
1037
        *,
        prefix: str = "",
    ) -> None:
1038
        super().__init__()
1039

1040
1041
        self.config = config
        assert not config.hidden_size % config.num_attention_heads
1042
        self.total_num_heads = config.num_attention_heads
1043
        self.head_dim = config.hidden_size // config.num_attention_heads
1044
        assert self.total_num_heads * self.head_dim == config.hidden_size
1045

1046
        use_data_parallel = is_vit_use_data_parallel()
1047
1048
1049
        self.qkv_proj = QKVParallelLinear(
            hidden_size=config.hidden_size,
            head_size=self.head_dim,
1050
            total_num_heads=self.total_num_heads,
1051
1052
1053
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
1054
            disable_tp=use_data_parallel,
1055
1056
1057
1058
1059
1060
1061
        )
        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",
1062
1063
1064
1065
1066
            disable_tp=use_data_parallel,
        )

        self.tp_size = (
            1 if use_data_parallel else get_tensor_model_parallel_world_size()
1067
        )
1068
        self.n_heads = divide(config.num_attention_heads, self.tp_size)
1069
1070
1071
1072

    def forward(
        self,
        hidden_states: torch.Tensor,
1073
        attention_mask: torch.Tensor,
1074
        position_embeddings: torch.Tensor,
1075
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
1076
        batch, patches, _ = hidden_states.size()
1077

1078
1079
        qkv_states, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv_states.chunk(3, dim=-1)
1080

1081
1082
1083
        # 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)
1084
        v = v.view(batch, patches, self.n_heads, self.head_dim)
1085
        cos, sin = position_embeddings
1086
        q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0)
1087

1088
1089
1090
1091
        if USE_XFORMERS_OPS:
            # Transpose q and k back for attention
            q = q.transpose(1, 2).contiguous()
            k = k.transpose(1, 2).contiguous()
1092
            out = xops.memory_efficient_attention(q, k, v, attn_bias=attention_mask)
1093
        else:
1094
            v = v.transpose(1, 2)
1095
            out = nn.functional.scaled_dot_product_attention(
1096
1097
                q, k, v, attn_mask=attention_mask
            )
1098
            out = out.transpose(1, 2)
1099

1100
        out = out.reshape(batch, patches, self.n_heads * self.head_dim)
1101
        attn_output, _ = self.o_proj(out)
1102

1103
        return attn_output, None
1104
1105
1106


class PixtralHFTransformerBlock(nn.Module):
1107
1108
1109
    def __init__(
        self,
        config: PixtralVisionConfig,
1110
        quant_config: QuantizationConfig | None = None,
1111
1112
1113
        *,
        prefix: str = "",
    ) -> None:
1114
        super().__init__()
1115

1116
        self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5)
1117
        self.attention = PixtralHFAttention(
1118
1119
1120
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.attention",
1121
1122
        )
        self.feed_forward = PixtralHFMLP(
1123
1124
1125
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.feed_forward",
1126
        )
1127
1128
1129
1130
1131
        self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5)

    def forward(
        self,
        hidden_states: torch.Tensor,
1132
        attention_mask: torch.Tensor,
1133
1134
        position_embeddings: torch.Tensor,
    ) -> torch.Tensor:
1135
1136
1137
1138
1139
        r, _ = self.attention.forward(
            self.attention_norm(hidden_states),
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
        )
1140
1141
1142
1143
1144
1145
1146
        h = hidden_states + r
        r = self.feed_forward.forward(self.ffn_norm(h))
        out = h + r
        return out


class PixtralHFTransformer(nn.Module):
1147
1148
1149
    def __init__(
        self,
        config: PixtralVisionConfig,
1150
        quant_config: QuantizationConfig | None = None,
1151
        *,
1152
        num_hidden_layers_override: int | None = None,
1153
1154
        prefix: str = "",
    ) -> None:
1155
        super().__init__()
1156
1157
1158
1159
1160
1161

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

1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
        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)
            ]
        )
1172
1173
1174
1175

    def forward(
        self,
        x: torch.Tensor,
1176
        attention_mask: torch.Tensor,
1177
        position_embeddings: torch.Tensor,
1178
        return_all_hidden_states: bool,
1179
    ) -> torch.Tensor:
1180
        hidden_states_pool = [x]
1181

1182
1183
        for layer in self.layers:
            x = layer(x, attention_mask, position_embeddings)
1184
1185
1186
1187
1188
1189
            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
1190
1191
1192
1193
        return x


class PixtralHFVisionModel(nn.Module):
1194
1195
1196
    def __init__(
        self,
        config: PixtralVisionConfig,
1197
        quant_config: QuantizationConfig | None = None,
1198
        *,
1199
1200
        num_hidden_layers_override: int | None = None,
        require_post_norm: bool | None = None,
1201
1202
        prefix: str = "",
    ) -> None:
1203
1204
1205
        super().__init__()

        self.config = config
1206

1207
        self.patch_conv = Conv2dLayer(
1208
1209
1210
1211
1212
1213
1214
            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)
1215
1216
        self.transformer = PixtralHFTransformer(
            config,
1217
            quant_config=quant_config,
1218
1219
1220
1221
1222
1223
1224
1225
1226
            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)} "
1227
1228
                "layers."
            )
1229
1230
1231
1232
1233

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

1234
1235
        self.dtype = next(self.parameters()).dtype
        self.device = next(self.parameters()).device
1236
        self.patch_positional_embedding = PixtralRotaryEmbedding(config, self.device)
1237
1238
1239

    def forward(
        self,
1240
        pixel_values: list[torch.Tensor],
1241
        *,
1242
1243
        select_layers: list[int] | None = None,
        feature_select_strategy: VisionFeatureSelectStrategy | None = None,
1244
    ) -> tuple[torch.Tensor, ...]:
1245
1246
        """
        Args:
1247
1248
1249
1250
            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
1251
            select_layers: Layer indices whose features should be
1252
1253
                concatenated and used as the visual encoder output. If none
                are provided, the last layer is used.
1254

1255
1256
1257
1258
1259
1260
        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 = [
1261
            self.patch_conv(img.unsqueeze(0).to(self.dtype)) for img in pixel_values
1262
1263
        ]

1264
        patch_embeds = [p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list]
1265
1266
        embed_sizes = [p.shape[1] for p in patch_embeds]

1267
        # flatten to a single sequence
1268
        patch_embeds = torch.cat(patch_embeds, dim=1)
1269
1270
1271
1272
1273
        patch_embeds = self.ln_pre(patch_embeds)

        # positional embeddings
        position_ids = position_ids_in_meshgrid(
            patch_embeds_list,
1274
1275
1276
            max_width=self.config.image_size // self.config.patch_size,
        ).to(self.device)
        position_embedding = self.patch_positional_embedding(patch_embeds, position_ids)
1277
1278
1279

        if USE_XFORMERS_OPS:
            attention_mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
1280
1281
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
            )
1282
1283
        else:
            from transformers.models.pixtral.modeling_pixtral import (
1284
1285
1286
                generate_block_attention_mask,
            )

1287
            attention_mask = generate_block_attention_mask(
1288
1289
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], patch_embeds
            )
1290

1291
1292
1293
1294
        out = self.transformer(
            patch_embeds,
            attention_mask,
            position_embedding,
1295
1296
            return_all_hidden_states=select_layers is not None,
        )
1297

1298
1299
1300
1301
1302
1303
1304
        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,
        )
1305

1306
        # squeeze dim 0 and split into separate tensors for each image
1307
        return torch.split(out.squeeze(0), embed_sizes)
1308
1309
1310

    # (TODO) Add prefix argument for filtering out weights to be loaded
    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
1311
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
1312
1313
1314
1315
1316
1317
1318
1319
        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),
        ]
1320
        params_dict = dict(self.named_parameters())
1321
        loaded_params: set[str] = set()
1322
        layer_count = len(self.transformer.layers)
1323
1324

        for name, loaded_weight in weights:
1325
1326
1327
1328
1329
1330
            # 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

1331
            for param_name, weight_name, shard_id in stacked_params_mapping:
1332
1333
                if weight_name not in name:
                    continue
1334
1335
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
1336
1337
1338
1339
1340
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
1341
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
1342
                weight_loader(param, loaded_weight)
1343
1344
            loaded_params.add(name)
        return loaded_params