8ffe3f5d78
### What this PR does / why we need it?
This PR introduces optimized Triton implementations for the
rejection_random_sample_kernel delivering superior performance compared
to the existing Triton implementations. The new Triton kernels maintain
full functional accuracy while delivering significant performance
improvements across various batch sizes and MTP configurations.
### Does this PR introduce _any_ user-facing change?
Yes, this PR modifies rejection_sampler.py to use optimized Triton
kernels:
rejection_random_sample_kernel is modified and optimized
### How was this patch tested?
performance benchmark results:
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<!--StartFragment-->
Batch Size | MTP | origin implementation(us) | optimized version(us)
-- | -- | -- | --
1 | 1 | 2.934 | 3.64
8 | 1 | 4.467 | 4
32 | 1 | 6.98 | 4.54
64 | 1 | 11.087 | 6.42
128 | 1 | 13.414 | 7.84
256 | 1 | 19.66 | 8.487
512 | 1 | 39.908 | 11.62
1024 | 1 | 81.781 | 18.16
2048 | 1 | 137.923 | 32.934
1 | 2 | 3.4 | 4.02
8 | 2 | 3.74 | 4.24
32 | 2 | 6.373 | 7.394
64 | 2 | 9.747 | 6.46
128 | 2 | 12.98 | 7.76
256 | 2 | 20.834 | 9.787
512 | 2 | 39.314 | 13.56
1024 | 2 | 83.135 | 22.387
2048 | 2 | 157.563 | 40.607
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- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: 1024daniel <xxltju324@gmail.com>
96 lines
4.2 KiB
Python
96 lines
4.2 KiB
Python
import pytest
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import torch
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from vllm.v1.sample.rejection_sampler import \
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rejection_random_sample_kernel as original_rejection_random_sample_kernel
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from vllm_ascend.ops.triton.reject_sample import (
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cal_grid_and_block_size, rejection_random_sample_kernel)
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from vllm_ascend.ops.triton.triton_utils import init_device_properties_triton
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@pytest.fixture(scope="function", autouse=True)
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def setup_device_properties():
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init_device_properties_triton()
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yield
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@pytest.mark.parametrize("max_spec_len", [1, 2, 3])
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@pytest.mark.parametrize("vocab_size", [151_936])
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@pytest.mark.parametrize("batch_size", [1, 8, 32, 64, 128, 256, 512, 1024])
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@torch.inference_mode()
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def test_rejection_random_sample(max_spec_len, vocab_size, batch_size):
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device = 'npu'
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torch.manual_seed(0)
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draft_probs = torch.rand(batch_size * max_spec_len,
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vocab_size,
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dtype=torch.float32,
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device=device)
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target_probs = torch.rand(batch_size * max_spec_len,
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vocab_size,
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dtype=torch.float32,
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device=device)
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bonus_token_ids = torch.randint(low=0,
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high=vocab_size,
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size=(batch_size, 1),
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dtype=torch.int64,
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device=device)
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draft_token_ids = torch.randint(low=0,
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high=vocab_size,
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size=(batch_size * max_spec_len, ),
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dtype=torch.int64,
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device=device)
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output_token_ids = torch.empty((batch_size, max_spec_len + 1),
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dtype=torch.int64,
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device=device)
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original_output_token_ids = output_token_ids.clone()
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num_tokens = draft_token_ids.shape[0]
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uniform_probs = torch.rand((num_tokens, ),
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dtype=torch.float32,
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device=device)
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num_draft_tokens = [max_spec_len] * batch_size
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num_draft_tokens = torch.tensor(num_draft_tokens,
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dtype=torch.int32,
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device=device)
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cu_num_draft_tokens = torch.cumsum(num_draft_tokens,
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dim=0,
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dtype=torch.int32)
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is_greedy_ptr = torch.full((batch_size, ),
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False,
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dtype=torch.bool,
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device=device)
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recovered_ids = torch.zeros_like(draft_token_ids,
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dtype=torch.int64,
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device=device)
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grid, block_size = cal_grid_and_block_size(batch_size)
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original_rejection_random_sample_kernel[(batch_size, )](
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original_output_token_ids,
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cu_num_draft_tokens,
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draft_token_ids,
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draft_probs,
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target_probs,
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bonus_token_ids,
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recovered_ids,
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uniform_probs,
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is_greedy_ptr,
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max_spec_len,
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vocab_size,
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NO_DRAFT_PROBS=draft_probs is None,
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)
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rejection_random_sample_kernel[(grid, )](output_token_ids,
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cu_num_draft_tokens,
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draft_token_ids,
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draft_probs,
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target_probs,
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bonus_token_ids,
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recovered_ids,
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uniform_probs,
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is_greedy_ptr,
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max_spec_len,
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vocab_size,
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batch_size,
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NO_DRAFT_PROBS=draft_probs
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is None,
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BLOCK_SIZE=block_size)
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torch.npu.synchronize()
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assert torch.equal(original_output_token_ids, output_token_ids)
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