# 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. import tvm import tvm.testing from tvm import te from tvm.contrib import miopen import numpy as np import pytest requires_miopen = pytest.mark.skipif( tvm.get_global_func("tvm.contrib.miopen.conv2d.setup", True) is None, reason="MIOpen is not enabled", ) @tvm.testing.requires_rocm @requires_miopen def test_conv2d(): in_channel = 3 out_channel = 64 filter_h = 3 filter_w = 3 pad_h = 1 pad_w = 1 stride_h = 1 stride_w = 1 dilation_h = 1 dilation_w = 1 xshape = [1, in_channel, 128, 128] wshape = (out_channel, in_channel, filter_h, filter_w) X = te.placeholder(xshape, name="X") W = te.placeholder(wshape, name="W") Y = miopen.conv2d_forward( X, W, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, conv_mode=0, data_type=1 ) yshape = [x.value for x in Y.shape] from tvm import topi s = te.create_schedule(Y.op) def verify(): dev = tvm.rocm(0) f = tvm.build(s, [X, W, Y], "rocm --host=llvm", name="conv2d") x = tvm.nd.array(np.random.uniform(-1, 1, xshape).astype(np.float32), dev) w = tvm.nd.array(np.random.uniform(-1, 1, wshape).astype(np.float32), dev) y = tvm.nd.array(np.random.uniform(-1, 1, yshape).astype(np.float32), dev) f(x, w, y) Y_ref = topi.nn.conv2d_nchw( X, W, (stride_h, stride_w), (pad_h, pad_w), (dilation_h, dilation_w) ) s_ref = te.create_schedule(Y_ref.op) f_ref = tvm.build(s_ref, [X, W, Y_ref], "rocm --host=llvm") y_ref = tvm.nd.array(np.random.uniform(-1, 1, yshape).astype(np.float32), dev) f_ref(x, w, y_ref) print("Max abs diff:", np.max(np.abs(y.numpy() - y_ref.numpy()))) tvm.testing.assert_allclose(y.numpy(), y_ref.numpy(), atol=1e-3) verify() def verify_softmax(shape, axis, dtype="float32", log_softmax=False): miopen_op = miopen.log_softmax if log_softmax else miopen.softmax testing_op = ( tvm.topi.testing.log_softmax_python if log_softmax else tvm.topi.testing.softmax_python ) A = te.placeholder(shape, dtype=dtype, name="A") B = miopen_op(A, axis) s = te.create_schedule([B.op]) dev = tvm.rocm(0) a_np = np.random.uniform(size=shape).astype(dtype) b_np = testing_op(a_np) a = tvm.nd.array(a_np, dev) b = tvm.nd.array(b_np, dev) f = tvm.build(s, [A, B], target="rocm --host=llvm", name="softmax") f(a, b) tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-3) def verify_softmax_4d(shape, dtype="float32", log_softmax=False): miopen_op = miopen.log_softmax if log_softmax else miopen.softmax testing_op = ( tvm.topi.testing.log_softmax_python if log_softmax else tvm.topi.testing.softmax_python ) A = te.placeholder(shape, dtype=dtype, name="A") B = miopen_op(A, axis=1) s = te.create_schedule([B.op]) dev = tvm.rocm(0) n, c, h, w = shape a_np = np.random.uniform(size=shape).astype(dtype) b_np = testing_op(a_np.transpose(0, 2, 3, 1).reshape(h * w, c)) b_np = b_np.reshape(n, h, w, c).transpose(0, 3, 1, 2) a = tvm.nd.array(a_np, dev) b = tvm.nd.array(b_np, dev) f = tvm.build(s, [A, B], target="rocm --host=llvm", name="softmax") f(a, b) tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-3) @tvm.testing.requires_rocm @requires_miopen def test_softmax(): verify_softmax((32, 10), -1) verify_softmax((3, 4), -1) verify_softmax_4d((1, 16, 256, 256)) verify_softmax_4d((1, 16, 256, 256)) verify_softmax((32, 10), -1, log_softmax=True) verify_softmax((3, 4), -1, log_softmax=True) verify_softmax_4d((1, 16, 256, 256), log_softmax=True) if __name__ == "__main__": test_conv2d()