derek.holloway
7502bf64a4
CodeQL / Analyze (actions) (push) Canceled after 0s
CodeQL / Analyze (python) (push) Canceled after 0s
pre-commit / pre-commit (push) Canceled after 0s
Multi-Arch CI / setup (push) Canceled after 0s
Publish build_manylinux_x86_64 images / publish_build_manylinux_x86_64 (push) Canceled after 0s
Setup (Multi-Arch) / setup (push) Canceled after 0s
Multi-Arch CI / Linux::${{ fromJSON(needs.setup.outputs.linux_build_config || '{}').build_variant_label || 'skip' }} (push) Canceled after 0s
Publish TheRock Docker image / build-and-push-image (push) Canceled after 0s
Publish no_rocm_image_ubi10 image / publish_no_rocm_image_ubi10 (push) Canceled after 0s
Multi-Arch CI / Windows::${{ fromJSON(needs.setup.outputs.windows_build_config || '{}').build_variant_label || 'skip' }} (push) Canceled after 0s
Publish TheRock Docker image / build-and-push-image (push) Canceled after 0s
Multi-Arch CI / Manifest Diff (push) Canceled after 0s
TheRock Manifest Diff Report / Generate Manifest Diff Report (push) Canceled after 0s
Multi-Arch CI / CI Summary (push) Canceled after 0s
Publish no_rocm_image_ubuntu24_04 images / publish_no_rocm_image_ubuntu24_04 (push) Canceled after 0s
Publish no_rocm_image_ubuntu24_04_ocl_rt images / publish_no_rocm_image_ubuntu24_04_ocl_rt (push) Canceled after 0s
Publish TheRock Docker image / build-and-push-image (push) Canceled after 0s
Publish TheRock Docker image / build-and-push-image (push) Canceled after 0s
Publish no_rocm_image_ubuntu24_04 images / publish_no_rocm_image_ubuntu24_04_rocgdb (push) Canceled after 0s
Publish no_rocm_image_ubuntu24_04_rocgdb images / publish_no_rocm_image_ubuntu24_04_rocgdb (push) Canceled after 0s
Publish no_rocm_image_ubuntu24_04 images / publish_no_rocm_image_ubuntu24_04_ocl_rt (push) Canceled after 0s
Publish TheRock Docker image / build-and-push-image (push) Canceled after 0s
Unit Tests / Unit Tests :: ubuntu-24.04 (push) Canceled after 0s
Unit Tests / Unit Tests :: windows-2022 (push) Canceled after 0s
Publish no_rocm_image_ubuntu24_04 images / publish_no_rocm_image_ubuntu24_04_media (push) Canceled after 0s
Unit Tests / Unit Tests Summary (push) Canceled after 0s
Publish no_rocm_image_ubuntu24_04 images / publish_no_rocm_image_ubuntu24_04_openmpi (push) Canceled after 0s
416 lines
18 KiB
C++
416 lines
18 KiB
C++
// Copyright © Advanced Micro Devices, Inc., or its affiliates.
|
|
// SPDX-License-Identifier: MIT
|
|
|
|
#include <algorithm>
|
|
#include <gtest/gtest.h>
|
|
#include <hip/hip_runtime.h>
|
|
#include <memory>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
#include <hipdnn_frontend.hpp>
|
|
#include <hipdnn_frontend/node/ConvolutionWgradNode.hpp>
|
|
#include <hipdnn_test_sdk/constants/ConvWgradConstants.hpp>
|
|
#include <hipdnn_test_sdk/utilities/IntegrationTestFixture.hpp>
|
|
#include <hipdnn_test_sdk/utilities/LiftingTestHelpers.hpp>
|
|
#include <hipdnn_test_sdk/utilities/TestUtilities.hpp>
|
|
#include <hipdnn_test_sdk/utilities/TestableGraph.hpp>
|
|
#include <hipdnn_test_sdk/utilities/ToVec.hpp>
|
|
|
|
using namespace hipdnn_frontend;
|
|
using namespace hipdnn_frontend::graph;
|
|
using namespace hipdnn_tests::constants;
|
|
using hipdnn_tests::IntegrationTestFixture;
|
|
using hipdnn_tests::liftGraph;
|
|
using hipdnn_tests::liftGraphWithoutFinalization;
|
|
using hipdnn_tests::TestableGraphLifting;
|
|
using hipdnn_tests::toVec;
|
|
|
|
namespace
|
|
{
|
|
// Lifts a frontend graph via build_operation_graph(handle), then
|
|
// reconstructs it with fromBackendDescriptor() for verification.
|
|
class IntegrationConvolutionWgradDescriptorLifting : public IntegrationTestFixture
|
|
{
|
|
protected:
|
|
/// Builds a standard ConvolutionWrw graph for round-trip testing.
|
|
static std::shared_ptr<TestableGraphLifting> buildGraph()
|
|
{
|
|
auto graph = std::make_shared<TestableGraphLifting>();
|
|
graph->set_name("ConvolutionWrwLiftingTestGraph")
|
|
.set_compute_data_type(DataType::FLOAT)
|
|
.set_intermediate_data_type(DataType::FLOAT)
|
|
.set_io_data_type(DataType::FLOAT);
|
|
|
|
auto dy = std::make_shared<TensorAttributes>();
|
|
dy->set_uid(K_WGRAD_TENSOR_DY_UID).set_name("dy").set_data_type(DataType::FLOAT);
|
|
dy->set_dim(toVec(K_WGRAD_TENSOR_DY_DIMS)).set_stride(toVec(K_WGRAD_TENSOR_DY_STRIDES));
|
|
|
|
auto x = std::make_shared<TensorAttributes>();
|
|
x->set_uid(K_WGRAD_TENSOR_X_UID).set_name("x").set_data_type(DataType::FLOAT);
|
|
x->set_dim(toVec(K_WGRAD_TENSOR_X_DIMS)).set_stride(toVec(K_WGRAD_TENSOR_X_STRIDES));
|
|
|
|
ConvWgradAttributes attrs;
|
|
attrs.set_name("test_op");
|
|
attrs.set_convolution_mode(ConvolutionMode::CONVOLUTION);
|
|
attrs.set_pre_padding(toVec(K_WGRAD_CONV_PADDING));
|
|
attrs.set_post_padding(toVec(K_WGRAD_CONV_PADDING));
|
|
attrs.set_stride(toVec(K_WGRAD_CONV_STRIDE));
|
|
attrs.set_dilation(toVec(K_WGRAD_CONV_DILATION));
|
|
|
|
auto dw = graph->conv_wgrad(dy, x, attrs);
|
|
dw->set_dim(toVec(K_WGRAD_TENSOR_DW_DIMS));
|
|
dw->set_uid(K_WGRAD_TENSOR_DW_UID).set_output(true).set_name("dw");
|
|
|
|
return graph;
|
|
}
|
|
};
|
|
|
|
// Builds a standard ConvolutionWrw graph, lowers via build_operation_graph(handle),
|
|
// lifts back with fromBackendDescriptor(), and performs comprehensive field-by-field
|
|
// validation of graph data types, tensor attributes, and operation parameters.
|
|
TEST_F(IntegrationConvolutionWgradDescriptorLifting, BasicConvolutionWrwRoundTrip)
|
|
{
|
|
auto originalGraph = buildGraph();
|
|
|
|
auto liftedGraph = liftGraph(*originalGraph, _handle);
|
|
ASSERT_NE(liftedGraph, nullptr);
|
|
|
|
// Verify graph-level data types
|
|
EXPECT_EQ(liftedGraph->get_compute_data_type(), DataType::FLOAT);
|
|
EXPECT_EQ(liftedGraph->get_intermediate_data_type(), DataType::FLOAT);
|
|
EXPECT_EQ(liftedGraph->get_io_data_type(), DataType::FLOAT);
|
|
|
|
// Verify tensors by UID
|
|
auto tensorMap = liftedGraph->getTensorsByUid();
|
|
ASSERT_EQ(tensorMap.size(), 3u);
|
|
|
|
// Verify x tensor
|
|
ASSERT_NE(tensorMap.count(K_WGRAD_TENSOR_X_UID), 0u);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_X_UID]->get_uid(), K_WGRAD_TENSOR_X_UID);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_X_UID]->get_dim(), toVec(K_WGRAD_TENSOR_X_DIMS));
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_X_UID]->get_stride(), toVec(K_WGRAD_TENSOR_X_STRIDES));
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_X_UID]->get_data_type(), DataType::FLOAT);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_X_UID]->get_name(), "x");
|
|
|
|
// Verify dy tensor
|
|
ASSERT_NE(tensorMap.count(K_WGRAD_TENSOR_DY_UID), 0u);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DY_UID]->get_uid(), K_WGRAD_TENSOR_DY_UID);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DY_UID]->get_dim(), toVec(K_WGRAD_TENSOR_DY_DIMS));
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DY_UID]->get_stride(), toVec(K_WGRAD_TENSOR_DY_STRIDES));
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DY_UID]->get_data_type(), DataType::FLOAT);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DY_UID]->get_name(), "dy");
|
|
|
|
// Verify dw tensor (strides are inferred by infer_properties)
|
|
ASSERT_NE(tensorMap.count(K_WGRAD_TENSOR_DW_UID), 0u);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DW_UID]->get_uid(), K_WGRAD_TENSOR_DW_UID);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DW_UID]->get_dim(), toVec(K_WGRAD_TENSOR_DW_DIMS));
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DW_UID]->get_stride(), toVec(K_WGRAD_TENSOR_DW_STRIDES));
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DW_UID]->get_data_type(), DataType::FLOAT);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DW_UID]->get_name(), "dw");
|
|
|
|
// Verify sub-node count and type
|
|
auto& subNodes = liftedGraph->getSubNodes();
|
|
ASSERT_EQ(subNodes.size(), 1u)
|
|
<< "Expected 1 operation node in lifted graph"; // NOLINT(readability-implicit-bool-conversion)
|
|
|
|
auto* opNode = dynamic_cast<ConvolutionWgradNode*>(subNodes[0].get());
|
|
ASSERT_NE(opNode, nullptr)
|
|
<< "Expected a ConvolutionWgradNode"; // NOLINT(readability-implicit-bool-conversion)
|
|
|
|
// Verify mode
|
|
EXPECT_EQ(opNode->attributes.get_convolution_mode(), ConvolutionMode::CONVOLUTION);
|
|
|
|
// Verify pre_padding
|
|
EXPECT_EQ(opNode->attributes.get_pre_padding(), toVec(K_WGRAD_CONV_PADDING));
|
|
// Verify post_padding
|
|
EXPECT_EQ(opNode->attributes.get_post_padding(), toVec(K_WGRAD_CONV_PADDING));
|
|
// Verify stride
|
|
EXPECT_EQ(opNode->attributes.get_stride(), toVec(K_WGRAD_CONV_STRIDE));
|
|
// Verify dilation
|
|
EXPECT_EQ(opNode->attributes.get_dilation(), toVec(K_WGRAD_CONV_DILATION));
|
|
|
|
// Verify operation name
|
|
EXPECT_EQ(opNode->attributes.get_name(), "test_op");
|
|
}
|
|
|
|
// After lifting, verifies tensor objects in the node attributes are the same
|
|
// shared_ptr instances as in the tensor map (pointer equality).
|
|
TEST_F(IntegrationConvolutionWgradDescriptorLifting, ConvolutionWrwTensorSharingPreserved)
|
|
{
|
|
auto originalGraph = buildGraph();
|
|
|
|
auto liftedGraph = liftGraph(*originalGraph, _handle);
|
|
ASSERT_NE(liftedGraph, nullptr);
|
|
|
|
auto tensorMap = liftedGraph->getTensorsByUid();
|
|
|
|
auto& subNodes = liftedGraph->getSubNodes();
|
|
ASSERT_EQ(subNodes.size(), 1u);
|
|
|
|
auto* opNode = dynamic_cast<ConvolutionWgradNode*>(subNodes[0].get());
|
|
ASSERT_NE(opNode, nullptr);
|
|
|
|
// Verify x tensor sharing
|
|
EXPECT_EQ(opNode->attributes.get_x()->get_uid(), K_WGRAD_TENSOR_X_UID);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_X_UID].get(), opNode->attributes.get_x().get());
|
|
// Verify dy tensor sharing
|
|
EXPECT_EQ(opNode->attributes.get_dy()->get_uid(), K_WGRAD_TENSOR_DY_UID);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DY_UID].get(), opNode->attributes.get_dy().get());
|
|
// Verify dw tensor sharing
|
|
EXPECT_EQ(opNode->attributes.get_dw()->get_uid(), K_WGRAD_TENSOR_DW_UID);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DW_UID].get(), opNode->attributes.get_dw().get());
|
|
|
|
// Verify tensor names
|
|
EXPECT_EQ(opNode->attributes.get_x()->get_name(), "x");
|
|
EXPECT_EQ(opNode->attributes.get_dy()->get_name(), "dy");
|
|
EXPECT_EQ(opNode->attributes.get_dw()->get_name(), "dw");
|
|
}
|
|
|
|
// Builds a ConvolutionWrw graph, serializes to binary, creates a backend descriptor
|
|
// from bytes (no handle, no finalize), calls fromBackendDescriptor(), and verifies
|
|
// all fields survive the backend C API serialization path.
|
|
TEST_F(IntegrationConvolutionWgradDescriptorLifting, ConvolutionWrwLiftWithoutFinalization)
|
|
{
|
|
auto originalGraph = buildGraph();
|
|
|
|
auto liftedGraph = liftGraphWithoutFinalization(*originalGraph);
|
|
ASSERT_NE(liftedGraph, nullptr);
|
|
|
|
// Verify graph-level data types
|
|
EXPECT_EQ(liftedGraph->get_compute_data_type(), DataType::FLOAT);
|
|
EXPECT_EQ(liftedGraph->get_intermediate_data_type(), DataType::FLOAT);
|
|
EXPECT_EQ(liftedGraph->get_io_data_type(), DataType::FLOAT);
|
|
|
|
// Verify the lifted graph has 1 operation node
|
|
auto& subNodes = liftedGraph->getSubNodes();
|
|
ASSERT_EQ(subNodes.size(), 1u);
|
|
|
|
auto* opNode = dynamic_cast<ConvolutionWgradNode*>(subNodes[0].get());
|
|
ASSERT_NE(opNode, nullptr);
|
|
|
|
// Verify mode
|
|
EXPECT_EQ(opNode->attributes.get_convolution_mode(), ConvolutionMode::CONVOLUTION);
|
|
|
|
// Verify pre_padding
|
|
EXPECT_EQ(opNode->attributes.get_pre_padding(), toVec(K_WGRAD_CONV_PADDING));
|
|
// Verify post_padding
|
|
EXPECT_EQ(opNode->attributes.get_post_padding(), toVec(K_WGRAD_CONV_PADDING));
|
|
// Verify stride
|
|
EXPECT_EQ(opNode->attributes.get_stride(), toVec(K_WGRAD_CONV_STRIDE));
|
|
// Verify dilation
|
|
EXPECT_EQ(opNode->attributes.get_dilation(), toVec(K_WGRAD_CONV_DILATION));
|
|
|
|
// Verify operation name
|
|
EXPECT_EQ(opNode->attributes.get_name(), "test_op");
|
|
|
|
// Verify tensor dims and strides
|
|
auto tensorMap = liftedGraph->getTensorsByUid();
|
|
ASSERT_EQ(tensorMap.size(), 3u);
|
|
|
|
ASSERT_NE(tensorMap.count(K_WGRAD_TENSOR_X_UID), 0u);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_X_UID]->get_dim(), toVec(K_WGRAD_TENSOR_X_DIMS));
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_X_UID]->get_stride(), toVec(K_WGRAD_TENSOR_X_STRIDES));
|
|
ASSERT_NE(tensorMap.count(K_WGRAD_TENSOR_DY_UID), 0u);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DY_UID]->get_dim(), toVec(K_WGRAD_TENSOR_DY_DIMS));
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DY_UID]->get_stride(), toVec(K_WGRAD_TENSOR_DY_STRIDES));
|
|
ASSERT_NE(tensorMap.count(K_WGRAD_TENSOR_DW_UID), 0u);
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DW_UID]->get_dim(), toVec(K_WGRAD_TENSOR_DW_DIMS));
|
|
EXPECT_EQ(tensorMap[K_WGRAD_TENSOR_DW_UID]->get_stride(), toVec(K_WGRAD_TENSOR_DW_STRIDES));
|
|
}
|
|
|
|
// Creates tensors without explicit set_uid(), verifies that auto-assigned UIDs
|
|
// survive the lifting round trip and are all distinct.
|
|
TEST_F(IntegrationConvolutionWgradDescriptorLifting, AutoAssignedUidsPreservedInLiftingRoundTrip)
|
|
{
|
|
constexpr std::array<int64_t, 4> K_AUTO_X_DIMS = {1, 4, 8, 8};
|
|
constexpr std::array<int64_t, 4> K_AUTO_X_STRIDES = {256, 64, 8, 1};
|
|
constexpr std::array<int64_t, 4> K_AUTO_DY_DIMS = {1, 16, 6, 6};
|
|
constexpr std::array<int64_t, 4> K_AUTO_DY_STRIDES = {576, 36, 6, 1};
|
|
constexpr std::array<int64_t, 4> K_AUTO_DW_DIMS = {16, 4, 5, 5};
|
|
constexpr std::array<int64_t, 4> K_AUTO_DW_STRIDES = {100, 25, 5, 1};
|
|
|
|
auto graph = std::make_shared<TestableGraphLifting>();
|
|
graph->set_name("AutoUidWgradLiftTest")
|
|
.set_compute_data_type(DataType::FLOAT)
|
|
.set_intermediate_data_type(DataType::FLOAT)
|
|
.set_io_data_type(DataType::FLOAT);
|
|
|
|
auto x = std::make_shared<TensorAttributes>();
|
|
x->set_name("x").set_data_type(DataType::FLOAT);
|
|
x->set_dim(toVec(K_AUTO_X_DIMS)).set_stride(toVec(K_AUTO_X_STRIDES));
|
|
|
|
auto dy = std::make_shared<TensorAttributes>();
|
|
dy->set_name("dy").set_data_type(DataType::FLOAT);
|
|
dy->set_dim(toVec(K_AUTO_DY_DIMS)).set_stride(toVec(K_AUTO_DY_STRIDES));
|
|
|
|
ConvWgradAttributes convAttrs;
|
|
convAttrs.set_name("auto_uid_wgrad_op");
|
|
convAttrs.set_pre_padding({1, 1});
|
|
convAttrs.set_post_padding({1, 1});
|
|
convAttrs.set_stride({1, 1});
|
|
convAttrs.set_dilation({1, 1});
|
|
convAttrs.set_convolution_mode(ConvolutionMode::CONVOLUTION);
|
|
|
|
auto dw = graph->conv_wgrad(dy, x, convAttrs);
|
|
dw->set_dim(toVec(K_AUTO_DW_DIMS));
|
|
dw->set_output(true).set_name("dw");
|
|
|
|
auto liftedGraph = liftGraph(*graph, _handle);
|
|
ASSERT_NE(liftedGraph, nullptr);
|
|
|
|
auto tensorMap = liftedGraph->getTensorsByUid();
|
|
ASSERT_EQ(tensorMap.size(), 3u)
|
|
<< "Expected 3 tensors in lifted graph"; // NOLINT(readability-implicit-bool-conversion)
|
|
|
|
// Collect all UIDs and verify they are distinct
|
|
std::vector<int64_t> uids;
|
|
uids.reserve(tensorMap.size());
|
|
for(const auto& [uid, tensor] : tensorMap)
|
|
{
|
|
uids.push_back(uid);
|
|
}
|
|
std::sort(uids.begin(), uids.end());
|
|
EXPECT_EQ(std::adjacent_find(uids.begin(), uids.end()), uids.end())
|
|
<< "All auto-assigned UIDs must be distinct"; // NOLINT(readability-implicit-bool-conversion)
|
|
|
|
// Verify the node references tensors with auto-assigned UIDs
|
|
auto& subNodes = liftedGraph->getSubNodes();
|
|
ASSERT_EQ(subNodes.size(), 1u);
|
|
|
|
auto* opNode = dynamic_cast<ConvolutionWgradNode*>(subNodes[0].get());
|
|
ASSERT_NE(opNode, nullptr);
|
|
|
|
// Verify tensor dims survived the round trip
|
|
auto xUid = opNode->attributes.get_x()->get_uid();
|
|
auto dyUid = opNode->attributes.get_dy()->get_uid();
|
|
auto dwUid = opNode->attributes.get_dw()->get_uid();
|
|
|
|
EXPECT_NE(xUid, dyUid);
|
|
EXPECT_NE(xUid, dwUid);
|
|
EXPECT_NE(dyUid, dwUid);
|
|
|
|
EXPECT_EQ(tensorMap[xUid]->get_dim(), toVec(K_AUTO_X_DIMS));
|
|
EXPECT_EQ(tensorMap[dyUid]->get_dim(), toVec(K_AUTO_DY_DIMS));
|
|
EXPECT_EQ(tensorMap[dwUid]->get_dim(), toVec(K_AUTO_DW_DIMS));
|
|
EXPECT_EQ(tensorMap[dwUid]->get_stride(), toVec(K_AUTO_DW_STRIDES));
|
|
}
|
|
|
|
// Builds a conv wgrad graph with asymmetric padding (pre_padding={1,1},
|
|
// post_padding={2,2}), lowers, lifts, and verifies asymmetric padding
|
|
// values survive the round trip.
|
|
TEST_F(IntegrationConvolutionWgradDescriptorLifting, AsymmetricPaddingPreservedInLiftingRoundTrip)
|
|
{
|
|
constexpr std::array<int64_t, 2> K_ASYM_PRE_PADDING = {1, 1};
|
|
constexpr std::array<int64_t, 2> K_ASYM_POST_PADDING = {2, 2};
|
|
constexpr std::array<int64_t, 4> K_ASYM_DW_DIMS = {64, 3, 4, 4};
|
|
|
|
auto graph = std::make_shared<TestableGraphLifting>();
|
|
graph->set_name("AsymPaddingWgradLiftTest")
|
|
.set_compute_data_type(DataType::FLOAT)
|
|
.set_intermediate_data_type(DataType::FLOAT)
|
|
.set_io_data_type(DataType::FLOAT);
|
|
|
|
auto dy = std::make_shared<TensorAttributes>();
|
|
dy->set_uid(K_WGRAD_TENSOR_DY_UID).set_name("dy").set_data_type(DataType::FLOAT);
|
|
dy->set_dim(toVec(K_WGRAD_TENSOR_DY_DIMS)).set_stride(toVec(K_WGRAD_TENSOR_DY_STRIDES));
|
|
|
|
auto x = std::make_shared<TensorAttributes>();
|
|
x->set_uid(K_WGRAD_TENSOR_X_UID).set_name("x").set_data_type(DataType::FLOAT);
|
|
x->set_dim(toVec(K_WGRAD_TENSOR_X_DIMS)).set_stride(toVec(K_WGRAD_TENSOR_X_STRIDES));
|
|
|
|
ConvWgradAttributes convAttrs;
|
|
convAttrs.set_name("asym_wgrad_op");
|
|
convAttrs.set_pre_padding(toVec(K_ASYM_PRE_PADDING));
|
|
convAttrs.set_post_padding(toVec(K_ASYM_POST_PADDING));
|
|
convAttrs.set_stride({1, 1});
|
|
convAttrs.set_dilation({1, 1});
|
|
convAttrs.set_convolution_mode(ConvolutionMode::CONVOLUTION);
|
|
|
|
auto dw = graph->conv_wgrad(dy, x, convAttrs);
|
|
dw->set_dim(toVec(K_ASYM_DW_DIMS));
|
|
dw->set_uid(K_WGRAD_TENSOR_DW_UID).set_output(true).set_name("dw");
|
|
|
|
auto liftedGraph = liftGraph(*graph, _handle);
|
|
ASSERT_NE(liftedGraph, nullptr);
|
|
|
|
auto& subNodes = liftedGraph->getSubNodes();
|
|
ASSERT_EQ(subNodes.size(), 1u);
|
|
|
|
auto* opNode = dynamic_cast<ConvolutionWgradNode*>(subNodes[0].get());
|
|
ASSERT_NE(opNode, nullptr)
|
|
<< "Expected a ConvolutionWgradNode"; // NOLINT(readability-implicit-bool-conversion)
|
|
|
|
EXPECT_EQ(opNode->attributes.get_pre_padding(), toVec(K_ASYM_PRE_PADDING));
|
|
EXPECT_EQ(opNode->attributes.get_post_padding(), toVec(K_ASYM_POST_PADDING));
|
|
EXPECT_EQ(opNode->attributes.get_stride(), std::vector<int64_t>({1, 1}));
|
|
EXPECT_EQ(opNode->attributes.get_dilation(), std::vector<int64_t>({1, 1}));
|
|
}
|
|
|
|
// Exercises the JSON serialize/deserialize path with a handle (full finalization)
|
|
// for a conv wgrad graph.
|
|
TEST_F(IntegrationConvolutionWgradDescriptorLifting, JsonRoundTripWithHandle)
|
|
{
|
|
auto originalGraph = buildGraph();
|
|
|
|
auto result = originalGraph->validate();
|
|
ASSERT_EQ(result.code, ErrorCode::OK) << result.err_msg;
|
|
|
|
// Serialize to JSON (auto-lowers internally)
|
|
std::string jsonData;
|
|
result = originalGraph->serialize(jsonData);
|
|
ASSERT_EQ(result.code, ErrorCode::OK) << result.err_msg;
|
|
ASSERT_FALSE(jsonData.empty());
|
|
|
|
// Deserialize from JSON with handle
|
|
auto liftedGraph = std::make_shared<TestableGraphLifting>();
|
|
result = liftedGraph->deserialize(_handle, jsonData);
|
|
ASSERT_EQ(result.code, ErrorCode::OK) << result.err_msg;
|
|
|
|
// Verify graph-level attributes
|
|
EXPECT_EQ(liftedGraph->get_name(), "ConvolutionWrwLiftingTestGraph");
|
|
EXPECT_EQ(liftedGraph->get_compute_data_type(), DataType::FLOAT);
|
|
EXPECT_EQ(liftedGraph->get_intermediate_data_type(), DataType::FLOAT);
|
|
EXPECT_EQ(liftedGraph->get_io_data_type(), DataType::FLOAT);
|
|
|
|
// Verify tensors by UID
|
|
auto tensorMap = liftedGraph->getTensorsByUid();
|
|
ASSERT_EQ(tensorMap.size(), 3u) << "Expected 3 tensors (dy, x, dw)";
|
|
|
|
hipdnn_tests::verifyTensorInGraph(tensorMap,
|
|
K_WGRAD_TENSOR_DY_UID,
|
|
"dy",
|
|
toVec(K_WGRAD_TENSOR_DY_DIMS),
|
|
toVec(K_WGRAD_TENSOR_DY_STRIDES),
|
|
DataType::FLOAT);
|
|
hipdnn_tests::verifyTensorInGraph(tensorMap,
|
|
K_WGRAD_TENSOR_X_UID,
|
|
"x",
|
|
toVec(K_WGRAD_TENSOR_X_DIMS),
|
|
toVec(K_WGRAD_TENSOR_X_STRIDES),
|
|
DataType::FLOAT);
|
|
hipdnn_tests::verifyTensorInGraph(tensorMap,
|
|
K_WGRAD_TENSOR_DW_UID,
|
|
"dw",
|
|
toVec(K_WGRAD_TENSOR_DW_DIMS),
|
|
toVec(K_WGRAD_TENSOR_DW_STRIDES),
|
|
DataType::FLOAT);
|
|
|
|
// Verify sub-node count and type
|
|
auto& subNodes = liftedGraph->getSubNodes();
|
|
ASSERT_EQ(subNodes.size(), 1u) << "Expected 1 operation node in lifted graph";
|
|
|
|
auto* opNode = dynamic_cast<ConvolutionWgradNode*>(subNodes[0].get());
|
|
ASSERT_NE(opNode, nullptr) << "Expected a ConvolutionWgradNode";
|
|
|
|
// Verify convolution parameters
|
|
EXPECT_EQ(opNode->attributes.get_convolution_mode(), ConvolutionMode::CONVOLUTION);
|
|
EXPECT_EQ(opNode->attributes.get_pre_padding(), toVec(K_WGRAD_CONV_PADDING));
|
|
EXPECT_EQ(opNode->attributes.get_post_padding(), toVec(K_WGRAD_CONV_PADDING));
|
|
EXPECT_EQ(opNode->attributes.get_stride(), toVec(K_WGRAD_CONV_STRIDE));
|
|
EXPECT_EQ(opNode->attributes.get_dilation(), toVec(K_WGRAD_CONV_DILATION));
|
|
EXPECT_EQ(opNode->attributes.get_name(), "test_op");
|
|
}
|
|
|
|
} // namespace
|