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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into quantize_transpiler_update

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revert-13637-optimize-opyreader
Dang Qingqing 7 years ago
parent 49ca3a32ed 3043f51b3a
commit ba8ba3000b
27 changed files with 595 additions and 569 deletions
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84
paddle/fluid/framework/details/cow_ptr.h
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@ -20,41 +20,79 @@ namespace paddle {
namespace framework {
namespace details {
template <class T>
class COWPtr {
// Change it to thread safe flags if needed.
class ThreadUnsafeOwnershipFlags {
public:
typedef std::shared_ptr<T> RefPtr;
explicit ThreadUnsafeOwnershipFlags(bool flag) : flag_(flag) {}
private:
RefPtr m_sp;
ThreadUnsafeOwnershipFlags(const ThreadUnsafeOwnershipFlags& other) = delete;
ThreadUnsafeOwnershipFlags& operator=(
const ThreadUnsafeOwnershipFlags& other) = delete;
ThreadUnsafeOwnershipFlags(ThreadUnsafeOwnershipFlags&& other) = default;
void detach() {
T* tmp = m_sp.get();
if (!(tmp == nullptr || m_sp.unique())) {
m_sp = RefPtr(new T(*tmp));
void SetOwnership(bool flag) { flag_ = flag; }
// Invoke the callback if it is not owned.
template <typename Callback>
void AcquireOwnershipOnce(Callback acquire) {
if (!flag_) {
acquire();
flag_ = true;
}
}
public:
COWPtr() : m_sp(nullptr) {}
explicit COWPtr(T* t) : m_sp(t) {}
explicit COWPtr(const RefPtr& refptr) : m_sp(refptr) {}
private:
bool flag_;
};
const T& Data() const { return operator*(); }
// Copy-On-Write pointer.
// It will hold a T* pointer, and only copy once when `MutableData` is invoked.
//
// The template parameter OwnershipFlags should have:
// * a constructor takes a bool. True if own.
// * SetOwnership(bool flag).
// * AcquireOwnershipOnce(Callback). It will invoke the callback if it is not
// owned.
//
// https://en.wikipedia.org/wiki/Copy-on-write
template <typename T, typename OwnershipFlags = ThreadUnsafeOwnershipFlags>
class COWPtr {
public:
// Ctor from raw pointer.
explicit COWPtr(T* ptr) : payload_(ptr), ownership_{true} {}
T* MutableData() { return operator->(); }
// Move methods. Steal ownership from origin
COWPtr(COWPtr&& other)
: payload_(other.payload_), ownership_{std::move(other.ownership_)} {}
COWPtr& operator=(COWPtr&& origin) = default;
const T& operator*() const { return *m_sp; }
T& operator*() {
detach();
return *m_sp;
// Copy methods. Not own payload
COWPtr(const COWPtr& other) : payload_(other.payload_), ownership_{false} {}
COWPtr& operator=(const COWPtr& other) {
payload_ = other.payload_;
ownership_.SetOwnership(false);
return *this;
}
const T* operator->() const { return m_sp.operator->(); }
T* operator->() {
detach();
return m_sp.operator->();
// Access read only data.
const T& Data() const { return *payload_; }
// Access mutable data. If the data is not owned, the data will be copied
// before.
T* MutableData() {
ownership_.AcquireOwnershipOnce(
[this] { payload_.reset(new T(*payload_)); });
return payload_.get();
}
private:
// Actual data pointer.
std::shared_ptr<T> payload_;
// Ownership flag.
OwnershipFlags ownership_;
};
} // namespace details
} // namespace framework
} // namespace paddle

8
paddle/fluid/framework/details/cow_ptr_test.cc
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@ -30,14 +30,6 @@ TEST(COWPtr, all) {
ASSERT_EQ(ptr2.Data(), 10);
}
TEST(COWPtr, change_old) {
COWPtr<int> ptr(new int{0});
COWPtr<int> ptr2 = ptr;
*ptr.MutableData() = 10;
ASSERT_EQ(ptr2.Data(), 0);
ASSERT_EQ(ptr.Data(), 10);
}
} // namespace details
} // namespace framework
} // namespace paddle

42
paddle/fluid/framework/details/multi_devices_graph_pass.cc
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@ -210,43 +210,6 @@ std::vector<std::string> MultiDevSSAGraphBuilder::FindDistTrainRecvVars(
return recv_vars;
}
bool MultiDevSSAGraphBuilder::IsDistTrainOp(
ir::Node *node, const std::vector<std::string> &send_vars,
const std::vector<std::string> &recv_vars) const {
if (send_vars.size() == 0 || recv_vars.size() == 0) {
return false;
}
/**
* Check any of opvars contains `.block` and in sendvars
*/
auto checker = [](const std::vector<std::string> &opvars,
const std::vector<std::string> &rpc_vars) -> bool {
for (auto &var : opvars) {
// a variable name with the suffix `.block` means it's a splited
// variable by (DistributeTranspiler)
// [python/paddle/fluid/transpiler/distribute_transpiler.py]
if (var.find(".block") != std::string::npos &&
std::find(rpc_vars.begin(), rpc_vars.end(), var) != rpc_vars.end()) {
return true;
}
}
return false;
};
std::vector<std::string> input_var_names;
std::vector<std::string> output_var_names;
for (ir::Node *input : node->inputs) {
input_var_names.push_back(input->Name());
}
for (ir::Node *output : node->outputs) {
output_var_names.push_back(output->Name());
}
return checker(output_var_names, send_vars) ||
checker(input_var_names, recv_vars);
}
size_t MultiDevSSAGraphBuilder::GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const {
int64_t numel_sum = 0;
@ -370,7 +333,9 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
}
}
is_dist_train = true;
} else if (IsDistTrainOp(node, send_vars, recv_vars)) {
} else if (boost::get<int>(node->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) ==
static_cast<int>(OpRole::kDist)) {
int op_dev_id = CreateDistTrainOp(&result, node);
if (node->Op()->Type() == "concat") {
auto origin_param_name = node->Op()->OutputArgumentNames()[0];
@ -736,6 +701,7 @@ int MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
.emplace(varname, op_dev_id);
}
} else {
LOG(ERROR) << "got unexpected dist op: " << node->Op()->Type();
PADDLE_THROW(
"the distribute training related op should be in [split_byref, "
"concat].");

6
paddle/fluid/framework/details/multi_devices_graph_pass.h
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@ -51,12 +51,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
int CreateRPCOp(ir::Graph *result, ir::Node *node) const;
int CreateDistTrainOp(ir::Graph *result, ir::Node *node) const;
/**
* Is this operator as the end-point operator before/after send operator.
*/
bool IsDistTrainOp(ir::Node *node, const std::vector<std::string> &send_vars,
const std::vector<std::string> &recv_vars) const;
std::vector<std::string> FindDistTrainSendVars(
const std::vector<ir::Node *> &nodes) const;

567
paddle/fluid/framework/mixed_vector.h
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1
paddle/fluid/framework/op_proto_maker.cc
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@ -120,6 +120,7 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
{static_cast<int>(OpRole::kForward),
static_cast<int>(OpRole::kBackward),
static_cast<int>(OpRole::kOptimize), static_cast<int>(OpRole::kRPC),
static_cast<int>(OpRole::kDist), static_cast<int>(OpRole::kLRSched),
static_cast<int>(OpRole::kLoss) | static_cast<int>(OpRole::kForward),
static_cast<int>(OpRole::kLoss) |
static_cast<int>(OpRole::kBackward),

6
paddle/fluid/framework/op_proto_maker.h
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@ -26,7 +26,13 @@ enum class OpRole {
kForward = 0x0000,
kBackward = 0x0001,
kOptimize = 0x0002,
// RPC role is for send/recv releated op
kRPC = 0x0003,
// Dist role is for split_byref/split_selected_rows/concat
// used for distributed training.
kDist = 0x0004,
// Tag all learning rate scheduler operators.
kLRSched = 0x0005,
kLoss = 0x0100,
// The default value of op's role. This should be only used for unittests and

28
paddle/fluid/operators/detection_map_op.h
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@ -76,8 +76,8 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
int class_num = ctx.Attr<int>("class_num");
auto& label_lod = in_label->lod();
auto& detect_lod = in_detect->lod();
auto label_lod = in_label->lod();
auto detect_lod = in_detect->lod();
PADDLE_ENFORCE_EQ(label_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(label_lod[0].size(), detect_lod[0].size(),
@ -166,11 +166,11 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto labels = framework::EigenTensor<T, 2>::From(input_label);
auto detect = framework::EigenTensor<T, 2>::From(input_detect);
auto& label_lod = input_label.lod();
auto& detect_lod = input_detect.lod();
auto label_lod = input_label.lod();
auto detect_lod = input_detect.lod();
int batch_size = label_lod[0].size() - 1;
auto& label_index = label_lod[0];
auto label_index = label_lod[0];
for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<Box>> boxes;
@ -274,6 +274,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
output_true_pos->set_lod(true_pos_lod);
output_false_pos->set_lod(false_pos_lod);
return;
}
void GetInputPos(const framework::Tensor& input_pos_count,
@ -291,7 +292,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto SetData = [](const framework::LoDTensor& pos_tensor,
std::map<int, std::vector<std::pair<T, int>>>& pos) {
const T* pos_data = pos_tensor.data<T>();
auto& pos_data_lod = pos_tensor.lod()[0];
auto pos_data_lod = pos_tensor.lod()[0];
for (size_t i = 0; i < pos_data_lod.size() - 1; ++i) {
for (size_t j = pos_data_lod[i]; j < pos_data_lod[i + 1]; ++j) {
T score = pos_data[j * 2];
@ -316,23 +317,20 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
std::map<int, std::vector<std::pair<T, int>>>* false_pos) const {
int batch_size = gt_boxes.size();
for (int n = 0; n < batch_size; ++n) {
auto& image_gt_boxes = gt_boxes[n];
for (auto& image_gt_box : image_gt_boxes) {
auto image_gt_boxes = gt_boxes[n];
for (auto it = image_gt_boxes.begin(); it != image_gt_boxes.end(); ++it) {
size_t count = 0;
auto& labeled_bboxes = image_gt_box.second;
auto labeled_bboxes = it->second;
if (evaluate_difficult) {
count = labeled_bboxes.size();
} else {
for (auto& box : labeled_bboxes) {
if (!box.is_difficult) {
++count;
}
}
for (size_t i = 0; i < labeled_bboxes.size(); ++i)
if (!(labeled_bboxes[i].is_difficult)) ++count;
}
if (count == 0) {
continue;
}
int label = image_gt_box.first;
int label = it->first;
if (label_pos_count->find(label) == label_pos_count->end()) {
(*label_pos_count)[label] = count;
} else {

8
paddle/fluid/operators/distributed/variable_response.cc
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@ -92,9 +92,14 @@ bool VariableResponse::CopyLodTensorData(
::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& ctx, const framework::DDim& dims,
int length) {
auto server_var = GetVar();
if (!server_var) {
LOG(ERROR) << "recved var should not on current server: "
<< meta_.varname();
return false;
}
auto* tensor = GetVar()->GetMutable<framework::LoDTensor>();
tensor->Resize(dims);
framework::LoD lod;
for (int i = 0; i < meta_.lod_level(); ++i) {
framework::Vector<size_t> v;
@ -107,7 +112,6 @@ bool VariableResponse::CopyLodTensorData(
void* tensor_data =
tensor->mutable_data(ctx.GetPlace(), ToTypeIndex(meta_.data_type()));
if (!ReadRaw(input, ctx, tensor->place(), tensor_data, length)) {
return false;
}

2
paddle/fluid/operators/extract_rows_op.cc
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@ -50,7 +50,7 @@ class ExtractRowsOp : public framework::OperatorBase {
auto &in = scope.FindVar(Input("X"))->Get<framework::SelectedRows>();
auto out = scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
auto &in_rows = in.rows();
auto in_rows = in.rows();
auto out_dim = framework::make_ddim(
std::vector<int64_t>{static_cast<int64_t>(in_rows.size()), 1});
auto dst_ptr = out->mutable_data<int64_t>(out_dim, in.place());

14
paddle/fluid/operators/math/selected_rows_functor.cu
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@ -60,9 +60,11 @@ struct SelectedRowsAdd<platform::CUDADeviceContext, T> {
auto out_place = context.GetPlace();
PADDLE_ENFORCE(platform::is_gpu_place(out_place));
memory::Copy(boost::get<platform::CUDAPlace>(out_place), out_data,
boost::get<platform::CUDAPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T), context.stream());
memory::Copy(
boost::get<platform::CUDAPlace>(out_place), out_data,
boost::get<platform::CUDAPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(context).stream());
auto* in2_data = in2_value.data<T>();
memory::Copy(boost::get<platform::CUDAPlace>(out_place),
@ -107,7 +109,7 @@ struct SelectedRowsAddTensor<platform::CUDADeviceContext, T> {
PADDLE_ENFORCE_EQ(in1_height, out_dims[0]);
auto& in1_value = input1.value();
framework::Vector<int64_t> in1_rows(input1.rows());
auto& in1_rows = input1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2.numel() / in1_height);
@ -146,7 +148,7 @@ struct SelectedRowsAddTo<platform::CUDADeviceContext, T> {
auto in1_height = input1.height();
PADDLE_ENFORCE_EQ(in1_height, input2->height());
auto& in1_rows = input1.rows();
framework::Vector<int64_t> in1_rows(input1.rows());
auto& in2_rows = *(input2->mutable_rows());
auto& in1_value = input1.value();
@ -206,7 +208,7 @@ struct SelectedRowsAddToTensor<platform::CUDADeviceContext, T> {
PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
auto& in1_value = input1.value();
framework::Vector<int64_t> in1_rows(input1.rows());
auto& in1_rows = input1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);

8
paddle/fluid/operators/math/selected_rows_functor_test.cu
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@ -20,7 +20,9 @@ limitations under the License. */
TEST(selected_rows_functor, gpu_add) {
paddle::platform::CUDAPlace gpu_place(0);
paddle::platform::CPUPlace cpu_place;
paddle::platform::CUDADeviceContext ctx(gpu_place);
paddle::platform::CUDADeviceContext& ctx =
*reinterpret_cast<paddle::platform::CUDADeviceContext*>(
paddle::platform::DeviceContextPool::Instance().Get(gpu_place));
paddle::operators::math::SetConstant<paddle::platform::CUDADeviceContext,
float>
functor;
@ -132,7 +134,9 @@ TEST(selected_rows_functor, gpu_add) {
TEST(selected_rows_functor, gpu_add_to) {
paddle::platform::CUDAPlace gpu_place(0);
paddle::platform::CPUPlace cpu_place;
paddle::platform::CUDADeviceContext ctx(gpu_place);
paddle::platform::CUDADeviceContext& ctx =
*reinterpret_cast<paddle::platform::CUDADeviceContext*>(
paddle::platform::DeviceContextPool::Instance().Get(gpu_place));
paddle::operators::math::SetConstant<paddle::platform::CUDADeviceContext,
float>
functor;

1
paddle/fluid/operators/sum_op.h
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@ -123,6 +123,7 @@ class SumKernel : public framework::OpKernel<T> {
out_value->Resize(framework::make_ddim(in_dim));
out_value->mutable_data<T>(context.GetPlace());
// if all the input sparse vars are empty, no need to
// merge these vars.
if (first_dim == 0UL) {

4
paddle/fluid/pybind/const_value.cc
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@ -36,7 +36,9 @@ void BindConstValue(pybind11::module* m) {
.value("Backward", framework::OpRole::kBackward)
.value("Optimize", framework::OpRole::kOptimize)
.value("Loss", framework::OpRole::kLoss)
.value("RPC", framework::OpRole::kRPC);
.value("RPC", framework::OpRole::kRPC)
.value("Dist", framework::OpRole::kDist)
.value("LRSched", framework::OpRole::kLRSched);
op_proto_and_checker_maker.def(
"kOpRoleAttrName", framework::OpProtoAndCheckerMaker::OpRoleAttrName);

24
python/paddle/fluid/framework.py
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@ -1509,6 +1509,30 @@ class Program(object):
self._op_role_var = []
self._current_role = OpRole.Forward
@contextlib.contextmanager
def _lr_schedule_guard(self):
"""
A with guard to set :code:`LRSched` :code:`OpRole` and
:code:`OpRoleVar` automatically. The :code:`OpRoleVar` is
set to the target learning rate.
Notes: This is a very low level API. Users should not use it directly.
Examples:
>>> p, g = backward(...)
>>> with program.lr_schedule_guard():
>>> lr = lr * decay
"""
OpRole = core.op_proto_and_checker_maker.OpRole
self._current_role = OpRole.LRSched
# TODO(typhoonzero): how to set target learning rate var
self._op_role_var = []
yield
self._op_role_var = []
self._current_role = OpRole.Forward
def __str__(self):
"""
Get the protobuf debug string of this Program.

4
python/paddle/fluid/initializer.py
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@ -74,7 +74,7 @@ class Initializer(object):
directly, but need to use one of its implementations.
"""
def __init_(self):
def __init__(self):
pass
def __call__(self, param, block):
@ -293,7 +293,7 @@ class TruncatedNormalInitializer(Initializer):
assert loc is not None
assert scale is not None
assert seed is not None
super(NormalInitializer, self).__init__()
super(TruncatedNormalInitializer, self).__init__()
self._mean = loc
self._std_dev = scale
self._seed = seed

137
python/paddle/fluid/layers/learning_rate_scheduler.py
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@ -27,7 +27,7 @@ from . import nn
from . import ops
from . import tensor
from ..initializer import init_on_cpu
from ..framework import default_main_program, Parameter
from ..framework import default_main_program, Parameter, unique_name
__all__ = [
'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
@ -63,11 +63,12 @@ def noam_decay(d_model, warmup_steps):
Returns:
The decayed learning rate.
"""
global_step = _decay_step_counter(1)
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter(1)
a = global_step**-0.5
b = (warmup_steps**-1.5) * global_step
lr_value = (d_model**-0.5) * ops.elementwise_min(a, b)
a = global_step**-0.5
b = (warmup_steps**-1.5) * global_step
lr_value = (d_model**-0.5) * ops.elementwise_min(a, b)
return lr_value
@ -108,14 +109,15 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
sgd_optimizer.minimize(avg_cost)
"""
global_step = _decay_step_counter()
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
return decayed_lr
return decayed_lr
def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
@ -136,14 +138,15 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
Returns:
The decayed learning rate
"""
global_step = _decay_step_counter()
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * ops.exp(-1 * decay_rate * div_res)
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * ops.exp(-1 * decay_rate * div_res)
return decayed_lr
return decayed_lr
def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
@ -181,15 +184,16 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
staircase=True))
sgd_optimizer.minimize(avg_cost)
"""
global_step = _decay_step_counter()
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate / (1 + decay_rate * div_res)
decayed_lr = learning_rate / (1 + decay_rate * div_res)
return decayed_lr
return decayed_lr
def polynomial_decay(learning_rate,
@ -220,25 +224,28 @@ def polynomial_decay(learning_rate,
Returns:
Variable: The decayed learning rate
"""
global_step = _decay_step_counter()
if cycle:
div_res = ops.ceil(global_step / decay_steps)
zero_var = tensor.fill_constant(shape=[1], dtype='float32', value=0.0)
one_var = tensor.fill_constant(shape=[1], dtype='float32', value=1.0)
with control_flow.Switch() as switch:
with switch.case(global_step == zero_var):
tensor.assign(input=one_var, output=div_res)
decay_steps = decay_steps * div_res
else:
decay_steps_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(decay_steps))
global_step = ops.elementwise_min(x=global_step, y=decay_steps_var)
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
if cycle:
div_res = ops.ceil(global_step / decay_steps)
zero_var = tensor.fill_constant(
shape=[1], dtype='float32', value=0.0)
one_var = tensor.fill_constant(
shape=[1], dtype='float32', value=1.0)
with control_flow.Switch() as switch:
with switch.case(global_step == zero_var):
tensor.assign(input=one_var, output=div_res)
decay_steps = decay_steps * div_res
else:
decay_steps_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(decay_steps))
global_step = ops.elementwise_min(x=global_step, y=decay_steps_var)
decayed_lr = (learning_rate - end_learning_rate) * \
((1 - global_step / decay_steps) ** power) + end_learning_rate
return decayed_lr
decayed_lr = (learning_rate - end_learning_rate) * \
((1 - global_step / decay_steps) ** power) + end_learning_rate
return decayed_lr
def piecewise_decay(boundaries, values):
@ -266,34 +273,36 @@ def piecewise_decay(boundaries, values):
"""
with default_main_program()._lr_schedule_guard():
if len(values) - len(boundaries) != 1:
raise ValueError("len(values) - len(boundaries) should be 1")
if len(values) - len(boundaries) != 1:
raise ValueError("len(values) - len(boundaries) should be 1")
global_step = _decay_step_counter()
global_step = _decay_step_counter()
lr = tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
lr = tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
with control_flow.Switch() as switch:
for i in range(len(boundaries)):
boundary_val = tensor.fill_constant(
with control_flow.Switch() as switch:
for i in range(len(boundaries)):
boundary_val = tensor.fill_constant(
shape=[1],
dtype='float32',
value=float(boundaries[i]),
force_cpu=True)
value_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(values[i]))
with switch.case(global_step < boundary_val):
tensor.assign(value_var, lr)
last_value_var = tensor.fill_constant(
shape=[1],
dtype='float32',
value=float(boundaries[i]),
force_cpu=True)
value_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(values[i]))
with switch.case(global_step < boundary_val):
tensor.assign(value_var, lr)
last_value_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(values[len(values) - 1]))
with switch.default():
tensor.assign(last_value_var, lr)
value=float(values[len(values) - 1]))
with switch.default():
tensor.assign(last_value_var, lr)
return lr

3
python/paddle/fluid/tests/unittests/CMakeLists.txt
Unescape View File

@ -80,7 +80,8 @@ if(WITH_DISTRIBUTE)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext SERIAL)
endif(NOT APPLE)
py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
#FIXME(gongwb): random fails.
#py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
endif()
py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)

2
python/paddle/fluid/tests/unittests/op_test.py
Unescape View File

@ -345,7 +345,7 @@ class OpTest(unittest.TestCase):
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) +
"\nExpect " + str(expect_t) + "\n" + "But Got" +
str(actual_t) + " in class " + self.__class__.__name__)
str(actual_t))
if isinstance(expect, tuple):
self.assertListEqual(actual.recursive_sequence_lengths(),
expect[1], "Output (" + out_name +

5
python/paddle/fluid/tests/unittests/test_detection_map_op.py
Unescape View File

@ -20,7 +20,6 @@ import six
import sys
import collections
import math
import paddle.fluid as fluid
from op_test import OpTest
@ -33,7 +32,7 @@ class TestDetectionMAPOp(OpTest):
self.detect = np.array(self.detect).astype('float32')
self.mAP = np.array(self.mAP).astype('float32')
if len(self.class_pos_count) > 0:
if (len(self.class_pos_count) > 0):
self.class_pos_count = np.array(self.class_pos_count).astype(
'int32')
self.true_pos = np.array(self.true_pos).astype('float32')
@ -274,7 +273,7 @@ class TestDetectionMAPOp11Point(TestDetectionMAPOp):
class TestDetectionMAPOpMultiBatch(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOpMultiBatch, self).init_test_case()
self.class_pos_count = [0, 2, 1, 0]
self.class_pos_count = [0, 2, 1]
self.true_pos_lod = [[0, 3, 2]]
self.true_pos = [[0.7, 1.], [0.3, 0.], [0.2, 1.], [0.8, 0.], [0.1, 1.]]
self.false_pos_lod = [[0, 3, 2]]

6
python/paddle/fluid/tests/unittests/test_dist_mnist.py
Unescape View File

@ -22,7 +22,7 @@ class TestDistMnist2x2(TestDistBase):
self._sync_mode = True
self._use_reduce = False
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
@ -31,7 +31,7 @@ class TestDistMnist2x2WithMemopt(TestDistBase):
self._sync_mode = True
self._mem_opt = True
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
@ -40,7 +40,7 @@ class TestDistMnistAsync(TestDistBase):
self._sync_mode = False
self._use_reduce = False
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=200)

13
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
Unescape View File

@ -21,7 +21,16 @@ class TestDistSeResneXt2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=1e-7)
class TestDistseResnXt2x2WithMemopt(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._mem_opt = True
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=1e-7)
@ -29,7 +38,7 @@ class TestDistSeResneXt2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)

4
python/paddle/fluid/tests/unittests/test_dist_transformer.py
Unescape View File

@ -59,7 +59,7 @@ class TestDistTransformer2x2Sync(TestDistBase):
def _setup_config(self):
self._sync_mode = True
def test_transformer(self):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1e-5)
@ -68,7 +68,7 @@ class TestDistTransformer2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
def test_transformer(self):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1.0)

17
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
Unescape View File

@ -17,19 +17,28 @@ import unittest
from test_dist_base import TestDistBase
class TestDistSeResneXt2x2(TestDistBase):
class TestDistW2V2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_word2vec.py", delta=1e-4)
class TestDistSeResneXt2x2Async(TestDistBase):
class TestDistW2V2x2WithMemOpt(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._mem_opt = True
def test_dist_train(self):
self.check_with_place("dist_word2vec.py", delta=1e-4)
class TestDistW2V2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_word2vec.py", delta=1)

29
python/paddle/fluid/transpiler/details/program_utils.py
Unescape View File

@ -21,13 +21,12 @@ import paddle
def delete_ops(block, ops):
try:
start = list(block.ops).index(ops[0])
end = list(block.ops).index(ops[-1])
[block._remove_op(start) for _ in six.moves.range(end - start + 1)]
except Exception as e:
raise e
block.program._sync_with_cpp()
for op in ops:
try:
idx = list(block.ops).index(op)
block._remove_op(idx)
except Exception as e:
print(e)
def find_op_by_input_arg(block, arg_name):
@ -37,10 +36,18 @@ def find_op_by_input_arg(block, arg_name):
return -1
def find_op_by_output_arg(block, arg_name):
for index, op in enumerate(block.ops):
if arg_name in op.output_arg_names:
return index
def find_op_by_output_arg(block, arg_name, reverse=False):
if reverse:
pos = len(block.ops) - 1
while pos >= 0:
op = block.ops[pos]
if arg_name in op.output_arg_names:
return pos
pos -= 1
else:
for index, op in enumerate(block.ops):
if arg_name in op.output_arg_names:
return index
return -1

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