glm4_1v.py 64.4 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
27
28

# Adapted from
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/Glm4v/modeling_Glm4v.py
# Copyright 2025 The vLLM team.
# Copyright 2025 The ZhipuAI Team.
# Copyright 2025 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 GLM-4V model compatible with HuggingFace weights."""

29
import itertools
30
import math
31
from collections.abc import Callable, Iterable, Mapping, Sequence
32
from functools import partial
33
from typing import Annotated, Any, Literal, TypeAlias
34
35
36
37
38
39

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
40
from transformers import BatchFeature
Yuxuan Zhang's avatar
Yuxuan Zhang committed
41
from transformers.models.glm4v.configuration_glm4v import Glm4vVisionConfig
42
from transformers.models.glm4v.image_processing_glm4v import (
43
44
45
46
    Glm4vImageProcessor,
    smart_resize,
)
from transformers.models.glm4v.video_processing_glm4v import Glm4vVideoProcessor
47
48
from transformers.video_utils import VideoMetadata

49
from vllm.attention.backends.registry import AttentionBackendEnum
50
51
52
53
from vllm.attention.layer import (
    check_upstream_fa_availability,
    maybe_get_vit_flash_attn_backend,
)
54
from vllm.config import VllmConfig
55
from vllm.config.multimodal import BaseDummyOptions, VideoDummyOptions
56
from vllm.distributed import get_tensor_model_parallel_world_size, parallel_state
57
58
from vllm.distributed import utils as dist_utils
from vllm.logger import init_logger
59
from vllm.model_executor.layers.conv import Conv2dLayer, Conv3dLayer
60
from vllm.model_executor.layers.layernorm import RMSNorm
61
62
63
64
65
66
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
67
from vllm.model_executor.layers.quantization import QuantizationConfig
68
from vllm.model_executor.layers.rotary_embedding import get_rope
69
70
71
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.multimodal import MULTIMODAL_REGISTRY
72
73
from vllm.multimodal.inputs import (
    MultiModalDataDict,
74
    MultiModalFeatureSpec,
75
76
77
78
79
80
81
82
83
84
85
86
    MultiModalFieldConfig,
    MultiModalKwargsItems,
    VideoItem,
)
from vllm.multimodal.parse import ImageSize, MultiModalDataItems, MultiModalDataParser
from vllm.multimodal.processing import (
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
)
87
88
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
89
from vllm.utils.tensor_schema import TensorSchema, TensorShape
90
91

from ..layers.activation import SiluAndMul
92
93
94
from .interfaces import (
    MultiModalEmbeddings,
    SupportsLoRA,
95
    SupportsMRoPE,
96
97
98
99
100
101
102
103
104
105
    SupportsMultiModal,
    SupportsPP,
)
from .qwen2_vl import _create_qwen2vl_field_factory, apply_rotary_pos_emb_vision
from .utils import (
    AutoWeightsLoader,
    WeightsMapper,
    init_vllm_registered_model,
    maybe_prefix,
)
106
107
108
109
from .vision import (
    get_vit_attn_backend,
    run_dp_sharded_mrope_vision_model,
)
110
111
112
113
114
115
116
117
118

logger = init_logger(__name__)

# For profile run
_MAX_FRAMES_PER_VIDEO = 600

# === Vision Inputs === #


119
class Glm4vImagePixelInputs(TensorSchema):
120
    """
121
122
123
124
125
    Dimensions:
        - np: Number of patches
        - cpp: Number of channels * patch_size * patch_size
        - ni: Number of images
        - g: Grid dimensions (3 for grid_t, grid_h, grid_w)
126
    """
127

128
    type: Literal["pixel_values"] = "pixel_values"
129

130
131
    pixel_values: Annotated[torch.Tensor, TensorShape("np", "cpp")]
    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
132
133


134
class Glm4vImageEmbeddingInputs(TensorSchema):
135
    """
136
137
138
139
140
    Dimensions:
        - f: Number of image features (varies based on image resolution)
        - h: Hidden size (must match language model backbone)
        - n: Number of images
        - g: Grid dimensions (3 for grid_t, grid_h, grid_w)
141
    """
142

143
144
145
146
    type: Literal["image_embeds"] = "image_embeds"

    image_embeds: Annotated[torch.Tensor, TensorShape("f", "h")]
    image_grid_thw: Annotated[torch.Tensor, TensorShape("n", 3)]
147
148


149
Glm4vImageInputs: TypeAlias = Glm4vImagePixelInputs | Glm4vImageEmbeddingInputs
150
151


152
class Glm4vVideoPixelInputs(TensorSchema):
153
    """
154
155
156
157
158
    Dimensions:
        - np: Number of patches
        - ctpp: Number of channels * temporal_patch_size *
            patch_size * patch_size
        - f: Number of frames
159
        - g: Grid dimensions (3 for grid_t which is usually 1 for processed
160
          video, grid_h, grid_w)
161
    """
162

163
    type: Literal["pixel_values_videos"] = "pixel_values_videos"
164

165
    pixel_values_videos: Annotated[torch.Tensor, TensorShape("np", "ctpp")]
166
    video_grid_thw: Annotated[torch.Tensor, TensorShape("f", 3)]
167
168


169
class Glm4vVideoEmbeddingInputs(TensorSchema):
170
    """
171
172
173
    Dimensions:
        - p: Number of video patches across all frames
        - h: Hidden size (must match language model backbone)
174
        - f: Number of frames
175
        - g: Grid dimensions (3 for grid_t which is usually 1 for processed
176
          video, grid_h, grid_w)
177
    """
178

179
    type: Literal["video_embeds"] = "video_embeds"
180

181
    video_embeds: Annotated[torch.Tensor, TensorShape("p", "h")]
182
    video_grid_thw: Annotated[torch.Tensor, TensorShape("f", 3)]
183
184


185
Glm4vVideoInputs: TypeAlias = Glm4vVideoPixelInputs | Glm4vVideoEmbeddingInputs
186

187
# ==== Vision Encoder ==== #
188
189
190
191
192
193
194
195


class Glm4vVisionMLP(nn.Module):
    def __init__(
        self,
        in_features: int,
        hidden_features: int,
        bias: bool = False,
196
        quant_config: QuantizationConfig | None = None,
197
        prefix: str = "",
198
        use_data_parallel: bool = False,
199
200
    ):
        super().__init__()
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=in_features,
            output_sizes=[hidden_features] * 2,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
            disable_tp=use_data_parallel,
        )
        self.down_proj = RowParallelLinear(
            hidden_features,
            in_features,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
            disable_tp=use_data_parallel,
        )
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
        self.act_fn = SiluAndMul()

    def forward(self, x: torch.Tensor):
        x, _ = self.gate_up_proj(x)
        x = self.act_fn(x)
        x, _ = self.down_proj(x)
        return x


def all_gather_interleave(local_tensor, hidden_size: int, tp_size: int):
    """All-gather the input tensor interleavely across model parallel group."""
    import torch.distributed as dist

    gathered_tensors = [torch.zeros_like(local_tensor) for _ in range(tp_size)]
    dist.all_gather(
        gathered_tensors,
        local_tensor,
        group=parallel_state.get_tp_group().device_group,
    )

    gathered_tensors_split = [
238
        torch.split(tensor, hidden_size // tp_size, -1) for tensor in gathered_tensors
239
240
241
242
243
244
245
246
247
248
249
250
251
252
    ]
    ordered_tensors = [
        tensor for pair in zip(*gathered_tensors_split) for tensor in pair
    ]
    result_tensor = torch.cat(ordered_tensors, dim=-1)
    return result_tensor


class Glm4vVisionAttention(nn.Module):
    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        projection_size: int,
253
        quant_config: QuantizationConfig | None = None,
254
        prefix: str = "",
255
        use_data_parallel: bool = False,
256
        attn_backend_override: AttentionBackendEnum | None = None,
257
258
259
    ) -> None:
        super().__init__()
        # Per attention head and per partition values.
260
261
262
263
264
265
        self.tp_size = (
            1 if use_data_parallel else get_tensor_model_parallel_world_size()
        )
        self.tp_rank = (
            0 if use_data_parallel else parallel_state.get_tensor_model_parallel_rank()
        )
266
        self.hidden_size_per_attention_head = dist_utils.divide(
267
268
            projection_size, num_heads
        )
269
        self.num_attention_heads_per_partition = dist_utils.divide(
270
271
            num_heads, self.tp_size
        )
272

273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
        self.qkv = QKVParallelLinear(
            hidden_size=embed_dim,
            head_size=self.hidden_size_per_attention_head,
            total_num_heads=num_heads,
            total_num_kv_heads=num_heads,
            bias=False,
            quant_config=quant_config,
            # Change qkv prefix to align with GLM-4.5V-FP8 quantization cfg
            prefix=f"{prefix}.qkv_proj" if quant_config else f"{prefix}.qkv",
            disable_tp=use_data_parallel,
        )
        self.proj = RowParallelLinear(
            input_size=projection_size,
            output_size=embed_dim,
            quant_config=quant_config,
            prefix=f"{prefix}.proj",
            bias=False,
            disable_tp=use_data_parallel,
        )
292
293

        # Detect attention implementation.
294
295
        self.attn_backend = get_vit_attn_backend(
            head_size=self.hidden_size_per_attention_head,
296
            dtype=torch.get_default_dtype(),
297
            attn_backend_override=attn_backend_override,
298
        )
299
        self.use_upstream_fa = False
300

301
302
        self.attn_backend, self.flash_attn_varlen_func = (
            maybe_get_vit_flash_attn_backend(
303
304
                self.attn_backend,
                self.use_upstream_fa,
305
                attn_backend_override=attn_backend_override,
306
            )
307
        )
308

309
        if self.attn_backend not in {
310
311
312
313
            AttentionBackendEnum.FLASH_ATTN,
            AttentionBackendEnum.TORCH_SDPA,
            AttentionBackendEnum.XFORMERS,
            AttentionBackendEnum.ROCM_AITER_FA,
314
315
        }:
            raise RuntimeError(
316
317
                f"GLM-4V does not support {self.attn_backend} backend now."
            )
318

319
        self.is_flash_attn_backend = self.attn_backend in {
320
321
            AttentionBackendEnum.FLASH_ATTN,
            AttentionBackendEnum.ROCM_AITER_FA,
322
323
        }

324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
    def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
        # [s, b, 3 * head * head_dim]
        seq_len, bs, _ = qkv.shape

        # [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] -> 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

    def forward(
342
343
344
        self,
        x: torch.Tensor,
        cu_seqlens: torch.Tensor,
345
346
        rotary_pos_emb_cos: torch.Tensor,
        rotary_pos_emb_sin: torch.Tensor,
347
348
        max_seqlen: int | None = None,  # Only used for Flash Attention
        seqlens: list[int] | None = None,  # Only used for xFormers
349
350
351
352
353
354
355
356
    ) -> torch.Tensor:
        # [s, b, c] --> [s, b, head * 3 * head_dim]
        x, _ = self.qkv(x)

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

357
        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v))
358
        if rotary_pos_emb_cos is not None and rotary_pos_emb_sin is not None:
359
360
            # [2 * b, s, heads, head_dim]
            qk_concat = torch.cat([q, k], dim=0)
361
362
363
            qk_rotated = apply_rotary_pos_emb_vision(
                qk_concat, rotary_pos_emb_cos, rotary_pos_emb_sin
            )
364
            q, k = torch.chunk(qk_rotated, 2, dim=0)
365

366
        if self.is_flash_attn_backend:
367
368
            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])

369
            output = self.flash_attn_varlen_func(
370
371
372
373
374
375
376
                q,
                k,
                v,
                cu_seqlens_q=cu_seqlens,
                cu_seqlens_k=cu_seqlens,
                max_seqlen_q=max_seqlen,
                max_seqlen_k=max_seqlen,
377
                dropout_p=0.0,
378
379
380
                causal=False,
            )

381
382
383
            context_layer = rearrange(
                output, "(b s) h d -> s b (h d)", b=batch_size
            ).contiguous()
384
        elif self.attn_backend == AttentionBackendEnum.TORCH_SDPA:
385
386
387
388
389
390
391
392
            # Execute attention entry by entry for speed & less VRAM.
            outputs = []
            for i in range(1, len(cu_seqlens)):
                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]
393
394
395
396
                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)
397
398
399
                output_i = rearrange(output_i, "b h s d -> b s h d ")
                outputs.append(output_i)
            context_layer = torch.cat(outputs, dim=1)
400
401
402
            context_layer = rearrange(
                context_layer, "b s h d -> s b (h d)"
            ).contiguous()
403
        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
404
405
406
            from xformers import ops as xops
            from xformers.ops.fmha.attn_bias import BlockDiagonalMask

407
408
409
            attn_bias = BlockDiagonalMask.from_seqlens(
                q_seqlen=seqlens, kv_seqlen=None, device=q.device
            )
410
411

            context_layer = xops.memory_efficient_attention_forward(
412
413
414
415
416
                q, k, v, attn_bias=attn_bias, p=0, scale=None
            )
            context_layer = rearrange(
                context_layer, "b s h d -> s b (h d)"
            ).contiguous()
417
418
419
420
421
422
423
424
425
426
427

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


class Glm4vVisionBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_hidden_dim: int,
428
429
        norm_layer: Callable[[int], nn.Module] | None = None,
        quant_config: QuantizationConfig | None = None,
430
        prefix: str = "",
431
        use_data_parallel: bool = False,
432
        attn_backend_override: AttentionBackendEnum | None = None,
433
434
435
436
437
438
439
440
441
442
443
444
    ) -> 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)
        self.attn = Glm4vVisionAttention(
            embed_dim=dim,
            num_heads=num_heads,
            projection_size=dim,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
445
            use_data_parallel=use_data_parallel,
446
            attn_backend_override=attn_backend_override,
447
448
449
450
451
452
        )
        self.mlp = Glm4vVisionMLP(
            dim,
            mlp_hidden_dim,
            bias=False,
            quant_config=quant_config,
453
            prefix=f"{prefix}.mlp",
454
            use_data_parallel=use_data_parallel,
455
456
457
        )

    def forward(
458
459
460
        self,
        x: torch.Tensor,
        cu_seqlens: torch.Tensor,
461
462
        rotary_pos_emb_cos: torch.Tensor,
        rotary_pos_emb_sin: torch.Tensor,
463
464
        max_seqlen: int | None = None,  # Only used for Flash Attention
        seqlens: list[int] | None = None,  # Only used for xFormers
465
    ) -> torch.Tensor:
466
        x_attn = self.attn(
467
468
            self.norm1(x),
            cu_seqlens=cu_seqlens,
469
470
            rotary_pos_emb_cos=rotary_pos_emb_cos,
            rotary_pos_emb_sin=rotary_pos_emb_sin,
471
472
473
            max_seqlen=max_seqlen,
            seqlens=seqlens,
        )
474
475
        x_fused_norm, residual = self.norm2(x, residual=x_attn)
        x = residual + self.mlp(x_fused_norm)
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493

        return x


class Glm4vVisionPatchEmbed(nn.Module):
    def __init__(
        self,
        patch_size: int = 14,
        temporal_patch_size: int = 1,
        in_channels: int = 3,
        hidden_size: int = 1536,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.hidden_size = hidden_size

        kernel_size = (temporal_patch_size, patch_size, patch_size)
494
495
        self.proj = Conv3dLayer(
            in_channels,
496
            hidden_size,
497
498
            kernel_size=kernel_size,
            stride=kernel_size,
499
500
501
502
            bias=True,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
503
504
505
        L, C = x.shape
        x = x.view(L, -1, self.temporal_patch_size, self.patch_size, self.patch_size)
        x = self.proj(x).view(L, self.hidden_size)
506
507
508
509
510
511
512
513
        return x


class Glm4vPatchMerger(nn.Module):
    def __init__(
        self,
        d_model: int,
        context_dim: int,
514
        quant_config: QuantizationConfig | None = None,
515
        bias: bool = False,
516
        prefix: str = "",
517
        use_data_parallel: bool = False,
518
519
520
    ) -> None:
        super().__init__()
        self.hidden_size = d_model
521
522
523
524
525
526
527
528
529
        self.proj = ColumnParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=bias,
            gather_output=True,
            quant_config=quant_config,
            prefix=f"{prefix}.proj",
            disable_tp=use_data_parallel,
        )
530
        self.post_projection_norm = nn.LayerNorm(self.hidden_size)
531
        self.gate_up_proj = MergedColumnParallelLinear(
532
533
534
535
            input_size=self.hidden_size,
            output_sizes=[context_dim] * 2,
            bias=bias,
            quant_config=quant_config,
536
            prefix=f"{prefix}.gate_up_proj",
537
            disable_tp=use_data_parallel,
538
        )
539
        self.down_proj = RowParallelLinear(
540
541
542
543
            context_dim,
            self.hidden_size,
            bias=bias,
            quant_config=quant_config,
544
            prefix=f"{prefix}.down_proj",
545
            disable_tp=use_data_parallel,
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
        )
        self.act_fn = SiluAndMul()
        self.extra_activation_func = nn.GELU()

    def forward(self, x: torch.Tensor):
        x, _ = self.proj(x)
        x = self.extra_activation_func(self.post_projection_norm(x))
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class Glm4vVisionEmbeddings(nn.Module):
    def __init__(self, config: Glm4vVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

567
        self.num_patches = (self.image_size // self.patch_size) ** 2
568
        self.num_positions = self.num_patches
569
        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
570
571
572
573
574
575
        self.register_buffer(
            "position_ids",
            torch.arange(self.num_positions).expand((1, -1)),
            persistent=False,
        )

576
577
578
    def forward(
        self, embeddings, lengths, image_shapes, h_coords, w_coords
    ) -> torch.Tensor:
579
580
581
582
583
584
585
586
587
588
        pos_embed_weight = self.position_embedding.weight
        hidden_size = pos_embed_weight.shape[1]
        total_seq = h_coords.shape[0]
        device = pos_embed_weight.device

        # Move coordinates to correct device
        h_coords, w_coords = h_coords.to(device), w_coords.to(device)

        # Handle empty sequence case
        if total_seq == 0:
589
590
591
            adapted_pos_embed = torch.empty(
                0, hidden_size, device=device, dtype=pos_embed_weight.dtype
            )
592
593
594
        else:
            # Convert inputs to tensors if needed
            if isinstance(lengths, list):
595
                lengths = torch.tensor(lengths, device=device, dtype=torch.long)
596
            if not isinstance(image_shapes, torch.Tensor):
597
598
599
                image_shapes = torch.tensor(
                    image_shapes, device=device, dtype=torch.long
                )
600
601
602
603

            # Prepare 2D position embedding
            orig_size_sq = pos_embed_weight.shape[0]
            orig_size = int(orig_size_sq**0.5)
604
605
606
607
608
609
            pos_embed_2d = (
                pos_embed_weight.view(orig_size, orig_size, hidden_size)
                .permute(2, 0, 1)
                .unsqueeze(0)
                .to(device=device, dtype=torch.float32)
            )
610
611

            # Calculate target dimensions for each patch
612
613
614
615
616
617
618
619
620
621
            # Add bounds checking for data parallel mode
            if len(lengths) > image_shapes.shape[0]:
                # In data parallel mode, some GPUs might not have all
                # image shapes
                # Use available image shapes, cycling if necessary
                target_h_list = []
                target_w_list = []
                for i in range(len(lengths)):
                    # Cycle through available shapes
                    shape_idx = i % image_shapes.shape[0]
622
623
624
625
626
627
628
629
                    target_h_list.append(image_shapes[shape_idx, 1].repeat(lengths[i]))
                    target_w_list.append(image_shapes[shape_idx, 2].repeat(lengths[i]))
                target_h = torch.cat(target_h_list).to(
                    device=device, dtype=torch.float32
                )
                target_w = torch.cat(target_w_list).to(
                    device=device, dtype=torch.float32
                )
630
            else:
631
632
633
634
635
636
                target_h = torch.cat(
                    [image_shapes[i, 1].repeat(lengths[i]) for i in range(len(lengths))]
                ).to(device=device, dtype=torch.float32)
                target_w = torch.cat(
                    [image_shapes[i, 2].repeat(lengths[i]) for i in range(len(lengths))]
                ).to(device=device, dtype=torch.float32)
637
638
639
640
641
642
643
644

            # Normalize coordinates to [-1, 1] range for grid_sample
            h_coords = h_coords.to(device=device, dtype=torch.float32)
            w_coords = w_coords.to(device=device, dtype=torch.float32)
            norm_w = ((w_coords + 0.5) / target_w) * 2 - 1
            norm_h = ((h_coords + 0.5) / target_h) * 2 - 1

            # Create sampling grid
645
            grid = torch.stack((norm_w, norm_h), dim=-1).unsqueeze(0).unsqueeze(2)
646
647
648
649
650
651
652
653
654
655
656
657

            # Perform bicubic interpolation
            interpolated_embed_fp32 = F.grid_sample(
                pos_embed_2d,
                grid,
                mode="bicubic",
                align_corners=False,
                padding_mode="border",
            )

            # Reshape and convert back to original dtype
            adapted_pos_embed_fp32 = (
658
659
660
661
662
                interpolated_embed_fp32.squeeze(0).squeeze(-1).permute(1, 0)
            )
            adapted_pos_embed = adapted_pos_embed_fp32.to(pos_embed_weight.dtype).to(
                embeddings.device
            )
663
664
665
666
667
668
669
670
671
672
673

        # Add adapted position encoding to embeddings
        embeddings = embeddings + adapted_pos_embed
        return embeddings


class Glm4vVisionTransformer(nn.Module):
    def __init__(
        self,
        vision_config: Glm4vVisionConfig,
        norm_eps: float = 1e-6,
674
        quant_config: QuantizationConfig | None = None,
675
        prefix: str = "",
676
        use_data_parallel: bool = False,
677
        attn_backend_override: AttentionBackendEnum | None = None,
678
679
680
681
682
683
684
685
686
    ) -> None:
        super().__init__()

        patch_size = vision_config.patch_size
        temporal_patch_size = vision_config.temporal_patch_size
        in_channels = vision_config.in_channels
        depth = vision_config.depth
        self.hidden_size = vision_config.hidden_size
        self.num_heads = vision_config.num_heads
687
        self.use_data_parallel = use_data_parallel
688
689
690
691
692
693
694
695
696
697
698
699
700
701

        self.patch_size = vision_config.patch_size
        self.spatial_merge_size = vision_config.spatial_merge_size
        self.out_hidden_size = vision_config.out_hidden_size

        self.patch_embed = Glm4vVisionPatchEmbed(
            patch_size=patch_size,
            temporal_patch_size=temporal_patch_size,
            in_channels=in_channels,
            hidden_size=self.hidden_size,
        )

        norm_layer = partial(RMSNorm, eps=norm_eps)
        head_dim = self.hidden_size // self.num_heads
702
703
704
705
706
707
708
        self.rotary_pos_emb = get_rope(
            head_size=head_dim,
            rotary_dim=head_dim // 2,
            max_position=8192,
            base=10000.0,
            is_neox_style=True,
        )
709
710
711
712
713
714
715
716
717
718
        self.blocks = nn.ModuleList(
            [
                Glm4vVisionBlock(
                    dim=self.hidden_size,
                    num_heads=self.num_heads,
                    mlp_hidden_dim=vision_config.out_hidden_size,
                    norm_layer=norm_layer,
                    quant_config=quant_config,
                    prefix=f"{prefix}.blocks.{layer_idx}",
                    use_data_parallel=self.use_data_parallel,
719
                    attn_backend_override=attn_backend_override,
720
721
722
723
                )
                for layer_idx in range(depth)
            ]
        )
724
725
726
727
728
        self.merger = Glm4vPatchMerger(
            d_model=vision_config.out_hidden_size,
            context_dim=vision_config.intermediate_size,
            quant_config=quant_config,
            bias=False,
729
            prefix=f"{prefix}.merger",
730
            use_data_parallel=self.use_data_parallel,
731
732
733
        )
        self.embeddings = Glm4vVisionEmbeddings(vision_config)

734
735
736
        self.post_conv_layernorm = RMSNorm(
            vision_config.hidden_size, eps=vision_config.rms_norm_eps
        )
737
        self.downsample = Conv2dLayer(
738
739
740
741
742
            in_channels=vision_config.hidden_size,
            out_channels=vision_config.out_hidden_size,
            kernel_size=vision_config.spatial_merge_size,
            stride=vision_config.spatial_merge_size,
        )
743
744
745
        self.post_layernorm = RMSNorm(
            vision_config.hidden_size, eps=vision_config.rms_norm_eps
        )
746

747
        self.attn_backend = get_vit_attn_backend(
748
749
750
            head_size=head_dim,
            dtype=torch.get_default_dtype(),
            attn_backend_override=attn_backend_override,
751
        )
752
753
754
        if (
            self.attn_backend != AttentionBackendEnum.FLASH_ATTN
            and check_upstream_fa_availability(torch.get_default_dtype())
755
        ):
756
            self.attn_backend = AttentionBackendEnum.FLASH_ATTN
757
758
759
760
761
762
763
764
765

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

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

766
767
768
    def rot_pos_emb(
        self, grid_thw: torch.Tensor
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
769
770
771
772
        pos_ids = []
        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)
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
            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))
794
795
        pos_ids = torch.cat(pos_ids, dim=0)
        max_grid_size = grid_thw[:, 1:].max()
796
797
798
799
800
801
802
803
804
805
806
807

        # Use pre-computed cos_sin_cache from RotaryEmbedding
        cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)

        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
        cos_w = cos[pos_ids[:, 1]]
        sin_h = sin[pos_ids[:, 0]]
        sin_w = sin[pos_ids[:, 1]]

        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
        return cos_combined, sin_combined, pos_ids
808
809
810
811

    def compute_attn_mask_seqlen(
        self,
        cu_seqlens: torch.Tensor,
812
    ) -> tuple[int | None, list[int] | None]:
813
814
        max_seqlen, seqlens = None, None
        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
815
        if (
816
817
            self.attn_backend == AttentionBackendEnum.FLASH_ATTN
            or self.attn_backend == AttentionBackendEnum.ROCM_AITER_FA
818
        ):
819
820
821
822
823
824
            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
        return max_seqlen, seqlens

    def forward(
        self,
        x: torch.Tensor,
825
        grid_thw: list[list[int]],
826
    ) -> torch.Tensor:
827
828
829
        # Convert grid_thw to tensor (always expecting list format now)
        grid_thw = torch.tensor(grid_thw, device=x.device, dtype=torch.long)

830
831
832
833
834
835
        # patchify
        x = x.to(device=self.device, dtype=self.dtype)
        x = self.patch_embed(x)
        x = self.post_conv_layernorm(x)

        # compute position embedding
836
837
838
        rotary_pos_emb_cos, rotary_pos_emb_sin, image_type_ids = self.rot_pos_emb(
            grid_thw
        )
839
        # compute cu_seqlens
840
841
842
        cu_seqlens = torch.repeat_interleave(
            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
        ).cumsum(dim=0, dtype=torch.int32)
843
844
845
846
        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)

        # pre-compute seqlens for attn mask to reduce cuMemcpy operations
        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
847
848
849
        x = self.embeddings(
            x, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1]
        )
850
851
852
853
854
855
856

        # transformers
        x = x.unsqueeze(1)
        for blk in self.blocks:
            x = blk(
                x,
                cu_seqlens=cu_seqlens,
857
858
                rotary_pos_emb_cos=rotary_pos_emb_cos,
                rotary_pos_emb_sin=rotary_pos_emb_sin,
859
860
861
862
863
864
865
                max_seqlen=max_seqlen,
                seqlens=seqlens,
            )

        # adapter
        x = self.post_layernorm(x)

866
        x = x.view(-1, self.spatial_merge_size, self.spatial_merge_size, x.shape[-1])
867
868
869
870
871
872
        x = x.permute(0, 3, 1, 2)
        x = self.downsample(x).view(-1, self.out_hidden_size)
        x = self.merger(x)

        return x

873
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("attn.qkv.", "attn.q.", "q"),
            ("attn.qkv.", "attn.k.", "k"),
            ("attn.qkv.", "attn.v.", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        loaded_params: set[str] = set()

        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]
897
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
898
899
900
901
902
903
904
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class Glm4vProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
Yuxuan Zhang's avatar
Yuxuan Zhang committed
905
        return self.ctx.get_hf_config()
906
907
908
909

    def get_tokenizer(self):
        return self.ctx.tokenizer

910
    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
911
912
        return {"image": None, "video": 1}

913
914
    def get_image_processor(self, **kwargs: object) -> Glm4vImageProcessor:
        return self.get_hf_processor(**kwargs).image_processor
915

916
917
    def get_video_processor(self, **kwargs: object) -> Glm4vVideoProcessor:
        return self.get_hf_processor(**kwargs).video_processor
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935

    def _get_vision_info(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int = 16,
        do_resize: bool = True,
        max_image_pixels: int = 28 * 28 * 2 * 30000,
    ) -> tuple[ImageSize, int]:
        hf_config = self.get_hf_config()
        vision_config = hf_config.vision_config
        patch_size = vision_config.patch_size
        merge_size = vision_config.spatial_merge_size
        temporal_patch_size = vision_config.temporal_patch_size
        if do_resize:
            resized_height, resized_width = smart_resize(
                num_frames=num_frames
936
937
                if num_frames > temporal_patch_size
                else temporal_patch_size,
938
939
940
941
942
                height=image_height,
                width=image_width,
                factor=patch_size * merge_size,
                max_pixels=max_image_pixels,
            )
943
            preprocessed_size = ImageSize(width=resized_width, height=resized_height)
944
        else:
945
            preprocessed_size = ImageSize(width=image_width, height=image_height)
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960

        # 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)
        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

    def get_image_size_with_most_features(self) -> ImageSize:
961
962
963
        max_image_size, _ = self._get_vision_info(
            image_width=9999999, image_height=9999999
        )
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
        return max_image_size

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        _, num_image_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            max_image_pixels=28 * 28 * 2 * 6144,
        )
        return num_image_tokens

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

        return self.get_num_image_tokens(
            image_width=target_width,
            image_height=target_height,
        )

    def get_num_video_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int,
    ) -> int:
        _, num_video_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            num_frames=num_frames,
            max_image_pixels=28 * 28 * 2 * 30000,
        )
        return num_video_tokens

    def _get_max_video_frames(self, max_tokens: int) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        num_frames = 0

        while True:
            next_num_frames = num_frames + 1
            next_max_tokens = self.get_num_video_tokens(
                image_width=target_width,
                image_height=target_height,
                num_frames=next_num_frames,
            )
            if next_max_tokens > max_tokens or next_max_tokens == 0:
                break

            num_frames = next_num_frames

        return num_frames

    def get_num_frames_with_most_features(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
        max_images = mm_counts.get("image", 0)
        max_videos = mm_counts.get("video", 0)

        max_image_tokens = self.get_max_image_tokens() * max_images
1030
1031
1032
1033
        max_total_frames = self._get_max_video_frames(seq_len - max_image_tokens)
        max_frames_per_video = min(
            max_total_frames // max(max_videos, 1), _MAX_FRAMES_PER_VIDEO
        )
1034
1035
1036

        return max(max_frames_per_video, 1)

1037
1038
1039
    def _get_video_second_idx(
        self, metadata: dict[str, Any], total_frames: int
    ) -> list[int]:
1040
1041
        video_processor = self.get_video_processor()

1042
        video_fps = metadata.get("fps", video_processor.fps)
1043
1044
        meta_frames = metadata.get("total_num_frames", total_frames)
        max_frame_idx = meta_frames - 1
1045
        duration = metadata.get("duration", round(max_frame_idx / video_fps) + 1)
1046
1047
1048
        do_sample_frames = metadata["do_sample_frames"]
        if not do_sample_frames:
            frame_indices = metadata["frames_indices"]
1049
        else:
1050
1051
            if duration <= video_processor.max_duration:
                n = int(math.floor(duration * video_processor.fps))
1052
                frame_indices = [
1053
1054
1055
                    min(
                        max_frame_idx,
                        int(math.ceil(i * video_fps / video_processor.fps)),
1056
1057
                    )
                    for i in range(n)
1058
                ]
1059
            else:
1060
                num_samples = int(video_processor.max_duration * video_processor.fps)
1061
1062
1063
                if num_samples >= meta_frames:
                    frame_indices = list(range(meta_frames))
                else:
1064
1065
1066
                    target_seconds = np.linspace(
                        0, duration, num_samples, endpoint=True
                    )
1067
1068
1069
1070
                    frame_indices = [
                        min(max_frame_idx, int(math.ceil(t * video_fps)))
                        for t in target_seconds
                    ]
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087

        seen, uniq = set(), []
        for idx in frame_indices:
            if idx not in seen:
                seen.add(idx)
                uniq.append(idx)
        if len(uniq) & 1:
            uniq.append(uniq[-1])
        frame_indices = uniq

        full_second_idxs = [int(idx / video_fps) for idx in frame_indices]
        timestamps_list = full_second_idxs[::2]
        selected_timestamps = []
        for idx in range(0, len(timestamps_list)):
            selected_timestamps.append(timestamps_list[idx])
        return selected_timestamps

1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
    def _construct_video_placeholder(
        self,
        video_array: np.ndarray,
        metadata: dict[str, Any],
        grid_thw: torch.Tensor,
    ) -> str:
        hf_processor = self.get_hf_processor()
        tokenizer = self.get_tokenizer()
        image_processor = hf_processor.image_processor

        hf_config = self.get_hf_config()
        boi_token_id = hf_config.image_start_token_id
        eoi_token_id = hf_config.image_end_token_id
        bov_token_id = hf_config.video_start_token_id
        eov_token_id = hf_config.video_end_token_id
        merge_length = image_processor.merge_size**2

        assert isinstance(grid_thw, torch.Tensor)
        timestamps = self._get_video_second_idx(metadata, len(video_array))
        frames_idx_token = [
1108
            tokenizer.encode(str(i), add_special_tokens=False) for i in timestamps
1109
1110
1111
1112
1113
1114
1115
        ]
        T, H, W = grid_thw
        num_tokens_per_frame = int(H * W) // merge_length
        placeholder = []
        placeholder.append(bov_token_id)
        for frame_idx in frames_idx_token:
            placeholder.append(boi_token_id)
1116
            placeholder.extend([hf_processor.video_token_id] * num_tokens_per_frame)
1117
1118
1119
1120
1121
1122
            placeholder.append(eoi_token_id)
            placeholder.extend(frame_idx)
        placeholder.append(eov_token_id)

        return placeholder

1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146

class Glm4vDummyInputsBuilder(BaseDummyInputsBuilder[Glm4vProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

        hf_config = self.info.get_hf_config()
        hf_processor = self.info.get_hf_processor()
        tokenizer = self.info.get_tokenizer()

        image_token: str = hf_processor.image_token
        video_token_ids = [
            hf_config.video_start_token_id,
            hf_processor.video_token_id,
            hf_config.video_end_token_id,
        ]
        video_token = tokenizer.decode(video_token_ids)

        return image_token * num_images + video_token * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
1147
        mm_options: Mapping[str, BaseDummyOptions] | None = None,
1148
1149
1150
1151
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

1152
        target_width, target_height = self.info.get_image_size_with_most_features()
1153
        target_num_frames = self.info.get_num_frames_with_most_features(
1154
1155
            seq_len, mm_counts
        )
1156
1157
1158
1159

        image_overrides = mm_options.get("image") if mm_options else None
        video_overrides = mm_options.get("video") if mm_options else None

1160
        return {
1161
1162
1163
1164
1165
1166
1167
            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            ),
            "video": self._get_dummy_videos(
1168
1169
1170
1171
                width=target_width,
                height=target_height,
                num_frames=target_num_frames,
                num_videos=num_videos,
1172
                overrides=video_overrides,
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
            ),
        }

    def _get_dummy_videos(
        self,
        *,
        width: int,
        height: int,
        num_frames: int,
        num_videos: int,
1183
        overrides: VideoDummyOptions | None = None,
1184
    ) -> list[VideoItem]:
1185
1186
1187
1188
1189
1190
        if overrides:
            if overrides.num_frames:
                if overrides.num_frames > num_frames:
                    logger.warning(
                        "video.num_frames override (%d) exceeds model's "
                        "maximum number of frames (%d), will be ignored",
1191
1192
1193
                        overrides.num_frames,
                        num_frames,
                    )
1194
1195
1196
1197
1198
                num_frames = min(num_frames, overrides.num_frames)
            if overrides.width:
                if overrides.width > width:
                    logger.warning(
                        "video.width override (%d) exceeds model's "
1199
1200
1201
1202
                        "maximum width (%d), will be ignored",
                        overrides.width,
                        width,
                    )
1203
1204
1205
1206
1207
1208
                width = min(width, overrides.width)
            if overrides.height:
                if overrides.height > height:
                    logger.warning(
                        "video.height override (%d) exceeds model's "
                        "maximum height (%d), will be ignored",
1209
1210
1211
                        overrides.height,
                        height,
                    )
1212
                height = min(height, overrides.height)
1213

1214
1215
1216
1217
1218
1219
1220
        video = np.full((num_frames, width, height, 3), 255, dtype=np.uint8)
        video_items = []
        for i in range(num_videos):
            video_metadata = {
                "fps": 2.0,
                "duration": num_frames / 2.0,
                "total_num_frames": num_frames,
1221
                "frames_indices": [i for i in range(num_frames)],
1222
                "video_backend": "opencv",
1223
                "do_sample_frames": False,
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
            }
            video_item = (video.copy(), video_metadata)
            video_items.append(video_item)

        return video_items


class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
    def _get_data_parser(self) -> MultiModalDataParser:
        return MultiModalDataParser(video_needs_metadata=True)

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        mm_data = dict(mm_data)
        processor = self.info.get_hf_processor(**mm_kwargs)

        # GLM-4.1V use `image_token_id` as video placeholder, we need to
        # replace it with `video_token_id` for video processing. So we
        # separate video processing from image processing.
1248
1249
1250
1251
1252
        if (
            "videos" in mm_data
            and isinstance(mm_data["videos"], list)
            and len(mm_data["videos"]) > 0
        ):
1253
1254
1255
1256
1257
            video_grid_thw_lst = []
            pixel_values_videos_lst = []
            for item in mm_data.pop("videos", []):
                video_array, metadata = item

1258
1259
1260
                # don't update mm_kwargs inplace
                video_mm_kwargs = dict(**mm_kwargs)
                video_mm_kwargs["do_sample_frames"] = metadata.get(
1261
1262
                    "do_sample_frames", True
                )
1263
1264
1265

                video_mm_data = dict()
                video_mm_data["videos"] = [[video_array]]
1266
1267

                unuse_metadata = ["do_sample_frames"]
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
                video_mm_data["video_metadata"] = [
                    [
                        VideoMetadata(
                            **{
                                k: metadata[k]
                                for k in metadata
                                if k not in unuse_metadata
                            }
                        )
                    ]
                ]
1279
1280
1281
1282

                video_outputs = super()._call_hf_processor(
                    prompt="<|begin_of_video|><|video|><|end_of_video|>",
                    mm_data=video_mm_data,
1283
                    mm_kwargs=video_mm_kwargs,
1284
1285
                    tok_kwargs=tok_kwargs,
                )
1286
1287
1288
1289
1290
                input_ids = video_outputs.pop("input_ids")
                input_ids[input_ids == processor.image_token_id] = (
                    processor.video_token_id
                )
                video_placeholder = processor.tokenizer.batch_decode(input_ids)[0]
1291
1292
1293
                prompt = prompt.replace(
                    "<|begin_of_video|><|video|><|end_of_video|>",
                    video_placeholder,
1294
                    1,
1295
1296
                )

1297
                video_grid_thw_lst.append(video_outputs["video_grid_thw"])
1298
                pixel_values_videos_lst.append(video_outputs["pixel_values_videos"])
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
            video_outputs = dict(
                pixel_values_videos=torch.cat(pixel_values_videos_lst),
                video_grid_thw=torch.cat(video_grid_thw_lst),
            )
        else:
            video_outputs = dict()

        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )
        combined_outputs = dict(
            processed_outputs,
            **video_outputs,
        )
        return BatchFeature(combined_outputs)

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
1323
        return _create_qwen2vl_field_factory(
1324
1325
            self.info.get_hf_config().vision_config.spatial_merge_size
        )(hf_inputs)
1326
1327
1328
1329
1330

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, Any],
1331
        out_mm_kwargs: MultiModalKwargsItems,
1332
1333
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
1334
        image_processor = self.info.get_image_processor(**hf_processor_mm_kwargs)
1335
1336
1337
1338

        merge_length = image_processor.merge_size**2

        def get_image_replacement_glm4v(item_idx: int):
1339
1340
            out_item = out_mm_kwargs["image"][item_idx]
            grid_thw = out_item["image_grid_thw"].data
1341
1342
1343
1344
1345
1346
            assert isinstance(grid_thw, torch.Tensor)

            num_tokens = int(grid_thw.prod()) // merge_length
            return [hf_processor.image_token_id] * num_tokens

        def get_video_replacement_glm4v(item_idx: int):
1347
1348
            out_item = out_mm_kwargs["video"][item_idx]
            grid_thw = out_item["video_grid_thw"].data
1349
1350
1351
            assert isinstance(grid_thw, torch.Tensor)

            video, metadata = mm_items["video"][item_idx]
1352
            placeholder = self.info._construct_video_placeholder(
1353
1354
                video, metadata, grid_thw
            )
1355
1356
1357
1358
            return PromptUpdateDetails.select_token_id(
                placeholder,
                embed_token_id=hf_processor.video_token_id,
            )
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378

        return [
            PromptReplacement(
                modality="image",
                target=hf_processor.image_token,
                replacement=get_image_replacement_glm4v,
            ),
            PromptReplacement(
                modality="video",
                target="<|begin_of_video|><|video|><|end_of_video|>",
                replacement=get_video_replacement_glm4v,
            ),
        ]


@MULTIMODAL_REGISTRY.register_processor(
    Glm4vMultiModalProcessor,
    info=Glm4vProcessingInfo,
    dummy_inputs=Glm4vDummyInputsBuilder,
)
1379
class Glm4vForConditionalGeneration(
1380
    nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP, SupportsMRoPE
1381
):
1382
1383
    merge_by_field_config = True

1384
1385
1386
1387
1388
1389
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
1390
        "gate_up_proj": ["gate_up_proj"],
1391
1392
1393
1394
1395
1396
1397
1398
    }

    # To ensure correct weight loading and mapping.
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "lm_head.": "language_model.lm_head.",
            "model.language_model.": "language_model.model.",
            "model.visual.": "visual.",
1399
1400
        }
    )
1401

1402
1403
    supports_encoder_tp_data = True

1404
    @classmethod
1405
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
1406
1407
1408
1409
1410
1411
1412
        if modality.startswith("image"):
            return "<|begin_of_image|><|image|><|end_of_image|>"
        if modality.startswith("video"):
            return "<|begin_of_video|><|video|><|end_of_video|>"

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

1413
1414
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
Yuxuan Zhang's avatar
Yuxuan Zhang committed
1415
        config = vllm_config.model_config.hf_config
1416
1417
1418
1419
1420
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config

        self.config = config
        self.multimodal_config = multimodal_config
1421
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
1422

1423
1424
1425
1426
1427
        attn_backend_override = (
            multimodal_config.mm_encoder_attn_backend
            if multimodal_config is not None
            else None
        )
1428
1429
1430
        self.visual = Glm4vVisionTransformer(
            config.vision_config,
            norm_eps=getattr(config, "rms_norm_eps", 1e-5),
1431
            quant_config=quant_config,
1432
            prefix=maybe_prefix(prefix, "visual"),
1433
            use_data_parallel=self.use_data_parallel,
1434
            attn_backend_override=attn_backend_override,
1435
1436
        )

Yuxuan Zhang's avatar
Yuxuan Zhang committed
1437
1438
1439
1440
1441
1442
1443
        if config.model_type == "glm4v":
            architectures = ["Glm4ForCausalLM"]
        elif config.model_type == "glm4v_moe":
            architectures = ["Glm4MoeForCausalLM"]
        else:
            architectures = None

1444
1445
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
Yuxuan Zhang's avatar
Yuxuan Zhang committed
1446
1447
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
1448
1449
            architectures=architectures,
        )
1450
1451

        self.make_empty_intermediate_tensors = (
1452
1453
            self.language_model.make_empty_intermediate_tensors
        )
1454
1455

    def _parse_and_validate_image_input(
1456
        self, **kwargs: object
1457
    ) -> Glm4vImageInputs | None:
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
        pixel_values = kwargs.pop("pixel_values", None)
        image_embeds = kwargs.pop("image_embeds", None)
        image_grid_thw = kwargs.pop("image_grid_thw", None)

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None:
            return Glm4vImagePixelInputs(
                type="pixel_values",
                pixel_values=pixel_values,
                image_grid_thw=image_grid_thw,
            )

        if image_embeds is not None:
            return Glm4vImageEmbeddingInputs(
                type="image_embeds",
                image_embeds=image_embeds,
                image_grid_thw=image_grid_thw,
            )

    def _parse_and_validate_video_input(
1480
        self, **kwargs: object
1481
    ) -> Glm4vVideoInputs | None:
1482
1483
1484
        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
        video_embeds = kwargs.pop("video_embeds", None)
        video_grid_thw = kwargs.pop("video_grid_thw", None)
1485

1486
1487
        if pixel_values_videos is None and video_embeds is None:
            return None
1488

1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
        if pixel_values_videos is not None:
            return Glm4vVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_videos=pixel_values_videos,
                video_grid_thw=video_grid_thw,
            )

        if video_embeds is not None:
            return Glm4vVideoEmbeddingInputs(
                type="video_embeds",
                video_embeds=video_embeds,
                video_grid_thw=video_grid_thw,
            )

    def _process_image_input(
1504
1505
        self, image_input: Glm4vImageInputs
    ) -> tuple[torch.Tensor, ...]:
1506
1507
        grid_thw = image_input["image_grid_thw"]
        assert grid_thw.ndim == 2
1508
        grid_thw_list = grid_thw.tolist()
1509
1510
1511
1512
1513

        if image_input["type"] == "image_embeds":
            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
        else:
            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
1514
            if self.use_data_parallel:
1515
1516
1517
                return run_dp_sharded_mrope_vision_model(
                    self.visual, pixel_values, grid_thw.tolist(), rope_type="rope_3d"
                )
1518
            else:
1519
                image_embeds = self.visual(pixel_values, grid_thw=grid_thw.tolist())
1520
        merge_size = self.visual.spatial_merge_size
1521
1522
1523
1524
        sizes = (
            torch.tensor(grid_thw_list, dtype=torch.long).prod(-1)
            // (merge_size * merge_size)
        ).tolist()
1525
        return image_embeds.split(sizes)
1526
1527

    def _process_video_input(
1528
1529
        self, video_input: Glm4vVideoInputs
    ) -> tuple[torch.Tensor, ...]:
1530
1531
        grid_thw = video_input["video_grid_thw"]
        assert grid_thw.ndim == 2
1532
        grid_thw_list = grid_thw.tolist()
1533
1534
1535
1536
1537

        if video_input["type"] == "video_embeds":
            video_embeds = video_input["video_embeds"].type(self.visual.dtype)
        else:
            pixel_values_videos = video_input["pixel_values_videos"].type(
1538
1539
                self.visual.dtype
            )
1540
            if self.use_data_parallel:
1541
1542
1543
1544
1545
1546
                return run_dp_sharded_mrope_vision_model(
                    self.visual,
                    pixel_values_videos,
                    grid_thw.tolist(),
                    rope_type="rope_3d",
                )
1547
            else:
1548
1549
1550
                video_embeds = self.visual(
                    pixel_values_videos, grid_thw=grid_thw.tolist()
                )
1551
1552
        # Split concatenated embeddings for each video item.
        merge_size = self.visual.spatial_merge_size
1553
1554
1555
1556
        sizes = (
            torch.tensor(grid_thw_list, dtype=torch.long).prod(-1)
            // (merge_size * merge_size)
        ).tolist()
1557
        return video_embeds.split(sizes)
1558
1559
1560
1561
1562
1563
1564

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

        # Preserve the order of modalities if there are multiple of them
        # from the order of kwargs.
        for input_key in kwargs:
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
            if (
                input_key in ("pixel_values", "image_embeds")
                and "image" not in mm_input_by_modality
            ):
                mm_input_by_modality["image"] = self._parse_and_validate_image_input(
                    **kwargs
                )
            if (
                input_key in ("pixel_values_videos", "video_embeds")
                and "video" not in mm_input_by_modality
            ):
                mm_input_by_modality["video"] = self._parse_and_validate_video_input(
                    **kwargs
                )
1579
1580
1581
1582
1583
        return mm_input_by_modality

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

1584
    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
1585
        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
1586
1587
1588
1589
        if not mm_input_by_modality:
            return None

        # The result multimodal_embeddings is tuple of tensors, with each
1590
        # tensor corresponding to a multimodal data item (image or video).
1591
1592
1593
1594
1595
1596
1597
        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 mm_input_by_modality:
            multimodal_input = mm_input_by_modality[modality]
            if modality == "image":
1598
1599
                image_embeddings = self._process_image_input(multimodal_input)
                multimodal_embeddings += tuple(image_embeddings)
1600
1601
            if modality == "video":
                video_embeddings = self._process_video_input(multimodal_input)
1602
                multimodal_embeddings += tuple(video_embeddings)
1603
1604
        return multimodal_embeddings

1605
1606
1607
    def get_mrope_input_positions(
        self,
        input_tokens: list[int],
1608
        mm_features: list[MultiModalFeatureSpec],
1609
    ) -> tuple[torch.Tensor, int]:
1610
1611
1612
1613
1614
1615
        kwargs = MultiModalFeatureSpec.gather_kwargs(
            mm_features,
            {"image_grid_thw", "video_grid_thw"},
        )
        image_grid_thw = [item.tolist() for item in kwargs.get("image_grid_thw", [])]
        video_grid_thw = [item.tolist() for item in kwargs.get("video_grid_thw", [])]
1616

1617
        hf_config = self.config
1618
1619
1620
1621
1622
1623
        image_token_id = hf_config.image_token_id
        video_start_token_id = hf_config.video_start_token_id
        video_end_token_id = hf_config.video_end_token_id
        spatial_merge_size = hf_config.vision_config.spatial_merge_size
        llm_pos_ids_list: list = []

1624
        if image_grid_thw or video_grid_thw:
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
            input_token_type: list[str] = []
            video_check_flg = False
            for token in input_tokens:
                if token == video_start_token_id:
                    video_check_flg = True
                elif token == video_end_token_id:
                    video_check_flg = False

                if (token == image_token_id) and (video_check_flg is False):
                    input_token_type.append("image")
                elif (token == image_token_id) and (video_check_flg is True):
                    input_token_type.append("video")
                else:
                    input_token_type.append("text")

            input_type_group: list[tuple[str, int, int]] = []
            for key, group_iter in itertools.groupby(
                enumerate(input_token_type), lambda x: x[1]
            ):
                group_list = list(group_iter)
                start_index = group_list[0][0]
                end_index = group_list[-1][0] + 1
                input_type_group.append((key, start_index, end_index))

            video_frame_num = 1
            mm_data_idx = 0
            for modality_type, start_idx, end_idx in input_type_group:
                st_idx = (
                    llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
                )
                if modality_type == "image":
1656
                    t, h, w = image_grid_thw[mm_data_idx]
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
1683
1684
1685
1686
1687
1688
                    llm_grid_t, llm_grid_h, llm_grid_w = (
                        t,
                        h // spatial_merge_size,
                        w // spatial_merge_size,
                    )

                    t_index = (
                        torch.arange(llm_grid_t)
                        .view(-1, 1)
                        .expand(-1, llm_grid_h * llm_grid_w)
                        .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]) + st_idx
                    )
                    mm_data_idx += 1

                elif modality_type == "video":
                    t, h, w = (
                        video_frame_num,
1689
                        *image_grid_thw[mm_data_idx][1:],
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
1733
1734
1735
1736
1737
                    )
                    llm_grid_t, llm_grid_h, llm_grid_w = (
                        t,
                        h // spatial_merge_size,
                        w // spatial_merge_size,
                    )

                    for t_idx in range(llm_grid_t):
                        t_index = (
                            torch.tensor(t_idx)
                            .view(-1, 1)
                            .expand(-1, llm_grid_h * llm_grid_w)
                            .flatten()
                        )
                        h_index = (
                            torch.arange(llm_grid_h)
                            .view(1, -1, 1)
                            .expand(1, -1, llm_grid_w)
                            .flatten()
                        )
                        w_index = (
                            torch.arange(llm_grid_w)
                            .view(1, 1, -1)
                            .expand(1, llm_grid_h, -1)
                            .flatten()
                        )
                        llm_pos_ids_list.append(
                            torch.stack([t_index, h_index, w_index]) + st_idx
                        )

                    mm_data_idx += 1
                    video_frame_num += 1

                else:
                    text_len = end_idx - start_idx
                    llm_pos_ids_list.append(
                        torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
                    )
                    video_frame_num = 1

        else:
            text_len = len(input_tokens)
            llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1))

        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
        mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
        return llm_positions, mrope_position_delta

1738
1739
1740
1741
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
1742
1743
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
1744
        **kwargs: object,
1745
    ) -> torch.Tensor | IntermediateTensors:
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
        """Run forward pass for GLM-4V.

        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 GLM-4V
                opensource models), the shape will be `(3, seq_len)`,
                otherwise it will be `(seq_len,).
1756
1757
1758
1759
            intermediate_tensors: Optional intermediate tensors for pipeline
                parallelism.
            inputs_embeds: Optional pre-computed input embeddings.
            **kwargs: Additional keyword arguments.
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
        """
        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.language_model.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
1775
    ) -> torch.Tensor | None:
1776
        return self.language_model.compute_logits(hidden_states)
1777

1778
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
1779
1780
1781
1782
1783
1784
1785
1786
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
Jee Jee Li's avatar
Jee Jee Li committed
1787
            language_model="language_model.model",
1788
1789
1790
            connector="visual.merger.",
            tower_model="visual.",
        )
Jee Jee Li's avatar
Jee Jee Li committed
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809


@MULTIMODAL_REGISTRY.register_processor(
    Glm4vMultiModalProcessor,
    info=Glm4vProcessingInfo,
    dummy_inputs=Glm4vDummyInputsBuilder,
)
class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }