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Merged
LeiWang1999 merged 1 commit into from
Sep 23, 2025
Merged

[AMD] refactor MatrixCoreIntrinEmitter #860

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4 changes: 4 additions & 0 deletions testing/python/amd/test_tilelang_gemm_mfma_intrinsic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -234,6 +234,10 @@ def test_assert_tl_matmul():
assert_tl_matmul_correctness(128, 128, 128, "int8", "int32", accum_dtype="int32")
assert_tl_matmul_correctness(128, 256, 256, "int8", "int32", accum_dtype="int32")
assert_tl_matmul_correctness(128, 256, 256, "int8", "int32", accum_dtype="int32", k_pack=2)
assert_tl_matmul_correctness(
128, 256, 256, "int8", "int32", b_transposed=False, accum_dtype="int32")
assert_tl_matmul_correctness(
128, 256, 256, "int8", "int32", b_transposed=False, accum_dtype="int32", k_pack=2)


if __name__ == "__main__":
Expand Down
111 changes: 39 additions & 72 deletions testing/python/amd/test_tilelang_gemm_mfma_preshuffle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,7 @@
from tilelang import tvm as tvm
import tilelang.language as T
from tilelang.intrinsics import make_mfma_swizzle_layout as make_swizzle_layout
from tilelang.intrinsics.mfma_macro_generator import (
MatrixCoreIntrinEmitter,)
from tilelang.intrinsics.mfma_macro_generator import MatrixCorePreshuffleIntrinEmitter
from tilelang.transform import simplify_prim_func

tilelang.testing.set_random_seed(0)
Expand All @@ -22,16 +21,8 @@ def tl_matmul(
b_transposed=True,
k_pack=1,
b_preshuffle=False,
b_g2l_load=False,
):
assert in_dtype in [
"float16",
"int8",
], "Currently only float16 and int8 are supported"
assert out_dtype in [
"float16",
"float32",
"int32",
], "Currently only float16, float32 and int32 are supported"

micro_size_x = micro_size_y = micro_size_k = 16

Expand All @@ -47,15 +38,14 @@ def tl_matmul(
if b_preshuffle:
block_row_warps = 1
block_col_warps = 4
warp_row_tiles = 128
warp_col_tiles = 32
warp_row_tiles = 64
warp_col_tiles = 16

chunk = 32 * k_pack
chunk = 256 * k_pack

pack_size_k = micro_size_k * k_pack

shared_scope = "shared"
cache_write_shared = False

block_M = block_row_warps * warp_row_tiles
block_N = block_col_warps * warp_col_tiles
Expand All @@ -68,6 +58,7 @@ def tl_matmul(
pack_size_k, micro_size_y)
else:
B_shape = (N, K) if b_transposed else (K, N)

A_shared_shape = (block_K, block_M) if a_transposed else (block_M, block_K)
if b_preshuffle:
B_shared_shape = (block_N // micro_size_y, block_K // pack_size_k, micro_size_y,
Expand All @@ -76,12 +67,6 @@ def tl_matmul(
micro_size_y)
else:
B_shared_shape = (block_N, block_K) if b_transposed else (block_K, block_N)
C_shared_shape = (
block_M // micro_size_x,
block_N // micro_size_y,
micro_size_x,
micro_size_y,
)

warp_size = 64
threads = warp_size * (block_row_warps * block_col_warps)
Expand All @@ -92,7 +77,7 @@ def tl_matmul(
warp_cols = warp_col_tiles // micro_size_y

# MMA Wrapper to Auto Generate Code for MMA
mfma_emitter = MatrixCoreIntrinEmitter(
mfma_emitter = MatrixCorePreshuffleIntrinEmitter(
a_dtype=in_dtype,
b_dtype=in_dtype,
accum_dtype=accum_dtype,
Expand All @@ -117,7 +102,6 @@ def main(

A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
A_local = T.alloc_local((warp_rows * local_size_a), in_dtype)
B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
Expand All @@ -126,12 +110,15 @@ def main(
A_shared: make_swizzle_layout(A_shared),
})

num_ko = K // block_K
num_ki = block_K // (k_pack * micro_size_k)

# Improve L2 Cache
T.use_swizzle(panel_size=10)

T.clear(C_local)

for ko in T.Pipelined((K // block_K), num_stages=0):
for ko in T.Pipelined(num_ko, num_stages=0):

# Load A into shared memory
if a_transposed:
Expand All @@ -140,7 +127,7 @@ def main(
T.copy(A[by * block_M, ko * block_K], A_shared)

# Load B into shared memory
if b_preshuffle:
if b_g2l_load is False:
if b_transposed:
for j, k, jj, kk in T.Parallel(block_N // micro_size_y,
block_K // pack_size_k, micro_size_y,
Expand All @@ -153,53 +140,37 @@ def main(
micro_size_y):
B_shared[k, j, kk, jj] = B[ko * block_K // pack_size_k + k,
bx * block_N // micro_size_y + j, kk, jj]
else:
if b_transposed:
T.copy(B[bx * block_N, ko * block_K], B_shared)
else:
T.copy(B[ko * block_K, bx * block_N], B_shared)

for ki in T.serial(0, (block_K // (k_pack * micro_size_k))):
for ki in T.serial(0, num_ki):

# Load A into fragment
# Load A S2L
mfma_emitter.ldmatrix_a(
A_local,
A_shared,
ki,
)

# Load B into fragment
mfma_emitter.ldmatrix_b(
B_local,
B_shared,
ki,
)
if b_g2l_load:
# Load B G2L
mfma_emitter.ldmatrix_b(B_local, B, ki + ko * num_ki, pid_m=by, pid_n=bx)
else:
# Load B S2L
mfma_emitter.ldmatrix_b(
B_local,
B_shared,
ki,
)

# Perform Matrix Multiplication
mfma_emitter.mfma(A_local, B_local, C_local)

# Perform STMatrix
if cache_write_shared:
mfma_emitter.stmatrix(
C_local,
C_shared,
)

# Store shared into global
for i, j in T.Parallel(block_M, block_N):
C[by * block_M + i, bx * block_N + j] = C_shared[
i // micro_size_x,
j // micro_size_y,
i % micro_size_x,
j % micro_size_y,
]
else:
mfma_emitter.stmatrix(
C_local,
C,
pid_m=by,
pid_n=bx,
)
mfma_emitter.stmatrix(
C_local,
C,
pid_m=by,
pid_n=bx,
)

return main

Expand Down Expand Up @@ -232,9 +203,10 @@ def assert_tl_matmul_correctness(M,
a_transposed=False,
b_transposed=True,
k_pack=1,
b_preshuffle=False):
b_preshuffle=False,
b_g2l_load=False):
matmul = tl_matmul(M, N, K, in_dtype, out_dtype, accum_dtype, a_transposed, b_transposed,
k_pack, b_preshuffle)
k_pack, b_preshuffle, b_g2l_load)
print(matmul)
kernel = tilelang.compile(matmul)
src_code = kernel.get_kernel_source()
Expand Down Expand Up @@ -285,30 +257,25 @@ def assert_tl_matmul_correctness(M,

print(C)
print(ref_c)

torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)


@tilelang.testing.requires_rocm
def test_assert_tl_matmul():
assert_tl_matmul_correctness(128, 128, 128, "int8", "int32", accum_dtype="int32")
assert_tl_matmul_correctness(128, 256, 256, "int8", "int32", accum_dtype="int32")
assert_tl_matmul_correctness(
128, 256, 256, "int8", "int32", b_transposed=False, accum_dtype="int32")
assert_tl_matmul_correctness(128, 256, 256, "int8", "int32", accum_dtype="int32", k_pack=2)

assert_tl_matmul_correctness(
128, 128, 128, "int8", "int32", accum_dtype="int32", b_preshuffle=True)
256, 256, 256, "int8", "int32", accum_dtype="int32", b_preshuffle=True)
assert_tl_matmul_correctness(
128, 256, 256, "int8", "int32", accum_dtype="int32", b_preshuffle=True)
256, 256, 256, "int8", "int32", accum_dtype="int32", b_preshuffle=True)
assert_tl_matmul_correctness(
128, 256, 256, "int8", "int32", b_transposed=False, accum_dtype="int32", b_preshuffle=True)
256, 256, 256, "int8", "int32", b_transposed=False, accum_dtype="int32", b_preshuffle=True)

assert_tl_matmul_correctness(
128, 256, 256, "int8", "int32", accum_dtype="int32", k_pack=2, b_preshuffle=True)
256, 256, 512, "int8", "int32", accum_dtype="int32", k_pack=2, b_preshuffle=True)
assert_tl_matmul_correctness(
128,
256,
256,
512,
"int8",
"int32",
b_transposed=False,
Expand Down
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