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resample.cpp
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/*
* Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*/
#include <catch2/catch_test_macros.hpp>
#include "../utils/helpers.h"
#include <cudnn_frontend.h>
TEST_CASE("Resample Max Pooling NHWC Inference", "[resample][pooling][max][graph]") {
namespace fe = cudnn_frontend;
// This example shows running max pooling graphs when in inference mode.
// See details about support surface in
// https://docs.nvidia.com/deeplearning/cudnn/developer/graph-api.html#resamplefwd
constexpr int N = 8;
constexpr int H = 56;
constexpr int W = 56;
constexpr int C = 8;
fe::graph::Graph graph{};
graph.set_io_data_type(fe::DataType_t::HALF).set_compute_data_type(fe::DataType_t::FLOAT);
auto X = graph.tensor(fe::graph::Tensor_attributes().set_dim({N, C, H, W}).set_stride({H * W * C, 1, W * C, C}));
auto [Y, Index] = graph.resample(X,
fe::graph::Resample_attributes()
.set_generate_index(false)
.set_resampling_mode(fe::ResampleMode_t::MAXPOOL)
.set_padding_mode(fe::PaddingMode_t::NEG_INF_PAD)
.set_window({2, 3})
.set_stride({4, 5})
.set_pre_padding({2, 3})
.set_post_padding({4, 5}));
Y->set_output(true);
assert(Index == nullptr);
// Create a unique_ptr for the cuDNN handle
auto handle_ptr = create_cudnn_handle();
auto handle = *handle_ptr;
REQUIRE(graph.validate().is_good());
REQUIRE(graph.build_operation_graph(handle).is_good());
REQUIRE(graph.create_execution_plans({fe::HeurMode_t::A}).is_good());
REQUIRE(graph.check_support().is_good());
REQUIRE(graph.build_plans(fe::BuildPlanPolicy_t::HEURISTICS_CHOICE).is_good());
Surface<half> X_gpu(N * H * W * C);
Surface<half> Y_gpu(N * H * W * C);
std::unordered_map<std::shared_ptr<fe::graph::Tensor_attributes>, void*> variant_pack = {{X, X_gpu.devPtr},
{Y, Y_gpu.devPtr}};
int64_t workspace_size = 0;
REQUIRE(graph.get_workspace_size(workspace_size).is_good());
Surface<int8_t> workspace(workspace_size);
REQUIRE(graph.execute(handle, variant_pack, workspace.devPtr).is_good());
}
TEST_CASE("Resample Max Pooling NHWC Training", "[resample][pooling][max][graph]") {
namespace fe = cudnn_frontend;
// This example shows running NHWC max pooling graphs.
// Support for NHWC max pooling has a fast path which can dump index tensor from forward pass.
// This mean backward pass to skip reading full X tensor and instead just use this index tensor.
// See details about support surface and index tensor in
// https://docs.nvidia.com/deeplearning/cudnn/developer/graph-api.html#resamplefwd
constexpr int N = 8;
constexpr int H = 56;
constexpr int W = 56;
constexpr int C = 8;
fe::graph::Graph graph{};
graph.set_io_data_type(fe::DataType_t::HALF).set_compute_data_type(fe::DataType_t::FLOAT);
auto X = graph.tensor(fe::graph::Tensor_attributes().set_dim({N, C, H, W}).set_stride({H * W * C, 1, W * C, C}));
auto [Y, Index] = graph.resample(X,
fe::graph::Resample_attributes()
.set_generate_index(true)
.set_resampling_mode(fe::ResampleMode_t::MAXPOOL)
.set_padding_mode(fe::PaddingMode_t::NEG_INF_PAD)
.set_window({2, 3})
.set_stride({4, 5})
.set_pre_padding({2, 3})
.set_post_padding({4, 5}));
Y->set_output(true);
Index->set_output(true).set_data_type(fe::DataType_t::INT8);
// Create a unique_ptr for the cuDNN handle
auto handle_ptr = create_cudnn_handle();
auto handle = *handle_ptr;
REQUIRE(graph.validate().is_good());
auto const status = graph.build_operation_graph(handle);
if (cudnn_frontend::detail::get_backend_version() >= 8600)
REQUIRE(status.is_good());
else {
REQUIRE(status.is_bad());
SKIP("Using index tensor is not supported pre 8.6.");
}
REQUIRE(graph.create_execution_plans({fe::HeurMode_t::A}).is_good());
REQUIRE(graph.check_support().is_good());
REQUIRE(graph.build_plans(fe::BuildPlanPolicy_t::HEURISTICS_CHOICE).is_good());
Surface<half> X_gpu(N * H * W * C);
Surface<half> Y_gpu(N * H * W * C);
Surface<int8_t> Index_gpu(N * H * W * C / 8);
std::unordered_map<std::shared_ptr<fe::graph::Tensor_attributes>, void*> variant_pack = {
{X, X_gpu.devPtr}, {Y, Y_gpu.devPtr}, {Index, Index_gpu.devPtr}};
int64_t workspace_size = 0;
REQUIRE(graph.get_workspace_size(workspace_size).is_good());
Surface<int8_t> workspace(workspace_size);
REQUIRE(graph.execute(handle, variant_pack, workspace.devPtr).is_good());
}
TEST_CASE("Resample Avg Pooling", "[resample][pooling][average][graph]") {
namespace fe = cudnn_frontend;
// This example shows running average pooling graphs.
// There is no difference between NHWC and NCHW support surface.
// See details about support surface in
// https://docs.nvidia.com/deeplearning/cudnn/developer/graph-api.html#resamplefwd
constexpr int N = 8;
constexpr int H = 56;
constexpr int W = 56;
constexpr int C = 8;
fe::graph::Graph graph{};
graph.set_io_data_type(fe::DataType_t::HALF).set_compute_data_type(fe::DataType_t::FLOAT);
auto X = graph.tensor(fe::graph::Tensor_attributes().set_dim({N, C, H, W}).set_stride({H * W * C, 1, W * C, C}));
auto [Y, Index] = graph.resample(X,
fe::graph::Resample_attributes()
.set_generate_index(true)
.set_resampling_mode(fe::ResampleMode_t::AVGPOOL_INCLUDE_PADDING)
.set_padding_mode(fe::PaddingMode_t::ZERO_PAD)
.set_window({2, 3})
.set_stride({4, 5})
.set_pre_padding({2, 3})
.set_post_padding({4, 5}));
Y->set_output(true);
assert(Index == nullptr);
// Create a unique_ptr for the cuDNN handle
auto handle_ptr = create_cudnn_handle();
auto handle = *handle_ptr;
REQUIRE(graph.validate().is_good());
REQUIRE(graph.build_operation_graph(handle).is_good());
REQUIRE(graph.create_execution_plans({fe::HeurMode_t::A}).is_good());
REQUIRE(graph.check_support().is_good());
REQUIRE(graph.build_plans(fe::BuildPlanPolicy_t::HEURISTICS_CHOICE).is_good());
Surface<half> X_gpu(N * H * W * C);
Surface<half> Y_gpu(N * H * W * C);
std::unordered_map<std::shared_ptr<fe::graph::Tensor_attributes>, void*> variant_pack = {{X, X_gpu.devPtr},
{Y, Y_gpu.devPtr}};
int64_t workspace_size = 0;
REQUIRE(graph.get_workspace_size(workspace_size).is_good());
Surface<int8_t> workspace(workspace_size);
REQUIRE(graph.execute(handle, variant_pack, workspace.devPtr).is_good());
}