[Bugfix] Fix PP for ChatGLM and Molmo (#9422)

docs/source/models/supported_models.rst

@@ -425,7 +425,7 @@ Text Generation
     -
   * - :code:`MolmoForCausalLM`
     - Molmo
-    - Image
+    - T + I
     - :code:`allenai/Molmo-7B-D-0924`, :code:`allenai/Molmo-72B-0924`, etc.
     -
     - ✅︎

tests/distributed/test_pipeline_parallel.py

@@ -118,11 +118,8 @@ class PPTestSettings:
 # The values displayed here are only a rough indicator of the size of the model
 
 # yapf: disable
-GENERATION_MODEL_SETTINGS = {
-    # [DETAILED TESTS]
-    "meta-llama/Meta-Llama-3-8B": PPTestSettings.detailed(),
-    "microsoft/Phi-3-mini-4k-instruct": PPTestSettings.detailed(trust_remote_code=True, multi_node_only=True),  # noqa: E501
-    # [FAST TESTS]
+TEXT_GENERATION_MODELS = {
+    # [Decoder-only]
     # Uses Llama
     # "BAAI/AquilaChat-7B": PPTestSettings.fast(),
     "Snowflake/snowflake-arctic-instruct": PPTestSettings.fast(tp_base=8, trust_remote_code=True),  # noqa: E501
@@ -151,6 +148,7 @@ GENERATION_MODEL_SETTINGS = {
     "core42/jais-13b-chat": PPTestSettings.fast(),
     # TODO: Implement PP
     # "ai21labs/AI21-Jamba-1.5-Mini": PPTestSettings.fast(),
+    "meta-llama/Meta-Llama-3-8B": PPTestSettings.detailed(),
     "openbmb/MiniCPM-2B-sft-bf16": PPTestSettings.fast(trust_remote_code=True),
     "openbmb/MiniCPM3-4B": PPTestSettings.fast(trust_remote_code=True),
     # Uses Llama
@@ -163,6 +161,7 @@ GENERATION_MODEL_SETTINGS = {
     "facebook/opt-iml-max-1.3b": PPTestSettings.fast(),
     "OrionStarAI/Orion-14B-Chat": PPTestSettings.fast(trust_remote_code=True),
     "microsoft/phi-2": PPTestSettings.fast(),
+    "microsoft/Phi-3-mini-4k-instruct": PPTestSettings.detailed(trust_remote_code=True, multi_node_only=True),  # noqa: E501
     "microsoft/Phi-3-small-8k-instruct": PPTestSettings.fast(trust_remote_code=True),  # noqa: E501
     "microsoft/Phi-3.5-MoE-instruct": PPTestSettings.fast(trust_remote_code=True),  # noqa: E501
     "adept/persimmon-8b-chat": PPTestSettings.fast(),
@@ -174,40 +173,40 @@ GENERATION_MODEL_SETTINGS = {
     "upstage/solar-pro-preview-instruct": PPTestSettings.fast(tp_base=2),
     # FIXME: Cannot load tokenizer in latest transformers version
     # "xverse/XVERSE-7B-Chat": PPTestSettings.fast(trust_remote_code=True),
+    # [Encoder-only]
+    # TODO: Implement PP
+    # "facebook/bart-base": PPTestSettings.fast(),
 }
 
-EMBEDDING_MODEL_SETTINGS = {  # type: ignore[var-annotated]
-    # [FAST TESTS]
+EMBEDDING_MODELS = {  # type: ignore[var-annotated]
+    # [Text-only]
     "intfloat/e5-mistral-7b-instruct": PPTestSettings.fast(),
     "BAAI/bge-multilingual-gemma2": PPTestSettings.fast(),
     "Qwen/Qwen2.5-Math-RM-72B": PPTestSettings.fast(tp_base=4, trust_remote_code=True),  # noqa: E501
 }
 
-MULTIMODAL_MODEL_SETTINGS = {
-    # [FAST TESTS]
+MULTIMODAL_MODELS = {
+    # [Decoder-only]
     "Salesforce/blip2-opt-2.7b": PPTestSettings.fast(),
     "facebook/chameleon-7b": PPTestSettings.fast(),
     "adept/fuyu-8b": PPTestSettings.fast(),
+    "THUDM/glm-4v-9b": PPTestSettings.fast(trust_remote_code=True),
     "OpenGVLab/InternVL2-1B": PPTestSettings.fast(trust_remote_code=True),
     "llava-hf/llava-1.5-7b-hf": PPTestSettings.fast(),
     "llava-hf/llava-v1.6-mistral-7b-hf": PPTestSettings.fast(),
     "llava-hf/LLaVA-NeXT-Video-7B-hf": PPTestSettings.fast(),
     "llava-hf/llava-onevision-qwen2-0.5b-ov-hf": PPTestSettings.fast(),
     "openbmb/MiniCPM-Llama3-V-2_5": PPTestSettings.fast(trust_remote_code=True),
-    # TODO: Implement PP
-    # "meta-llama/Llama-3.2-11B-Vision-Instruct": PPTestSettings.fast(),
+    "allenai/Molmo-7B-D-0924": PPTestSettings.fast(trust_remote_code=True),
     "microsoft/Phi-3-vision-128k-instruct": PPTestSettings.fast(trust_remote_code=True),  # noqa: E501
     "mistralai/Pixtral-12B-2409": PPTestSettings.fast(tp_base=2, tokenizer_mode="mistral"),  # noqa: E501
     "Qwen/Qwen-VL-Chat": PPTestSettings.fast(trust_remote_code=True),
     "Qwen/Qwen2-Audio-7B-Instruct": PPTestSettings.fast(),
     "Qwen/Qwen2-VL-2B-Instruct": PPTestSettings.fast(),
     "fixie-ai/ultravox-v0_3": PPTestSettings.fast(),
-}
-
-CONDITIONAL_GENERATION_MODEL_SETTINGS = {  # type: ignore[var-annotated]
-    # [FAST TESTS]
+    # [Encoder-decoder]
     # TODO: Implement PP
-    # "facebook/bart-base": PPTestSettings.fast(),
+    # "meta-llama/Llama-3.2-11B-Vision-Instruct": PPTestSettings.fast(),
 }
 # yapf: enable
 
@@ -323,7 +322,7 @@ def _compare_tp(
     ("model_name", "parallel_setup", "distributed_backend", "task",
      "test_options"),
     [
-        params for model_name, settings in GENERATION_MODEL_SETTINGS.items()
+        params for model_name, settings in TEXT_GENERATION_MODELS.items()
         for params in settings.iter_params(model_name)
         if model_name in TEST_MODELS
     ],
@@ -350,7 +349,7 @@ def test_tp_language_generation(
     ("model_name", "parallel_setup", "distributed_backend", "task",
      "test_options"),
     [
-        params for model_name, settings in EMBEDDING_MODEL_SETTINGS.items()
+        params for model_name, settings in EMBEDDING_MODELS.items()
         for params in settings.iter_params(model_name)
         if model_name in TEST_MODELS
     ],
@@ -377,7 +376,7 @@ def test_tp_language_embedding(
     ("model_name", "parallel_setup", "distributed_backend", "task",
      "test_options"),
     [
-        params for model_name, settings in MULTIMODAL_MODEL_SETTINGS.items()
+        params for model_name, settings in MULTIMODAL_MODELS.items()
         for params in settings.iter_params(model_name)
         if model_name in TEST_MODELS
     ],

vllm/model_executor/models/chatglm.py

@@ -13,8 +13,9 @@ from torch.nn import LayerNorm
 
 from vllm.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig
-from vllm.distributed import get_tensor_model_parallel_world_size
-from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
+                         token_inputs)
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -22,8 +23,7 @@ from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -39,7 +39,9 @@ from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
                            SequenceData)
 from vllm.transformers_utils.configs import ChatGLMConfig
 
-from .interfaces import SupportsLoRA, SupportsMultiModal
+from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
+from .utils import (is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers)
 
 logger = init_logger(__name__)
 
@@ -150,6 +152,10 @@ def find_all_positions(input_ids: List[int], target: int) -> List[int]:
 
 
 def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
+    multi_modal_data = inputs.get("multi_modal_data")
+    if multi_modal_data is None or "image" not in multi_modal_data:
+        return inputs
+
     hf_config = ctx.get_hf_config(ChatGLMConfig)
     vision_config = getattr(hf_config, 'vision_config', None)
 
@@ -161,8 +167,8 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
         msg = f"Unsupported vision config: {type(vision_config)}"
         raise NotImplementedError(msg)
 
-    input_ids = inputs.get("prompt_token_ids")
-    position_ids = inputs.get("position_ids")
+    input_ids = inputs["prompt_token_ids"]
+
     tokenizer = cached_get_tokenizer(
         ctx.model_config.model,
         trust_remote_code=ctx.model_config.trust_remote_code)
@@ -171,20 +177,19 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
         raw_batch_data = tokenizer.apply_chat_template(
             conversation=[{
                 "role": "user",
-                "image": inputs['multi_modal_data']["image"],
-                "content": inputs['prompt']
+                "image": multi_modal_data["image"],
+                "content": inputs['prompt'],
             }],
             add_generation_prompt=True,
             tokenize=True,
             return_tensors="pt",
-            return_dict=True).data
+            return_dict=True,
+        ).data
     except Exception:
         logger.error("Failed to process content (%s)", inputs['prompt'])
         raise
     input_ids = raw_batch_data['input_ids'][0].tolist()
 
-    if position_ids is None:
-        position_ids = list(range(len(input_ids)))
     boi_token_id = hf_config.boi_token_id
     eoi_token_id = hf_config.eoi_token_id
     boi_positions = find_all_positions(input_ids, boi_token_id)
@@ -193,7 +198,6 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
     assert len(boi_positions) == len(eoi_positions)
 
     new_input_ids = []
-    new_position_ids = []
     final_processed_position = 0
     final_processed_position = 0
 
@@ -201,29 +205,28 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
         assert boi_position < eoi_position
         new_input_ids.extend(input_ids[final_processed_position:boi_position +
                                        1])
-        new_position_ids.extend(
-            list(range(final_processed_position, boi_position + 1)))
         new_input_ids.extend([input_ids[boi_position + 1]] *
                              image_placeholder_length)
-        new_position_ids.extend([boi_position + 1] * image_placeholder_length)
         final_processed_position = eoi_position
 
     new_input_ids.extend(input_ids[final_processed_position:])
-    new_position_ids.extend(
-        list(range(final_processed_position, len(input_ids))))
 
-    assert len(new_input_ids) == len(new_position_ids)
+    prompt = inputs.get("prompt")
+    if prompt is None:
+        prompt = tokenizer.decode(new_input_ids)
 
-    inputs["prompt_token_ids"] = new_input_ids
-    inputs["position_ids"] = new_position_ids
-    return inputs
+    return token_inputs(
+        prompt_token_ids=new_input_ids,
+        prompt=prompt,
+        multi_modal_data=multi_modal_data,
+    )
 
 
 class GLMAttention(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: ChatGLMConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
@@ -314,7 +317,7 @@ class GLMMLP(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: ChatGLMConfig,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -357,7 +360,7 @@ class GLMBlock(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: ChatGLMConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
@@ -428,9 +431,10 @@ class GLMTransformer(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: ChatGLMConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.post_layer_norm = config.post_layer_norm
@@ -439,10 +443,11 @@ class GLMTransformer(nn.Module):
         self.num_layers = config.num_layers
 
         # Transformer layers.
-        self.layers = nn.ModuleList([
-            GLMBlock(config, cache_config, quant_config)
-            for i in range(self.num_layers)
-        ])
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            self.num_layers,
+            lambda prefix: GLMBlock(config, cache_config, quant_config),
+            prefix=f"{prefix}.layers",
+        )
 
         if self.post_layer_norm:
             layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
@@ -450,6 +455,10 @@ class GLMTransformer(nn.Module):
             self.final_layernorm = layer_norm_func(
                 config.hidden_size, eps=config.layernorm_epsilon)
 
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.hidden_size))
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -457,16 +466,16 @@ class GLMTransformer(nn.Module):
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
-        for i in range(self.num_layers):
+        for i in range(self.start_layer, self.end_layer):
             layer = self.layers[i]
             hidden_states = layer(
                 hidden_states=hidden_states,
                 position_ids=position_ids,
-                kv_cache=kv_caches[i],
+                kv_cache=kv_caches[i - self.start_layer],
                 attn_metadata=attn_metadata,
             )
         # Final layer norm.
-        if self.post_layer_norm:
+        if get_pp_group().is_last_rank and self.post_layer_norm:
             hidden_states = self.final_layernorm(hidden_states)
 
         return hidden_states
@@ -476,7 +485,7 @@ class ChatGLMModel(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: ChatGLMConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
@@ -504,6 +513,9 @@ class ChatGLMModel(nn.Module):
         else:
             self.vision = None
 
+        self.make_empty_intermediate_tensors = (
+            self.encoder.make_empty_intermediate_tensors)
+
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> GLMImagePixelInputs:
 
@@ -529,24 +541,26 @@ class ChatGLMModel(nn.Module):
         intermediate_tensors: Optional[IntermediateTensors] = None,
         **kwargs: object,
     ) -> torch.Tensor:
-
-        inputs_embeds = self.embedding(input_ids)
-        image_input = self._parse_and_validate_image_input(**kwargs)
-
-        if image_input["pixel_values"] is not None:
-            pixel_values = image_input["pixel_values"].to(
-                dtype=inputs_embeds.dtype)
-            image_embeds = self.vision(pixel_values)
-
-            boi_token_id = self.config.boi_token_id
-            eoi_token_id = self.config.eoi_token_id
-
-            inputs_embeds = merge_glm_vision_embeddings(
-                input_ids=input_ids,
-                inputs_embeds=inputs_embeds,
-                vision_embeddings=image_embeds,
-                boi_token_id=boi_token_id,
-                eoi_token_id=eoi_token_id)
+        if intermediate_tensors is None:
+            inputs_embeds = self.embedding(input_ids)
+            image_input = self._parse_and_validate_image_input(**kwargs)
+
+            if image_input["pixel_values"] is not None:
+                pixel_values = image_input["pixel_values"].to(
+                    dtype=inputs_embeds.dtype)
+                image_embeds = self.vision(pixel_values)
+
+                boi_token_id = self.config.boi_token_id
+                eoi_token_id = self.config.eoi_token_id
+
+                inputs_embeds = merge_glm_vision_embeddings(
+                    input_ids=input_ids,
+                    inputs_embeds=inputs_embeds,
+                    vision_embeddings=image_embeds,
+                    boi_token_id=boi_token_id,
+                    eoi_token_id=eoi_token_id)
+        else:
+            inputs_embeds = intermediate_tensors["hidden_states"]
 
         # Run encoder.
         hidden_states = self.encoder(
@@ -555,6 +569,9 @@ class ChatGLMModel(nn.Module):
             kv_caches=kv_caches,
             attn_metadata=attn_metadata,
         )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
         return hidden_states
 
 
@@ -562,7 +579,8 @@ class ChatGLMModel(nn.Module):
 @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_glmv_image_tokens)
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_glmv)
 @INPUT_REGISTRY.register_input_processor(input_processor_for_glmv)
-class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
+class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
+                         SupportsMultiModal):
     packed_modules_mapping = {
         "query_key_value": ["query_key_value"],
         "dense_h_to_4h": ["dense_h_to_4h"]
@@ -610,7 +628,8 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
                 intermediate_tensors: Optional[IntermediateTensors] = None,
                 **kwargs) -> torch.Tensor:
         hidden_states = self.transformer(input_ids, positions, kv_caches,
-                                         attn_metadata, **kwargs)
+                                         attn_metadata, intermediate_tensors,
+                                         **kwargs)
         return hidden_states
 
     def compute_logits(
@@ -656,6 +675,8 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
             # Skip loading extra bias for GPTQ models.
             if name.endswith(".bias") and name not in params_dict:
                 continue
+            if is_pp_missing_parameter(name, self):
+                continue
             param = params_dict[name]
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)

vllm/model_executor/models/molmo.py

@@ -30,21 +30,21 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.models.interfaces import SupportsMultiModal
-from vllm.model_executor.models.utils import make_layers
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalInputs
+from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
                            SequenceData)
 from vllm.transformers_utils.processor import get_processor
 
-from .utils import get_vit_attn_backend
+from .interfaces import SupportsMultiModal, SupportsPP
+from .utils import (get_vit_attn_backend,
+                    make_empty_intermediate_tensors_factory, make_layers)
 
 # TODO: hard-coded for now. Consider making it configurable.
 VIT_LAYERS = [-2, -9]
@@ -744,6 +744,10 @@ class MolmoModel(nn.Module):
         assert config.layer_norm_type == "rms"
         self.norm = RMSNorm(config.hidden_size, config.layer_norm_eps)
 
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -925,16 +929,19 @@ def pad_images(
 
 
 def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
-    prompt = inputs.get("prompt", None)
-    multi_modal_data = inputs.get("multi_modal_data", None)
-    if multi_modal_data is not None:
-        image = multi_modal_data.get("image", None)
-    else:
-        image = None
+    prompt = inputs.get("prompt")
+    multi_modal_data = inputs.get("multi_modal_data")
+    image = None if multi_modal_data is None else multi_modal_data.get("image")
+
     processor = cached_get_processor(ctx.model_config.model,
                                      trust_remote_code=True,
                                      revision=ctx.model_config.code_revision)
 
+    model_config = ctx.model_config
+    tokenizer = cached_get_tokenizer(
+        model_config.tokenizer,
+        trust_remote_code=model_config.trust_remote_code)
+
     # NOTE: message formatting for raw text prompt is only applied for
     # offline inference; for online inference, the prompt is always in
     # instruction format and tokenized.
@@ -997,9 +1004,13 @@ def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
 
     multi_modal_data = dict(image=image_data)
 
+    prompt = inputs.get("prompt")
+    if prompt is None:
+        prompt = tokenizer.decode(out["input_ids"])
+
     return token_inputs(
         prompt_token_ids=out["input_ids"],
-        prompt=inputs["prompt"],
+        prompt=prompt,
         multi_modal_data=multi_modal_data,
     )
 
@@ -1008,7 +1019,7 @@ def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
 @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_molmo_image_tokens)
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_molmo)
 @INPUT_REGISTRY.register_input_processor(input_processor_for_molmo)
-class MolmoForCausalLM(nn.Module, SupportsMultiModal):
+class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
 
     def __init__(
         self,
@@ -1040,6 +1051,9 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal):
                                                 or config.vocab_size)
         self.sampler = Sampler()
 
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
     def _parse_and_validate_image_input(
         self,
         **kwargs: object,
@@ -1123,31 +1137,36 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal):
         positions: torch.LongTensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
         **kwargs: object,
     ) -> SamplerOutput:
+        if intermediate_tensors is not None:
+            input_ids = None
+            inputs_embeds = None
+        else:
+            image_input = self._parse_and_validate_image_input(**kwargs)
 
-        image_input = self._parse_and_validate_image_input(**kwargs)
-
-        if image_input is not None:
-            inputs_embeds = self.model.embed_tokens(input_ids)
-            image_features = self._process_image_input(image_input)
+            if image_input is not None:
+                inputs_embeds = self.model.embed_tokens(input_ids)
+                image_features = self._process_image_input(image_input)
 
-            inputs_embeds = self._merge_multimodal_embeddings(
-                inputs_embeds,
-                image_features,
-                image_input["image_input_idx"],
-                image_input["seq_len"],
-            )
+                inputs_embeds = self._merge_multimodal_embeddings(
+                    inputs_embeds,
+                    image_features,
+                    image_input["image_input_idx"],
+                    image_input["seq_len"],
+                )
 
-            input_ids = None
-        else:
-            inputs_embeds = None
+                input_ids = None
+            else:
+                inputs_embeds = None
 
         hidden_states = self.model(
             input_ids=input_ids,
             positions=positions,
             kv_caches=kv_caches,
             attn_metadata=attn_metadata,
+            intermediate_tensors=intermediate_tensors,
             inputs_embeds=inputs_embeds,
         )
 

vllm/model_executor/models/qwen2_rm.py

@@ -119,5 +119,6 @@ class Qwen2ForRewardModel(nn.Module, SupportsPP):
         return self._pooler(hidden_states, pooling_metadata)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        loader = AutoWeightsLoader(self)
+        loader = AutoWeightsLoader(self,
+                                   ignore_unexpected_prefixes=["lm_head."])
         loader.load_weights(weights)

vllm/model_executor/models/qwen2_vl.py

@@ -61,6 +61,7 @@ from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalDataDict,
                              MultiModalInputs)
 from vllm.multimodal.base import MultiModalData
 from vllm.multimodal.image import cached_get_image_processor
+from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors, SequenceData
 from vllm.transformers_utils.config import uses_mrope
 from vllm.transformers_utils.processor import cached_get_processor
@@ -817,7 +818,7 @@ def input_processor_for_qwen2_vl(
     min_pixels: Optional[int] = None,
     max_pixels: Optional[int] = None,
 ) -> DecoderOnlyInputs:
-    multi_modal_data = inputs.get("multi_modal_data", None)
+    multi_modal_data = inputs.get("multi_modal_data")
     if multi_modal_data is None:
         return inputs
 
@@ -830,6 +831,7 @@ def input_processor_for_qwen2_vl(
     min_pixels = min_pixels if min_pixels else image_processor.min_pixels
     max_pixels = max_pixels if max_pixels else image_processor.max_pixels
 
+    model_config = ctx.model_config
     hf_config = ctx.get_hf_config(Qwen2VLConfig)
 
     # To avoid redundant processing of vision objects (resize, rescale, etc.),
@@ -845,14 +847,11 @@ def input_processor_for_qwen2_vl(
     #                       return_tensors="pt")
     #    prompt_token_ids = inputs["input_ids"][0].tolist()
 
-    prompt_token_ids = inputs.get("prompt_token_ids", None)
-    if prompt_token_ids is None:
-        prompt = inputs["prompt"]
-        prompt_token_ids = processor.tokenizer(
-            prompt,
-            padding=True,
-            return_tensors=None,
-        )["input_ids"]
+    tokenizer = cached_get_tokenizer(
+        model_config.tokenizer,
+        trust_remote_code=model_config.trust_remote_code)
+
+    prompt_token_ids = inputs["prompt_token_ids"]
 
     # Expand image pad tokens.
 
@@ -894,9 +893,13 @@ def input_processor_for_qwen2_vl(
                                               min_pixels=min_pixels,
                                               max_pixels=max_pixels)
 
+    prompt = inputs.get("prompt")
+    if prompt is None:
+        prompt = tokenizer.decode(prompt_token_ids)
+
     return token_inputs(
         prompt_token_ids=prompt_token_ids,
-        prompt=inputs["prompt"],
+        prompt=prompt,
         multi_modal_data=multi_modal_data,
     )
 

vllm/model_executor/models/utils.py

@@ -79,6 +79,9 @@ class AutoWeightsLoader:
 
     Similarly, the weight loading logic for individual parameters can be
     overridden by defining a ``weight_loader`` method.
+
+    Detailed weight loading information can be viewed by setting the
+    environment variable ``VLLM_LOGGING_LEVEL=DEBUG``.
     """
 
     def __init__(
@@ -136,20 +139,27 @@ class AutoWeightsLoader:
             weight_qualname = self._get_qualname(base_prefix, weight_name)
 
             if self._can_skip(weight_qualname):
+                logger.debug("Skipping weight %s", weight_qualname)
+
                 continue
 
             if weight_name != "":
-                if not self._can_ignore_unexpected(weight_qualname):
-                    raise ValueError(
-                        f"Attempted to load nested weight '{weight_qualname}' "
-                        f"into a single parameter '{base_prefix}'")
+                if self._can_ignore_unexpected(weight_qualname):
+                    logger.debug("Ignoring weight %s", weight_qualname)
 
-                continue
+                    continue
+
+                raise ValueError(
+                    f"Attempted to load nested weight '{weight_qualname}' "
+                    f"into a single parameter '{base_prefix}'")
 
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)
             weight_loader(param, weight_data)
 
+            logger.debug("Loaded weight %s with shape %s", weight_qualname,
+                         param.shape)
+
             yield weight_qualname
 
     def _load_module(
@@ -175,21 +185,41 @@ class AutoWeightsLoader:
         for child_prefix, child_weights in self._groupby_prefix(weights):
             prefix = self._get_qualname(base_prefix, child_prefix)
 
-            if self._can_skip(prefix):
-                continue
-
             if child_prefix in child_modules:
+                if self._can_skip(prefix + "."):
+                    logger.debug("Skipping module %s", prefix)
+
+                    continue
+
                 yield from self._load_module(prefix,
                                              child_modules[child_prefix],
                                              child_weights)
             elif child_prefix in child_params:
+                if self._can_skip(prefix):
+                    logger.debug("Skipping param %s", prefix)
+
+                    continue
+
                 yield from self._load_param(prefix, child_params[child_prefix],
                                             child_weights)
             else:
-                if not self._can_ignore_unexpected(prefix):
-                    msg = (f"There is no module or parameter named '{prefix}' "
-                           f"in {type(self.module).__name__}")
-                    raise ValueError(msg)
+                can_skip_module = self._can_skip(prefix + ".")
+                can_skip_param = self._can_skip(prefix)
+                if can_skip_module or can_skip_param:
+                    logger.debug("Skipping missing %s", prefix)
+
+                    continue
+
+                can_ignore_module = self._can_ignore_unexpected(prefix + ".")
+                can_ignore_param = self._can_ignore_unexpected(prefix)
+                if can_ignore_module or can_ignore_param:
+                    logger.debug("Ignoring missing %s", prefix)
+
+                    continue
+
+                msg = (f"There is no module or parameter named '{prefix}' "
+                       f"in {type(self.module).__name__}")
+                raise ValueError(msg)
 
     def load_weights(
         self,