102 lines
3.1 KiB
Python
102 lines
3.1 KiB
Python
import torch
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# vllm imports
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from vllm import cache_ops
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from vllm import attention_ops
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_PARTITION_SIZE = 512
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def reshape_and_cache(
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key: torch.Tensor,
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value: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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slots: torch.Tensor,
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):
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cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots)
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def attention(
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out: torch.Tensor,
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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kv_head_mapping: torch.Tensor,
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softmax_scale: float,
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block_tables: torch.Tensor,
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input_lengths: torch.Tensor,
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max_s: int,
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):
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# Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
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# Copyright 2023 The vLLM team. All rights
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# 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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#
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# value_cache => [num_blocks, num_heads, head_size, block_size]
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block_size = value_cache.shape[3]
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num_seqs, num_heads, head_size = query.shape
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max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
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# NOTE(woosuk): We use a simple heuristic to decide whether to use
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# PagedAttention V1 or V2. If the number of partitions is 1, we use
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# V1 to avoid the overhead of reduction. Also, if the number of
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# sequences or heads is large, we use V1 since there is enough work
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# to parallelize.
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use_v1 = max_num_partitions == 1 or num_seqs * num_heads > 512
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if use_v1:
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attention_ops.paged_attention_v1(
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out,
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query,
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key_cache,
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value_cache,
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kv_head_mapping,
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softmax_scale,
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block_tables,
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input_lengths,
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block_size,
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max_s,
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None,
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)
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else:
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# Run PagedAttention V2.
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assert _PARTITION_SIZE % block_size == 0
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tmp_output = torch.empty(
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size=(num_seqs, num_heads, max_num_partitions, head_size),
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dtype=out.dtype,
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device=out.device,
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)
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exp_sums = torch.empty(
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size=(num_seqs, num_heads, max_num_partitions),
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dtype=torch.float32,
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device=out.device,
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)
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max_logits = torch.empty_like(exp_sums)
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attention_ops.paged_attention_v2(
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out,
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exp_sums,
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max_logits,
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tmp_output,
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query,
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key_cache,
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value_cache,
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kv_head_mapping,
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softmax_scale,
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block_tables,
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input_lengths,
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block_size,
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max_s,
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None,
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)
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