# 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 numpy as np import tvm from tvm import relay from tvm.relay.transform import gradient from tvm.relay.testing import ctx_list, run_infer_type def sigmoid(x): one = np.ones_like(x) return one / (one + np.exp(-x)) def relu(x): x_copy = np.copy(x) np.maximum(x_copy, 0, x_copy) return x_copy def test_unary_op(): def check_single_op(opfunc, ref): shape = (10, 4) dtype = 'float32' tp = relay.TensorType(shape, dtype) x = relay.var("x", tp) y = opfunc(x) if ref is not None: data = np.random.rand(*shape).astype(dtype) ref_grad = ref(data) fwd_func = relay.Function([x], y) fwd_func = run_infer_type(fwd_func) bwd_func = run_infer_type(gradient(fwd_func)) for target, ctx in ctx_list(): intrp = relay.create_executor(ctx=ctx, target=target) op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data) np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01) for opfunc, ref in [(tvm.relay.log, lambda x: 1 / x), (tvm.relay.exp, np.exp), (tvm.relay.sigmoid, lambda x: sigmoid(x) * (1 - sigmoid(x))), (tvm.relay.tanh, lambda x: 1 - np.tanh(x) * np.tanh(x)), (tvm.relay.sqrt, lambda x: 0.5 * np.power(x, -0.5)), (tvm.relay.abs, lambda x: np.where(x < 0, -np.ones_like(x), np.ones_like(x))), (relay.nn.relu, lambda x: np.where(x < 0, np.zeros_like(x), np.ones_like(x))), (tvm.relay.cos, lambda x: -1.0 * np.sin(x)), (tvm.relay.sin, lambda x: np.cos(x))]: check_single_op(opfunc, ref) def test_binary_op(): def inst(vars, sh): return [vars.get(s, s) for s in sh] def check_binary_op(opfunc, ref): s = (5, 10, 5) t = relay.TensorType((5, 10, 5)) x = relay.var("x", t) y = relay.var("y", t) z = opfunc(x, y) x_data = np.random.rand(*s).astype(t.dtype) y_data = np.random.rand(*s).astype(t.dtype) ref_grad0, ref_grad1 = ref(x_data, y_data) fwd_func = relay.Function([x, y], z) fwd_func = run_infer_type(fwd_func) bwd_func = run_infer_type(gradient(fwd_func)) for target, ctx in ctx_list(): intrp = relay.create_executor(ctx=ctx, target=target) op_res, (op_grad0, op_grad1) = intrp.evaluate(bwd_func)(x_data, y_data) np.testing.assert_allclose(op_grad0.asnumpy(), ref_grad0, rtol=0.01) np.testing.assert_allclose(op_grad1.asnumpy(), ref_grad1, rtol=0.01) for opfunc, ref in [(relay.add, lambda x, y: [np.ones_like(x), np.ones_like(y)]), (relay.subtract, lambda x, y: [np.ones_like(x), -np.ones_like(y)]), (relay.multiply, lambda x, y: [y, x]), (relay.divide, lambda x, y: [1 / y, - x / (y**2)])]: check_binary_op(opfunc, ref) if __name__ == "__main__": test_unary_op() test_binary_op()