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Oct 22, 2025
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[Example] Add block level high performance gemv example #1097

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d62824c
add alloc_reducer gemv example
LeiWang1999 Oct 22, 2025
7739e39
test
LeiWang1999 Oct 22, 2025
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208 changes: 131 additions & 77 deletions examples/gemv/example_gemv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,75 +216,122 @@ def main(
return main


def get_best_config(N, K):

def get_configs():
iter_params = dict(BLOCK_N=[2, 4, 8, 32, 64, 128], reduce_threads=[4, 8, 32])
return [
dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())
]

@autotune(
configs=get_configs(),
warmup=3,
rep=20,
)
@jit(
out_idx=[-1],
target="auto",
)
def kernel(
BLOCK_N=None,
reduce_threads=None,
def get_block_template_configs():
iter_params = dict(
block_M=[2, 4, 8, 32, 64, 128],
block_N=[2, 4, 8, 32, 64, 128],
num_stages=[0, 1, 2, 3, 4],
threads=[32, 64, 128, 256])
return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())]


@tl.autotune(
configs=get_block_template_configs(),
warmup=3,
rep=20,
)
@tl.jit(
pass_configs={
tl.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
tl.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
},
out_idx=[2],
)
def gemv_alloc_reducer(M,
N,
block_M=128,
block_N=128,
num_stages=2,
threads=256,
dtype: str = "float16",
accum_dtype: str = "float"):

@T.prim_func
def main(a: T.Tensor((M, N), dtype), x: T.Tensor(N, dtype), o: T.Tensor(M,
dtype)): # type: ignore
with T.Kernel(T.ceildiv(M, block_M), threads=threads) as i0_m:
o_reducer = T.alloc_reducer(block_M, accum_dtype, replication="all")
T.clear(o_reducer)
for i0_n in T.Pipelined(T.ceildiv(N, block_N), num_stages=num_stages):
a_smem = T.alloc_shared((block_M, block_N), dtype)
T.copy(a[i0_m * block_M, i0_n * block_N], a_smem)
a_frag = T.alloc_fragment((block_M, block_N), dtype)
T.copy(a_smem, a_frag)
x_frag = T.alloc_fragment(block_N, dtype)
T.copy(x[i0_n * block_N], x_frag)
for i1_m, i1_n in T.Parallel(block_M, block_N):
o_reducer[i1_m] += a_frag[i1_m, i1_n] * x_frag[i1_n]
T.finalize_reducer(o_reducer)
T.copy(o_reducer, o[i0_m * block_M])

return main


def get_thread_template_configs():
iter_params = dict(BLOCK_N=[2, 4, 8, 32, 64, 128], reduce_threads=[4, 8, 32])
return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())]


@autotune(
configs=get_thread_template_configs(),
warmup=3,
rep=20,
)
@jit(
out_idx=[-1],
target="auto",
)
def get_autotuned_kernel(
N,
K,
BLOCK_N=None,
reduce_threads=None,
):
dtype = "float16"
accum_dtype = "float"
MAX_TRANSACTION_SIZE_IN_BITS = 128
TILE_K = MAX_TRANSACTION_SIZE_IN_BITS // DataType(dtype).bits
BLOCK_K = reduce_threads * TILE_K

@T.prim_func
def main(
A: T.Tensor((K,), dtype),
B: T.Tensor((N, K), dtype),
C: T.Tensor((N,), dtype),
):
dtype = "float16"
accum_dtype = "float"
MAX_TRANSACTION_SIZE_IN_BITS = 128
TILE_K = MAX_TRANSACTION_SIZE_IN_BITS // DataType(dtype).bits
BLOCK_K = reduce_threads * TILE_K

@T.prim_func
def main(
A: T.Tensor((K,), dtype),
B: T.Tensor((N, K), dtype),
C: T.Tensor((N,), dtype),
):
with T.Kernel(T.ceildiv(N, BLOCK_N), threads=(BLOCK_N, reduce_threads)) as bn:
tn = T.get_thread_binding(0)
tk = T.get_thread_binding(1)
A_local = T.alloc_local((TILE_K,), dtype)
B_local = T.alloc_local((TILE_K,), dtype)
C_accum = T.alloc_local((1,), accum_dtype)

T.clear(C_accum)
for bk in T.serial(T.ceildiv(K, BLOCK_K)):
for k in T.vectorized(TILE_K):
A_local[k] = A[bk * BLOCK_K + tk * TILE_K + k]
B_local[k] = B[bn * BLOCK_N + tn, bk * BLOCK_K + tk * TILE_K + k]
for k in T.serial(TILE_K):
C_accum[0] += A_local[k].astype(accum_dtype) * B_local[k].astype(
accum_dtype)
C_reduced = T.alloc_local((1,), accum_dtype)
with T.attr(
T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]),
"reduce_scope",
T.reinterpret(T.uint64(0), dtype="handle"),
):
T.evaluate(
T.tvm_thread_allreduce(
T.uint32(1),
C_accum[0],
True,
C_reduced[0],
tk,
dtype="handle",
))

C[bn * BLOCK_N + tn] = C_reduced[0]

return main

return kernel()
with T.Kernel(T.ceildiv(N, BLOCK_N), threads=(BLOCK_N, reduce_threads)) as bn:
tn = T.get_thread_binding(0)
tk = T.get_thread_binding(1)
A_local = T.alloc_local((TILE_K,), dtype)
B_local = T.alloc_local((TILE_K,), dtype)
C_accum = T.alloc_local((1,), accum_dtype)

T.clear(C_accum)
for bk in T.serial(T.ceildiv(K, BLOCK_K)):
for k in T.vectorized(TILE_K):
A_local[k] = A[bk * BLOCK_K + tk * TILE_K + k]
B_local[k] = B[bn * BLOCK_N + tn, bk * BLOCK_K + tk * TILE_K + k]
for k in T.serial(TILE_K):
C_accum[0] += A_local[k].astype(accum_dtype) * B_local[k].astype(accum_dtype)
C_reduced = T.alloc_local((1,), accum_dtype)
with T.attr(
T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]),
"reduce_scope",
T.reinterpret(T.uint64(0), dtype="handle"),
):
T.evaluate(
T.tvm_thread_allreduce(
T.uint32(1),
C_accum[0],
True,
C_reduced[0],
tk,
dtype="handle",
))

C[bn * BLOCK_N + tn] = C_reduced[0]

return main


def check_correctness_and_bench(kernel, N, K, bench_ref=True):
Expand All @@ -297,7 +344,7 @@ def check_correctness_and_bench(kernel, N, K, bench_ref=True):
print(f"TileLang Latency: {latency} ms\n")


def main():
def main(do_bench: bool = True):
parser = argparse.ArgumentParser(description="GEMV Example")
parser.add_argument("--n", type=int, default=1024, help="Matrix dimension N")
parser.add_argument("--k", type=int, default=1024, help="Matrix dimension K")
Expand All @@ -308,16 +355,23 @@ def main():
check_correctness_and_bench(splitk_gemv(N, K, 32, 32, 32), N, K)
check_correctness_and_bench(splitk_gemv_vectorized(N, K, 2, 32), N, K)
check_correctness_and_bench(splitk_gemv_vectorized_tvm(N, K, 2, 32), N, K)
check_correctness_and_bench(gemv_alloc_reducer(N, K, block_M=128, block_N=128), N, K)

print("Test passed!")

best_result = get_best_config(N, K)
best_config = best_result.config
kernel = splitk_gemv_vectorized_tvm(N, K, **best_config)
profiler = kernel.get_profiler()
latency = profiler.do_bench(lambda x, y: x @ y.T, warmup=500)
print(f"Torch Latency: {latency} ms")
latency = profiler.do_bench(kernel, warmup=500)
print(f"TileLang Latency: {latency} ms\n")
if not do_bench:
best_result = get_autotuned_kernel(N, K)
best_config = best_result.config
kernel = splitk_gemv_vectorized_tvm(N, K, **best_config)
profiler = kernel.get_profiler()
latency = profiler.do_bench(lambda x, y: x @ y.T, warmup=500)
print(f"Torch Latency: {latency} ms")
tilelang_thread_latency = profiler.do_bench(kernel, warmup=500)
print(f"TileLang SIMT Latency: {tilelang_thread_latency} ms\n")
kernel = gemv_alloc_reducer(N, K)
profiler = kernel.get_profiler()
tilelang_tile_latency = profiler.do_bench(kernel, warmup=500)
print(f"TileLang BlockReduce Latency: {tilelang_tile_latency} ms\n")


if __name__ == "__main__":
Expand Down
2 changes: 1 addition & 1 deletion examples/gemv/test_example_gemv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@


def test_example_gemv():
example_gemv.main()
example_gemv.main(do_bench=False)


if __name__ == "__main__":
Expand Down