36 lines
1.4 KiB
Python
36 lines
1.4 KiB
Python
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/kernels/test_moe.py
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# Copyright 2023 The vLLM team.
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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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import torch
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def concat_and_cache_mla(
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kv_c_normed: torch.Tensor, # [num_tokens, num_kv_head, nope]
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k_pe: torch.Tensor, # [num_tokens, num_kv_head, rope]
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kv_cache: torch.
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Tensor, # [num_blocks, block_size, num_kv_head, nope + rope]
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slot_mapping, # [num_tokens]
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):
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num_blocks = kv_cache.size()[0]
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block_size = kv_cache.size()[1]
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num_kv_head = k_pe.size()[1]
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idx_for_copy = slot_mapping // block_size * block_size + slot_mapping % block_size
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kv_cache = kv_cache.view(num_blocks * block_size, num_kv_head, -1)
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kv_cache[idx_for_copy] = torch.cat([kv_c_normed.unsqueeze(1), k_pe],
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dim=-1)
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