[Model] Improve DotsOCRForCausalLM (#25466)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/models/dots_ocr.py

@@ -7,11 +7,13 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn import LayerNorm
-from transformers.modeling_utils import PreTrainedModel
 from transformers.models.qwen2_vl import Qwen2VLProcessor
 
 from vllm.attention.layer import check_upstream_fa_availability
 from vllm.config import VllmConfig
+from vllm.distributed import utils as dist_utils
+from vllm.distributed.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -19,10 +21,14 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.interfaces import (MultiModalEmbeddings,
+                                                   SupportsLoRA,
                                                    SupportsMultiModal,
                                                    SupportsPP)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.models.qwen2 import Qwen2ForCausalLM
+from vllm.model_executor.models.qwen2_5_vl import Qwen2_5_VisionAttention
 from vllm.model_executor.models.qwen2_vl import (Qwen2VLDummyInputsBuilder,
                                                  Qwen2VLMultiModalProcessor,
                                                  Qwen2VLProcessingInfo)
@@ -38,6 +44,8 @@ from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.dotsocr import (DotsOCRConfig,
                                                      DotsVisionConfig)
 
+from .vision import run_dp_sharded_mrope_vision_model
+
 IMAGE_TOKEN = "<|imgpad|>"
 
 
@@ -181,6 +189,8 @@ class PatchMerger(nn.Module):
         context_dim: int,
         spatial_merge_size: int = 2,
         pre_norm="layernorm",
+        prefix: str = "",
+        use_data_parallel: bool = False,
     ) -> None:
         super().__init__()
         self.hidden_size = context_dim * (spatial_merge_size**2)
@@ -189,21 +199,21 @@ class PatchMerger(nn.Module):
             self.ln_q = LayerNorm(context_dim, eps=1e-6)
         elif self.pre_norm == "rmsnorm":
             self.ln_q = RMSNorm(context_dim, eps=1e-6)
-        else:
-            print("no norm in patch merger")
 
         self.mlp = nn.Sequential(
             ColumnParallelLinear(self.hidden_size,
                                  self.hidden_size,
                                  bias=True,
                                  return_bias=False,
-                                 disable_tp=True),
+                                 prefix=f"{prefix}.0",
+                                 disable_tp=use_data_parallel),
             nn.GELU(),
             RowParallelLinear(self.hidden_size,
                               dim,
                               bias=True,
                               return_bias=False,
-                              disable_tp=True),
+                              prefix=f"{prefix}.2",
+                              disable_tp=use_data_parallel),
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -223,38 +233,36 @@ class DotsVisionAttention(nn.Module):
                  bias: bool = True,
                  *,
                  quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = "") -> None:
+                 prefix: str = "",
+                 use_data_parallel: bool = False) -> None:
         super().__init__()
-        from vllm.distributed import (parallel_state,
-                                      tensor_model_parallel_all_gather)
-        from vllm.distributed import utils as dist_utils
 
         self.embed_dim = dim
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.tp_size = parallel_state.get_tensor_model_parallel_world_size()
-        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
-        self.num_heads_per_partition = dist_utils.divide(
+        self.tp_size = (1 if use_data_parallel else
+                        get_tensor_model_parallel_world_size())
+        self.tp_rank = (0 if use_data_parallel else
+                        get_tensor_model_parallel_rank())
+        self.hidden_size_per_attention_head = dist_utils.divide(dim, num_heads)
+        self.num_attention_heads_per_partition = dist_utils.divide(
             num_heads, self.tp_size)
-
         # qkv/proj follow Qwen2-VL style; bias controlled by arg
-        self.qkv = QKVParallelLinear(hidden_size=dim,
-                                     head_size=dim // num_heads,
+        self.qkv = QKVParallelLinear(
+            hidden_size=dim,
+            head_size=self.hidden_size_per_attention_head,
             total_num_heads=num_heads,
             bias=bias,
             quant_config=quant_config,
-                                     prefix=f"{prefix}.qkv")
+            prefix=f"{prefix}.qkv",
+            disable_tp=use_data_parallel)
         self.proj = RowParallelLinear(input_size=dim,
                                       output_size=dim,
                                       bias=bias,
                                       quant_config=quant_config,
-                                      prefix=f"{prefix}.proj")
-        self._all_gather = tensor_model_parallel_all_gather
-        self._split_last = dist_utils.split_tensor_along_last_dim
-
+                                      prefix=f"{prefix}.proj",
+                                      disable_tp=use_data_parallel)
         # Select attention backend
-        self.attn_backend = get_vit_attn_backend(self.head_dim,
-                                                 torch.get_default_dtype())
+        self.attn_backend = get_vit_attn_backend(
+            self.hidden_size_per_attention_head, torch.get_default_dtype())
         self.use_upstream_fa = False
         if self.attn_backend != _Backend.FLASH_ATTN and \
                 check_upstream_fa_availability(torch.get_default_dtype()):
@@ -270,19 +278,6 @@ class DotsVisionAttention(nn.Module):
             _Backend.FLASH_ATTN, _Backend.ROCM_AITER_FA
         }
 
-    def _split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
-        # qkv: [S, B, 3*dim]
-        seq_len, bs, _ = qkv.shape
-        if self.tp_size > 1:
-            qkv = self._all_gather(qkv)
-        q, k, v = qkv.chunk(3, dim=2)
-        if self.tp_size > 1:
-            q = self._split_last(q, num_partitions=self.tp_size)[self.tp_rank]
-            k = self._split_last(k, num_partitions=self.tp_size)[self.tp_rank]
-            v = self._split_last(v, num_partitions=self.tp_size)[self.tp_rank]
-        new_shape = (seq_len, bs, self.num_heads_per_partition, self.head_dim)
-        return (q.view(*new_shape), k.view(*new_shape), v.view(*new_shape))
-
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -295,7 +290,7 @@ class DotsVisionAttention(nn.Module):
         # [S, C] -> [S, B=1, C]
         x = hidden_states.unsqueeze(1)
         x, _ = self.qkv(x)
-        q, k, v = self._split_qkv(x)
+        q, k, v = Qwen2_5_VisionAttention.split_qkv(self, x)
         bs = q.shape[1]
         # [S,B,H,D] -> [B,S,H,D]
         q = q.permute(1, 0, 2, 3).contiguous()
@@ -327,8 +322,9 @@ class DotsVisionAttention(nn.Module):
                                             max_seqlen_k=max_seqlen,
                                             dropout_p=0.0,
                                             causal=False)
-            context_layer = output.view(bs, -1, self.num_heads_per_partition,
-                                        self.head_dim)
+            context_layer = output.view(bs, -1,
+                                        self.num_attention_heads_per_partition,
+                                        self.hidden_size_per_attention_head)
         elif self.attn_backend == _Backend.TORCH_SDPA:
             outputs = []
             for i in range(1, len(cu_seqlens)):
@@ -368,7 +364,8 @@ class DotsSwiGLUFFN(nn.Module):
                  config,
                  *,
                  quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = ""):
+                 prefix: str = "",
+                 use_data_parallel: bool = False):
         super().__init__()
         hidden_features = config.intermediate_size
         in_features = config.embed_dim
@@ -380,13 +377,13 @@ class DotsSwiGLUFFN(nn.Module):
                                                bias=bias,
                                                quant_config=quant_config,
                                                prefix=f"{prefix}.fc13",
-                                               disable_tp=True)
+                                               disable_tp=use_data_parallel)
         self.fc2 = RowParallelLinear(hidden_features,
                                      in_features,
                                      bias=bias,
                                      quant_config=quant_config,
                                      prefix=f"{prefix}.fc2",
-                                     disable_tp=True)
+                                     disable_tp=use_data_parallel)
         self.act_fn = SiluAndMul()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -397,28 +394,36 @@ class DotsSwiGLUFFN(nn.Module):
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
-        params = dict(self.named_parameters())
-        loaded: set[str] = set()
-        for name, w in weights:
-            # Map fc1 -> fc13 (shard 0)
-            if name.startswith("fc1."):
-                tgt = name.replace("fc1.", "fc13.")
-                if tgt in params:
-                    params[tgt].weight_loader(params[tgt], w, 0)
-                    loaded.add(tgt)
+        stacked_params_mapping = [
+            ("fc13", "fc1", 0),
+            ("fc13", "fc3", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        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
-            # Map fc3 -> fc13 (shard 1)
-            if name.startswith("fc3."):
-                tgt = name.replace("fc3.", "fc13.")
-                if tgt in params:
-                    params[tgt].weight_loader(params[tgt], w, 1)
-                    loaded.add(tgt)
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
                     continue
-            # Pass-through for fc2 and others
-            if name in params:
-                params[name].weight_loader(params[name], w)
-                loaded.add(name)
-        return loaded
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
 
 
 class DotsPatchEmbed(nn.Module):
@@ -463,25 +468,28 @@ class DotsViTPreprocessor(nn.Module):
 
 class DotsVisionBlock(nn.Module):
 
-    def __init__(self,
+    def __init__(
+        self,
         config,
         *,
         quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = ""):
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
         super().__init__()
 
-        self.attn = DotsVisionAttention(
-            config,
+        self.attn = DotsVisionAttention(config,
                                         config.embed_dim,
                                         num_heads=config.num_attention_heads,
                                         bias=config.use_bias,
                                         quant_config=quant_config,
                                         prefix=f"{prefix}.attn",
-        )
+                                        use_data_parallel=use_data_parallel)
         self.norm1 = RMSNorm(config.embed_dim, eps=config.rms_norm_eps)
         self.mlp = DotsSwiGLUFFN(config,
                                  quant_config=quant_config,
-                                 prefix=f"{prefix}.mlp")
+                                 prefix=f"{prefix}.mlp",
+                                 use_data_parallel=use_data_parallel)
         self.norm2 = RMSNorm(config.embed_dim, eps=config.rms_norm_eps)
 
     def forward(self,
@@ -502,7 +510,7 @@ class DotsVisionBlock(nn.Module):
         return hidden_states
 
 
-class DotsVisionTransformer(PreTrainedModel):
+class DotsVisionTransformer(nn.Module):
 
     def __init__(
         self,
@@ -512,8 +520,9 @@ class DotsVisionTransformer(PreTrainedModel):
         num_hidden_layers_override: Optional[int] = None,
         require_post_norm: Optional[bool] = None,
         prefix: str = "",
+        use_data_parallel: bool = False,
     ) -> None:
-        super().__init__(config)
+        super().__init__()
         self.config = config
         self.spatial_merge_size = config.spatial_merge_size
 
@@ -526,14 +535,15 @@ class DotsVisionTransformer(PreTrainedModel):
         if self.attn_backend != _Backend.FLASH_ATTN and \
                 check_upstream_fa_availability(torch.get_default_dtype()):
             self.attn_backend = _Backend.FLASH_ATTN
-
+        self.out_hidden_size = config.hidden_size
         # Keep blocks for compatibility with other vision towers
         num_layers = (config.num_hidden_layers if num_hidden_layers_override
                       is None else num_hidden_layers_override)
         self.blocks = nn.ModuleList([
             DotsVisionBlock(config,
                             quant_config=quant_config,
-                            prefix=f"{prefix}.blocks.{i}")
+                            prefix=f"{prefix}.blocks.{i}",
+                            use_data_parallel=use_data_parallel)
             for i in range(num_layers)
         ])
         if require_post_norm is None:
@@ -548,6 +558,7 @@ class DotsVisionTransformer(PreTrainedModel):
             dim=config.hidden_size,
             context_dim=config.embed_dim,
             spatial_merge_size=config.spatial_merge_size,
+            use_data_parallel=use_data_parallel,
         )
 
     @property
@@ -604,7 +615,11 @@ class DotsVisionTransformer(PreTrainedModel):
         return max_seqlen, seqlens
 
     def forward(self, hidden_states: torch.Tensor,
-                grid_thw: torch.Tensor) -> torch.Tensor:
+                grid_thw: list[list[int]]) -> torch.Tensor:
+        # Convert grid_thw to tensor (always expecting list format now)
+        grid_thw = torch.tensor(grid_thw,
+                                device=hidden_states.device,
+                                dtype=torch.long)
         hidden_states = hidden_states.to(self.dtype)
         hidden_states = self.patch_embed(hidden_states, grid_thw)
 
@@ -638,7 +653,8 @@ class DotsVisionTransformer(PreTrainedModel):
     info=DotsOCRProcessingInfo,
     dummy_inputs=DotsOCRDummyInputsBuilder,
 )
-class DotsOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+class DotsOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP,
+                         SupportsLoRA):
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_substr={
             ".attn.qkv_proj.": ".attn.qkv.",
@@ -650,6 +666,21 @@ class DotsOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
         },
     )
 
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+        ".attn.qkv": [".attn.qkv"],
+        "fc13": ["fc1", "fc3"],
+    }
+    supports_encoder_tp_data = True
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
         if modality.startswith("image"):
@@ -660,19 +691,18 @@ class DotsOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
 
         self.config: DotsOCRConfig = vllm_config.model_config.hf_config
         self.quant_config = vllm_config.quant_config
-        self.multimodal_config = vllm_config.model_config.multimodal_config
-
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
         if isinstance(self.config.vision_config, dict):
             vision_config = DotsVisionConfig(**self.config.vision_config)
             self.config.vision_config = vision_config
         else:
             vision_config = self.config.vision_config
-
         self.vision_tower = DotsVisionTransformer(
             vision_config,
             quant_config=self.quant_config,
             prefix=maybe_prefix(prefix, "vision_tower"),
-        )
+            use_data_parallel=self.use_data_parallel)
         self.language_model: Qwen2ForCausalLM = init_vllm_registered_model(
             vllm_config=vllm_config,
             hf_config=self.config,
@@ -744,6 +774,15 @@ class DotsOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
         else:
             pixel_values = image_input["pixel_values"].type(
                 self.vision_tower.dtype)
+
+            if self.use_data_parallel:
+                return run_dp_sharded_mrope_vision_model(
+                    self.vision_tower,
+                    pixel_values,
+                    grid_thw_list,
+                    rope_type="rope_3d",
+                )
+            else:
                 image_embeds = self.vision_tower(
                     pixel_values, grid_thw)[:, :self.config.hidden_size]
 
@@ -822,3 +861,13 @@ class DotsOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
                                                    torch.Tensor]]) -> set[str]:
         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(
+            language_model="language_model",
+            connector="vision_tower.merger",
+            tower_model="vision_tower.",
+        )