88c56e3bf2
### What this PR does / why we need it?
Fix lint failed due to the merging of a previous PR.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
431 lines
20 KiB
Python
431 lines
20 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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from __future__ import annotations
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from contextlib import contextmanager, nullcontext
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import numpy as np
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import torch
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import torch_npu
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from vllm.config import CUDAGraphMode
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from vllm.logger import logger
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.kv_cache_interface import AttentionSpec, KVCacheConfig, MambaSpec
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ
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from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
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_NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN = 1
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class NPUModelRunner310(NPUModelRunner):
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# Inherited from parent runner; annotated here to satisfy strict type checks.
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uniform_decode_query_len: int
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._acl_format = ACL_FORMAT_FRACTAL_NZ
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if self.speculative_config is not None and self.speculative_config.method == "ngram":
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# 310P ngram requires decode-only graph shapes to be built with q_len=1.
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# Keep dispatcher's internal query_len in sync to avoid key-init assert.
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self.cudagraph_dispatcher.uniform_decode_query_len = _NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN
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@contextmanager
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def temporary_modify_uniform_decode_query_len(self):
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# This is only needed for the 310P ngram path where dispatcher uses q_len=1
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# while runner's default uniform_decode_query_len remains 1 + num_spec_tokens.
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# TODO: remove this temporary override after upstream supports independent
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# decode capture query_len for backend-specific paths.
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if self.speculative_config is None or self.speculative_config.method != "ngram":
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yield
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return
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original_uniform_decode_query_len = self.uniform_decode_query_len
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self.uniform_decode_query_len = _NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN
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try:
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yield
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finally:
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self.uniform_decode_query_len = original_uniform_decode_query_len
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def _determine_batch_execution_and_padding(
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self,
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num_tokens: int,
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num_reqs: int,
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num_scheduled_tokens_np: np.ndarray,
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max_num_scheduled_tokens: int,
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use_cascade_attn: bool,
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allow_microbatching: bool = False,
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force_eager: bool = False,
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force_uniform_decode: bool | None = None,
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force_has_lora: bool | None = None,
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force_num_active_loras: int | None = None,
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num_encoder_reqs: int = 0,
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):
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if self.attn_state in (AscendAttentionState.ChunkedPrefill, AscendAttentionState.PrefillCacheHit):
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force_eager = True
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if force_uniform_decode is None and self.attn_state == AscendAttentionState.DecodeOnly:
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decode_query_len = _NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN
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if (
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max_num_scheduled_tokens == decode_query_len
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and num_tokens == max_num_scheduled_tokens * num_reqs
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and np.all(self.input_batch.num_computed_tokens_cpu[:num_reqs] > 0)
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):
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# Respect explicit caller override: only force when unset.
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force_uniform_decode = True
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return super()._determine_batch_execution_and_padding(
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num_tokens=num_tokens,
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num_reqs=num_reqs,
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num_scheduled_tokens_np=num_scheduled_tokens_np,
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max_num_scheduled_tokens=max_num_scheduled_tokens,
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use_cascade_attn=use_cascade_attn,
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allow_microbatching=allow_microbatching,
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force_eager=force_eager,
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force_uniform_decode=force_uniform_decode,
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force_has_lora=force_has_lora,
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force_num_active_loras=force_num_active_loras,
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num_encoder_reqs=num_encoder_reqs,
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)
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def _pad_query_start_loc_for_fia(
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self,
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num_tokens_padded: int,
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num_reqs_padded: int,
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num_reqs: int,
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cudagraph_runtime_mode: CUDAGraphMode | None = None,
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batch_desc_num_reqs: int | None = None,
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) -> int:
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# Keep this aligned with the dispatcher because batch_desc.num_reqs is
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# generated by dispatcher._create_padded_batch_descriptor().
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# For 310P ngram we intentionally set dispatcher q_len=1, while runner's
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# default uniform_decode_query_len may remain 1 + num_spec_tokens.
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uniform_decode_query_len = self.cudagraph_dispatcher.uniform_decode_query_len
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if num_tokens_padded == num_reqs_padded * uniform_decode_query_len:
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# Uniform-batch case: num_reqs must be no greater than num_reqs_padded
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assert num_reqs <= num_reqs_padded
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last_loc = self.query_start_loc.np[num_reqs]
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self.query_start_loc.np[num_reqs + 1 : num_reqs_padded + 1] = (
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self.arange_np[1 : num_reqs_padded + 1 - num_reqs] * uniform_decode_query_len + last_loc
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)
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else:
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# Mixed-batch case: num_reqs must equal num_reqs_padded
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assert num_reqs == num_reqs_padded
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# Insert a dummy request instead of setting query_start_loc[num_reqs] = num_tokens_padded directly
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self.query_start_loc.np[num_reqs_padded + 1] = num_tokens_padded
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num_reqs_padded = num_reqs_padded + 1
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self.query_start_loc.copy_to_gpu()
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return num_reqs_padded
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@torch.inference_mode()
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def _dummy_run(
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self,
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num_tokens: int,
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with_prefill: bool = False,
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cudagraph_runtime_mode=None,
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force_attention: bool = False,
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uniform_decode: bool = False,
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is_profile: bool = False,
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create_mixed_batch: bool = False,
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allow_microbatching: bool = True,
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skip_eplb: bool = False,
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remove_lora: bool = True,
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is_graph_capturing: bool = False,
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num_active_loras: int = 0,
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):
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temporary_context = self.temporary_modify_uniform_decode_query_len() if uniform_decode else nullcontext()
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with temporary_context:
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return super()._dummy_run(
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num_tokens=num_tokens,
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with_prefill=with_prefill,
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cudagraph_runtime_mode=cudagraph_runtime_mode,
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force_attention=force_attention,
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uniform_decode=uniform_decode,
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is_profile=is_profile,
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create_mixed_batch=create_mixed_batch,
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allow_microbatching=allow_microbatching,
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skip_eplb=skip_eplb,
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remove_lora=remove_lora,
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is_graph_capturing=is_graph_capturing,
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num_active_loras=num_active_loras,
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)
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def _check_and_update_cudagraph_mode(
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self,
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attention_backends,
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kv_cache_groups,
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) -> None:
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# 910B does not need this branch because runner/dispatcher query_len are
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# naturally consistent there. 310P ngram needs temporary alignment.
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with self.temporary_modify_uniform_decode_query_len():
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super()._check_and_update_cudagraph_mode(attention_backends, kv_cache_groups)
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def initialize_kv_cache_tensors(self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
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"""
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Initialize the memory buffer for KV cache.
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Args:
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kv_cache_config: The KV cache config
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Returns:
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Dict[str, torch.Tensor]: A map between layer names to their
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corresponding memory buffer for KV cache.
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"""
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# 310P limitation: KV transfer is not supported
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if self.vllm_config.kv_transfer_config is not None:
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raise ValueError("KV cache transfer is not supported for 310P.")
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if self.use_sparse:
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raise ValueError("Deepseek Sparse Attention is not supported for 310P.")
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if self.model_config.use_mla:
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raise ValueError("MLAAttention is not supported for 310P.")
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# Initialize the memory size for KV cache
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kv_cache_size = self._calculate_kv_cache_tensors_size(kv_cache_config)
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# Allocate and reshape KV cache Tensors
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kv_caches = self._allocate_kv_cache_and_reshape_tensors(kv_cache_config, kv_cache_size)
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# Set up cross-layer KV cache sharing
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for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
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logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
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kv_caches[layer_name] = kv_caches[target_layer_name]
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from vllm.v1.worker.utils import bind_kv_cache
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bind_kv_cache(kv_caches, self.compilation_config.static_forward_context, self.kv_caches)
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return kv_caches
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def _calculate_kv_cache_tensors_size(self, kv_cache_config: KVCacheConfig) -> dict[str, int]:
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"""
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Initializes the KV cache size. The buffer needs to be reshaped to the desired shape before being used by
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the models.
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Args:
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kv_cache_config: The KV cache config
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Returns:
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dict[str, int]: A map between layer names to their
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corresponding memory buffer size.
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"""
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# init kv cache tensors
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kv_cache_sizes: dict[str, int] = {}
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for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
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# TODO: REFACTOR ME to sharing hybrid cache
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for idx in range(len(kv_cache_tensor.shared_by)):
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layer_name = kv_cache_tensor.shared_by[idx]
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if "linear_attn" in layer_name and layer_name not in kv_cache_sizes:
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# for mamba linear attention
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kv_cache_size = kv_cache_tensor.size
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for layer_name_inner in kv_cache_tensor.shared_by:
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# shared the kvcache between the self_attn specs in the same group
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if "linear_attn" in layer_name_inner:
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kv_cache_sizes[layer_name_inner] = kv_cache_size
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elif "attn" in layer_name and layer_name not in kv_cache_sizes:
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kv_tensor_split_factor = 2
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kv_tensor_size = int(kv_cache_tensor.size // kv_tensor_split_factor)
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for layer_name_inner in kv_cache_tensor.shared_by:
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# shared the kvcache between the self_attn specs in the same group
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if "attn" in layer_name_inner and "linear_attn" not in layer_name_inner:
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kv_cache_sizes[layer_name_inner] = kv_tensor_size
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layer_names = set()
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for group in kv_cache_config.kv_cache_groups:
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for layer_name in group.layer_names:
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if layer_name in self.runner_only_attn_layers:
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continue
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layer_names.add(layer_name)
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assert layer_names == set(kv_cache_sizes.keys()), "Some layers are not correctly initialized"
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return kv_cache_sizes
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def _allocate_kv_cache_and_reshape_tensors(
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self,
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kv_cache_config: KVCacheConfig,
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kv_cache_sizes: dict[str, int],
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) -> dict[str, torch.Tensor]:
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"""
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Allocate the KV cache tensors to the desired shape and dtype.
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Args:
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kv_cache_config: The KV cache config
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kv_cache_sizes: The KV cache size of each layer
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Returns:
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dict[str, torch.Tensor]: A map between layer names to their
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corresponding memory buffer for KV cache.
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"""
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kv_caches: dict[str, torch.Tensor] = {}
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for group in self._kv_cache_spec_attn_group_iterator():
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kv_cache_spec = group.kv_cache_spec
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attn_backend = group.backend
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for layer_name in group.layer_names:
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if layer_name in self.runner_only_attn_layers:
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continue
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if isinstance(kv_cache_spec, AttentionSpec):
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kv_tensor_size = kv_cache_sizes[layer_name]
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assert kv_tensor_size is not None
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sum_page_size_bytes = kv_tensor_size * 2
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assert sum_page_size_bytes % kv_cache_spec.page_size_bytes == 0
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num_blocks = sum_page_size_bytes // kv_cache_spec.page_size_bytes
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assert num_blocks >= kv_cache_config.num_blocks
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if hasattr(attn_backend, "get_supported_kernel_block_sizes") and self.use_hybrid_blocks:
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block_size = attn_backend.get_supported_kernel_block_sizes()[0]
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block_size_chunk = kv_cache_spec.block_size // block_size
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kv_cache_shape = attn_backend.get_kv_cache_shape(
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num_blocks * block_size_chunk,
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block_size,
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kv_cache_spec.num_kv_heads,
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kv_cache_spec.head_size,
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)
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else:
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kv_cache_shape = self.attn_backend.get_kv_cache_shape(
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num_blocks, kv_cache_spec.block_size, kv_cache_spec.num_kv_heads, kv_cache_spec.head_size
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)
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dtype = kv_cache_spec.dtype
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k_shape = kv_cache_shape[1:]
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v_shape = k_shape
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k_cache = torch_npu.empty_with_format(
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size=k_shape, dtype=dtype, device=self.device, acl_format=self._acl_format
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)
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v_cache = torch_npu.empty_with_format(
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size=v_shape, dtype=dtype, device=self.device, acl_format=self._acl_format
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)
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kv_caches[layer_name] = (k_cache, v_cache)
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elif isinstance(kv_cache_spec, MambaSpec):
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tensor_size = kv_cache_sizes[layer_name]
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dtype = kv_cache_spec.dtype
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tensor_element_size = torch.tensor([], dtype=dtype).element_size()
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raw_tensor = torch.zeros(tensor_size // tensor_element_size, dtype=dtype, device=self.device)
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assert tensor_size is not None
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assert tensor_size % kv_cache_spec.page_size_bytes == 0
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num_blocks = tensor_size // kv_cache_spec.page_size_bytes
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assert num_blocks >= kv_cache_config.num_blocks
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state_tensors = []
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target_idx = 0
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start_idx = 0
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for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
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# normally, there is conv state and ssm state in this loop. And there is only
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# a conv state in some special models.
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target_shape = (num_blocks, *shape)
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target_idx += torch.prod(torch.tensor(target_shape)).item()
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tensor = raw_tensor[start_idx:target_idx].view(target_shape)
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start_idx = target_idx
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state_tensors.append(tensor)
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kv_caches[layer_name] = state_tensors
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else:
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raise ValueError("Unknown KV cache spec type.")
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return kv_caches
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# Override this function because of tensor.copy_(other) accuracy issue.
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# TODO: This override will be removed after tensor.copy_(other) accuracy issue is resolved.
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def _prepare_input_ids(
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self,
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scheduler_output: SchedulerOutput,
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total_num_scheduled_tokens: int,
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cu_num_tokens: np.ndarray,
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) -> None:
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"""Prepare the input IDs for the current batch.
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Carefully handles the `prev_sampled_token_ids` which can be cached
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from the previous engine iteration, in which case those tokens on the
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GPU need to be copied into the corresponding slots into input_ids."""
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if self.input_batch.prev_sampled_token_ids is None:
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# Normal scheduling case
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self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
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if self.enable_prompt_embeds:
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self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
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self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
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return
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# Async scheduling case, where some decode requests from the previous
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# iteration won't have entries in input_ids_cpu and need to be copied
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# on the NPU from prev_sampled_token_ids.
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prev_req_id_to_index = self.input_batch.prev_req_id_to_index
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assert prev_req_id_to_index is not None
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sample_flattened_indices: list[int] = []
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spec_flattened_indices: list[int] = []
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prev_common_req_indices: list[int] = []
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prev_draft_token_indices: list[int] = []
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indices_match = True
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max_flattened_index = -1
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total_num_spec_tokens = 0
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scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
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for req_id, cur_index in self.input_batch.req_id_to_index.items():
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if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
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prev_common_req_indices.append(prev_index)
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draft_len = len(scheduled_spec_tokens.get(req_id, ()))
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total_num_spec_tokens += draft_len
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flattened_index = cu_num_tokens[cur_index].item() - 1
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sample_flattened_indices.append(flattened_index - draft_len)
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spec_flattened_indices.extend(range(flattened_index - draft_len + 1, flattened_index + 1))
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start = prev_index * self.num_spec_tokens
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prev_draft_token_indices.extend(range(start, start + draft_len))
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indices_match &= prev_index == flattened_index
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max_flattened_index = max(max_flattened_index, flattened_index)
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num_common_tokens = len(sample_flattened_indices)
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total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
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if num_common_tokens < total_without_spec:
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self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
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if self.enable_prompt_embeds:
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self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
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self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
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if num_common_tokens == 0:
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return
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if indices_match and max_flattened_index == (num_common_tokens - 1):
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# NOTE: Override the copy_ function here
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indices = torch.arange(num_common_tokens, device=self.input_ids.gpu.device)
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source = self.input_batch.prev_sampled_token_ids[:num_common_tokens, 0]
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self.input_ids.gpu.index_copy_(0, indices, source)
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if self.enable_prompt_embeds:
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self.is_token_ids.gpu[:num_common_tokens] = True
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return
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# Upload the index tensors asynchronously so the scatter can be non-blocking.
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sampled_tokens_index_tensor = torch.tensor(
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sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
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).to(self.device, non_blocking=True)
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prev_common_req_indices_tensor = torch.tensor(
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prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
self.input_ids.gpu.scatter_(
|
|
dim=0,
|
|
index=sampled_tokens_index_tensor,
|
|
src=self.input_batch.prev_sampled_token_ids[prev_common_req_indices_tensor, 0],
|
|
)
|
|
# Scatter the draft tokens after the sampled tokens are scattered.
|
|
if self._draft_token_ids is None or not spec_flattened_indices:
|
|
return
|
|
assert isinstance(self._draft_token_ids, torch.Tensor)
|
|
draft_tokens_index_tensor = torch.tensor(
|
|
spec_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
prev_draft_token_indices_tensor = torch.tensor(
|
|
prev_draft_token_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
draft_token_ids = self._draft_token_ids.to(dtype=torch.int32)
|
|
self.input_ids.gpu.scatter_(
|
|
dim=0,
|
|
index=draft_tokens_index_tensor,
|
|
src=draft_token_ids.flatten()[prev_draft_token_indices_tensor],
|
|
)
|