forked from EngineX-Hygon/enginex-hygon-vllm
248 lines
9.0 KiB
Python
248 lines
9.0 KiB
Python
import triton
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import triton.language as tl
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import torch
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from functools import reduce
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import pytest
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import torch
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import math
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@pytest.mark.parametrize("shape_pair,dim", [
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(((4, 8, 512), (4, 8, 64)), 2),
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(((8, 8, 512), (8, 8, 64)), 2),
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(((16, 8, 512), (16, 8, 64)), 2),
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(((32, 8, 512), (32, 8, 64)), 2),
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(((64, 8, 512), (64, 8, 64)), 2),
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(((128, 8, 512), (128, 8, 64)), 2),
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(((256, 8, 512), (256, 8, 64)), 2),
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(((512, 8, 512), (512, 8, 64)), 2),
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(((672, 8, 512), (672, 8, 64)), 2),
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(((768, 8, 512), (768, 8, 64)), 2),
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(((896, 8, 512), (896, 8, 64)), 2),
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(((1024, 8, 512), (1024, 8, 64)), 2),
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(((4, 16, 512), (4, 16, 64)), 2),
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(((8, 16, 512), (8, 16, 64)), 2),
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(((16, 16, 512), (16, 16, 64)), 2),
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(((32, 16, 512), (32, 16, 64)), 2),
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(((64, 16, 512), (64, 16, 64)), 2),
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(((128, 16, 512), (128, 16, 64)), 2),
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(((256, 16, 512), (256, 16, 64)), 2),
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(((512, 16, 512), (512, 16, 64)), 2),
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(((672, 16, 512), (672, 16, 64)), 2),
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(((768, 16, 512), (768, 16, 64)), 2),
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(((896, 16, 512), (896, 16, 64)), 2),
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(((1024, 16, 512), (1024, 16, 64)), 2),
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(((4, 32, 512), (4, 32, 64)), 2),
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(((8, 32, 512), (8, 32, 64)), 2),
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(((16, 32, 512), (16, 32, 64)), 2),
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(((32, 32, 512), (32, 32, 64)), 2),
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(((64, 32, 512), (64, 32, 64)), 2),
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(((128, 32, 512), (128, 32, 64)), 2),
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(((256, 32, 512), (256, 32, 64)), 2),
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(((512, 32, 512), (512, 32, 64)), 2),
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(((672, 32, 512), (672, 32, 64)), 2),
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(((768, 32, 512), (768, 32, 64)), 2),
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(((896, 32, 512), (896, 32, 64)), 2),
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(((1024, 32, 512), (1024, 32, 64)), 2),
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])
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def test_concat_Acc(shape_pair, dim):
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torch.manual_seed(1)
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shape1, shape2 = shape_pair
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M = shape1[0]
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N = shape1[1]
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x_sizes = [M, N, 512]
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x_strides = [512, 512*M, 1]
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x_max_index = M * N * 512
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x_required_length = x_max_index
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x_data = torch.arange(x_required_length,device='cuda').bfloat16()
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x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
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# print("形状:", x.shape) # [4, 8, 512]
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# print("步幅:", x.stride()) # (1536, 192, 1)
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y_sizes = [M, N, 64]
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y_strides = [1536*(N//8), 192, 1]
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y_max_index = 1536*(N//8) * M
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y_required_length = y_max_index
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y_data = torch.arange(y_required_length,device='cuda').bfloat16()
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y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
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expected = torch.cat([x,y], dim=dim)
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result = concat_helper(x, y, dim=dim)
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assert torch.allclose(result, expected, rtol=1e-5, atol=1e-5), "Mismatch"
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@triton.jit
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def concat_kernel(
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A_ptr, B_ptr, C_ptr,
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A_section_numel, B_section_numel, C_section_numel,
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Per_block,
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section_num,
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M,
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N,
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Astride_0,
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Astride_1,
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Astride_2,
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Bstride_0,
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Bstride_1,
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Bstride_2,
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BLOCK_SIZE: tl.constexpr
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):
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block_idx = tl.program_id(0)
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for sub_section_index in range(Per_block):
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sub_offset = block_idx * Per_block + sub_section_index
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M_idx = sub_offset // N
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N_idx = sub_offset % N
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if sub_offset <= section_num-1:
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C_ptr_block_start = C_ptr + sub_offset * C_section_numel
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A_ptr_block_start = A_ptr + M_idx * Astride_0 + N_idx * Astride_1
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B_ptr_block_start = B_ptr + M_idx * Bstride_0 + N_idx * Bstride_1
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for offset in range(0, A_section_numel, BLOCK_SIZE):
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offset_idx = offset + tl.arange(0, BLOCK_SIZE)
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mask = offset_idx < A_section_numel
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val_from_A = tl.load(A_ptr_block_start + offset_idx, mask=mask)
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tl.store(C_ptr_block_start + offset_idx, val_from_A, mask=mask)
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for offset in range(0, B_section_numel, BLOCK_SIZE):
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offset_idx = offset + tl.arange(0, BLOCK_SIZE)
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mask = offset_idx < B_section_numel
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val_from_B = tl.load(B_ptr_block_start + offset_idx, mask=mask)
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tl.store(C_ptr_block_start + A_section_numel + offset_idx, val_from_B, mask=mask)
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def concat_helper(A:torch.Tensor, B:torch.Tensor, dim:int):
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output_shape = list(A.shape)
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output_shape[dim] = A.shape[dim] + B.shape[dim]
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C = torch.empty(output_shape, device=A.device, dtype=A.dtype)
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if dim!=0 :
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block_num = reduce(lambda x, y: x * y, output_shape[:dim])
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Per_block = 1
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unit_offset_A, unit_offset_B, unit_offset_C = A.shape[dim],B.shape[dim],C.shape[dim]
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if (A.shape[1]==8 and A.shape[0] > 512) or ( A.shape[1]==16 and A.shape[0] > 256):
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Per_block = 2
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if ( A.shape[1]==32 and A.shape[2] == 512 and A.shape[0] > 256):
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Per_block = 8
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num_blocks = math.ceil(block_num/Per_block)
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concat_kernel[(num_blocks,)](
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A, B, C,
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unit_offset_A, unit_offset_B, unit_offset_C,
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Per_block,
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block_num,
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output_shape[0],
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output_shape[1],
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A.stride(0),
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A.stride(1),
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A.stride(2),
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B.stride(0),
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B.stride(1),
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B.stride(2),
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BLOCK_SIZE=1024)
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return C
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assert False, "not support"
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configs = []
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configs.append(
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triton.testing.Benchmark(
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x_names=['M','N'],
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x_vals=[(4,8),(8,8),(16,8),(32,8),(64,8),(96,8),(128,8),(256,8),(512,8),(768,8),(1024,8), \
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(4,16),(8,16),(16,16),(32,16),(64,16),(96,16),(128,16),(256,16),(512,16),(768,16),(1024,16), \
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(4,32),(8,32),(16,32),(32,32),(64,32),(96,32),(128,32),(256,32),(512,32),(768,32),(1024,32)],
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x_log=True,
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line_arg='provider',
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line_vals=['triton', 'torch'],
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line_names=['Triton', 'Torch'],
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styles=[('blue', '-'), ('green', '-')],
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ylabel='s',
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plot_name='concat-dim2',
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args={"dim":2},
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),
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)
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@triton.testing.perf_report(configs)
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def benchmark(M, N, provider, dim):
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x_sizes = [M, N, 512]
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x_strides = [512, 512*M, 1]
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x_max_index = M * N * 512
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x_required_length = x_max_index
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x_data = torch.arange(x_required_length,device='cuda').bfloat16()
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x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
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# print("形状:", x.shape) # [M, 8, 512]
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# print("步幅:", x.stride()) # (512, 512*M, 1)
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y_sizes = [M, N, 64]
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y_strides = [1536*(N//8), 192, 1]
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y_max_index = 1536*(N//8) * M
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y_required_length = y_max_index
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y_data = torch.arange(y_required_length,device='cuda').bfloat16()
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y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
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# print("形状:", y.shape) # [M, 8, 64]
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# print("步幅:", y.stride()) # (1536, 192, 1)
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quantiles = [0.5, 0.2, 0.8]
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if provider == 'torch':
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ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.cat([x,y],dim=dim), quantiles=quantiles)
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if provider == 'triton':
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ms, min_ms, max_ms = triton.testing.do_bench(lambda: concat_helper(x, y,dim=dim), quantiles=quantiles)
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return (ms*1000), (max_ms*1000), (min_ms*1000)
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# @triton.testing.perf_report(configs)
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# def benchmark_16(size, provider, dim):
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# x = torch.rand([size,16,512], device='cuda', dtype=torch.bfloat16)
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# y = torch.rand([size,16,64], device='cuda', dtype=torch.bfloat16)
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# quantiles = [0.5, 0.2, 0.8]
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# if provider == 'torch':
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# ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.cat([x,y],dim=dim), quantiles=quantiles)
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# if provider == 'triton':
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# ms, min_ms, max_ms = triton.testing.do_bench(lambda: concat_helper(x, y,dim=dim), quantiles=quantiles)
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# return (ms*1000), (max_ms*1000), (min_ms*1000)
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# @triton.testing.perf_report(configs)
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# def benchmark_32(size, provider, dim):
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# x = torch.rand([size,32,512], device='cuda', dtype=torch.bfloat16)
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# y = torch.rand([size,32,64], device='cuda', dtype=torch.bfloat16)
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# quantiles = [0.5, 0.2, 0.8]
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# if provider == 'torch':
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# ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.cat([x,y],dim=dim), quantiles=quantiles)
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# if provider == 'triton':
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# ms, min_ms, max_ms = triton.testing.do_bench(lambda: concat_helper(x, y,dim=dim), quantiles=quantiles)
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# return (ms*1000), (max_ms*1000), (min_ms*1000)
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# @triton.testing.perf_report(configs)
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# def benchmark_prefill(size, provider, dim):
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# x = torch.rand([size,32,128], device='cuda', dtype=torch.bfloat16)
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# y = torch.rand([size,32,64], device='cuda', dtype=torch.bfloat16)
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# quantiles = [0.5, 0.2, 0.8]
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# if provider == 'torch':
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# ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.cat([x,y],dim=dim), quantiles=quantiles)
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# if provider == 'triton':
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# ms, min_ms, max_ms = triton.testing.do_bench(lambda: concat_helper(x, y,dim=dim), quantiles=quantiles)
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# return (ms*1000), (max_ms*1000), (min_ms*1000)
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if __name__ == '__main__':
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benchmark.run(save_path="./triton_test",print_data=True)
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# benchmark_16.run(save_path="./triton_test_16",print_data=True)
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# benchmark_32.run(save_path="./triton_test_32",print_data=True)
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# benchmark_prefill.run(save_path="./triton_test_prefill",print_data=True)
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