提交 5b7fd00d 编写于 作者: Z Zhuoyuan 提交者: GitHub

Merge pull request #4280 from Superjom/feature/recurrent_op_backward_fix

make RecurrentOp's backward compatible with framework
上级 5862667c b545b5b8
develop 1.8.5 2.0.1-rocm-post 2.4.1 Ligoml-patch-1 OliverLPH-patch-1 OliverLPH-patch-2 PaddlePM-patch-1 PaddlePM-patch-2 ZHUI-patch-1 add_default_att add_kylinv10 add_model_benchmark_ci add_some_yaml_config addfile all_new_design_exec ascendrc ascendrelease bugfix-eval-frame-leakgae cherry-pick-fix-customOP-random-fail cherry_undefined_var compile_windows cp_2.4_fix_numpy delete_2.0.1-rocm-post delete_add_default_att delete_all_new_design_exec delete_ascendrc delete_compile_windows delete_delete_addfile delete_disable_iterable_dataset_unittest delete_fix_dataloader_memory_leak delete_fix_imperative_dygraph_error delete_fix_retry_ci delete_fix_undefined_var delete_improve_sccache delete_incubate/lite delete_paddle_tiny_install delete_paralleltest delete_prv-disable-more-cache delete_revert-31068-fix_conv3d_windows delete_revert-31562-mean delete_revert-33630-bug-fix delete_revert-34159-add_npu_bce_logical_dev delete_revert-34910-spinlocks_for_allocator 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github/fork/chenwhql/saveload/remove_save_load_config github/fork/cryoco/pass-compatibility-trt github/fork/danleifeng/isempty_api2.0 github/fork/frankwhzhang/api_transfer github/fork/hbwx24/error_msg/cuda_kernel_error_msg github/fork/heavengate/cherry_yolo_box github/fork/heavengate/update_yolo_box github/fork/iclementine/rnn_fix github/fork/iducn/testestse github/fork/jczaja/prv-25537-fix github/fork/jeff41404/release/1.8 github/fork/jiweibo/api_2.0 github/fork/jiweibo/fix_lite_resnet50_test github/fork/juncaipeng/fix_doc_1 github/fork/lfchener/sample_code github/fork/littletomatodonkey/fix_reg_doc github/fork/liym27/dy2stat_update_assign_to_rc20 github/fork/luotao1/profiler_ut github/fork/mapingshuo/add_wait github/fork/mapingshuo/doc_2.0 github/fork/mapingshuo/zero-0.5 github/fork/miraiwk/dev github/fork/pangyoki/add-Categorical-class-branch github/fork/pangyoki/add-multinomial-op-branch github/fork/pangyoki/fix-test_distritbution-CI github/fork/qjing666/doublegrad 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github/fork/zhhsplendid/refine_api_test_ptb_lm github/fork/zhhsplendid/refine_api_test_resnet github/fork/zhhsplendid/refine_api_test_simnet github/fork/zhiqiu/dev/refine_initializer github/fork/zhiqiu/dev/remove_inplace_argument github/fork/zlsh80826/nvinfer_plugin_var_len_cuda11 hack_event improve_sccache incuabte/new_frl incubate/frl_train_eval incubate/infrt incubate/lite incubate/new_frl incubate/new_frl_rc incubate/stride inplace_addto layer_norm make_flag_adding_easier master matmul_double_grad move_embedding_to_phi move_histogram_to_pten move_sgd_to_phi move_slice_to_pten move_temporal_shift_to_phi move_yolo_box_to_phi npu_fix_alloc numel operator_opt paddle_tiny_install paralleltest pass-compile-eval-frame preln_ernie prv-disable-more-cache prv-md-even-more prv-onednn-2.5 prv-reshape-mkldnn-ut2 pten_tensor_refactor release-deleted/2.5 release-rc/2.5 release/0.11.0 release/0.12.0 release/0.13.0 release/0.14.0 release/0.15.0 release/1.0.0 release/1.1 release/1.2 release/1.3 release/1.4 release/1.5 release/1.6 release/1.7 release/1.8 release/2.0 release/2.0-alpha release/2.0-beta release/2.0-rc release/2.0-rc1 release/2.1 release/2.2 release/2.3 release/2.3-fc-ernie-fix release/2.4 release/2.5 release/lite-0.1 release/llm_2.5 revert-24981-add_device_attr_for_regulization revert-26856-strategy_example2 revert-27520-disable_pr revert-31068-fix_conv3d_windows revert-31562-mean revert-32290-develop-hardlabel revert-33037-forci revert-33475-fix_cifar_label_dimension revert-33630-bug-fix revert-34159-add_npu_bce_logical_dev revert-34406-add_copy_from_tensor revert-34910-spinlocks_for_allocator revert-35069-revert-34910-spinlocks_for_allocator revert-36057-dev/read_flags_in_ut revert-36201-refine_fast_threaded_ssa_graph_executor revert-36985-add_license revert-37318-refactor_dygraph_to_eager revert-37926-eager_coreops_500 revert-37956-revert-37727-pylayer_support_tuple revert-38100-mingdong revert-38301-allocation_rearrange_pr 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test_model_benchmark_ci zhiqiu-patch-1 v2.5.1 v2.5.0 v2.5.0-rc1 v2.5.0-rc0 v2.4.2 v2.4.1 v2.4.0 v2.4.0-rc0 v2.3.2 v2.3.1 v2.3.0 v2.3.0-rc0 v2.2.2 v2.2.1 v2.2.0 v2.2.0-rc0 v2.2.0-bak0 v2.1.3 v2.1.2 v2.1.1 v2.1.0 v2.1.0-rc0 v2.0.2 v2.0.1 v2.0.0 v2.0.0-rc1 v2.0.0-rc0 v2.0.0-beta0 v2.0.0-alpha0 v1.8.5 v1.8.4 v1.8.3 v1.8.2 v1.8.1 v1.8.0 v1.7.2 v1.7.1 v1.7.0 v1.6.3 v1.6.2 v1.6.1 v1.6.0 v1.6.0-rc0 v1.5.2 v1.5.1 v1.5.0 v1.4.1 v1.4.0 v1.3.2 v1.3.1 v1.3.0 v1.2.1 v1.2.0 v1.1.0 v1.0.2 v1.0.1 v1.0.0 v1.0.0-rc0 v0.15.0 v0.15.0-rc0 v0.14.0 v0.13.0 v0.12.0 v0.11.1a2 v0.11.1a1 v0.11.0 lite-v0.1
6 合并请求!11636[IMPORTANT] MKLDNN layout: Support for sum operator,!8482Release/0.11.0,!8190Release/0.11.0,!8189Release/0.11.0,!6633给线性回归的get-started代码加上了预测的示例~~,!4615Feature/tensor array add python binding
......@@ -60,8 +60,8 @@ std::string OperatorBase::Output(const std::string& name) const {
const std::vector<std::string>& OperatorBase::Outputs(
const std::string& name) const {
auto it = outputs_.find(name);
PADDLE_ENFORCE(it != outputs_.end(), "Op %s does not have output %s", type_,
name);
PADDLE_ENFORCE(it != outputs_.end(), "Op %s does not have output called %s",
type_, name);
return it->second;
}
......
......@@ -80,7 +80,6 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
// Now all variables in scope must be created outside of op.
PADDLE_ENFORCE_NOT_NULL(stepnet_);
PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs");
PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "net_op has no outputs");
if (seq_len_ > step_scopes->size()) {
for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
......@@ -129,8 +128,8 @@ const rnn::ArgumentName RecurrentOp::kArgName{
"memories", "pre_memories", "boot_memories"};
const rnn::ArgumentName RecurrentGradientOp::kArgName{
"step_net", "step_scopes", "outlink@grad", "inlink@grad",
"memories", "pre_memories", "boot_memories@grad"};
"step_net", "step_scopes@GRAD", "outlinks@GRAD", "inlinks@GRAD",
"memories", "pre_memories", "boot_memories@GRAD"};
RecurrentOp::RecurrentOp(const std::string& type,
const framework::VariableNameMap& inputs,
......@@ -226,13 +225,13 @@ RecurrentGradientOp::RecurrentGradientOp(
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {
rnn::InitArgument(kArgName, &arg_, *this);
rnn::InitArgument(kArgName, &arg_, *this, true /*is grad*/);
alg_.Init(&arg_, &stepnet_);
}
} // namespace operators
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(
recurrent, paddle::operators::RecurrentOp,
paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);
REGISTER_OP(recurrent, paddle::operators::RecurrentOp,
paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker,
recurrent_grad, paddle::operators::RecurrentGradientOp);
......@@ -22,7 +22,7 @@ namespace paddle {
namespace operators {
// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
// TODO(Yan Chunwei):
// TODO(Superjom)
// 1. No-padding computing for sequences with indifinite length in one batch.
// 2. Hierarchical RNN for sequence with sub-sequence.
// 3. Internal Memory.
......@@ -177,6 +177,9 @@ class RecurrentGradientOp : public framework::OperatorBase {
static const rnn::ArgumentName kArgName;
/*
* set a stepnet that is created according to a RecurrentOp's stepnet.
*/
void set_stepnet(std::unique_ptr<OperatorBase> net) {
stepnet_ = std::move(net);
}
......
......@@ -109,15 +109,14 @@ void LinkMemories(const std::vector<Scope*>& scopes,
}
void InitArgument(const ArgumentName& name, Argument* arg,
const framework::OperatorBase& op) {
arg->step_scopes = op.Output(name.step_scopes);
const framework::OperatorBase& op, bool is_grad) {
arg->step_scopes =
is_grad ? op.Input(name.step_scopes) : op.Output(name.step_scopes);
arg->inlinks = op.Inputs(name.inlinks);
arg->outlinks = op.Outputs(name.outlinks);
auto boot_memories = op.Inputs(name.boot_memories);
auto boot_memories =
is_grad ? op.Outputs(name.boot_memories) : op.Inputs(name.boot_memories);
// attributes
auto memories = op.Attr<std::vector<std::string>>(name.memories);
auto pre_memories = op.Attr<std::vector<std::string>>(name.pre_memories);
......
......@@ -78,7 +78,7 @@ void LinkMemories(const std::vector<Scope*>& step_scopes,
const int offset, bool infer_shape_mode);
void InitArgument(const ArgumentName& name, Argument* arg,
const framework::OperatorBase& op);
const framework::OperatorBase& op, bool is_grad = false);
} // namespace rnn
} // namespace operators
......
......@@ -3,6 +3,7 @@ import paddle.v2.framework.core as core
import unittest
import numpy as np
from paddle.v2.framework.op import Operator, RecurrentOp
from op_test import get_numeric_gradient
def py_sigmoid(x):
......@@ -47,7 +48,7 @@ class PySimpleRNN(object):
else:
pre_mem = self.h_boot
xW = np.matmul(x, self.W)
hU = np.matmul(mem, self.U)
hU = np.matmul(pre_mem, self.U)
sum = xW + hU
self.mems[step_id] = py_sigmoid(sum)
......@@ -68,7 +69,7 @@ def create_tensor(scope, name, shape, np_data):
return tensor
class TestRecurrentOp(unittest.TestCase):
class RecurrentOpTest(unittest.TestCase):
'''
Test RNNOp
......@@ -158,6 +159,42 @@ class TestRecurrentOp(unittest.TestCase):
print
print 'py_output', py_output
self.assertEqual(pd_output.shape, py_output.shape)
self.assertTrue(np.isclose(pd_output, py_output, rtol=0.1).all())
class RecurrentGradientOpTest(unittest.TestCase):
def create_forward_op(self):
self.forward_op = RecurrentOp(
# inputs
inlinks=["x"],
boot_memories=["h_boot"],
step_net="stepnet",
# outputs
outlinks=["h"],
step_scopes="step_scopes",
# attributes
pre_memories=["h@pre"],
memories=["h@alias"])
# create a stepnet for RNN
stepnet = core.Net.create()
x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
sig_op = Operator("sigmoid", X="sum", Y="h@alias")
for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
stepnet.append_op(op)
stepnet.complete_add_op(True)
self.forward_op.set_stepnet(stepnet)
def create_gradient_op(self):
a = set()
backward_op = core.RecurrentOp.backward(self.forward_op, a)
def test_grad(self):
self.create_forward_op()
self.create_gradient_op()
if __name__ == '__main__':
......
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