mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-21 06:40:23 +08:00
Lei Wang
157e65bdf8
* installation script fix * readme typo fix * doc fix for dequantize gemm * [Doc] remove CODE_OF_CONDUCT.md and SECURITY.md; update references in CONTRIBUTING.md * [Doc] add unit tests for AnnotateDeviceRegions transform; remove SUPPORT.md
98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
# Copyright (c) Microsoft Corporation.
|
|
# Licensed under the MIT License.
|
|
from tilelang import tvm as tvm
|
|
import tilelang as tl
|
|
import tilelang.language as T
|
|
import tilelang.testing
|
|
|
|
|
|
def modify(
|
|
with_B: bool = False,
|
|
with_bias: bool = False,
|
|
):
|
|
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Buffer((64, 64)),
|
|
B: T.Buffer((64, 64)),
|
|
C: T.Buffer((64, 64)),
|
|
D: T.Buffer((64, 64)),
|
|
bias: T.Buffer((64, 64)),
|
|
):
|
|
if with_B:
|
|
if with_bias:
|
|
T.gemm(A, bias, D)
|
|
T.gemm(A, B, D)
|
|
else:
|
|
with T.block():
|
|
A_shared = T.alloc_shared((64, 64), dtype="float32")
|
|
C_shared = T.alloc_shared((64, 64), dtype="float32")
|
|
D_shared = T.alloc_shared((64, 64), dtype="float32")
|
|
T.copy(A, A_shared)
|
|
T.copy(C, C_shared)
|
|
T.gemm(A_shared, C_shared, D_shared)
|
|
T.copy(D_shared, D)
|
|
|
|
return main
|
|
|
|
|
|
def test_modify(with_B=False, with_bias=False):
|
|
tester = modify(with_B=with_B, with_bias=with_bias)
|
|
mod = tvm.IRModule({tester.attrs["global_symbol"]: tester})
|
|
mod2 = tl.transform.Simplify()(mod)
|
|
assert mod != mod2
|
|
|
|
|
|
def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
|
|
|
|
@T.prim_func
|
|
def main(
|
|
a: T.handle,
|
|
b: T.handle,
|
|
c: T.handle,
|
|
):
|
|
A = T.match_buffer(a, (M, K), dtype=dtype)
|
|
B = T.match_buffer(b, (K, N), dtype=dtype)
|
|
C = T.match_buffer(c, (M, N), dtype=accum_dtype)
|
|
|
|
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
|
|
A_shared = T.alloc_shared((block_M, block_K), dtype)
|
|
B_shared = T.alloc_shared((block_K, block_N), 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=3):
|
|
T.copy(A[by * block_M, k * block_K], A_shared)
|
|
T.copy(B[k * block_K, bx * block_N], B_shared)
|
|
T.gemm(A_shared, B_shared, C_local)
|
|
|
|
T.copy(C_local, C[by * block_M, bx * block_N])
|
|
|
|
return main
|
|
|
|
|
|
def test_matmul():
|
|
func = matmul(1024, 1024, 1024, 128, 128, 32)
|
|
mod = tvm.IRModule({func.attrs["global_symbol"]: func})
|
|
mod = tl.transform.Simplify()(mod)
|
|
|
|
rt_mod, params = tl.lower(mod.functions_items()[0][1], runtime_only=False)
|
|
# TODO Profiler only support TensorType, not dynamic variable
|
|
profiler = tl.Profiler(rt_mod, params, result_idx=[2])
|
|
|
|
import torch
|
|
a = torch.randn(1024, 1024, dtype=torch.float16).cuda().half()
|
|
b = torch.randn(1024, 1024, dtype=torch.float16).cuda().half()
|
|
c = profiler(a, b)
|
|
|
|
ref_c = a @ b
|
|
ref_c = ref_c.float()
|
|
torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
|
|
|
|
# Get CUDA Source
|
|
# print(rt_mod.imported_modules[0].get_source())
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|