1ffca8673f
### What this PR does / why we need it?
Add basic 310p support. Only dense models work with eager mode now.
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
Signed-off-by: Shaoxu Cheng <2906339855@qq.com>
101 lines
4.2 KiB
Python
101 lines
4.2 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 typing import Any
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import torch
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import torch_npu
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from vllm.v1.kv_cache_interface import FullAttentionSpec, KVCacheConfig
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from vllm.v1.worker.utils import bind_kv_cache
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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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class NPUModelRunner310(NPUModelRunner):
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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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def _initialize_kv_cache_tensors_310p(self, kv_cache_config: KVCacheConfig) -> dict[str, Any]:
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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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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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assert len(kv_cache_tensor.shared_by) == 1, (
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"KV cache tensor shared by multiple layers is not supported in 310P."
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)
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kv_cache_sizes[kv_cache_tensor.shared_by[0]] = kv_cache_tensor.size
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kv_caches: dict[str, Any] = {}
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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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if not isinstance(kv_cache_spec, FullAttentionSpec):
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raise ValueError("Unknown KV cache spec type.")
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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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tensor_size = kv_cache_sizes[layer_name]
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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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if hasattr(attn_backend, "get_supported_block_size") and self.use_hybrid_blocks:
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block_size = attn_backend.get_supported_block_size()[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 = attn_backend.get_kv_cache_shape(
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num_blocks,
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kv_cache_spec.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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dtype = kv_cache_spec.dtype
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if "attn" in layer_name:
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k_tensor = torch.zeros(kv_cache_shape[1:], dtype=dtype, device=self.device)
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v_tensor = torch.zeros(kv_cache_shape[1:], dtype=dtype, device=self.device)
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k_cache = torch_npu.npu_format_cast(k_tensor, self._acl_format)
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v_cache = torch_npu.npu_format_cast(v_tensor, self._acl_format)
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kv_caches[layer_name] = (k_cache, v_cache)
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bind_kv_cache(
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kv_caches,
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self.compilation_config.static_forward_context,
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self.kv_caches,
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1, # 310p devices donnot support: hf_config.model_type == "longcat_flash"
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)
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return kv_caches
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def initialize_kv_cache_tensors(self, kv_cache_config: KVCacheConfig) -> dict[str, Any]:
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return self._initialize_kv_cache_tensors_310p(kv_cache_config)
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