755caeb06e
### What this PR does / why we need it? Replace multiple PyTorch operations with a fused Triton kernel to determine token indices for sampling during speculative decoding. This reduces kernel launch overhead and memory traffic, improving overall performance on Ascend hardware. --------- Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
# 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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# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/v1/spec_decode/utils.py
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from vllm.triton_utils import tl, triton
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@triton.jit
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def prepare_inputs_padded_kernel(
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cu_num_draft_tokens_ptr, # [num_reqs]
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valid_sampled_tokens_count_ptr, # [num_reqs]
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query_start_loc_gpu_ptr, # [num_reqs + 1]
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token_indices_to_sample_ptr, # [num_reqs] (output)
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num_reqs, # tl.int32
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BLOCK_SIZE: tl.constexpr,
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):
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pid = tl.program_id(axis=0)
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num_programs = tl.num_programs(axis=0)
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# Grid-Stride Loop:
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block_start_step = num_programs * BLOCK_SIZE
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for block_start in tl.range(pid * BLOCK_SIZE, num_reqs, block_start_step):
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offsets = block_start + tl.arange(0, BLOCK_SIZE)
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mask = offsets < num_reqs
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# Calculate num_draft_tokens from cu_num_draft_tokens, which is an inclusive
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# cumulative sum (first entry is the first value, not zero).
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cu_draft_curr = tl.load(cu_num_draft_tokens_ptr + offsets, mask=mask)
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prev_indices = offsets - 1
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has_prev = offsets > 0
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cu_draft_prev = tl.load(
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cu_num_draft_tokens_ptr + prev_indices,
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mask=mask & has_prev,
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other=0,
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)
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num_draft_tokens = tl.where(has_prev, cu_draft_curr - cu_draft_prev,
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cu_draft_curr)
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valid_count = tl.load(valid_sampled_tokens_count_ptr + offsets,
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mask=mask)
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num_rejected = num_draft_tokens + 1 - valid_count
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num_rejected = tl.where(num_draft_tokens > 0, num_rejected, 0)
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# query_start_loc[req_idx + 1] is the start position of the next request,
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# which is one past the last token of this request.
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q_last_tok_idx = tl.load(query_start_loc_gpu_ptr + offsets + 1,
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mask=mask) - 1
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index_to_sample = q_last_tok_idx - num_rejected
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tl.store(token_indices_to_sample_ptr + offsets,
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index_to_sample,
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mask=mask)
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