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提交 bd064c0f 编写于 作者: J JiabinYang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_reorg_op

上级 c0563285 d0fdcb2f
隐藏空白更改
内联 并排
Showing with 649 addition and 264 deletion
paddle/fluid/inference/api/demo_ci/run.sh
浏览文件 @ bd064c0f
......@@ -21,7 +21,7 @@ else
fi
USE_TENSORRT=OFF
if [ [-d"$TENSORRT_INCLUDE_DIR"] -a [-d"$TENSORRT_LIB_DIR"] ]; then
if [ -d "$TENSORRT_INCLUDE_DIR" -a -d "$TENSORRT_LIB_DIR" ]; then
USE_TENSORRT=ON
fi
......
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
浏览文件 @ bd064c0f
......@@ -42,16 +42,22 @@ class Pool2dOpConverter : public OpConverter {
boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
std::vector<int> paddings =
boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
bool ceil_mode = boost::get<bool>(op_desc.GetAttr("ceil_mode"));
nvinfer1::Dims input_shape = input1->getDimensions();
int nbDims = input_shape.nbDims;
nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
if (global_pooling == true) {
nvinfer1::Dims input_shape = input1->getDimensions();
int nbDims = input_shape.nbDims;
nv_ksize.d[0] = input_shape.d[nbDims - 2];
nv_ksize.d[1] = input_shape.d[nbDims - 1];
nv_strides.h() = 1;
nv_strides.w() = 1;
nv_paddings.h() = 0;
nv_paddings.w() = 0;
}
const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
......@@ -64,6 +70,36 @@ class Pool2dOpConverter : public OpConverter {
PADDLE_THROW("TensorRT unsupported pooling type!");
}
if (ceil_mode) {
nvinfer1::DimsHW pre_pad(0, 0);
nvinfer1::DimsHW post_pad(0, 0);
int input_height = input_shape.d[nbDims - 2];
int input_width = input_shape.d[nbDims - 1];
int floor_h_output_size =
(input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
int ceil_h_output_size =
(input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
strides[0] +
1;
int floor_w_output_size =
(input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
int ceil_w_output_size =
(input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) /
strides[1] +
1;
if (floor_h_output_size != ceil_h_output_size) {
post_pad.h() = strides[0] - 1;
}
if (floor_w_output_size != ceil_w_output_size) {
post_pad.w() = strides[1] - 1;
}
auto* layer = TRT_ENGINE_ADD_LAYER(
engine_, Padding, *const_cast<nvinfer1::ITensor*>(input1), pre_pad,
post_pad);
input1 = layer->getOutput(0);
}
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
*const_cast<nvinfer1::ITensor*>(input1),
nv_pool_type, nv_ksize);
......
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
浏览文件 @ bd064c0f
......@@ -20,18 +20,20 @@ namespace paddle {
namespace inference {
namespace tensorrt {
void test_pool2d(bool global_pooling) {
void test_pool2d(bool global_pooling, bool ceil_mode) {
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(5, parameters, scope, 1 << 15);
// The ITensor's Dims should not contain the batch size.
// So, the ITensor's Dims of input and output should be C * H * W.
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 13, 14));
if (global_pooling)
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 1, 1));
else if (ceil_mode)
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 7));
else
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 6));
// Prepare Op description
framework::OpDesc desc;
......@@ -39,7 +41,7 @@ void test_pool2d(bool global_pooling) {
desc.SetInput("X", {"pool2d-X"});
desc.SetOutput("Out", {"pool2d-Out"});
std::vector<int> ksize({2, 2});
std::vector<int> ksize({3, 3});
std::vector<int> strides({2, 2});
std::vector<int> paddings({0, 0});
std::string pooling_t = "max";
......@@ -49,6 +51,7 @@ void test_pool2d(bool global_pooling) {
desc.SetAttr("strides", strides);
desc.SetAttr("paddings", paddings);
desc.SetAttr("global_pooling", global_pooling);
desc.SetAttr("ceil_mode", ceil_mode);
LOG(INFO) << "set OP";
validator.SetOp(*desc.Proto());
......@@ -57,9 +60,10 @@ void test_pool2d(bool global_pooling) {
validator.Execute(3);
}
TEST(Pool2dOpConverter, normal) { test_pool2d(false); }
TEST(Pool2dOpConverter, normal) { test_pool2d(false, false); }
TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true, false); }
TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true); }
TEST(Pool2dOpConverter, test_ceil_mode) { test_pool2d(false, true); }
} // namespace tensorrt
} // namespace inference
......
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @ bd064c0f
......@@ -52,6 +52,9 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(
ctx->HasOutput("TargetBBox"),
"Output(TargetBBox) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BBoxInsideWeight"),
"Output(BBoxInsideWeight) of RpnTargetAssignOp should not be null");
auto anchor_dims = ctx->GetInputDim("Anchor");
auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
......@@ -68,6 +71,7 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ScoreIndex", {-1});
ctx->SetOutputDim("TargetLabel", {-1, 1});
ctx->SetOutputDim("TargetBBox", {-1, 4});
ctx->SetOutputDim("BBoxInsideWeight", {-1, 4});
}
protected:
......@@ -169,6 +173,7 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
const float rpn_positive_overlap,
const float rpn_negative_overlap, std::vector<int>* fg_inds,
std::vector<int>* bg_inds, std::vector<int>* tgt_lbl,
std::vector<int>* fg_fake, std::vector<T>* bbox_inside_weight,
std::minstd_rand engine, bool use_random) {
float epsilon = 0.00001;
int anchor_num = anchor_to_gt_max.dims()[0];
......@@ -201,12 +206,12 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
// Reservoir Sampling
int fg_num = static_cast<int>(rpn_fg_fraction * rpn_batch_size_per_im);
ReservoirSampling(fg_num, &fg_inds_fake, engine, use_random);
fg_num = static_cast<int>(fg_inds_fake.size());
for (int64_t i = 0; i < fg_num; ++i) {
int fg_fake_num = static_cast<int>(fg_inds_fake.size());
for (int64_t i = 0; i < fg_fake_num; ++i) {
target_label[fg_inds_fake[i]] = 1;
}
int bg_num = rpn_batch_size_per_im - fg_num;
int bg_num = rpn_batch_size_per_im - fg_fake_num;
for (int64_t i = 0; i < anchor_num; ++i) {
if (anchor_to_gt_max_data[i] < rpn_negative_overlap) {
bg_inds_fake.push_back(i);
......@@ -214,12 +219,28 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
}
ReservoirSampling(bg_num, &bg_inds_fake, engine, use_random);
bg_num = static_cast<int>(bg_inds_fake.size());
int fake_num = 0;
for (int64_t i = 0; i < bg_num; ++i) {
// fg fake found
if (target_label[bg_inds_fake[i]] == 1) {
fake_num++;
fg_fake->emplace_back(fg_inds_fake[0]);
for (int j = 0; j < 4; ++j) {
bbox_inside_weight->emplace_back(T(0.));
}
}
target_label[bg_inds_fake[i]] = 0;
}
for (int64_t i = 0; i < (fg_fake_num - fake_num) * 4; ++i) {
bbox_inside_weight->emplace_back(T(1.));
}
for (int64_t i = 0; i < anchor_num; ++i) {
if (target_label[i] == 1) fg_inds->emplace_back(i);
if (target_label[i] == 1) {
fg_inds->emplace_back(i);
fg_fake->emplace_back(i);
}
if (target_label[i] == 0) bg_inds->emplace_back(i);
}
fg_num = fg_inds->size();
......@@ -248,7 +269,8 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
std::vector<int> bg_inds;
std::vector<int> gt_inds;
std::vector<int> tgt_lbl;
std::vector<int> fg_fake;
std::vector<T> bbox_inside_weight;
// Calculate the max IoU between anchors and gt boxes
// Map from anchor to gt box that has highest overlap
auto place = ctx.GetPlace();
......@@ -275,32 +297,37 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
// Follow the Faster RCNN's implementation
ScoreAssign(anchor_by_gt_overlap_data, anchor_to_gt_max, gt_to_anchor_max,
rpn_batch_size_per_im, rpn_fg_fraction, rpn_positive_overlap,
rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, engine,
use_random);
rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, &fg_fake,
&bbox_inside_weight, engine, use_random);
int fg_num = fg_inds.size();
int bg_num = bg_inds.size();
gt_inds.reserve(fg_num);
for (int i = 0; i < fg_num; ++i) {
gt_inds.emplace_back(argmax[fg_inds[i]]);
int fg_fake_num = fg_fake.size();
gt_inds.reserve(fg_fake_num);
for (int i = 0; i < fg_fake_num; ++i) {
gt_inds.emplace_back(argmax[fg_fake[i]]);
}
Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t;
int* loc_index_data = loc_index_t.mutable_data<int>({fg_num}, place);
Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t, bbox_inside_weight_t;
int* loc_index_data = loc_index_t.mutable_data<int>({fg_fake_num}, place);
int* score_index_data =
score_index_t.mutable_data<int>({fg_num + bg_num}, place);
int* tgt_lbl_data = tgt_lbl_t.mutable_data<int>({fg_num + bg_num}, place);
int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_num}, place);
std::copy(fg_inds.begin(), fg_inds.end(), loc_index_data);
int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_fake_num}, place);
T* bbox_inside_weight_data =
bbox_inside_weight_t.mutable_data<T>({fg_fake_num, 4}, place);
std::copy(fg_fake.begin(), fg_fake.end(), loc_index_data);
std::copy(fg_inds.begin(), fg_inds.end(), score_index_data);
std::copy(bg_inds.begin(), bg_inds.end(), score_index_data + fg_num);
std::copy(tgt_lbl.begin(), tgt_lbl.end(), tgt_lbl_data);
std::copy(gt_inds.begin(), gt_inds.end(), gt_inds_data);
std::copy(bbox_inside_weight.begin(), bbox_inside_weight.end(),
bbox_inside_weight_data);
std::vector<Tensor> loc_score_tgtlbl_gt;
loc_score_tgtlbl_gt.emplace_back(loc_index_t);
loc_score_tgtlbl_gt.emplace_back(score_index_t);
loc_score_tgtlbl_gt.emplace_back(tgt_lbl_t);
loc_score_tgtlbl_gt.emplace_back(gt_inds_t);
loc_score_tgtlbl_gt.emplace_back(bbox_inside_weight_t);
return loc_score_tgtlbl_gt;
}
......@@ -318,6 +345,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
auto* score_index = context.Output<LoDTensor>("ScoreIndex");
auto* tgt_bbox = context.Output<LoDTensor>("TargetBBox");
auto* tgt_lbl = context.Output<LoDTensor>("TargetLabel");
auto* bbox_inside_weight = context.Output<LoDTensor>("BBoxInsideWeight");
PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL,
"RpnTargetAssignOp gt_boxes needs 1 level of LoD");
......@@ -340,7 +368,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index->mutable_data<int>({max_num}, place);
tgt_bbox->mutable_data<T>({max_num, 4}, place);
tgt_lbl->mutable_data<int>({max_num, 1}, place);
bbox_inside_weight->mutable_data<T>({max_num, 4}, place);
auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
std::random_device rnd;
......@@ -394,6 +422,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
Tensor sampled_score_index = loc_score_tgtlbl_gt[1];
Tensor sampled_tgtlbl = loc_score_tgtlbl_gt[2];
Tensor sampled_gt_index = loc_score_tgtlbl_gt[3];
Tensor sampled_bbox_inside_weight = loc_score_tgtlbl_gt[4];
int loc_num = sampled_loc_index.dims()[0];
int score_num = sampled_score_index.dims()[0];
......@@ -432,6 +461,8 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
AppendRpns<int>(score_index, total_score_num, &sampled_score_index_unmap);
AppendRpns<T>(tgt_bbox, total_loc_num * 4, &sampled_tgt_bbox);
AppendRpns<int>(tgt_lbl, total_score_num, &sampled_tgtlbl);
AppendRpns<T>(bbox_inside_weight, total_loc_num * 4,
&sampled_bbox_inside_weight);
total_loc_num += loc_num;
total_score_num += score_num;
......@@ -448,10 +479,12 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index->set_lod(loc_score);
tgt_bbox->set_lod(lod_loc);
tgt_lbl->set_lod(loc_score);
bbox_inside_weight->set_lod(lod_loc);
loc_index->Resize({total_loc_num});
score_index->Resize({total_score_num});
tgt_bbox->Resize({total_loc_num, 4});
tgt_lbl->Resize({total_score_num, 1});
bbox_inside_weight->Resize({total_loc_num, 4});
}
};
......@@ -514,6 +547,9 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
"TargetLabel",
"(Tensor<int>), The target labels of each anchor with shape "
"[F + B, 1], F and B are sampled foreground and backgroud number.");
AddOutput("BBoxInsideWeight",
"(Tensor), The bbox inside weight with shape "
"[F, 4], F is the sampled foreground number.");
AddComment(R"DOC(
This operator can be, for a given set of ground truth bboxes and the
anchors, to assign classification and regression targets to each prediction.
......
paddle/fluid/operators/fusion_gru_op.cc
浏览文件 @ bd064c0f
......@@ -16,10 +16,9 @@ limitations under the License. */
#include <cstring> // for memcpy
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
......@@ -174,58 +173,44 @@ class FusionGRUKernel : public framework::OpKernel<T> {
}
}
#define INIT_VEC_FUNC \
std::function<void(const int, const T *, T *)> act_gate, act_state; \
std::function<void(const int, const T*, const T*, const T*, T*)> cross; \
auto& act_gate_str = ctx.Attr<std::string>("gate_activation"); \
auto& act_state_str = ctx.Attr<std::string>("activation"); \
if (platform::jit::MayIUse(platform::jit::avx)) { \
math::VecActivations<T, platform::jit::avx> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::avx>; \
} else { \
math::VecActivations<T, platform::jit::isa_any> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::isa_any>; \
}
#define INIT_BASE_INPUT_OUTPUT \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse");
#define INIT_BASE_SIZES \
auto x_dims = x->dims(); /* T x M*/ \
auto wh_dims = wh->dims(); /* D x 3D*/ \
const int total_T = x_dims[0]; \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D3 = wh_dims[1]; \
const int D2 = D * 2;
#define INIT_BASE_DEFINES \
auto* x = ctx.Input<LoDTensor>("X"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto x_lod = x->lod(); \
auto x_dims = x->dims(); /* T x M*/ \
auto wh_dims = wh->dims(); /* D x 3D*/ \
const int total_T = x_dims[0]; \
const int D3 = wh_dims[1]
#define INIT_OTHER_DEFINES \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D2 = D * 2; \
const auto& ker = math::jitkernel::KernelPool::Instance() \
.template Get<math::jitkernel::GRUKernel<T>, \
const std::string&, const std::string&>( \
ctx.Attr<std::string>("gate_activation"), \
ctx.Attr<std::string>("activation"), D); \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
auto place = ctx.GetPlace(); \
T* xx_data = xx->mutable_data<T>(place)
void SeqCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
INIT_VEC_FUNC
auto x_lod = x->lod();
INIT_BASE_DEFINES;
INIT_OTHER_DEFINES;
const int N = x_lod[0].size() - 1;
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : nullptr;
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
const T* wh_state_data = wh_data + D * D2;
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(place);
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, D3, M, x_data, wx_data,
xx_data,
......@@ -252,14 +237,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
if (h0_data) {
prev_hidden_data = h0_data + bid * D;
} else {
// W: {W_update, W_reset; W_state}
// update gate
act_gate(D, xx_data, xx_data);
// state gate
act_state(D, xx_data + D2, xx_data + D2);
// out = a*b
blas.VMUL(D, xx_data, xx_data + D2, hidden_out_data);
// save prev
ker->ComputeH1(xx_data, hidden_out_data);
prev_hidden_data = hidden_out_data;
tstart = 1;
move_step();
......@@ -269,17 +247,12 @@ class FusionGRUKernel : public framework::OpKernel<T> {
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D2, D, static_cast<T>(1),
prev_hidden_data, D, wh_data, D2, static_cast<T>(1), xx_data,
D3);
act_gate(D2, xx_data, xx_data);
// rt = rt*ht_1 inplace result
blas.VMUL(D, prev_hidden_data, xx_data + D, hidden_out_data);
ker->ComputeHtPart1(xx_data, prev_hidden_data, hidden_out_data);
// gemm rt * Ws
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D, D, static_cast<T>(1),
hidden_out_data, D, wh_state_data, D, static_cast<T>(1),
xx_data + D2, D3);
act_state(D, xx_data + D2, xx_data + D2);
// out = zt*ht~ + (1-zt)*ht_1
cross(D, xx_data, xx_data + D2, prev_hidden_data, hidden_out_data);
ker->ComputeHtPart2(xx_data, prev_hidden_data, hidden_out_data);
// save prev
prev_hidden_data = hidden_out_data;
move_step();
......@@ -289,28 +262,19 @@ class FusionGRUKernel : public framework::OpKernel<T> {
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
if (x->lod()[0].size() == 2) {
INIT_BASE_DEFINES;
if (x_lod[0].size() == 2) {
xx->Resize({total_T, D3});
SeqCompute(ctx);
return;
}
INIT_VEC_FUNC
INIT_OTHER_DEFINES;
auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
auto* batched_input = ctx.Output<LoDTensor>("BatchedInput");
auto* batched_out = ctx.Output<LoDTensor>("BatchedOut");
const T* x_data = x->data<T>();
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* batched_input_data = batched_input->mutable_data<T>(ctx.GetPlace());
T* batched_out_data = batched_out->mutable_data<T>(ctx.GetPlace());
hidden_out->mutable_data<T>(ctx.GetPlace());
T* batched_input_data = batched_input->mutable_data<T>(place);
T* batched_out_data = batched_out->mutable_data<T>(place);
hidden_out->mutable_data<T>(place);
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
......@@ -336,7 +300,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* prev_hidden_data = nullptr;
if (h0) {
// reorder h0
T* reordered_h0_data = reordered_h0->mutable_data<T>(ctx.GetPlace());
T* reordered_h0_data = reordered_h0->mutable_data<T>(place);
const T* h0_data = h0->data<T>();
prev_hidden_data = reordered_h0_data;
size_t sz = sizeof(T) * D;
......@@ -350,12 +314,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* cur_out_data = batched_out_data;
// W: {W_update, W_reset; W_state}
for (int i = 0; i < max_bs; ++i) {
// update gate
act_gate(D, cur_in_data, cur_in_data);
// state gate
act_state(D, cur_in_data + D2, cur_in_data + D2);
// out = a*b
blas.VMUL(D, cur_in_data, cur_in_data + D2, cur_out_data);
ker->ComputeH1(cur_in_data, cur_out_data);
// add offset
cur_in_data += D3;
cur_out_data += D;
......@@ -380,10 +339,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* cur_out_data = batched_out_data;
T* cur_prev_hidden_data = prev_hidden_data;
for (int i = 0; i < cur_bs; ++i) {
act_gate(D2, cur_batched_data, cur_batched_data);
// rt = rt*ht_1 inplace result
blas.VMUL(D, cur_prev_hidden_data, cur_batched_data + D, cur_out_data);
ker->ComputeHtPart1(cur_batched_data, cur_prev_hidden_data,
cur_out_data);
cur_batched_data += D3;
cur_prev_hidden_data += D;
cur_out_data += D;
......@@ -397,12 +354,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
cur_prev_hidden_data = prev_hidden_data;
for (int i = 0; i < cur_bs; ++i) {
// ht~ = act_state(...)
act_state(D, cur_batched_data + D2, cur_batched_data + D2);
// out = zt*ht~ + (1-zt)*ht_1
cross(D, cur_batched_data, cur_batched_data + D2, cur_prev_hidden_data,
cur_out_data);
ker->ComputeHtPart2(cur_batched_data, cur_prev_hidden_data,
cur_out_data);
cur_batched_data += D3;
cur_prev_hidden_data += D;
cur_out_data += D;
......@@ -416,9 +369,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
batched_out->set_lod(batched_lod);
to_seq(dev_ctx, *batched_out, hidden_out);
}
#undef INIT_VEC_FUNC
#undef INIT_BASE_SIZES
#undef INIT_BASE_INPUT_OUTPUT
#undef INIT_OTHER_DEFINES
#undef INIT_BASE_DEFINES
};
} // namespace operators
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ bd064c0f
......@@ -68,6 +68,7 @@ cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selec
cc_test(im2col_test SRCS im2col_test.cc DEPS im2col)
cc_test(vol2col_test SRCS vol2col_test.cc DEPS vol2col)
cc_test(sequence_padding_test SRCS sequence_padding_test.cc DEPS sequence_padding)
cc_test(sequence_pooling_test SRCS sequence_pooling_test.cc DEPS sequence_pooling)
if(WITH_GPU)
nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function)
nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function)
......@@ -75,6 +76,6 @@ endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
cc_library(jit_kernel
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_lstm.cc
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc
DEPS cpu_info cblas)
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @ bd064c0f
......@@ -142,6 +142,15 @@ class LSTMKernel : public Kernel {
const T *wp_data = nullptr) const = 0;
};
template <typename T>
class GRUKernel : public Kernel {
public:
// compute h1 without h0
virtual void ComputeH1(T *gates, T *ht) const = 0;
virtual void ComputeHtPart1(T *gates, const T *ht_1, T *ht) const = 0;
virtual void ComputeHtPart2(T *gates, const T *ht_1, T *ht) const = 0;
};
} // namespace jitkernel
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/jit_kernel_lstm.cc → paddle/fluid/operators/math/jit_kernel_rnn.cc
浏览文件 @ bd064c0f
......@@ -136,6 +136,21 @@ static std::shared_ptr<const VActKernel<T>> GetActKernel(
return nullptr;
}
template <jit::cpu_isa_t isa>
static std::unique_ptr<AVXAct> GetAVXAct(const std::string& type) {
if (type == "sigmoid") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>());
} else if (type == "relu") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>());
} else if (type == "tanh") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>());
} else if (type == "identity" || type == "") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>());
}
PADDLE_THROW("Not support type: %s", type);
return nullptr;
}
/* LSTM JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class LSTMKernelImpl : public LSTMKernel<T> {
......@@ -192,61 +207,49 @@ class LSTMKernelImpl : public LSTMKernel<T> {
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl( \
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> { \
if (type == "sigmoid") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>()); \
} else if (type == "relu") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>()); \
} else if (type == "tanh") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>()); \
} else if (type == "identity" || type == "") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>()); \
} \
PADDLE_THROW("Not support type: %s", type); \
}; \
avx_act_gate_ = GetAVXAct(act_gate); \
avx_act_cand_ = GetAVXAct(act_cand); \
avx_act_cell_ = GetAVXAct(act_cell); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
const float* wp_data, float* checked) const { \
/* gates: W_ch, W_ih, W_fh, W_oh */ \
__m256 c, i, f, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
f = _mm256_loadu_ps(gates + 16); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = C_t-1 * fgated + cand_gated * igated*/ \
c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
i = _mm256_loadu_ps(ct_1); \
f = _mm256_mul_ps(i, avx_act_gate_->Compute(f)); \
f = _mm256_add_ps(c, f); \
_mm256_storeu_ps(ct, f); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1( \
float* gates, float* ct, float* ht, const float* wp_data) const { \
__m256 c, i, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = igated * cgated*/ \
c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
_mm256_storeu_ps(ct, c); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
#define INTRI8_FLOAT(isa) \
template <> \
LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl( \
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_cand_ = GetAVXAct<isa>(act_cand); \
avx_act_cell_ = GetAVXAct<isa>(act_cell); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
const float* wp_data, float* checked) const { \
/* gates: W_ch, W_ih, W_fh, W_oh */ \
__m256 c, i, f, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
f = _mm256_loadu_ps(gates + 16); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = C_t-1 * fgated + cand_gated * igated*/ \
c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
i = _mm256_loadu_ps(ct_1); \
f = _mm256_mul_ps(i, avx_act_gate_->Compute(f)); \
f = _mm256_add_ps(c, f); \
_mm256_storeu_ps(ct, f); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1( \
float* gates, float* ct, float* ht, const float* wp_data) const { \
__m256 c, i, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = igated * cgated*/ \
c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
_mm256_storeu_ps(ct, c); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
}
// TODO(TJ): optimize keq16
......@@ -354,6 +357,126 @@ REGISTER_JITKERNEL_ARGS(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
#undef JITKERNEL_DECLARE_LSTM
#undef JITKERNEL_KEY_LSTM
#undef JITKERNEL_NEW_LSTM_IMPL
/* GRU JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class GRUKernelImpl : public GRUKernel<T> {
public:
explicit GRUKernelImpl(const std::string& act_gate,
const std::string& act_state, int d)
: GRUKernel<T>() {
d_ = d;
d2_ = d * 2;
act_gate_d2_ = GetActKernel<T>(act_gate, d2_);
act_gate_d_ = GetActKernel<T>(act_gate, d);
act_state_d_ = GetActKernel<T>(act_state, d);
vmul_d_ = KernelPool::Instance().template Get<VMulKernel<T>>(d);
}
void ComputeH1(T* gates, T* ht) const override {
act_gate_d_->Compute(gates, gates);
act_state_d_->Compute(gates + d2_, gates + d2_);
vmul_d_->Compute(gates, gates + d2_, ht);
}
void ComputeHtPart1(T* gates, const T* ht_1, T* ht) const override {
// W: {W_update, W_reset; W_state}
act_gate_d2_->Compute(gates, gates);
vmul_d_->Compute(ht_1, gates + d_, ht);
}
void ComputeHtPart2(T* gates, const T* ht_1, T* ht) const override {
T* y = gates + d2_;
act_state_d_->Compute(y, y);
// out = zt*ht~ + (1-zt)*ht_1
for (int i = 0; i < d_; ++i) {
ht[i] = gates[i] * y[i] + (static_cast<T>(1) - gates[i]) * ht_1[i];
}
}
private:
int d_, d2_;
std::shared_ptr<const VActKernel<T>> act_gate_d2_, act_gate_d_, act_state_d_;
std::shared_ptr<const VMulKernel<T>> vmul_d_;
#ifdef __AVX__
std::unique_ptr<const AVXAct> avx_act_gate_, avx_act_state_;
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
GRUKernelImpl<float, isa, kEQ8>::GRUKernelImpl( \
const std::string& act_gate, const std::string& act_state, int d) \
: GRUKernel<float>() { \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_state_ = GetAVXAct<isa>(act_state); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeH1(float* gates, float* ht) \
const { \
__m256 u, s; \
/* W: {W_update, W_reset; W_state} */ \
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
s = _mm256_mul_ps(avx_act_gate_->Compute(u), avx_act_state_->Compute(s)); \
_mm256_storeu_ps(ht, s); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart1( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */ \
__m256 r, ht0; \
r = _mm256_loadu_ps(gates + 8); \
ht0 = _mm256_loadu_ps(ht_1); \
r = _mm256_mul_ps(avx_act_gate_->Compute(r), ht0); \
_mm256_storeu_ps(ht, r); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart2( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */ \
__m256 u, s, ht0; \
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
ht0 = _mm256_loadu_ps(ht_1); \
u = avx_act_gate_->Compute(u); \
s = _mm256_mul_ps(u, avx_act_state_->Compute(s)); \
u = _mm256_sub_ps(_mm256_set1_ps(1.f), u); \
u = _mm256_mul_ps(u, ht0); \
u = _mm256_add_ps(s, u); \
_mm256_storeu_ps(ht, u); \
}
#ifdef __AVX__
INTRI8_FLOAT(jit::avx);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT(jit::avx512f);
#endif
#define JITKERNEL_DECLARE_GRU(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const GRUKernel<ker_dtype>> KernelPool::Get< \
GRUKernel<ker_dtype>, const std::string&, const std::string&, int>( \
const std::string& act_gate, const std::string& act_state, int d)
#define JITKERNEL_KEY_GRU(ker_key, dtype_key) \
#ker_key #dtype_key + std::to_string(d) + act_gate + act_state
#define JITKERNEL_NEW_GRU_IMPL(ker, dtype, isa, k) \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_state, d));
REGISTER_JITKERNEL_ARGS(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
JITKERNEL_KEY_GRU, JITKERNEL_NEW_GRU_IMPL);
#undef INTRI8_FLOAT
#undef JITKERNEL_NEW_GRU_IMPL
#undef JITKERNEL_KEY_GRU
#undef JITKERNEL_DECLARE_GRU
} // namespace jitkernel
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @ bd064c0f
......@@ -157,6 +157,31 @@ class FirstSeqPoolFunctor {
}
};
template <typename T>
class SumSeqPoolGradFunctor {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& out_grad,
framework::LoDTensor* in_grad) {
auto lod = in_grad->lod()[0];
int64_t out_w = out_grad.numel() / out_grad.dims()[0];
int64_t in_w = in_grad->numel() / in_grad->dims()[0];
PADDLE_ENFORCE(in_w == out_w);
const T* out_g_data = out_grad.data<T>();
T* in_g_data = in_grad->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
int64_t in_offset = lod[i] * in_w;
const T* out_pos = out_g_data + i * out_w;
T* in_pos = in_g_data + in_offset;
for (int r = 0; r != h; ++r) {
blas.VCOPY(in_w, out_pos, in_pos + r * in_w);
}
}
}
};
template <typename T>
class SequencePoolFunctor<platform::CPUDeviceContext, T> {
public:
......@@ -231,9 +256,15 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
math::SetConstant<platform::CPUDeviceContext, T> functor;
functor(context, in_grad, 0);
}
if (pooltype == "SUM") {
math::SumSeqPoolGradFunctor<T> sum_pool_grad;
sum_pool_grad(context, out_grad, in_grad);
return;
}
auto lod = in_grad->lod()[0];
auto& place = *context.eigen_device();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]),
static_cast<int>(lod[i + 1]));
......@@ -247,12 +278,6 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
if (pooltype == "AVERAGE") {
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
} else if (pooltype == "SUM") {
const T* out_g_data = out_g_t.data<T>();
T* in_g_data = in_g_t.mutable_data<T>(context.GetPlace());
for (int r = 0; r != h; ++r) {
blas.VCOPY(w, out_g_data, in_g_data + r * w);
}
} else if (pooltype == "SQRT") {
in_g_e.device(place) =
(out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
......
paddle/fluid/operators/math/sequence_pooling_test.cc 0 → 100644
浏览文件 @ bd064c0f
/* 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/math/sequence_pooling.h"
#include <gtest/gtest.h>
#include <vector>
template <typename DeviceContext, typename Place, typename T>
void TestSequencePoolingSum(const paddle::framework::LoD& lod) {
paddle::framework::LoDTensor cpu_out_grad;
paddle::framework::LoDTensor cpu_in_grad;
paddle::framework::LoDTensor out_grad;
paddle::framework::LoDTensor in_grad;
const size_t second_dim = 128u;
// construct out_grad's tensor in cpu
const size_t out_first_dim = lod[0].size() - 1;
auto out_dims = paddle::framework::make_ddim(
{static_cast<int64_t>(out_first_dim), static_cast<int64_t>(second_dim)});
cpu_out_grad.mutable_data<T>(out_dims, paddle::platform::CPUPlace());
for (int64_t i = 0; i < cpu_out_grad.numel(); ++i) {
cpu_out_grad.data<T>()[i] = static_cast<T>(i);
}
// copy to dst out_grad
auto* place = new Place();
DeviceContext* context = new DeviceContext(*place);
if (paddle::platform::is_cpu_place(*place)) {
out_grad = cpu_out_grad;
} else {
TensorCopySync(cpu_out_grad, *place, &out_grad);
}
// construct in_grad
in_grad.set_lod(lod);
auto in_dims = paddle::framework::make_ddim(
{static_cast<int64_t>(lod[0].back()), static_cast<int64_t>(second_dim)});
in_grad.mutable_data<T>(in_dims, context->GetPlace());
// check tensor contruction result
PADDLE_ENFORCE_EQ(in_grad.dims().size(), out_grad.dims().size());
for (int64_t i = 1; i < out_grad.dims().size(); ++i) {
PADDLE_ENFORCE_EQ(in_grad.dims()[i], out_grad.dims()[i]);
}
// call functor
paddle::operators::math::SequencePoolGradFunctor<DeviceContext, T>()(
*context, "SUM", out_grad, &in_grad);
if (paddle::platform::is_cpu_place(*place)) {
cpu_in_grad = in_grad;
} else {
TensorCopySync(in_grad, paddle::platform::CPUPlace(), &cpu_in_grad);
cpu_in_grad.set_lod(in_grad.lod());
}
EXPECT_EQ(in_grad.numel(), lod[0].back() * second_dim);
EXPECT_EQ(in_grad.lod(), lod);
if (paddle::platform::is_cpu_place(*place)) {
for (int64_t i = 0; i < in_grad.lod()[0].size() - 1; ++i) {
int64_t begin = in_grad.lod()[0][i];
int64_t end = in_grad.lod()[0][i + 1];
paddle::framework::Tensor tmp = in_grad.Slice(begin, end);
for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
for (int64_t m = 0; m != second_dim; ++m) {
EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
out_grad.data<T>()[m + i * second_dim]);
}
}
}
} else {
for (int64_t i = 0; i < cpu_in_grad.lod()[0].size() - 1; ++i) {
int64_t begin = cpu_in_grad.lod()[0][i];
int64_t end = cpu_in_grad.lod()[0][i + 1];
paddle::framework::Tensor tmp = cpu_in_grad.Slice(begin, end);
for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
for (int64_t m = 0; m != second_dim; ++m) {
EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
cpu_out_grad.data<T>()[m + i * second_dim]);
}
}
}
}
delete place;
delete context;
}
TEST(SequencePoolingGrad, CPU_SUM) {
paddle::framework::LoD lod1;
lod1.push_back(std::vector<size_t>{0, 10});
TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
paddle::platform::CPUPlace, float>(lod1);
paddle::framework::LoD lod2;
lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
paddle::platform::CPUPlace, float>(lod2);
}
#ifdef PADDLE_WITH_CUDA
TEST(SequencePoolingGrad, CUDA_SUM) {
paddle::framework::LoD lod1;
lod1.push_back(std::vector<size_t>{0, 10});
TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace, float>(lod1);
paddle::framework::LoD lod2;
lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace, float>(lod2);
}
#endif
paddle/fluid/operators/top_k_op.cu
浏览文件 @ bd064c0f
......@@ -262,31 +262,31 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
const T* src, int lds, int dim, int k,
int grid_dim, int num) {
__shared__ Pair<T> sh_topk[BlockSize];
__shared__ int maxid[BlockSize / 2];
const int tid = threadIdx.x;
const int warp = threadIdx.x / 32;
const int bid = blockIdx.x;
for (int i = bid; i < num; i += grid_dim) {
output += i * output_stride;
indices += i * k;
int top_num = k;
__shared__ int maxid[BlockSize / 2];
T* out = output + i * output_stride;
int64_t* inds = indices + i * k;
Pair<T> topk[MaxLength];
int beam = MaxLength;
Pair<T> max;
bool is_empty = false;
bool firststep = true;
for (int k = 0; k < MaxLength; k++) {
topk[k].set(-INFINITY, -1);
for (int j = 0; j < MaxLength; j++) {
topk[j].set(-INFINITY, -1);
}
while (k) {
while (top_num) {
ThreadGetTopK<T, MaxLength, BlockSize>(
topk, &beam, k, src + i * lds, &firststep, &is_empty, &max, dim, tid);
sh_topk[tid] = topk[0];
BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &output,
&indices, &beam, &k, tid, warp);
BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &out, &inds,
&beam, &top_num, tid, warp);
}
}
}
......@@ -327,13 +327,15 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
size_t k = static_cast<int>(ctx.Attr<int>("k"));
const T* input_data = input->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
// FIXME(typhoonzero): data is always converted to type T?
int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
size_t input_height = input->dims()[0];
size_t input_width = input->dims()[1];
framework::DDim inputdims = input->dims();
const size_t input_height = framework::product(
framework::slice_ddim(inputdims, 0, inputdims.size() - 1));
const size_t input_width = inputdims[inputdims.size() - 1];
if (k > input_width) k = input_width;
// NOTE: pass lds and dim same to input width.
......@@ -342,14 +344,12 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
const int kMaxHeight = 2048;
int gridx = input_height < kMaxHeight ? input_height : kMaxHeight;
auto& dev_ctx = ctx.cuda_device_context();
switch (GetDesiredBlockDim(input_width)) {
FIXED_BLOCK_DIM(
KeMatrixTopK<T, 5,
kBlockDim><<<gridx, kBlockDim, 0, dev_ctx.stream()>>>(
output_data, output->dims()[1], indices_data, input_data,
input_width, input_width, static_cast<int>(k), gridx,
input_height));
output_data, k, indices_data, input_data, input_width,
input_width, static_cast<int>(k), gridx, input_height));
default:
PADDLE_THROW("Error");
}
......
paddle/fluid/operators/top_k_op.h
浏览文件 @ bd064c0f
......@@ -34,7 +34,6 @@ class TopkKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
// Get the top k elements of each row of input tensor
// FIXME: only deal with matrix(2d tensor).
auto* input = ctx.Input<Tensor>("X");
auto* output = ctx.Output<Tensor>("Out");
auto* indices = ctx.Output<Tensor>("Indices");
......@@ -44,8 +43,6 @@ class TopkKernel : public framework::OpKernel<T> {
T* output_data = output->mutable_data<T>(ctx.GetPlace());
int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
auto eg_input = EigenMatrix<T>::From(*input);
// reshape input to a flattern matrix(like flat_inner_dims)
framework::DDim inputdims = input->dims();
const size_t row = framework::product(
......@@ -53,7 +50,7 @@ class TopkKernel : public framework::OpKernel<T> {
const size_t col = inputdims[inputdims.size() - 1];
Eigen::DSizes<int, 2> flat2dims(row, col);
// NOTE: eigen shape doesn't affect paddle tensor.
eg_input.reshape(flat2dims);
auto eg_input = EigenMatrix<T>::Reshape(*input, inputdims.size() - 1);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
......
python/paddle/fluid/layers/detection.py
浏览文件 @ bd064c0f
......@@ -116,8 +116,8 @@ def rpn_target_assign(bbox_pred,
Returns:
tuple:
A tuple(predicted_scores, predicted_location, target_label,
target_bbox) is returned. The predicted_scores and
predicted_location is the predicted result of the RPN.
target_bbox, bbox_inside_weight) is returned. The predicted_scores
and predicted_location is the predicted result of the RPN.
The target_label and target_bbox is the ground truth,
respectively. The predicted_location is a 2D Tensor with shape
[F, 4], and the shape of target_bbox is same as the shape of
......@@ -126,6 +126,8 @@ def rpn_target_assign(bbox_pred,
[F + B, 1], and the shape of target_label is same as the shape
of the predicted_scores, B is the number of the background
anchors, the F and B is depends on the input of this operator.
Bbox_inside_weight represents whether the predicted loc is fake_fg
or not and the shape is [F, 4].
Examples:
.. code-block:: python
......@@ -138,7 +140,7 @@ def rpn_target_assign(bbox_pred,
append_batch_size=False, dtype='float32')
gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
append_batch_size=False, dtype='float32')
loc_pred, score_pred, loc_target, score_target =
loc_pred, score_pred, loc_target, score_target, bbox_inside_weight =
fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
......@@ -152,6 +154,8 @@ def rpn_target_assign(bbox_pred,
target_label = helper.create_variable_for_type_inference(dtype='int32')
target_bbox = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype)
bbox_inside_weight = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype)
helper.append_op(
type="rpn_target_assign",
inputs={
......@@ -164,7 +168,8 @@ def rpn_target_assign(bbox_pred,
'LocationIndex': loc_index,
'ScoreIndex': score_index,
'TargetLabel': target_label,
'TargetBBox': target_bbox
'TargetBBox': target_bbox,
'BBoxInsideWeight': bbox_inside_weight
},
attrs={
'rpn_batch_size_per_im': rpn_batch_size_per_im,
......@@ -179,13 +184,14 @@ def rpn_target_assign(bbox_pred,
score_index.stop_gradient = True
target_label.stop_gradient = True
target_bbox.stop_gradient = True
bbox_inside_weight.stop_gradient = True
cls_logits = nn.reshape(x=cls_logits, shape=(-1, 1))
bbox_pred = nn.reshape(x=bbox_pred, shape=(-1, 4))
predicted_cls_logits = nn.gather(cls_logits, score_index)
predicted_bbox_pred = nn.gather(bbox_pred, loc_index)
return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox
return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox, bbox_inside_weight
def detection_output(loc,
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ bd064c0f
......@@ -301,7 +301,7 @@ class TestRpnTargetAssign(unittest.TestCase):
dtype='float32',
lod_level=1,
append_batch_size=False)
pred_scores, pred_loc, tgt_lbl, tgt_bbox = layers.rpn_target_assign(
pred_scores, pred_loc, tgt_lbl, tgt_bbox, bbox_inside_weight = layers.rpn_target_assign(
bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
......@@ -313,15 +313,18 @@ class TestRpnTargetAssign(unittest.TestCase):
rpn_straddle_thresh=0.0,
rpn_fg_fraction=0.5,
rpn_positive_overlap=0.7,
rpn_negative_overlap=0.3)
rpn_negative_overlap=0.3,
use_random=False)
self.assertIsNotNone(pred_scores)
self.assertIsNotNone(pred_loc)
self.assertIsNotNone(tgt_lbl)
self.assertIsNotNone(tgt_bbox)
self.assertIsNotNone(bbox_inside_weight)
assert pred_scores.shape[1] == 1
assert pred_loc.shape[1] == 4
assert pred_loc.shape[1] == tgt_bbox.shape[1]
print(str(program))
class TestGenerateProposals(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ bd064c0f
......@@ -78,9 +78,9 @@ if(WITH_DISTRIBUTE)
set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 200)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext)
set_tests_properties(test_dist_se_resnext PROPERTIES TIMEOUT 1000)
# TODO: fix this test
#py_test_modules(test_dist_transformer MODULES test_dist_transformer)
#set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
py_test_modules(test_dist_transformer MODULES test_dist_transformer)
set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
endif(NOT APPLE)
py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
endif()
......
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ bd064c0f
......@@ -35,7 +35,7 @@ import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid import core
from test_dist_base import TestDistRunnerBase, runtime_main
from test_dist_base import TestDistRunnerBase, runtime_main, RUN_STEP
import paddle.compat as cpt
from paddle.compat import long_type
......@@ -562,18 +562,12 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
pass_start_time = time.time()
for batch_id, data in enumerate(train_data()):
if batch_id >= 5:
if batch_id >= RUN_STEP:
break
feed_list = []
total_num_token = 0
#if TrainTaskConfig.local:
# lr_rate = lr_scheduler.update_learning_rate()
#for place_id, data_buffer in enumerate(
# split_data(
# data, num_part=dev_count)):
if TrainTaskConfig.local:
lr_rate = lr_scheduler.update_learning_rate()
......@@ -619,12 +613,11 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
init = True
# Validate and save the model for inference.
if batch_id == 0 or batch_id == 4:
if TrainTaskConfig.val_file_pattern is not None:
val_avg_cost, val_ppl = test()
print("[%f]" % val_avg_cost)
else:
assert (False)
if TrainTaskConfig.val_file_pattern is not None:
val_avg_cost, val_ppl = test()
print("[%f]" % val_avg_cost)
else:
assert (False)
#import transformer_reader as reader
......@@ -1701,7 +1694,7 @@ class DistTransformer2x2(TestDistRunnerBase):
def run_trainer(self, args):
TrainTaskConfig.use_gpu = args.use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
args.is_dist, not args.sync_mode)
if args.is_dist:
......
python/paddle/fluid/tests/unittests/test_dist_mnist.py
浏览文件 @ bd064c0f
......@@ -40,7 +40,8 @@ class TestDistMnistAsync(TestDistBase):
self._sync_mode = False
self._use_reduce = False
def test_dist_train(self):
# FIXME(typhoonzero): fix async mode test later
def no_test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=200)
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ bd064c0f
......@@ -40,7 +40,8 @@ class TestDistSeResneXt2x2Async(TestDistBase):
self._sync_mode = False
self._use_reader_alloc = False
def test_dist_train(self):
#FIXME(typhoonzero): fix async mode later
def no_test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py
浏览文件 @ bd064c0f
......@@ -42,7 +42,8 @@ class TestDistSimnetBow2x2DenseAsync(TestDistBase):
self._sync_mode = False
self._enforce_place = "CPU"
def test_simnet_bow(self):
#FIXME(typhoonzero): fix async tests later
def no_test_simnet_bow(self):
need_envs = {
"IS_DISTRIBUTED": '0',
"IS_SPARSE": '0',
......@@ -78,7 +79,8 @@ class TestDistSimnetBow2x2SparseAsync(TestDistBase):
self._sync_mode = False
self._enforce_place = "CPU"
def test_simnet_bow(self):
#FIXME(typhoonzero): fix async tests later
def no_test_simnet_bow(self):
need_envs = {
"IS_DISTRIBUTED": '0',
"IS_SPARSE": '1',
......
python/paddle/fluid/tests/unittests/test_dist_transformer.py
浏览文件 @ bd064c0f
......@@ -61,7 +61,8 @@ class TestDistTransformer2x2Sync(TestDistBase):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1e-5)
self.check_with_place(
"dist_transformer.py", delta=1e-5, check_error_log=False)
class TestDistTransformer2x2Async(TestDistBase):
......@@ -70,7 +71,8 @@ class TestDistTransformer2x2Async(TestDistBase):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1.0)
self.check_with_place(
"dist_transformer.py", delta=1.0, check_error_log=False)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_fusion_gru_op.py
浏览文件 @ bd064c0f
......@@ -125,6 +125,12 @@ class TestFusionGRUOpMD2(TestFusionGRUOp):
self.D = 8
class TestFusionGRUOpMD3(TestFusionGRUOp):
def set_confs(self):
self.M = 17
self.D = 15
class TestFusionGRUOpBS1(TestFusionGRUOp):
def set_confs(self):
self.lod = [[3]]
......
python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py
浏览文件 @ bd064c0f
......@@ -50,8 +50,10 @@ def rpn_target_assign(anchor_by_gt_overlap,
fg_inds, size=(len(fg_inds) - num_fg), replace=False)
else:
disable_inds = fg_inds[num_fg:]
labels[disable_inds] = -1
fg_inds = np.where(labels == 1)[0]
bbox_inside_weight = np.zeros((len(fg_inds), 4), dtype=np.float32)
num_bg = rpn_batch_size_per_im - np.sum(labels == 1)
bg_inds = np.where(anchor_to_gt_max < rpn_negative_overlap)[0]
......@@ -59,18 +61,27 @@ def rpn_target_assign(anchor_by_gt_overlap,
enable_inds = bg_inds[np.random.randint(len(bg_inds), size=num_bg)]
else:
enable_inds = bg_inds[:num_bg]
fg_fake_inds = np.array([], np.int32)
fg_value = np.array([fg_inds[0]], np.int32)
fake_num = 0
for bg_id in enable_inds:
if bg_id in fg_inds:
fake_num += 1
fg_fake_inds = np.hstack([fg_fake_inds, fg_value])
labels[enable_inds] = 0
bbox_inside_weight[fake_num:, :] = 1
fg_inds = np.where(labels == 1)[0]
bg_inds = np.where(labels == 0)[0]
loc_index = fg_inds
score_index = np.hstack((fg_inds, bg_inds))
loc_index = np.hstack([fg_fake_inds, fg_inds])
score_index = np.hstack([fg_inds, bg_inds])
labels = labels[score_index]
assert not np.any(labels == -1), "Wrong labels with -1"
gt_inds = anchor_to_gt_argmax[fg_inds]
gt_inds = anchor_to_gt_argmax[loc_index]
return loc_index, score_index, labels, gt_inds
return loc_index, score_index, labels, gt_inds, bbox_inside_weight
def get_anchor(n, c, h, w):
......@@ -123,9 +134,12 @@ def rpn_target_assign_in_python(all_anchors,
gt_boxes_slice = gt_boxes_slice[not_crowd_inds]
iou = _bbox_overlaps(inside_anchors, gt_boxes_slice)
loc_inds, score_inds, labels, gt_inds = rpn_target_assign(
iou, rpn_batch_size_per_im, rpn_positive_overlap,
rpn_negative_overlap, rpn_fg_fraction, use_random)
loc_inds, score_inds, labels, gt_inds, bbox_inside_weight = \
rpn_target_assign(iou, rpn_batch_size_per_im,
rpn_positive_overlap,
rpn_negative_overlap,
rpn_fg_fraction,
use_random)
# unmap to all anchor
loc_inds = inds_inside[loc_inds]
score_inds = inds_inside[score_inds]
......@@ -139,6 +153,7 @@ def rpn_target_assign_in_python(all_anchors,
score_indexes = score_inds
tgt_labels = labels
tgt_bboxes = box_deltas
bbox_inside_weights = bbox_inside_weight
else:
loc_indexes = np.concatenate(
[loc_indexes, loc_inds + i * anchor_num])
......@@ -146,8 +161,10 @@ def rpn_target_assign_in_python(all_anchors,
[score_indexes, score_inds + i * anchor_num])
tgt_labels = np.concatenate([tgt_labels, labels])
tgt_bboxes = np.vstack([tgt_bboxes, box_deltas])
bbox_inside_weights = np.vstack([bbox_inside_weights, \
bbox_inside_weight])
return loc_indexes, score_indexes, tgt_bboxes, tgt_labels
return loc_indexes, score_indexes, tgt_bboxes, tgt_labels, bbox_inside_weights
class TestRpnTargetAssignOp(OpTest):
......@@ -182,10 +199,12 @@ class TestRpnTargetAssignOp(OpTest):
rpn_fg_fraction = 0.5
use_random = False
loc_index, score_index, tgt_bbox, labels = rpn_target_assign_in_python(
all_anchors, gt_boxes, is_crowd, im_info, lod, rpn_straddle_thresh,
rpn_batch_size_per_im, rpn_positive_overlap, rpn_negative_overlap,
rpn_fg_fraction, use_random)
loc_index, score_index, tgt_bbox, labels, bbox_inside_weights = \
rpn_target_assign_in_python(all_anchors, gt_boxes, is_crowd,
im_info, lod, rpn_straddle_thresh,
rpn_batch_size_per_im, rpn_positive_overlap,
rpn_negative_overlap,
rpn_fg_fraction, use_random)
labels = labels[:, np.newaxis]
self.op_type = "rpn_target_assign"
......@@ -207,7 +226,8 @@ class TestRpnTargetAssignOp(OpTest):
'LocationIndex': loc_index.astype('int32'),
'ScoreIndex': score_index.astype('int32'),
'TargetBBox': tgt_bbox.astype('float32'),
'TargetLabel': labels.astype('int32')
'TargetLabel': labels.astype('int32'),
'BBoxInsideWeight': bbox_inside_weights.astype('float32')
}
def test_check_output(self):
......
python/paddle/fluid/tests/unittests/test_top_k_op.py
浏览文件 @ bd064c0f
......@@ -21,22 +21,27 @@ from op_test import OpTest
class TestTopkOp(OpTest):
def setUp(self):
self.set_args()
self.op_type = "top_k"
k = 1
input = np.random.random((32, 84)).astype("float32")
output = np.ndarray((32, k))
indices = np.ndarray((32, k)).astype("int64")
k = self.top_k
input = np.random.random((self.row, k)).astype("float32")
output = np.ndarray((self.row, k))
indices = np.ndarray((self.row, k)).astype("int64")
self.inputs = {'X': input}
self.attrs = {'k': k}
for rowid in range(32):
for rowid in range(self.row):
row = input[rowid]
output[rowid] = np.sort(row)[-k:]
indices[rowid] = row.argsort()[-k:]
output[rowid] = np.sort(row)[::-1][:k]
indices[rowid] = row.argsort()[::-1][:k]
self.outputs = {'Out': output, 'Indices': indices}
def set_args(self):
self.row = 32
self.top_k = 1
def test_check_output(self):
self.check_output()
......@@ -50,14 +55,39 @@ class TestTopkOp3d(OpTest):
output = np.ndarray((64, k))
indices = np.ndarray((64, k)).astype("int64")
# FIXME: should use 'X': input for a 3d input
self.inputs = {'X': input_flat_2d}
self.inputs = {'X': input}
self.attrs = {'k': k}
for rowid in range(64):
row = input_flat_2d[rowid]
output[rowid] = np.sort(row)[-k:]
indices[rowid] = row.argsort()[-k:]
output[rowid] = np.sort(row)[::-1][:k]
indices[rowid] = row.argsort()[::-1][:k]
self.outputs = {
'Out': output.reshape((32, 2, k)),
'Indices': indices.reshape((32, 2, k))
}
def test_check_output(self):
self.check_output()
class TestTopkOp2(OpTest):
def setUp(self):
self.op_type = "top_k"
k = 1
m = 2056
input = np.random.random((m, 84)).astype("float32")
output = np.ndarray((m, k))
indices = np.ndarray((m, k)).astype("int64")
self.inputs = {'X': input}
self.attrs = {'k': k}
for rowid in range(m):
row = input[rowid]
output[rowid] = -np.sort(-row)[:k]
indices[rowid] = (-row).argsort()[:k]
self.outputs = {'Out': output, 'Indices': indices}
......@@ -65,5 +95,17 @@ class TestTopkOp3d(OpTest):
self.check_output()
class TestTopkOp3(TestTopkOp):
def set_args(self):
self.row = 2056
self.top_k = 3
class TestTopkOp4(TestTopkOp):
def set_args(self):
self.row = 40000
self.top_k = 1
if __name__ == "__main__":
unittest.main()
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