qwen2_vl.py 65.8 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
# Adapted from
# https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Qwen2-VL model compatible with HuggingFace weights."""
27
from collections.abc import Iterable, Mapping, Sequence
28
from functools import partial
29
from typing import Annotated, Any, Callable, Literal, Optional, Union
30
31
32
33
34

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
35
from transformers import AutoConfig, BatchFeature, PretrainedConfig
36
37
from transformers.models.qwen2_vl import (Qwen2VLImageProcessor,
                                          Qwen2VLProcessor)
38
39
from transformers.models.qwen2_vl.configuration_qwen2_vl import (
    Qwen2VLConfig, Qwen2VLVisionConfig)
40
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
41
42
from transformers.models.qwen2_vl.video_processing_qwen2_vl import (
    Qwen2VLVideoProcessor)
43

44
from vllm.attention.layer import check_upstream_fa_availability
45
from vllm.config import VllmConfig
46
from vllm.distributed import parallel_state, tensor_model_parallel_all_gather
47
48
49
50
51
52
53
from vllm.distributed import utils as dist_utils
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import QuickGELU
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
54
from vllm.model_executor.models.module_mapping import MultiModelKeys
55
from vllm.multimodal import MULTIMODAL_REGISTRY
56
from vllm.multimodal.inputs import (ImageItem, ModalityData,
57
                                    MultiModalDataDict, MultiModalFieldConfig,
58
                                    MultiModalKwargsItems, VideoItem)
zhuwenwen's avatar
zhuwenwen committed
59
60
61
from vllm.multimodal.parse import (DictEmbeddingItems, ImageSize,
                                   ModalityDataItems, MultiModalDataItems,
                                   MultiModalDataParser)
62
from vllm.multimodal.processing import (BaseMultiModalProcessor,
63
64
                                        BaseProcessingInfo, PromptReplacement,
                                        PromptUpdate)
65
from vllm.multimodal.profiling import BaseDummyInputsBuilder
66
from vllm.platforms import _Backend, current_platform
67
from vllm.sequence import IntermediateTensors
68
from vllm.transformers_utils.config import uses_mrope
69
from vllm.transformers_utils.tokenizer import AnyTokenizer
70
from vllm.utils.tensor_schema import TensorSchema, TensorShape
71

72
from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMRoPE,
73
                         SupportsMultiModal, SupportsPP)
74
from .utils import (AutoWeightsLoader, WeightsMapper,
75
76
                    init_vllm_registered_model, maybe_prefix,
                    merge_multimodal_embeddings)
77
from .vision import get_vit_attn_backend, run_dp_sharded_mrope_vision_model
78

zhuwenwen's avatar
zhuwenwen committed
79
80
81
82
import os
import re
from vllm import _custom_ops as ops
from vllm.model_executor.utils import pad_weight, gemm_bank_conf
83
from vllm.platforms import current_platform
zhuwenwen's avatar
zhuwenwen committed
84

85
86
logger = init_logger(__name__)

87
# For profile run
88
_MAX_FRAMES_PER_VIDEO = 14
89

90
91
92
# === Vision Inputs === #


93
class Qwen2VLImagePixelInputs(TensorSchema):
94
    """
95
96
97
98
99
100
101
102
103
104
105
    Dimensions:
        - np: The total number of patches over each image over each prompt in
              the batch
        - ni: Number of images
        - cps: Number of channels * patch_size * patch_size
    
    Historical context:
        - pixel_values shape: (num_patches, num_channels * patch_size * 
          patch_size)
        - image_grid_thw shape: (num_images, 3) in (grid_t, grid_h, grid_w)
          format
106
    """
107
    type: Literal["pixel_values"]
108

109
110
111
112
    pixel_values: Annotated[
        torch.Tensor,
        TensorShape("np", "cps"),
    ]
113

114
115
116
117
118
119
120
121
122
123
124
125
    image_grid_thw: Annotated[
        torch.Tensor,
        TensorShape("ni", 3),
    ]


class Qwen2VLImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - nf: Number of image features
        - hs: Hidden size
        - ni: Number of images
126
    
127
128
129
130
131
132
133
    Historical context:
        - image_embeds shape: (num_image_features, hidden_size)
        - num_image_features varies based on the number and resolution of the
          images.
        - hidden_size must match the hidden size of language model backbone.
        - image_grid_thw shape: (num_images, 3) in (grid_t, grid_h, grid_w)
          format
134
    """
135
    type: Literal["image_embeds"]
136

137
138
139
140
141
142
143
144
145
    image_embeds: Annotated[
        torch.Tensor,
        TensorShape("nf", "hs"),
    ]

    image_grid_thw: Annotated[
        torch.Tensor,
        TensorShape("ni", 3),
    ]
146
147


148
149
150
151
Qwen2VLImageInputs = Union[Qwen2VLImagePixelInputs,
                           Qwen2VLImageEmbeddingInputs]


152
153
154
155
156
157
158
159
160
161
162
163
164
165
class Qwen2VLVideoPixelInputs(TensorSchema):
    """
    Dimensions:
        - np: The total number of patches over each video over each prompt in
              the batch
        - ctps: Number of channels * temporal_patch_size * patch_size * 
          patch_size
        - nv: Number of videos
    
    Historical context:
        - pixel_values_videos shape: (num_patches, num_channels * 
          temporal_patch_size * patch_size * patch_size)
        - video_grid_thw shape: (num_videos, 3) in (grid_t, grid_h, grid_w)
          format
166
    """
167
    type: Literal["pixel_values_videos"]
168

169
170
171
172
    pixel_values_videos: Annotated[
        torch.Tensor,
        TensorShape("np", "ctps"),
    ]
173

174
175
176
177
    video_grid_thw: Annotated[
        torch.Tensor,
        TensorShape("nv", 3),
    ]
178
179


180
181
182
183
184
185
class Qwen2VLVideoEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - nf: Number of video features
        - hs: Hidden size
        - nv: Number of videos
186
    
187
188
189
190
191
192
193
    Historical context:
        - video_embeds shape: (num_video_features, hidden_size)
        - num_video_features varies based on the number and resolution of the
          videos.
        - hidden_size must match the hidden size of language model backbone.
        - video_grid_thw shape: (num_videos, 3) in (grid_t, grid_h, grid_w)
          format
194
    """
195
    type: Literal["video_embeds"]
196

197
198
199
200
201
202
203
204
205
    video_embeds: Annotated[
        torch.Tensor,
        TensorShape("nf", "hs"),
    ]

    video_grid_thw: Annotated[
        torch.Tensor,
        TensorShape("nv", 3),
    ]
206
207


208
209
210
Qwen2VLVideoInputs = Union[Qwen2VLVideoPixelInputs,
                           Qwen2VLVideoEmbeddingInputs]

211
212
213
214
215
216
217
218
# === Vision Encoder === #


class Qwen2VisionMLP(nn.Module):

    def __init__(
        self,
        in_features: int,
219
        hidden_features: int,
220
        act_layer: type[nn.Module] = QuickGELU,
221
        quant_config: Optional[QuantizationConfig] = None,
222
        prefix: str = "",
223
        use_data_parallel: bool = False,
224
225
226
227
    ):
        super().__init__()
        self.fc1 = ColumnParallelLinear(in_features,
                                        hidden_features,
228
                                        quant_config=quant_config,
229
230
                                        prefix=f"{prefix}.fc1",
                                        disable_tp=use_data_parallel)
231
232
233
        self.act = act_layer()
        self.fc2 = RowParallelLinear(hidden_features,
                                     in_features,
234
                                     quant_config=quant_config,
235
236
                                     prefix=f"{prefix}.fc2",
                                     disable_tp=use_data_parallel)
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x_parallel, _ = self.fc1(x)
        x_parallel = self.act(x_parallel)
        x, _ = self.fc2(x_parallel)
        return x


def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
    if not interleaved:
        x1, x2 = x.chunk(2, dim=-1)
        return torch.cat((-x2, x1), dim=-1)
    else:
        x1, x2 = x[..., ::2], x[..., 1::2]
        return rearrange(torch.stack((-x2, x1), dim=-1),
                         "... d two -> ... (d two)",
                         two=2)


def apply_rotary_emb_torch(x: torch.Tensor,
                           cos: torch.Tensor,
                           sin: torch.Tensor,
                           interleaved: bool = False) -> torch.Tensor:
    """
    x: (batch_size, seqlen, nheads, headdim)
    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
    """
    ro_dim = cos.shape[-1] * 2
    assert ro_dim <= x.shape[-1]
    cos = repeat(
        cos,
        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
    sin = repeat(
        sin,
        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
    return torch.cat(
        [
            x[..., :ro_dim] * cos +
            rotate_half(x[..., :ro_dim], interleaved) * sin, x[..., ro_dim:]
        ],
        dim=-1,
    )


def apply_rotary_pos_emb_vision(t: torch.Tensor,
282
                                freqs: torch.Tensor) -> torch.Tensor:
283
284
285
    t_ = t.float()
    cos = freqs.cos()
    sin = freqs.sin()
zhuwenwen's avatar
zhuwenwen committed
286
    apply_rotary_emb = apply_rotary_emb_torch
287
288
    if current_platform.is_cuda():
        from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb
289
290
    if current_platform.is_rocm():
        from flash_attn.layers.rotary import apply_rotary_emb
zhuwenwen's avatar
zhuwenwen committed
291
    output = apply_rotary_emb(t_, cos, sin).type_as(t)
292
293
294
295
296
297
298
    return output


class Qwen2VisionAttention(nn.Module):

    def __init__(
        self,
299
300
301
        embed_dim: int,
        num_heads: int,
        projection_size: int,
302
        quant_config: Optional[QuantizationConfig] = None,
303
        prefix: str = "",
304
        use_data_parallel: bool = False,
305
306
307
    ) -> None:
        super().__init__()
        # Per attention head and per partition values.
308
309
        self.tp_size = (1 if use_data_parallel else
                        parallel_state.get_tensor_model_parallel_world_size())
310
        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
311
312
313
        self.hidden_size_per_attention_head = dist_utils.divide(
            projection_size, num_heads)
        self.num_attention_heads_per_partition = dist_utils.divide(
314
            num_heads, self.tp_size)
315
316
317

        self.qkv = ColumnParallelLinear(input_size=embed_dim,
                                        output_size=3 * projection_size,
318
                                        quant_config=quant_config,
319
320
                                        prefix=f"{prefix}.qkv",
                                        disable_tp=use_data_parallel)
321
322
        self.proj = RowParallelLinear(input_size=projection_size,
                                      output_size=embed_dim,
323
                                      quant_config=quant_config,
324
325
                                      prefix=f"{prefix}.proj",
                                      disable_tp=use_data_parallel)
326
327

        # Detect attention implementation.
328
329
330
331
332
333
334
335
336
337
        self.attn_backend = get_vit_attn_backend(
            head_size=self.hidden_size_per_attention_head,
            dtype=torch.get_default_dtype())
        self.use_upstream_fa = False
        if self.attn_backend != _Backend.FLASH_ATTN and \
            check_upstream_fa_availability(
                torch.get_default_dtype()):
            self.attn_backend = _Backend.FLASH_ATTN
            self.use_upstream_fa = True

338
        if self.attn_backend not in {
339
340
                _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS,
                _Backend.ROCM_AITER_FA
341
342
343
        }:
            raise RuntimeError(
                f"Qwen2-VL does not support {self.attn_backend} backend now.")
344
        self.is_flash_attn_backend = self.attn_backend in {
345
            _Backend.FLASH_ATTN, _Backend.ROCM_FLASH, _Backend.ROCM_AITER_FA
346
        }
347

348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
    def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
        # [s, b, 3 * head * head_dim]
        seq_len, bs, _ = qkv.shape
        if self.tp_size > 1:
            qkv = tensor_model_parallel_all_gather(qkv)

        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
        q, k, v = qkv.chunk(3, dim=2)

        # 3 * [s, b, head * head_dim]
        if self.tp_size > 1:
            splitter = partial(dist_utils.split_tensor_along_last_dim,
                               num_partitions=self.tp_size)
            q = splitter(q)[self.tp_rank]
            k = splitter(k)[self.tp_rank]
            v = splitter(v)[self.tp_rank]

        # 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim]
        new_shape = (seq_len, bs, self.num_attention_heads_per_partition,
                     self.hidden_size_per_attention_head)
        q, k, v = (x.view(*new_shape) for x in (q, k, v))
        return q, k, v

371
    def forward(
372
373
374
375
376
377
            self,
            x: torch.Tensor,
            cu_seqlens: torch.Tensor,
            rotary_pos_emb: torch.Tensor,
            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
            seqlens: Optional[list[int]] = None,  # Only used for xFormers
378
379
    ) -> torch.Tensor:

380
381
        # [s, b, c] --> [s, b, 3 * head * head_dim]
        x, _ = self.qkv(x)
382

383
384
        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim]
        q, k, v = self.split_qkv(x)
385
386
        batch_size = q.shape[1]

387
388
        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous()
                   for x in (q, k, v))
389
        if rotary_pos_emb is not None:
390
391
392
393
            # [2 * b, s, heads, head_dim]
            qk_concat = torch.cat([q, k], dim=0)
            qk_rotated = apply_rotary_pos_emb_vision(qk_concat, rotary_pos_emb)
            q, k = torch.chunk(qk_rotated, 2, dim=0)
394

395
        if self.is_flash_attn_backend:
396
397
398
            # if self.attn_backend == _Backend.ROCM_AITER_FA:
            #     from aiter import flash_attn_varlen_func
            # else:
399
400
401
402
            #     if self.use_upstream_fa:
            #         from flash_attn import flash_attn_varlen_func
            #     else:
            #         from vllm.vllm_flash_attn import flash_attn_varlen_func
403
            from flash_attn import flash_attn_varlen_func
404

405
            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
406
407
408
409
410
411
412
413

            output = flash_attn_varlen_func(q,
                                            k,
                                            v,
                                            cu_seqlens_q=cu_seqlens,
                                            cu_seqlens_k=cu_seqlens,
                                            max_seqlen_q=max_seqlen,
                                            max_seqlen_k=max_seqlen,
414
                                            dropout_p=0.0,
415
416
417
                                            causal=False)

            context_layer = rearrange(output,
418
419
                                      "(b s) h d -> s b (h d)",
                                      b=batch_size).contiguous()
420
        elif self.attn_backend == _Backend.TORCH_SDPA:
zhuwenwen's avatar
zhuwenwen committed
421
422
            # Execute attention entry by entry for speed & less VRAM.
            outputs = []
423
            for i in range(1, len(cu_seqlens)):
zhuwenwen's avatar
zhuwenwen committed
424
425
426
427
428
429
430
431
432
433
434
435
436
437
                start_idx = cu_seqlens[i - 1]
                end_idx = cu_seqlens[i]
                q_i = q[:, start_idx:end_idx]
                k_i = k[:, start_idx:end_idx]
                v_i = v[:, start_idx:end_idx]
                q_i, k_i, v_i = (rearrange(x, "b s h d -> b h s d")
                                 for x in [q_i, k_i, v_i])
                output_i = F.scaled_dot_product_attention(q_i,
                                                          k_i,
                                                          v_i,
                                                          dropout_p=0.0)
                output_i = rearrange(output_i, "b h s d -> b s h d ")
                outputs.append(output_i)
            context_layer = torch.cat(outputs, dim=1)
438
439
            context_layer = rearrange(context_layer,
                                      "b s h d -> s b (h d)").contiguous()
440
        elif self.attn_backend == _Backend.XFORMERS:
441
442
443
444
            from xformers import ops as xops
            from xformers.ops.fmha.attn_bias import BlockDiagonalMask

            attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens,
445
446
                                                       kv_seqlen=None,
                                                       device=q.device)
447
448
449

            context_layer = xops.memory_efficient_attention_forward(
                q, k, v, attn_bias=attn_bias, p=0, scale=None)
450
451
            context_layer = rearrange(context_layer,
                                      "b s h d -> s b (h d)").contiguous()
452
453
454
455
456
457
458
459
460
461
462
463

        output, _ = self.proj(context_layer)
        return output


class Qwen2VisionBlock(nn.Module):

    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_ratio: float,
464
        act_layer: type[nn.Module] = QuickGELU,
465
        norm_layer: Optional[Callable[[int], nn.Module]] = None,
466
        quant_config: Optional[QuantizationConfig] = None,
467
        prefix: str = "",
468
        use_data_parallel: bool = False,
469
470
471
472
473
474
475
476
477
478
479
    ) -> None:
        super().__init__()
        if norm_layer is None:
            norm_layer = partial(nn.LayerNorm, eps=1e-6)
        self.norm1 = norm_layer(dim)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)

        self.attn = Qwen2VisionAttention(embed_dim=dim,
                                         num_heads=num_heads,
                                         projection_size=dim,
480
                                         quant_config=quant_config,
481
482
                                         prefix=f"{prefix}.attn",
                                         use_data_parallel=use_data_parallel)
483
484
485
        self.mlp = Qwen2VisionMLP(dim,
                                  mlp_hidden_dim,
                                  act_layer=act_layer,
486
                                  quant_config=quant_config,
487
488
                                  prefix=f"{prefix}.mlp",
                                  use_data_parallel=use_data_parallel)
489

490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
    def forward(
            self,
            x: torch.Tensor,
            cu_seqlens: torch.Tensor,
            rotary_pos_emb: torch.Tensor,
            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
            seqlens: Optional[list[int]] = None,  # Only used for xFormers
    ) -> torch.Tensor:
        x = x + self.attn(
            self.norm1(x),
            cu_seqlens=cu_seqlens,
            rotary_pos_emb=rotary_pos_emb,
            max_seqlen=max_seqlen,
            seqlens=seqlens,
        )

506
507
508
509
510
511
512
513
514
515
        x = x + self.mlp(self.norm2(x))
        return x


class Qwen2VisionPatchEmbed(nn.Module):

    def __init__(
        self,
        patch_size: int = 14,
        temporal_patch_size: int = 2,
516
        in_channels: int = 3,
517
518
519
520
521
522
523
        embed_dim: int = 1152,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.embed_dim = embed_dim

524
525
        kernel_size = (temporal_patch_size, patch_size, patch_size)
        self.proj = nn.Conv3d(in_channels,
526
527
528
529
530
531
532
533
534
                              embed_dim,
                              kernel_size=kernel_size,
                              stride=kernel_size,
                              bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        L, C = x.shape
        x = x.view(L, -1, self.temporal_patch_size, self.patch_size,
                   self.patch_size)
535
536
        if os.environ.get('PYTORCH_MIOPEN_SUGGEST_NDHWC') == '1':
            x = x.to(memory_format=torch.channels_last_3d)
537
538
539
540
541
542
543
544
545
546
        x = self.proj(x).view(L, self.embed_dim)
        return x


class Qwen2VisionPatchMerger(nn.Module):

    def __init__(
        self,
        d_model: int,
        context_dim: int,
547
        norm_layer: Optional[Callable[[int], nn.Module]] = None,
548
549
        spatial_merge_size: int = 2,
        quant_config: Optional[QuantizationConfig] = None,
550
        prefix: str = "",
551
        use_data_parallel: bool = False,
552
553
554
555
556
557
558
559
560
561
    ) -> None:
        super().__init__()
        self.hidden_size = context_dim * (spatial_merge_size**2)
        if norm_layer is None:
            norm_layer = partial(nn.LayerNorm, eps=1e-6)
        self.ln_q = norm_layer(context_dim)
        self.mlp = nn.ModuleList([
            ColumnParallelLinear(self.hidden_size,
                                 self.hidden_size,
                                 bias=True,
562
                                 quant_config=quant_config,
563
564
                                 prefix=f"{prefix}.mlp.0",
                                 disable_tp=use_data_parallel),
565
566
567
568
            nn.GELU(),
            RowParallelLinear(self.hidden_size,
                              d_model,
                              bias=True,
569
                              quant_config=quant_config,
570
571
                              prefix=f"{prefix}.mlp.2",
                              disable_tp=use_data_parallel),
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
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
        ])

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.ln_q(x)
        x = x.view(-1, self.hidden_size)

        mlp_fc1, mlp_act, mlp_fc2 = self.mlp
        x_parallel, _ = mlp_fc1(x)
        x_parallel = mlp_act(x_parallel)
        out, _ = mlp_fc2(x_parallel)
        return out


class Qwen2VisionRotaryEmbedding(nn.Module):

    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        self.dim = dim
        self.theta = theta
        inv_freq = 1.0 / (theta
                          **(torch.arange(0, dim, 2, dtype=torch.float) / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self._seq_len_cached = 0
        self._freqs_cached = None

    def update_freqs_cache(self, seqlen: int) -> None:
        if seqlen > self._seq_len_cached:
            seqlen *= 2
            self._seq_len_cached = seqlen
            self.inv_freq = 1.0 / (self.theta**(torch.arange(
                0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device)
                                                / self.dim))
            seq = torch.arange(seqlen,
                               device=self.inv_freq.device,
                               dtype=self.inv_freq.dtype)
            freqs = torch.outer(seq, self.inv_freq)
            self._freqs_cached = freqs

    def forward(self, seqlen: int) -> torch.Tensor:
        self.update_freqs_cache(seqlen)
        return self._freqs_cached[:seqlen]


class Qwen2VisionTransformer(nn.Module):

    def __init__(
        self,
        vision_config: Qwen2VLVisionConfig,
        norm_eps: float = 1e-6,
        quant_config: Optional[QuantizationConfig] = None,
622
        prefix: str = "",
623
        use_data_parallel: bool = False,
624
625
626
    ) -> None:
        super().__init__()

627
628
629
630
631
632
633
634
635
        patch_size = vision_config.patch_size
        temporal_patch_size = vision_config.temporal_patch_size
        spatial_merge_size = vision_config.spatial_merge_size
        in_channels = vision_config.in_channels
        hidden_size = vision_config.hidden_size
        embed_dim = vision_config.embed_dim
        depth = vision_config.depth
        num_heads = vision_config.num_heads
        mlp_ratio = vision_config.mlp_ratio
636

637
638
639
        self.use_data_parallel = use_data_parallel
        self.out_hidden_size = vision_config.hidden_size

640
        self.spatial_merge_size = spatial_merge_size
641
642
        self.num_heads = num_heads
        self.embed_dim = embed_dim
643
644
645
646

        self.patch_embed = Qwen2VisionPatchEmbed(
            patch_size=patch_size,
            temporal_patch_size=temporal_patch_size,
647
            in_channels=in_channels,
648
649
650
651
652
653
654
655
            embed_dim=embed_dim,
        )

        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
        head_dim = embed_dim // num_heads
        self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)

        self.blocks = nn.ModuleList([
656
657
658
659
660
            Qwen2VisionBlock(dim=embed_dim,
                             num_heads=num_heads,
                             mlp_ratio=mlp_ratio,
                             norm_layer=norm_layer,
                             quant_config=quant_config,
661
662
                             prefix=f"{prefix}.blocks.{layer_idx}",
                             use_data_parallel=use_data_parallel)
663
            for layer_idx in range(depth)
664
665
666
667
668
669
        ])
        self.merger = Qwen2VisionPatchMerger(
            d_model=hidden_size,
            context_dim=embed_dim,
            norm_layer=norm_layer,
            quant_config=quant_config,
670
            prefix=f"{prefix}.merger",
671
            use_data_parallel=use_data_parallel,
672
        )
zhuwenwen's avatar
zhuwenwen committed
673

674
675
676
677
678
679
        self.attn_backend = get_vit_attn_backend(
            head_size=head_dim, dtype=torch.get_default_dtype())
        if self.attn_backend != _Backend.FLASH_ATTN and \
            check_upstream_fa_availability(
                torch.get_default_dtype()):
            self.attn_backend = _Backend.FLASH_ATTN
zhuwenwen's avatar
zhuwenwen committed
680
681
682
683
684
685
686
687
688
689
        
        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config
            
        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
        self.use_fa_pad = os.environ.get('FA_PAD') == '1'
        self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
690
691
692

    @property
    def dtype(self) -> torch.dtype:
693
        return self.patch_embed.proj.weight.dtype
694
695
696

    @property
    def device(self) -> torch.device:
697
        return self.patch_embed.proj.weight.device
698

699
    def rot_pos_emb(self, grid_thw: list[list[int]]) -> torch.Tensor:
700
        pos_ids = []
701
        max_grid_size = 0
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
        for t, h, w in grid_thw:
            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
            hpos_ids = hpos_ids.reshape(
                h // self.spatial_merge_size,
                self.spatial_merge_size,
                w // self.spatial_merge_size,
                self.spatial_merge_size,
            ).permute(0, 2, 1, 3).flatten()
            wpos_ids = wpos_ids.reshape(
                h // self.spatial_merge_size,
                self.spatial_merge_size,
                w // self.spatial_merge_size,
                self.spatial_merge_size,
            ).permute(0, 2, 1, 3).flatten()
            pos_ids.append(
                torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
719
            max_grid_size = max(max_grid_size, h, w)
720
721
722
723
724
        pos_ids = torch.cat(pos_ids, dim=0)
        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
        return rotary_pos_emb

725
726
727
728
    def compute_attn_mask_seqlen(
            self, cu_seqlens: torch.Tensor
    ) -> tuple[Optional[int], Optional[list[int]]]:
        max_seqlen, seqlens = None, None
729
730
        if (self.attn_backend == _Backend.FLASH_ATTN
                or self.attn_backend == _Backend.ROCM_AITER_FA):
731
732
733
734
735
            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
        elif self.attn_backend == _Backend.XFORMERS:
            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
        return max_seqlen, seqlens

736
737
738
    def forward(
        self,
        x: torch.Tensor,
739
        grid_thw: list[list[int]],
740
741
742
743
744
745
746
747
748
    ) -> torch.Tensor:
        # patchify
        x = x.to(device=self.device, dtype=self.dtype)
        x = self.patch_embed(x)

        # compute position embedding
        rotary_pos_emb = self.rot_pos_emb(grid_thw)

        # compute cu_seqlens
749
750
751
        grid_thw_ = torch.tensor(grid_thw)
        cu_seqlens = torch.repeat_interleave(grid_thw_[:, 1] * grid_thw_[:, 2],
                                             grid_thw_[:, 0]).cumsum(
752
753
754
755
756
                                                 dim=0, dtype=torch.int32)
        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)

        # transformers
        x = x.unsqueeze(1)
757

758
759
        # pre-compute seqlens for attn mask to reduce cuMemcpy operations
        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
760
        for blk in self.blocks:
761
762
763
764
765
766
767
            x = blk(
                x,
                cu_seqlens=cu_seqlens,
                rotary_pos_emb=rotary_pos_emb,
                max_seqlen=max_seqlen,
                seqlens=seqlens,
            )
768
769
770

        # adapter
        x = self.merger(x)
771

772
773
        return x

774
775
    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
776
777
778
779
780
781
782
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
783
        loaded_params: set[str] = set()
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800

        for name, loaded_weight in weights:
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)

                param = params_dict[name]
                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)
            loaded_params.add(name)
zhuwenwen's avatar
zhuwenwen committed
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
            
        if self.use_llama_nn and self.quant_method is None:
            lay_key_words = [
                "attn.qkv.weight",
                "attn.proj.weight",
                "mlp.fc1.weight",
                "mlp.fc2.weight",
                "mlp.0.weight",
                "mlp.2.weight",
                "self_attn.qkv_proj.weight",
                "self_attn.o_proj.weight",
                "mlp.gate_up_proj.weight",
                "mlp.down_proj.weight",
                "lm_head.weight",
            ]
            combined_words = "|".join(lay_key_words)
            
zhuwenwen's avatar
zhuwenwen committed
818
819
            # lay_qkv_words = ["attn.qkv.weight"]   
            # qkv_words = "|".join(lay_qkv_words)  
zhuwenwen's avatar
zhuwenwen committed
820
            
zhuwenwen's avatar
zhuwenwen committed
821
822
            # lay_qkv_bias_words = ["attn.qkv.bias"]   
            # qkv_bias_words = "|".join(lay_qkv_bias_words) 
zhuwenwen's avatar
zhuwenwen committed
823
            
zhuwenwen's avatar
zhuwenwen committed
824
825
            for layername in loaded_params:
                weight = params_dict[layername]
zhuwenwen's avatar
zhuwenwen committed
826
827
828
829
830
                # if self.use_fa_pad and (re.findall(qkv_bias_words, layername)):
                #     weight.data = pad_weight(weight.data, 32)
                    
                matches = re.findall(combined_words, layername)
                if matches:   
zhuwenwen's avatar
zhuwenwen committed
831
832
                    # if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
                    #     weight.data = pad_weight(weight.data, 32)  
zhuwenwen's avatar
zhuwenwen committed
833
834
835
836
837
838
839
840
841
842
843
844
845
                    
                    # if self.use_fa_pad and (re.findall(qkv_words, layername)):
                    #     if not gemm_bank_conf(weight.data.shape[0]):
                    #         weight.data = pad_weight(weight.data, 32)
                        
                    _weight = torch.zeros_like(weight.data)
                    ori_shape =_weight.shape
                    
                    ops.trans_w16_gemm(_weight, weight.data, _weight.shape[0], _weight.shape[1])
                    weight.data.copy_(_weight)
                    
                    weight.data=weight.data.reshape(ori_shape[1],-1)
                    
846
847
        return loaded_params

848

849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
def _create_qwen2vl_field_factory(
    spatial_merge_size: int
) -> Callable[
    [Mapping[str, torch.Tensor]],
        Mapping[str, MultiModalFieldConfig],
]:

    def _qwen2vl_field_config(hf_inputs: Mapping[str, torch.Tensor]):
        image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
        image_pixel_grid_sizes = image_grid_thw.prod(-1)
        image_embed_grid_sizes = (image_pixel_grid_sizes //
                                  spatial_merge_size // spatial_merge_size)

        video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
        video_grid_sizes = video_grid_thw.prod(-1)
        video_embed_grid_sizes = (video_grid_sizes // spatial_merge_size //
                                  spatial_merge_size)

        return dict(
            pixel_values=MultiModalFieldConfig.flat_from_sizes(
                "image", image_pixel_grid_sizes),
            image_embeds=MultiModalFieldConfig.flat_from_sizes(
                "image", image_embed_grid_sizes),
            image_grid_thw=MultiModalFieldConfig.batched("image"),
            pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
                "video", video_grid_sizes),
            video_embeds=MultiModalFieldConfig.flat_from_sizes(
                "video", video_embed_grid_sizes),
            video_grid_thw=MultiModalFieldConfig.batched("video"),
        )

    return _qwen2vl_field_config
881
882


Roger Wang's avatar
Roger Wang committed
883
class Qwen2VLMultiModalDataParser(MultiModalDataParser):
884

885
886
887
888
    def __init__(self, spatial_merge_size: int, *args, **kwargs):
        self._spatial_merge_size = spatial_merge_size
        super().__init__(*args, **kwargs)

889
890
891
    def _parse_image_data(
        self,
        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
892
    ) -> Optional[ModalityDataItems[Any, Any]]:
893
        if isinstance(data, dict):
zhuwenwen's avatar
zhuwenwen committed
894
895
896
897
            return DictEmbeddingItems(
                data,
                modality="image",
                required_fields={"image_embeds", "image_grid_thw"},
898
899
                fields_factory=_create_qwen2vl_field_factory(
                    self._spatial_merge_size),
zhuwenwen's avatar
zhuwenwen committed
900
            )
901

902
        return super()._parse_image_data(data)
903

904
    def _parse_video_data(
905
        self,
906
        data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
907
    ) -> Optional[ModalityDataItems[Any, Any]]:
908
        if isinstance(data, dict):
zhuwenwen's avatar
zhuwenwen committed
909
910
911
912
            return DictEmbeddingItems(
                data,
                modality="video",
                required_fields={"video_embeds", "video_grid_thw"},
913
914
                fields_factory=_create_qwen2vl_field_factory(
                    self._spatial_merge_size),
zhuwenwen's avatar
zhuwenwen committed
915
            )
916
917
918

        return super()._parse_video_data(data)

919

920
class Qwen2VLProcessingInfo(BaseProcessingInfo):
921

922
    def get_hf_config(self):
923
924
        return self.ctx.get_hf_config(Qwen2VLConfig)

925
    def get_hf_processor(self, **kwargs: object) -> Qwen2VLProcessor:
zhuwenwen's avatar
zhuwenwen committed
926
927
        return self.ctx.get_hf_processor(
            Qwen2VLProcessor,
928
            use_fast=kwargs.pop("use_fast", True),
zhuwenwen's avatar
zhuwenwen committed
929
930
931
            **kwargs,
        )

932
933
    def get_image_processor(self, **kwargs: object) -> Qwen2VLImageProcessor:
        return self.get_hf_processor(**kwargs).image_processor
934

935
936
937
    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": None, "video": None}

938
939
940
941
942
    def get_mm_max_tokens_per_item(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> Mapping[str, int]:
943
944
945
946
        max_image_tokens = self.get_max_image_tokens()
        max_video_tokens = self.get_max_video_tokens(seq_len, mm_counts)
        return {"image": max_image_tokens, "video": max_video_tokens}

947
948
949
950
951
952
953
    def _get_vision_info(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int = 1,
        do_resize: bool = True,
954
        image_processor: Optional[Qwen2VLImageProcessor],
955
    ) -> tuple[ImageSize, int]:
956
957
958
959
        if image_processor is None:
            image_processor = self.get_image_processor()

        hf_config = self.get_hf_config()
960
        vision_config = hf_config.vision_config
961
962
963
        patch_size = vision_config.patch_size
        merge_size = vision_config.spatial_merge_size
        temporal_patch_size = vision_config.temporal_patch_size
964

965
966
967
968
969
970
971
972
973
974
975
976
977
978
        if do_resize:
            resized_height, resized_width = smart_resize(
                height=image_height,
                width=image_width,
                factor=patch_size * merge_size,
                min_pixels=image_processor.min_pixels,
                max_pixels=image_processor.max_pixels,
            )
            preprocessed_size = ImageSize(width=resized_width,
                                          height=resized_height)
        else:
            preprocessed_size = ImageSize(width=image_width,
                                          height=image_height)

zhuwenwen's avatar
zhuwenwen committed
979
980
981
982
983
        # NOTE: Frames are padded to be divisible by `temporal_patch_size`
        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L294
        padded_num_frames = num_frames + num_frames % temporal_patch_size

        grid_t = max(padded_num_frames // temporal_patch_size, 1)
984
985
986
987
988
989
990
991
        grid_h = preprocessed_size.height // patch_size
        grid_w = preprocessed_size.width // patch_size

        num_patches = grid_t * grid_h * grid_w
        num_vision_tokens = num_patches // (merge_size**2)

        return preprocessed_size, num_vision_tokens

992
    def get_num_image_tokens(
993
994
995
996
        self,
        *,
        image_width: int,
        image_height: int,
997
        image_processor: Optional[Qwen2VLImageProcessor],
998
999
1000
1001
    ) -> int:
        _, num_image_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
1002
            num_frames=1,
1003
            image_processor=image_processor,
1004
1005
1006
        )
        return num_image_tokens

1007
    def get_num_video_tokens(
1008
1009
1010
1011
1012
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int,
1013
        image_processor: Optional[Qwen2VLImageProcessor],
1014
1015
1016
1017
1018
    ) -> int:
        _, num_video_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            num_frames=num_frames,
1019
            image_processor=image_processor,
1020
1021
1022
        )
        return num_video_tokens

1023
    def get_image_size_with_most_features(self) -> ImageSize:
1024
1025
1026
        max_image_size, _ = self._get_vision_info(
            image_width=9999999,
            image_height=9999999,
1027
            num_frames=1,
1028
            image_processor=None,
1029
1030
1031
        )
        return max_image_size

1032
1033
    def get_max_image_tokens(self) -> int:
        target_width, target_height = self.get_image_size_with_most_features()
1034

1035
        return self.get_num_image_tokens(
1036
1037
            image_width=target_width,
            image_height=target_height,
1038
            image_processor=None,
1039
        )
1040

1041
1042
1043
    def _get_max_video_frames(self,
                              max_tokens: int,
                              start_num_frames: int = 1) -> int:
1044
        target_width, target_height = self.get_image_size_with_most_features()
1045

1046
        num_frames = start_num_frames
1047
1048
1049

        while True:
            next_num_frames = num_frames + 1
1050
            next_max_tokens = self.get_num_video_tokens(
1051
1052
1053
                image_width=target_width,
                image_height=target_height,
                num_frames=next_num_frames,
1054
                image_processor=None,
1055
            )
1056

1057
            if next_max_tokens > max_tokens:
1058
1059
1060
1061
1062
1063
                break

            num_frames = next_num_frames

        return num_frames

1064
1065
1066
1067
    def get_num_frames_with_most_features(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
1068
        max_frames_per_video: int = _MAX_FRAMES_PER_VIDEO,
1069
1070
    ) -> int:
        max_videos = mm_counts.get("video", 0)
1071

1072
        max_total_frames = self._get_max_video_frames(seq_len)
zhuwenwen's avatar
zhuwenwen committed
1073
        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
1074
                                   max_frames_per_video)
1075

zhuwenwen's avatar
zhuwenwen committed
1076
        return max(max_frames_per_video, 1)
1077

1078
1079
1080
1081
1082
    def get_max_video_tokens(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
1083
        target_width, target_height = self.get_image_size_with_most_features()
1084

1085
        return self.get_num_video_tokens(
1086
1087
            image_width=target_width,
            image_height=target_height,
1088
1089
            num_frames=self.get_num_frames_with_most_features(
                seq_len, mm_counts),
1090
            image_processor=None,
1091
1092
        )

1093
1094
1095

class Qwen2VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen2VLProcessingInfo]):

1096
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
1097
1098
1099
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

1100
        hf_processor = self.info.get_hf_processor()
1101
1102
        image_token: str = hf_processor.image_token
        video_token: str = hf_processor.video_token
1103

1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
        return image_token * num_images + video_token * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

1114
1115
1116
        target_width, target_height = \
            self.info.get_image_size_with_most_features()
        target_num_frames = \
1117
            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
1118

1119
        return {
1120
1121
1122
1123
1124
1125
1126
1127
            "image":
            self._get_dummy_images(width=target_width,
                                   height=target_height,
                                   num_images=num_images),
            "video":
            self._get_dummy_videos(
                width=target_width,
                height=target_height,
1128
                num_frames=target_num_frames,
1129
1130
                num_videos=num_videos,
            )
1131
1132
        }

1133

1134
1135
class Qwen2VLMultiModalProcessor(BaseMultiModalProcessor[Qwen2VLProcessingInfo]
                                 ):
1136

1137
    def _get_data_parser(self) -> MultiModalDataParser:
1138
1139
        return Qwen2VLMultiModalDataParser(
            self.info.get_hf_config().vision_config.spatial_merge_size)
1140

1141
    def _get_prompt_updates(
1142
1143
        self,
        mm_items: MultiModalDataItems,
1144
        hf_processor_mm_kwargs: Mapping[str, Any],
1145
        out_mm_kwargs: MultiModalKwargsItems,
1146
    ) -> Sequence[PromptUpdate]:
1147
1148
1149
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        image_processor = self.info.get_image_processor(
            **hf_processor_mm_kwargs)
1150
1151
        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()
1152
1153

        placeholder = {
1154
1155
            "image": vocab[hf_processor.image_token],
            "video": vocab[hf_processor.video_token],
1156
        }
1157

1158
1159
1160
        merge_length = image_processor.merge_size**2

        def get_replacement_qwen2vl(item_idx: int, modality: str):
1161
1162
            out_item = out_mm_kwargs[modality][item_idx]
            grid_thw = out_item[f"{modality}_grid_thw"].data
1163
1164
            assert isinstance(grid_thw, torch.Tensor)

1165
1166
            num_tokens = int(grid_thw.prod()) // merge_length
            return [placeholder[modality]] * num_tokens
1167
1168
1169
1170

        return [
            PromptReplacement(
                modality=modality,
1171
                target=[placeholder[modality]],
1172
1173
1174
1175
                replacement=partial(get_replacement_qwen2vl,
                                    modality=modality),
            ) for modality in ("image", "video")
        ]
1176

1177
    def _get_mm_fields_config(
1178
        self,
1179
1180
1181
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
1182
1183
1184
        return _create_qwen2vl_field_factory(
            self.info.get_hf_config().vision_config.spatial_merge_size)(
                hf_inputs)
1185
1186


1187
1188
1189
@MULTIMODAL_REGISTRY.register_processor(Qwen2VLMultiModalProcessor,
                                        info=Qwen2VLProcessingInfo,
                                        dummy_inputs=Qwen2VLDummyInputsBuilder)
1190
class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
1191
                                      SupportsLoRA, SupportsPP, SupportsMRoPE):
1192

1193
    # To ensure correct weight loading and mapping.
1194
1195
1196
1197
1198
1199
1200
1201
1202
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            # mapping for new names in checkpoint saved after transformers v4.52
            "model.language_model.": "language_model.model.",
            "model.visual.": "visual.",
            # mapping for original checkpoint
            "lm_head.": "language_model.lm_head.",
            "model.": "language_model.model.",
        })
1203

1204
1205
    supports_encoder_tp_data = True

1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
    def get_mrope_input_positions(
        self,
        input_tokens: list[int],
        hf_config: PretrainedConfig,
        image_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
        video_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
        second_per_grid_ts: Optional[list[float]] = None,
        context_len: int = 0,
        seq_len: Optional[int] = None,
        audio_feature_lengths: Optional[torch.Tensor] = None,
        use_audio_in_video: bool = False,
    ) -> tuple[torch.Tensor, int]:
        """Get M-RoPE input positions for Qwen2-VL model."""
        if image_grid_thw is None:
            image_grid_thw = []
        if video_grid_thw is None:
            video_grid_thw = []
        if second_per_grid_ts is None:
            second_per_grid_ts = []

        image_token_id = hf_config.image_token_id
        video_token_id = hf_config.video_token_id
        vision_start_token_id = hf_config.vision_start_token_id
        spatial_merge_size = hf_config.vision_config.spatial_merge_size
        tokens_per_second = getattr(hf_config.vision_config,
                                    "tokens_per_second", 1.0)

        input_tokens_tensor = torch.tensor(input_tokens)
        vision_start_indices = torch.argwhere(
            input_tokens_tensor == vision_start_token_id).squeeze(1)
        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
        image_nums = (vision_tokens == image_token_id).sum()
        video_nums = (vision_tokens == video_token_id).sum()
        llm_pos_ids_list: list = []

        st = 0
        remain_images, remain_videos = image_nums, video_nums

        image_index, video_index = 0, 0
        for _ in range(image_nums + video_nums):
            video_second_per_grid_t = 0.0
            if remain_images > 0:
                try:
                    ed_image = input_tokens.index(image_token_id, st)
                except ValueError:
                    ed_image = len(input_tokens) + 1
            else:
                ed_image = len(input_tokens) + 1
            if remain_videos > 0:
                try:
                    ed_video = input_tokens.index(video_token_id, st)
                except ValueError:
                    ed_video = len(input_tokens) + 1
            else:
                ed_video = len(input_tokens) + 1
            if ed_image < ed_video:
                t, h, w = (
                    image_grid_thw[image_index][0],
                    image_grid_thw[image_index][1],
                    image_grid_thw[image_index][2],
                )
                image_index += 1
                remain_images -= 1
                ed = ed_image
            else:
                t, h, w = (
                    video_grid_thw[video_index][0],
                    video_grid_thw[video_index][1],
                    video_grid_thw[video_index][2],
                )
                video_second_per_grid_t = 1.0
                if second_per_grid_ts:
                    video_second_per_grid_t = second_per_grid_ts[video_index]
                video_index += 1
                remain_videos -= 1
                ed = ed_video

            llm_grid_t, llm_grid_h, llm_grid_w = \
                t, h // spatial_merge_size, w // spatial_merge_size
            text_len = ed - st

            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
                llm_pos_ids_list) > 0 else 0
            llm_pos_ids_list.append(
                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)

            t_index = (torch.arange(llm_grid_t).view(-1, 1).expand(
                -1, llm_grid_h * llm_grid_w) * video_second_per_grid_t *
                       tokens_per_second).long().flatten()

            h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
                llm_grid_t, -1, llm_grid_w).flatten()
            w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(
                llm_grid_t, llm_grid_h, -1).flatten()
            llm_pos_ids_list.append(
                torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
            st = ed + llm_grid_t * llm_grid_h * llm_grid_w

        if st < len(input_tokens):
            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
                llm_pos_ids_list) > 0 else 0
            text_len = len(input_tokens) - st
            llm_pos_ids_list.append(
                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)

        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
        mrope_position_delta = (llm_positions.max() + 1 -
                                len(input_tokens)).item()
        llm_positions = llm_positions[:, context_len:seq_len]

        return llm_positions, mrope_position_delta

1318
1319
1320
1321
1322
1323
1324
1325
1326
    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "<|vision_start|><|image_pad|><|vision_end|>"
        if modality.startswith("video"):
            return "<|vision_start|><|video_pad|><|vision_end|>"

        raise ValueError("Only image or video modality is supported")

1327
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
1328
        super().__init__()
1329
        config: Qwen2VLConfig = vllm_config.model_config.hf_config
1330
1331
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
1332

1333
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
1334
1335
1336
        self.config = config
        self.multimodal_config = multimodal_config

1337
1338
1339
1340
1341
        if multimodal_config.get_limit_per_prompt("image") or \
            multimodal_config.get_limit_per_prompt("video"):
            self.visual = Qwen2VisionTransformer(
                config.vision_config,
                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
1342
                quant_config=quant_config,
1343
                prefix=maybe_prefix(prefix, "visual"),
1344
                use_data_parallel=self.use_data_parallel,
1345
1346
1347
            )
        else:
            self.visual = None
1348

1349
1350
1351
1352
1353
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            prefix=maybe_prefix(prefix, "language_model"),
            architectures=["Qwen2ForCausalLM"],
        )
1354

1355
        self.make_empty_intermediate_tensors = (
1356
            self.language_model.make_empty_intermediate_tensors)
1357

1358
    def _validate_and_reshape_mm_tensor(self, mm_input: object,
1359
1360
1361
1362
1363
1364
1365
1366
1367
                                        name: str) -> torch.Tensor:
        if not isinstance(mm_input, (torch.Tensor, list)):
            raise ValueError(f"Incorrect type of {name}. "
                             f"Got type: {type(mm_input)}")
        if isinstance(mm_input, torch.Tensor):
            if mm_input.ndim == 2:
                return mm_input
            if mm_input.ndim != 3:
                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
1368
1369
                                 f"Got ndim: {mm_input.ndim} "
                                 f"(shape={mm_input.shape})")
1370
            return mm_input.reshape(-1, mm_input.shape[-1])
1371
1372
1373
1374
1375
1376
        else:
            return torch.concat(mm_input)

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[Qwen2VLImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
1377
        image_embeds = kwargs.pop("image_embeds", None)
1378
1379
        image_grid_thw = kwargs.pop("image_grid_thw", None)

1380
        if pixel_values is None and image_embeds is None:
1381
1382
            return None

1383
1384
1385
1386
1387
        if pixel_values is not None:
            pixel_values = self._validate_and_reshape_mm_tensor(
                pixel_values, "image pixel values")
            image_grid_thw = self._validate_and_reshape_mm_tensor(
                image_grid_thw, "image grid_thw")
1388

1389
            return Qwen2VLImagePixelInputs(type="pixel_values",
1390
                                           pixel_values=pixel_values,
1391
1392
1393
                                           image_grid_thw=image_grid_thw)

        if image_embeds is not None:
1394
1395
            image_embeds = self._validate_and_reshape_mm_tensor(
                image_embeds, "image embeds")
1396
1397
            image_grid_thw = self._validate_and_reshape_mm_tensor(
                image_grid_thw, "image grid_thw")
1398

1399
            return Qwen2VLImageEmbeddingInputs(type="image_embeds",
1400
1401
                                               image_embeds=image_embeds,
                                               image_grid_thw=image_grid_thw)
1402
1403
1404
1405

    def _parse_and_validate_video_input(
            self, **kwargs: object) -> Optional[Qwen2VLVideoInputs]:
        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
1406
        video_embeds = kwargs.pop("video_embeds", None)
1407
1408
        video_grid_thw = kwargs.pop("video_grid_thw", None)

1409
        if pixel_values_videos is None and video_embeds is None:
1410
1411
            return None

1412
1413
1414
1415
1416
        if pixel_values_videos is not None:
            pixel_values_videos = self._validate_and_reshape_mm_tensor(
                pixel_values_videos, "video pixel values")
            video_grid_thw = self._validate_and_reshape_mm_tensor(
                video_grid_thw, "video grid_thw")
1417

1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
            return Qwen2VLVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_videos=pixel_values_videos,
                video_grid_thw=video_grid_thw,
            )

        if video_embeds is not None:
            video_embeds = self._validate_and_reshape_mm_tensor(
                video_embeds, "video embeds")
            video_grid_thw = self._validate_and_reshape_mm_tensor(
                video_grid_thw, "video grid_thw")

            return Qwen2VLVideoEmbeddingInputs(type="video_embeds",
                                               video_embeds=video_embeds,
                                               video_grid_thw=video_grid_thw)
1433

1434
1435
1436
1437
1438
    def _process_image_input(
            self, image_input: Qwen2VLImageInputs) -> tuple[torch.Tensor, ...]:

        grid_thw = image_input["image_grid_thw"]
        assert grid_thw.ndim == 2
1439
        grid_thw_list = grid_thw.tolist()
1440

1441
        if image_input["type"] == "image_embeds":
1442
            image_embeds = image_input["image_embeds"]
1443
        else:
1444
            pixel_values = image_input["pixel_values"]
1445
1446
1447
1448
1449
1450
1451
1452
1453

            if self.use_data_parallel:
                return run_dp_sharded_mrope_vision_model(self.visual,
                                                         pixel_values,
                                                         grid_thw_list,
                                                         rope_type="rope_3d")
            else:
                image_embeds = self.visual(pixel_values,
                                           grid_thw=grid_thw_list)
1454
1455
1456

        # Split concatenated embeddings for each image item.
        merge_size = self.visual.spatial_merge_size
1457
1458
        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
                 (merge_size * merge_size)).tolist()
1459

1460
        return image_embeds.split(sizes)
1461
1462
1463

    def _process_video_input(
            self, video_input: Qwen2VLVideoInputs) -> tuple[torch.Tensor, ...]:
1464

1465
1466
        grid_thw = video_input["video_grid_thw"]
        assert grid_thw.ndim == 2
1467
        grid_thw_list = grid_thw.tolist()
1468

1469
        if video_input["type"] == "video_embeds":
1470
            video_embeds = video_input["video_embeds"]
1471
        else:
1472
            pixel_values_videos = video_input["pixel_values_videos"]
1473
1474
1475
1476
1477
1478
1479
1480
            if self.use_data_parallel:
                return run_dp_sharded_mrope_vision_model(self.visual,
                                                         pixel_values_videos,
                                                         grid_thw_list,
                                                         rope_type="rope_3d")
            else:
                video_embeds = self.visual(pixel_values_videos,
                                           grid_thw=grid_thw_list)
1481

1482
1483
        # Split concatenated embeddings for each video item.
        merge_size = self.visual.spatial_merge_size
1484
1485
        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
                 (merge_size * merge_size)).tolist()
1486

1487
        return video_embeds.split(sizes)
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504

    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        modalities = {}

        # Preserve the order of modalities if there are multiple of them
        # from the order of kwargs.
        for input_key in kwargs:
            if input_key in ("pixel_values",
                             "image_embeds") and "images" not in modalities:
                modalities["images"] = self._parse_and_validate_image_input(
                    **kwargs)
            if input_key in ("pixel_values_videos",
                             "video_embeds") and "videos" not in modalities:
                modalities["videos"] = self._parse_and_validate_video_input(
                    **kwargs)

        return modalities
1505

1506
1507
1508
    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

1509
1510
    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:
1511

1512
1513
        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
1514
            return []
1515

1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
        # The result multimodal_embeddings is tuple of tensors, with each
        # tensor correspoending to a multimodal data item (image or video).
        multimodal_embeddings: tuple[torch.Tensor, ...] = ()

        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
        for modality in modalities:
            if modality == "images":
                image_input = modalities["images"]
                vision_embeddings = self._process_image_input(image_input)
                multimodal_embeddings += vision_embeddings
            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_video_input(video_input)
                multimodal_embeddings += video_embeddings
1531
1532
1533
1534
1535
1536

        return multimodal_embeddings

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
1537
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
1538
    ) -> torch.Tensor:
1539
        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
1540
1541
        if multimodal_embeddings is not None \
            and len(multimodal_embeddings) != 0:
1542
1543
1544
            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, multimodal_embeddings,
                [self.config.image_token_id, self.config.video_token_id])
1545
1546
        return inputs_embeds

1547
1548
1549
    def get_input_embeddings_v0(
        self,
        input_ids: torch.Tensor,
1550
1551
        image_input: Optional[Qwen2VLImagePixelInputs] = None,
        video_input: Optional[Qwen2VLVideoPixelInputs] = None,
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
    ) -> torch.Tensor:
        inputs_embeds = self.get_input_embeddings(input_ids)
        if image_input is not None:
            image_embeds = self._process_image_input(image_input)
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                image_embeds,
                placeholder_token_id=self.config.image_token_id,
            )

        if video_input is not None:
            video_embeds = self._process_video_input(video_input)
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                video_embeds,
                placeholder_token_id=self.config.video_token_id,
            )
1571
1572
        return inputs_embeds

1573
1574
1575
1576
1577
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
1578
        inputs_embeds: Optional[torch.Tensor] = None,
1579
        **kwargs: object,
1580
    ) -> Union[torch.Tensor, IntermediateTensors]:
1581
1582
1583
1584
1585
1586
1587
1588
1589
        """Run forward pass for Qwen2-VL.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            positions: Flattened (concatenated) position ids corresponding to a
                batch.
                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
                opensource models), the shape will be `(3, seq_len)`,
1590
1591
1592
                otherwise it will be `(seq_len,)`.
            intermediate_tensors: Intermediate tensors from prior forward pass.
            inputs_embeds: Optional tensor of input embeddings.
1593
1594
        """

1595
        if intermediate_tensors is not None:
1596
            inputs_embeds = None
1597

1598
1599
1600
        # NOTE: In v1, inputs_embeds is always generated at model runner from
        # `get_multimodal_embeddings` and `get_input_embeddings`, this
        # condition is only for v0 compatibility.
1601
        elif inputs_embeds is None:
1602
1603
            image_input = self._parse_and_validate_image_input(**kwargs)
            video_input = self._parse_and_validate_video_input(**kwargs)
1604

1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
            if image_input is None and video_input is None:
                inputs_embeds = None
            else:
                if uses_mrope(self.config):
                    assert positions.ndim == 2 and positions.size(0) == 3, (
                        "multimodal section rotary embedding requires "
                        f"(3, seq_len) positions, but got {positions.size()}")
                inputs_embeds = self.get_input_embeddings_v0(
                    input_ids,
                    image_input=image_input,
                    video_input=video_input)
                input_ids = None
1617

1618
        hidden_states = self.language_model.model(
1619
1620
            input_ids=input_ids,
            positions=positions,
1621
            intermediate_tensors=intermediate_tensors,
1622
1623
1624
1625
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

1626
1627
1628
1629
    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> Optional[torch.Tensor]:
1630
        return self.language_model.compute_logits(hidden_states)
1631

1632
1633
    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
1634

1635
1636
1637
1638
        skip_prefixes = []
        if self.visual is None:
            skip_prefixes.extend(["visual."])
        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
1639
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
1640
1641
1642
1643
1644
1645
1646

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="language_model",
1647
1648
1649
            connector="visual.merger.",
            tower_model="visual.",
        )
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682


class Tarsier2MultiModalProcessor(Qwen2VLMultiModalProcessor):
    pass


class Tarsier2ImageProcessor(Qwen2VLImageProcessor):

    def __init__(
        self,
        size: Optional[dict[str, int]] = None,
        **kwargs,
    ) -> None:
        if size is not None and "min_pixels" in size and "max_pixels" in size:
            # Remap if Tarsier2-specific format is provided
            remapped_size = {
                "shortest_edge": size["min_pixels"],
                "longest_edge": size["max_pixels"]
            }
            super().__init__(size=remapped_size, **kwargs)
        else:
            super().__init__(size=size, **kwargs)


class Tarsier2Processor(Qwen2VLProcessor):

    def __init__(
        self,
        vision_config: dict,
        tokenizer: AnyTokenizer,
        **kwargs,
    ):
        self.image_processor = Tarsier2ImageProcessor(**vision_config)
1683
1684
1685
1686
1687
1688
        super().__init__(
            image_processor=self.image_processor,
            tokenizer=tokenizer,
            video_processor=Qwen2VLVideoProcessor(**vision_config),
            chat_template=None,
            **kwargs)
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732


class Tarsier2ProcessingInfo(Qwen2VLProcessingInfo):

    def get_hf_config(self) -> Qwen2VLConfig:
        model_path = self.ctx.model_config.model
        original_config = AutoConfig.from_pretrained(model_path)
        config_dict = original_config.to_dict()
        correct_config = Qwen2VLConfig.from_dict(config_dict)

        return correct_config

    def get_hf_processor(self, **kwargs: object) -> Tarsier2Processor:
        return Tarsier2Processor(
            vision_config=self.ctx.get_hf_image_processor_config(),
            tokenizer=self.get_tokenizer(),
            **kwargs,
        )

    def get_image_processor(self) -> Tarsier2ImageProcessor:
        return Tarsier2ImageProcessor(
            **self.ctx.get_hf_image_processor_config())


@MULTIMODAL_REGISTRY.register_processor(Tarsier2MultiModalProcessor,
                                        info=Tarsier2ProcessingInfo,
                                        dummy_inputs=Qwen2VLDummyInputsBuilder)
class Tarsier2ForConditionalGeneration(Qwen2VLForConditionalGeneration):
    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
        "vision_tower.": "visual.",
    })

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        # Tarsier2 uses llava as model_type, which will create a Qwen2VLConfig
        # as text_config, we need to reconstruct Qwen2VLConfig from LlavaConfig.
        config = vllm_config.model_config.hf_config
        qwen2vl_config = config.text_config
        qwen2vl_config.architectures = config.architectures
        vllm_config.model_config.hf_config = qwen2vl_config
        super().__init__(vllm_config=vllm_config, prefix=prefix)

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:

1733
1734
1735
1736
        skip_prefixes = []
        if self.visual is None:
            skip_prefixes.extend(["visual."])
        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
1737
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)