add qwen3

vllm-v0.6.2/vllm/worker/neuron_model_runner.py

@@ -0,0 +1,342 @@
+import os
+from dataclasses import dataclass
+from importlib.util import find_spec
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers_neuronx.config import GenerationConfig
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.model_executor.model_loader.neuron import get_neuron_model
+from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
+                             MultiModalKwargs)
+from vllm.sequence import IntermediateTensors, SequenceGroupMetadata
+from vllm.utils import is_pin_memory_available, make_tensor_with_pad
+from vllm.worker.model_runner_base import ModelRunnerBase, ModelRunnerInputBase
+
+if TYPE_CHECKING:
+    from vllm.attention.backends.abstract import AttentionBackend
+
+logger = init_logger(__name__)
+
+
+@dataclass(frozen=True)
+class ModelInputForNeuron(ModelRunnerInputBase):
+    """
+    Used by the NeuronModelRunner.
+    """
+    input_tokens: Optional[torch.Tensor] = None
+    input_positions: Optional[torch.Tensor] = None
+    input_block_ids: Optional[torch.Tensor] = None
+    sampling_metadata: Optional["SamplingMetadata"] = None
+    multi_modal_kwargs: Optional[BatchedTensorInputs] = None
+
+    def as_broadcastable_tensor_dict(
+            self) -> Dict[str, Union[int, torch.Tensor]]:
+        raise NotImplementedError("ModelInputForNeuron cannot be broadcast.")
+
+    @classmethod
+    def from_broadcasted_tensor_dict(
+        cls,
+        tensor_dict: Dict[str, Any],
+        attn_backend: Optional["AttentionBackend"] = None,
+    ) -> "ModelInputForNeuron":
+        assert attn_backend is None
+        return cls.from_broadcasted_tensor_dict(tensor_dict)
+
+
+class NeuronModelRunner(ModelRunnerBase[ModelInputForNeuron]):
+
+    # NEURON has an upper limit on the top_k
+    _MAX_NEURON_SAMPLING_TOP_K = 256
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+    ):
+        ModelRunnerBase.__init__(self, vllm_config)
+        model_config = self.model_config
+        if model_config is not None and model_config.get_sliding_window():
+            logger.warning("Sliding window is not supported on Neuron. "
+                           "The model will run without sliding window.")
+        self.device = self.device_config.device
+        self.pin_memory = is_pin_memory_available()
+
+        # Multi-modal data support
+        self.mm_registry = MULTIMODAL_REGISTRY
+        self.multi_modal_input_mapper = self.mm_registry \
+            .create_input_mapper(self.model_config)
+
+        # Lazy initialization.
+        self.model: nn.Module  # initialize after load_model.
+
+        # Once NEURON_ON_DEVICE_SAMPLING_DISABLED is set to a non-zero value,
+        # turn off on-device sampling.
+        self._on_device_sampling_disabled = int(
+            os.getenv("NEURON_ON_DEVICE_SAMPLING_DISABLED", "0"))
+
+        # NEURON needs to update sampling parameters when request IDs change
+        # across batches. This variable stores the previous batch's request IDs
+        # to determine if an update is needed.
+        self._previous_batch_request_ids: List[str] = []
+
+        if not self._on_device_sampling_disabled:
+            logger.warning(
+                "On-device sampling is turned on in Neuron by default, only "
+                "top_k, top_p, and temperature are current supported sampling "
+                "parameters. To turn off the on-device sampling, please set "
+                "the environment variable NEURON_ON_DEVICE_SAMPLING_DISABLED=1."
+            )
+            self.model_config.neuron_sampling_params = GenerationConfig(
+                max_length=self.scheduler_config.max_model_len,
+                do_sample=True,
+                per_batch_line=True,
+                top_k=[self._MAX_NEURON_SAMPLING_TOP_K] \
+                    * self.scheduler_config.max_num_seqs,
+                top_p=[1.0] * self.scheduler_config.max_num_seqs,
+                temperature=[1.0] * self.scheduler_config.max_num_seqs,
+                dynamic=True,
+                global_top_k=self._MAX_NEURON_SAMPLING_TOP_K)
+
+    def load_model(self) -> None:
+        if find_spec("transformers_neuronx") is not None:
+            self.model = get_neuron_model(
+                self.model_config,
+                parallel_config=self.parallel_config,
+                scheduler_config=self.scheduler_config)
+        else:
+            raise NotImplementedError(
+                "Supports only Transformer-NeuronX based models.")
+
+    def _prepare_prompt(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[int],
+               BatchedTensorInputs]:
+        assert len(seq_group_metadata_list) > 0
+        input_tokens: List[List[int]] = []
+        input_positions: List[List[int]] = []
+        input_block_ids: List[int] = []
+
+        seq_lens: List[int] = []
+        multi_modal_kwargs_list: List[MultiModalKwargs] = []
+        for seq_group_metadata in seq_group_metadata_list:
+            assert seq_group_metadata.is_prompt
+            seq_ids = list(seq_group_metadata.seq_data.keys())
+            assert len(seq_ids) == 1
+            seq_id = seq_ids[0]
+
+            seq_data = seq_group_metadata.seq_data[seq_id]
+            prompt_tokens = seq_data.get_token_ids()
+            seq_len = len(prompt_tokens)
+            seq_lens.append(seq_len)
+
+            input_tokens.append(prompt_tokens)
+            input_positions.append(list(range(seq_len)))
+
+            assert seq_group_metadata.block_tables is not None
+            block_table = seq_group_metadata.block_tables[seq_id]
+            assert len(block_table) == 1
+            input_block_ids.append(block_table[0])
+
+            mm_data = seq_group_metadata.multi_modal_data
+            if mm_data:
+                if self.mm_registry.has_processor(self.model_config):
+                    mm_kwargs = mm_data
+                else:
+                    mm_kwargs = self.multi_modal_input_mapper(
+                        mm_data,
+                        seq_group_metadata.mm_processor_kwargs,
+                    )
+
+                multi_modal_kwargs_list.append(mm_kwargs)
+
+        max_seq_len = max(seq_lens)
+        assert max_seq_len > 0
+        input_tokens = make_tensor_with_pad(input_tokens,
+                                            pad=0,
+                                            max_len=max_seq_len,
+                                            dtype=torch.long,
+                                            device=self.device)
+        input_positions = make_tensor_with_pad(input_positions,
+                                               pad=0,
+                                               max_len=max_seq_len,
+                                               dtype=torch.long,
+                                               device=self.device)
+        input_block_ids = torch.tensor(input_block_ids,
+                                       dtype=torch.long,
+                                       device=self.device)
+
+        multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
+
+        return (input_tokens, input_positions, input_block_ids, seq_lens,
+                multi_modal_kwargs)
+
+    def _prepare_decode(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        assert len(seq_group_metadata_list) > 0
+        input_tokens: List[List[int]] = []
+        input_positions: List[List[int]] = []
+        input_block_ids: List[int] = []
+        context_lens: List[int] = []
+
+        for seq_group_metadata in seq_group_metadata_list:
+            assert not seq_group_metadata.is_prompt
+
+            seq_ids = list(seq_group_metadata.seq_data.keys())
+
+            for seq_id in seq_ids:
+                seq_data = seq_group_metadata.seq_data[seq_id]
+                generation_token = seq_data.get_last_token_id()
+                input_tokens.append([generation_token])
+
+                seq_len = seq_data.get_len()
+                position = seq_len - 1
+                input_positions.append([position])
+                context_lens.append(seq_len)
+
+                assert seq_group_metadata.block_tables is not None
+                block_table = seq_group_metadata.block_tables[seq_id]
+                assert len(block_table) == 1
+                input_block_ids.append(block_table[0])
+
+        input_tokens = make_tensor_with_pad(input_tokens,
+                                            pad=0,
+                                            max_len=1,
+                                            dtype=torch.long,
+                                            device=self.device)
+        input_positions = make_tensor_with_pad(input_positions,
+                                               pad=0,
+                                               max_len=1,
+                                               dtype=torch.long,
+                                               device=self.device)
+        context_lens = torch.tensor(context_lens,
+                                    dtype=torch.int,
+                                    device=self.device)
+        input_block_ids = torch.tensor(input_block_ids,
+                                       dtype=torch.long,
+                                       device=self.device)
+
+        return input_tokens, input_positions, input_block_ids
+
+    def make_model_input_from_broadcasted_tensor_dict(
+            self, tensor_dict: Dict[str, Any]) -> ModelInputForNeuron:
+        return ModelInputForNeuron.from_broadcasted_tensor_dict(tensor_dict)
+
+    def prepare_model_input(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        virtual_engine: int = 0,
+        finished_requests_ids: Optional[List[str]] = None
+    ) -> ModelInputForNeuron:
+        multi_modal_kwargs = None
+        # NOTE: We assume that all sequences in the group are all prompts or
+        # all decodes.
+        is_prompt = seq_group_metadata_list[0].is_prompt
+        # Prepare input tensors.
+        if is_prompt:
+            (input_tokens, input_positions, input_block_ids, seq_lens,
+             multi_modal_kwargs
+             ) = self._prepare_prompt(seq_group_metadata_list)
+        else:
+            (input_tokens, input_positions,
+             input_block_ids) = self._prepare_decode(seq_group_metadata_list)
+            seq_lens = None
+        sampling_metadata = SamplingMetadata.prepare(
+            seq_group_metadata_list,
+            seq_lens,
+            # query_lens is not needed if chunked prefill is not
+            # supported. Since neuron worker doesn't support chunked prefill
+            # just use seq_lens instead.
+            seq_lens,
+            self.device,
+            self.pin_memory,
+            generators=self.get_generators(finished_requests_ids))
+
+        if not self._on_device_sampling_disabled:
+            # Once the request IDs are changed in current iteration, we will
+            # update the on-device sampling parameters.
+            current_batch_request_ids = [
+                seq_group_meta_data.request_id
+                for seq_group_meta_data in seq_group_metadata_list
+            ]
+            if current_batch_request_ids != self._previous_batch_request_ids:
+                self._update_neuron_sampling_params(sampling_metadata)
+                self._previous_batch_request_ids = current_batch_request_ids
+
+        return ModelInputForNeuron(input_tokens=input_tokens,
+                                   input_positions=input_positions,
+                                   input_block_ids=input_block_ids,
+                                   sampling_metadata=sampling_metadata,
+                                   multi_modal_kwargs=multi_modal_kwargs)
+
+    def _update_neuron_sampling_params(self,
+                                       sampling_metadata: SamplingMetadata):
+        # Update Neuron sampling parameters (GenerationConfig in Neuron)
+        current_sampling_params = self.model_config.neuron_sampling_params
+        assert current_sampling_params is not None, (
+            f"Failed to update sampling_params, "
+            f"current sampling params is {current_sampling_params}")
+
+        top_k = current_sampling_params.top_k
+        top_p = current_sampling_params.top_p
+        temperature = current_sampling_params.temperature
+        for index, sequence_group_to_sample in enumerate(
+                sampling_metadata.seq_groups):
+            top_k[index] = self._convert_to_neuron_top_k(
+                sequence_group_to_sample.sampling_params.top_k)
+            top_p[index] = sequence_group_to_sample.sampling_params.top_p
+            temperature[index] = \
+                sequence_group_to_sample.sampling_params.temperature
+
+        self.model.model.update_generation_config(current_sampling_params)
+
+    def _convert_to_neuron_top_k(self, top_k: int) -> int:
+        if top_k < 0 or top_k > self._MAX_NEURON_SAMPLING_TOP_K:
+            return self._MAX_NEURON_SAMPLING_TOP_K
+        return top_k
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        model_input: ModelInputForNeuron,
+        kv_caches: Optional[List[torch.Tensor]] = None,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        num_steps: int = 1,
+    ) -> Optional[List[SamplerOutput]]:
+        if num_steps > 1:
+            raise ValueError(
+                "NeuronModelRunner does not support multi-step execution.")
+
+        hidden_states = self.model(
+            input_ids=model_input.input_tokens,
+            positions=model_input.input_positions,
+            input_block_ids=model_input.input_block_ids,
+            **MultiModalKwargs.as_kwargs(model_input.multi_modal_kwargs or {},
+                                         device=self.device),
+        )
+
+        # Compute the logits only if the on-device sampling is turned off as
+        # on-device sampling outputs the token ids.
+        if self._on_device_sampling_disabled:
+            logits = self.model.compute_logits(hidden_states,
+                                               model_input.sampling_metadata)
+        else:
+            logits = hidden_states
+
+        # Sample the next token.
+        output = self.model.sample(
+            logits=logits,
+            sampling_metadata=model_input.sampling_metadata,
+        )
+        return [output]
+
+    @property
+    def vocab_size(self) -> int:
+        return self.model_config.get_vocab_size()