f1ffb5fb19
### What this PR does / why we need it?
vllm model runner v2 use uva buffer to prepare input data, but npu
doesn't support uva yet, this pr implement a uvawrapper class to mimic
gpu's uva backend. what's more, this pr make some modifications to adapt
to the newer main branch.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM main:
13397841ab
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
129 lines
4.2 KiB
Python
129 lines
4.2 KiB
Python
# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/sample/gumbel.py.
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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#
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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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import torch
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from vllm.triton_utils import tl, triton
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@triton.jit
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def _gumbel_sample_kernel(
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local_argmax_ptr,
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local_argmax_stride,
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local_max_ptr,
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local_max_stride,
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logits_ptr,
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logits_stride,
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idx_mapping_ptr,
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seeds_ptr,
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pos_ptr,
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temp_ptr,
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vocab_size,
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BLOCK_SIZE: tl.constexpr,
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APPLY_TEMPERATURE: tl.constexpr,
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):
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batch_idx = tl.program_id(0)
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req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
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block_idx = tl.program_id(1)
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block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
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mask = block < vocab_size
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logits = tl.load(
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logits_ptr + batch_idx * logits_stride + block,
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mask=mask,
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other=float("-inf"),
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)
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logits = logits.to(tl.float32)
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temp = tl.load(temp_ptr + req_state_idx).to(tl.float32)
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if temp != 0.0:
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# Calculate the seed for gumbel noise.
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seed = tl.load(seeds_ptr + req_state_idx)
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# NOTE(Ronald1995): change pos's dtype to tl.int32, because triton-ascend's
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# compiler doesn't support unint64 of pos arg.
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pos = tl.load(pos_ptr + batch_idx).to(tl.int32)
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gumbel_seed = tl.randint(seed, pos)
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# Generate gumbel noise.
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# NOTE(Ronald1995): r is tl.float64 in vllm, change it to tl.float32,
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# or triton-ascend's compiler will raise error.
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r = tl.rand(gumbel_seed, block).to(tl.float32)
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gumbel_noise = -tl.log(-tl.log(r + 1e-20) + 1e-20)
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gumbel_noise = gumbel_noise.to(tl.float32)
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# Apply temperature.
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if APPLY_TEMPERATURE:
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# NOTE(woosuk): Match the behavior of _temperature_kernel.
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# E.g., if the kernel uses tl.div_rn, we should use tl.div_rn here too.
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logits = logits / temp
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# Apply gumbel noise.
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logits = tl.where(mask, logits + gumbel_noise, float("-inf"))
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idx = tl.argmax(logits, axis=0)
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token_id = block_idx * BLOCK_SIZE + idx
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value = tl.max(logits, axis=0)
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tl.store(local_argmax_ptr + batch_idx * local_argmax_stride + block_idx, token_id)
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tl.store(local_max_ptr + batch_idx * local_max_stride + block_idx, value)
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def gumbel_sample(
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logits: torch.Tensor, # [num_reqs, vocab_size]
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idx_mapping: torch.Tensor, # [num_reqs]
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temperature: torch.Tensor, # [num_reqs]
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seed: torch.Tensor, # [num_reqs]
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pos: torch.Tensor, # [num_reqs]
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apply_temperature: bool,
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) -> torch.Tensor:
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num_reqs, vocab_size = logits.shape
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BLOCK_SIZE = 1024
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num_blocks = triton.cdiv(vocab_size, BLOCK_SIZE)
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local_argmax = torch.empty(
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num_reqs,
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num_blocks,
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dtype=torch.int64,
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device=logits.device,
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)
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local_max = torch.empty(
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num_reqs,
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num_blocks,
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dtype=torch.float32,
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device=logits.device,
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)
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# TODO(Ronald1995): Optimize the performance of the kernel in npu.
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_gumbel_sample_kernel[(num_reqs, num_blocks)](
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local_argmax,
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local_argmax.stride(0),
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local_max,
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local_max.stride(0),
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logits,
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logits.stride(0),
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idx_mapping,
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seed,
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pos,
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temperature,
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vocab_size,
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BLOCK_SIZE=BLOCK_SIZE,
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APPLY_TEMPERATURE=apply_temperature,
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)
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# NOTE(woosuk): Use int64 for later indexing.
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max_block_idx = local_max.argmax(dim=-1, keepdim=True)
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sampled = local_argmax.gather(dim=-1, index=max_block_idx).view(-1)
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return sampled
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