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97 lines
3.4 KiB
97 lines
3.4 KiB
/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <iostream>
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#include <memory>
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#include "common/common_test.h"
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#include "pipeline/jit/parse/python_adapter.h"
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#include "pipeline/jit/parse/data_converter.h"
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#include "frontend/operator/ops.h"
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#include "pipeline/pynative/pynative_execute.h"
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#include "utils/ms_context.h"
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#include "utils/utils.h"
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namespace py = pybind11;
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using pybind11::literals::operator"" _a;
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using Tensor = mindspore::tensor::Tensor;
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using TensorPtr = mindspore::tensor::TensorPtr;
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namespace mindspore {
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namespace pynative {
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class TestPynativeExecute : public UT::Common {
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public:
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TestPynativeExecute() {}
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};
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inline ValuePtr PyAttrValue(const py::object &obj) {
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ValuePtr converted_ret;
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bool converted = parse::ConvertData(obj, &converted_ret);
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if (!converted) {
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MS_LOG(EXCEPTION) << "attribute convert error with type:" << std::string(py::str(obj));
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}
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return converted_ret;
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}
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OpExecInfoPtr ConstructOpExecInfo() {
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py::str op_name = "Conv2D";
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py::object tensor_py_module = py::module::import("mindspore.common.tensor").attr("Tensor");
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py::object np_py_module = py::module::import("numpy");
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py::object np_ones = np_py_module.attr("ones");
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py::object np_float32 = np_py_module.attr("float32");
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py::tuple weight_dim = py::make_tuple(64, 3, 3, 3);
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py::object weight = tensor_py_module(np_float32(np_ones(weight_dim)));
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py::tuple op_params = py::make_tuple(weight);
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py::tuple inputs_dim = py::make_tuple(1, 3, 6, 6);
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py::object input = tensor_py_module(np_float32(np_ones(inputs_dim)));
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py::tuple op_inputs = py::make_tuple(input, weight);
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py::tuple kernel_size = py::make_tuple(3, 3);
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py::dict op_attrs = py::dict("out_channel"_a = 64, "kernel_size"_a = kernel_size, "mode"_a = 1, "pad_mode"_a = "same",
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"stride"_a = 1, "dilation"_a = 1, "group"_a = 1, "data_format"_a = kOpFormat_NCHW);
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auto conv_obj = prim::GetPythonOps("conv2d_prim", "gtest_input.pynative");
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py::none py_none;
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py::args args = py::make_tuple(conv_obj, op_name, op_inputs);
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py::list args_input = args[PY_INPUTS];
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return PynativeExecutor::GetInstance()->forward_executor()->GenerateOpExecInfo(args);
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}
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TEST_F(TestPynativeExecute, TestCreateContext) {
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auto ctx3 = MsContext::GetInstance();
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ASSERT_EQ(ctx3->backend_policy(), "vm");
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ASSERT_EQ(ctx3->get_param<std::string>(MS_CTX_DEVICE_TARGET), "CPU");
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ctx3->set_backend_policy("ge_only");
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ctx3->set_param<std::string>(MS_CTX_DEVICE_TARGET, "GPU");
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auto ctx4 = MsContext::GetInstance();
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ASSERT_EQ(ctx3.get(), ctx4.get());
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ASSERT_EQ(ctx4->backend_policy(), "ge_only");
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ASSERT_EQ(ctx4->get_param<std::string>(MS_CTX_DEVICE_TARGET), "GPU");
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}
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TEST_F(TestPynativeExecute, TestDefaultContext) {
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auto ctx = MsContext::GetInstance();
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ASSERT_EQ(std::string(ctx->backend_policy()), "ge_only");
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auto ctx2 = MsContext::GetInstance();
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ASSERT_EQ(ctx.get(), ctx2.get());
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}
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} // namespace pynative
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} // namespace mindspore
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