未验证 提交 24e988a4 编写于 作者: C chengduo 提交者: GitHub

Fix bug of scope_buffered_ssa_graph_executor (#18100)

* fix code bug
test=develop
上级 3f55ab0f
......@@ -31,7 +31,7 @@ paddle.fluid.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'pr
paddle.fluid.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd38c5b8b2b2e0bb19bcf1b581a80a7e4'))
paddle.fluid.DistributeTranspilerConfig.__init__
paddle.fluid.ParallelExecutor.__init__ (ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 1, 0, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.ParallelExecutor.drop_local_exe_scopes (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '80d857dc626612e2b2460d0154551e95'))
paddle.fluid.ParallelExecutor.drop_local_exe_scopes (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '77c739744ea5708b80fb1b37cc89db40'))
paddle.fluid.ParallelExecutor.run (ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '33ce6ec50f8eeb05d340e6b114b026fd'))
paddle.fluid.create_lod_tensor (ArgSpec(args=['data', 'recursive_seq_lens', 'place'], varargs=None, keywords=None, defaults=None), ('document', 'b82ea20e2dc5ff2372e0643169ca47ff'))
paddle.fluid.create_random_int_lodtensor (ArgSpec(args=['recursive_seq_lens', 'base_shape', 'place', 'low', 'high'], varargs=None, keywords=None, defaults=None), ('document', '74dc6d23185d90a7a50fbac19f5b65fb'))
......@@ -313,7 +313,7 @@ paddle.fluid.layers.StaticRNN.step_input (ArgSpec(args=['self', 'x'], varargs=No
paddle.fluid.layers.StaticRNN.step_output (ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None), ('document', '252890d4c3199a7623ab8667e13fd837'))
paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None), ('document', '7a0000520f179f35239956a5ba55119f'))
paddle.fluid.layers.reorder_lod_tensor_by_rank (ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None), ('document', '5b552a1f0f7eb4dacb768a975ba15d08'))
paddle.fluid.layers.Print (ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both')), ('document', 'a222dbad457441941e50b812e5af9c7e'))
paddle.fluid.layers.Print (ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both')), ('document', 'ee6c70867d317b0a87094ed23546215f'))
paddle.fluid.layers.is_empty (ArgSpec(args=['x', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '3011dc695f490afdf504dc24f628319a'))
paddle.fluid.layers.sigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a4e395ab004e7da34e94a0a1f9eee183'))
paddle.fluid.layers.logsigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5f2508c52e0a797bb9bd5e29d79ede78'))
......
......@@ -71,6 +71,7 @@ void ScopeBufferedSSAGraphExecutor::DropLocalExeScopes() {
if (local_scope_var != nullptr) {
auto &local_scope = *local_scope_var->GetMutable<Scope *>();
scope->DeleteScope(local_scope);
scope->EraseVars({std::string(details::kLocalExecScopeName)});
VLOG(3) << "Drop local execution scope: " << local_scope;
}
}
......
......@@ -135,33 +135,34 @@ struct Formater {
};
// TODO(ChunweiYan) there should be some other printers for TensorArray
class TensorPrintOp : public framework::OperatorBase {
class PrintOp : public framework::OperatorBase {
public:
TensorPrintOp(const std::string &type,
const framework::VariableNameMap &inputs,
PrintOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
TensorPrintOp(const TensorPrintOp &o)
: framework::OperatorBase(
static_cast<const framework::OperatorBase &>(o)) {
PADDLE_THROW("Not implemented.");
}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
const framework::Variable *in_var_ptr = nullptr;
std::string printed_var_name = "";
in_var_ptr = scope.FindVar(Input("In"));
printed_var_name = Inputs("In").front();
PADDLE_ENFORCE_NOT_NULL(in_var_ptr);
auto &in_tensor = in_var_ptr->Get<framework::LoDTensor>();
const auto in_var = scope.FindVar(Input("In"));
auto out_var = scope.FindVar(Output("Out"));
PADDLE_ENFORCE_NOT_NULL(in_var, "The input should not be found in scope",
Input("In"));
PADDLE_ENFORCE_NOT_NULL(out_var, "The output should not be found in scope",
Output("Out"));
auto &in_tensor = in_var->Get<framework::LoDTensor>();
framework::LoDTensor *out_tensor =
out_var->GetMutable<framework::LoDTensor>();
PrintValue(place, Inputs("In").front(), in_tensor);
framework::TensorCopy(in_tensor, place, out_tensor);
out_tensor->set_lod(in_tensor.lod());
}
void PrintValue(const platform::Place &place,
const std::string &printed_var_name,
const framework::LoDTensor &in_tensor) const {
std::string print_phase = Attr<std::string>("print_phase");
bool is_forward = Attr<bool>("is_forward");
......@@ -177,12 +178,12 @@ class TensorPrintOp : public framework::OperatorBase {
printed_tensor.set_lod(in_tensor.lod());
printed_tensor.Resize(in_tensor.dims());
if (platform::is_cpu_place(in_tensor.place())) {
if (is_cpu_place(in_tensor.place())) {
printed_tensor.ShareDataWith(in_tensor);
} else {
// copy data to cpu to print
platform::CPUPlace place;
framework::TensorCopy(in_tensor, place, &printed_tensor);
TensorCopy(in_tensor, place, &printed_tensor);
}
Formater formater;
......@@ -215,6 +216,7 @@ class PrintOpProtoAndCheckMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("In", "Input tensor to be displayed.");
AddOutput("Out", "The output tensor.");
AddAttr<int>("first_n", "Only log `first_n` number of times.");
AddAttr<std::string>("message", "A string message to print as a prefix.");
AddAttr<int>("summarize", "Number of elements printed.");
......@@ -239,10 +241,23 @@ tensor `t`.)DOC");
}
};
class InferShapeForward : public framework::InferShapeBase {
class PrintOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
VLOG(10) << "PrintOpInferShape";
PADDLE_ENFORCE(ctx->HasInput("In"), "Input(In) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null.");
ctx->ShareDim("In", /*->*/ "Out");
ctx->ShareLoD("In", /*->*/ "Out");
}
};
class PrintOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("In"), "Input(In) should not be null.");
void operator()(framework::InferVarTypeContext *ctx) const override {
auto input_type = ctx->GetType(ctx->Input("In")[0]);
auto out_name = ctx->Output("Out").front();
ctx->SetType(out_name, input_type);
}
};
......@@ -253,7 +268,8 @@ class PrintOpGradientMaker : public framework::SingleGradOpDescMaker {
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *op_desc_ptr = new framework::OpDesc();
op_desc_ptr->SetType("print");
op_desc_ptr->SetInput("In", InputGrad("In"));
op_desc_ptr->SetInput("In", OutputGrad("Out"));
op_desc_ptr->SetOutput("Out", InputGrad("In"));
op_desc_ptr->SetAttrMap(Attrs());
op_desc_ptr->SetAttr("is_forward", false);
return std::unique_ptr<framework::OpDesc>(op_desc_ptr);
......@@ -265,5 +281,6 @@ class PrintOpGradientMaker : public framework::SingleGradOpDescMaker {
namespace ops = paddle::operators;
REGISTER_OPERATOR(print, ops::TensorPrintOp, ops::PrintOpProtoAndCheckMaker,
ops::PrintOpGradientMaker, ops::InferShapeForward);
REGISTER_OPERATOR(print, ops::PrintOp, ops::PrintOpProtoAndCheckMaker,
ops::PrintOpGradientMaker, ops::PrintOpInferShape,
ops::PrintOpVarTypeInference);
......@@ -27,7 +27,7 @@ import six
import numpy as np
import subprocess
import multiprocessing
import sys
from .. import compat as cpt
from .proto import framework_pb2
......@@ -82,14 +82,14 @@ def _current_expected_place():
def _cpu_num():
cpu_num = os.environ.get('CPU_NUM', None)
if cpu_num is None:
if "CPU_NUM" not in os.environ.keys():
sys.stderr.write(
'The CPU_NUM is not specified, you should set CPU_NUM in '
'the environment variable list, i.e export CPU_NUM=1. CPU_NUM '
'indicates that how many CPUPlace are used in the current task.\n'
'!!! The default number of CPUPlaces is 1.')
'!!! The default number of CPUPlaces is 1.\n\n')
os.environ['CPU_NUM'] = str(1)
cpu_num = os.environ.get('CPU_NUM')
return int(cpu_num)
......
......@@ -165,8 +165,12 @@ def Print(input,
print the gradients of input tensor.
Returns:
Variable: Output tensor, same data with input tensor.
Variable: Output tensor.
NOTES:
The input and output are two different variables, and in the
following process, you should use the output variable but not the input,
otherwise, the print layer doesn't have backward.
Examples:
.. code-block:: python
......@@ -174,16 +178,18 @@ def Print(input,
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32")
fluid.layers.Print(input, message = "The content of input layer:")
input = fluid.layers.Print(input, message = "The content of input layer:")
# value = some_layer(...)
# Print(value, summarize=10,
# message="The content of some_layer: ")
'''
helper = LayerHelper('print', **locals())
helper = LayerHelper('print' + "_" + input.name, **locals())
output = helper.create_variable_for_type_inference(input.dtype)
helper.append_op(
type='print',
inputs={'In': input},
outputs={'Out': output},
attrs={
'first_n': first_n,
'summarize': summarize,
......@@ -194,7 +200,7 @@ def Print(input,
'print_tensor_lod': print_tensor_lod,
'print_phase': print_phase.upper()
})
return input
return output
class BlockGuard(object):
......
......@@ -324,6 +324,7 @@ class ParallelExecutor(object):
loss = fluid.layers.mean(hidden)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_program)
parallel_exe = fluid.ParallelExecutor(use_cuda=use_cuda,
......
......@@ -17,11 +17,13 @@ from __future__ import print_function
import unittest
import paddle.fluid.core as core
from paddle.fluid.executor import Executor
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.backward import append_backward
from paddle.fluid.framework import switch_main_program
from paddle.fluid.framework import Program
import numpy as np
from simple_nets import simple_fc_net, init_data
class TestPrintOpCPU(unittest.TestCase):
......@@ -89,5 +91,35 @@ class TestPrintOpGPU(TestPrintOpCPU):
self.x_tensor.set_recursive_sequence_lengths([[1, 1]])
class TestPrintOpBackward(unittest.TestCase):
def check_backward(self, use_cuda):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = simple_fc_net()
loss = fluid.layers.Print(loss)
fluid.optimizer.Adam().minimize(loss)
print_ops = [op for op in main.blocks[0].ops if op.type == u'print']
assert len(print_ops) == 2, "The number of print op should be 2"
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup)
binary = fluid.compiler.CompiledProgram(main).with_data_parallel(
loss_name=loss.name)
img, label = init_data()
feed_dict = {"image": img, "label": label}
exe.run(binary, feed_dict)
def test_fw_bw(self):
if core.is_compiled_with_cuda():
self.check_backward(use_cuda=True)
self.check_backward(use_cuda=False)
if __name__ == '__main__':
unittest.main()
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