wehub-resource-sync
122 lines
4.4 KiB
Python
122 lines
4.4 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
|
|
# or more contributor license agreements. See the NOTICE file
|
|
# distributed with this work for additional information
|
|
# regarding copyright ownership. The ASF licenses this file
|
|
# to you under the Apache License, Version 2.0 (the
|
|
# "License"); you may not use this file except in compliance
|
|
# with the License. You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing,
|
|
# software distributed under the License is distributed on an
|
|
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
|
# KIND, either express or implied. See the License for the
|
|
# specific language governing permissions and limitations
|
|
# under the License.
|
|
# ruff: noqa: E741
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import tvm
|
|
import tvm.testing
|
|
from tvm import te
|
|
from tvm.contrib import hipblas
|
|
from tvm.testing import env
|
|
|
|
|
|
def verify_matmul_add(in_dtype, out_dtype, rtol=1e-5):
|
|
n = 1024
|
|
l = 128
|
|
m = 236
|
|
A = te.placeholder((n, l), name="A", dtype=in_dtype)
|
|
B = te.placeholder((l, m), name="B", dtype=in_dtype)
|
|
C = hipblas.matmul(A, B, dtype=out_dtype)
|
|
|
|
def verify(target="rocm"):
|
|
if not tvm.get_global_func("tvm.contrib.hipblas.matmul", True):
|
|
print("skip because extern function is not available")
|
|
return
|
|
f = tvm.compile(te.create_prim_func([A, B, C]), target=target)
|
|
|
|
def run_and_check():
|
|
dev = tvm.rocm(0)
|
|
a = tvm.runtime.tensor(np.random.uniform(0, 128, size=(n, l)).astype(A.dtype), dev)
|
|
b = tvm.runtime.tensor(np.random.uniform(0, 128, size=(l, m)).astype(B.dtype), dev)
|
|
c = tvm.runtime.tensor(np.zeros((n, m), dtype=C.dtype), dev)
|
|
f(a, b, c)
|
|
tvm.testing.assert_allclose(
|
|
c.numpy(),
|
|
np.dot(a.numpy().astype(C.dtype), b.numpy().astype(C.dtype)),
|
|
rtol=rtol,
|
|
)
|
|
|
|
tvm.testing.run_with_gpu_lock(run_and_check)
|
|
|
|
verify()
|
|
|
|
|
|
def roundoff(v, d):
|
|
return int(np.floor((v + d - 1) / d) * d)
|
|
|
|
|
|
def verify_batch_matmul(Ashape, Bshape, Cshape, in_dtype, out_dtype, rtol=1e-5):
|
|
A = te.placeholder(Ashape, name="A", dtype=in_dtype)
|
|
B = te.placeholder(Bshape, name="B", dtype=in_dtype)
|
|
C = hipblas.batch_matmul(A, B, dtype=out_dtype)
|
|
|
|
f = tvm.compile(te.create_prim_func([A, B, C]), target="rocm")
|
|
|
|
def run_and_check():
|
|
dev = tvm.rocm(0)
|
|
if "int" in in_dtype:
|
|
a = tvm.runtime.tensor(np.random.uniform(1, 10, size=Ashape).astype(in_dtype), dev)
|
|
b = tvm.runtime.tensor(np.random.uniform(1, 10, size=Bshape).astype(in_dtype), dev)
|
|
else:
|
|
a = tvm.runtime.tensor(np.random.uniform(size=Ashape).astype(A.dtype), dev)
|
|
b = tvm.runtime.tensor(np.random.uniform(size=Bshape).astype(B.dtype), dev)
|
|
|
|
c = tvm.runtime.tensor(np.zeros(Cshape, dtype=C.dtype), dev)
|
|
f(a, b, c)
|
|
tvm.testing.assert_allclose(
|
|
c.numpy(),
|
|
np.matmul(a.numpy().astype(C.dtype), b.numpy().astype(C.dtype)).astype(C.dtype),
|
|
rtol=rtol,
|
|
)
|
|
|
|
tvm.testing.run_with_gpu_lock(run_and_check)
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@pytest.mark.skipif(not env.has_rocm(), reason="need rocm")
|
|
def test_matmul_add():
|
|
verify_matmul_add("float", "float", rtol=1e-3)
|
|
verify_matmul_add("float16", "float")
|
|
verify_matmul_add("float16", "float16", rtol=1e-2)
|
|
verify_matmul_add("int8", "int32")
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@pytest.mark.skipif(not env.has_rocm(), reason="need rocm")
|
|
def test_batch_matmul():
|
|
if not tvm.get_global_func("tvm.contrib.hipblas.batch_matmul", True):
|
|
print("skip because extern function is not available")
|
|
return
|
|
|
|
verify_batch_matmul((16, 1024, 128), (16, 128, 236), (16, 1024, 236), "float", "float")
|
|
verify_batch_matmul((16, 1024, 128), (1, 128, 236), (16, 1024, 236), "float", "float")
|
|
verify_batch_matmul((16, 1024, 128), (16, 128, 236), (16, 1024, 236), "float16", "float")
|
|
verify_batch_matmul((16, 1024, 128), (1, 128, 236), (16, 1024, 236), "float16", "float")
|
|
verify_batch_matmul(
|
|
(16, 1024, 128), (16, 128, 236), (16, 1024, 236), "float16", "float16", rtol=1e-2
|
|
)
|
|
verify_batch_matmul(
|
|
(16, 1024, 128), (1, 128, 236), (16, 1024, 236), "float16", "float16", rtol=1e-2
|
|
)
|
|
|
|
verify_batch_matmul((16, 1024, 128), (16, 128, 236), (16, 1024, 236), "int8", "int32")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tvm.testing.main()
|