wehub-resource-sync
189 lines
7.6 KiB
Python
189 lines
7.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Any
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import torch
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import torch.nn as nn
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from vllm.config import VllmConfig
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from vllm.config.compilation import CUDAGraphMode
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from vllm.v1.core.sched.output import NewRequestData
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.worker.gpu.attn_utils import (
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build_attn_metadata,
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compute_mm_prefix_ranges,
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)
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from vllm.v1.worker.gpu.input_batch import InputBatch
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from vllm.v1.worker.gpu.mm.encoder_cache import EncoderCache
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from vllm.v1.worker.gpu.mm.rope import get_rope_state
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from vllm.v1.worker.gpu.model_states.interface import ModelState
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from vllm.v1.worker.gpu.model_states.mm_pruning import maybe_create_mm_pruner
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from vllm.v1.worker.gpu.states import RequestState
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from vllm.v1.worker.utils import AttentionGroup
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class DefaultModelState(ModelState):
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def __init__(
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self,
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vllm_config: VllmConfig,
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model: nn.Module,
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encoder_cache: EncoderCache | None,
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device: torch.device,
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):
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super().__init__(vllm_config, model, encoder_cache, device)
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self.rope_state = get_rope_state(
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self.model_config,
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model,
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max_num_reqs=self.max_num_reqs,
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max_num_tokens=self.max_num_tokens,
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max_model_len=self.max_model_len,
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device=self.device,
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)
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# Pruner is used for multimodal embedding pruning (EVS).
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self.mm_pruner = maybe_create_mm_pruner(
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self.model_config, model, self.rope_state, encoder_cache
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)
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def add_request(self, req_index: int, new_req_data: NewRequestData) -> None:
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if self.rope_state is not None:
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assert new_req_data.prefill_token_ids is not None
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self.rope_state.init_prefill_positions(
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req_index,
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self.model,
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new_req_data.prefill_token_ids,
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mm_features=new_req_data.mm_features,
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)
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def apply_staged_writes(self) -> None:
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if self.rope_state is not None:
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self.rope_state.apply_staged_writes()
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def dummy_inputs_embeds(self, num_tokens: int) -> torch.Tensor:
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"""Pre-allocated inputs_embeds buffer for dummy runs (contents unused)."""
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return self.encoder_runner.inputs_embeds[:num_tokens]
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def get_mm_embeddings(
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self,
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scheduled_encoder_inputs: dict[str, list[int]],
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input_batch: InputBatch,
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req_states: RequestState,
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) -> torch.Tensor:
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mm_hashes, mm_kwargs = self.encoder_runner.prepare_mm_inputs(
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scheduled_encoder_inputs
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)
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if mm_kwargs:
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# Execute the multimodal encoder.
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encoder_outputs = self.encoder_runner.execute_mm_encoder(mm_kwargs)
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# Cache the encoder outputs by mm_hash
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self.encoder_cache.encoder_outputs.update(zip(mm_hashes, encoder_outputs))
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mm_embeds, is_mm_embed = super().gather_mm_embeddings(input_batch)
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if self.mm_pruner is not None and mm_embeds:
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# EVS: recompute mrope positions for pruned media.
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mm_embeds = self.mm_pruner.recompute(mm_embeds, input_batch, req_states)
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# We must flush the staged rope updates for prepare_inputs() to pick up.
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self.apply_staged_writes()
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# Use unpadded input_ids to match is_mm_embed size (num_tokens).
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# input_batch.input_ids may be padded for CUDA graphs.
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input_ids_unpadded = input_batch.input_ids[: input_batch.num_tokens]
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inputs_embeds = self.encoder_runner.get_inputs_embeds(
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input_ids_unpadded, mm_embeds, is_mm_embed
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)
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return inputs_embeds[: input_batch.num_tokens_after_padding]
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def gather_mm_embeddings(
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self, input_batch: InputBatch, draft_lookahead: int = 0
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) -> tuple[list[torch.Tensor], torch.Tensor]:
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mm_embeds, is_mm_embed = super().gather_mm_embeddings(
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input_batch, draft_lookahead
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)
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if self.mm_pruner is not None:
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# EVS: strip the appended mrope-position channels.
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mm_embeds = self.mm_pruner.strip(mm_embeds)
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return mm_embeds, is_mm_embed
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def prepare_inputs(
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self, input_batch: InputBatch, req_states: RequestState
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) -> dict[str, torch.Tensor | None]:
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if self.rope_state is None:
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return {} # Common case (1D positions).
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self.rope_state.prepare_positions(
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input_batch.idx_mapping,
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input_batch.query_start_loc,
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req_states.prefill_len.gpu,
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req_states.num_computed_tokens.gpu,
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)
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positions = self.rope_state.get_positions(input_batch.num_tokens_after_padding)
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return {"positions": positions}
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def prepare_dummy_inputs(self, num_reqs: int, num_tokens: int) -> dict[str, Any]:
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model_inputs = {}
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if self.supports_mm_inputs:
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inputs_embeds = self.encoder_runner.inputs_embeds[:num_tokens]
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model_inputs["inputs_embeds"] = inputs_embeds
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if self.rope_state is not None:
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model_inputs["positions"] = self.rope_state.get_positions(num_tokens)
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return model_inputs
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def prepare_attn(
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self,
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input_batch: InputBatch,
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cudagraph_mode: CUDAGraphMode,
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block_tables: tuple[torch.Tensor, ...],
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slot_mappings: torch.Tensor,
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attn_groups: list[list[AttentionGroup]],
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kv_cache_config: KVCacheConfig,
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for_capture: bool = False,
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) -> dict[str, Any]:
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if cudagraph_mode == CUDAGraphMode.FULL:
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# Use padded sizes - padding is handled by model_runner.prepare_attn.
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num_reqs = input_batch.num_reqs_after_padding
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num_tokens = input_batch.num_tokens_after_padding
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else:
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# For piecewise cudagraphs and eager, use unpadded sizes.
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num_reqs = input_batch.num_reqs
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num_tokens = input_batch.num_tokens
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query_start_loc_cpu = torch.from_numpy(input_batch.query_start_loc_np)
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max_query_len = input_batch.num_scheduled_tokens.max().item()
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seq_lens_cpu_upper_bound = input_batch.seq_lens_cpu_upper_bound
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if for_capture:
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# Capture with worst-case max_seq_len so the graph is valid at any replay.
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max_seq_len = self.max_model_len
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else:
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max_seq_len = seq_lens_cpu_upper_bound[:num_reqs].max().item()
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req_doc_ranges: dict[int, list[tuple[int, int]]] | None = None
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if (
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self.supports_mm_inputs
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and self.encoder_cache is not None
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and self.model_config.is_mm_prefix_lm
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):
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req_doc_ranges = compute_mm_prefix_ranges(
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req_ids=input_batch.req_ids,
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mm_features=self.encoder_cache.mm_features,
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sliding_window=self.model_config.get_sliding_window(),
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)
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attn_metadata = build_attn_metadata(
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attn_groups=attn_groups,
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num_reqs=num_reqs,
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num_tokens=num_tokens,
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query_start_loc_gpu=input_batch.query_start_loc,
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query_start_loc_cpu=query_start_loc_cpu,
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max_query_len=max_query_len,
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seq_lens=input_batch.seq_lens,
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max_seq_len=max_seq_len,
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block_tables=block_tables,
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slot_mappings=slot_mappings,
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kv_cache_config=kv_cache_config,
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seq_lens_cpu_upper_bound=seq_lens_cpu_upper_bound,
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dcp_local_seq_lens=input_batch.dcp_local_seq_lens,
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positions=input_batch.positions,
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mm_req_doc_ranges=req_doc_ranges,
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for_cudagraph_capture=for_capture,
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rswa_prefix_lens=input_batch.prompt_lens,
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)
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return attn_metadata
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