[VLM] Add TP support for Phi-4-MM

examples/offline_inference/audio_language.py

@@ -77,6 +77,7 @@ def run_phi4mm(questions: str, audio_count: int):
         enable_lora=True,
         max_lora_rank=320,
         lora_extra_vocab_size=0,
+        limit_mm_per_prompt={"audio": audio_count},
     )
     lora_request = LoRARequest("speech", 1, speech_lora_path)
     # To maintain code compatibility in this script, we add LoRA here.

vllm/model_executor/models/phi4mm.py

@@ -15,7 +15,7 @@
 from transformers.utils import logging
 
 from vllm.config import VllmConfig
-from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.distributed import get_pp_group
 from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                          InputContext)
 from vllm.inputs.data import TokenInputs, token_inputs
@@ -34,7 +34,7 @@
 
 from .interfaces import SupportsLoRA, SupportsMultiModal
 from .phi4mm_audio import AudioEmbedding
-from .utils import maybe_prefix
+from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
 from .vision_siglip_navit import get_siglip_vision_model
 
 # <|endoftext10|> (see vocab.json in hf model)
@@ -352,12 +352,6 @@ def __init__(self,
         # n_embed or hidden_size
         hidden_size = config.n_embd if hasattr(
             config, 'n_embd') else config.hidden_size
-        if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'):
-            embd_drop = config.embd_pdrop if hasattr(
-                config, 'embd_pdrop') else config.embed_pdrop
-            self.drop = nn.Dropout(embd_drop)
-        else:
-            self.drop = None
 
         # layer_idx to output the img features
         if isinstance(config.img_processor, dict):
@@ -1431,6 +1425,20 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
         ],
     }
 
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={
+            "base_layer.": "",
+        },
+        orig_to_new_prefix={
+            "model.embed_tokens_extend.audio_embed.audio_projection.vision.":
+            "embed_tokens_extend.audio_projection_for_vision.",
+            "model.embed_tokens_extend.audio_embed.audio_projection.speech.":
+            "embed_tokens_extend.audio_projection.",
+            "model.embed_tokens_extend.audio_embed.": "embed_tokens_extend.",
+            "model.embed_tokens_extend.image_embed.": "vision_encoder.",
+        },
+    )
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -1445,8 +1453,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.lora_config = lora_config
 
         # Tensor/Pipeline parallel not supported for now.
-        assert get_tensor_model_parallel_world_size(
-        ) == 1, "tensor parallel is not supported"
         assert get_pp_group(
         ).world_size == 1, "pipeline parallel is not supported"
 
@@ -1686,44 +1692,6 @@ def merge_image_features_to_inputs_embeds(
         )
         return merged_embeds
 
-    def load_weights(self, weights: Iterable[Tuple[str,
-                                                   torch.Tensor]]) -> None:
-        weights = {name: weight for name, weight in weights}
-        adjusted_weights = {}
-
-        for name, weight in weights.items():
-            # NOTE vision-speech tasks use a separate projection layer
-            audio_proj_4v = \
-                "model.embed_tokens_extend.audio_embed.audio_projection.vision"
-            if name.startswith(audio_proj_4v):
-                name = name.replace(
-                    audio_proj_4v,
-                    "embed_tokens_extend.audio_projection_for_vision")
-
-            name = (name.replace(
-                "model.embed_tokens_extend.audio_embed."\
-                    "audio_projection.speech.",
-                "embed_tokens_extend.audio_projection.",
-            ).replace(
-                "model.embed_tokens_extend.audio_embed.",
-                "embed_tokens_extend.",
-            ).replace("model.embed_tokens_extend.image_embed.",
-                      "vision_encoder."))
-            # NOTE: this is deal with LoRA injection, where `base_layer`
-            # remains as the original layer in the model
-            if name.endswith(".base_layer.weight"):
-                name = name.replace(".base_layer.weight", ".weight")
-            adjusted_weights[name] = weight
-
-        missing_keys, unexpected_keys = self.load_state_dict(adjusted_weights,
-                                                             strict=False)
-        logger.debug("*** missing keys:")
-        for key in missing_keys:
-            logger.debug(key)
-        logger.debug("**** unexpected keys:")
-        for key in unexpected_keys:
-            logger.debug(key)
-
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -1796,6 +1764,13 @@ def sample(
         next_tokens = self.sampler(logits, sampling_metadata)
         return next_tokens
 
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> None:
+        weights = ((name, data) for name, data in weights
+                   if "lora" not in name)
+        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
@@ -1804,4 +1779,4 @@ def get_mm_mapping(self) -> MultiModelKeys:
             language_model="model.",
             connector=["audio_projection_for_vision", "audio_projection"],
             tower_model=["vision_encoder", "embed_tokens_extend"],
-        )
+        )