[FEAT] [Performance] Enable DP for ViT in Qwen2.5VL (#22742)

Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

tests/multimodal/test_utils.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import base64
+import math
 import mimetypes
 import os
 from tempfile import NamedTemporaryFile, TemporaryDirectory
@@ -20,6 +21,8 @@ from vllm.distributed.parallel_state import (init_distributed_environment,
 from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.inputs import PlaceholderRange
 from vllm.multimodal.utils import (MediaConnector, argsort_mm_positions,
+                                   get_load_balance_assignment,
+                                   run_dp_sharded_mrope_vision_model,
                                    run_dp_sharded_vision_model)
 from vllm.platforms import current_platform
 from vllm.utils import get_open_port, update_environment_variables
@@ -425,8 +428,8 @@ def run_dp_sharded_vision_model_vs_direct(local_rank: int, world_size: int,
     # Set random seed for reproducibility
     current_platform.seed_everything(0)
 
-    device = torch.device(f"cuda:{local_rank}")
-    torch.cuda.set_device(device)
+    device = f"{current_platform.device_name}:{local_rank}"
+    current_platform.set_device(device)
     torch.set_default_device(device)
 
     update_environment_variables({
@@ -463,3 +466,322 @@ def run_dp_sharded_vision_model_vs_direct(local_rank: int, world_size: int,
 
     # Check that the outputs are close (they should be identical)
     assert torch.allclose(direct_output, sharded_output, rtol=1e-5, atol=1e-5)
+
+
+@pytest.mark.parametrize(
+    "sizes,num_gpus,expected_shuffle_indices,expected_gpu_sample_counts,"
+    "expected_grouped_sizes_per_gpu,test_description",
+    [
+        # Empty input
+        ([], 2, [], [0, 0], [0, 0], "empty input"),
+
+        # Fewer samples than GPUs
+        ([100, 200], 4, [1, 0], [1, 1, 0, 0], [200, 100, 0, 0
+                                               ], "fewer samples than GPUs"),
+
+        # Single GPU
+        ([100, 200, 300], 1, [2, 1, 0], [3], [600], "single GPU"),
+
+        # Balanced assignment
+        ([100, 100, 100, 100
+          ], 2, [0, 2, 1, 3], [2, 2], [200, 200], "balanced assignment"),
+
+        # Unbalanced sizes - this one is trickier since the algorithm is greedy
+        ([1000, 100, 200, 50], 2, [0, 2, 1, 3
+                                   ], [1, 3], [1000, 350], "unbalanced sizes"),
+    ],
+)
+def test_get_load_balance_assignment_cases(sizes, num_gpus,
+                                           expected_shuffle_indices,
+                                           expected_gpu_sample_counts,
+                                           expected_grouped_sizes_per_gpu,
+                                           test_description):
+    """Test get_load_balance_assignment with various input cases."""
+    result = get_load_balance_assignment(sizes, num_gpus=num_gpus)
+    (shuffle_indices, gpu_sample_counts, grouped_sizes_per_gpu) = result
+
+    # Common assertions for all cases
+    assert len(shuffle_indices) == len(sizes)
+    assert len(gpu_sample_counts) == num_gpus
+    assert len(grouped_sizes_per_gpu) == num_gpus
+    assert sum(gpu_sample_counts) == len(sizes)
+
+    assert shuffle_indices == expected_shuffle_indices
+
+    assert gpu_sample_counts == expected_gpu_sample_counts
+    assert grouped_sizes_per_gpu == expected_grouped_sizes_per_gpu
+
+
+class SimpleMRopeVisionModel(torch.nn.Module):
+    """A simple vision model for testing mrope functionality."""
+
+    def __init__(self, spatial_merge_size: int = 2, out_hidden_size: int = 64):
+        super().__init__()
+        self.spatial_merge_size = spatial_merge_size
+        self.out_hidden_size = out_hidden_size
+        self.linear = torch.nn.Linear(768, out_hidden_size)
+
+    def forward(self, pixel_values: torch.Tensor,
+                grid_thw_list: list[list[int]]):
+        """Simple forward pass that simulates spatial merging."""
+        # Apply linear transformation
+        embeddings = self.linear(pixel_values)
+
+        # Simulate spatial merging by reducing the number of patches
+        merge_factor = self.spatial_merge_size * self.spatial_merge_size
+
+        # Group patches and merge spatially
+        merged_embeddings = []
+        start_idx = 0
+
+        for grid_thw in grid_thw_list:
+            num_patches = math.prod(grid_thw)
+            end_idx = start_idx + num_patches
+
+            # Get patches for this image
+            image_patches = embeddings[start_idx:end_idx]
+
+            # Simulate spatial merging by averaging groups of patches
+            merged_patches = num_patches // merge_factor
+            if merged_patches > 0:
+                # Reshape and average to simulate merging
+                reshaped = image_patches[:merged_patches * merge_factor].view(
+                    merged_patches, merge_factor, -1)
+                merged = reshaped.mean(dim=1)
+                merged_embeddings.append(merged)
+
+            start_idx = end_idx
+
+        if merged_embeddings:
+            return torch.cat(merged_embeddings, dim=0)
+        else:
+            return torch.empty((0, self.out_hidden_size),
+                               device=pixel_values.device,
+                               dtype=pixel_values.dtype)
+
+
+@multi_gpu_test(num_gpus=2)
+@pytest.mark.parametrize(
+    "batch_size",
+    [
+        1,  # Single image
+        3,  # Small batch
+        5,  # Odd batch size (for testing padding)
+    ],
+)
+def test_run_dp_sharded_mrope_vision_model(batch_size: int):
+    world_size = 2
+    # Launch processes
+    mp.spawn(
+        run_dp_sharded_mrope_vision_model_vs_direct,
+        args=(
+            world_size,
+            batch_size,
+            get_open_port(),
+        ),
+        nprocs=world_size,
+    )
+
+
+def run_dp_sharded_mrope_vision_model_vs_direct(local_rank: int,
+                                                world_size: int,
+                                                batch_size: int,
+                                                master_port: int):
+    """
+    Test that run_dp_sharded_mrope_vision_model produces the same results as 
+    calling the model directly.
+    """
+    # Set random seed for reproducibility
+    current_platform.seed_everything(0)
+    device = f"{current_platform.device_name}:{local_rank}"
+    current_platform.set_device(device)
+    torch.set_default_device(device)
+
+    update_environment_variables({
+        'RANK': str(local_rank),
+        'LOCAL_RANK': str(local_rank),
+        'WORLD_SIZE': str(world_size),
+        'MASTER_ADDR': 'localhost',
+        'MASTER_PORT': str(master_port),
+    })
+
+    # initialize distributed
+    init_distributed_environment()
+    initialize_model_parallel(tensor_model_parallel_size=world_size)
+
+    # Create test data
+    grid_thw_list = []
+    pixel_values_list = []
+
+    for i in range(batch_size):
+        # Varying image sizes for better testing
+        t, h, w = 1, 4 + i, 4 + i
+        grid_thw_list.append([t, h, w])
+
+        num_patches = t * h * w
+        # Create random pixel values for this image
+        image_pixels = torch.randn(num_patches, 768)
+        pixel_values_list.append(image_pixels)
+
+    # Concatenate all pixel values
+    pixel_values = torch.cat(pixel_values_list, dim=0)
+
+    # Create a simple mrope vision model
+    vision_model = SimpleMRopeVisionModel()
+
+    # Run the model directly on the full input (only on rank 0)
+    if local_rank == 0:
+        with torch.inference_mode():
+            direct_output = vision_model(pixel_values, grid_thw_list)
+
+    # Run the model through the sharded function
+    with torch.inference_mode():
+        sharded_output = run_dp_sharded_mrope_vision_model(
+            vision_model, pixel_values, grid_thw_list)
+        sharded_output = torch.cat(sharded_output, dim=0)
+
+    # Check that the world size is setup correctly
+    assert get_tensor_model_parallel_world_size() == world_size
+
+    # Compare outputs (only on rank 0)
+    if local_rank == 0:
+        # Check that the outputs have the same shape
+        assert direct_output.shape == sharded_output.shape
+        # Check that the outputs are close (they should be identical)
+        assert torch.allclose(direct_output,
+                              sharded_output,
+                              rtol=1e-5,
+                              atol=1e-5)
+
+
+@multi_gpu_test(num_gpus=2)
+def test_run_dp_sharded_mrope_vision_model_empty_input():
+    world_size = 2
+    mp.spawn(
+        run_dp_sharded_mrope_vision_model_empty_input_worker,
+        args=(world_size, get_open_port()),
+        nprocs=world_size,
+    )
+
+
+def run_dp_sharded_mrope_vision_model_empty_input_worker(
+        local_rank: int, world_size: int, master_port: int):
+    """Test run_dp_sharded_mrope_vision_model with empty input."""
+    # Set up distributed environment
+    device = f"{current_platform.device_name}:{local_rank}"
+    current_platform.set_device(device)
+    torch.set_default_device(device)
+
+    update_environment_variables({
+        'RANK': str(local_rank),
+        'LOCAL_RANK': str(local_rank),
+        'WORLD_SIZE': str(world_size),
+        'MASTER_ADDR': 'localhost',
+        'MASTER_PORT': str(master_port),
+    })
+
+    init_distributed_environment()
+    initialize_model_parallel(tensor_model_parallel_size=world_size)
+
+    # Create empty inputs
+    pixel_values = torch.empty((0, 768))
+    grid_thw_list: list[list[int]] = []
+
+    vision_model = SimpleMRopeVisionModel()
+
+    # Should handle empty input gracefully
+    with torch.inference_mode():
+        output = run_dp_sharded_mrope_vision_model(vision_model, pixel_values,
+                                                   grid_thw_list)
+
+    assert len(output) == 0
+
+
+@multi_gpu_test(num_gpus=4)
+def test_run_dp_sharded_mrope_vision_model_uneven_load():
+    world_size = 4
+    mp.spawn(
+        run_dp_sharded_mrope_vision_model_uneven_load_worker,
+        args=(world_size, get_open_port()),
+        nprocs=world_size,
+    )
+
+
+def run_dp_sharded_mrope_vision_model_uneven_load_worker(
+        local_rank: int, world_size: int, master_port: int):
+    """Test run_dp_sharded_mrope_vision_model with uneven load distribution."""
+    # Set up distributed environment
+    current_platform.seed_everything(123)
+    device = f"{current_platform.device_name}:{local_rank}"
+    current_platform.set_device(device)
+    torch.set_default_device(device)
+
+    update_environment_variables({
+        'RANK': str(local_rank),
+        'LOCAL_RANK': str(local_rank),
+        'WORLD_SIZE': str(world_size),
+        'MASTER_ADDR': 'localhost',
+        'MASTER_PORT': str(master_port),
+    })
+
+    init_distributed_environment()
+    initialize_model_parallel(tensor_model_parallel_size=world_size)
+
+    # Create images with very different sizes
+    grid_thw_list = [
+        [1, 2, 2],  # Small: 4 patches
+        [1, 8, 8],  # Large: 64 patches  
+        [1, 3, 3],  # Medium: 9 patches
+    ]
+
+    pixel_values_list = []
+    for grid_thw in grid_thw_list:
+        num_patches = math.prod(grid_thw)
+        image_pixels = torch.randn(num_patches, 768)
+        pixel_values_list.append(image_pixels)
+
+    pixel_values = torch.cat(pixel_values_list, dim=0)
+    vision_model = SimpleMRopeVisionModel()
+
+    # Should handle uneven distribution without errors
+    with torch.inference_mode():
+        output_tuple = run_dp_sharded_mrope_vision_model(
+            vision_model, pixel_values, grid_thw_list)
+
+    # Verify output shape is reasonable
+    merge_factor = vision_model.spatial_merge_size**2
+    expected_output_patches = list(
+        math.prod(grid_thw) // merge_factor for grid_thw in grid_thw_list)
+
+    for i, output in enumerate(output_tuple):
+        assert output.shape[0] == expected_output_patches[i]
+        assert output.shape[1] == vision_model.out_hidden_size
+
+
+@pytest.mark.parametrize("spatial_merge_size", [2, 4])
+def test_simple_mrope_vision_model_spatial_merge(spatial_merge_size: int):
+    """Test SimpleMRopeVisionModel with different spatial merge sizes."""
+    device = current_platform.device_type
+
+    grid_thw_list = [[1, 4, 4], [1, 6, 6]]  # Two images
+    pixel_values_list = []
+
+    for grid_thw in grid_thw_list:
+        num_patches = math.prod(grid_thw)
+        image_pixels = torch.randn(num_patches, 768, device=device)
+        pixel_values_list.append(image_pixels)
+
+    pixel_values = torch.cat(pixel_values_list, dim=0)
+    vision_model = SimpleMRopeVisionModel(
+        spatial_merge_size=spatial_merge_size).to(device)
+
+    with torch.inference_mode():
+        output = vision_model(pixel_values, grid_thw_list)
+
+    # Verify output dimensions based on spatial merging
+    total_patches = sum(math.prod(grid_thw) for grid_thw in grid_thw_list)
+    merge_factor = spatial_merge_size**2
+    expected_output_patches = total_patches // merge_factor
+
+    assert output.shape[0] == expected_output_patches
+    assert output.shape[1] == vision_model.out_hidden_size

vllm/model_executor/layers/linear.py

@@ -437,7 +437,7 @@ class MergedReplicatedLinear(ReplicatedLinear):
             shard_offset = sum(self.output_sizes[:loaded_shard_id])
             shard_size = self.output_sizes[loaded_shard_id]
 
-        param[shard_offset:shard_offset + shard_size] = loaded_weight
+        param.data[shard_offset:shard_offset + shard_size] = loaded_weight
 
 
 @CustomOp.register("column_parallel_linear")

vllm/model_executor/models/qwen2_5_vl.py

@@ -45,10 +45,14 @@ from vllm.logger import init_logger
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.layers.activation import get_act_and_mul_fn
 from vllm.model_executor.layers.layernorm import RMSNorm
+# yapf: disable
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                MergedColumnParallelLinear,
+                                               MergedReplicatedLinear,
                                                QKVParallelLinear,
+                                               ReplicatedLinear,
                                                RowParallelLinear)
+# yapf: enable
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.gptq_marlin import (
@@ -57,6 +61,7 @@ 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
 from vllm.multimodal.inputs import MultiModalFieldConfig
+from vllm.multimodal.utils import run_dp_sharded_mrope_vision_model
 from vllm.platforms import _Backend
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.config import uses_mrope
@@ -170,19 +175,25 @@ class Qwen2_5_VisionMLP(nn.Module):
                  bias: bool = False,
                  act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
                  quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = ""):
+                 prefix: str = "",
+                 use_data_parallel: bool = False):
         super().__init__()
-        self.gate_up_proj = MergedColumnParallelLinear(
+        cls_gate_up_proj = (MergedReplicatedLinear if use_data_parallel else
+                            MergedColumnParallelLinear)
+        self.gate_up_proj = cls_gate_up_proj(
             input_size=in_features,
             output_sizes=[hidden_features] * 2,  # [gate_proj, up_proj]
             bias=bias,
             quant_config=quant_config,
             prefix=f"{prefix}.gate_up_proj")
-        self.down_proj = RowParallelLinear(hidden_features,
-                                           in_features,
-                                           bias=bias,
-                                           quant_config=quant_config,
-                                           prefix=f"{prefix}.down_proj")
+
+        cls_down_proj = (ReplicatedLinear
+                         if use_data_parallel else RowParallelLinear)
+        self.down_proj = cls_down_proj(hidden_features,
+                                       in_features,
+                                       bias=bias,
+                                       quant_config=quant_config,
+                                       prefix=f"{prefix}.down_proj")
         self.act_fn = act_fn
 
     def forward(self, x: torch.Tensor):
@@ -220,28 +231,42 @@ class Qwen2_5_VisionAttention(nn.Module):
         projection_size: int,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        use_data_parallel: bool = False,
     ) -> None:
         super().__init__()
         # Per attention head and per partition values.
-        self.tp_size = parallel_state.get_tensor_model_parallel_world_size()
+        self.tp_size = (1 if use_data_parallel else
+                        parallel_state.get_tensor_model_parallel_world_size())
         self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
         self.hidden_size_per_attention_head = dist_utils.divide(
             projection_size, num_heads)
         self.num_attention_heads_per_partition = dist_utils.divide(
             num_heads, self.tp_size)
 
-        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=True,
-            quant_config=quant_config,
-            prefix=f"{prefix}.qkv")
-        self.proj = RowParallelLinear(input_size=projection_size,
-                                      output_size=embed_dim,
-                                      quant_config=quant_config,
-                                      prefix=f"{prefix}.proj")
+        if use_data_parallel:
+            self.qkv = ReplicatedLinear(embed_dim,
+                                        self.hidden_size_per_attention_head *
+                                        3 * num_heads,
+                                        bias=True,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.qkv")
+
+        else:
+            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=True,
+                quant_config=quant_config,
+                prefix=f"{prefix}.qkv")
+
+        cls_proj = (ReplicatedLinear
+                    if use_data_parallel else RowParallelLinear)
+        self.proj = cls_proj(input_size=projection_size,
+                             output_size=embed_dim,
+                             quant_config=quant_config,
+                             prefix=f"{prefix}.proj")
 
         # Detect attention implementation.
         self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
@@ -302,8 +327,6 @@ class Qwen2_5_VisionAttention(nn.Module):
             k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
 
         if self.is_flash_attn_backend:
-            # from vllm_flash_attn.flash_attn_interface import (
-            #   flash_attn_varlen_func)
             if self.attn_backend == _Backend.ROCM_AITER_FA:
                 from aiter import flash_attn_varlen_func
             else:
@@ -370,23 +393,27 @@ class Qwen2_5_VisionBlock(nn.Module):
         norm_layer: Optional[Callable[[int], nn.Module]] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        use_data_parallel: bool = False,
     ) -> 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 = Qwen2_5_VisionAttention(embed_dim=dim,
-                                            num_heads=num_heads,
-                                            projection_size=dim,
-                                            quant_config=quant_config,
-                                            prefix=f"{prefix}.attn")
+        self.attn = Qwen2_5_VisionAttention(
+            embed_dim=dim,
+            num_heads=num_heads,
+            projection_size=dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+            use_data_parallel=use_data_parallel)
         self.mlp = Qwen2_5_VisionMLP(dim,
                                      mlp_hidden_dim,
                                      act_fn=act_fn,
                                      bias=True,
                                      quant_config=quant_config,
-                                     prefix=f"{prefix}.mlp")
+                                     prefix=f"{prefix}.mlp",
+                                     use_data_parallel=use_data_parallel)
 
     def forward(
             self,
@@ -445,24 +472,30 @@ class Qwen2_5_VisionPatchMerger(nn.Module):
         spatial_merge_size: int = 2,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        use_data_parallel: bool = False,
     ) -> 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)
+
+        cls_fc1 = (ReplicatedLinear
+                   if use_data_parallel else ColumnParallelLinear)
+        cls_fc2 = (ReplicatedLinear
+                   if use_data_parallel else RowParallelLinear)
         self.mlp = nn.ModuleList([
-            ColumnParallelLinear(self.hidden_size,
-                                 self.hidden_size,
-                                 bias=True,
-                                 quant_config=quant_config,
-                                 prefix=f"{prefix}.mlp.0"),
+            cls_fc1(self.hidden_size,
+                    self.hidden_size,
+                    bias=True,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.mlp.0"),
             nn.GELU(),
-            RowParallelLinear(self.hidden_size,
-                              d_model,
-                              bias=True,
-                              quant_config=quant_config,
-                              prefix=f"{prefix}.mlp.2"),
+            cls_fc2(self.hidden_size,
+                    d_model,
+                    bias=True,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.mlp.2"),
         ])
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -514,6 +547,7 @@ class Qwen2_5_VisionTransformer(nn.Module):
         norm_eps: float = 1e-6,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        use_data_parallel: bool = False,
     ) -> None:
         super().__init__()
 
@@ -523,6 +557,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
         depth = vision_config.depth
         self.hidden_size = vision_config.hidden_size
         self.num_heads = vision_config.num_heads
+        self.use_data_parallel = use_data_parallel
+        self.out_hidden_size = vision_config.out_hidden_size
 
         # args for get_window_index_thw
         self.window_size = vision_config.window_size
@@ -550,7 +586,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
                                     vision_config.hidden_act),
                                 norm_layer=norm_layer,
                                 quant_config=quant_config,
-                                prefix=f"{prefix}.blocks.{layer_idx}")
+                                prefix=f"{prefix}.blocks.{layer_idx}",
+                                use_data_parallel=use_data_parallel)
             for layer_idx in range(depth)
         ])
         self.merger = Qwen2_5_VisionPatchMerger(
@@ -560,6 +597,7 @@ class Qwen2_5_VisionTransformer(nn.Module):
             spatial_merge_size=self.spatial_merge_size,
             quant_config=quant_config,
             prefix=f"{prefix}.merger",
+            use_data_parallel=use_data_parallel,
         )
         self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
 
@@ -767,7 +805,6 @@ class Qwen2_5_VisionTransformer(nn.Module):
                 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)
@@ -840,6 +877,8 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
         config: Qwen2_5_VLConfig = vllm_config.model_config.hf_config
         multimodal_config = vllm_config.model_config.multimodal_config
 
+        self.use_data_parallel = (vllm_config.parallel_config.
+                                  enable_multimodal_encoder_data_parallel)
         self.config = config
         self.multimodal_config = multimodal_config
 
@@ -851,6 +890,7 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
                 quant_config=self._maybe_ignore_quant_config(
                     self.quant_config),
                 prefix=maybe_prefix(prefix, "visual"),
+                use_data_parallel=self.use_data_parallel,
             )
         else:
             self.visual = None
@@ -973,7 +1013,13 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
             image_embeds = image_input["image_embeds"].type(self.visual.dtype)
         else:
             pixel_values = image_input["pixel_values"]
-            image_embeds = self.visual(pixel_values, grid_thw=grid_thw_list)
+
+            if self.use_data_parallel:
+                return run_dp_sharded_mrope_vision_model(
+                    self.visual, pixel_values, grid_thw_list)
+            else:
+                image_embeds = self.visual(pixel_values,
+                                           grid_thw=grid_thw_list)
 
         # Split concatenated embeddings for each image item.
         # Using prod on grid_thw_list instead of grid_thw.prod avoids CUDA sync
@@ -995,8 +1041,12 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
             video_embeds = video_input["video_embeds"].type(self.visual.dtype)
         else:
             pixel_values_videos = video_input["pixel_values_videos"]
-            video_embeds = self.visual(pixel_values_videos,
-                                       grid_thw=grid_thw_list)
+            if self.use_data_parallel:
+                return run_dp_sharded_mrope_vision_model(
+                    self.visual, pixel_values_videos, grid_thw_list)
+            else:
+                video_embeds = self.visual(pixel_values_videos,
+                                           grid_thw=grid_thw_list)
 
         # Split concatenated embeddings for each video item.
         merge_size = self.visual.spatial_merge_size

vllm/model_executor/models/qwen2_vl.py

@@ -329,8 +329,6 @@ class Qwen2VisionAttention(nn.Module):
             k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
 
         if self.is_flash_attn_backend:
-            # from vllm_flash_attn.flash_attn_interface import (
-            #   flash_attn_varlen_func)
             if self.attn_backend == _Backend.ROCM_AITER_FA:
                 from aiter import flash_attn_varlen_func
             else:

vllm/multimodal/utils.py

@@ -3,6 +3,8 @@
 
 import asyncio
 import atexit
+import itertools
+import math
 from collections.abc import Iterable
 from concurrent.futures import ThreadPoolExecutor
 from itertools import groupby
@@ -465,6 +467,219 @@ def run_dp_sharded_vision_model(image_input: torch.Tensor,
     return vision_embeddings
 
 
+def get_load_balance_assignment(
+    sizes: list[int],
+    num_gpus: int = 2,
+) -> tuple[list[int], list[int], list[int]]:
+    """
+    Generate load balancing assignment and metadata 
+    for distributing data across GPUs.
+    The load is determined by the total image sizes,
+    not the number of images.
+    
+    Args:
+        sizes: The size of each image
+        num_gpus: Number of GPUs to balance across
+    
+    Returns:
+        shuffle_indices: 
+            Indices to reorder data for balanced loading
+        gpu_sample_counts: 
+            Number of samples assigned to each GPU
+        grouped_sizes_per_gpu: 
+            Total size assigned to each GPU
+    
+    Example:
+        ```
+        sizes = [1000, 100, 200, 50]
+        num_gpus=2
+        ```
+
+    """
+
+    n_samples = len(sizes)
+
+    # Handle edge cases
+    if n_samples == 0:
+        return [], [0] * num_gpus, [0] * num_gpus
+
+    # Use greedy algorithm - balance by total size, not sample count
+    gpu_assignments = [list[int]() for _ in range(num_gpus)]
+    gpu_loads = [0] * num_gpus  # This tracks total SIZE, not sample count
+
+    # Sort indices by size (largest first for better load balancing)
+    # sizes = [1000, 100, 200, 50]
+    # large_to_small_indices = [0, 2, 1, 3]
+    large_to_small_indices = sorted(range(n_samples),
+                                    key=lambda i: sizes[i],
+                                    reverse=True)
+
+    for idx in large_to_small_indices:
+        # Find GPU with minimum current load (by total size)
+        min_gpu = min(range(num_gpus), key=lambda i: gpu_loads[i])
+        gpu_assignments[min_gpu].append(idx)
+        gpu_loads[min_gpu] += sizes[idx]
+
+    # Create shuffle indices and counts
+    shuffle_indices = list[int]()
+    gpu_sample_counts = list[int]()
+    for gpu_id in range(num_gpus):
+        # GPU_0 = [1000] = [0]
+        # GPU_1 = [200, 100, 50] = [2, 1, 3]
+        # shuffle_indices = [0, 2, 1, 3]
+        shuffle_indices.extend(gpu_assignments[gpu_id])
+        # GPU_0 = [1]
+        # GPU_1 = [3]
+        # gpu_sample_counts = [1, 3]
+        gpu_sample_counts.append(len(gpu_assignments[gpu_id]))
+
+    return (shuffle_indices, gpu_sample_counts, gpu_loads)
+
+
+def run_dp_sharded_mrope_vision_model(
+    vision_model: torch.nn.Module,
+    pixel_values: torch.Tensor,
+    grid_thw_list: list[list[int]],
+) -> tuple[torch.Tensor, ...]:
+    """Run a vision model with data parallelism (DP) sharding. 
+    The function will shard the input image tensor on the 
+    first dimension and run the vision model.
+    This function is used to run the vision model with mrope.
+    
+    Args:
+        vision_model (torch.nn.Module): Vision model.
+        pixel_values (torch.Tensor): Image/Video input tensor.
+        grid_thw_list: List of grid dimensions for each image
+    Returns:
+        torch.Tensor: Output image embeddings
+
+    Example:
+        ```
+        vision_model.out_hidden_size = 64
+        vision_model.spatial_merge_size = 2
+        pixel_values.shape = (1350, channel)
+        grid_thw_list = [[1, 10, 100], [1, 10, 10], [1, 10, 20], [1, 50]]
+        tp_size=2
+        ```
+
+    """
+    tp_size = get_tensor_model_parallel_world_size()
+
+    # GPU_0 tp_rank_local = 0
+    # GPU_1 tp_rank_local = 1
+    tp_rank_local = get_tensor_model_parallel_rank()
+
+    # patches_per_image = [1000, 100, 200, 50]
+    patches_per_image = [math.prod(grid_thw) for grid_thw in grid_thw_list]
+    # patches_per_image = [0, 1000, 1100, 1300, 1350]
+    cum_patches_per_image = [0, *itertools.accumulate(patches_per_image)]
+
+    # Get load balancing assignment with all metadata
+    # image_to_tp_rank = [0, 2, 1, 3]
+    # gpu_sample_counts = [1, 3]
+    # grouped_pixel_values_len = [1000, 350]
+    (image_to_tp_rank, gpu_sample_counts,
+     grouped_pixel_values_len) = get_load_balance_assignment(
+         patches_per_image, tp_size)
+
+    # cu_gpu_sample_counts = [0, 1, 4]
+    cum_gpu_sample_counts = [0, *itertools.accumulate(gpu_sample_counts)]
+
+    # GPU_0 image_idxs_local = [0]
+    # GPU_1 image_idxs_local = [2, 1, 3]
+    image_idxs_local = image_to_tp_rank[cum_gpu_sample_counts[tp_rank_local]:
+                                        cum_gpu_sample_counts[tp_rank_local +
+                                                              1]]
+
+    # Get the pixel values for the local images based on the image_idxs_local
+    if len(image_idxs_local) > 0:
+        pixel_values_local = torch.cat([
+            pixel_values[cum_patches_per_image[i]:cum_patches_per_image[i + 1]]
+            for i in image_idxs_local
+        ])
+    else:
+        # Handle case where this rank has no images
+        pixel_values_local = torch.empty((0, pixel_values.shape[1]),
+                                         device=pixel_values.device,
+                                         dtype=pixel_values.dtype)
+    # embed_dim_reduction_factor = 2 * 2
+    embed_dim_reduction_factor = (vision_model.spatial_merge_size *
+                                  vision_model.spatial_merge_size)
+
+    # Find the max length across all ranks
+    # The output embedding of every DP rank has to be
+    # padded to this length for tensor_model_parallel_all_gather
+    # to work
+    max_len_per_rank = max(
+        grouped_pixel_values_len) // embed_dim_reduction_factor
+    local_grid_thw_list = [grid_thw_list[i] for i in image_idxs_local]
+
+    # Run the vision model on the local pixel_values_local
+    if pixel_values_local.shape[0] > 0:
+        image_embeds_local = vision_model(pixel_values_local,
+                                          local_grid_thw_list)
+    else:
+        # Handle empty case
+        image_embeds_local = torch.empty((0, vision_model.out_hidden_size),
+                                         device=pixel_values.device,
+                                         dtype=pixel_values.dtype)
+
+    # Pad the output based on max_len_per_rank
+    # for tensor_model_parallel_all_gather to work
+    current_len = image_embeds_local.shape[0]
+    if current_len < max_len_per_rank:
+        padding_size = max_len_per_rank - current_len
+        padding = torch.empty((padding_size, image_embeds_local.shape[1]),
+                              dtype=image_embeds_local.dtype,
+                              device=image_embeds_local.device)
+        image_embeds_local_padded = torch.cat([image_embeds_local, padding],
+                                              dim=0)
+    else:
+        image_embeds_local_padded = image_embeds_local
+
+    # Do all_gather to collect embeddings from all ranks
+    gathered_embeds = tensor_model_parallel_all_gather(
+        image_embeds_local_padded, dim=0)
+
+    # Remove padding and reconstruct per-rank embeddings
+    rank_embeddings = list[torch.Tensor]()
+    for rank in range(tp_size):
+        start_idx = rank * max_len_per_rank
+        end_idx = start_idx + (grouped_pixel_values_len[rank] //
+                               embed_dim_reduction_factor)
+        rank_embeddings.append(gathered_embeds[start_idx:end_idx])
+
+    patches_per_output_image = [(patch_size // embed_dim_reduction_factor)
+                                for patch_size in patches_per_image]
+
+    # Reconstruct embeddings in the original order
+    original_order_embeddings = [None] * len(grid_thw_list)
+    current_idx = 0
+    for rank in range(tp_size):
+        count = gpu_sample_counts[rank]
+        if count > 0:
+            # Get images assigned to this rank in shuffled order
+            # GPU_0 = image_idxs_local  [0]
+            # GPU_1 = image_idxs_local  [2, 1, 3]
+            rank_images = image_to_tp_rank[current_idx:current_idx + count]
+
+            rank_embed = rank_embeddings[rank]
+            # Split rank embeddings back to individual images
+            embed_start = 0
+            for img_idx in rank_images:
+                img_patches = patches_per_output_image[img_idx]
+                original_order_embeddings[img_idx] = rank_embed[
+                    embed_start:embed_start + img_patches]
+                embed_start += img_patches
+            current_idx += count
+
+    out_embeddings = tuple(embed for embed in original_order_embeddings
+                           if embed is not None)
+    assert len(out_embeddings) == len(
+        original_order_embeddings), "Found unassigned embeddings"
+    return out_embeddings
+
+
 def fetch_audio(
     audio_url: str,
     audio_io_kwargs: Optional[dict[str, Any]] = None,