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PaddleDetection
提交 dfbac603 编写于 作者: P peizhilin
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Merge remote-tracking branch 'upstream/develop' into windows/build

上级 7c8c9dc9 dd6fd4c7
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AUTHORS.md
浏览文件 @ dfbac603
......@@ -25,6 +25,7 @@
| kexinzhao | Ke-Xin Zhao |
| kuke | Yi-Bing Liu |
| lcy-seso | Ying Cao |
| cjld | Dun Liang |
| lipeng-unisound | Peng Li |
| liuyuan | Yuan Liu |
| livc | Zhao Li |
......
paddle/fluid/API.spec
浏览文件 @ dfbac603
......@@ -103,6 +103,7 @@ paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 's
paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
paddle.fluid.layers.group_norm ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None))
paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax'], varargs=None, keywords=None, defaults=(False, -100, False, False))
paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/operators/group_norm_op.cc 0 → 100644
浏览文件 @ dfbac603
/* Copyright (c) 2018 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. */
#include "paddle/fluid/operators/group_norm_op.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
class GroupNormOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Y"),
"Output(Y) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Mean"),
"Output(Mean) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Variance"),
"Output(Variance) of GroupNormOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
auto channel_num = x_dim[1];
auto batch_size = x_dim[0];
auto groups = ctx->Attrs().Get<int>("groups");
PADDLE_ENFORCE_LE(
groups, channel_num,
"'groups' must be less equal than the number of channels.");
PADDLE_ENFORCE_GE(groups, 1, "'groups' must be greater equal than 1.");
if (ctx->HasInput("Scale")) {
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], channel_num);
}
if (ctx->HasInput("Bias")) {
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], channel_num);
}
ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
ctx->SetOutputDim("Mean", {batch_size, groups});
ctx->SetOutputDim("Variance", {batch_size, groups});
ctx->ShareLoD("X", "Y");
}
};
class GroupNormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input tensor.");
AddInput("Scale",
"Scale is a 1-dimensional tensor of size C"
"that is applied to the output.")
.AsDispensable();
AddInput("Bias",
"Bias is a 1-dimensional tensor of size C "
"that is applied to the output")
.AsDispensable();
AddOutput("Y", "Result after normalization.");
AddOutput("Mean", "Mean of each group.").AsIntermediate();
AddOutput("Variance", "Variance of each group.").AsIntermediate();
AddAttr<float>("epsilon",
"Constant for numerical stability [default 1e-5].")
.SetDefault(1e-5)
.AddCustomChecker([](const float &epsilon) {
PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 1.0f,
"'epsilon' should be between 0.0 and 1.0.");
});
AddAttr<int>("groups", "The number of groups that divided from channels.")
.AddCustomChecker([](const int &groups) {
PADDLE_ENFORCE_GT(groups, 0, "'groups' should be greater than zero.");
});
AddComment(R"DOC(
Group Normalization
Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`_
)DOC");
}
};
class GroupNormGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
// check input
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Mean"),
"Input(Mean) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Variance"),
"Input(Variance) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@GRAD) of GroupNormOp should not be null.");
// check output
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
if (ctx->HasOutput(framework::GradVarName("Scale"))) {
ctx->SetOutputDim(framework::GradVarName("Scale"),
ctx->GetInputDim("Scale"));
}
if (ctx->HasOutput(framework::GradVarName("Bias"))) {
ctx->SetOutputDim(framework::GradVarName("Bias"),
ctx->GetInputDim("Bias"));
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
const auto *var = ctx.InputVar(framework::GradVarName("Y"));
if (var == nullptr) {
PADDLE_THROW("can't find Y@GRAD");
}
const Tensor *t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
}
if (t == nullptr) {
PADDLE_THROW("can't find Y@GRAD");
}
return framework::OpKernelType(framework::ToDataType(t->type()),
ctx.GetPlace());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(group_norm, ops::GroupNormOp, ops::GroupNormOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp);
REGISTER_OP_CPU_KERNEL(
group_norm, ops::GroupNormKernel<paddle::platform::CPUDeviceContext, float>,
ops::GroupNormKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
group_norm_grad,
ops::GroupNormGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GroupNormGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/group_norm_op.cu 0 → 100644
浏览文件 @ dfbac603
/* Copyright (c) 2018 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. */
#include <cub/cub.cuh>
#include "paddle/fluid/operators/group_norm_op.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void GroupNormForwardGetMeanAndVar(const T* x, int N, int C,
int imsize, int groups,
int group_size, T* mean, T* var) {
int gid = blockIdx.y;
int cid = blockIdx.x;
int bid = blockIdx.z;
int number = min(group_size, static_cast<int>(C - gid * group_size));
int ccid = gid * group_size + cid;
if (ccid >= C) return;
T x_mean = 0, x_var = 0;
for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
T val = x[(bid * C + ccid) * imsize + imid];
x_mean += val;
x_var += val * val;
}
x_mean /= number * imsize;
x_var /= number * imsize;
__shared__ T s_mem[2];
if (threadIdx.x == 0) {
s_mem[0] = s_mem[1] = 0;
}
__syncthreads();
paddle::platform::CudaAtomicAdd(&s_mem[0], x_mean);
paddle::platform::CudaAtomicAdd(&s_mem[1], x_var);
__syncthreads();
if (threadIdx.x == 0) {
paddle::platform::CudaAtomicAdd(&mean[bid * groups + gid], s_mem[0]);
paddle::platform::CudaAtomicAdd(&var[bid * groups + gid], s_mem[1]);
}
}
template <typename T>
__global__ void GroupNormForward(const T* x, const T* mean, const T* var,
const T* scale, const T* bias, int N, int C,
int imsize, int groups, int group_size,
T epsilon, T* y, T* real_var) {
int gid = blockIdx.y;
int cid = blockIdx.x;
int bid = blockIdx.z;
int ccid = gid * group_size + cid;
if (ccid >= C) return;
T x_mean = mean[bid * groups + gid];
T x_var = var[bid * groups + gid];
x_var = x_var - x_mean * x_mean;
T var_inv = 1.0 / sqrt(x_var + epsilon);
if (cid == 0 && threadIdx.x == 0) real_var[bid * groups + gid] = x_var;
for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
T val = x[(bid * C + ccid) * imsize + imid];
val = (val - x_mean) * var_inv;
if (scale) val *= scale[gid * group_size + cid];
if (bias) val += bias[gid * group_size + cid];
y[(bid * C + ccid) * imsize + imid] = val;
}
}
template <typename T>
class GroupNormKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto* scale = ctx.Input<Tensor>("Scale");
auto* bias = ctx.Input<Tensor>("Bias");
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Output<Tensor>("Y");
auto* mean = ctx.Output<Tensor>("Mean");
auto* var = ctx.Output<Tensor>("Variance");
const auto groups = ctx.Attr<int>("groups");
const auto x_dims = x->dims();
const int group_size = (x_dims[1] - 1) / groups + 1;
y->mutable_data<T>(ctx.GetPlace());
mean->mutable_data<T>(ctx.GetPlace());
var->mutable_data<T>(ctx.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> set_zero;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
Tensor temp_var;
temp_var.mutable_data<T>(var->dims(), ctx.GetPlace());
set_zero(dev_ctx, mean, static_cast<T>(0));
set_zero(dev_ctx, &temp_var, static_cast<T>(0));
auto* x_data = x->data<T>();
auto* y_data = y->data<T>();
auto* mean_data = mean->data<T>();
auto* var_data = var->data<T>();
auto* temp_var_data = temp_var.data<T>();
const T* scale_data = nullptr;
if (scale) scale_data = scale->data<T>();
const T* bias_data = nullptr;
if (bias) bias_data = bias->data<T>();
int imsize = x_dims[2] * x_dims[3];
int block_size = std::min(512, imsize);
dim3 grid(group_size, groups, x_dims[0]);
dim3 threads(block_size, 1, 1);
GroupNormForwardGetMeanAndVar<T><<<grid, threads, 0, dev_ctx.stream()>>>(
x_data, x_dims[0], x_dims[1], imsize, groups, group_size, mean_data,
temp_var_data);
GroupNormForward<T><<<grid, threads, 0, dev_ctx.stream()>>>(
x_data, mean_data, temp_var_data, scale_data, bias_data, x_dims[0],
x_dims[1], imsize, groups, group_size, epsilon, y_data, var_data);
}
};
template <typename T>
__global__ void GroupNormBackwardGetMeanAndVar(
const T* x, const T* mean, const T* var, const T* scale, const T* d_y,
int N, int C, int imsize, int groups, int group_size, T epsilon, T* d_x,
T* d_mean, T* d_var, T* d_scale, T* d_bias) {
int gid = blockIdx.y;
int cid = blockIdx.x;
int bid = blockIdx.z;
int number = min(group_size, static_cast<int>(C - gid * group_size));
int ccid = gid * group_size + cid;
if (ccid >= C) return;
T x_mean = mean[bid * groups + gid];
T x_var = var[bid * groups + gid];
T var_inv = 1.0 / sqrt(x_var + epsilon);
T d_var_inv = 0, d_x_mean = 0;
T d_mean_data = 0, d_var_data = 0, d_scale_data = 0, d_bias_data = 0;
for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
T tmp = x[(bid * C + ccid) * imsize + imid];
T val = (tmp - x_mean) * var_inv;
T dval = d_y[(bid * C + ccid) * imsize + imid];
if (d_bias) d_bias_data += dval;
if (d_scale) d_scale_data += val * dval;
if (scale) dval = dval * scale[ccid];
d_var_data += (tmp - x_mean) * dval;
T d_tmp = dval * var_inv;
if (d_x) d_x[(bid * C + ccid) * imsize + imid] = d_tmp;
d_mean_data -= d_tmp;
}
__shared__ T s_mem[4];
if (threadIdx.x == 0) {
s_mem[0] = s_mem[1] = 0;
if (d_scale) s_mem[2] = 0;
if (d_bias) s_mem[3] = 0;
}
__syncthreads();
paddle::platform::CudaAtomicAdd(&s_mem[0], d_mean_data);
paddle::platform::CudaAtomicAdd(&s_mem[1], d_var_data);
if (d_scale) paddle::platform::CudaAtomicAdd(&s_mem[2], d_scale_data);
if (d_bias) paddle::platform::CudaAtomicAdd(&s_mem[3], d_bias_data);
__syncthreads();
if (threadIdx.x == 0) {
paddle::platform::CudaAtomicAdd(&d_mean[bid * groups + gid], s_mem[0]);
paddle::platform::CudaAtomicAdd(&d_var[bid * groups + gid], s_mem[1]);
if (d_scale) paddle::platform::CudaAtomicAdd(&d_scale[ccid], s_mem[2]);
if (d_bias) paddle::platform::CudaAtomicAdd(&d_bias[ccid], s_mem[3]);
}
}
template <typename T>
__global__ void GroupNormBackward(const T* x, const T* mean, const T* var,
const T* d_mean, const T* d_var, int N, int C,
int imsize, int groups, int group_size,
T epsilon, T* d_x) {
int gid = blockIdx.y;
int cid = blockIdx.x;
int bid = blockIdx.z;
int number = min(group_size, static_cast<int>(C - gid * group_size));
int ccid = gid * group_size + cid;
if (ccid >= C) return;
T x_mean = mean[bid * groups + gid];
T x_var = var[bid * groups + gid];
T d_x_mean = d_mean[bid * groups + gid];
T d_var_inv = d_var[bid * groups + gid];
T d_x_var =
-1.0 / (2 * (x_var + epsilon) * sqrt(x_var + epsilon)) * d_var_inv;
d_x_mean -= 2 * d_x_var * x_mean;
d_x_var /= number * imsize;
d_x_mean /= number * imsize;
for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
T tmp = x[(bid * C + ccid) * imsize + imid];
if (d_x)
d_x[(bid * C + ccid) * imsize + imid] += d_x_mean + tmp * 2 * d_x_var;
}
}
template <typename T>
class GroupNormGradKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto* x = ctx.Input<Tensor>("X");
auto* mean = ctx.Input<Tensor>("Mean");
auto* var = ctx.Input<Tensor>("Variance");
auto* scale = ctx.Input<Tensor>("Scale");
auto* d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto groups = ctx.Attr<int>("groups");
// init output
auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto* d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
const auto& x_dims = x->dims();
const int group_size = (x_dims[1] - 1) / groups + 1;
T* d_x_data = nullptr;
if (d_x) {
d_x->mutable_data<T>(ctx.GetPlace());
d_x_data = d_x->data<T>();
}
math::SetConstant<platform::CUDADeviceContext, T> set_zero;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
Tensor temp_var;
temp_var.mutable_data<T>(var->dims(), ctx.GetPlace());
set_zero(dev_ctx, &temp_var, static_cast<T>(0));
T* temp_var_data = temp_var.data<T>();
Tensor temp_mean;
temp_mean.mutable_data<T>(var->dims(), ctx.GetPlace());
set_zero(dev_ctx, &temp_mean, static_cast<T>(0));
T* temp_mean_data = temp_mean.data<T>();
auto* x_data = x->data<T>();
auto* y_data = d_y->data<T>();
auto* mean_data = mean->data<T>();
auto* var_data = var->data<T>();
T* d_scale_data = nullptr;
if (d_scale) {
d_scale->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_scale, static_cast<T>(0));
d_scale_data = d_scale->data<T>();
}
T* d_bias_data = nullptr;
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_bias, static_cast<T>(0));
d_bias_data = d_bias->data<T>();
}
const T* scale_data = nullptr;
if (scale) scale_data = scale->data<T>();
int imsize = x_dims[2] * x_dims[3];
int block_size = std::min(512, imsize);
dim3 grid(group_size, groups, x_dims[0]);
dim3 threads(block_size, 1, 1);
GroupNormBackwardGetMeanAndVar<T><<<grid, threads, 0, dev_ctx.stream()>>>(
x_data, mean_data, var_data, scale_data, y_data, x_dims[0], x_dims[1],
imsize, groups, group_size, epsilon, d_x_data, temp_mean_data,
temp_var_data, d_scale_data, d_bias_data);
GroupNormBackward<T><<<grid, threads, 0, dev_ctx.stream()>>>(
x_data, mean_data, var_data, temp_mean_data, temp_var_data, x_dims[0],
x_dims[1], imsize, groups, group_size, epsilon, d_x_data);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
group_norm,
ops::GroupNormKernel<paddle::platform::CUDADeviceContext, float>,
ops::GroupNormKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
group_norm_grad,
ops::GroupNormGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::GroupNormGradKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/group_norm_op.h 0 → 100644
浏览文件 @ dfbac603
/* Copyright (c) 2016 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
#include <algorithm>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
template <typename DeviceContext, typename T>
class GroupNormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto* scale = ctx.Input<Tensor>("Scale");
auto* bias = ctx.Input<Tensor>("Bias");
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Output<Tensor>("Y");
auto* mean = ctx.Output<Tensor>("Mean");
auto* var = ctx.Output<Tensor>("Variance");
const auto groups = ctx.Attr<int>("groups");
const auto x_dims = x->dims();
const int group_size = (x_dims[1] - 1) / groups + 1;
y->mutable_data<T>(ctx.GetPlace());
mean->mutable_data<T>(ctx.GetPlace());
var->mutable_data<T>(ctx.GetPlace());
auto* x_data = x->data<T>();
auto* y_data = y->data<T>();
auto* mean_data = mean->data<T>();
auto* var_data = var->data<T>();
const T* scale_data = nullptr;
if (scale) scale_data = scale->data<T>();
const T* bias_data = nullptr;
if (bias) bias_data = bias->data<T>();
int imsize = x_dims[2] * x_dims[3];
auto* iter_x_data = x_data;
auto* iter_y_data = y_data;
for (int bid = 0; bid < x_dims[0]; bid++)
for (int gid = 0; gid < groups; gid++) {
T x_mean = 0, x_var = 0;
int number = std::min(group_size,
static_cast<int>(x_dims[1] - gid * group_size));
auto* tmp = iter_x_data;
for (int cid = 0; cid < number; cid++) {
for (int imid = 0; imid < imsize; imid++, iter_x_data++) {
x_mean += iter_x_data[0];
x_var += iter_x_data[0] * iter_x_data[0];
}
}
x_mean /= number * imsize;
x_var /= number * imsize;
x_var = x_var - x_mean * x_mean;
T var_inv = 1.0 / sqrt(x_var + epsilon);
mean_data[bid * groups + gid] = x_mean;
var_data[bid * groups + gid] = x_var;
for (int cid = 0; cid < number; cid++) {
for (int imid = 0; imid < imsize; imid++, tmp++, iter_y_data++) {
T val = (tmp[0] - x_mean) * var_inv;
if (scale_data) val *= scale_data[gid * group_size + cid];
if (bias_data) val += bias_data[gid * group_size + cid];
iter_y_data[0] = val;
}
}
}
}
};
template <typename DeviceContext, typename T>
class GroupNormGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto* x = ctx.Input<Tensor>("X");
auto* mean = ctx.Input<Tensor>("Mean");
auto* var = ctx.Input<Tensor>("Variance");
auto* scale = ctx.Input<Tensor>("Scale");
auto* d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto groups = ctx.Attr<int>("groups");
// init output
auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto* d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
const auto& x_dims = x->dims();
const int group_size = (x_dims[1] - 1) / groups + 1;
// TODO(liangdun): need to check d_x is null
math::SetConstant<DeviceContext, T> set_zero;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
T* d_x_data = nullptr;
if (d_x) {
d_x->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_x, static_cast<T>(0));
d_x_data = d_x->data<T>();
}
auto* x_data = x->data<T>();
auto* y_data = d_y->data<T>();
auto* mean_data = mean->data<T>();
auto* var_data = var->data<T>();
T* d_scale_data = nullptr;
if (d_scale) {
d_scale->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_scale, static_cast<T>(0));
d_scale_data = d_scale->data<T>();
}
T* d_bias_data = nullptr;
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_bias, static_cast<T>(0));
d_bias_data = d_bias->data<T>();
}
const T* scale_data = nullptr;
if (scale) scale_data = scale->data<T>();
int imsize = x_dims[2] * x_dims[3];
auto* iter_x_data = x_data;
auto* iter_d_x_data = d_x_data;
auto* iter_y_data = y_data;
for (int bid = 0; bid < x_dims[0]; bid++)
for (int gid = 0; gid < groups; gid++) {
T x_mean = mean_data[bid * groups + gid];
T x_var = var_data[bid * groups + gid];
T var_inv = 1.0 / sqrt(x_var + epsilon);
int number = std::min(group_size,
static_cast<int>(x_dims[1] - gid * group_size));
auto* tmp = iter_x_data;
auto* tmp2 = iter_d_x_data;
T d_var_inv = 0, d_x_mean = 0;
for (int cid = 0; cid < number; cid++) {
for (int imid = 0; imid < imsize;
imid++, tmp++, iter_y_data++, iter_d_x_data++) {
T val = (tmp[0] - x_mean) * var_inv;
T dval = iter_y_data[0];
if (d_bias_data) d_bias_data[gid * group_size + cid] += dval;
if (d_scale_data)
d_scale_data[gid * group_size + cid] += val * dval;
if (scale_data) dval = scale_data[gid * group_size + cid] * dval;
d_var_inv += (tmp[0] - x_mean) * dval;
T d_tmp = dval * var_inv;
if (d_x_data) iter_d_x_data[0] += d_tmp;
d_x_mean -= d_tmp;
}
}
T d_x_var =
-1.0 / (2 * (x_var + epsilon) * sqrt(x_var + epsilon)) * d_var_inv;
d_x_mean -= 2 * d_x_var * x_mean;
d_x_var /= number * imsize;
d_x_mean /= number * imsize;
iter_d_x_data = tmp2;
if (d_x_data) {
for (int cid = 0; cid < number; cid++) {
for (int imid = 0; imid < imsize;
imid++, iter_x_data++, iter_d_x_data++) {
iter_d_x_data[0] += d_x_mean;
iter_d_x_data[0] += iter_x_data[0] * 2 * d_x_var;
}
}
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/detail/activation_functions.h
浏览文件 @ dfbac603
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <math.h>
#include <string>
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/hostdevice.h"
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ dfbac603
......@@ -37,6 +37,7 @@ limitations under the License. */
#include "paddle/fluid/memory/allocation/allocator_strategy.h"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/place.h"
......@@ -86,6 +87,9 @@ bool IsCompiledWithDIST() {
}
PYBIND11_PLUGIN(core) {
// Not used, just make sure cpu_info.cc is linked.
paddle::platform::CpuTotalPhysicalMemory();
paddle::memory::allocation::UseAllocatorStrategyGFlag();
py::module m("core", "C++ core of PaddlePaddle");
......
python/paddle/fluid/contrib/utils/__init__.py 0 → 100644
浏览文件 @ dfbac603
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from __future__ import print_function
from . import hdfs_utils
from .hdfs_utils import *
__all__ = hdfs_utils.__all__
python/paddle/fluid/contrib/utils/hdfs_utils.py 0 → 100644
浏览文件 @ dfbac603
# Copyright (c) 2018 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.
"""HDFS Utils"""
import os
import subprocess
import multiprocessing
from datetime import datetime
import re
import copy
import errno
import logging
__all__ = ["HDFSClient", "multi_download"]
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
_logger = logging.getLogger("hdfs_utils")
_logger.setLevel(logging.INFO)
class HDFSClient(object):
def __init__(self, hadoop_home, configs):
self.pre_commands = []
hadoop_bin = '%s/bin/hadoop' % hadoop_home
self.pre_commands.append(hadoop_bin)
dfs = 'fs'
self.pre_commands.append(dfs)
for k, v in configs.iteritems():
config_command = '-D%s=%s' % (k, v)
self.pre_commands.append(config_command)
def __run_hdfs_cmd(self, commands, retry_times=5):
whole_commands = copy.deepcopy(self.pre_commands)
whole_commands.extend(commands)
print('Running system command: {0}'.format(' '.join(whole_commands)))
ret_code = 0
ret_out = None
ret_err = None
for x in range(retry_times + 1):
proc = subprocess.Popen(
whole_commands, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(output, errors) = proc.communicate()
ret_code, ret_out, ret_err = proc.returncode, output, errors
if ret_code:
_logger.warn(
'Times: %d, Error running command: %s. Return code: %d, Error: %s'
% (x, ' '.join(whole_commands), proc.returncode, errors))
else:
break
return ret_code, ret_out, ret_err
def upload(self, hdfs_path, local_path, overwrite=False, retry_times=5):
"""
upload the local file to hdfs
args:
local_file_path: the local file path
remote_file_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
return:
True or False
"""
assert hdfs_path is not None
assert local_path is not None and os.path.exists(local_path)
if os.path.isdir(local_path):
_logger.warn(
"The Local path: {} is dir and I will support it later, return".
format(local_path))
return
base = os.path.basename(local_path)
if not self.is_exist(hdfs_path):
self.makedirs(hdfs_path)
else:
if self.is_exist(os.path.join(hdfs_path, base)):
if overwrite:
_logger.error(
"The HDFS path: {} is exist and overwrite is True, delete it".
format(hdfs_path))
self.delete(hdfs_path)
else:
_logger.error(
"The HDFS path: {} is exist and overwrite is False, return".
format(hdfs_path))
return False
put_commands = ["-put", local_path, hdfs_path]
returncode, output, errors = self.__run_hdfs_cmd(put_commands,
retry_times)
if returncode:
_logger.error("Put local path: {} to HDFS path: {} failed".format(
local_path, hdfs_path))
return False
else:
_logger.info("Put local path: {} to HDFS path: {} successfully".
format(local_path, hdfs_path))
return True
def download(self, hdfs_path, local_path, overwrite=False, unzip=False):
"""
download from hdfs
args:
local_file_path: the local file path
remote_file_path: remote dir on hdfs
return:
True or False
"""
_logger.info('Downloading %r to %r.', hdfs_path, local_path)
_logger.info('Download of %s to %r complete.', hdfs_path, local_path)
if not self.is_exist(hdfs_path):
print("HDFS path: {} do not exist".format(hdfs_path))
return False
if self.is_dir(hdfs_path):
_logger.error(
"The HDFS path: {} is dir and I will support it later, return".
format(hdfs_path))
if os.path.exists(local_path):
base = os.path.basename(hdfs_path)
local_file = os.path.join(local_path, base)
if os.path.exists(local_file):
if overwrite:
os.remove(local_file)
else:
_logger.error(
"The Local path: {} is exist and overwrite is False, return".
format(local_file))
return False
self.make_local_dirs(local_path)
download_commands = ["-get", hdfs_path, local_path]
returncode, output, errors = self.__run_hdfs_cmd(download_commands)
if returncode:
_logger.error("Get local path: {} from HDFS path: {} failed".format(
local_path, hdfs_path))
return False
else:
_logger.info("Get local path: {} from HDFS path: {} successfully".
format(local_path, hdfs_path))
return True
def is_exist(self, hdfs_path=None):
"""
whether the remote hdfs path exists?
args:
remote_file_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
fs_name: The default values are the same as in the job configuration
fs_ugi: The default values are the same as in the job configuration
return:
True or False
"""
exist_cmd = ['-test', '-e', hdfs_path]
returncode, output, errors = self.__run_hdfs_cmd(
exist_cmd, retry_times=1)
if returncode:
_logger.error("HDFS is_exist HDFS path: {} failed".format(
hdfs_path))
return False
else:
_logger.info("HDFS is_exist HDFS path: {} successfully".format(
hdfs_path))
return True
def is_dir(self, hdfs_path=None):
"""
whether the remote hdfs path exists?
args:
remote_file_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
fs_name: The default values are the same as in the job configuration
fs_ugi: The default values are the same as in the job configuration
return:
True or False
"""
if not self.is_exist(hdfs_path):
return False
dir_cmd = ['-test', '-d', hdfs_path]
returncode, output, errors = self.__run_hdfs_cmd(dir_cmd, retry_times=1)
if returncode:
_logger.error("HDFS path: {} failed is not a directory".format(
hdfs_path))
return False
else:
_logger.info("HDFS path: {} successfully is a directory".format(
hdfs_path))
return True
def delete(self, hdfs_path):
"""Remove a file or directory from HDFS.
:param hdfs_path: HDFS path.
:param recursive: Recursively delete files and directories. By default,
this method will raise an :class:`HdfsError` if trying to delete a
non-empty directory.
This function returns `True` if the deletion was successful and `False` if
no file or directory previously existed at `hdfs_path`.
"""
_logger.info('Deleting %r.', hdfs_path)
if not self.is_exist(hdfs_path):
_logger.warn("HDFS path: {} do not exist".format(hdfs_path))
return True
if self.is_dir(hdfs_path):
del_cmd = ['-rmr', hdfs_path]
else:
del_cmd = ['-rm', hdfs_path]
returncode, output, errors = self.__run_hdfs_cmd(del_cmd, retry_times=0)
if returncode:
_logger.error("HDFS path: {} delete files failure".format(
hdfs_path))
return False
else:
_logger.info("HDFS path: {} delete files successfully".format(
hdfs_path))
return True
def rename(self, hdfs_src_path, hdfs_dst_path, overwrite=False):
"""Move a file or folder.
:param hdfs_src_path: Source path.
:param hdfs_dst_path: Destination path. If the path already exists and is
a directory, the source will be moved into it. If the path exists and is
a file, or if a parent destination directory is missing, this method will
raise an :class:`HdfsError`.
"""
assert hdfs_src_path is not None
assert hdfs_dst_path is not None
if not self.is_exist(hdfs_src_path):
_logger.info("HDFS path do not exist: {}".format(hdfs_src_path))
if self.is_exist(hdfs_dst_path) and not overwrite:
_logger.error("HDFS path is exist: {} and overwrite=False".format(
hdfs_dst_path))
rename_command = ['-mv', hdfs_src_path, hdfs_dst_path]
returncode, output, errors = self.__run_hdfs_cmd(
rename_command, retry_times=1)
if returncode:
_logger.error("HDFS rename path: {} to {} failed".format(
hdfs_src_path, hdfs_dst_path))
return False
else:
_logger.info("HDFS rename path: {} to {} successfully".format(
hdfs_src_path, hdfs_dst_path))
return True
@staticmethod
def make_local_dirs(local_path):
try:
os.makedirs(local_path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
def makedirs(self, hdfs_path):
"""Create a remote directory, recursively if necessary.
:param hdfs_path: Remote path. Intermediate directories will be created
appropriately.
"""
_logger.info('Creating directories to %r.', hdfs_path)
assert hdfs_path is not None
if self.is_exist(hdfs_path):
return
mkdirs_commands = ['-mkdir', hdfs_path]
returncode, output, errors = self.__run_hdfs_cmd(
mkdirs_commands, retry_times=1)
if returncode:
_logger.error("HDFS mkdir path: {} failed".format(hdfs_path))
return False
else:
_logger.error("HDFS mkdir path: {} successfully".format(hdfs_path))
return True
def ls(self, hdfs_path):
assert hdfs_path is not None
if not self.is_exist(hdfs_path):
return []
ls_commands = ['-ls', hdfs_path]
returncode, output, errors = self.__run_hdfs_cmd(
ls_commands, retry_times=1)
if returncode:
_logger.error("HDFS list path: {} failed".format(hdfs_path))
return []
else:
_logger.info("HDFS list path: {} successfully".format(hdfs_path))
ret_lines = []
regex = re.compile('\s+')
out_lines = output.strip().split("\n")
for line in out_lines:
re_line = regex.split(line)
if len(re_line) == 8:
ret_lines.append(re_line[7])
return ret_lines
def lsr(self, hdfs_path, only_file=True, sort=True):
def sort_by_time(v1, v2):
v1_time = datetime.strptime(v1[1], '%Y-%m-%d %H:%M')
v2_time = datetime.strptime(v2[1], '%Y-%m-%d %H:%M')
return v1_time > v2_time
assert hdfs_path is not None
if not self.is_exist(hdfs_path):
return []
ls_commands = ['-lsr', hdfs_path]
returncode, output, errors = self.__run_hdfs_cmd(
ls_commands, retry_times=1)
if returncode:
_logger.error("HDFS list all files: {} failed".format(hdfs_path))
return []
else:
_logger.info("HDFS list all files: {} successfully".format(
hdfs_path))
lines = []
regex = re.compile('\s+')
out_lines = output.strip().split("\n")
for line in out_lines:
re_line = regex.split(line)
if len(re_line) == 8:
if only_file and re_line[0][0] == "d":
continue
else:
lines.append(
(re_line[7], re_line[5] + " " + re_line[6]))
if sort:
sorted(lines, cmp=sort_by_time)
ret_lines = [ret[0] for ret in lines]
return ret_lines
def multi_upload(client,
hdfs_path,
local_path,
multi_processes=5,
overwrite=False):
"""
:param overwrite: will overwrite hdfs file or not
:param multi_processes: the upload data process at the same time, default=5
:param client: instance of HDFSClient
:param hdfs_path: path on hdfs
:param local_path: path on local
:return:
"""
def __subprocess_upload(datas):
for data in datas:
re_path = os.path.relpath(os.path.dirname(data), local_path)
hdfs_re_path = os.path.join(hdfs_path, re_path)
client.upload(hdfs_re_path, data, overwrite, retry_times=5)
def get_local_files(path):
rlist = []
if not os.path.isdir(path):
return rlist
for dirname, folder, files in os.walk(path):
for i in files:
t = os.path.join(dirname, i)
rlist.append(t)
return rlist
assert isinstance(client, HDFSClient)
all_files = get_local_files(local_path)
if not all_files:
_logger.info("there are nothing need to upload, exit")
return
_logger.info("Start {} multi process to upload datas".format(
multi_processes))
procs = []
for i in range(multi_processes):
process_datas = all_files[i::multi_processes]
p = multiprocessing.Process(
target=__subprocess_upload, args=(process_datas, ))
procs.append(p)
p.start()
# complete the processes
for proc in procs:
proc.join()
_logger.info("Finish {} multi process to upload datas".format(
multi_processes))
def multi_download(client,
hdfs_path,
local_path,
trainer_id,
trainers,
multi_processes=5):
"""
multi_download
:param client: instance of HDFSClient
:param hdfs_path: path on hdfs
:param local_path: path on local
:param trainer_id: current trainer id
:param trainers: all trainers number
:param multi_processes: the download data process at the same time, default=5
:return: None
"""
def __subprocess_download(datas):
for data in datas:
re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
local_re_path = os.path.join(local_path, re_path)
client.download(data, local_re_path)
assert isinstance(client, HDFSClient)
client.make_local_dirs(local_path)
_logger.info("Make local dir {} successfully".format(local_path))
all_need_download = client.lsr(hdfs_path, sort=True)
need_download = all_need_download[trainer_id::trainers]
_logger.info("Get {} files From all {} files need to be download from {}".
format(len(need_download), len(all_need_download), hdfs_path))
_logger.info("Start {} multi process to download datas".format(
multi_processes))
procs = []
for i in range(multi_processes):
process_datas = need_download[i::multi_processes]
p = multiprocessing.Process(
target=__subprocess_download, args=(process_datas, ))
procs.append(p)
p.start()
# complete the processes
for proc in procs:
proc.join()
_logger.info("Finish {} multi process to download datas".format(
multi_processes))
local_downloads = []
for data in need_download:
data_name = os.path.basename(data)
re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
local_re_path = os.path.join(local_path, re_path, data_name)
local_downloads.append(local_re_path)
return local_downloads
if __name__ == "__main__":
hadoop_home = "/home/client/hadoop-client/hadoop/"
configs = {
"fs.default.name": "hdfs://xxx.hadoop.com:54310",
"hadoop.job.ugi": "hello,hello123"
}
client = HDFSClient(hadoop_home, configs)
client.ls("/user/com/train-25")
files = client.lsr("/user/com/train-25/models")
downloads = multi_download(
client,
"/user/com/train-25/model",
"/home/xx/data1",
1,
5,
multi_processes=5)
multi_upload(client, "/user/com/train-25/model", "/home/xx/data1")
python/paddle/fluid/layers/nn.py
浏览文件 @ dfbac603
......@@ -85,6 +85,7 @@ __all__ = [
'row_conv',
'multiplex',
'layer_norm',
'group_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
......@@ -2547,6 +2548,84 @@ def layer_norm(input,
return helper.append_activation(layer_norm_out)
@templatedoc()
def group_norm(input,
groups,
epsilon=1e-05,
param_attr=None,
bias_attr=None,
act=None,
data_layout='NCHW',
name=None):
"""
**Group Normalization Layer**
Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`
Args:
input(Variable): The input tensor variable.
groups(int): The number of groups that divided from channels.
epsilon(float): The small value added to the variance to prevent
division by zero.
param_attr(ParamAttr|None): The parameter attribute for the learnable
scale :math:`g`. If it is set to False, no scale will be added to the output units.
If it is set to None, the bias is initialized one. Default: None.
bias_attr(ParamAttr|None): The parameter attribute for the learnable
bias :math:`b`. If it is set to False, no bias will be added to the output units.
If it is set to None, the bias is initialized zero. Default: None.
act(str): Activation to be applied to the output of group normalizaiton.
data_layout(string|NCHW): Only NCHW is supported.
name (str): The name of this layer. It is optional.
Returns:
Variable: A tensor variable which is the result after applying group normalization on the input.
Examples:
>>> data = fluid.layers.data(name='data', shape=[8, 32, 32],
>>> dtype='float32')
>>> x = fluid.layers.group_norm(input=data, groups=4)
"""
helper = LayerHelper('group_norm', **locals())
dtype = helper.input_dtype()
# create intput and parameters
inputs = {'X': input}
input_shape = input.shape
if data_layout != 'NCHW':
raise ValueError("unsupported data layout:" + data_layout)
param_shape = [input_shape[1]]
if param_attr:
scale = helper.create_parameter(
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
default_initializer=Constant(1.0))
inputs['Scale'] = scale
if bias_attr:
bias = helper.create_parameter(
attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
inputs['Bias'] = bias
# create output
mean_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
variance_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
group_norm_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="group_norm",
inputs=inputs,
outputs={
"Y": group_norm_out,
"Mean": mean_out,
"Variance": variance_out,
},
attrs={"epsilon": epsilon,
"groups": groups})
return helper.append_activation(group_norm_out)
def conv2d_transpose(input,
num_filters,
output_size=None,
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ dfbac603
......@@ -23,11 +23,11 @@ if(NOT WITH_DISTRIBUTE)
LIST(REMOVE_ITEM TEST_OPS test_dist_text_classification)
endif(NOT WITH_DISTRIBUTE)
if(WITH_GPU)
if (${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op)
endif()
endif(WITH_GPU)
if (NOT ${WITH_GPU})
LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op)
elseif(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op)
endif()
list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
list(REMOVE_ITEM TEST_OPS test_modified_huber_loss_op) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5184
......@@ -81,10 +81,12 @@ list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
list(REMOVE_ITEM TEST_OPS test_dist_transformer)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer)
list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
list(REMOVE_ITEM TEST_OPS test_interpolate_op)
foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP})
endforeach(TEST_OP)
py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR} SERIAL)
py_test_modules(test_interpolate_op MODULES test_interpolate_op SERIAL)
if(WITH_DISTRIBUTE)
py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ dfbac603
......@@ -381,8 +381,8 @@ class OpTest(unittest.TestCase):
outs.sort(key=len)
checker(outs)
def __assert_is_close(self, numeric_grads, analytic_grads, names,
max_relative_error, msg_prefix):
def _assert_is_close(self, numeric_grads, analytic_grads, names,
max_relative_error, msg_prefix):
for a, b, name in six.moves.zip(numeric_grads, analytic_grads, names):
abs_a = np.abs(a)
......@@ -451,9 +451,9 @@ class OpTest(unittest.TestCase):
analytic_grads = self._get_gradient(inputs_to_check, place,
output_names, no_grad_set)
self.__assert_is_close(numeric_grads, analytic_grads, inputs_to_check,
max_relative_error,
"Gradient Check On %s" % str(place))
self._assert_is_close(numeric_grads, analytic_grads, inputs_to_check,
max_relative_error,
"Gradient Check On %s" % str(place))
@staticmethod
def _numpy_to_lod_tensor(np_value, lod, place):
......
python/paddle/fluid/tests/unittests/test_group_norm_op.py 0 → 100644
浏览文件 @ dfbac603
# Copyright (c) 2018 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.
from __future__ import print_function
import unittest
import numpy as np
from operator import mul
import paddle.fluid.core as core
import paddle.fluid as fluid
from op_test import OpTest
from testsuite import create_op
def group_norm_naive(x, scale, bias, epsilon, groups):
N, C, H, W = x.shape
G = groups
x = x.reshape((N * G, -1))
mean = np.mean(x, axis=1, keepdims=True)
var = np.var(x, axis=1, keepdims=True)
output = (x - mean) / np.sqrt(var + epsilon)
output = output.reshape((N, C, H, W)) * scale.reshape(
(-1, 1, 1)) + bias.reshape((-1, 1, 1))
return output, mean.reshape((N, G)), var.reshape((N, G))
class TestGroupNormOp(OpTest):
def setUp(self):
self.op_type = "group_norm"
self.data_format = "NCHW"
self.dtype = np.float32
self.shape = (2, 4, 3, 3)
self.attrs = {'epsilon': 1e-5, 'groups': 2}
self.compare_between_place = False
self.init_test_case()
input = np.random.random(self.shape).astype(self.dtype)
scale = np.random.random([self.shape[1]]).astype(self.dtype)
bias = np.random.random([self.shape[1]]).astype(self.dtype)
output, mean, var = group_norm_naive(
input, scale, bias, self.attrs['epsilon'], self.attrs['groups'])
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(input),
'Scale': OpTest.np_dtype_to_fluid_dtype(scale),
'Bias': OpTest.np_dtype_to_fluid_dtype(bias)
}
self.outputs = {'Y': output, 'Mean': mean, 'Variance': var}
def test_check_output(self):
atol = 1e-4
place = core.CPUPlace()
self.check_output_with_place(place, atol=atol)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, atol=atol)
def do_compare_between_place(self):
if not core.is_compiled_with_cuda(): return
place = core.CPUPlace()
place2 = core.CUDAPlace(0)
self.scope = core.Scope()
op_inputs = self.inputs if hasattr(self, "inputs") else dict()
op_outputs = self.outputs if hasattr(self, "outputs") else dict()
op_attrs = self.attrs if hasattr(self, "attrs") else dict()
self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
op_attrs)
inputs_to_check = set(['X', 'Scale', 'Bias'])
output_names = 'Y'
cpu_grads = self._get_gradient(inputs_to_check, place, output_names,
None)
gpu_grads = self._get_gradient(inputs_to_check, place2, output_names,
None)
self._assert_is_close(cpu_grads, gpu_grads, inputs_to_check, 0.005,
"Gradient Check On %s" % str(place))
def test_check_grad(self):
if self.compare_between_place:
self.do_compare_between_place()
return
place = core.CPUPlace()
self.check_grad_with_place(
place, set(['X', 'Scale', 'Bias']), 'Y', max_relative_error=0.01)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_grad_with_place(
place,
set(['X', 'Scale', 'Bias']),
'Y',
max_relative_error=0.01)
def init_test_case(self):
pass
class TestGroupNormOp1(TestGroupNormOp):
def init_test_case(self):
self.attrs['groups'] = 1
class TestGroupNormOp2(TestGroupNormOp):
def init_test_case(self):
self.attrs['groups'] = 4
class TestGroupNormOpBigEps1(TestGroupNormOp):
def init_test_case(self):
self.attrs['groups'] = 1
self.attrs['epsilon'] = 0.5
class TestGroupNormOpBigEps2(TestGroupNormOp):
def init_test_case(self):
self.attrs['groups'] = 4
self.attrs['epsilon'] = 0.5
class TestGroupNormOpBigEps3(TestGroupNormOp):
def init_test_case(self):
self.attrs['epsilon'] = 0.5
class TestGroupNormOpLargeData(TestGroupNormOp):
def init_test_case(self):
self.shape = (2, 32, 64, 64)
self.attrs['groups'] = 8
self.compare_between_place = True
if __name__ == '__main__':
unittest.main()
tools/manylinux1/Dockerfile.x64
浏览文件 @ dfbac603
......@@ -36,17 +36,21 @@ RUN cd /opt && wget -q --no-check-certificate https://github.com/google/protobuf
tar xzf protobuf-cpp-3.1.0.tar.gz && \
cd protobuf-3.1.0 && ./configure && make -j4 && make install && cd .. && rm -f protobuf-cpp-3.1.0.tar.gz
RUN wget -O /root/requirements.txt https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt
RUN wget https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt -O /root/requirements.txt
RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install -r /root/requirements.txt && \
LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install -r /root/requirements.txt && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install -r /root/requirements.txt && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.6.0/bin/pip3 install -r /root/requirements.txt && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.7.0/bin/pip3 install -r /root/requirements.txt && \
go get github.com/Masterminds/glide && \
rm -rf /root/requirements.txt
RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python
LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.6.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.7.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python
RUN wget -O /opt/swig-2.0.12.tar.gz https://cytranet.dl.sourceforge.net/project/swig/swig/swig-2.0.12/swig-2.0.12.tar.gz && \
cd /opt && tar xzf swig-2.0.12.tar.gz && cd /opt/swig-2.0.12 && ./configure && make && make install && cd /opt && rm swig-2.0.12.tar.gz
......
tools/manylinux1/build_scripts/build.sh
浏览文件 @ dfbac603
......@@ -9,12 +9,12 @@ set -ex
# remove others to expedite build and reduce docker image size. The original
# manylinux docker image project builds many python versions.
# NOTE We added back 3.5.1, since auditwheel requires python 3.3+
CPYTHON_VERSIONS="2.7.11 3.5.1"
CPYTHON_VERSIONS="3.7.0 3.6.0 3.5.1 2.7.11"
# openssl version to build, with expected sha256 hash of .tar.gz
# archive
OPENSSL_ROOT=openssl-1.0.2l
OPENSSL_HASH=ce07195b659e75f4e1db43552860070061f156a98bb37b672b101ba6e3ddf30c
OPENSSL_ROOT=openssl-1.1.0i
OPENSSL_HASH=ebbfc844a8c8cc0ea5dc10b86c9ce97f401837f3fa08c17b2cdadc118253cf99
EPEL_RPM_HASH=e5ed9ecf22d0c4279e92075a64c757ad2b38049bcf5c16c4f2b75d5f6860dc0d
DEVTOOLS_HASH=a8ebeb4bed624700f727179e6ef771dafe47651131a00a78b342251415646acc
PATCHELF_HASH=d9afdff4baeacfbc64861454f368b7f2c15c44d245293f7587bbf726bfe722fb
......@@ -25,7 +25,7 @@ AUTOCONF_HASH=954bd69b391edc12d6a4a51a2dd1476543da5c6bbf05a95b59dc0dd6fd4c2969
# Dependencies for compiling Python that we want to remove from
# the final image after compiling Python
PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel"
PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel libffi-devel"
# Libraries that are allowed as part of the manylinux1 profile
MANYLINUX1_DEPS="glibc-devel libstdc++-devel glib2-devel libX11-devel libXext-devel libXrender-devel mesa-libGL-devel libICE-devel libSM-devel ncurses-devel freetype-devel libpng-devel"
......@@ -61,7 +61,7 @@ yum -y install bzip2 make git patch unzip bison yasm diffutils \
wget -q https://cmake.org/files/v3.5/cmake-3.5.2.tar.gz && tar xzf cmake-3.5.2.tar.gz && \
cd cmake-3.5.2 && ./bootstrap && \
make -j4 && make install && cd .. && rm cmake-3.5.2.tar.gz
make -j8 && make install && cd .. && rm cmake-3.5.2.tar.gz
# Install newest autoconf
......@@ -77,11 +77,13 @@ mkdir -p /opt/python
build_cpythons $CPYTHON_VERSIONS
PY35_BIN=/opt/python/cp35-cp35m/bin
PY36_BIN=/opt/python/cp36-cp36m/bin
PY37_BIN=/opt/python/cp37-cp37m/bin
# NOTE Since our custom manylinux image builds pythons with shared
# libpython, we need to add libpython's dir to LD_LIBRARY_PATH before running
# python.
ORIGINAL_LD_LIBRARY_PATH="${LD_LIBRARY_PATH}"
LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib"
LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib:$(dirname ${PY36_BIN})/lib:$(dirname ${PY37_BIN})/lib"
# Our openssl doesn't know how to find the system CA trust store
# (https://github.com/pypa/manylinux/issues/53)
......@@ -119,9 +121,8 @@ ln -s $PY35_BIN/auditwheel /usr/local/bin/auditwheel
# final image
yum -y erase wireless-tools gtk2 libX11 hicolor-icon-theme \
avahi freetype bitstream-vera-fonts \
${PYTHON_COMPILE_DEPS} > /dev/null 2>&1
yum -y install ${MANYLINUX1_DEPS}
yum -y clean all > /dev/null 2>&1
${PYTHON_COMPILE_DEPS} > /dev/null 2>&1 || true
yum -y install ${MANYLINUX1_DEPS} && yum -y clean all > /dev/null 2>&1 || true
yum list installed
# we don't need libpython*.a, and they're many megabytes
find /opt/_internal -name '*.a' -print0 | xargs -0 rm -f
......
tools/manylinux1/build_scripts/build_utils.sh
浏览文件 @ dfbac603
......@@ -52,9 +52,17 @@ function do_cpython_build {
# NOTE --enable-shared for generating libpython shared library needed for
# linking of some of the nupic.core test executables.
CFLAGS="-Wformat" ./configure --prefix=${prefix} --enable-shared $unicode_flags > /dev/null
make -j2 > /dev/null
make install > /dev/null
if [ $(lex_pyver $py_ver) -ge $(lex_pyver 3.7) ]; then
# NOTE python 3.7 should be installed via make altinstall rather than
# make install, and we should specify the location of ssl
CFLAGS="-Wformat" ./configure --prefix=${prefix} --with-openssl=/usr/local/ssl --enable-shared $unicode_flags > /dev/null
make -j8 > /dev/null
make altinstall > /dev/null
else
CFLAGS="-Wformat" ./configure --prefix=${prefix} --enable-shared $unicode_flags > /dev/null
make -j8 > /dev/null
make install > /dev/null
fi
popd
echo "ZZZ looking for libpython"
find / -name 'libpython*.so*'
......@@ -64,6 +72,9 @@ function do_cpython_build {
if [ -e ${prefix}/bin/python3 ]; then
ln -s python3 ${prefix}/bin/python
fi
if [ -e ${prefix}/bin/python3.7 ]; then
ln -s python3.7 ${prefix}/bin/python
fi
# NOTE Make libpython shared library visible to python calls below
LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/python get-pip.py
LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/pip install wheel
......
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