diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec
index 66d4aee09a..c9886cd118 100644
--- a/paddle/fluid/API.spec
+++ b/paddle/fluid/API.spec
@@ -160,6 +160,12 @@ paddle.fluid.layers.relu ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.log ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.elu ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
+paddle.fluid.layers.relu6 ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None))
+paddle.fluid.layers.pow ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
+paddle.fluid.layers.stanh ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None))
+paddle.fluid.layers.hard_sigmoid ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None))
+paddle.fluid.layers.swish ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None))
@@ -276,12 +282,6 @@ paddle.fluid.layers.softsign ArgSpec(args=[], varargs='args', keywords='kwargs',
paddle.fluid.layers.brelu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.leaky_relu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.soft_relu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.elu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.relu6 ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.pow ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.stanh ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.hard_sigmoid ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.swish ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.uniform_random ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
diff --git a/paddle/fluid/framework/details/reference_count_op_handle.h b/paddle/fluid/framework/details/reference_count_op_handle.h
index 71db8d952f..fc479a4c4a 100644
--- a/paddle/fluid/framework/details/reference_count_op_handle.h
+++ b/paddle/fluid/framework/details/reference_count_op_handle.h
@@ -22,6 +22,7 @@
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
@@ -46,17 +47,15 @@ class ReferenceCountOpHandle : public OpHandleBase {
const std::vector<std::string> &var_names,
GarbageCollector<Tensor> *gc,
AtomicReferenceCountMap *ref_cnts)
- : OpHandleBase(node),
- scope_(scope),
- var_names_(var_names),
- gc_(gc),
- ref_cnts_(ref_cnts) {
+ : OpHandleBase(node), scope_(scope), gc_(gc), ref_cnts_(ref_cnts) {
dev_ctx_ = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place));
if (IsStreamGarabageCollector()) {
PADDLE_ENFORCE(cudaSetDevice(place.device));
PADDLE_ENFORCE(cudaEventCreateWithFlags(&event_, cudaEventDisableTiming));
}
+
+ for (auto &name : var_names) AddVar(name);
}
~ReferenceCountOpHandle() {
@@ -69,19 +68,35 @@ class ReferenceCountOpHandle : public OpHandleBase {
std::string Name() const override { return "reference_count"; }
+ void AddVar(const std::string &name) {
+ auto it = var_names_.find(name);
+ if (it != var_names_.end())
+ ++(it->second);
+ else
+ var_names_[name] = 1;
+ }
+
protected:
void RunImpl() override {
auto *exec_scope = scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
- std::vector<LoDTensor *> tensors;
- for (auto &name : var_names_) {
+ std::vector<Tensor *> tensors;
+ for (auto &pair : var_names_) {
+ auto &name = pair.first;
auto it = ref_cnts_->find(name);
if (it == ref_cnts_->end()) continue;
auto *var = exec_scope->FindVar(name);
- if (var == nullptr || !var->IsType<LoDTensor>()) continue;
-
- if (it->second.fetch_sub(1) <= 1) {
- tensors.emplace_back(var->GetMutable<LoDTensor>());
+ if (var == nullptr) continue;
+
+ if (var->IsType<LoDTensor>()) {
+ if (it->second.fetch_sub(pair.second) <= pair.second) {
+ tensors.emplace_back(var->GetMutable<LoDTensor>());
+ }
+ } else if (var->IsType<SelectedRows>()) {
+ if (it->second.fetch_sub(pair.second) <= pair.second) {
+ tensors.emplace_back(
+ var->GetMutable<SelectedRows>()->mutable_value());
+ }
}
}
@@ -91,7 +106,7 @@ class ReferenceCountOpHandle : public OpHandleBase {
}
private:
- void ClearTensors(const std::vector<LoDTensor *> &tensors) {
+ void ClearTensors(const std::vector<Tensor *> &tensors) {
auto *gc = dynamic_cast<StreamGarbageCollector<Tensor> *>(gc_);
if (gc != nullptr) {
auto compute_stream = dev_ctx_->stream();
@@ -112,7 +127,7 @@ class ReferenceCountOpHandle : public OpHandleBase {
const Scope *scope_;
platform::CUDADeviceContext *dev_ctx_;
- std::vector<std::string> var_names_;
+ std::unordered_map<std::string, int> var_names_;
GarbageCollector<Tensor> *gc_; // not own
AtomicReferenceCountMap *ref_cnts_; // not own
cudaEvent_t event_;
diff --git a/paddle/fluid/framework/details/reference_count_pass.cc b/paddle/fluid/framework/details/reference_count_pass.cc
index 344754d5a1..b1ce551ce7 100644
--- a/paddle/fluid/framework/details/reference_count_pass.cc
+++ b/paddle/fluid/framework/details/reference_count_pass.cc
@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
+#include <queue>
#include <string>
#include <vector>
@@ -23,6 +24,25 @@ namespace paddle {
namespace framework {
namespace details {
+static ComputationOpHandle *FindNextComputationOpHandle(VarHandle *var_in) {
+ std::queue<VarHandleBase *> queue;
+ queue.push(var_in);
+ do {
+ auto *var = queue.front();
+ queue.pop();
+ for (auto *op : var->PendingOps()) {
+ auto *compute_op = dynamic_cast<ComputationOpHandle *>(op);
+ if (compute_op != nullptr && compute_op->GetPlace() == var_in->place_) {
+ return compute_op;
+ }
+ for (auto *out_var : op->Outputs()) {
+ queue.push(out_var);
+ }
+ }
+ } while (!queue.empty());
+ return nullptr;
+}
+
std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
auto &ref_cnts = Get<DeviceReferenceCountMap>(kGlobalReferenceCount);
@@ -34,6 +54,9 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
// Step 2: Find all variables in non-computation ops which refers to variables
// in computation ops
std::unordered_set<std::string> names;
+ std::unordered_map<OpHandleBase *, std::unique_ptr<ReferenceCountOpHandle>>
+ compute_ref_cnt_map;
+
auto get_ref_cnts_from_compute_op = [&](
const std::unique_ptr<OpHandleBase> &op,
const std::vector<VarHandleBase *> &vars) {
@@ -54,15 +77,18 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
VarDesc *var_desc = var_handle->Node()->Var();
auto var_name = var_handle->Node()->Name();
- // This is wierd but there is really some variables without var_desc
+ // This is weird but there is really some variables without var_desc
// in computation_op
if (var_desc == nullptr) {
if (compute_op->Node()->Op()->Block()->FindVar(var_name) == nullptr)
continue;
} else {
- if (var_desc->Persistable() ||
- var_desc->Proto()->type().type() != proto::VarType::LOD_TENSOR)
+ if (var_desc->Persistable()) continue;
+ auto var_type = var_desc->Proto()->type().type();
+ if (var_type != proto::VarType::LOD_TENSOR &&
+ var_type != proto::VarType::SELECTED_ROWS) {
continue;
+ }
}
// compute op only runs in one device
@@ -93,12 +119,33 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
if (ref_cnts.count(place.device) &&
ref_cnts[place.device]->count(var_name)) {
++(*ref_cnts[place.device])[var_name];
+
+ auto *next_compute_op = FindNextComputationOpHandle(var_handle);
+ if (next_compute_op != nullptr) {
+ if (compute_ref_cnt_map.count(next_compute_op)) {
+ compute_ref_cnt_map[next_compute_op]->AddVar(var_name);
+ VLOG(5) << "Add reference count of " << var_name << " to Operator "
+ << next_compute_op->Name();
+ } else {
+ // Create new reference_count_op_handle
+ ir::Node *ref_cnt_node = graph->CreateEmptyNode(
+ "reference_count", ir::Node::Type::kOperation);
+ auto *ref_cnt_handle = new ReferenceCountOpHandle(
+ ref_cnt_node, next_compute_op->GetScope(), place, {var_name},
+ gcs[place.device].get(), cur_ref_cnts[place.device].get());
+ if (next_compute_op->Outputs().empty()) {
+ auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
+ next_compute_op->AddOutput(dep_var);
+ graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
+ }
+ ref_cnt_handle->AddInput(next_compute_op->Outputs().front());
+ compute_ref_cnt_map[next_compute_op].reset(ref_cnt_handle);
+ }
+ }
}
}
};
- std::unordered_map<OpHandleBase *, ReferenceCountOpHandle *>
- compute_ref_cnt_map;
auto &all_ops = graph->Get<GraphOps>(kGraphOps);
for (auto &op : all_ops) {
auto in_var_names = get_ref_cnts_from_compute_op(op, op->Inputs());
@@ -113,11 +160,13 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
auto *ref_cnt_handle = new ReferenceCountOpHandle(
ref_cnt_node, compute_op->GetScope(), place, in_var_names,
gcs[place.device].get(), cur_ref_cnts[place.device].get());
- auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
- compute_op->AddOutput(dep_var);
- ref_cnt_handle->AddInput(dep_var);
- graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
- compute_ref_cnt_map[compute_op] = ref_cnt_handle;
+ if (compute_op->Outputs().empty()) {
+ auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
+ compute_op->AddOutput(dep_var);
+ graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
+ }
+ ref_cnt_handle->AddInput(compute_op->Outputs().front());
+ compute_ref_cnt_map[compute_op].reset(ref_cnt_handle);
}
for (auto &op : all_ops) {
@@ -131,7 +180,11 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
new_all_ops.emplace_back(std::move(op));
auto it = compute_ref_cnt_map.find(new_all_ops.back().get());
if (it != compute_ref_cnt_map.end()) {
- new_all_ops.emplace_back(it->second);
+ // Add LeafNode to ReferenceCountOpHandle
+ auto *dummy_leaf = new DummyVarHandle(graph->CreateControlDepVar());
+ graph->Get<GraphDepVars>(kGraphDepVars).emplace(dummy_leaf);
+ it->second->AddOutput(dummy_leaf);
+ new_all_ops.emplace_back(std::move(it->second));
}
}
diff --git a/paddle/fluid/operators/adam_op.h b/paddle/fluid/operators/adam_op.h
index 5b27068c9e..4cb1f3a80e 100644
--- a/paddle/fluid/operators/adam_op.h
+++ b/paddle/fluid/operators/adam_op.h
@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <math.h> // for sqrt in CPU and CUDA
#include <Eigen/Dense>
+#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
@@ -306,26 +307,43 @@ class AdamOpKernel : public framework::OpKernel<T> {
VLOG(3) << "grad row size is 0!!";
return;
}
- // merge duplicated rows if any.
- // The rows of grad_merge have been sorted inside MergeAdd functor
- scatter::MergeAdd<DeviceContext, T> merge_func;
- auto& grad_merge = *(ctx.scope()
- .NewScope()
- .Var("sparse_adam_grad_merge")
- ->GetMutable<framework::SelectedRows>());
- merge_func(ctx.template device_context<DeviceContext>(), grad,
- &grad_merge);
+
+ std::vector<int64_t> cpu_rows(grad.rows().begin(), grad.rows().end());
+ bool is_strict_sorted = true;
+ for (size_t i = 1; i < cpu_rows.size(); ++i) {
+ if (cpu_rows[i - 1] >= cpu_rows[i]) {
+ is_strict_sorted = false;
+ break;
+ }
+ }
+
+ const framework::SelectedRows* grad_merge_ptr;
+ if (is_strict_sorted) {
+ grad_merge_ptr = &grad;
+ } else {
+ // merge duplicated rows if any.
+ // The rows of grad_merge have been sorted inside MergeAdd functor
+ scatter::MergeAdd<DeviceContext, T> merge_func;
+ auto* grad_merge_var = const_cast<framework::Scope&>(ctx.scope())
+ .Var()
+ ->GetMutable<framework::SelectedRows>();
+ merge_func(ctx.template device_context<DeviceContext>(), grad,
+ grad_merge_var);
+ grad_merge_ptr = grad_merge_var;
+ }
+
+ auto& grad_merge = *grad_merge_ptr;
auto& grad_tensor = grad_merge.value();
const T* grad_data = grad_tensor.template data<T>();
- int64_t* rows = nullptr;
-// When compiled without CUDA, the CUDAMutableData() interface should not be
+ const int64_t* rows = nullptr;
+// When compiled without CUDA, the CUDAData() interface should not be
// provided.
#if defined(PADDLE_WITH_CUDA)
if (platform::is_gpu_place(ctx.GetPlace())) {
- rows = grad_merge.mutable_rows()->CUDAMutableData(ctx.GetPlace());
+ rows = grad_merge.rows().CUDAData(ctx.GetPlace());
} else {
#endif
- rows = grad_merge.mutable_rows()->data();
+ rows = grad_merge.rows().data();
#if defined(PADDLE_WITH_CUDA)
}
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 058d939464..58384aa0f5 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -45,8 +45,9 @@ __all__ = [
'lod_reset', 'lrn', 'pad', 'pad_constant_like', 'label_smooth', 'roi_pool',
'dice_loss', 'image_resize', 'image_resize_short', 'resize_bilinear',
'gather', 'scatter', 'sequence_scatter', 'random_crop', 'mean_iou', 'relu',
- 'log', 'crop', 'rank_loss', 'prelu', 'flatten', 'sequence_mask', 'stack',
- 'pad2d', 'unstack', 'sequence_enumerate', 'expand', 'sequence_concat',
+ 'log', 'crop', 'rank_loss', 'elu', 'relu6', 'pow', 'stanh', 'hard_sigmoid',
+ 'swish', 'prelu', 'flatten', 'sequence_mask', 'stack', 'pad2d', 'unstack',
+ 'sequence_enumerate', 'expand', 'sequence_concat',
'uniform_random_batch_size_like', 'gaussian_random', 'sampling_id',
'gaussian_random_batch_size_like', 'sum', 'slice', 'shape'
]
@@ -5828,6 +5829,148 @@ def pad2d(input,
return out
+@templatedoc()
+def elu(x, alpha=1.0, name=None):
+ """
+ ${comment}
+ Args:
+ x(${x_type}): ${x_comment}
+ alpha(${alpha_type}|1.0): ${alpha_comment}
+ name(str|None): A name for this layer(optional). If set None, the layer
+ will be named automatically.
+
+ Returns:
+ output(${out_type}): ${out_comment}
+ """
+ helper = LayerHelper('elu', **locals())
+ out = helper.create_tmp_variable(dtype=x.dtype)
+ helper.append_op(
+ type='elu',
+ inputs={'X': x},
+ outputs={'Out': out},
+ attrs={'alpha': alpha})
+ return out
+
+
+@templatedoc()
+def relu6(x, threshold=6.0, name=None):
+ """
+ ${comment}
+ Args:
+ x(${x_type}): ${x_comment}
+ threshold(${threshold_type}|6.0): ${threshold_comment}
+ name(str|None): A name for this layer(optional). If set None, the layer
+ will be named automatically.
+
+ Returns:
+ output(${out_type}): ${out_comment}
+ """
+ helper = LayerHelper('relu6', **locals())
+ out = helper.create_tmp_variable(dtype=x.dtype)
+ helper.append_op(
+ type='relu6',
+ inputs={'X': x},
+ outputs={'Out': out},
+ attrs={'threshold': threshold})
+ return out
+
+
+@templatedoc()
+def pow(x, factor=1.0, name=None):
+ """
+ ${comment}
+ Args:
+ x(${x_type}): ${x_comment}
+ factor(${factor_type}|1.0): ${factor_comment}
+ name(str|None): A name for this layer(optional). If set None, the layer
+ will be named automatically.
+
+ Returns:
+ output(${out_type}): ${out_comment}
+ """
+ helper = LayerHelper('pow', **locals())
+ out = helper.create_tmp_variable(dtype=x.dtype)
+ helper.append_op(
+ type='pow',
+ inputs={'X': x},
+ outputs={'Out': out},
+ attrs={'factor': factor})
+ return out
+
+
+@templatedoc()
+def stanh(x, scale_a=2.0 / 3.0, scale_b=1.7159, name=None):
+ """
+ ${comment}
+ Args:
+ x(${x_type}): ${x_comment}
+ scale_a(${scale_a_type}|2.0 / 3.0): ${scale_a_comment}
+ scale_b(${scale_b_type}|1.7159): ${scale_b_comment}
+ name(str|None): A name for this layer(optional). If set None, the layer
+ will be named automatically.
+
+ Returns:
+ output(${out_type}): ${out_comment}
+ """
+ helper = LayerHelper('stanh', **locals())
+ out = helper.create_tmp_variable(dtype=x.dtype)
+ helper.append_op(
+ type='stanh',
+ inputs={'X': x},
+ outputs={'Out': out},
+ attrs={'scale_a': scale_a,
+ 'scale_b': scale_b})
+ return out
+
+
+@templatedoc()
+def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
+ """
+ ${comment}
+ Args:
+ x(${x_type}): ${x_comment}
+ slope(${slope_type}|0.2): ${slope_comment}
+ offset(${offset_type}|0.5): ${offset_comment}
+ name(str|None): A name for this layer(optional). If set None, the layer
+ will be named automatically.
+
+ Returns:
+ output(${out_type}): ${out_comment}
+ """
+ helper = LayerHelper('hard_sigmoid', **locals())
+ out = helper.create_tmp_variable(dtype=x.dtype)
+ helper.append_op(
+ type='hard_sigmoid',
+ inputs={'X': x},
+ outputs={'Out': out},
+ attrs={'slope': slope,
+ 'offset': offset})
+ return out
+
+
+@templatedoc()
+def swish(x, beta=1.0, name=None):
+ """
+ ${comment}
+ Args:
+ x(${x_type}): ${x_comment}
+ beta(${beta_type}|1.0): ${beta_comment}
+ name(str|None): A name for this layer(optional). If set None, the layer
+ will be named automatically.
+
+ Returns:
+ output(${out_type}): ${out_comment}
+ """
+ helper = LayerHelper('swish', **locals())
+ out = helper.create_tmp_variable(dtype=x.dtype)
+ helper.append_op(
+ type='swish',
+ inputs={'X': x},
+ outputs={'Out': out},
+ attrs={'slope': beta})
+ return out
+
+
def prelu(x, mode, param_attr=None, name=None):
"""
Equation:
diff --git a/python/paddle/fluid/layers/ops.py b/python/paddle/fluid/layers/ops.py
index 5191c88274..b2ae77e867 100644
--- a/python/paddle/fluid/layers/ops.py
+++ b/python/paddle/fluid/layers/ops.py
@@ -36,12 +36,6 @@ __activations__ = [
'brelu',
'leaky_relu',
'soft_relu',
- 'elu',
- 'relu6',
- 'pow',
- 'stanh',
- 'hard_sigmoid',
- 'swish',
]
__all__ = [