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triton/python/tests/test_gemm.py
goostavz c28cfd821b [Triton-MLIR][Backend] Fix convert_layout blocked->shared in non-default order (#876) 
This PR fix the problem of TN/NT GEMM correctness when no SCF involved.
I'll continue to clean up getLinearIndex/getMultiDimIndex in a uniformed
way which should be benifical to avoid different kinds of order issues.
This is not fully done yet, just merge to sync the code.
2022-11-15 09:02:46 +08:00

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import pytest
import torch
from torch.testing import assert_close
import triton
import triton.language as tl
@triton.jit
def matmul_no_scf_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
):
offs_m = tl.arange(0, M)
offs_n = tl.arange(0, N)
offs_k = tl.arange(0, 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
a = tl.load(a_ptrs)
b = tl.load(b_ptrs)
c = tl.dot(a, b)
c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
tl.store(c_ptrs, c)
# TODO: num_warps could only be 4 for now
@pytest.mark.parametrize('SHAPE,NUM_WARPS,TRANS_A,TRANS_B', [
(shape, num_warps, trans_a, trans_b)
for shape in [
[128, 256, 32],
[256, 128, 16],
[128, 16, 32],
[32, 128, 64],
[128, 128, 64],
[64, 128, 128],
]
for num_warps in [2, 4]
for trans_a in [False, True]
for trans_b in [False, True]
])
def test_gemm_no_scf(SHAPE, NUM_WARPS, TRANS_A, TRANS_B):
SIZE_M, SIZE_N, SIZE_K = SHAPE
if (TRANS_A):
a = torch.randn((SIZE_K, SIZE_M), device='cuda', dtype=torch.float16).T
else:
a = torch.randn((SIZE_M, SIZE_K), device='cuda', dtype=torch.float16)
if (TRANS_B):
b = torch.randn((SIZE_N, SIZE_K), device='cuda', dtype=torch.float16).T
else:
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)
@pytest.mark.parametrize('SHAPE,NUM_WARPS,TRANS_A,TRANS_B', [
(shape, num_warps, trans_a, trans_b)
for shape in [
[64, 128, 128],
[128, 128, 128],
[16, 8, 32],
[32, 16, 64],
[32, 16, 64],
]
for num_warps in [1, 2, 4]
for trans_a in [False, True]
for trans_b in [False, True]
])
def test_gemm_no_scf_int8(SHAPE, NUM_WARPS, TRANS_A, TRANS_B):
SIZE_M, SIZE_N, SIZE_K = SHAPE
if (TRANS_A):
a = torch.randint(-5, 5, (SIZE_K, SIZE_M), device='cuda', dtype=torch.int8).T
else:
a = torch.randint(-5, 5, (SIZE_M, SIZE_K), device='cuda', dtype=torch.int8)
if (TRANS_B):
b = torch.randint(-5, 5, (SIZE_N, SIZE_K), device='cuda', dtype=torch.int8).T
else:
b = torch.randint(-5, 5, (SIZE_K, SIZE_N), device='cuda', dtype=torch.int8)
c = torch.empty((SIZE_M, SIZE_N), device=a.device, dtype=torch.int32)
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)
aa = a.cpu()
bb = b.cpu()
golden = torch.matmul(aa.float(), bb.float()).int()
torch.set_printoptions(profile="full")
torch.testing.assert_close(c.cpu(), golden, 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)
def get_variant_golden(a, b):
SIZE_M = a.shape[0]
SIZE_K = a.shape[1]
SIZE_N = b.shape[1]
assert a.shape[1] == b.shape[0]
zero_M_K = torch.zeros((SIZE_M, SIZE_K)).cuda()
zero_3M_K = torch.zeros((3 * SIZE_M, SIZE_K)).cuda()
zero_K_N = torch.zeros((SIZE_K, SIZE_N)).cuda()
zero_3K_N = torch.zeros((3 * SIZE_K, SIZE_N)).cuda()
a_padded = torch.cat((a, zero_M_K, zero_M_K), 0)
a_padded = torch.cat((a_padded, zero_3M_K, zero_3M_K), 1)
b_padded = torch.cat((b, zero_K_N, zero_K_N), 0)
b_padded = torch.cat((b_padded, zero_3K_N, zero_3K_N), 1)
c_padded = torch.matmul(a_padded, b_padded)
return c_padded[:SIZE_M, :SIZE_N]
@pytest.mark.parametrize('SIZE_M,SIZE_N,SIZE_K,NUM_WARPS,BLOCK_SIZE_M,BLOCK_SIZE_N,BLOCK_SIZE_K,TRANS_A,TRANS_B', [
# Non-forloop
[64, 32, 64, 4, 64, 32, 64, False, False],
[128, 64, 128, 4, 128, 64, 128, False, False],
# K-Forloop
[64, 32, 128, 4, 64, 32, 64, False, False],
[128, 16, 128, 4, 128, 16, 32, False, False],
[32, 16, 128, 4, 32, 16, 32, False, False],
[32, 64, 128, 4, 32, 64, 32, False, False],
[32, 128, 256, 4, 32, 128, 64, False, False],
[64, 128, 64, 4, 64, 128, 32, False, False],
[64, 64, 128, 4, 64, 64, 32, False, False],
[128, 128, 64, 4, 128, 128, 32, False, False],
[128, 128, 128, 4, 128, 128, 32, False, False],
[128, 128, 256, 4, 128, 128, 64, False, False],
[128, 256, 128, 4, 128, 256, 32, False, False],
[256, 128, 64, 4, 256, 128, 16, False, False],
[128, 64, 128, 4, 128, 64, 32, False, False],
# TODO[goostavz]: fix these cases
#[128, 64, 128, 4, 128, 64, 32, True, False],
#[128, 64, 128, 4, 128, 64, 32, False, True],
])
def test_gemm(SIZE_M, SIZE_N, SIZE_K, NUM_WARPS, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K, TRANS_A, TRANS_B):
if (TRANS_A):
a = torch.randn((SIZE_K, SIZE_M), device='cuda', dtype=torch.float16).T
else:
a = torch.randn((SIZE_M, SIZE_K), device='cuda', dtype=torch.float16)
if (TRANS_B):
b = torch.randn((SIZE_N, SIZE_K), device='cuda', dtype=torch.float16).T
else:
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)
# It's not easy to get a proper error threshold in different size
# Here the gemm calculation is padded to a different size in order to get
# a variant version of the golden result. And the error between golden and
# golden_variant provide reference on selecting the proper rtol / atol.
golden_variant = get_variant_golden(a, b)
golden_diff = golden - golden_variant
golden_abs_err = torch.max(torch.abs(golden_diff)).item()
golden_rel_err = torch.max(torch.abs(golden_diff / golden)).item()
torch.set_printoptions(profile="full")
assert_close(c, golden, rtol=max(1e-4, 1.5 * golden_rel_err), atol=max(1e-4, 1.5 * golden_abs_err), check_dtype=False)
@pytest.mark.parametrize('M,N,K,num_warps,block_M,block_N,block_K', [
[32, 32, 16, 4, 32, 32, 16],
[32, 16, 16, 4, 32, 32, 16],
[128, 8, 8, 4, 32, 32, 16],
# TODO[Superjomn]: fix it later
# [127, 41, 43, 4, 32, 32, 16],
])
def test_gemm_fmadot(M, N, K, num_warps, block_M, block_N, block_K):
@triton.jit
def matmul_kernel(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
):
pid = tl.program_id(axis=0)
# num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
pid_m = pid // num_pid_n
pid_n = pid % num_pid_n
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[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_mask = (offs_am[:, None] < M) & (offs_k[None, :] < K)
b_mask = (offs_k[:, None] < K) & (offs_bn[None, :] < N)
a = tl.load(a_ptrs, a_mask)
b = tl.load(b_ptrs, b_mask)
# NOTE the allow_tf32 should be false to force the dot op to do fmadot lowering
accumulator += tl.dot(a, b, allow_tf32=False)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
offs_k += BLOCK_SIZE_K
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, accumulator, c_mask)
a = torch.randn((M, K), device='cuda', dtype=torch.float32)
b = torch.randn((K, N), device='cuda', dtype=torch.float32)
c = torch.empty((M, N), device=a.device, dtype=torch.float32)
grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),)
matmul_kernel[grid](a, b, c,
M, N, K,
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),
BLOCK_SIZE_M=block_M, BLOCK_SIZE_N=block_N, BLOCK_SIZE_K=block_K)
golden = torch.matmul(a, b)
torch.testing.assert_close(c, golden)
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