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提交 83ca657f 编写于 作者: T Tao Luo
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Merge branch 'develop' into resnet50_ut

上级 21ee3059 35b713c3
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Showing with 288 addition and 195 deletion
paddle/fluid/API.spec
浏览文件 @ 83ca657f
......@@ -145,14 +145,14 @@ paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, key
paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None))
paddle.fluid.layers.expand ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_concat ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'out', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None, None))
paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None))
paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32', False))
paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
......@@ -160,6 +160,12 @@ paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shap
paddle.fluid.layers.sum ArgSpec(args=['x', 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.logical_and ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.logical_or ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.logical_xor ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.logical_not ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......@@ -225,12 +231,6 @@ paddle.fluid.layers.is_empty ArgSpec(args=['x', 'cond'], varargs=None, keywords=
paddle.fluid.layers.mean ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.mul ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.clip ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.clip_by_norm ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
......
paddle/fluid/framework/scope.cc
浏览文件 @ 83ca657f
......@@ -20,13 +20,6 @@ limitations under the License. */
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/string/printf.h"
// The mutex is not needed by training and inference, only for distribution.
#if PADDLE_WITH_DISTRIBUTE
#define WITH_LOCK 1
#else
#define WITH_LOCK 0
#endif
DEFINE_bool(benchmark, false,
"Doing memory benchmark. It will make deleting scope synchronized, "
"and add some memory usage logs."
......@@ -56,24 +49,18 @@ int64_t GetEagerDeletionThreshold() {
Scope::~Scope() { DropKids(); }
Scope& Scope::NewScope() const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
kids_.push_back(new Scope(this));
return *kids_.back();
}
Variable* Scope::Var(const std::string& name) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return VarInternal(name);
}
Variable* Scope::Var(std::string* name) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
if (name != nullptr) {
*name = new_name;
......@@ -82,39 +69,29 @@ Variable* Scope::Var(std::string* name) {
}
Variable* Scope::FindVar(const std::string& name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return FindVarInternal(name);
}
const Scope* Scope::FindScope(const Variable* var) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return FindScopeInternal(var);
}
void Scope::DropKids() {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
for (Scope* s : kids_) delete s;
kids_.clear();
}
bool Scope::HasKid(const Scope* scope) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
return it != this->kids_.end();
}
std::vector<std::string> Scope::LocalVarNames() const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
std::vector<std::string> known_vars;
known_vars.reserve(this->vars_.size());
for (auto& p : vars_) {
......@@ -124,9 +101,7 @@ std::vector<std::string> Scope::LocalVarNames() const {
}
void Scope::DeleteScope(Scope* scope) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
PADDLE_ENFORCE(it != this->kids_.end(), "Cannot find %p as kid scope", scope);
this->kids_.erase(it);
......@@ -139,9 +114,7 @@ void Scope::DeleteScope(Scope* scope) const {
}
void Scope::EraseVars(const std::vector<std::string>& var_names) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
......@@ -154,16 +127,12 @@ void Scope::EraseVars(const std::vector<std::string>& var_names) {
void Scope::Rename(const std::string& origin_name,
const std::string& new_name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
RenameInternal(origin_name, new_name);
}
std::string Scope::Rename(const std::string& origin_name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
RenameInternal(origin_name, new_name);
return new_name;
......
paddle/fluid/platform/dynload/cublas.h
浏览文件 @ 83ca657f
......@@ -55,7 +55,7 @@ extern void *cublas_dso_handle;
struct DynLoad__##__name { \
template <typename... Args> \
inline cublasStatus_t operator()(Args... args) { \
return __name(args...); \
return ::__name(args...); \
} \
}; \
extern DynLoad__##__name __name
......
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @ 83ca657f
......@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#define GLOG_NO_ABBREVIATED_SEVERITIES
#define GOOGLE_GLOG_DLL_DECL
#include <glog/logging.h>
#include <cudnn.h>
#include <mutex> // NOLINT
......@@ -50,8 +53,8 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
#define DECLARE_DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
return __name(args...); \
inline cudnnStatus_t operator()(Args... args) { \
return ::__name(args...); \
} \
}; \
extern DynLoad__##__name __name
......
paddle/fluid/platform/dynload/curand.h
浏览文件 @ 83ca657f
......@@ -44,7 +44,7 @@ extern void *curand_dso_handle;
struct DynLoad__##__name { \
template <typename... Args> \
curandStatus_t operator()(Args... args) { \
return __name(args...); \
return ::__name(args...); \
} \
}; \
extern DynLoad__##__name __name
......
paddle/fluid/platform/dynload/dynamic_loader.cc
浏览文件 @ 83ca657f
......@@ -107,7 +107,11 @@ static inline void* GetDsoHandleFromDefaultPath(const std::string& dso_path,
static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
const std::string& dso_name,
bool throw_on_error = true) {
#if !defined(_WIN32)
int dynload_flags = RTLD_LAZY | RTLD_LOCAL;
#else
int dynload_flags = 0;
#endif // !_WIN32
void* dso_handle = nullptr;
std::string dlPath = dso_name;
......@@ -117,10 +121,15 @@ static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
// search xxx.so from custom path
dlPath = join(search_root, dso_name);
dso_handle = dlopen(dlPath.c_str(), dynload_flags);
#if !defined(_WIN32)
auto errorno = dlerror();
#else
auto errorno = GetLastError();
#endif // !_WIN32
// if not found, search from default path
if (nullptr == dso_handle) {
LOG(WARNING) << "Failed to find dynamic library: " << dlPath << " ("
<< dlerror() << ")";
<< errorno << ")";
if (dlPath.find("nccl") != std::string::npos) {
std::cout
<< "You may need to install 'nccl2' from NVIDIA official website: "
......@@ -139,10 +148,15 @@ static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
"export LD_LIBRARY_PATH=... \n Note: After Mac OS 10.11, "
"using the DYLD_LIBRARY_PATH is impossible unless System "
"Integrity Protection (SIP) is disabled.";
#if !defined(_WIN32)
auto errorno = dlerror();
#else
auto errorno = GetLastError();
#endif // !_WIN32
if (throw_on_error) {
PADDLE_ENFORCE(nullptr != dso_handle, error_msg, dlPath, dlerror());
PADDLE_ENFORCE(nullptr != dso_handle, error_msg, dlPath, errorno);
} else if (nullptr == dso_handle) {
LOG(WARNING) << string::Sprintf(error_msg, dlPath, dlerror());
LOG(WARNING) << string::Sprintf(error_msg, dlPath, errorno);
}
return dso_handle;
......
paddle/scripts/paddle_build.sh
浏览文件 @ 83ca657f
......@@ -395,7 +395,7 @@ EOF
ctest --output-on-failure -j $1
# make install should also be test when unittest
make install -j 8
pip install /usr/local/opt/paddle/share/wheels/*.whl
pip install ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]] ; then
paddle version
fi
......@@ -750,7 +750,7 @@ function main() {
cmake_gen ${PYTHON_ABI:-""}
build
run_test
assert_api_not_changed
assert_api_not_changed ${PYTHON_ABI:-""}
;;
*)
print_usage
......
python/paddle/fluid/clip.py
浏览文件 @ 83ca657f
......@@ -271,7 +271,8 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
"All parameters' 'clip_norm' of a same group should be the same"
)
local_norm_var = layers.reduce_sum(input=layers.pow(x=grad, factor=2.0))
square = grad * grad
local_norm_var = layers.cast(layers.reduce_sum(input=square), 'float64')
context[self.group_name].append(local_norm_var)
self.context = context
......@@ -281,6 +282,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
if group_scale_name not in self.context:
group_norm_var = layers.sums(input=self.context[self.group_name])
group_norm_var = layers.sqrt(x=group_norm_var)
group_norm_var = layers.cast(group_norm_var, 'float32')
clip_var = self.context[self.group_name + "_clip"]
group_scale_var = layers.elementwise_div(
x=clip_var,
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 83ca657f
......@@ -21,7 +21,7 @@ from .. import core
from ..framework import Program, Variable, Operator
from ..layer_helper import LayerHelper, unique_name
from ..initializer import force_init_on_cpu
from .ops import logical_and, logical_not, logical_or
from .nn import logical_and, logical_not, logical_or
import numpy
import warnings
import six
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 83ca657f
......@@ -51,7 +51,9 @@ __all__ = [
'expand', 'sequence_concat', 'scale', 'elementwise_add', 'elementwise_div',
'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
'elementwise_pow', 'uniform_random_batch_size_like', 'gaussian_random',
'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'shape'
'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'shape',
'logical_and', 'logical_or', 'logical_xor', 'logical_not', 'clip',
'clip_by_norm'
]
......@@ -6630,8 +6632,6 @@ def _elementwise_op(helper):
assert y is not None, 'y cannot be None in {}'.format(op_type)
axis = helper.kwargs.get('axis', -1)
use_mkldnn = helper.kwargs.get('use_mkldnn', False)
out = helper.kwargs.get('out', None)
if out is None:
name = helper.kwargs.get('name', None)
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
......@@ -6650,13 +6650,7 @@ def _elementwise_op(helper):
@templatedoc()
def scale(x,
scale=1.0,
bias=0.0,
bias_after_scale=True,
out=None,
act=None,
name=None):
def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
"""
${comment}
......@@ -6665,7 +6659,6 @@ def scale(x,
scale(${scale_type}): ${scale_comment}
bias(${bias_type}): ${bias_comment}
bias_after_scale(${bias_after_scale_type}): ${bias_after_scale_comment}
out(Tensor): Output tensor.
act(basestring|None): Activation applied to the output.
name(basestring|None): Name of the output.
......@@ -6674,7 +6667,6 @@ def scale(x,
"""
helper = LayerHelper('scale', **locals())
if out is None:
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
......@@ -6693,73 +6685,31 @@ def scale(x,
return helper.append_activation(out)
def elementwise_add(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_add(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_add', **locals()))
def elementwise_div(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_div(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_div', **locals()))
def elementwise_sub(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_sub(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_sub', **locals()))
def elementwise_mul(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_mul(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_mul', **locals()))
def elementwise_max(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_max(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_max', **locals()))
def elementwise_min(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_min(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_min', **locals()))
def elementwise_pow(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_pow(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
......@@ -6771,7 +6721,168 @@ for func in [
func.__doc__ = _generate_doc_string_(
op_proto,
additional_args_lines=[
"out (Tensor): The output tensor of elementwise op.",
"act (basestring|None): Activation applied to the output.",
"name (basestring|None): Name of the output."
])
def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
helper = LayerHelper(op_name, **locals())
if binary_op:
assert x.dtype == y.dtype
if out is None:
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
if binary_op:
helper.append_op(
type=op_name, inputs={"X": x,
"Y": y}, outputs={"Out": out})
else:
helper.append_op(type=op_name, inputs={"X": x}, outputs={"Out": out})
return out
@templatedoc()
def logical_and(x, y, out=None, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_and", x=x, y=y, name=name, out=out, binary_op=True)
@templatedoc()
def logical_or(x, y, out=None, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_or", x=x, y=y, name=name, out=out, binary_op=True)
@templatedoc()
def logical_xor(x, y, out=None, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_xor", x=x, y=y, name=name, out=out, binary_op=True)
@templatedoc()
def logical_not(x, out=None, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_not", x=x, y=None, name=name, out=out, binary_op=False)
@templatedoc()
def clip(x, min, max, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
min(${min_type}): ${min_comment}
max(${max_type}): ${max_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper("clip", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="clip",
inputs={"X": x},
attrs={"min": min,
"max": max},
outputs={"Out": out})
return out
@templatedoc()
def clip_by_norm(x, max_norm, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
max_norm(${max_norm_type}): ${max_norm_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper("clip_by_norm", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="clip_by_norm",
inputs={"X": x},
attrs={"max_norm": max_norm},
outputs={"Out": out})
return out
python/paddle/fluid/layers/ops.py
浏览文件 @ 83ca657f
......@@ -39,12 +39,6 @@ __all__ = [
'mean',
'mul',
'sigmoid_cross_entropy_with_logits',
'clip',
'clip_by_norm',
'logical_and',
'logical_or',
'logical_xor',
'logical_not',
'maxout',
]
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ 83ca657f
......@@ -22,7 +22,7 @@ class TestDistSeResneXt2x2(TestDistBase):
self._sync_mode = True
self._use_reader_alloc = False
def test_dist_train(self):
def no_test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......@@ -40,7 +40,7 @@ class TestDistSeResneXt2x2Async(TestDistBase):
self._sync_mode = False
self._use_reader_alloc = False
def test_dist_train(self):
def no_test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......
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