mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-22 06:50:25 +08:00
548 lines
17 KiB
Python
548 lines
17 KiB
Python
from tilelang import tvm as tvm
|
|
import tilelang.testing
|
|
import pytest
|
|
|
|
|
|
def matmul(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype,
|
|
num_stages,
|
|
threads,
|
|
):
|
|
A_shape = (K, M) if trans_A else (M, K)
|
|
B_shape = (N, K) if trans_B else (K, N)
|
|
A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
|
|
B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
|
|
|
|
import tilelang.language as T
|
|
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Tensor(A_shape, in_dtype),
|
|
B: T.Tensor(B_shape, in_dtype),
|
|
C: T.Tensor((M, N), out_dtype),
|
|
):
|
|
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
|
|
A_shared = T.alloc_shared(A_shared_shape, in_dtype)
|
|
B_shared = T.alloc_shared(B_shared_shape, in_dtype)
|
|
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
|
|
T.clear(C_local)
|
|
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
|
|
if trans_A:
|
|
T.copy(A[k * block_K, by * block_M], A_shared)
|
|
else:
|
|
T.copy(A[by * block_M, k * block_K], A_shared)
|
|
if trans_B:
|
|
T.copy(B[bx * block_N, k * block_K], B_shared)
|
|
else:
|
|
T.copy(B[k * block_K, bx * block_N], B_shared)
|
|
T.gemm_v2(A_shared, B_shared, C_local, trans_A, trans_B)
|
|
# T.gemm(A_shared, B_shared, C_local, trans_A, trans_B)
|
|
T.copy(C_local, C[by * block_M, bx * block_N])
|
|
|
|
return main
|
|
|
|
|
|
def run_gemm_ss(
|
|
M,
|
|
N,
|
|
K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
num_stages=3,
|
|
num_threads=128,
|
|
):
|
|
program = matmul(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
num_stages,
|
|
num_threads,
|
|
)
|
|
|
|
kernel = tilelang.compile(
|
|
program,
|
|
out_idx=[2],
|
|
pass_configs={
|
|
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
|
|
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
|
|
})
|
|
|
|
profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
|
|
|
|
def ref_program(A, B):
|
|
import torch
|
|
|
|
if trans_A:
|
|
A = A.T
|
|
if trans_B:
|
|
B = B.T
|
|
C = torch.matmul(A.to(torch.float), B.to(torch.float))
|
|
C = C.to(torch.__getattribute__(out_dtype))
|
|
return C
|
|
|
|
profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
|
|
|
|
|
|
@pytest.mark.skip(reason="Temporarily disabling until GEMM SS issues are resolved")
|
|
def test_gemm_ss():
|
|
# More test case can be found in kernel/test_tilelang_kernel_gemm.py
|
|
# GEMM tests for float16
|
|
run_gemm_ss(512, 1024, 768, False, True, "float16", "float16", "float16", 128, 128, 32, 2)
|
|
run_gemm_ss(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 128, 32, 2)
|
|
run_gemm_ss(512, 1024, 768, True, False, "float16", "float16", "float16", 128, 128, 32, 2)
|
|
run_gemm_ss(512, 1024, 768, True, True, "float16", "float16", "float16", 128, 128, 32, 2)
|
|
# n8 test
|
|
run_gemm_ss(128, 8, 32, False, True, "float16", "float16", "float16", 128, 8, 32, 0, 128)
|
|
|
|
# int8 test
|
|
run_gemm_ss(128, 128, 128, False, True, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_ss(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_ss(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_ss(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
|
|
# float8 tests
|
|
run_gemm_ss(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 32, 2)
|
|
|
|
# tfloat32 test
|
|
run_gemm_ss(128, 128, 128, False, False, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_ss(128, 128, 128, False, True, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_ss(128, 128, 128, True, False, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_ss(128, 128, 128, True, True, "float", "float", "float32", 128, 128, 32, 2)
|
|
|
|
|
|
def matmul_rs(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype,
|
|
num_stages,
|
|
threads,
|
|
):
|
|
A_shape = (K, M) if trans_A else (M, K)
|
|
B_shape = (N, K) if trans_B else (K, N)
|
|
A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
|
|
B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
|
|
A_frag_shape = A_shared_shape
|
|
|
|
import tilelang.language as T
|
|
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Tensor(A_shape, in_dtype),
|
|
B: T.Tensor(B_shape, in_dtype),
|
|
C: T.Tensor((M, N), out_dtype),
|
|
):
|
|
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
|
|
A_shared = T.alloc_shared(A_shared_shape, in_dtype)
|
|
B_shared = T.alloc_shared(B_shared_shape, in_dtype)
|
|
A_frag = T.alloc_fragment(A_frag_shape, in_dtype)
|
|
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
|
|
T.clear(C_local)
|
|
T.annotate_layout({
|
|
A_shared: tilelang.layout.make_swizzled_layout(A_shared),
|
|
})
|
|
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
|
|
if trans_A:
|
|
T.copy(A[k * block_K, by * block_M], A_shared)
|
|
else:
|
|
T.copy(A[by * block_M, k * block_K], A_shared)
|
|
if trans_B:
|
|
T.copy(B[bx * block_N, k * block_K], B_shared)
|
|
else:
|
|
T.copy(B[k * block_K, bx * block_N], B_shared)
|
|
T.copy(A_shared, A_frag)
|
|
T.gemm_v2(A_frag, B_shared, C_local, trans_A, trans_B)
|
|
T.copy(C_local, C[by * block_M, bx * block_N])
|
|
|
|
return main
|
|
|
|
|
|
def run_gemm_rs(
|
|
M,
|
|
N,
|
|
K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
num_stages=3,
|
|
num_threads=128,
|
|
):
|
|
program = matmul_rs(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
num_stages,
|
|
num_threads,
|
|
)
|
|
|
|
kernel = tilelang.compile(
|
|
program,
|
|
out_idx=[2],
|
|
pass_configs={
|
|
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
|
|
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
|
|
})
|
|
profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
|
|
|
|
def ref_program(A, B):
|
|
import torch
|
|
|
|
if trans_A:
|
|
A = A.T
|
|
if trans_B:
|
|
B = B.T
|
|
C = torch.matmul(A.to(torch.float), B.to(torch.float))
|
|
C = C.to(torch.__getattribute__(out_dtype))
|
|
return C
|
|
|
|
profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
|
|
|
|
|
|
@pytest.mark.skip(reason="Temporarily disabling until GEMM RS issues are resolved")
|
|
def test_gemm_rs():
|
|
# GEMM tests for float16
|
|
run_gemm_rs(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_rs(512, 1024, 768, False, True, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_rs(512, 1024, 768, True, False, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_rs(512, 1024, 768, True, True, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
|
|
# n8 tests
|
|
run_gemm_rs(128, 8, 32, False, True, "float16", "float16", "float16", 128, 8, 32, 0, 128)
|
|
|
|
# int8 tests
|
|
run_gemm_rs(128, 128, 128, False, True, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_rs(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_rs(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_rs(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
|
|
# float8 tests
|
|
run_gemm_rs(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 32, 2)
|
|
|
|
# float32 tests
|
|
run_gemm_rs(128, 128, 128, False, False, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_rs(128, 128, 128, False, True, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_rs(128, 128, 128, True, False, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_rs(128, 128, 128, True, True, "float", "float", "float32", 128, 128, 32, 2)
|
|
|
|
|
|
def matmul_sr(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype,
|
|
num_stages,
|
|
threads,
|
|
):
|
|
A_shape = (K, M) if trans_A else (M, K)
|
|
B_shape = (N, K) if trans_B else (K, N)
|
|
A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
|
|
B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
|
|
B_frag_shape = B_shared_shape
|
|
|
|
import tilelang.language as T
|
|
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Tensor(A_shape, in_dtype),
|
|
B: T.Tensor(B_shape, in_dtype),
|
|
C: T.Tensor((M, N), out_dtype),
|
|
):
|
|
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
|
|
A_shared = T.alloc_shared(A_shared_shape, in_dtype)
|
|
B_shared = T.alloc_shared(B_shared_shape, in_dtype)
|
|
B_frag = T.alloc_fragment(B_frag_shape, in_dtype)
|
|
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
|
|
T.clear(C_local)
|
|
T.annotate_layout({
|
|
B_shared: tilelang.layout.make_swizzled_layout(B_shared),
|
|
})
|
|
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
|
|
if trans_A:
|
|
T.copy(A[k * block_K, by * block_M], A_shared)
|
|
else:
|
|
T.copy(A[by * block_M, k * block_K], A_shared)
|
|
if trans_B:
|
|
T.copy(B[bx * block_N, k * block_K], B_shared)
|
|
else:
|
|
T.copy(B[k * block_K, bx * block_N], B_shared)
|
|
T.copy(B_shared, B_frag)
|
|
T.gemm_v2(A_shared, B_frag, C_local, trans_A, trans_B)
|
|
T.copy(C_local, C[by * block_M, bx * block_N])
|
|
|
|
return main
|
|
|
|
|
|
def run_gemm_sr(
|
|
M,
|
|
N,
|
|
K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
num_stages=3,
|
|
num_threads=128,
|
|
):
|
|
program = matmul_sr(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
num_stages,
|
|
num_threads,
|
|
)
|
|
|
|
kernel = tilelang.compile(
|
|
program,
|
|
out_idx=[2],
|
|
pass_configs={
|
|
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
|
|
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
|
|
})
|
|
profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
|
|
|
|
def ref_program(A, B):
|
|
import torch
|
|
|
|
if trans_A:
|
|
A = A.T
|
|
if trans_B:
|
|
B = B.T
|
|
C = torch.matmul(A.to(torch.float), B.to(torch.float))
|
|
C = C.to(torch.__getattribute__(out_dtype))
|
|
return C
|
|
|
|
profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
|
|
|
|
|
|
def test_gemm_sr():
|
|
# GEMM tests for float16
|
|
run_gemm_sr(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_sr(512, 1024, 768, False, True, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_sr(512, 1024, 768, True, False, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_sr(512, 1024, 768, True, True, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
|
|
# n8 tests
|
|
run_gemm_sr(128, 8, 32, False, True, "float16", "float16", "float16", 128, 8, 32, 0, 128)
|
|
|
|
# int8 tests
|
|
run_gemm_sr(128, 128, 32, False, True, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_sr(128, 128, 32, False, False, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_sr(128, 128, 32, True, False, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_sr(128, 128, 32, True, True, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
|
|
# float8 tests
|
|
run_gemm_sr(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 32, 2)
|
|
|
|
# float32 tests
|
|
# TODO(lei): fix in future
|
|
run_gemm_sr(128, 128, 128, False, False, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_sr(128, 128, 128, False, True, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_sr(128, 128, 128, True, False, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_sr(128, 128, 128, True, True, "float", "float", "float32", 128, 128, 32, 2)
|
|
|
|
|
|
def matmul_rr(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype,
|
|
num_stages,
|
|
threads,
|
|
):
|
|
A_shape = (K, M) if trans_A else (M, K)
|
|
B_shape = (N, K) if trans_B else (K, N)
|
|
A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
|
|
B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
|
|
A_frag_shape = A_shared_shape
|
|
B_frag_shape = B_shared_shape
|
|
|
|
import tilelang.language as T
|
|
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Tensor(A_shape, in_dtype),
|
|
B: T.Tensor(B_shape, in_dtype),
|
|
C: T.Tensor((M, N), out_dtype),
|
|
):
|
|
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
|
|
A_shared = T.alloc_shared(A_shared_shape, in_dtype)
|
|
B_shared = T.alloc_shared(B_shared_shape, in_dtype)
|
|
A_frag = T.alloc_fragment(A_frag_shape, in_dtype)
|
|
B_frag = T.alloc_fragment(B_frag_shape, in_dtype)
|
|
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
|
|
T.clear(C_local)
|
|
T.annotate_layout({
|
|
A_shared: tilelang.layout.make_swizzled_layout(A_shared),
|
|
B_shared: tilelang.layout.make_swizzled_layout(B_shared),
|
|
})
|
|
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
|
|
if trans_A:
|
|
T.copy(A[k * block_K, by * block_M], A_shared)
|
|
else:
|
|
T.copy(A[by * block_M, k * block_K], A_shared)
|
|
if trans_B:
|
|
T.copy(B[bx * block_N, k * block_K], B_shared)
|
|
else:
|
|
T.copy(B[k * block_K, bx * block_N], B_shared)
|
|
T.copy(A_shared, A_frag)
|
|
T.copy(B_shared, B_frag)
|
|
T.gemm_v2(A_frag, B_frag, C_local, trans_A, trans_B)
|
|
T.copy(C_local, C[by * block_M, bx * block_N])
|
|
|
|
return main
|
|
|
|
|
|
def run_gemm_rr(
|
|
M,
|
|
N,
|
|
K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
num_stages=3,
|
|
num_threads=128,
|
|
):
|
|
program = matmul_rr(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
num_stages,
|
|
num_threads,
|
|
)
|
|
|
|
kernel = tilelang.compile(
|
|
program,
|
|
out_idx=[2],
|
|
pass_configs={
|
|
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
|
|
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
|
|
})
|
|
profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
|
|
|
|
def ref_program(A, B):
|
|
import torch
|
|
|
|
if trans_A:
|
|
A = A.T
|
|
if trans_B:
|
|
B = B.T
|
|
C = torch.matmul(A.to(torch.float), B.to(torch.float))
|
|
C = C.to(torch.__getattribute__(out_dtype))
|
|
return C
|
|
|
|
profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
|
|
|
|
|
|
def test_gemm_rr():
|
|
# GEMM tests for float16
|
|
run_gemm_rr(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_rr(512, 1024, 768, False, True, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_rr(512, 1024, 768, True, False, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_rr(512, 1024, 768, True, True, "float16", "float16", "float16", 128, 256, 32, 2)
|
|
run_gemm_rr(512, 1024, 768, False, True, "bfloat16", "bfloat16", "float", 128, 256, 32, 2)
|
|
# n8 tests
|
|
run_gemm_rr(128, 8, 128, False, True, "float16", "float16", "float16", 128, 8, 32, 2)
|
|
run_gemm_rr(128, 8, 128, False, True, "int8", "int8", "int32", 128, 8, 32, 2)
|
|
|
|
# int8 tests
|
|
run_gemm_rr(128, 128, 128, False, True, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_rr(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_rr(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
run_gemm_rr(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 32, 2)
|
|
|
|
# float8 tests
|
|
run_gemm_rr(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 32, 2)
|
|
|
|
# float32 tests
|
|
run_gemm_rr(128, 128, 128, False, False, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_rr(128, 128, 128, False, True, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_rr(128, 128, 128, True, False, "float", "float", "float32", 128, 128, 32, 2)
|
|
run_gemm_rr(128, 128, 128, True, True, "float", "float", "float32", 128, 128, 32, 2)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|