a73bd6caf4
<!-- Thanks for sending a pull request! BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html --> ### What this PR does / why we need it? <!-- - Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. - Please clarify why the changes are needed. For instance, the use case and bug description. - Fixes # --> Set div_mode to False to use the ACLNN kernel, which is crucial when using ACL Graph. ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as API, interface or other behavior changes. Documentation-only updates are not considered user-facing changes. --> ### How was this patch tested? <!-- CI passed with new added/existing test. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
295 lines
12 KiB
Python
295 lines
12 KiB
Python
# 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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from typing import List, Optional
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import torch
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from vllm.model_executor.layers.linear import ColumnParallelLinear
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# Implementation of vanilla chunked prefill, should be removed after the kernel is ready for
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# all the corner case
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def vanilla_chunked_prefill(
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output: torch.Tensor,
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query: torch.Tensor, # (num_tokens, heads, head_size)
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key_cache: torch.Tensor, # (num_blocks, block_size, kv_heads, head_size)
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value_cache: torch.
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Tensor, # (num_blocks, block_size, kv_heads, head_size,)
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block_tables: torch.Tensor, # (num_seqs, max_num_blocks_per_seq)
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cu_seqlen_q: torch.Tensor, # (num_seqs + 1,)
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cu_seqlen_k: torch.Tensor, # (num_seqs + 1,)
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max_seqlen_q: int,
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max_seqlen_k: int,
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scale: float,
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alibi_slopes: Optional[torch.Tensor],
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causal: bool = True,
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) -> None:
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num_query_heads = query.shape[1]
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head_dim = value_cache.shape[3]
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num_kv_heads = value_cache.shape[2]
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block_size = value_cache.shape[1]
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num_batch = cu_seqlen_q.shape[0] - 1
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max_num_blocks_per_seq = block_tables.shape[1]
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key = key_cache[block_tables].view(num_batch,
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max_num_blocks_per_seq * block_size,
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num_kv_heads, head_dim)
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value = value_cache[block_tables].view(num_batch,
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max_num_blocks_per_seq * block_size,
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num_kv_heads, head_dim)
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key = key[:, :max_seqlen_k, :, :]
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value = value[:, :max_seqlen_k, :, :]
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seqlen_k = cu_seqlen_k[1:] - cu_seqlen_k[:-1]
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seqlen_q = cu_seqlen_q[1:] - cu_seqlen_q[:-1]
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seqlen_q = seqlen_q.view(-1, 1)
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seqlen_k = seqlen_k.view(-1, 1)
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seqlen_diff = seqlen_k - seqlen_q
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q_idx_mask = (torch.arange(0, max_seqlen_q,
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device="npu").view(1, -1).repeat(num_batch, 1))
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k_idx_mask = (torch.arange(0, max_seqlen_k,
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device="npu").view(1, -1).repeat(num_batch, 1))
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q_mask = q_idx_mask < seqlen_q
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k_mask = k_idx_mask < seqlen_k
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# calculate idx for causal mask of query [batch, max_seqlen_q]
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causal_mask_idx = (q_idx_mask + seqlen_diff)[q_mask]
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# generate causal mask [batch, max_seqlen_q, max_seqlen_k]
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tril_mask = torch.tril(torch.ones(max_seqlen_k, max_seqlen_k,
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device="npu"))
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tril_mask[tril_mask == 0] = float("-inf")
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tril_mask[tril_mask == 1] = 0
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causal_mask = tril_mask[causal_mask_idx]
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causal_mask_padding = torch.empty([num_batch, max_seqlen_q, max_seqlen_k],
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device="npu").fill_(float("-inf"))
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causal_mask_padding[q_mask] = causal_mask
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# to [batch, num_heads, max_seqlen_q, max_seqlen_k]
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causal_mask_padding = causal_mask_padding.unsqueeze(1)
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pad_q = torch.zeros(
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[num_batch, max_seqlen_q, num_query_heads, head_dim],
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device="npu",
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dtype=query.dtype,
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)
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pad_k = torch.zeros(
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[num_batch, max_seqlen_k, num_kv_heads, head_dim],
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device="npu",
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dtype=key.dtype,
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)
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pad_v = torch.zeros(
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[num_batch, max_seqlen_k, num_kv_heads, head_dim],
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device="npu",
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dtype=value.dtype,
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)
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pad_q[q_mask] = query
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pad_k[k_mask] = key[k_mask]
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pad_v[k_mask] = value[k_mask]
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if num_query_heads > num_kv_heads:
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pad_k = pad_k.view(
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[num_batch, max_seqlen_k, num_kv_heads, 1, head_dim])
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pad_k = pad_k.repeat(1, 1, 1, num_query_heads // num_kv_heads, 1).view(
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[num_batch, max_seqlen_k, num_query_heads, head_dim])
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pad_v = pad_v.view(
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[num_batch, max_seqlen_k, num_kv_heads, 1, head_dim])
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pad_v = pad_v.repeat(1, 1, 1, num_query_heads // num_kv_heads, 1).view(
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[num_batch, max_seqlen_k, num_query_heads, head_dim])
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# permute to [b, h, n, k]
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pad_q = pad_q.permute(0, 2, 1, 3)
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pad_k = pad_k.permute(0, 2, 1, 3)
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pad_v = pad_v.permute(0, 2, 1, 3)
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attn_mask = torch.empty([num_batch, 1, 1, max_seqlen_k],
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device="npu").fill_(float("-inf"))
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attn_mask[:, :, :, :max_seqlen_k].masked_fill_(k_mask[:, None, None, :], 0)
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# [b, h, f, t]
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attn_weights = torch.einsum("bhqd,bhkd->bhqk", pad_q, pad_k)
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attn_weights *= scale
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attn_mask = attn_mask.float()
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attn_weights = attn_weights + attn_mask
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if causal:
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attn_weights = attn_weights + causal_mask_padding
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attn_weights = torch.softmax(attn_weights, dim=-1)
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attn_output = torch.einsum("bhqk,bhkd->bhqd", attn_weights, pad_v.float())
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attn_output = attn_output.permute(0, 2, 1, 3)
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attn_output = (attn_output[q_mask].view([-1, num_query_heads,
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head_dim]).to(output.dtype))
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output.copy_(attn_output)
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return attn_output
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def vanilla_chunked_prefill_mla(
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output: torch.Tensor, # (num_tokens, num_heads, v_head_dim)
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query: torch.Tensor, # (num_tokens, num_heads, nope_dim + rope_dim)
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kv_cache: torch.Tensor, # (num_blocks, block_size, latent_kv)
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block_tables: torch.Tensor, # (batch_size, max_num_blocks_per_seq)
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query_lens: torch.Tensor, # (batch_size)
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context_lens: torch.Tensor, # (batch_size)
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kv_b_proj: ColumnParallelLinear, # ()
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max_query_len: int,
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max_context_len: int,
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nope_dim: int,
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rope_dim: int,
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v_head_dim: int,
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scale: float,
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alibi_slopes: Optional[torch.Tensor],
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causal: bool = True) -> None:
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batch_size = block_tables.size(0)
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assert query_lens.size(0) == batch_size
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num_heads = query.size(1)
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block_size = kv_cache.size(1)
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latent_kv_dim = kv_cache.size(3) - rope_dim
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max_num_blocks_per_seq = block_tables.size(1)
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batch_size = query_lens.size(0)
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kv_cache = kv_cache.squeeze()
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# select kv_c out as [batch_size, max_context_len, latent_kv + rope_dim]
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cache_kv_c_pe = kv_cache[block_tables].view(
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batch_size, max_num_blocks_per_seq * block_size,
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latent_kv_dim + rope_dim)[:, :max_context_len, :]
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# get kv_c and k_pe
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# cached_kv_c: [batch_size, max_context_len, latent_kv]
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# cached_k_pe: [batch_size, max_context_len, rope_dim]
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cache_kv_c = cache_kv_c_pe[:, :, :latent_kv_dim]
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cache_k_pe = cache_kv_c_pe[:, :, latent_kv_dim:]
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# get k_rope and v
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# k_nope: [batch_size, max_context_len, num_heads, nope_dim]
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# value: [batch_size, max_context_len, num_heads, v_head_dim]
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k_nope, value = kv_b_proj(cache_kv_c)[0].view(
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batch_size, max_context_len, num_heads,
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nope_dim + v_head_dim).split([nope_dim, v_head_dim], dim=-1)
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# key: [batch_size, max_context_len, num_hads, rope_dim + nope_dim]
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key = torch.cat(
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[k_nope, cache_k_pe.unsqueeze(2).expand(-1, -1, num_heads, -1)],
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dim=-1)
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context_lens = context_lens.view(-1, 1).to("npu")
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query_lens = query_lens.view(-1, 1).to("npu")
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seq_diff = context_lens - query_lens
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q_idx_mask = (torch.arange(0, max_query_len,
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device="npu").view(1, -1).repeat(batch_size, 1))
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kv_c_idx_mask = (torch.arange(0, max_context_len,
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device="npu").view(1,
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-1).repeat(batch_size, 1))
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kv_c_mask = kv_c_idx_mask < context_lens
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q_mask = q_idx_mask < query_lens
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# calculate idx for causal mask of query [batch, max_seqlen_q]
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causal_mask_idx = (q_idx_mask + seq_diff)[q_mask]
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# generate causal mask [batch, max_seqlen_q, max_seqlen_k]
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tril_mask = torch.tril(
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torch.ones(max_context_len, max_context_len, device="npu"))
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tril_mask[tril_mask == 0] = float("-inf")
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tril_mask[tril_mask == 1] = 0
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causal_mask = tril_mask[causal_mask_idx]
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causal_mask_padding = torch.empty(
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[batch_size, max_query_len, max_context_len],
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device="npu").fill_(float("-inf"))
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causal_mask_padding[q_mask] = causal_mask
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# to [batch, num_heads, max_seqlen_q, max_seqlen_k]
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causal_mask_padding = causal_mask_padding.unsqueeze(1)
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pad_q = torch.zeros(
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[batch_size, max_query_len, num_heads, rope_dim + nope_dim],
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device="npu",
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dtype=query.dtype,
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)
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pad_k = torch.zeros(
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[batch_size, max_context_len, num_heads, rope_dim + nope_dim],
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device="npu",
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dtype=key.dtype,
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)
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pad_v = torch.zeros(
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[batch_size, max_context_len, num_heads, v_head_dim],
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device="npu",
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dtype=value.dtype,
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)
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pad_q[q_mask] = query
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pad_k[kv_c_mask] = key[kv_c_mask]
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pad_v[kv_c_mask] = value[kv_c_mask]
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pad_q = pad_q.permute(0, 2, 1, 3)
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pad_k = pad_k.permute(0, 2, 1, 3)
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pad_v = pad_v.permute(0, 2, 1, 3)
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attn_mask = torch.empty([batch_size, 1, 1, max_context_len],
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device="npu").fill_(float("-inf"))
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attn_mask[:, :, :, :max_context_len].masked_fill_(
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kv_c_mask[:, None, None, :], 0)
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# [b, h, f, t]
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attn_weights = torch.einsum("bhqd,bhkd->bhqk", pad_q, pad_k)
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attn_weights *= scale
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attn_mask = attn_mask.float()
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attn_weights = attn_weights + attn_mask
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if causal:
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attn_weights = attn_weights + causal_mask_padding
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attn_weights = torch.softmax(attn_weights, dim=-1)
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attn_output = torch.einsum("bhqk,bhkd->bhqd", attn_weights, pad_v.float())
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attn_output = attn_output.permute(0, 2, 1, 3)
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attn_output = (attn_output[q_mask].view([-1, num_heads,
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v_head_dim]).to(output.dtype))
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output = output.view_as(attn_output)
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output.copy_(attn_output)
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return attn_output
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def vanilla_decode_mla(
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query: torch.Tensor, # [num_tokens, num_heads, latent_dim + rope_dim]
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key_cache: torch.
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Tensor, # [num_blocks, block_size, num_kv_heads, latent_dim + rope_dim]
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num_kv_heads: int,
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num_heads: int,
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scale: float,
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block_table: torch.Tensor, # [batch_size, max_block_size]
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context_lens: List[int],
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mla_vhead_size: int,
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rope_dim: int,
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output: torch.Tensor):
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batch_size = block_table.size()[0]
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max_block_size = block_table.size()[1]
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reduce_dim = key_cache.size()[-1]
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block_size = key_cache.size()[1]
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latent_dim = reduce_dim - rope_dim
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kv_c_and_pe = key_cache[block_table].view(
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[batch_size, max_block_size * block_size, num_kv_heads, reduce_dim])
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max_context_len = max(context_lens)
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context_lens = torch.tensor(context_lens, device="npu").view(batch_size, 1)
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# [batch_size, max_context_len, num_kv_heads, latent_dim + rope_dim]
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# since the kv head is 1 in deepseek, we use expand here for perf
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kv_c_and_pe = kv_c_and_pe[:, :max_context_len, :, :].expand(
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-1, -1, num_heads, 1)
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kv_c = kv_c_and_pe[..., :latent_dim]
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kv_idx_mask = (torch.arange(0, max_context_len,
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device="npu").view(1,
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-1).repeat(batch_size, 1))
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# [batch_size, max_context_len]
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kv_idx_mask = kv_idx_mask < context_lens
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query = query.unsqueeze(1)
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attn_weights = torch.einsum("bqhd,bkhd->bhqk", query, kv_c_and_pe)
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attn_weights *= scale
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attn_weights = attn_weights + kv_idx_mask[:, -1, -1, :].float()
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attn_weights = torch.softmax(attn_weights, dim=-1)
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attn_output = torch.einsum("bhqk,bkhd->bqhd", attn_weights,
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kv_c.float()).view(-1, num_heads, latent_dim)
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output.copy_(attn_output)
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return output
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