test_topi_batch_matmul.py 2.49 KB
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# 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.
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"""Test code for batch_matmul operator"""
import numpy as np
import tvm
import topi
import topi.testing
from topi.util import get_const_tuple
from tvm.contrib.pickle_memoize import memoize

from common import get_all_backend

def verify_batch_matmul(batch, M, N, K):
    x = tvm.placeholder((batch, M, K), name='x')
    y = tvm.placeholder((batch, N, K), name='y')
    dtype = x.dtype

    # use memoize to pickle the test data for next time use
    @memoize("topi.tests.test_topi_batch_matmul")
    def get_ref_data():
        a_np = np.random.uniform(size=(batch, M, K)).astype(dtype)
        b_np = np.random.uniform(size=(batch, N, K)).astype(dtype)
        c_np = topi.testing.batch_matmul(a_np, b_np)
        return (a_np, b_np, c_np)
    # get the test data
    a_np, b_np, c_np = get_ref_data()

    def check_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)
        with tvm.target.create(device):
            out = topi.nn.batch_matmul(x, y)
            s = topi.generic.schedule_batch_matmul([out])
        a = tvm.nd.array(a_np, ctx)
        b = tvm.nd.array(b_np, ctx)
        c = tvm.nd.array(np.zeros(get_const_tuple(out.shape), dtype=dtype), ctx)
        f = tvm.build(s, [x, y, out], device, name="dense")
        f(a, b, c)
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)

    for device in get_all_backend():
        check_device(device)

def test_batch_matmul():
    verify_batch_matmul(1, 16, 16, 32)
    verify_batch_matmul(5, 16, 16, 32)
    verify_batch_matmul(5, 16, 20, 32)
    verify_batch_matmul(30, 16, 20, 32)


if __name__ == "__main__":
    test_batch_matmul()