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[Triton-MLIR] Minor fixes related with scf/swizzling support (#791)

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1, Disable static loop unrolling in the frontend by default;
2, A minor fix in axisAnalysis in order to support scf;
3, A minor fix in TritonGPUToLLVM to support swizzling.
This commit is contained in:
goostavz
2022-10-21 11:46:28 +08:00
committed by GitHub
parent dc0588a898
commit c4726333bf
4 changed files with 79 additions and 26 deletions
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95
python/tests/test_gemm.py
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@@ -1,13 +1,13 @@
# import pytest
# import torch
# from torch.testing import assert_close
import pytest
import torch
from torch.testing import assert_close
import triton
import triton.language as tl
@triton.jit
def matmul_kernel(
def matmul_no_scf_kernel(
a_ptr, b_ptr, c_ptr,
stride_am, stride_ak,
stride_bk, stride_bn,
@@ -30,23 +30,72 @@ def matmul_kernel(
# TODO: num_warps could only be 4 for now
# @pytest.mark.parametrize('SIZE_M,SIZE_N,SIZE_K,NUM_WARPS', [
# [128, 256, 32, 4],
# [256, 128, 16, 4],
# [128, 16, 32, 4],
# [32, 128, 64, 4],
@pytest.mark.parametrize('SIZE_M,SIZE_N,SIZE_K,NUM_WARPS', [
[128, 256, 32, 4],
[256, 128, 16, 4],
[128, 16, 32, 4],
[32, 128, 64, 4],
])
def test_gemm_no_scf(SIZE_M, SIZE_N, SIZE_K, NUM_WARPS):
a = torch.randn((SIZE_M, SIZE_K), device='cuda', dtype=torch.float16)
b = torch.randn((SIZE_K, SIZE_N), device='cuda', dtype=torch.float16)
c = torch.empty((SIZE_M, SIZE_N), device=a.device, dtype=torch.float32)
grid = lambda META: (1, )
matmul_no_scf_kernel[grid](a_ptr=a, b_ptr=b, c_ptr=c,
stride_am=a.stride(0), stride_ak=a.stride(1),
stride_bk=b.stride(0), stride_bn=b.stride(1),
stride_cm=c.stride(0), stride_cn=c.stride(1),
M=SIZE_M, N=SIZE_N, K=SIZE_K,
num_warps=NUM_WARPS)
golden = torch.matmul(a, b)
torch.set_printoptions(profile="full")
assert_close(c, golden, rtol=1e-3, atol=1e-3, check_dtype=False)
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
M: tl.constexpr, N: tl.constexpr, K: tl.constexpr,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
):
offs_m = tl.arange(0, BLOCK_SIZE_M)
offs_n = tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, K, BLOCK_SIZE_K):
a = tl.load(a_ptrs)
b = tl.load(b_ptrs)
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
tl.store(c_ptrs, accumulator)
# TODO: DotConversion in TritonGPUToLLVM cannot support non-splat C for the moment
# @pytest.mark.parametrize('SIZE_M,SIZE_N,SIZE_K,NUM_WARPS,BLOCK_SIZE_M,BLOCK_SIZE_N,BLOCK_SIZE_K', [
# [128, 256, 128, 4, 128, 256, 32],
# # [256, 128, 64, 4, 256, 128, 16],
# # [128, 16, 128, 4, 128, 16, 32],
# # [32, 128, 256, 4, 32, 128, 64],
# ])
# def test_gemm_impl(SIZE_M, SIZE_N, SIZE_K, NUM_WARPS):
# a = torch.randn((SIZE_M, SIZE_K), device='cuda', dtype=torch.float16)
# b = torch.randn((SIZE_K, SIZE_N), device='cuda', dtype=torch.float16)
# c = torch.empty((SIZE_M, SIZE_N), device=a.device, dtype=torch.float32)
# grid = lambda META: (1, )
# matmul_kernel[grid](a_ptr=a, b_ptr=b, c_ptr=c,
# stride_am=a.stride(0), stride_ak=a.stride(1),
# stride_bk=b.stride(0), stride_bn=b.stride(1),
# stride_cm=c.stride(0), stride_cn=c.stride(1),
# M=SIZE_M, N=SIZE_N, K=SIZE_K,
# num_warps=NUM_WARPS)
# golden = torch.matmul(a, b)
# torch.set_printoptions(profile="full")
# assert_close(c, golden, rtol=1e-3, atol=1e-3, check_dtype=False)
# def test_gemm(SIZE_M, SIZE_N, SIZE_K, NUM_WARPS, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K):
# a = torch.randn((SIZE_M, SIZE_K), device='cuda', dtype=torch.float16)
# b = torch.randn((SIZE_K, SIZE_N), device='cuda', dtype=torch.float16)
# c = torch.empty((SIZE_M, SIZE_N), device=a.device, dtype=torch.float32)
# grid = lambda META: (1, )
# matmul_kernel[grid](a_ptr=a, b_ptr=b, c_ptr=c,
# stride_am=a.stride(0), stride_ak=a.stride(1),
# stride_bk=b.stride(0), stride_bn=b.stride(1),
# stride_cm=c.stride(0), stride_cn=c.stride(1),
# M=a.shape[0], N=b.shape[1], K=a.shape[1],
# BLOCK_SIZE_M=BLOCK_SIZE_M, BLOCK_SIZE_N=BLOCK_SIZE_N, BLOCK_SIZE_K=BLOCK_SIZE_K,
# num_warps=NUM_WARPS)
# golden = torch.matmul(a, b)
# torch.set_printoptions(profile="full")
# assert_close(c, golden, rtol=1e-3, atol=1e-3, check_dtype=False)