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[Cherry-pick]add tensor support for gaussian_random_op test=develop (#24389) #24500

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Merged
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May 15, 2020
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[Cherry-pick]add tensor support for gaussian_random_op test=develop (#24389) #24500

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35 changes: 7 additions & 28 deletions paddle/fluid/operators/fill_constant_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -20,48 +20,26 @@ limitations under the License. */
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/utils.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

inline framework::DDim GetShape(const framework::ExecutionContext &ctx) {
inline framework::DDim GetShape(const framework::ExecutionContext &ctx,
std::string op_type) {
// 1. shape is a Tensor
if (ctx.HasInput("ShapeTensor")) {
auto *shape_tensor = ctx.Input<framework::LoDTensor>("ShapeTensor");
auto *shape_data = shape_tensor->data<int>();
framework::Tensor cpu_shape_tensor;
if (platform::is_gpu_place(shape_tensor->place())) {
TensorCopySync(*shape_tensor, platform::CPUPlace(), &cpu_shape_tensor);
shape_data = cpu_shape_tensor.data<int>();
}
auto vec_shape =
std::vector<int>(shape_data, shape_data + shape_tensor->numel());
auto vec_shape = GetDataFromTensor<int>(shape_tensor);
return framework::make_ddim(vec_shape);
}

// 2. shape is a list/tuple containing Tensor
auto shape_tensor_list = ctx.MultiInput<framework::Tensor>("ShapeTensorList");
if (shape_tensor_list.size() > 0) {
std::vector<int> vec_shape;
for (size_t i = 0; i < shape_tensor_list.size(); ++i) {
auto tensor = shape_tensor_list[i];
PADDLE_ENFORCE_EQ(
tensor->dims(), framework::make_ddim({1}),
platform::errors::InvalidArgument(
"If the element type of 'shape'(tensor_list type) in "
"FillConstantOp is Tensor, the shape of this Tensor element must "
"be [1]. But received the Tensor element's shape is [%s]",
tensor->dims()));
if (platform::is_gpu_place(tensor->place())) {
framework::Tensor temp;
TensorCopySync(*tensor, platform::CPUPlace(), &temp);
vec_shape.push_back(*temp.data<int>());
} else {
vec_shape.push_back(*tensor->data<int>());
}
}
auto vec_shape = GetDataFromTensorList(shape_tensor_list);
return framework::make_ddim(vec_shape);
}

Expand Down Expand Up @@ -115,7 +93,8 @@ class FillConstantKernel : public framework::OpKernel<T> {
}
value = tensor_data[0];
}
auto shape = GetShape(ctx);
const std::string op_type = "fill_constant";
auto shape = GetShape(ctx, op_type);

if (out_var->IsType<framework::LoDTensor>()) {
tensor = out_var->GetMutable<framework::LoDTensor>();
Expand Down
84 changes: 74 additions & 10 deletions paddle/fluid/operators/gaussian_random_op.cc
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Expand Up @@ -14,16 +14,45 @@ limitations under the License. */

#include <random>
#include "paddle/fluid/framework/op_registry.h"

#include "paddle/fluid/operators/fill_constant_op.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;
template <typename T>
class CPUGaussianRandomKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
float mean = context.Attr<float>("mean");
float std = context.Attr<float>("std");
auto* tensor = context.Output<framework::Tensor>("Out");

unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
std::minstd_rand engine;
if (seed == 0) {
seed = std::random_device()();
}
engine.seed(seed);
std::normal_distribution<T> dist(mean, std);

const std::string op_type = "gaussian_random";
auto shape = GetShape(context, op_type);
tensor->Resize(shape);
int64_t size = tensor->numel();
T* data = tensor->mutable_data<T>(context.GetPlace());

for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
}
};

template <typename T>
class CPUGaussianRandomBatchSizeLikeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
float mean = context.Attr<float>("mean");
Expand Down Expand Up @@ -58,12 +87,26 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
for (auto dim : shape) {
temp.push_back(static_cast<int64_t>(dim));
}
PADDLE_ENFORCE_GT(
shape.size(), 0UL,
platform::errors::InvalidArgument(
"Attribute(shape) of GaussianRandomOp must be set "
"and shape.size() > 0, but reveived shape.size() is %d",
shape.size()));
if (shape.empty() && ctx->HasInput("ShapeTensor")) {
auto shape_dims = ctx->GetInputDim("ShapeTensor");
int num_ele = 1;
for (int i = 0; i < shape_dims.size(); ++i) {
num_ele *= shape_dims[i];
}
auto vec_dims = std::vector<int>(num_ele, -1);
ctx->SetOutputDim("Out", framework::make_ddim(vec_dims));

return;
}
if (!(ctx->HasInput("ShapeTensor") && !ctx->HasInputs("ShapeTensorList"))) {
PADDLE_ENFORCE_GT(
shape.size(), 0UL,
platform::errors::InvalidArgument(
"Attribute(shape) of GaussianRandomOp must be set "
"and shape.size() > 0, but reveived shape.size() is %d",
shape.size()));
}

ctx->SetOutputDim("Out", framework::make_ddim(temp));
}

Expand All @@ -85,6 +128,16 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype")),
ctx.device_context(), layout, library);
}

framework::OpKernelType GetKernelTypeForVar(
const std::string& var_name, const Tensor& tensor,
const framework::OpKernelType& expected_kernel_type) const override {
if (var_name == "ShapeTensor" || var_name == "ShapeTensorList") {
return expected_kernel_type;
}
return framework::OpKernelType(expected_kernel_type.data_type_,
tensor.place(), tensor.layout());
}
};

class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
Expand All @@ -94,7 +147,18 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {

AddAttr<std::vector<int64_t>>("shape",
"(vector<int64_t>) "
"The dimension of random tensor.");
"The dimension of random tensor.")
.SetDefault({});
AddInput("ShapeTensor",
"(Tensor<int>), optional). The shape of the output."
"It has a higher priority than Attr(shape).")
.AsDispensable();
AddInput("ShapeTensorList",
"(vector<Tensor<int>>, optional). The shape of the output. "
"It has a higher priority than Attr(shape)."
"The shape of the element in vector must be [1].")
.AsDuplicable()
.AsDispensable();
AddAttr<float>("mean",
"(float, default 0.0) "
"mean of random tensor.")
Expand Down Expand Up @@ -135,5 +199,5 @@ REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp,
REGISTER_OP_CPU_KERNEL(gaussian_random, ops::CPUGaussianRandomKernel<float>,
ops::CPUGaussianRandomKernel<double>);
REGISTER_OP_CPU_KERNEL(gaussian_random_batch_size_like,
ops::CPUGaussianRandomKernel<float>,
ops::CPUGaussianRandomKernel<double>);
ops::CPUGaussianRandomBatchSizeLikeKernel<float>,
ops::CPUGaussianRandomBatchSizeLikeKernel<double>);
34 changes: 30 additions & 4 deletions paddle/fluid/operators/gaussian_random_op.cu
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Expand Up @@ -15,6 +15,7 @@ limitations under the License. */
#include <thrust/transform.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/fill_constant_op.h"

namespace paddle {
namespace operators {
Expand All @@ -41,7 +42,6 @@ class GPUGaussianRandomKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
if (seed == 0) {
std::random_device rd;
Expand All @@ -50,19 +50,45 @@ class GPUGaussianRandomKernel : public framework::OpKernel<T> {
T mean = static_cast<T>(context.Attr<float>("mean"));
T std = static_cast<T>(context.Attr<float>("std"));
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
const std::string op_type = "gaussian_random";
auto shape = GetShape(context, op_type);
tensor->Resize(shape);
T* data = tensor->mutable_data<T>(context.GetPlace());

int64_t size = tensor->numel();
thrust::transform(index_sequence_begin, index_sequence_begin + size,
thrust::device_ptr<T>(data),
GaussianGenerator<T>(mean, std, seed));
}
};

template <typename T>
class GPUGaussianRandomBatchSizeLikeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
if (seed == 0) {
std::random_device rd;
seed = rd();
}
T mean = static_cast<T>(context.Attr<float>("mean"));
T std = static_cast<T>(context.Attr<float>("std"));
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
int64_t size = tensor->numel();
thrust::transform(index_sequence_begin, index_sequence_begin + size,
thrust::device_ptr<T>(data),
GaussianGenerator<T>(mean, std, seed));
}
};
} // namespace operators
} // namespace paddle

REGISTER_OP_CUDA_KERNEL(gaussian_random,
paddle::operators::GPUGaussianRandomKernel<float>,
paddle::operators::GPUGaussianRandomKernel<double>);
REGISTER_OP_CUDA_KERNEL(gaussian_random_batch_size_like,
paddle::operators::GPUGaussianRandomKernel<float>,
paddle::operators::GPUGaussianRandomKernel<double>);
REGISTER_OP_CUDA_KERNEL(
gaussian_random_batch_size_like,
paddle::operators::GPUGaussianRandomBatchSizeLikeKernel<float>,
paddle::operators::GPUGaussianRandomBatchSizeLikeKernel<double>);
7 changes: 6 additions & 1 deletion paddle/fluid/operators/mkldnn/gaussian_random_mkldnn_op.cc
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Expand Up @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */

#include <string>
#include "paddle/fluid/operators/fill_constant_op.h"
#include "paddle/fluid/operators/mean_op.h"

namespace paddle {
Expand All @@ -26,7 +27,6 @@ class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
float mean = context.Attr<float>("mean");
float std = context.Attr<float>("std");
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());

unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
std::minstd_rand engine;
Expand All @@ -35,6 +35,11 @@ class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
}
engine.seed(seed);
std::normal_distribution<T> dist(mean, std);

const std::string op_type = "gaussian_random";
auto shape = GetShape(context, op_type);
tensor->Resize(shape);
T* data = tensor->mutable_data<T>(context.GetPlace());
int64_t size = tensor->numel();
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
Expand Down
5 changes: 3 additions & 2 deletions python/paddle/fluid/layers/distributions.py
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Expand Up @@ -357,8 +357,9 @@ def sample(self, shape, seed=0):
output_shape = shape + batch_shape
zero_tmp = tensor.fill_constant_batch_size_like(
self.loc + self.scale, batch_shape + shape, self.loc.dtype, 0.)
normal_random_tmp = nn.gaussian_random_batch_size_like(
zero_tmp, zero_tmp.shape, mean=0., std=1., seed=seed)
zero_tmp_shape = nn.shape(zero_tmp)
normal_random_tmp = nn.gaussian_random(
zero_tmp_shape, mean=0., std=1., seed=seed)
output = normal_random_tmp * (zero_tmp + self.scale) + self.loc
return nn.reshape(output, output_shape)
else:
Expand Down
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