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239 lines (196 loc) · 10.8 KB
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/*
* SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*/
#include <catch2/catch_test_macros.hpp>
#include "../utils/helpers.h"
#include <cudnn_frontend.h>
TEST_CASE("Forward Training LayerNorm and Bitmask Clamped ReLU", "[layernorm][graph][clamped_relu_bitmask]") {
// Compatibility checks
if constexpr (CUDNN_VERSION < 91300) {
SKIP("LayerNorm with relu using bitmask is not supported in cudnn versions prior to 9.13.0");
}
if (check_device_arch_newer_than("ampere") == false) {
SKIP("LayerNorm requires Ampere and up");
}
namespace fe = cudnn_frontend;
fe::graph::Graph graph;
graph.set_io_data_type(fe::DataType_t::FLOAT)
.set_intermediate_data_type(fe::DataType_t::FLOAT)
.set_compute_data_type(fe::DataType_t::FLOAT);
auto batch_size = 4;
auto seq_length = 1024;
auto hidden_size = 128;
auto X = graph.tensor(fe::graph::Tensor_attributes()
.set_name("X")
.set_dim({batch_size * seq_length, hidden_size, 1, 1})
.set_stride({hidden_size, 1, hidden_size, hidden_size}));
auto scale = graph.tensor(fe::graph::Tensor_attributes()
.set_name("scale")
.set_dim({1, hidden_size, 1, 1})
.set_stride({hidden_size, 1, hidden_size, hidden_size})
.set_data_type(fe::DataType_t::FLOAT));
auto bias = graph.tensor(fe::graph::Tensor_attributes()
.set_name("bias")
.set_dim({1, hidden_size, 1, 1})
.set_stride({hidden_size, 1, hidden_size, hidden_size})
.set_data_type(fe::DataType_t::FLOAT));
float scalar_epsilon = 1e-05f;
fe::graph::Tensor_attributes s_epsilon(scalar_epsilon);
auto epsilon = graph.tensor(s_epsilon.set_name("epsilon"));
float lower_clip = 0.0f;
float upper_clip = 6.0f;
fe::graph::Tensor_attributes s_lower_clip(lower_clip);
fe::graph::Tensor_attributes s_upper_clip(upper_clip);
auto relu_lower_bound = graph.tensor(s_lower_clip.set_name("relu_lower_bound"));
auto relu_upper_bound = graph.tensor(s_upper_clip.set_name("relu_upper_bound"));
// Apply LayerNorm
auto layernorm_options =
fe::graph::Layernorm_attributes().set_forward_phase(fe::NormFwdPhase_t::TRAINING).set_epsilon(epsilon);
auto [ln_output, mean, inv_variance] = graph.layernorm(X, scale, bias, layernorm_options);
mean->set_output(true).set_data_type(fe::DataType_t::FLOAT);
inv_variance->set_output(true).set_data_type(fe::DataType_t::FLOAT);
// Apply clamped ReLU to the LayerNorm output
auto relu_attributes = fe::graph::Pointwise_attributes()
.set_mode(fe::PointwiseMode_t::RELU_FWD)
.set_compute_data_type(fe::DataType_t::FLOAT)
.set_relu_lower_clip(lower_clip)
.set_relu_upper_clip(upper_clip);
auto Y = graph.pointwise(ln_output, relu_attributes);
Y->set_output(true);
Y->set_name("ReLU(Y)");
// Generate bitmask for clamped ReLU
auto relu_lower_clip_mask_attr = fe::graph::Pointwise_attributes()
.set_mode(fe::PointwiseMode_t::CMP_GT)
.set_compute_data_type(fe::DataType_t::FLOAT);
auto lower_mask = graph.pointwise(Y, relu_lower_bound, relu_lower_clip_mask_attr);
lower_mask->set_data_type(fe::DataType_t::BOOLEAN);
lower_mask->set_name("lower_mask");
auto relu_upper_clip_mask_attr = fe::graph::Pointwise_attributes()
.set_mode(fe::PointwiseMode_t::CMP_LT)
.set_compute_data_type(fe::DataType_t::FLOAT);
auto upper_mask = graph.pointwise(Y, relu_upper_bound, relu_upper_clip_mask_attr);
upper_mask->set_data_type(fe::DataType_t::BOOLEAN);
upper_mask->set_name("upper_mask");
auto logical_and_attr = fe::graph::Pointwise_attributes()
.set_mode(fe::PointwiseMode_t::LOGICAL_AND)
.set_compute_data_type(fe::DataType_t::BOOLEAN);
auto bitmask = graph.pointwise(lower_mask, upper_mask, logical_and_attr);
bitmask->set_data_type(fe::DataType_t::BOOLEAN);
bitmask->set_name("relu_bitmask");
bitmask->set_output(true);
// Create a unique_ptr for the cuDNN handle
auto handle_ptr = create_cudnn_handle();
auto handle = *handle_ptr;
// Print the graph
std::cout << graph << std::endl;
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(handle).is_good());
REQUIRE(graph.build_plans(handle).is_good());
Surface<float> X_tensor(batch_size * seq_length * hidden_size);
Surface<float> Mean_tensor(batch_size * seq_length);
Surface<float> Var_tensor(batch_size * seq_length);
Surface<float> Scale_tensor(hidden_size);
Surface<float> Bias_tensor(hidden_size);
Surface<float> Y_tensor(batch_size * seq_length * hidden_size);
Surface<uint8_t> Relu_Bitmask_tensor(batch_size * seq_length * hidden_size);
int64_t workspace_size;
REQUIRE(graph.get_workspace_size(workspace_size).is_good());
Surface<int8_t> workspace(workspace_size);
std::unordered_map<std::shared_ptr<fe::graph::Tensor_attributes>, void*> variant_pack = {
{X, X_tensor.devPtr},
{mean, Mean_tensor.devPtr},
{inv_variance, Var_tensor.devPtr},
{scale, Scale_tensor.devPtr},
{bias, Bias_tensor.devPtr},
{Y, Y_tensor.devPtr},
{bitmask, Relu_Bitmask_tensor.devPtr}};
REQUIRE(graph.execute(handle, variant_pack, workspace.devPtr).is_good());
}
TEST_CASE("Clamped DReLU using Bitmask and Backward LayerNorm", "[layernorm][graph][DRelu_bitmask_DLN]") {
// Compatibility checks
if constexpr (CUDNN_VERSION < 91300) {
SKIP("LayerNorm with relu using bitmask is not supported in cudnn versions prior to 9.13.0");
}
if (check_device_arch_newer_than("ampere") == false) {
SKIP("LayerNorm requires Ampere and up");
}
namespace fe = cudnn_frontend;
fe::graph::Graph graph;
graph.set_io_data_type(fe::DataType_t::FLOAT)
.set_intermediate_data_type(fe::DataType_t::FLOAT)
.set_compute_data_type(fe::DataType_t::FLOAT);
auto batch_size = 4;
auto seq_length = 1024;
auto hidden_size = 128;
auto X = graph.tensor(fe::graph::Tensor_attributes()
.set_name("X")
.set_dim({batch_size * seq_length, hidden_size, 1, 1})
.set_stride({hidden_size, 1, hidden_size, hidden_size}));
auto DY = graph.tensor(fe::graph::Tensor_attributes()
.set_name("DY")
.set_dim({batch_size * seq_length, hidden_size, 1, 1})
.set_stride({hidden_size, 1, hidden_size, hidden_size}));
auto scale = graph.tensor(fe::graph::Tensor_attributes()
.set_name("scale")
.set_dim({1, hidden_size, 1, 1})
.set_stride({hidden_size, 1, hidden_size, hidden_size})
.set_data_type(fe::DataType_t::FLOAT));
auto mean = graph.tensor(fe::graph::Tensor_attributes()
.set_name("mean")
.set_dim({batch_size * seq_length, 1, 1, 1})
.set_stride({1, 1, 1, 1})
.set_data_type(fe::DataType_t::FLOAT));
auto inv_variance = graph.tensor(fe::graph::Tensor_attributes()
.set_name("inv_variance")
.set_dim({batch_size * seq_length, 1, 1, 1})
.set_stride({1, 1, 1, 1})
.set_data_type(fe::DataType_t::FLOAT));
auto mask = graph.tensor(fe::graph::Tensor_attributes()
.set_name("mask")
.set_dim({batch_size * seq_length, hidden_size, 1, 1})
.set_stride({hidden_size, 1, hidden_size, hidden_size})
.set_data_type(fe::DataType_t::BOOLEAN));
auto mul_options = fe::graph::Pointwise_attributes().set_mode(fe::PointwiseMode_t::MUL);
auto applied_bitmask_DY_output = graph.pointwise(DY, mask, mul_options);
auto DLN_options = fe::graph::Layernorm_backward_attributes().set_saved_mean_and_inv_variance(mean, inv_variance);
auto [DX, dscale, dbias] = graph.layernorm_backward(applied_bitmask_DY_output, X, scale, DLN_options);
DX->set_output(true);
dscale->set_output(true).set_data_type(fe::DataType_t::FLOAT);
dbias->set_output(true).set_data_type(fe::DataType_t::FLOAT);
// Create a unique_ptr for the cuDNN handle
auto handle_ptr = create_cudnn_handle();
auto handle = *handle_ptr;
// Print the graph
std::cout << graph << std::endl;
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(handle).is_good());
REQUIRE(graph.build_plans(handle).is_good());
Surface<float> X_tensor(batch_size * seq_length * hidden_size);
Surface<float> DY_tensor(batch_size * seq_length * hidden_size);
Surface<float> Mean_tensor(batch_size * seq_length);
Surface<float> Inv_variance_tensor(batch_size * seq_length);
Surface<uint8_t> Mask_tensor(batch_size * seq_length * hidden_size);
Surface<float> Scale_tensor(hidden_size);
Surface<float> Dscale_tensor(hidden_size);
Surface<float> Dbias_tensor(hidden_size);
Surface<float> DX_tensor(batch_size * seq_length * hidden_size);
int64_t workspace_size = 0;
REQUIRE(graph.get_workspace_size(workspace_size).is_good());
Surface<int8_t> workspace(workspace_size);
std::unordered_map<std::shared_ptr<fe::graph::Tensor_attributes>, void*> variant_pack = {
{X, X_tensor.devPtr},
{DY, DY_tensor.devPtr},
{mean, Mean_tensor.devPtr},
{inv_variance, Inv_variance_tensor.devPtr},
{mask, Mask_tensor.devPtr},
{scale, Scale_tensor.devPtr},
{dscale, Dscale_tensor.devPtr},
{dbias, Dbias_tensor.devPtr},
{DX, DX_tensor.devPtr}};
REQUIRE(graph.execute(handle, variant_pack, workspace.devPtr).is_good());
}