tzhouam
54977bd590
- Introduced OmniGPUModelRunner for enhanced GPU model execution with state management. - Added OmniModelInputForGPU and its builder to support additional information in model inputs. - Implemented methods for updating request states and handling multimodal inputs. - Enhanced input preparation for CUDA graph execution and LoRA integration.
199 lines
8.8 KiB
Python
199 lines
8.8 KiB
Python
from dataclasses import dataclass
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from typing import Optional, Dict, Any, List
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import itertools
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import torch
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from vllm.worker.model_runner import ModelInputForGPU, ModelInputForGPUBuilder
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from vllm.utils import flatten_2d_lists, async_tensor_h2d
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from vllm.lora.layers import LoRAMapping
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from vllm.sequence import MultiModalKwargs
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@dataclass(frozen=True)
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class OmniModelInputForGPU(ModelInputForGPU):
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# New: optional additional information dict for current scheduled tokens
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additional_information: Optional[Dict[str, Any]] = None
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class OmniModelInputForGPUBuilder(ModelInputForGPUBuilder):
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def build(self) -> OmniModelInputForGPU:
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"""Finalize the builder intermediate data and
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create on-device tensors.
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"""
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# Combine and flatten intermediate data.
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input_tokens = list[int]()
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inputs_embeds_list = list[torch.Tensor]()
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token_types = list[int]()
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for inter_data in self.inter_data_list:
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for cur_input_tokens in inter_data.input_tokens:
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input_tokens.extend(cur_input_tokens)
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for cur_token_types in inter_data.token_types:
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token_types.extend(cur_token_types)
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if inter_data.inputs_embeds is not None:
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inputs_embeds_list.append(
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inter_data.inputs_embeds.to(
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dtype=self.runner.model_config.dtype,
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device=self.runner.device))
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# Support v1 direct-transfer prompt embeds attached on request state
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if inter_data.inputs_embeds is None:
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try:
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# Locate req_id -> request state to fetch decoded CPU embeds
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req_id = inter_data.request_id # type: ignore[attr-defined]
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req_state = getattr(self.runner, "requests", {}).get(req_id)
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pe_cpu = getattr(req_state, "prompt_embeds_cpu", None)
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if pe_cpu is not None:
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inter_data.inputs_embeds = pe_cpu.to(
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dtype=self.runner.model_config.dtype,
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device=self.runner.device)
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inputs_embeds_list.append(inter_data.inputs_embeds)
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except Exception:
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pass
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inputs_embeds: Optional[torch.Tensor]
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if len(inputs_embeds_list) == 0:
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inputs_embeds = None
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else:
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inputs_embeds = torch.cat(inputs_embeds_list, dim=0).to(
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dtype=self.runner.model_config.dtype,
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device=self.runner.device)
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assert len(inputs_embeds) == len(input_tokens)
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if not input_tokens and inputs_embeds is None:
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# This may happen when all prefill requests hit
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# prefix caching and there is no decode request.
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return self.model_input_cls()
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mrope_input_positions: Optional[List[List[int]]] = None
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if any(inter_data.mrope_input_positions is not None
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for inter_data in self.inter_data_list):
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mrope_input_positions = [[] for _ in range(3)]
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for idx in range(3):
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for inter_data in self.inter_data_list:
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msections = inter_data.mrope_input_positions
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if msections is None:
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for _seq_input_positions in inter_data.input_positions:
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mrope_input_positions[idx].extend(
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_seq_input_positions)
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else:
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for _seq_mrope_input_positions in msections:
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mrope_input_positions[idx].extend(
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_seq_mrope_input_positions[idx])
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input_positions = None
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else:
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input_positions = []
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for inter_data in self.inter_data_list:
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for cur_input_positions in inter_data.input_positions:
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input_positions.extend(cur_input_positions)
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seq_lens = []
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query_lens = []
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max_decode_seq_len = 0
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max_encoder_seq_len = 0
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for inter_data in self.inter_data_list:
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seq_lens.extend(inter_data.seq_lens)
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query_lens.extend(inter_data.query_lens)
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if not inter_data.is_prompt:
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max_decode_seq_len = max(max_decode_seq_len,
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max(inter_data.seq_lens))
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if self.runner.model_config.is_encoder_decoder:
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max_encoder_seq_len = max(max_encoder_seq_len,
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inter_data.encoder_seq_len)
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# Mapping from request IDs to sequence IDs. Used for Jamba models
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# that manages the cache by itself.
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request_ids_to_seq_ids = {
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data.request_id: data.seq_ids
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for data in self.inter_data_list
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}
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cuda_graph_pad_size = self._get_cuda_graph_pad_size(
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num_seqs=len(seq_lens),
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max_decode_seq_len=max_decode_seq_len,
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max_encoder_seq_len=max_encoder_seq_len)
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batch_size = len(input_tokens)
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if cuda_graph_pad_size != -1:
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# If cuda graph can be used, pad tensors accordingly.
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# See `capture_model` API for more details.
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# vLLM uses cuda graph only for decoding requests.
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batch_size += cuda_graph_pad_size
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# Tokens and positions.
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if cuda_graph_pad_size:
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input_tokens.extend(itertools.repeat(0, cuda_graph_pad_size))
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assert self.runner.device is not None
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input_tokens_tensor = async_tensor_h2d(input_tokens, torch.long,
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self.runner.device,
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self.runner.pin_memory)
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token_types_tensor = async_tensor_h2d(token_types, torch.long,
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self.runner.device,
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self.runner.pin_memory) \
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if token_types else None
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if mrope_input_positions is not None:
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for idx in range(3):
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mrope_input_positions[idx].extend(
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itertools.repeat(0, cuda_graph_pad_size))
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input_positions_tensor = async_tensor_h2d(mrope_input_positions,
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torch.long,
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self.runner.device,
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self.runner.pin_memory)
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else:
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input_positions.extend(itertools.repeat(0, cuda_graph_pad_size))
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input_positions_tensor = async_tensor_h2d(input_positions,
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torch.long,
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self.runner.device,
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self.runner.pin_memory)
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# Sequence and query lengths.
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if cuda_graph_pad_size:
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seq_lens.extend(itertools.repeat(1, cuda_graph_pad_size))
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# Attention metadata.
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attn_metadata = self.attn_metadata_builder.build(
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seq_lens, query_lens, cuda_graph_pad_size, batch_size)
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# LoRA data.
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lora_requests = set()
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lora_mapping = None
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if self.enable_lora:
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lora_requests = set(r for data in self.inter_data_list
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for r in data.lora_requests)
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lora_index_mapping = flatten_2d_lists([
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flatten_2d_lists(inter_data.lora_index_mapping)
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for inter_data in self.inter_data_list
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])
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if cuda_graph_pad_size:
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lora_index_mapping.extend(
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itertools.repeat(0, cuda_graph_pad_size))
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lora_prompt_mapping = flatten_2d_lists([
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flatten_2d_lists(inter_data.lora_prompt_mapping)
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for inter_data in self.inter_data_list
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])
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lora_mapping = LoRAMapping(
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**dict(index_mapping=lora_index_mapping,
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prompt_mapping=lora_prompt_mapping,
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is_prefill=not self.decode_only))
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# Multi-modal data.
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multi_modal_kwargs_list = [
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data.multi_modal_kwargs for data in self.inter_data_list
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if data.multi_modal_kwargs is not None
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]
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multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
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return self.model_input_cls(
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input_tokens=input_tokens_tensor,
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inputs_embeds=inputs_embeds,
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input_positions=input_positions_tensor,
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token_types=token_types_tensor,
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attn_metadata=attn_metadata,
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seq_lens=seq_lens,
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query_lens=query_lens,
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lora_mapping=lora_mapping,
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lora_requests=lora_requests,
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multi_modal_kwargs=multi_modal_kwargs,
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request_ids_to_seq_ids=request_ids_to_seq_ids,
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finished_requests_ids=self.finished_requests_ids) |