derek.holloway
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431 lines
22 KiB
C++
431 lines
22 KiB
C++
// Copyright © Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include <gtest/gtest.h>
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#include <hip/hip_runtime.h>
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#include <memory>
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#include <hipdnn_flatbuffers_sdk/data_objects/graph_generated.h>
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#include <hipdnn_flatbuffers_sdk/data_objects/layernorm_backward_attributes_generated.h>
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#include <hipdnn_frontend.hpp>
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#include <hipdnn_test_sdk/constants/LayernormBackwardConstants.hpp>
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#include <hipdnn_test_sdk/utilities/IntegrationTestFixture.hpp>
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#include <hipdnn_test_sdk/utilities/LoweringTestHelpers.hpp>
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#include <hipdnn_test_sdk/utilities/TestUtilities.hpp>
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#include <hipdnn_test_sdk/utilities/TestableGraph.hpp>
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#include <hipdnn_test_sdk/utilities/ToVec.hpp>
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using namespace hipdnn_frontend;
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using namespace hipdnn_frontend::graph;
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using namespace hipdnn_tests::constants;
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using hipdnn_tests::buildTensorMap;
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using hipdnn_tests::IntegrationTestFixture;
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using hipdnn_tests::lowerAndDeserialize;
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using hipdnn_tests::TestableGraphLowering;
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using hipdnn_tests::toVec;
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using DataTypeSdk = hipdnn_flatbuffers_sdk::data_objects::DataType;
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using NodeAttrType = hipdnn_flatbuffers_sdk::data_objects::NodeAttributes;
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namespace
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{
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// Lowers a frontend graph via build_operation_graph_via_descriptors, then
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// retrieves the serialized graph and deserializes it for verification.
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class IntegrationLayernormBackwardDescriptorLowering : public IntegrationTestFixture
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{
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protected:
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/// Builds and lowers a graph, returning the deserialized GraphT.
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/// Callers set up attrs before calling; this creates tensors, calls the
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/// graph method, validates, lowers, serializes, and deserializes.
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hipdnn_flatbuffers_sdk::data_objects::GraphT
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buildAndDeserialize(LayernormBackwardAttributes& attrs, bool withOptionals, bool manualUids)
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{
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auto graph = std::make_shared<TestableGraphLowering>();
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graph->set_name("LayernormBackwardIntegrationTest")
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.set_compute_data_type(DataType::FLOAT)
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.set_intermediate_data_type(DataType::FLOAT)
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.set_io_data_type(DataType::FLOAT);
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auto dy = std::make_shared<TensorAttributes>();
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if(manualUids)
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{
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dy->set_uid(K_LAYERNORMBACKWARD_TENSOR_DY_UID);
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}
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dy->set_name("dy")
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.set_data_type(DataType::FLOAT)
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.set_dim(toVec(K_LAYERNORMBACKWARD_TENSOR_DY_DIMS))
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.set_stride(toVec(K_LAYERNORMBACKWARD_TENSOR_DY_STRIDES));
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auto x = std::make_shared<TensorAttributes>();
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if(manualUids)
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{
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x->set_uid(K_LAYERNORMBACKWARD_TENSOR_X_UID);
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}
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x->set_name("x")
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.set_data_type(DataType::FLOAT)
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.set_dim(toVec(K_LAYERNORMBACKWARD_TENSOR_X_DIMS))
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.set_stride(toVec(K_LAYERNORMBACKWARD_TENSOR_X_STRIDES));
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auto scale = std::make_shared<TensorAttributes>();
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if(manualUids)
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{
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scale->set_uid(K_LAYERNORMBACKWARD_TENSOR_SCALE_UID);
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}
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scale->set_name("scale")
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.set_data_type(DataType::FLOAT)
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.set_dim(toVec(K_LAYERNORMBACKWARD_TENSOR_SCALE_DIMS))
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.set_stride(toVec(K_LAYERNORMBACKWARD_TENSOR_SCALE_STRIDES));
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if(withOptionals)
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{
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auto mean = std::make_shared<TensorAttributes>();
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if(manualUids)
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{
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mean->set_uid(K_LAYERNORMBACKWARD_TENSOR_MEAN_UID);
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}
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mean->set_name("mean")
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.set_data_type(DataType::FLOAT)
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.set_dim(toVec(K_LAYERNORMBACKWARD_TENSOR_MEAN_DIMS))
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.set_stride(toVec(K_LAYERNORMBACKWARD_TENSOR_MEAN_STRIDES));
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attrs.set_mean(std::move(mean));
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auto invVariance = std::make_shared<TensorAttributes>();
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if(manualUids)
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{
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invVariance->set_uid(K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_UID);
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}
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invVariance->set_name("inv_variance")
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.set_data_type(DataType::FLOAT)
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.set_dim(toVec(K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_DIMS))
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.set_stride(toVec(K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_STRIDES));
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attrs.set_inv_variance(std::move(invVariance));
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auto epsilon = std::make_shared<TensorAttributes>();
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if(manualUids)
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{
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epsilon->set_uid(K_LAYERNORMBACKWARD_TENSOR_EPSILON_UID);
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}
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epsilon->set_name("epsilon")
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.set_data_type(DataType::FLOAT)
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.set_dim(toVec(K_LAYERNORMBACKWARD_TENSOR_EPSILON_DIMS))
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.set_stride(toVec(K_LAYERNORMBACKWARD_TENSOR_EPSILON_STRIDES));
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attrs.set_epsilon(std::move(epsilon));
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}
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auto [dx, dscale, dbias] = graph->layernorm_backward(dy, x, scale, attrs);
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if(manualUids)
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{
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dx->set_uid(K_LAYERNORMBACKWARD_TENSOR_DX_UID);
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}
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dx->set_output(true)
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.set_name("dx")
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.set_dim(toVec(K_LAYERNORMBACKWARD_TENSOR_DX_DIMS))
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.set_stride(toVec(K_LAYERNORMBACKWARD_TENSOR_DX_STRIDES));
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if(manualUids)
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{
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dscale->set_uid(K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID);
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}
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dscale->set_output(true)
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.set_name("dscale")
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.set_dim(toVec(K_LAYERNORMBACKWARD_TENSOR_DSCALE_DIMS))
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.set_stride(toVec(K_LAYERNORMBACKWARD_TENSOR_DSCALE_STRIDES));
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if(manualUids)
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{
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dbias->set_uid(K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID);
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}
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dbias->set_output(true)
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.set_name("dbias")
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.set_dim(toVec(K_LAYERNORMBACKWARD_TENSOR_DBIAS_DIMS))
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.set_stride(toVec(K_LAYERNORMBACKWARD_TENSOR_DBIAS_STRIDES));
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return lowerAndDeserialize(*graph, _handle);
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}
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};
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// Lowering round-trip: builds a graph, lowers via descriptors, and verifies
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// the deserialized FlatBuffer attributes match.
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TEST_F(IntegrationLayernormBackwardDescriptorLowering,
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LayernormBackwardLoweringRoundTripWithOptionals)
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{
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LayernormBackwardAttributes attrs;
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attrs.set_name("test_op");
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auto graphT = buildAndDeserialize(attrs, true, true);
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// Verify tensors
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ASSERT_EQ(graphT.tensors.size(), 9u);
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// Verify tensor attributes
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auto tensorMap = buildTensorMap(graphT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_DY_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DY_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DY_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DY_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DY_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DY_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_X_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_X_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_X_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_X_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_X_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_X_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_SCALE_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_SCALE_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_SCALE_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_SCALE_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_SCALE_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_SCALE_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_MEAN_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_MEAN_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_MEAN_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_MEAN_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_MEAN_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_MEAN_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_UID]->data_type,
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DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_DX_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DX_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DX_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DX_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DX_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DX_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DSCALE_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DSCALE_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DBIAS_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DBIAS_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID]->data_type, DataTypeSdk::FLOAT);
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// Verify operation node
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ASSERT_EQ(graphT.nodes.size(), 1u);
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auto& node = graphT.nodes[0];
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EXPECT_EQ(node->compute_data_type, DataTypeSdk::FLOAT);
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auto* opNode = node->attributes.AsLayernormBackwardAttributes();
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ASSERT_NE(opNode, nullptr);
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// Verify required tensor UIDs
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EXPECT_EQ(opNode->dy_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_DY_UID);
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EXPECT_EQ(opNode->x_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_X_UID);
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EXPECT_EQ(opNode->scale_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_SCALE_UID);
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EXPECT_EQ(opNode->mean_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_MEAN_UID);
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EXPECT_EQ(opNode->inv_variance_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_UID);
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EXPECT_EQ(opNode->dx_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_DX_UID);
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EXPECT_EQ(opNode->dscale_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID);
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EXPECT_EQ(opNode->dbias_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID);
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EXPECT_EQ(opNode->epsilon_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_EPSILON_UID);
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// Verify operation name preserved through lowering
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EXPECT_EQ(node->name, "test_op");
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}
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// Lowering round-trip: builds a graph, lowers via descriptors, and verifies
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// the deserialized FlatBuffer attributes match.
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TEST_F(IntegrationLayernormBackwardDescriptorLowering,
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LayernormBackwardLoweringRoundTripWithoutOptionals)
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{
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LayernormBackwardAttributes attrs;
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attrs.set_name("test_op");
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auto graphT = buildAndDeserialize(attrs, false, true);
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// Verify tensors
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ASSERT_EQ(graphT.tensors.size(), 6u);
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// Verify tensor attributes
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auto tensorMap = buildTensorMap(graphT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_DY_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DY_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DY_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DY_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DY_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DY_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_X_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_X_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_X_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_X_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_X_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_X_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_SCALE_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_SCALE_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_SCALE_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_SCALE_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_SCALE_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_SCALE_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_DX_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DX_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DX_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DX_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DX_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DX_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DSCALE_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DSCALE_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID]->data_type, DataTypeSdk::FLOAT);
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ASSERT_NE(tensorMap.count(K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID), 0u);
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID]->dims,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DBIAS_DIMS));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID]->strides,
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toVec(K_LAYERNORMBACKWARD_TENSOR_DBIAS_STRIDES));
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EXPECT_EQ(tensorMap[K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID]->data_type, DataTypeSdk::FLOAT);
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// Verify operation node
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ASSERT_EQ(graphT.nodes.size(), 1u);
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auto& node = graphT.nodes[0];
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EXPECT_EQ(node->compute_data_type, DataTypeSdk::FLOAT);
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auto* opNode = node->attributes.AsLayernormBackwardAttributes();
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ASSERT_NE(opNode, nullptr);
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// Verify required tensor UIDs
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EXPECT_EQ(opNode->dy_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_DY_UID);
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EXPECT_EQ(opNode->x_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_X_UID);
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EXPECT_EQ(opNode->scale_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_SCALE_UID);
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EXPECT_EQ(opNode->dx_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_DX_UID);
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EXPECT_EQ(opNode->dscale_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_DSCALE_UID);
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EXPECT_EQ(opNode->dbias_tensor_uid, K_LAYERNORMBACKWARD_TENSOR_DBIAS_UID);
|
|
|
|
// Verify operation name preserved through lowering
|
|
EXPECT_EQ(node->name, "test_op");
|
|
}
|
|
|
|
// Lowering round-trip: builds a graph, lowers via descriptors, and verifies
|
|
// the deserialized FlatBuffer attributes match.
|
|
TEST_F(IntegrationLayernormBackwardDescriptorLowering,
|
|
LayernormBackwardLoweringRoundTripAutoAssignedUids)
|
|
{
|
|
LayernormBackwardAttributes attrs;
|
|
attrs.set_name("test_op");
|
|
|
|
auto graphT = buildAndDeserialize(attrs, true, false);
|
|
|
|
// Verify tensors
|
|
ASSERT_EQ(graphT.tensors.size(), 9u);
|
|
std::unordered_set<int64_t> uids;
|
|
for(const auto& t : graphT.tensors)
|
|
{
|
|
uids.insert(t->uid);
|
|
}
|
|
EXPECT_EQ(uids.size(), 9u)
|
|
<< "Tensor UIDs are not unique"; // NOLINT(readability-implicit-bool-conversion)
|
|
|
|
// Verify operation node
|
|
ASSERT_EQ(graphT.nodes.size(), 1u);
|
|
auto& node = graphT.nodes[0];
|
|
EXPECT_EQ(node->compute_data_type, DataTypeSdk::FLOAT);
|
|
|
|
auto* opNode = node->attributes.AsLayernormBackwardAttributes();
|
|
ASSERT_NE(opNode, nullptr);
|
|
|
|
// Tensor UIDs in the node should match tensors in the graph
|
|
EXPECT_TRUE(uids.count(opNode->dy_tensor_uid) > 0)
|
|
<< "DY tensor UID " << opNode->dy_tensor_uid
|
|
<< " not found in graph tensors"; // NOLINT(readability-implicit-bool-conversion)
|
|
EXPECT_TRUE(uids.count(opNode->x_tensor_uid) > 0)
|
|
<< "X tensor UID " << opNode->x_tensor_uid
|
|
<< " not found in graph tensors"; // NOLINT(readability-implicit-bool-conversion)
|
|
EXPECT_TRUE(uids.count(opNode->scale_tensor_uid) > 0)
|
|
<< "Scale tensor UID " << opNode->scale_tensor_uid
|
|
<< " not found in graph tensors"; // NOLINT(readability-implicit-bool-conversion)
|
|
ASSERT_TRUE(opNode->mean_tensor_uid.has_value());
|
|
EXPECT_TRUE(uids.count(opNode->mean_tensor_uid.value()) > 0)
|
|
<< "Mean tensor UID " << opNode->mean_tensor_uid.value()
|
|
<< " not found in graph tensors"; // NOLINT(readability-implicit-bool-conversion)
|
|
ASSERT_TRUE(opNode->inv_variance_tensor_uid.has_value());
|
|
EXPECT_TRUE(uids.count(opNode->inv_variance_tensor_uid.value()) > 0)
|
|
<< "Inverse variance tensor UID " << opNode->inv_variance_tensor_uid.value()
|
|
<< " not found in graph tensors"; // NOLINT(readability-implicit-bool-conversion)
|
|
EXPECT_TRUE(uids.count(opNode->dx_tensor_uid) > 0)
|
|
<< "DX tensor UID " << opNode->dx_tensor_uid
|
|
<< " not found in graph tensors"; // NOLINT(readability-implicit-bool-conversion)
|
|
EXPECT_TRUE(uids.count(opNode->dscale_tensor_uid) > 0)
|
|
<< "DScale tensor UID " << opNode->dscale_tensor_uid
|
|
<< " not found in graph tensors"; // NOLINT(readability-implicit-bool-conversion)
|
|
EXPECT_TRUE(uids.count(opNode->dbias_tensor_uid) > 0)
|
|
<< "DBias tensor UID " << opNode->dbias_tensor_uid
|
|
<< " not found in graph tensors"; // NOLINT(readability-implicit-bool-conversion)
|
|
ASSERT_TRUE(opNode->epsilon_tensor_uid.has_value());
|
|
EXPECT_TRUE(uids.count(opNode->epsilon_tensor_uid.value()) > 0)
|
|
<< "Epsilon tensor UID " << opNode->epsilon_tensor_uid.value()
|
|
<< " not found in graph tensors"; // NOLINT(readability-implicit-bool-conversion)
|
|
|
|
// All nine tensor UIDs referenced by the node should be distinct
|
|
const std::unordered_set<int64_t> nodeUids = {opNode->dy_tensor_uid,
|
|
opNode->x_tensor_uid,
|
|
opNode->scale_tensor_uid,
|
|
opNode->mean_tensor_uid.value(),
|
|
opNode->inv_variance_tensor_uid.value(),
|
|
opNode->dx_tensor_uid,
|
|
opNode->dscale_tensor_uid,
|
|
opNode->dbias_tensor_uid,
|
|
opNode->epsilon_tensor_uid.value()};
|
|
EXPECT_EQ(nodeUids.size(), 9u)
|
|
<< "Node tensor UIDs are not distinct"; // NOLINT(readability-implicit-bool-conversion)
|
|
|
|
// Verify tensor attributes
|
|
auto tensorMap = buildTensorMap(graphT);
|
|
ASSERT_NE(tensorMap.count(opNode->dy_tensor_uid), 0u);
|
|
EXPECT_EQ(tensorMap[opNode->dy_tensor_uid]->dims, toVec(K_LAYERNORMBACKWARD_TENSOR_DY_DIMS));
|
|
EXPECT_EQ(tensorMap[opNode->dy_tensor_uid]->strides,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_DY_STRIDES));
|
|
EXPECT_EQ(tensorMap[opNode->dy_tensor_uid]->data_type, DataTypeSdk::FLOAT);
|
|
ASSERT_NE(tensorMap.count(opNode->x_tensor_uid), 0u);
|
|
EXPECT_EQ(tensorMap[opNode->x_tensor_uid]->dims, toVec(K_LAYERNORMBACKWARD_TENSOR_X_DIMS));
|
|
EXPECT_EQ(tensorMap[opNode->x_tensor_uid]->strides,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_X_STRIDES));
|
|
EXPECT_EQ(tensorMap[opNode->x_tensor_uid]->data_type, DataTypeSdk::FLOAT);
|
|
ASSERT_NE(tensorMap.count(opNode->scale_tensor_uid), 0u);
|
|
EXPECT_EQ(tensorMap[opNode->scale_tensor_uid]->dims,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_SCALE_DIMS));
|
|
EXPECT_EQ(tensorMap[opNode->scale_tensor_uid]->strides,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_SCALE_STRIDES));
|
|
EXPECT_EQ(tensorMap[opNode->scale_tensor_uid]->data_type, DataTypeSdk::FLOAT);
|
|
ASSERT_NE(tensorMap.count(opNode->mean_tensor_uid.value()), 0u);
|
|
EXPECT_EQ(tensorMap[opNode->mean_tensor_uid.value()]->dims,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_MEAN_DIMS));
|
|
EXPECT_EQ(tensorMap[opNode->mean_tensor_uid.value()]->strides,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_MEAN_STRIDES));
|
|
EXPECT_EQ(tensorMap[opNode->mean_tensor_uid.value()]->data_type, DataTypeSdk::FLOAT);
|
|
ASSERT_NE(tensorMap.count(opNode->inv_variance_tensor_uid.value()), 0u);
|
|
EXPECT_EQ(tensorMap[opNode->inv_variance_tensor_uid.value()]->dims,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_DIMS));
|
|
EXPECT_EQ(tensorMap[opNode->inv_variance_tensor_uid.value()]->strides,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_INV_VARIANCE_STRIDES));
|
|
EXPECT_EQ(tensorMap[opNode->inv_variance_tensor_uid.value()]->data_type, DataTypeSdk::FLOAT);
|
|
ASSERT_NE(tensorMap.count(opNode->dx_tensor_uid), 0u);
|
|
EXPECT_EQ(tensorMap[opNode->dx_tensor_uid]->dims, toVec(K_LAYERNORMBACKWARD_TENSOR_DX_DIMS));
|
|
EXPECT_EQ(tensorMap[opNode->dx_tensor_uid]->strides,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_DX_STRIDES));
|
|
EXPECT_EQ(tensorMap[opNode->dx_tensor_uid]->data_type, DataTypeSdk::FLOAT);
|
|
ASSERT_NE(tensorMap.count(opNode->dscale_tensor_uid), 0u);
|
|
EXPECT_EQ(tensorMap[opNode->dscale_tensor_uid]->dims,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_DSCALE_DIMS));
|
|
EXPECT_EQ(tensorMap[opNode->dscale_tensor_uid]->strides,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_DSCALE_STRIDES));
|
|
EXPECT_EQ(tensorMap[opNode->dscale_tensor_uid]->data_type, DataTypeSdk::FLOAT);
|
|
ASSERT_NE(tensorMap.count(opNode->dbias_tensor_uid), 0u);
|
|
EXPECT_EQ(tensorMap[opNode->dbias_tensor_uid]->dims,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_DBIAS_DIMS));
|
|
EXPECT_EQ(tensorMap[opNode->dbias_tensor_uid]->strides,
|
|
toVec(K_LAYERNORMBACKWARD_TENSOR_DBIAS_STRIDES));
|
|
EXPECT_EQ(tensorMap[opNode->dbias_tensor_uid]->data_type, DataTypeSdk::FLOAT);
|
|
|
|
// Verify operation name preserved through lowering
|
|
EXPECT_EQ(node->name, "test_op");
|
|
}
|
|
|
|
} // namespace
|