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未验证 提交 22bbd547 编写于 作者: Y Yiqun Liu 提交者： GitHub
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Add the support of fp16 in fusion_group (#22239)

上级 d97475d5
隐藏空白更改
内联 并排
Showing with 570 addition and 194 deletion
paddle/fluid/framework/ir/fusion_group/code_generator.cc
浏览文件 @ 22bbd547
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <sstream>
#include <unordered_set>
#include "paddle/fluid/framework/ir/fusion_group/code_generator_helper.h"
#include "paddle/fluid/framework/ir/fusion_group/cuda_resources.h"
#include "paddle/fluid/framework/ir/fusion_group/operation.h"
namespace paddle {
......@@ -27,13 +28,14 @@ CodeGenerator::CodeGenerator() {
// Only support elementwise operations now.
code_templates_.resize(1);
CodeTemplate elementwise_t(elementwise_cuda_template);
CodeTemplate elementwise_t(cuda_kernel_template_1d);
code_templates_[0] = elementwise_t;
}
std::string CodeGenerator::Generate(SubGraph* subgraph) {
std::vector<OperationExpression> expressions = ConvertToExpressions(subgraph);
return Generate(subgraph->GetFuncName(), expressions);
return Generate(subgraph->GetFuncName(), subgraph->GetDataType(),
expressions);
}
static bool HasInput(Node* n, std::string name) {
......@@ -100,9 +102,9 @@ std::vector<OperationExpression> CodeGenerator::ConvertToExpressions(
// In order to get the right result of expression, we need to calculate and
// store the expression as suffix Expressions using vector.
std::string CodeGenerator::Generate(
std::string func_name, std::vector<OperationExpression> expressions) {
std::string func_name, std::string dtype,
const std::vector<OperationExpression>& expressions) {
// TODO(liuyiqun): Check whether all expressions are elementwise operations.
std::string dtype = "float";
std::set<int> input_ids = DistilInputIds(expressions);
std::set<int> output_ids = DistilOutputIds(expressions);
......@@ -111,6 +113,15 @@ std::string CodeGenerator::Generate(
template_var.Add("parameters", EmitParameters(input_ids, output_ids, dtype));
template_var.Add("compute_body",
EmitComputeBody(expressions, input_ids, output_ids, dtype));
std::string predefined_cuda_functions;
if (dtype == "float") {
predefined_cuda_functions = predefined_cuda_functions_fp32;
} else if (dtype == "double") {
predefined_cuda_functions = predefined_cuda_functions_fp64;
} else if (dtype == "float16") {
predefined_cuda_functions = predefined_cuda_functions_fp16;
}
return predefined_cuda_functions + code_templates_[0].Format(template_var);
}
......@@ -173,9 +184,10 @@ std::string CodeGenerator::EmitComputeBody(
std::string dtype) {
std::ostringstream compute;
std::unordered_set<int> used;
std::string compute_dtype = (dtype == "float16") ? "float" : dtype;
for (size_t i = 0; i < expressions.size(); i++) {
VLOG(3) << DebugString(expressions[i]);
compute << expressions[i].GetExpression(dtype, &used);
compute << expressions[i].GetExpression(compute_dtype, &used);
}
// Load input to temporal variables.
......@@ -183,14 +195,23 @@ std::string CodeGenerator::EmitComputeBody(
for (auto id : input_ids) {
if (output_ids.find(id) == output_ids.end() &&
used.find(id) != used.end()) {
load << dtype << " " << TmpName(id) << " = " << ArgName(id) << "[idx];";
if (dtype == "float16") {
load << "float " << TmpName(id) << " = __half2float(" << ArgName(id)
<< "[idx]);";
} else {
load << dtype << " " << TmpName(id) << " = " << ArgName(id) << "[idx];";
}
}
}
// Store temporal variables to memory.
std::ostringstream store;
for (auto id : output_ids) {
store << ArgName(id) << "[idx] = " << TmpName(id) << ";";
if (dtype == "float16") {
store << ArgName(id) << "[idx] = __float2half(" << TmpName(id) << ");";
} else {
store << ArgName(id) << "[idx] = " << TmpName(id) << ";";
}
}
return load.str() + compute.str() + store.str();
......
paddle/fluid/framework/ir/fusion_group/code_generator.h
浏览文件 @ 22bbd547
......@@ -30,8 +30,8 @@ class CodeGenerator {
public:
CodeGenerator();
std::string Generate(std::string func_name,
std::vector<OperationExpression> expressions);
std::string Generate(std::string func_name, std::string dtype,
const std::vector<OperationExpression>& expressions);
std::string Generate(SubGraph* subgraph);
......
paddle/fluid/framework/ir/fusion_group/code_generator_helper.h
浏览文件 @ 22bbd547
......@@ -149,31 +149,6 @@ class CodeTemplate {
std::string template_str_;
};
static const char predefined_cuda_functions[] = R"(
__device__ float real_exp(float x) { return ::expf(x); }
__device__ double real_exp(double x) { return ::exp(x); }
__device__ float real_log(float x) { return ::logf(x); }
__device__ double real_log(double x) { return ::log(x); }
__device__ float real_min(float x, float y) { return ::fminf(x, y); }
__device__ double real_min(double x, double y) { return ::fmin(x, y); }
__device__ float real_max(float x, float y) { return ::fmaxf(x, y); }
__device__ double real_max(double x, double y) { return ::fmax(x, y); }
)";
static const char elementwise_cuda_template[] = R"(
extern "C" __global__ void $func_name($parameters) {
for(int idx = blockIdx.x * blockDim.x + threadIdx.x;
idx < N;
idx += gridDim.x * blockDim.x) {
$compute_body
}
}
)";
static std::string DebugString(const OperationExpression& expr) {
std::stringstream ret;
ret << "Op(" << expr.GetOpType() << "), inputs:{";
......
paddle/fluid/framework/ir/fusion_group/code_generator_tester.cc
浏览文件 @ 22bbd547
......@@ -22,6 +22,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/operators/math.h"
#include "paddle/fluid/platform/device_code.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/init.h"
#ifdef PADDLE_WITH_CUDA
......@@ -88,7 +89,8 @@ inline float elementwise_mul_grad_dy(float x, float y, float out, float dout) {
void CheckOutput(const std::vector<OperationExpression>& expressions,
const std::vector<LoDTensor> cpu_tensors,
const std::vector<int> input_ids_of_subgraph,
const std::vector<int> output_ids_of_subgraph, int i) {
const std::vector<int> output_ids_of_subgraph, int i,
float eps) {
std::vector<float> var(cpu_tensors.size());
for (auto id : input_ids_of_subgraph) {
if (id >= 0) {
......@@ -138,7 +140,12 @@ void CheckOutput(const std::vector<OperationExpression>& expressions,
for (auto id : output_ids_of_subgraph) {
float actual = cpu_tensors[id].data<float>()[i];
float expect = var[id];
EXPECT_LT(fabs(actual - expect), 1.E-05);
if (fabs(actual - expect) > eps) {
LOG(INFO) << "Precision check failed from i = " << id
<< ", expect: " << expect << ", actual: " << actual;
EXPECT_LT(fabs(actual - expect), eps);
break;
}
}
}
......@@ -162,33 +169,49 @@ void SetupRandomCPUTensor(LoDTensor* tensor) {
namespace fusion_group = paddle::framework::ir::fusion_group;
template <typename T>
void TestMainImpl(std::string func_name, std::string code_str,
std::vector<paddle::framework::LoDTensor> cpu_tensors, int n,
std::vector<int> input_ids, std::vector<int> output_ids) {
bool is_float16 = std::type_index(typeid(T)) ==
std::type_index(typeid(paddle::platform::float16));
paddle::framework::InitDevices(false, {0});
paddle::platform::CUDAPlace place = paddle::platform::CUDAPlace(0);
paddle::platform::CUDADeviceCode device_code(place, func_name, code_str);
device_code.Compile();
device_code.Compile(is_float16);
std::vector<paddle::framework::LoDTensor> gpu_tensors(cpu_tensors.size());
std::vector<paddle::framework::LoDTensor> tmp_cpu_tensors(cpu_tensors.size());
std::vector<float*> gpu_ptrs(gpu_tensors.size());
std::vector<T*> gpu_ptrs(gpu_tensors.size());
std::vector<void*> args;
args.push_back(&n);
for (auto id : input_ids) {
if (id >= 0) {
gpu_ptrs[id] =
gpu_tensors[id].mutable_data<float>(cpu_tensors[id].dims(), place);
gpu_tensors[id].mutable_data<T>(cpu_tensors[id].dims(), place);
fusion_group::SetupRandomCPUTensor<float>(&cpu_tensors[id]);
TensorCopySync(cpu_tensors[id], place, &gpu_tensors[id]);
if (is_float16) {
paddle::platform::float16* tmp_cpu_ptr =
tmp_cpu_tensors[id].mutable_data<paddle::platform::float16>(
cpu_tensors[id].dims(), paddle::platform::CPUPlace());
const float* cpu_ptr = cpu_tensors[id].data<float>();
for (int64_t i = 0; i < cpu_tensors[id].numel(); ++i) {
tmp_cpu_ptr[i] = paddle::platform::float16(cpu_ptr[i]);
}
TensorCopySync(tmp_cpu_tensors[id], place, &gpu_tensors[id]);
} else {
TensorCopySync(cpu_tensors[id], place, &gpu_tensors[id]);
}
args.push_back(&gpu_ptrs[id]);
}
}
for (auto id : output_ids) {
gpu_ptrs[id] =
gpu_tensors[id].mutable_data<float>(cpu_tensors[id].dims(), place);
gpu_tensors[id].mutable_data<T>(cpu_tensors[id].dims(), place);
args.push_back(&gpu_ptrs[id]);
}
......@@ -200,38 +223,93 @@ void TestMainImpl(std::string func_name, std::string code_str,
paddle::platform::DeviceContextPool::Instance().Get(place));
dev_ctx->Wait();
// Copy the results back to CPU.
for (auto id : output_ids) {
TensorCopySync(gpu_tensors[id], paddle::platform::CPUPlace(),
&cpu_tensors[id]);
if (is_float16) {
paddle::platform::float16* tmp_cpu_ptr =
tmp_cpu_tensors[id].mutable_data<paddle::platform::float16>(
cpu_tensors[id].dims(), paddle::platform::CPUPlace());
TensorCopySync(gpu_tensors[id], paddle::platform::CPUPlace(),
&tmp_cpu_tensors[id]);
float* cpu_ptr = cpu_tensors[id].mutable_data<float>(
cpu_tensors[id].dims(), paddle::platform::CPUPlace());
for (int64_t i = 0; i < cpu_tensors[id].numel(); ++i) {
cpu_ptr[i] = static_cast<float>(tmp_cpu_ptr[i]);
}
} else {
TensorCopySync(gpu_tensors[id], paddle::platform::CPUPlace(),
&cpu_tensors[id]);
}
}
}
void TestElementwiseMain(
std::string func_name, std::string code_str,
std::vector<fusion_group::OperationExpression> expressions,
std::vector<int> input_ids, std::vector<int> output_ids,
std::string dtype) {
std::unordered_set<int> ids;
for (auto id : input_ids) {
ids.insert(id);
}
for (auto id : output_ids) {
ids.insert(id);
}
// Prepare CPU tensors which always hold float.
std::vector<paddle::framework::LoDTensor> cpu_tensors(ids.size());
auto dims = paddle::framework::make_ddim(
{static_cast<int64_t>(256), static_cast<int64_t>(1024)});
for (size_t i = 0; i < cpu_tensors.size(); ++i) {
cpu_tensors[i].mutable_data<float>(dims, paddle::platform::CPUPlace());
}
int n = cpu_tensors[0].numel();
if (dtype == "float16") {
TestMainImpl<paddle::platform::float16>(func_name, code_str, cpu_tensors, n,
input_ids, output_ids);
} else {
TestMainImpl<float>(func_name, code_str, cpu_tensors, n, input_ids,
output_ids);
}
// Check the results
float eps = (dtype == "float16") ? 1E-2 : 1E-5;
for (int i = 0; i < n; i++) {
fusion_group::CheckOutput(expressions, cpu_tensors, input_ids, output_ids,
i, eps);
}
}
void TestMain(std::string func_name,
std::vector<fusion_group::OperationExpression> expressions,
std::vector<paddle::framework::LoDTensor> cpu_tensors, int n,
std::vector<int> input_ids, std::vector<int> output_ids) {
std::vector<int> input_ids, std::vector<int> output_ids,
std::string dtype) {
fusion_group::OperationMap::Init();
fusion_group::CodeGenerator code_generator;
std::string code_str = code_generator.Generate(func_name, expressions);
std::string code_str = code_generator.Generate(func_name, dtype, expressions);
VLOG(3) << code_str;
TestMainImpl(func_name, code_str, cpu_tensors, n, input_ids, output_ids);
LOG(INFO) << "dtype: " << dtype;
TestElementwiseMain(func_name, code_str, expressions, input_ids, output_ids,
dtype);
}
std::vector<fusion_group::OperationExpression> TestMain(
fusion_group::SubGraph* subgraph,
std::vector<paddle::framework::LoDTensor> cpu_tensors, int n,
std::vector<int> input_ids, std::vector<int> output_ids) {
void TestMain(fusion_group::SubGraph* subgraph, std::vector<int> input_ids,
std::vector<int> output_ids) {
fusion_group::OperationMap::Init();
fusion_group::CodeGenerator code_generator;
std::string code_str = code_generator.Generate(subgraph);
VLOG(3) << code_str;
TestMainImpl(subgraph->GetFuncName(), code_str, cpu_tensors, n, input_ids,
output_ids);
// Need to check the accuracy according to expressions.
return code_generator.ConvertToExpressions(subgraph);
std::vector<fusion_group::OperationExpression> expressions =
code_generator.ConvertToExpressions(subgraph);
LOG(INFO) << "dtype: " << subgraph->GetDataType();
TestElementwiseMain(subgraph->GetFuncName(), code_str, expressions, input_ids,
output_ids, subgraph->GetDataType());
}
TEST(code_generator, elementwise) {
......@@ -248,30 +326,16 @@ TEST(code_generator, elementwise) {
std::vector<fusion_group::OperationExpression> expressions = {
exp1, exp2, exp3, exp4, exp5};
// Prepare CPU tensors
std::vector<paddle::framework::LoDTensor> cpu_tensors(9);
auto dims = paddle::framework::make_ddim(
{static_cast<int64_t>(256), static_cast<int64_t>(1024)});
for (size_t i = 0; i < cpu_tensors.size(); ++i) {
cpu_tensors[i].mutable_data<float>(dims, paddle::platform::CPUPlace());
}
// Expressions:
// Op(elementwise_mul), inputs:{0,1}, outputs:{2}
// Op(elementwise_add), inputs:{2,3}, outputs:{4}
// Op(elementwise_sub), inputs:{4,5}, outputs:{6}
// Op(relu), inputs:{6}, outputs:{7}
// Op(sigmoid), inputs:{7}, outputs:{8}
int n = cpu_tensors[0].numel();
std::vector<int> input_ids = {0, 1, 3, 5};
std::vector<int> output_ids = {2, 4, 6, 7, 8};
TestMain("elementwise_kernel_0", expressions, cpu_tensors, n, input_ids,
output_ids);
// Check the results
for (int i = 0; i < n; i++) {
fusion_group::CheckOutput(expressions, cpu_tensors, input_ids, output_ids,
i);
for (std::string dtype : {"float", "float16"}) {
// Expressions:
// Op(elementwise_mul), inputs:{0,1}, outputs:{2}
// Op(elementwise_add), inputs:{2,3}, outputs:{4}
// Op(elementwise_sub), inputs:{4,5}, outputs:{6}
// Op(relu), inputs:{6}, outputs:{7}
// Op(sigmoid), inputs:{7}, outputs:{8}
std::vector<int> input_ids = {0, 1, 3, 5};
std::vector<int> output_ids = {2, 4, 6, 7, 8};
TestMain("elementwise_kernel_0", expressions, input_ids, output_ids, dtype);
}
}
......@@ -286,32 +350,19 @@ TEST(code_generator, elementwise_grad) {
{4, 5});
std::vector<fusion_group::OperationExpression> expressions = {exp1, exp2};
// Prepare CPU tensors
std::vector<paddle::framework::LoDTensor> cpu_tensors(8);
auto dims = paddle::framework::make_ddim(
{static_cast<int64_t>(256), static_cast<int64_t>(1024)});
for (size_t i = 0; i < cpu_tensors.size(); ++i) {
cpu_tensors[i].mutable_data<float>(dims, paddle::platform::CPUPlace());
}
// Expressions:
// Op(relu_grad), inputs:{2,3,7}, outputs:{6}
// Op(elementwise_mul_grad), inputs:{0,1,2,6}, outputs:{4,5}
int n = cpu_tensors[0].numel();
std::vector<int> input_ids = {0, 1, 2, 3, 7};
std::vector<int> output_ids = {4, 5, 6};
TestMain("elementwise_grad_kernel_0", expressions, cpu_tensors, n, input_ids,
output_ids);
// Check the results
for (int i = 0; i < n; i++) {
fusion_group::CheckOutput(expressions, cpu_tensors, input_ids, output_ids,
i);
for (std::string dtype : {"float", "float16"}) {
// Expressions:
// Op(relu_grad), inputs:{2,3,7}, outputs:{6}
// Op(elementwise_mul_grad), inputs:{0,1,2,6}, outputs:{4,5}
std::vector<int> input_ids = {0, 1, 2, 3, 7};
std::vector<int> output_ids = {4, 5, 6};
TestMain("elementwise_grad_kernel_0", expressions, input_ids, output_ids,
dtype);
}
}
std::unique_ptr<paddle::framework::ir::Graph> BuildGraph(
bool backward = false) {
std::unique_ptr<paddle::framework::ir::Graph> BuildGraph(bool backward,
std::string dtype) {
// inputs operator output
// --------------------------------------------------------
// x0 sigmoid -> tmp_0
......@@ -353,6 +404,14 @@ std::unique_ptr<paddle::framework::ir::Graph> BuildGraph(
std::unique_ptr<paddle::framework::ir::Graph> graph(
new paddle::framework::ir::Graph(layers.main_program()));
auto proto_dtype = (dtype == "float16")
? paddle::framework::proto::VarType::FP16
: paddle::framework::proto::VarType::FP32;
for (auto* n : graph->Nodes()) {
if (n && n->IsVar() && n->Var()) {
n->Var()->SetDataType(proto_dtype);
}
}
#ifdef __clang__
return graph;
#else
......@@ -401,66 +460,40 @@ std::unordered_set<paddle::framework::ir::Node*> DistilGradNodes(
}
TEST(code_generator, subgraph) {
std::unique_ptr<paddle::framework::ir::Graph> graph = BuildGraph(false);
fusion_group::SubGraph subgraph(0, "elementwise_kernel_1", true,
graph->Nodes());
// Prepare CPU tensors
std::vector<paddle::framework::LoDTensor> cpu_tensors(9);
auto dims = paddle::framework::make_ddim(
{static_cast<int64_t>(256), static_cast<int64_t>(1024)});
for (size_t i = 0; i < cpu_tensors.size(); ++i) {
cpu_tensors[i].mutable_data<float>(dims, paddle::platform::CPUPlace());
}
// Expressions generated by code_generator (they may be different):
// Op(sigmoid), inputs:{0}, outputs:{4}
// Op(elementwise_mul), inputs:{4,1}, outputs:{7}
// Op(tanh), inputs:{2}, outputs:{5}
// Op(elementwise_mul), inputs:{3,5}, outputs:{6}
// Op(elementwise_add), inputs:{7,6}, outputs:{8}
int n = cpu_tensors[0].numel();
std::vector<int> input_ids = {0, 1, 2, 3};
std::vector<int> output_ids = {4, 5, 6, 7, 8};
std::vector<fusion_group::OperationExpression> expressions =
TestMain(&subgraph, cpu_tensors, n, input_ids, output_ids);
// Check the results
for (int i = 0; i < n; i++) {
fusion_group::CheckOutput(expressions, cpu_tensors, input_ids, output_ids,
i);
for (std::string dtype : {"float", "float16"}) {
std::unique_ptr<paddle::framework::ir::Graph> graph =
BuildGraph(false, dtype);
fusion_group::SubGraph subgraph(0, "elementwise_kernel_1", true,
graph->Nodes());
// Expressions generated by code_generator (they may be different):
// Op(sigmoid), inputs:{0}, outputs:{4}
// Op(elementwise_mul), inputs:{4,1}, outputs:{7}
// Op(tanh), inputs:{2}, outputs:{5}
// Op(elementwise_mul), inputs:{3,5}, outputs:{6}
// Op(elementwise_add), inputs:{7,6}, outputs:{8}
std::vector<int> input_ids = {0, 1, 2, 3};
std::vector<int> output_ids = {4, 5, 6, 7, 8};
TestMain(&subgraph, input_ids, output_ids);
}
}
TEST(code_generator, subgraph_grad) {
std::unique_ptr<paddle::framework::ir::Graph> graph = BuildGraph(true);
fusion_group::SubGraph subgraph(0, "elementwise_grad_kernel_1", true,
DistilGradNodes(graph));
// Prepare CPU tensors
std::vector<paddle::framework::LoDTensor> cpu_tensors(18);
auto dims = paddle::framework::make_ddim(
{static_cast<int64_t>(256), static_cast<int64_t>(1024)});
for (size_t i = 0; i < cpu_tensors.size(); ++i) {
cpu_tensors[i].mutable_data<float>(dims, paddle::platform::CPUPlace());
}
// Expressions generated by code_generator (they may be different):
// Op(elementwise_add_grad), inputs:{1,2,3,0}, outputs:{11,10}
// Op(elementwise_mul_grad), inputs:{5,4,2,10}, outputs:{17,13}
// Op(elementwise_mul_grad), inputs:{7,6,1,11}, outputs:{12,15}
// Op(sigmoid_grad), inputs:{8,7,12}, outputs:{16}
// Op(tanh_grad), inputs:{9,4,13}, outputs:{14}
int n = cpu_tensors[0].numel();
std::vector<int> input_ids = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
std::vector<int> output_ids = {10, 11, 12, 13, 14, 15, 16, 17};
std::vector<fusion_group::OperationExpression> expressions =
TestMain(&subgraph, cpu_tensors, n, input_ids, output_ids);
// Check the results
for (int i = 0; i < n; i++) {
fusion_group::CheckOutput(expressions, cpu_tensors, input_ids, output_ids,
i);
for (std::string dtype : {"float", "float16"}) {
std::unique_ptr<paddle::framework::ir::Graph> graph =
BuildGraph(true, dtype);
fusion_group::SubGraph subgraph(0, "elementwise_grad_kernel_1", true,
DistilGradNodes(graph));
// Expressions generated by code_generator (they may be different):
// Op(elementwise_add_grad), inputs:{1,2,3,0}, outputs:{11,10}
// Op(elementwise_mul_grad), inputs:{5,4,2,10}, outputs:{17,13}
// Op(elementwise_mul_grad), inputs:{7,6,1,11}, outputs:{12,15}
// Op(sigmoid_grad), inputs:{8,7,12}, outputs:{16}
// Op(tanh_grad), inputs:{9,4,13}, outputs:{14}
std::vector<int> input_ids = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
std::vector<int> output_ids = {10, 11, 12, 13, 14, 15, 16, 17};
TestMain(&subgraph, input_ids, output_ids);
}
}
#endif
paddle/fluid/framework/ir/fusion_group/cuda_resources.h 0 → 100644
浏览文件 @ 22bbd547
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
namespace paddle {
namespace framework {
namespace ir {
namespace fusion_group {
static constexpr char predefined_cuda_functions_fp32[] = R"(
__device__ inline float real_exp(float x) { return ::expf(x); }
__device__ inline float real_log(float x) { return ::logf(x); }
)";
static constexpr char predefined_cuda_functions_fp64[] = R"(
__device__ inline double real_exp(double x) { return ::exp(x); }
__device__ inline double real_log(double x) { return ::log(x); }
)";
static constexpr char predefined_cuda_functions_fp16[] = R"(
__device__ inline float real_exp(float x) { return ::expf(x); }
__device__ inline float real_log(float x) { return ::logf(x); }
#define __HALF_TO_US(var) *(reinterpret_cast<unsigned short *>(&(var)))
#define __HALF_TO_CUS(var) *(reinterpret_cast<const unsigned short *>(&(var)))
struct __align__(2) __half {
__device__ __half() { }
protected:
unsigned short __x;
};
__device__ __half __float2half(const float f) {
__half val;
asm("{ cvt.rn.f16.f32 %0, %1; }\n" : "=h"(__HALF_TO_US(val)
) : "f"(f));
return val;
}
__device__ float __half2float(const __half h) {
float val;
asm("{ cvt.f32.f16 %0, %1; }\n" : "=f"(val) : "h"(__HALF_TO_CUS(h)));
return val;
}
#undef __HALF_TO_US
#undef __HALF_TO_CUS
typedef __half float16;
)";
static constexpr char cuda_kernel_template_1d[] = R"(
extern "C" __global__ void $func_name($parameters) {
for(int idx = blockIdx.x * blockDim.x + threadIdx.x;
idx < N;
idx += gridDim.x * blockDim.x) {
$compute_body
}
}
)";
} // namespace fusion_group
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/fusion_group/fusion_group_pass.cc
浏览文件 @ 22bbd547
......@@ -32,8 +32,7 @@ void FusionGroupPass::ApplyImpl(ir::Graph* graph) const {
if (Get<bool>("use_gpu")) {
fusion_group::OperationMap::Init();
int num_elementwise_groups = DetectFusionGroup(graph, 0);
VLOG(3) << "Detect " << num_elementwise_groups
<< " elementwise fusion groups.";
AddStatis(num_elementwise_groups);
}
}
......@@ -49,23 +48,23 @@ int FusionGroupPass::DetectFusionGroup(Graph* graph, int type) const {
size_t min_subgraph_size = 2;
bool save_intermediate_out = true;
for (auto& vec : subgraphs) {
if (vec.size() >= min_subgraph_size) {
std::string func_name = "fused_elementwise_" + std::to_string(index++);
fusion_group::SubGraph subgraph(
type, func_name, save_intermediate_out,
std::unordered_set<Node*>(vec.begin(), vec.end()));
VLOG(3) << "subgraph: {\n"
<< DebugString(subgraph.SortedNodes()) << "}\n";
GenerateCode(&subgraph);
InsertFusionGroupOp(graph, &subgraph);
num_subgraphs++;
fusion_group::SubGraph subgraph(
type, "", save_intermediate_out,
std::unordered_set<Node*>(vec.begin(), vec.end()));
VLOG(3) << "subgraph: {\n" << DebugString(subgraph.SortedNodes()) << "}\n";
if (subgraph.IsValid(min_subgraph_size)) {
subgraph.SetFuncName("fused_elementwise_" + std::to_string(index++));
if (GenerateCode(&subgraph)) {
InsertFusionGroupOp(graph, &subgraph);
num_subgraphs++;
}
}
}
return num_subgraphs;
}
void FusionGroupPass::GenerateCode(fusion_group::SubGraph* subgraph) const {
bool FusionGroupPass::GenerateCode(fusion_group::SubGraph* subgraph) const {
fusion_group::CodeGenerator code_generator;
std::string code_str = code_generator.Generate(subgraph);
VLOG(3) << code_str;
......@@ -74,10 +73,12 @@ void FusionGroupPass::GenerateCode(fusion_group::SubGraph* subgraph) const {
platform::CUDAPlace place = platform::CUDAPlace(0);
std::unique_ptr<platform::CUDADeviceCode> device_code(
new platform::CUDADeviceCode(place, subgraph->GetFuncName(), code_str));
device_code->Compile();
platform::DeviceCodePool& pool = platform::DeviceCodePool::Init({place});
pool.Set(std::move(device_code));
bool is_compiled = device_code->Compile();
if (is_compiled) {
platform::DeviceCodePool& pool = platform::DeviceCodePool::Init({place});
pool.Set(std::move(device_code));
}
return is_compiled;
}
static int ExtractOpRole(fusion_group::SubGraph* subgraph) {
......
paddle/fluid/framework/ir/fusion_group/fusion_group_pass.h
浏览文件 @ 22bbd547
......@@ -29,7 +29,7 @@ class FusionGroupPass : public FusePassBase {
private:
int DetectFusionGroup(Graph* graph, int type = 0) const;
void GenerateCode(fusion_group::SubGraph* subgraph) const;
bool GenerateCode(fusion_group::SubGraph* subgraph) const;
void InsertFusionGroupOp(Graph* graph,
fusion_group::SubGraph* subgraph) const;
......
paddle/fluid/framework/ir/fusion_group/fusion_group_pass_tester.cc
浏览文件 @ 22bbd547
......@@ -59,6 +59,11 @@ std::unique_ptr<Graph> BuildElementwiseListGraph(bool backward = false) {
}
std::unique_ptr<Graph> graph(new Graph(layers.main_program()));
for (auto* n : graph->Nodes()) {
if (n && n->IsVar() && n->Var()) {
n->Var()->SetDataType(proto::VarType::FP32);
}
}
#ifdef __clang__
return graph;
#else
......@@ -116,6 +121,11 @@ std::unique_ptr<Graph> BuildElementwiseTreeGraph(bool backward = false) {
}
std::unique_ptr<Graph> graph(new Graph(layers.main_program()));
for (auto* n : graph->Nodes()) {
if (n && n->IsVar() && n->Var()) {
n->Var()->SetDataType(proto::VarType::FP32);
}
}
#ifdef __clang__
return graph;
#else
......
paddle/fluid/framework/ir/fusion_group/operation.cc
浏览文件 @ 22bbd547
......@@ -91,7 +91,7 @@ void OperationMap::InsertUnaryElementwiseOperations() {
// relu:
// out = f(x) = x > 0 ? x : 0
// dx = dout * (out > 0 ? 1 : 0)
insert_handler("relu", "real_max(${0}, 0)", {"${1} > 0 ? ${2} : 0"});
insert_handler("relu", "${0} > 0 ? ${0} : 0", {"${1} > 0 ? ${2} : 0"});
// sigmoid:
// out = f(x) = 1.0 / (1.0 + exp(-x))
// dx = dout * out * (1 - out)
......@@ -133,9 +133,24 @@ void OperationMap::InsertBinaryElementwiseOperations() {
// dy = dout * x
insert_handler("elementwise_mul", "${0} * ${1}",
{"${3} * ${1}", "${3} * ${0}"});
insert_handler("elementwise_div", "${0} / ${1}", {});
insert_handler("elementwise_min", "real_min(${0}, ${1})", {});
insert_handler("elementwise_max", "real_max(${0}, ${1})", {});
// elementwise_div:
// out = x / y
// dx = dout / y
// dy = - dout * out / y
insert_handler("elementwise_div", "${0} / ${1}",
{"${3} / ${1}", "- ${3} * ${2} / ${1}"});
// elementwise_min:
// out = x < y ? x : y
// dx = dout * (x < y)
// dy = dout * (x >= y)
insert_handler("elementwise_min", "${0} < ${1} ? ${0} : ${1}",
{"${3} * (${0} < ${1})", "${3} * (${0} >= ${1})"});
// elementwise_max:
// out = x > y ? x : y
// dx = dout * (x > y)
// dy = dout * (x <= y)
insert_handler("elementwise_max", "${0} > ${1} ? ${0} : ${1}",
{"${3} * (${0} > ${1})", "${3} * (${0} <= ${1})"});
}
} // namespace fusion_group
......
paddle/fluid/framework/ir/fusion_group/subgraph.h
浏览文件 @ 22bbd547
......@@ -49,11 +49,23 @@ class SubGraph {
}
}
}
ExtractDataType();
}
bool IsEmpty() { return nodes_set_.empty(); }
bool IsValid(int min_subgraph_size) {
int num_operations = GetNumOperations();
if (num_operations < min_subgraph_size) {
VLOG(2) << "There are only " << num_operations
<< " operations in the subgraph. Expected at least "
<< min_subgraph_size;
return false;
}
return ExtractDataType();
}
int GetType() const { return type_; }
std::string GetDataType() const { return data_type_; }
void SetFuncName(std::string func_name) { func_name_ = func_name; }
std::string GetFuncName() const { return func_name_; }
......@@ -150,6 +162,37 @@ class SubGraph {
}
private:
bool ExtractDataType() {
bool is_first = true;
proto::VarType::Type data_type = proto::VarType::FP32;
for (auto* n : nodes_set_) {
if (n && n->IsVar() && n->Var()) {
if (n->Var()->GetType() != proto::VarType::LOD_TENSOR) {
// All var node in a subgraph should hold a LoDTensor.
return false;
}
if (is_first) {
data_type = n->Var()->GetDataType();
is_first = false;
} else if (n->Var()->GetDataType() != data_type) {
// DataType of VarDesc in a subgraph is not the same.
return false;
}
}
}
if (data_type == proto::VarType::FP32) {
data_type_ = "float";
} else if (data_type == proto::VarType::FP64) {
data_type_ = "double";
} else if (data_type == proto::VarType::FP16) {
data_type_ = "float16";
} else {
VLOG(2) << "Only support fp32, fp64 and fp16 in fusion_group.";
return false;
}
return true;
}
void TopologicalSort() {
if (!is_sorted_) {
std::unordered_map<Node*, std::vector<Node*>> inputs_map;
......@@ -203,6 +246,7 @@ class SubGraph {
private:
int type_{-1};
std::string data_type_;
std::string func_name_;
bool save_intermediate_out_{true};
......
paddle/fluid/framework/ir/pass_tester_helper.h
浏览文件 @ 22bbd547
......@@ -33,8 +33,9 @@ struct Layers {
const ProgramDesc& main_program() { return program_; }
VarDesc* data(std::string name, std::vector<int64_t> shape = {},
bool is_persistable = false) {
return lod_tensor(name, shape, is_persistable);
bool is_persistable = false,
proto::VarType::Type data_type = proto::VarType::FP32) {
return lod_tensor(name, shape, is_persistable, data_type);
}
VarDesc* conv2d(VarDesc* input, VarDesc* filter, VarDesc* bias,
......@@ -379,9 +380,11 @@ struct Layers {
private:
VarDesc* lod_tensor(std::string name, std::vector<int64_t> shape = {},
bool is_persistable = false) {
bool is_persistable = false,
proto::VarType::Type data_type = proto::VarType::FP32) {
auto* var = program_.MutableBlock(0)->Var(name);
var->SetType(proto::VarType::LOD_TENSOR);
var->SetDataType(data_type);
var->SetShape(shape);
var->SetPersistable(is_persistable);
return var;
......
paddle/fluid/operators/fused/fusion_group_op.cu.cc
浏览文件 @ 22bbd547
......@@ -13,10 +13,11 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/fused/fusion_group_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
fusion_group,
ops::FusionGroupKernel<paddle::platform::CUDADeviceContext, double>,
ops::FusionGroupKernel<paddle::platform::CUDADeviceContext, float>);
fusion_group, ops::FusionGroupKernel<plat::CUDADeviceContext, float>,
ops::FusionGroupKernel<plat::CUDADeviceContext, double>,
ops::FusionGroupKernel<plat::CUDADeviceContext, plat::float16>);
paddle/fluid/platform/device_code.cc
浏览文件 @ 22bbd547
......@@ -13,11 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/platform/device_code.h"
#include <sys/stat.h>
#include <algorithm>
#include <set>
#include <utility>
#include "paddle/fluid/platform/enforce.h"
DECLARE_string(cuda_dir);
namespace paddle {
namespace platform {
......@@ -79,6 +82,46 @@ DeviceCodePool::DeviceCodePool(const std::vector<platform::Place>& places) {
}
#ifdef PADDLE_WITH_CUDA
static std::string FindCUDAIncludePath() {
auto EndWith = [](std::string str, std::string substr) -> bool {
size_t pos = str.rfind(substr);
return pos != std::string::npos && pos == (str.length() - substr.length());
};
struct stat st;
std::string cuda_include_path;
if (!FLAGS_cuda_dir.empty()) {
cuda_include_path = FLAGS_cuda_dir;
if (EndWith(cuda_include_path, "/")) {
cuda_include_path.erase(cuda_include_path.end() - 1);
}
for (std::string suffix : {"/lib", "/lib64"}) {
if (EndWith(FLAGS_cuda_dir, suffix)) {
cuda_include_path.erase(cuda_include_path.end() - suffix.length());
break;
}
}
if (!EndWith(cuda_include_path, "include")) {
cuda_include_path += "/include";
}
// Whether the cuda_include_path exists on the file system.
if (stat(cuda_include_path.c_str(), &st) == 0) {
return cuda_include_path;
}
}
cuda_include_path = "/usr/local/cuda/include";
if (stat(cuda_include_path.c_str(), &st) == 0) {
return cuda_include_path;
}
LOG(WARNING) << "Cannot find CUDA include path."
<< "Please check whether CUDA is installed in the default "
"installation path, or specify it by export "
"FLAGS_cuda_dir=xxx.";
return "";
}
CUDADeviceCode::CUDADeviceCode(const Place& place, const std::string& name,
const std::string& kernel) {
if (!is_gpu_place(place)) {
......@@ -91,7 +134,7 @@ CUDADeviceCode::CUDADeviceCode(const Place& place, const std::string& name,
kernel_ = kernel;
}
bool CUDADeviceCode::Compile() {
bool CUDADeviceCode::Compile(bool include_path) {
is_compiled_ = false;
if (!dynload::HasNVRTC() || !dynload::HasCUDADriver()) {
LOG(WARNING)
......@@ -116,8 +159,14 @@ bool CUDADeviceCode::Compile() {
int compute_capability = dev_ctx->GetComputeCapability();
std::string compute_flag =
"--gpu-architecture=compute_" + std::to_string(compute_capability);
const std::vector<const char*> options = {"--std=c++11",
compute_flag.c_str()};
std::vector<const char*> options = {"--std=c++11", compute_flag.c_str()};
if (include_path) {
std::string cuda_include_path = FindCUDAIncludePath();
if (!cuda_include_path.empty()) {
std::string include_option = "--include-path=" + cuda_include_path;
options.push_back(include_option.c_str());
}
}
nvrtcResult compile_result =
dynload::nvrtcCompileProgram(program, // program
options.size(), // numOptions
......
paddle/fluid/platform/device_code.h
浏览文件 @ 22bbd547
......@@ -31,7 +31,7 @@ namespace platform {
class DeviceCode {
public:
virtual ~DeviceCode() {}
virtual bool Compile() = 0;
virtual bool Compile(bool include_path = false) = 0;
virtual void Launch(const size_t n, std::vector<void*>* args) const = 0;
Place GetPlace() const { return place_; }
......@@ -48,7 +48,7 @@ class CUDADeviceCode : public DeviceCode {
public:
explicit CUDADeviceCode(const Place& place, const std::string& name,
const std::string& kernel);
bool Compile() override;
bool Compile(bool include_path = false) override;
void Launch(const size_t n, std::vector<void*>* args) const override;
void SetNumThreads(int num_threads) { num_threads_ = num_threads; }
......
python/paddle/fluid/tests/unittests/ir/CMakeLists.txt
浏览文件 @ 22bbd547
......@@ -2,7 +2,7 @@ file(GLOB TEST_IR_PASSES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_IR_PASSES "${TEST_IR_PASSES}")
if(NOT WITH_GPU OR WIN32 OR APPLE)
LIST(REMOVE_ITEM TEST_IR_PASSES test_ir_fusion_group)
LIST(REMOVE_ITEM TEST_IR_PASSES test_ir_fusion_group_pass)
endif()
foreach(target ${TEST_IR_PASSES})
......
python/paddle/fluid/tests/unittests/ir/test_ir_fusion_group.py → python/paddle/fluid/tests/unittests/ir/test_ir_fusion_group_pass.py
浏览文件 @ 22bbd547
......@@ -17,92 +17,125 @@ import unittest
import numpy as np
from pass_test import PassTest
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
class FusionGroupPassTest(PassTest):
def setUp(self):
def build_program(self, dtype):
with fluid.program_guard(self.main_program, self.startup_program):
data1 = fluid.data(name="data1", shape=[32, 128], dtype="float32")
data2 = fluid.data(name="data2", shape=[32, 128], dtype="float32")
data3 = fluid.data(name="data3", shape=[32, 128], dtype="float32")
tmp_1 = fluid.layers.elementwise_add(data1, data2)
tmp_2 = fluid.layers.elementwise_mul(data3, tmp_1)
self.feeds = {
"data1": np.random.random((32, 128)).astype("float32"),
"data2": np.random.random((32, 128)).astype("float32"),
"data3": np.random.random((32, 128)).astype("float32")
}
self.fetch_list = [tmp_1, tmp_2]
self.feed_vars = self._prepare_feed_vars([32, 128], dtype, 2)
self.feed_vars.append(
fluid.data(
name="data2", shape=[128, 128], dtype=dtype))
# subgraph with only 1 op node
tmp_0 = self.feed_vars[0] * self.feed_vars[1]
tmp_1 = layers.mul(tmp_0, self.feed_vars[2])
# subgraph with 2 op nodes
tmp_2 = layers.relu(tmp_0 + tmp_1)
self.fetch_list = [tmp_2]
self.num_fused_ops = 1
def setUp(self):
self.build_program("float32")
self.feeds = self._feed_random_data(self.feed_vars)
self.pass_names = "fusion_group_pass"
self.fused_op_type = "fusion_group"
self.num_fused_ops = 1
def _prepare_feed_vars(self, shape, dtype, num_data):
feed_vars = []
for i in range(num_data):
var = fluid.data(name=("data" + str(i)), shape=shape, dtype=dtype)
feed_vars.append(var)
return feed_vars
def _feed_random_data(self, feed_vars):
feeds = {}
for var in feed_vars:
if var.type != fluid.core.VarDesc.VarType.LOD_TENSOR:
raise TypeError("Feed data of non LoDTensor is not supported.")
shape = var.shape
if var.dtype == fluid.core.VarDesc.VarType.FP32:
dtype = "float32"
elif var.dtype == fluid.core.VarDesc.VarType.FP64:
dtype = "float64"
elif var.dtype == fluid.core.VarDesc.VarType.FP16:
dtype = "float16"
else:
raise ValueError("Unsupported dtype %s" % var.dtype)
feeds[var.name] = np.random.random(shape).astype(dtype)
return feeds
def test_check_output(self):
use_gpu_set = []
if core.is_compiled_with_cuda():
use_gpu_set.append(True)
for use_gpu in use_gpu_set:
self.pass_attrs = {"fusion_group_pass": {"use_gpu": use_gpu}}
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
self.check_output_with_place(place, startup_on_cpu=False)
self.pass_attrs = {"fusion_group_pass": {"use_gpu": True}}
self.check_output_with_place(fluid.CUDAPlace(0))
class FusionGroupPassTest1(FusionGroupPassTest):
def setUp(self):
def build_program(self, dtype):
with fluid.program_guard(self.main_program, self.startup_program):
data = []
for i in range(5):
data.append(
fluid.data(
name=("data" + str(i)),
shape=[32, 128],
dtype="float32"))
tmp_1 = (
fluid.layers.assign(data[0]) * fluid.layers.sigmoid(data[1])
) + (fluid.layers.sigmoid(data[2]) * fluid.layers.tanh(data[3]))
tmp_2 = fluid.layers.tanh(tmp_1) + fluid.layers.sigmoid(data[4])
self.feeds = {}
for i in range(5):
self.feeds["data" + str(i)] = np.random.random(
(32, 128)).astype("float32")
self.feed_vars = self._prepare_feed_vars([32, 128], dtype, 5)
tmp_0 = layers.assign(self.feed_vars[0])
# subgraph with 9 op nodes
tmp_1 = tmp_0 * layers.sigmoid(self.feed_vars[1]) + layers.sigmoid(
self.feed_vars[2]) * layers.tanh(self.feed_vars[3])
tmp_2 = layers.tanh(tmp_1) + layers.sigmoid(self.feed_vars[4])
self.fetch_list = [tmp_1, tmp_2]
self.pass_names = "fusion_group_pass"
self.fused_op_type = "fusion_group"
self.num_fused_ops = 1
class FusionGroupPassTest2(FusionGroupPassTest):
def setUp(self):
def build_program(self, dtype):
with fluid.program_guard(self.main_program, self.startup_program):
data = []
for i in range(3):
data.append(
fluid.data(
name=("data" + str(i)),
shape=[32, 128],
dtype="float32"))
data.append(
self.feed_vars = self._prepare_feed_vars([32, 128], dtype, 3)
self.feed_vars.append(
fluid.data(
name="data3", shape=[128, 32], dtype="float32"))
tmp_1 = fluid.layers.relu((data[0] - data[1]) * data[2])
tmp_2 = fluid.layers.sigmoid(data[3])
tmp_3 = fluid.layers.relu(tmp_2)
tmp_4 = fluid.layers.mul(tmp_1, tmp_3)
self.feeds = {}
for i in range(3):
self.feeds["data" + str(i)] = np.random.random(
(32, 128)).astype("float32")
self.feeds["data3"] = np.random.random((128, 32)).astype("float32")
self.fetch_list = [tmp_1, tmp_2, tmp_3, tmp_4]
name="data3", shape=[128, 32], dtype=dtype))
# subgraph with 3 op nodes
tmp_1 = layers.relu(
(self.feed_vars[0] - self.feed_vars[1]) * self.feed_vars[2])
# subgraph with 2 op nodes
tmp_2 = layers.relu(layers.sigmoid(self.feed_vars[3]))
tmp_3 = layers.mul(tmp_1, tmp_2)
self.fetch_list = [tmp_1, tmp_2, tmp_3]
self.num_fused_ops = 2
class FusionGroupPassTestFP64(FusionGroupPassTest):
def setUp(self):
self.build_program("float64")
self.feeds = self._feed_random_data(self.feed_vars)
self.pass_names = "fusion_group_pass"
self.fused_op_type = "fusion_group"
self.num_fused_ops = 2
class FusionGroupPassTestFP16(FusionGroupPassTest):
def build_program(self, dtype):
with fluid.program_guard(self.main_program, self.startup_program):
self.feed_vars = self._prepare_feed_vars([32, 128], dtype, 2)
self.feed_vars.append(
fluid.data(
name="data2", shape=[128, 128], dtype=dtype))
# subgraph with only 1 op node
tmp_0 = self.feed_vars[0] * self.feed_vars[1]
tmp_1 = layers.mul(tmp_0, self.feed_vars[2])
tmp_2 = layers.cast(tmp_0, dtype="float16")
tmp_3 = layers.cast(tmp_1, dtype="float16")
# subgraph with 2 op nodes
tmp_4 = layers.relu(tmp_2 + tmp_3)
tmp_5 = layers.cast(tmp_4, dtype=dtype)
self.fetch_list = [tmp_5]
self.num_fused_ops = 1
if __name__ == "__main__":
......
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