Merge remote-tracking branch 'mirror/main'

vllm/engine/llm_engine.py

@@ -254,7 +254,7 @@ class LLMEngine:
             "num_scheduler_steps=%d, chunked_prefill_enabled=%s "
             "multi_step_stream_outputs=%s, enable_prefix_caching=%s, "
             "use_async_output_proc=%s, use_cached_outputs=%s, "
-            "mm_processor_kwargs=%s)",
+            "chat_template_text_format=%s, mm_processor_kwargs=%s)",
             VLLM_VERSION,
             model_config.model,
             speculative_config,
@@ -289,6 +289,7 @@ class LLMEngine:
             cache_config.enable_prefix_caching,
             model_config.use_async_output_proc,
             use_cached_outputs,
+            model_config.chat_template_text_format,
             model_config.mm_processor_kwargs,
         )
         # TODO(woosuk): Print more configs in debug mode.
@@ -646,10 +647,24 @@ class LLMEngine:
         prompt_adapter_request: Optional[PromptAdapterRequest],
         trace_headers: Optional[Mapping[str, str]] = None,
         priority: int = 0,
-    ) -> SequenceGroup:
+    ) -> Optional[SequenceGroup]:
         """Add a processed request to the engine's request pool.
         return the created sequence group.
         """
+        if isinstance(params, SamplingParams) and params.n > 1:
+            ParallelSampleSequenceGroup.add_request(
+                request_id,
+                self,
+                params,
+                processed_inputs=processed_inputs,
+                arrival_time=arrival_time,
+                lora_request=lora_request,
+                trace_headers=trace_headers,
+                prompt_adapter_request=prompt_adapter_request,
+                priority=priority,
+            )
+            return None
+
         self._validate_model_inputs(processed_inputs)
         # Create the sequences.
         block_size = self.cache_config.block_size
@@ -720,7 +735,7 @@ class LLMEngine:
         trace_headers: Optional[Mapping[str, str]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
         priority: int = 0,
-    ) -> Optional[SequenceGroup]:
+    ) -> None:
         ...
 
     @overload
@@ -734,7 +749,7 @@ class LLMEngine:
         trace_headers: Optional[Mapping[str, str]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
         priority: int = 0,
-    ) -> Optional[SequenceGroup]:
+    ) -> None:
         ...
 
     @deprecate_kwargs(
@@ -753,7 +768,7 @@ class LLMEngine:
             priority: int = 0,
             *,
             inputs: Optional[PromptType] = None,  # DEPRECATED
-    ) -> Optional[SequenceGroup]:
+    ) -> None:
         """Add a request to the engine's request pool.
 
         The request is added to the request pool and will be processed by the
@@ -797,22 +812,6 @@ class LLMEngine:
             >>> # continue the request processing
             >>> ...
         """
-
-        if isinstance(params, SamplingParams) and params.n > 1:
-            ParallelSampleSequenceGroup.add_request(
-                request_id,
-                self,
-                params,
-                prompt=prompt,
-                arrival_time=arrival_time,
-                lora_request=lora_request,
-                trace_headers=trace_headers,
-                prompt_adapter_request=prompt_adapter_request,
-                priority=priority,
-                inputs=inputs,
-            )
-            return None
-
         if inputs is not None:
             prompt = inputs
         assert prompt is not None and params is not None
@@ -843,7 +842,7 @@ class LLMEngine:
         processed_inputs["mm_processor_kwargs"] = preprocessed_inputs.get(
             "mm_processor_kwargs")
 
-        return self._add_processed_request(
+        self._add_processed_request(
             request_id=request_id,
             processed_inputs=processed_inputs,
             params=params,
@@ -1612,7 +1611,7 @@ class LLMEngine:
         # KV Cache Usage in %
         num_total_gpu = self.cache_config.num_gpu_blocks
         gpu_cache_usage_sys = 0.
-        if num_total_gpu is not None:
+        if num_total_gpu:  # Guard against both None and 0
             num_free_gpu = sum(
                 scheduler.block_manager.get_num_free_gpu_blocks()
                 for scheduler in self.scheduler)
@@ -1620,7 +1619,7 @@ class LLMEngine:
 
         num_total_cpu = self.cache_config.num_cpu_blocks
         cpu_cache_usage_sys = 0.
-        if num_total_cpu is not None and num_total_cpu > 0:
+        if num_total_cpu:  # Guard against both None and 0
             num_free_cpu = sum(
                 scheduler.block_manager.get_num_free_cpu_blocks()
                 for scheduler in self.scheduler)

vllm/engine/output_processor/single_step.py

-from typing import Dict, List, Tuple
+from typing import List
 
 from vllm.config import SchedulerConfig
 from vllm.core.scheduler import Scheduler
@@ -6,9 +6,8 @@ from vllm.engine.output_processor.interfaces import (
     SequenceGroupOutputProcessor)
 from vllm.engine.output_processor.stop_checker import StopChecker
 from vllm.logger import init_logger
-from vllm.sequence import (CompletionSequenceGroupOutput, Sequence,
-                           SequenceGroup, SequenceGroupOutput, SequenceOutput,
-                           SequenceStatus)
+from vllm.sequence import (CompletionSequenceGroupOutput, SequenceGroup,
+                           SequenceGroupOutput)
 from vllm.transformers_utils.detokenizer import Detokenizer
 from vllm.utils import Counter
 
@@ -114,104 +113,22 @@ class SingleStepOutputProcessor(SequenceGroupOutputProcessor):
                                         outputs: SequenceGroupOutput,
                                         is_async: bool) -> None:
         sampling_params = seq_group.sampling_params
-        if sampling_params.n == 1:
-            # only have one output sample
-            sample = outputs.samples[0]
-            # only have one sequence
-            seq = seq_group.seqs[0]
-            if not is_async:
-                seq.append_token_id(sample.output_token, sample.logprobs)
-            if sampling_params.detokenize and self.detokenizer:
-                new_char_count = self.detokenizer.decode_sequence_inplace(
-                    seq, sampling_params)
-            else:
-                new_char_count = 0
-            self.stop_checker.maybe_stop_sequence(
-                seq,
-                new_char_count,
-                sampling_params,
-                lora_req=seq_group.lora_request,
-            )
-            if seq.is_finished():
-                for scheduler in self.scheduler:
-                    scheduler.free_seq(seq)
-            return
-
-        # TODO: Add support for async for beam search
-        assert not is_async
-
-        # Process samples
-        samples = outputs.samples
-        parent_seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
-        parent_child_dict: Dict[int, List[SequenceOutput]] = {
-            parent_seq.seq_id: []
-            for parent_seq in parent_seqs
-        }
-        for sample in samples:
-            # Guard against a KeyError which can occur if the request was
-            # aborted while the output was generated
-            if (child_list :=
-                    parent_child_dict.get(sample.parent_seq_id)) is not None:
-                child_list.append(sample)
-        # List of (child, parent)
-        child_seqs: List[Tuple[Sequence, Sequence]] = []
-
-        # Process the child samples for each parent sequence
-        for parent in parent_seqs:
-            child_samples: List[SequenceOutput] = parent_child_dict[
-                parent.seq_id]
-            if len(child_samples) == 0:
-                # This parent sequence has no children samples. Remove
-                # the parent sequence from the sequence group since it will
-                # not be used in the future iterations.
-                parent.status = SequenceStatus.FINISHED_ABORTED
-                seq_group.remove(parent.seq_id)
-                for scheduler in self.scheduler:
-                    scheduler.free_seq(parent)
-                continue
-            # Fork the parent sequence if there are multiple child samples.
-            for child_sample in child_samples[:-1]:
-                new_child_seq_id: int = next(self.seq_counter)
-                child = parent.fork(new_child_seq_id)
-                child.append_token_id(child_sample.output_token,
-                                      child_sample.logprobs)
-                child_seqs.append((child, parent))
-            # Continue the parent sequence for the last child sample.
-            # We reuse the parent sequence here to reduce redundant memory
-            # copies, especially when using non-beam search sampling methods.
-            last_child_sample = child_samples[-1]
-            parent.append_token_id(last_child_sample.output_token,
-                                   last_child_sample.logprobs)
-            child_seqs.append((parent, parent))
-
-        for seq, _ in child_seqs:
-            if sampling_params.detokenize and self.detokenizer:
-                new_char_count = self.detokenizer.decode_sequence_inplace(
-                    seq, sampling_params)
-            else:
-                new_char_count = 0
-            self.stop_checker.maybe_stop_sequence(
-                seq,
-                new_char_count,
-                sampling_params,
-                lora_req=seq_group.lora_request,
-            )
-
-        # For newly created child sequences, add them to the sequence group
-        # and fork them in block manager if they are not finished.
-        for seq, parent in child_seqs:
-            if seq is not parent:
-                seq_group.add(seq)
-                if not seq.is_finished():
-                    for scheduler in self.scheduler:
-                        scheduler.fork_seq(parent, seq)
-
-        # Free the finished and selected parent sequences' memory in block
-        # manager. Keep them in the sequence group as candidate output.
-        # NOTE: we need to fork the new sequences before freeing the
-        # old sequences.
-        for seq, parent in child_seqs:
-            if seq is parent and seq.is_finished():
-                for scheduler in self.scheduler:
-                    scheduler.free_seq(seq)
-        return
+
+        sample = outputs.samples[0]
+        seq = seq_group.first_seq
+        if not is_async:
+            seq.append_token_id(sample.output_token, sample.logprobs)
+        if sampling_params.detokenize and self.detokenizer:
+            new_char_count = self.detokenizer.decode_sequence_inplace(
+                seq, sampling_params)
+        else:
+            new_char_count = 0
+        self.stop_checker.maybe_stop_sequence(
+            seq,
+            new_char_count,
+            sampling_params,
+            lora_req=seq_group.lora_request,
+        )
+        if seq.is_finished():
+            for scheduler in self.scheduler:
+                scheduler.free_seq(seq)

vllm/entrypoints/chat_utils.py

@@ -121,7 +121,7 @@ class ConversationMessage(TypedDict, total=False):
     role: Required[str]
     """The role of the message's author."""
 
-    content: Optional[str]
+    content: Union[Optional[str], List[Dict[str, str]]]
     """The contents of the message"""
 
     tool_call_id: Optional[str]
@@ -196,7 +196,10 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
         elif modality == "audio":
             if model_type == "ultravox":
                 return "<|reserved_special_token_0|>"
-            raise TypeError(f"Unknown {modality} model type: {model_type}")
+            if model_type == "qwen2_audio":
+                return (f"Audio {current_count}: "
+                        f"<|audio_bos|><|AUDIO|><|audio_eos|>")
+            raise TypeError(f"Unknown model type: {model_type}")
         elif modality == "video":
             if model_type == "qwen2_vl":
                 return "<|vision_start|><|video_pad|><|vision_end|>"
@@ -428,7 +431,7 @@ MM_PARSER_MAP: Dict[str, Callable[[ChatCompletionContentPartParam], str]] = {
 def _parse_chat_message_content_mm_part(
         part: ChatCompletionContentPartParam) -> Tuple[str, str]:
     """
-    Parses a given multi modal content part based on its type.
+    Parses a given multi-modal content part based on its type.
 
     Args:
         part: A dict containing the content part, with a potential 'type' field.
@@ -482,54 +485,76 @@ def _parse_chat_message_content_parts(
     role: str,
     parts: Iterable[ChatCompletionContentPartParam],
     mm_tracker: BaseMultiModalItemTracker,
+    chat_template_text_format: str,
 ) -> List[ConversationMessage]:
-    texts: List[str] = []
+    content: List[Union[str, Dict[str, str]]] = []
 
     mm_parser = mm_tracker.create_parser()
-    keep_multimodal_content = \
+    wrap_dicts = \
         mm_tracker._model_config.hf_config.model_type in \
-            MODEL_KEEP_MULTI_MODAL_CONTENT
+            MODEL_KEEP_MULTI_MODAL_CONTENT or \
+        (chat_template_text_format == "openai")
 
-    has_image = False
     for part in parts:
-        if isinstance(part, str):  # Handle plain text parts
-            text = _TextParser(part)
-            texts.append(text)
-        else:  # Handle structured dictionary parts
-            part_type, content = _parse_chat_message_content_mm_part(part)
-
-            # if part_type is text/refusal/image_url/audio_url but
-            # content is empty, logg a warning and skip
-            if part_type in VALID_MESSAGE_CONTENT_MM_PART_TYPES and not content:
-                logger.warning("Skipping multimodal part "
-                               "with empty / unparsable content.")
-                continue
-
-            if part_type in ("text", "refusal"):
-                texts.append(content)
-            elif part_type == "image_url":
-                mm_parser.parse_image(content)
-                has_image = True
-            elif part_type == "audio_url":
-                mm_parser.parse_audio(content)
-            else:
-                raise NotImplementedError(f"Unknown part type: {part_type}")
-
+        parse_res = _parse_chat_message_content_part(
+            part,
+            mm_parser,
+            wrap_dicts=wrap_dicts,
+        )
+        if parse_res:
+            content.append(parse_res)
+
+    if wrap_dicts:
+        # Parsing wraps images and texts as interleaved dictionaries
+        return [ConversationMessage(role=role,
+                                    content=content)]  # type: ignore
+    texts = cast(List[str], content)
     text_prompt = "\n".join(texts)
-    if keep_multimodal_content:
-        text_prompt = "\n".join(texts)
-        role_content = [{'type': 'text', 'text': text_prompt}]
+    mm_placeholder_counts = mm_parser.mm_placeholder_counts()
+    if mm_placeholder_counts:
+        text_prompt = _get_full_multimodal_text_prompt(mm_placeholder_counts,
+                                                       text_prompt)
+    return [ConversationMessage(role=role, content=text_prompt)]
+
+
+def _parse_chat_message_content_part(
+        part: ChatCompletionContentPartParam,
+        mm_parser: BaseMultiModalContentParser,
+        wrap_dicts: bool) -> Optional[Union[str, Dict[str, str]]]:
+    """Parses a single part of a conversation. If wrap_dicts is True,
+    structured dictionary pieces for texts and images will be
+    wrapped in dictionaries, i.e., {"type": "text", "text", ...} and
+    {"type": "image"}, respectively. Otherwise multimodal data will be
+    handled by mm_parser, and texts will be returned as strings to be joined
+    with multimodal placeholders.
+    """
+    if isinstance(part, str):  # Handle plain text parts
+        text = _TextParser(part)
+        return text
+
+    # Handle structured dictionary parts
+    part_type, content = _parse_chat_message_content_mm_part(part)
+
+    # if part_type is text/refusal/image_url/audio_url but
+    # content is empty, log a warning and skip
+    if part_type in VALID_MESSAGE_CONTENT_MM_PART_TYPES and not content:
+        logger.warning(
+            "Skipping multimodal part (type: '%s')"
+            "with empty / unparsable content.", part_type)
+        return None
 
-        if has_image:
-            role_content = [{'type': 'image'}] + role_content
-        return [ConversationMessage(role=role,
-                                    content=role_content)]  # type: ignore
-    else:
-        mm_placeholder_counts = mm_parser.mm_placeholder_counts()
-        if mm_placeholder_counts:
-            text_prompt = _get_full_multimodal_text_prompt(
-                mm_placeholder_counts, text_prompt)
-        return [ConversationMessage(role=role, content=text_prompt)]
+    if part_type in ("text", "refusal"):
+        return {'type': 'text', 'text': content} if wrap_dicts else content
+
+    if part_type == "image_url":
+        mm_parser.parse_image(content)
+        return {'type': 'image'} if wrap_dicts else None
+
+    if part_type == "audio_url":
+        mm_parser.parse_audio(content)
+        return {'type': 'audio'} if wrap_dicts else None
+
+    raise NotImplementedError(f"Unknown part type: {part_type}")
 
 
 # No need to validate using Pydantic again
@@ -540,6 +565,7 @@ _ToolParser = partial(cast, ChatCompletionToolMessageParam)
 def _parse_chat_message_content(
     message: ChatCompletionMessageParam,
     mm_tracker: BaseMultiModalItemTracker,
+    chat_template_text_format: str,
 ) -> List[ConversationMessage]:
     role = message["role"]
     content = message.get("content")
@@ -555,6 +581,7 @@ def _parse_chat_message_content(
         role,
         content,  # type: ignore
         mm_tracker,
+        chat_template_text_format,
     )
 
     for result_msg in result:
@@ -598,7 +625,11 @@ def parse_chat_messages(
     mm_tracker = MultiModalItemTracker(model_config, tokenizer)
 
     for msg in messages:
-        sub_messages = _parse_chat_message_content(msg, mm_tracker)
+        sub_messages = _parse_chat_message_content(
+            msg,
+            mm_tracker,
+            model_config.chat_template_text_format,
+        )
 
         conversation.extend(sub_messages)
 
@@ -616,7 +647,11 @@ def parse_chat_messages_futures(
     mm_tracker = AsyncMultiModalItemTracker(model_config, tokenizer)
 
     for msg in messages:
-        sub_messages = _parse_chat_message_content(msg, mm_tracker)
+        sub_messages = _parse_chat_message_content(
+            msg,
+            mm_tracker,
+            model_config.chat_template_text_format,
+        )
 
         conversation.extend(sub_messages)
 

vllm/entrypoints/openai/serving_chat.py

@@ -384,7 +384,7 @@ class OpenAIServingChat(OpenAIServing):
                     # Send response to echo the input portion of the
                     # last message
                     if request.echo or request.continue_final_message:
-                        last_msg_content: str = ""
+                        last_msg_content: Union[str, List[Dict[str, str]]] = ""
                         if conversation and "content" in conversation[
                                 -1] and conversation[-1].get("role") == role:
                             last_msg_content = conversation[-1]["content"] or ""
@@ -724,10 +724,13 @@ class OpenAIServingChat(OpenAIServing):
             choices.append(choice_data)
 
         if request.echo or request.continue_final_message:
-            last_msg_content = ""
+            last_msg_content: Union[str, List[Dict[str, str]]] = ""
             if conversation and "content" in conversation[-1] and conversation[
                     -1].get("role") == role:
                 last_msg_content = conversation[-1]["content"] or ""
+            if isinstance(last_msg_content, list):
+                last_msg_content = "\n".join(msg['text']
+                                             for msg in last_msg_content)
 
             for choice in choices:
                 full_message = last_msg_content + (choice.message.content

vllm/executor/ray_utils.py

@@ -10,7 +10,7 @@ from vllm.executor.msgspec_utils import decode_hook, encode_hook
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.sequence import ExecuteModelRequest, IntermediateTensors
-from vllm.utils import get_ip, is_hip, is_xpu
+from vllm.utils import get_ip, is_hip
 from vllm.worker.worker_base import WorkerWrapperBase
 
 logger = init_logger(__name__)
@@ -231,7 +231,7 @@ def initialize_ray_cluster(
     assert_ray_available()
 
     # Connect to a ray cluster.
-    if is_hip() or is_xpu():
+    if is_hip() or current_platform.is_xpu():
         ray.init(address=ray_address,
                  ignore_reinit_error=True,
                  num_gpus=parallel_config.world_size)

vllm/model_executor/custom_op.py

@@ -7,7 +7,7 @@ import vllm.envs as envs
 from vllm.compilation.levels import CompilationLevel
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import is_hip, is_xpu, print_warning_once
+from vllm.utils import is_hip, print_warning_once
 
 logger = init_logger(__name__)
 
@@ -78,7 +78,7 @@ class CustomOp(nn.Module):
             return self.forward_cpu
         elif current_platform.is_tpu():
             return self.forward_tpu
-        elif is_xpu():
+        elif current_platform.is_xpu():
             return self.forward_xpu
         else:
             return self.forward_cuda

vllm/model_executor/layers/quantization/awq.py

@@ -5,7 +5,8 @@ import os
 import torch.nn.functional as F
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.parameter import (GroupQuantScaleParameter,
@@ -37,10 +38,12 @@ class AWQConfig(QuantizationConfig):
         weight_bits: int,
         group_size: int,
         zero_point: bool,
+        modules_to_not_convert: Optional[List[str]] = None,
     ) -> None:
         self.weight_bits = weight_bits
         self.group_size = group_size
         self.zero_point = zero_point
+        self.modules_to_not_convert = modules_to_not_convert or []
 
         if self.weight_bits != 4:
             raise ValueError(
@@ -51,7 +54,8 @@ class AWQConfig(QuantizationConfig):
     def __repr__(self) -> str:
         return (f"AWQConfig(weight_bits={self.weight_bits}, "
                 f"group_size={self.group_size}, "
-                f"zero_point={self.zero_point})")
+                f"zero_point={self.zero_point}, "
+                f"modules_to_not_convert={self.modules_to_not_convert})")
 
     def get_name(self) -> str:
         return "awq"
@@ -77,11 +81,15 @@ class AWQConfig(QuantizationConfig):
         weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
         group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
         zero_point = cls.get_from_keys(config, ["zero_point"])
-        return cls(weight_bits, group_size, zero_point)
+        modules_to_not_convert = cls.get_from_keys_or(
+            config, ["modules_to_not_convert"], None)
+        return cls(weight_bits, group_size, zero_point, modules_to_not_convert)
 
     def get_quant_method(self, layer: torch.nn.Module,
-                         prefix: str) -> Optional["AWQLinearMethod"]:
+                         prefix: str) -> Optional["LinearMethodBase"]:
         if isinstance(layer, LinearBase):
+            if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
+                return UnquantizedLinearMethod()
             return AWQLinearMethod(self)
         return None
 
@@ -89,6 +97,10 @@ class AWQConfig(QuantizationConfig):
         return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
 
 
+def is_layer_skipped_awq(prefix: str, modules_to_not_convert: List[str]):
+    return any(module_name in prefix for module_name in modules_to_not_convert)
+
+
 class AWQLinearMethod(LinearMethodBase):
     """Linear method for AWQ.
 

vllm/model_executor/models/baichuan.py

@@ -28,6 +28,7 @@ import os
 import re
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
@@ -264,6 +265,7 @@ class BaiChuanDecoderLayer(nn.Module):
         return hidden_states, residual
 
 
+@support_torch_compile
 class BaiChuanModel(nn.Module):
 
     def __init__(self,
@@ -527,7 +529,9 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                         qweight.data=torch.cat((qweight.data,qweight_pad),dim=1).contiguous()
 
 class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
-    """Baichuan 13B and Baichuan2 7B/13B."""
+    """Baichuan 13B and Baichuan2 7B/13B.
+    NOTE: the class name has a lower case 'c'.
+    """
 
     def __init__(
         self,
@@ -545,7 +549,9 @@ class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
 
 
 class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
-    """Baichuan 7B."""
+    """Baichuan 7B.
+    NOTE: the class name has an upper case 'C'.
+    """
 
     def __init__(
         self,

vllm/model_executor/models/blip.py

@@ -122,7 +122,7 @@ def input_processor_for_blip(
 # Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/blip/modeling_blip.py#L164 # noqa
 class BlipVisionEmbeddings(nn.Module):
 
-    def __init__(self, config: BlipVisionConfig):
+    def __init__(self, config: Union[BlipVisionConfig, Blip2VisionConfig]):
         super().__init__()
 
         self.config = config
@@ -167,9 +167,10 @@ class BlipParallelAttention(nn.Module):
 
     def __init__(
         self,
-        config: BlipVisionConfig,
+        config: Union[BlipVisionConfig, Blip2VisionConfig],
         quant_config: Optional[QuantizationConfig] = None,
-    ):
+        prefix: str = "",
+    ) -> None:
         super().__init__()
         self.config = config
         self.embed_dim = config.hidden_size
@@ -189,11 +190,13 @@ class BlipParallelAttention(nn.Module):
             self.num_heads,
             bias=config.qkv_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv",
         )
         self.projection = RowParallelLinear(
             self.embed_dim,
             self.embed_dim,
             quant_config=quant_config,
+            prefix=f"{prefix}.projection",
         )
 
         self.tp_size = get_tensor_model_parallel_world_size()
@@ -235,9 +238,12 @@ class BlipParallelAttention(nn.Module):
 
 class BlipMLP(nn.Module):
 
-    def __init__(self,
-                 config: BlipVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        config: BlipVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         self.config = config
@@ -246,11 +252,13 @@ class BlipMLP(nn.Module):
         self.fc1 = ColumnParallelLinear(config.hidden_size,
                                         config.intermediate_size,
                                         bias=True,
-                                        quant_config=quant_config)
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.fc1")
         self.fc2 = RowParallelLinear(config.intermediate_size,
                                      config.hidden_size,
                                      bias=True,
-                                     quant_config=quant_config)
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.fc2")
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.fc1(hidden_states)
@@ -262,24 +270,32 @@ class BlipMLP(nn.Module):
 
 class BlipEncoderLayer(nn.Module):
 
-    def __init__(self,
-                 config: BlipVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        config: BlipVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         # fallback to sdpa attention if tp unavailable
         num_heads = config.num_attention_heads
         tp_size = get_tensor_model_parallel_world_size()
         if USE_XFORMERS_OPS and num_heads % tp_size == 0:
-            self.self_attn = BlipParallelAttention(config,
-                                                   quant_config=quant_config)
+            self.self_attn = BlipParallelAttention(
+                config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.self_attn",
+            )
         else:
             # Blip doesn't have SDPA attention implemented in transformers
             # use eager attention instead for cpu backend
             self.self_attn = BlipAttention(config)
         self.layer_norm1 = nn.LayerNorm(config.hidden_size,
                                         eps=config.layer_norm_eps)
-        self.mlp = BlipMLP(config, quant_config=quant_config)
+        self.mlp = BlipMLP(config,
+                           quant_config=quant_config,
+                           prefix=f"{prefix}.mlp")
         self.layer_norm2 = nn.LayerNorm(config.hidden_size,
                                         eps=config.layer_norm_eps)
 
@@ -307,10 +323,13 @@ class BlipEncoder(nn.Module):
         config: BlipConfig
     """
 
-    def __init__(self,
-                 config: BlipVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None,
-                 num_hidden_layers_override: Optional[int] = None):
+    def __init__(
+        self,
+        config: BlipVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        num_hidden_layers_override: Optional[int] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         self.config = config
@@ -321,8 +340,10 @@ class BlipEncoder(nn.Module):
             num_hidden_layers = num_hidden_layers_override
 
         self.layers = nn.ModuleList([
-            BlipEncoderLayer(config=config, quant_config=quant_config)
-            for _ in range(num_hidden_layers)
+            BlipEncoderLayer(config=config,
+                             quant_config=quant_config,
+                             prefix=f"{prefix}.layers.{layer_idx}")
+            for layer_idx in range(num_hidden_layers)
         ])
 
     def forward(self, inputs_embeds: torch.Tensor):
@@ -337,10 +358,15 @@ class BlipVisionModel(nn.Module):
     config_class = BlipVisionConfig
     main_input_name = "pixel_values"
 
-    def __init__(self,
-                 config: BlipVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None,
-                 num_hidden_layers_override: Optional[int] = None):
+    def __init__(
+        self,
+        config: BlipVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: Optional[bool] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         tp_size = get_tensor_model_parallel_world_size()
@@ -354,19 +380,24 @@ class BlipVisionModel(nn.Module):
             config=config,
             quant_config=quant_config,
             num_hidden_layers_override=num_hidden_layers_override,
+            prefix=f"{prefix}.encoder",
         )
 
+        num_hidden_layers = config.num_hidden_layers
         if len(self.encoder.layers) > config.num_hidden_layers:
             raise ValueError(
-                f"The original encoder only has {config.num_hidden_layers} "
+                f"The original encoder only has {num_hidden_layers} "
                 f"layers, but you requested {len(self.encoder.layers)} layers."
             )
-        elif len(self.encoder.layers) == config.num_hidden_layers:
+
+        # If possible, skip post_layernorm to conserve memory
+        if require_post_norm is None:
+            require_post_norm = len(self.encoder.layers) == num_hidden_layers
+
+        if require_post_norm:
             self.post_layernorm = nn.LayerNorm(config.hidden_size,
                                                eps=config.layer_norm_eps)
         else:
-            # post_layernorm is unused when we extract intermediate features
-            # In this case, we can skip it to conserve memory
             self.post_layernorm = None
 
     def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:

vllm/model_executor/models/blip2.py

@@ -490,7 +490,7 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
         self.multimodal_config = multimodal_config
 
         # TODO: Optionally initializes this for supporting embeddings.
-        self.vision_model = BlipVisionModel(config.vision_config)
+        self.vision_model = BlipVisionModel(config.vision_config, quant_config)
 
         self.query_tokens = nn.Parameter(
             torch.zeros(1, config.num_query_tokens,

vllm/model_executor/models/bloom.py

@@ -26,6 +26,7 @@ import os
 import re
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig
 from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
@@ -226,6 +227,7 @@ class BloomBlock(nn.Module):
         return output
 
 
+@support_torch_compile
 class BloomModel(nn.Module):
 
     def __init__(

vllm/model_executor/models/chatglm.py

@@ -15,8 +15,9 @@ import re
 
 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
@@ -24,8 +25,7 @@ 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 (
@@ -41,11 +41,13 @@ from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
                            SequenceData)
 from vllm.transformers_utils.configs import ChatGLMConfig
 
+from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
+from .utils import (is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers)
+
 from vllm import _custom_ops as ops
 from vllm.model_executor.utils import pad_weight, gemm_bank_conf
 
-from .interfaces import SupportsLoRA, SupportsMultiModal
-
 logger = init_logger(__name__)
 
 
@@ -155,6 +157,10 @@ 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)
 
@@ -166,8 +172,8 @@ 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)
@@ -176,20 +182,19 @@ 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)
@@ -198,7 +203,6 @@ 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
 
@@ -206,29 +210,28 @@ 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,
     ):
@@ -326,7 +329,7 @@ class GLMMLP(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: ChatGLMConfig,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -369,7 +372,7 @@ class GLMBlock(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: ChatGLMConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
@@ -440,9 +443,10 @@ 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
@@ -451,10 +455,11 @@ 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
@@ -462,6 +467,10 @@ 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,
@@ -469,16 +478,16 @@ 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
@@ -488,7 +497,7 @@ class ChatGLMModel(nn.Module):
 
     def __init__(
         self,
-        config,
+        config: ChatGLMConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
@@ -516,6 +525,9 @@ 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:
 
@@ -541,24 +553,26 @@ 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(
@@ -567,6 +581,9 @@ 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
 
 
@@ -574,7 +591,8 @@ 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"]
@@ -631,7 +649,8 @@ 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(
@@ -677,6 +696,8 @@ 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/clip.py

@@ -192,6 +192,7 @@ class CLIPParallelAttention(nn.Module):
         self,
         config: CLIPVisionConfig,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.config = config
@@ -211,12 +212,14 @@ class CLIPParallelAttention(nn.Module):
             head_size=self.head_dim,
             total_num_heads=self.num_heads,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
         )
 
         self.out_proj = RowParallelLinear(
             input_size=self.embed_dim,
             output_size=self.embed_dim,
             quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
         )
 
         self.tp_size = get_tensor_model_parallel_world_size()
@@ -259,20 +262,25 @@ class CLIPParallelAttention(nn.Module):
 
 class CLIPMLP(nn.Module):
 
-    def __init__(self,
-                 config: CLIPVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
         self.config = config
         self.activation_fn = get_act_fn(config.hidden_act)
         self.fc1 = ColumnParallelLinear(config.hidden_size,
                                         config.intermediate_size,
                                         bias=True,
-                                        quant_config=quant_config)
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.fc1")
         self.fc2 = RowParallelLinear(config.intermediate_size,
                                      config.hidden_size,
                                      bias=True,
-                                     quant_config=quant_config)
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.fc2")
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.fc1(hidden_states)
@@ -284,21 +292,29 @@ class CLIPMLP(nn.Module):
 
 class CLIPEncoderLayer(nn.Module):
 
-    def __init__(self,
-                 config: CLIPVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         num_heads = config.num_attention_heads
         tp_size = get_tensor_model_parallel_world_size()
         if USE_XFORMERS_OPS and num_heads % tp_size == 0:
-            self.self_attn = CLIPParallelAttention(config,
-                                                   quant_config=quant_config)
+            self.self_attn = CLIPParallelAttention(
+                config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.self_attn",
+            )
         else:
             self.self_attn = CLIPSdpaAttention(config)
         self.layer_norm1 = nn.LayerNorm(config.hidden_size,
                                         eps=config.layer_norm_eps)
-        self.mlp = CLIPMLP(config, quant_config=quant_config)
+        self.mlp = CLIPMLP(config,
+                           quant_config=quant_config,
+                           prefix=f"{prefix}.mlp")
         self.layer_norm2 = nn.LayerNorm(config.hidden_size,
                                         eps=config.layer_norm_eps)
 
@@ -327,11 +343,15 @@ class CLIPEncoder(nn.Module):
         config: CLIPConfig
     """
 
-    def __init__(self,
-                 config: CLIPVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None,
-                 num_hidden_layers_override: Optional[int] = None):
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        num_hidden_layers_override: Optional[int] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
+
         self.config = config
 
         if num_hidden_layers_override is None:
@@ -339,8 +359,10 @@ class CLIPEncoder(nn.Module):
         else:
             num_hidden_layers = num_hidden_layers_override
         self.layers = nn.ModuleList([
-            CLIPEncoderLayer(config=config, quant_config=quant_config)
-            for _ in range(num_hidden_layers)
+            CLIPEncoderLayer(config=config,
+                             quant_config=quant_config,
+                             prefix=f"{prefix}.layers.{layer_idx}")
+            for layer_idx in range(num_hidden_layers)
         ])
 
     def forward(self, inputs_embeds: torch.Tensor):
@@ -354,11 +376,17 @@ class CLIPEncoder(nn.Module):
 
 class CLIPVisionTransformer(nn.Module):
 
-    def __init__(self,
-                 config: CLIPVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None,
-                 num_hidden_layers_override: Optional[int] = None):
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: Optional[bool] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
+
         self.config = config
         embed_dim = config.hidden_size
 
@@ -370,19 +398,25 @@ class CLIPVisionTransformer(nn.Module):
         self.encoder = CLIPEncoder(
             config=config,
             quant_config=quant_config,
-            num_hidden_layers_override=num_hidden_layers_override)
+            num_hidden_layers_override=num_hidden_layers_override,
+            prefix=f"{prefix}.encoder",
+        )
 
+        num_hidden_layers = config.num_hidden_layers
         if len(self.encoder.layers) > config.num_hidden_layers:
             raise ValueError(
-                f"The original encoder only has {config.num_hidden_layers} "
+                f"The original encoder only has {num_hidden_layers} "
                 f"layers, but you requested {len(self.encoder.layers)} layers."
             )
-        elif len(self.encoder.layers) == config.num_hidden_layers:
+
+        # If possible, skip post_layernorm to conserve memory
+        if require_post_norm is None:
+            require_post_norm = len(self.encoder.layers) == num_hidden_layers
+
+        if require_post_norm:
             self.post_layernorm = nn.LayerNorm(embed_dim,
                                                eps=config.layer_norm_eps)
         else:
-            # post_layernorm is unused when we extract intermediate features
-            # In this case, we can skip it to conserve memory
             self.post_layernorm = None
 
     def forward(
@@ -405,10 +439,15 @@ class CLIPVisionModel(nn.Module):
     config_class = CLIPVisionConfig
     main_input_name = "pixel_values"
 
-    def __init__(self,
-                 config: CLIPVisionConfig,
-                 quant_config: Optional[QuantizationConfig] = None,
-                 num_hidden_layers_override: Optional[int] = None):
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: Optional[bool] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         tp_size = get_tensor_model_parallel_world_size()
@@ -418,7 +457,10 @@ class CLIPVisionModel(nn.Module):
         self.vision_model = CLIPVisionTransformer(
             config=config,
             quant_config=quant_config,
-            num_hidden_layers_override=num_hidden_layers_override)
+            num_hidden_layers_override=num_hidden_layers_override,
+            require_post_norm=require_post_norm,
+            prefix=f"{prefix}.vision_model",
+        )
 
     def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
         return self.vision_model(pixel_values)

vllm/model_executor/models/commandr.py

@@ -28,6 +28,7 @@ from torch import nn
 from transformers import CohereConfig
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -250,6 +251,7 @@ class CohereDecoderLayer(nn.Module):
         return hidden_states, residual
 
 
+@support_torch_compile
 class CohereModel(nn.Module):
 
     def __init__(

vllm/model_executor/models/exaone.py

@@ -29,6 +29,7 @@ import torch
 from torch import nn
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
@@ -311,6 +312,7 @@ class ExaoneDecoderLayer(nn.Module):
         return hidden_states, residual
 
 
+@support_torch_compile
 class ExaoneModel(nn.Module):
 
     def __init__(

vllm/model_executor/models/florence2.py

+import math
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.attention import AttentionMetadata
+from vllm.config import CacheConfig
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.bart import (BartDecoder, BartEncoder,
+                                             BartParallelLMHead,
+                                             BartScaledWordEmbedding)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .utils import AutoWeightsLoader
+
+
+class Florence2LanguageModel(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.config = config
+
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.shared = BartScaledWordEmbedding(self.vocab_size, config.d_model)
+        self.encoder = BartEncoder(config,
+                                   cache_config=cache_config,
+                                   quant_config=quant_config)
+        self.decoder = BartDecoder(config,
+                                   cache_config=cache_config,
+                                   quant_config=quant_config)
+
+        if self.config.tie_word_embeddings:
+            self.encoder.embed_tokens.weight = self.shared.weight
+            self.decoder.embed_tokens.weight = self.shared.weight
+
+    def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
+                encoder_input_ids: torch.Tensor,
+                encoder_positions: torch.Tensor, kv_caches: List[torch.Tensor],
+                attn_metadata: AttentionMetadata) -> torch.Tensor:
+        r"""
+        Args:
+            input_ids
+                Indices of *decoder* input sequence tokens in the vocabulary.
+                Padding will be ignored by default should you
+                provide it.
+            positions
+                Positions of *decoder* input sequence tokens.
+            encoder_input_ids
+                Indices of *encoder* input sequence tokens in the vocabulary.
+            encoder_positions:
+                Positions of *encoder* input sequence tokens.
+            kv_caches:
+                Layer-wise list of KV cache tensors
+            attn_metadata:
+                vLLM Attention metadata structure
+        Returns:
+            Model output torch.Tensor
+        """
+
+        encoder_hidden_states = None
+
+        if encoder_input_ids.numel() > 0:
+            # Run encoder attention if a non-zero number of encoder tokens
+            # are provided as input
+            encoder_hidden_states = self.encoder(input_ids=encoder_input_ids,
+                                                 positions=encoder_positions,
+                                                 kv_caches=kv_caches,
+                                                 attn_metadata=attn_metadata)
+
+        # decoder outputs consists of
+        # (dec_features, past_key_value, dec_hidden, dec_attn)
+        decoder_outputs = self.decoder(
+            decoder_input_ids=input_ids,
+            decoder_positions=positions,
+            encoder_hidden_states=encoder_hidden_states,
+            kv_caches=kv_caches,
+            attn_metadata=attn_metadata)
+
+        return decoder_outputs
+
+
+class Florence2LanguageForConditionalGeneration(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.config = config
+        self.model = Florence2LanguageModel(config,
+                                            cache_config=cache_config,
+                                            quant_config=quant_config)
+        embed_scale = math.sqrt(
+            config.d_model) if config.scale_embedding else 1.0
+
+        self.vocab_size = config.vocab_size
+        self.lm_head = BartParallelLMHead(self.vocab_size,
+                                          config.d_model,
+                                          embed_scale=embed_scale)
+
+        self.logits_processor = LogitsProcessor(self.vocab_size,
+                                                config.vocab_size)
+        self.sampler = Sampler()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        encoder_input_ids: torch.Tensor,
+        encoder_positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            input_ids
+                torch.Tensor of *decoder* input token ids.
+            positions
+                torch.Tensor of *decoder* position indices.
+            encoder_input_ids
+                torch.Tensor of *encoder* input token ids.
+            encoder_positions
+                torch.Tensor of *encoder* position indices
+            kv_caches:
+                Layer-wise list of KV cache tensors
+            attn_metadata:
+                vLLM Attention metadata structure
+        Returns:
+            Output torch.Tensor
+        """
+        return self.model(input_ids, positions, encoder_input_ids,
+                          encoder_positions, kv_caches, attn_metadata)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(self, logits: torch.Tensor,
+               sampling_metadata: SamplingMetadata) -> SamplerOutput:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+
+                param = params_dict[name.replace(weight_name, param_name)]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if "final_logits_bias" in name:
+                    continue
+                if self.config.tie_word_embeddings and "embed_tokens" in name:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+class Florence2ForConditionalGeneration(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+
+        # TODO(Isotr0py): Add vision backbone
+        self.language_model = Florence2LanguageForConditionalGeneration(
+            config=config.text_config,
+            cache_config=cache_config,
+            quant_config=quant_config)
+
+    @property
+    def sampler(self):
+        return self.language_model.sampler
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        *,
+        encoder_input_ids: torch.Tensor,
+        encoder_positions: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            input_ids
+                torch.Tensor of *decoder* input token ids.
+            positions
+                torch.Tensor of *decoder* position indices.
+            encoder_input_ids
+                torch.Tensor of *encoder* input token ids.
+            encoder_positions
+                torch.Tensor of *encoder* position indices
+            kv_caches:
+                Layer-wise list of KV cache tensors
+            attn_metadata:
+                vLLM Attention metadata structure
+        Returns:
+            Output torch.Tensor
+        """
+        return self.language_model(input_ids, positions, encoder_input_ids,
+                                   encoder_positions, kv_caches, attn_metadata)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> SamplerOutput:
+        return self.language_model.sample(logits, sampling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        skip_prefixes = [
+            'image_projection', "vision_tower", "image_proj_norm",
+            "image_pos_embed", "visual_temporal_embed"
+        ]
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
+        loader.load_weights(weights)

vllm/model_executor/models/gemma.py

@@ -22,6 +22,7 @@ from torch import nn
 from transformers import GemmaConfig
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
@@ -239,6 +240,7 @@ class GemmaDecoderLayer(nn.Module):
         return hidden_states, residual
 
 
+@support_torch_compile
 class GemmaModel(nn.Module):
 
     def __init__(

vllm/model_executor/models/gpt2.py

@@ -24,6 +24,7 @@ from torch import nn
 from transformers import GPT2Config
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig
 from vllm.distributed.parallel_state import (
     get_pp_group, get_tensor_model_parallel_world_size)
@@ -182,6 +183,7 @@ class GPT2Block(nn.Module):
         return hidden_states
 
 
+@support_torch_compile
 class GPT2Model(nn.Module):
 
     def __init__(

vllm/model_executor/models/idefics2_vision_model.py

@@ -113,7 +113,8 @@ class Idefics2VisionAttention(nn.Module):
         self,
         config: Idefics2Config,
         quant_config: Optional[QuantizationConfig] = None,
-    ):
+        prefix: str = "",
+    ) -> None:
         super().__init__()
         self.config = config
         self.embed_dim = config.hidden_size
@@ -130,12 +131,14 @@ class Idefics2VisionAttention(nn.Module):
             self.head_dim,
             self.num_heads,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
         )
         self.out_proj = RowParallelLinear(
             self.embed_dim,
             self.embed_dim,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
         )
         self.tp_size = get_tensor_model_parallel_world_size()
         self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
@@ -178,7 +181,8 @@ class Idefics2VisionMLP(nn.Module):
         self,
         config: Idefics2Config,
         quant_config: Optional[QuantizationConfig] = None,
-    ):
+        prefix: str = "",
+    ) -> None:
         super().__init__()
         self.config = config
         self.activation_fn = get_act_fn(config.hidden_act)
@@ -187,12 +191,14 @@ class Idefics2VisionMLP(nn.Module):
             config.intermediate_size,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
         )
         self.fc2 = RowParallelLinear(
             config.intermediate_size,
             config.hidden_size,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -204,13 +210,22 @@ class Idefics2VisionMLP(nn.Module):
 
 class Idefics2EncoderLayer(nn.Module):
 
-    def __init__(self, config: Idefics2Config):
+    def __init__(
+        self,
+        config: Idefics2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
         self.embed_dim = config.hidden_size
-        self.self_attn = Idefics2VisionAttention(config)
+        self.self_attn = Idefics2VisionAttention(config,
+                                                 quant_config=quant_config,
+                                                 prefix=f"{prefix}.self_attn")
         self.layer_norm1 = nn.LayerNorm(self.embed_dim,
                                         eps=config.layer_norm_eps)
-        self.mlp = Idefics2VisionMLP(config)
+        self.mlp = Idefics2VisionMLP(config,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.mlp")
         self.layer_norm2 = nn.LayerNorm(self.embed_dim,
                                         eps=config.layer_norm_eps)
 
@@ -245,12 +260,20 @@ class Idefics2Encoder(nn.Module):
         config: Idefics2Config
     """
 
-    def __init__(self, config: Idefics2Config):
+    def __init__(
+        self,
+        config: Idefics2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
+
         self.config = config
         self.layers = nn.ModuleList([
-            Idefics2EncoderLayer(config)
-            for _ in range(config.num_hidden_layers)
+            Idefics2EncoderLayer(config,
+                                 quant_config=quant_config,
+                                 prefix=f"{prefix}.layers.{layer_idx}")
+            for layer_idx in range(config.num_hidden_layers)
         ])
 
     def forward(
@@ -275,12 +298,20 @@ class Idefics2Encoder(nn.Module):
 
 class Idefics2VisionTransformer(nn.Module):
 
-    def __init__(self, config: Idefics2VisionConfig):
+    def __init__(
+        self,
+        config: Idefics2VisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
+
         embed_dim = config.hidden_size
         self.config = config
         self.embeddings = Idefics2VisionEmbeddings(config)
-        self.encoder = Idefics2Encoder(config)
+        self.encoder = Idefics2Encoder(config,
+                                       quant_config=quant_config,
+                                       prefix=f"{prefix}.encoder")
         self.post_layernorm = nn.LayerNorm(embed_dim,
                                            eps=config.layer_norm_eps)
 

vllm/model_executor/models/intern_vit.py

@@ -137,6 +137,7 @@ class InternParallelAttention(nn.Module):
         quant_config: Optional[QuantizationConfig] = None,
         *,
         num_dummy_heads: int = 0,
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -165,6 +166,7 @@ class InternParallelAttention(nn.Module):
             num_dummy_heads + self.num_heads,
             bias=config.qkv_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv",
         )
 
         self.qk_normalization = config.qk_normalization
@@ -181,6 +183,7 @@ class InternParallelAttention(nn.Module):
             self.dummy_dim,
             self.embed_dim,
             quant_config=quant_config,
+            prefix=f"{prefix}.proj",
         )
 
     def _apply_qk_norm(self, q: torch.Tensor, k: torch.Tensor):
@@ -284,20 +287,26 @@ class InternSdpaAttention(nn.Module):
 
 class InternMLP(nn.Module):
 
-    def __init__(self,
-                 config: PretrainedConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
+
         self.config = config
         self.activation_fn = get_act_fn(config.hidden_act)
         self.fc1 = ColumnParallelLinear(config.hidden_size,
                                         config.intermediate_size,
                                         bias=True,
-                                        quant_config=quant_config)
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.fc1")
         self.fc2 = RowParallelLinear(config.intermediate_size,
                                      config.hidden_size,
                                      bias=True,
-                                     quant_config=quant_config)
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.fc2")
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.fc1(hidden_states)
@@ -315,6 +324,7 @@ class InternVisionEncoderLayer(nn.Module):
         quant_config: Optional[QuantizationConfig] = None,
         *,
         num_dummy_heads: int = 0,
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -324,9 +334,12 @@ class InternVisionEncoderLayer(nn.Module):
 
         self.attn = self._init_attn(config,
                                     quant_config,
-                                    num_dummy_heads=num_dummy_heads)
+                                    num_dummy_heads=num_dummy_heads,
+                                    prefix=f"{prefix}.attn")
 
-        self.mlp = InternMLP(config, quant_config=quant_config)
+        self.mlp = InternMLP(config,
+                             quant_config=quant_config,
+                             prefix=f"{prefix}.mlp")
         self.norm1 = NORM2FN[self.norm_type](self.embed_dim,
                                              eps=config.layer_norm_eps)
         self.norm2 = NORM2FN[self.norm_type](self.embed_dim,
@@ -343,6 +356,7 @@ class InternVisionEncoderLayer(nn.Module):
         quant_config: Optional[QuantizationConfig],
         *,
         num_dummy_heads: int,
+        prefix: str = "",
     ):
         # fallback to sdpa attention if tp unavailable
         tp_size = get_tensor_model_parallel_world_size()
@@ -351,7 +365,8 @@ class InternVisionEncoderLayer(nn.Module):
         if USE_XFORMERS_OPS and (num_heads + num_dummy_heads) % tp_size == 0:
             return InternParallelAttention(config,
                                            quant_config=quant_config,
-                                           num_dummy_heads=num_dummy_heads)
+                                           num_dummy_heads=num_dummy_heads,
+                                           prefix=prefix)
 
         return InternSdpaAttention(config, num_dummy_heads=num_dummy_heads)
 
@@ -377,6 +392,7 @@ class InternVisionEncoder(nn.Module):
         *,
         num_hidden_layers_override: Optional[int] = None,
         num_dummy_heads: int = 0,
+        prefix: str = "",
     ):
         super().__init__()
 
@@ -390,8 +406,9 @@ class InternVisionEncoder(nn.Module):
         self.layers = nn.ModuleList([
             InternVisionEncoderLayer(config,
                                      quant_config,
-                                     num_dummy_heads=num_dummy_heads)
-            for _ in range(num_hidden_layers)
+                                     num_dummy_heads=num_dummy_heads,
+                                     prefix=f"{prefix}.layers.{layer_idx}")
+            for layer_idx in range(num_hidden_layers)
         ])
 
     def forward(self, inputs_embeds: torch.Tensor):
@@ -412,7 +429,8 @@ class InternVisionModel(nn.Module):
         *,
         num_hidden_layers_override: Optional[int] = None,
         num_dummy_heads: int = 0,
-    ):
+        prefix: str = "",
+    ) -> None:
         super().__init__()
 
         self.config = config
@@ -423,6 +441,7 @@ class InternVisionModel(nn.Module):
             quant_config=quant_config,
             num_hidden_layers_override=num_hidden_layers_override,
             num_dummy_heads=num_dummy_heads,
+            prefix=f"{prefix}.encoder",
         )
 
     def get_input_embeddings(self):