* 1. remove paddle.unique_with_counts api, which counts as unique api
2. add paddle.math.increment(x, value=1.0, name=None) api
3. replace paddle.sums with paddle.add_n api
4. update paddle.metric.accuracy api (add name parameter)
* fix doc and unittest of 2.0 lr_scheduler
* fix doc of 2.0 lr_scheduler
* fix unittest
* fix english doc of lr_scheduler
* fix api name of lr scheduler
* fix api name of lr scheduler
* add load_op_xpu for Baidu Kunlun, test=kunlun
* add is_compiled_with_xpu for unit test, test=kunlun
* add is_compiled_with_xpu for unit test, test=kunlun
* replace config by kwargs
* change save path form dir to prefix
* fix failed unittests
* revert unittest name change
* polish en docs
* add more tests for coverage
* Add api of constant in paddle.nn.initializer
* Add api of constant in paddle.nn.initializer
* Add api of constant in paddle.nn.initializer
* Add api of constant in paddle.nn.initializer
* Add api of constant in paddle.nn.initializer
* Add api of constant in paddle.nn.initializer
* Add api of constant in paddle.nn.initializer
* modify doc for Layer, test=develop
* modify doc for Layer2, test=develop
* set dtype default value to float32, test=develop
* add example code for paddle.nn.Layer, test=develop
* set create_parameter and create_variable dtype default value to None, test=develop
* modify some example code, tet=develop
* refine, test=develop
* del no ues code, test=develop
* modify doc, example code, args, test=develop
* modify doc, test=develop
* fix huber_loss ans npair_loss doc and example code, test=document_fix
* remove disable_static in example code, test=document_fix
* remove huber_loss and refine npair_loss example code, test=document_fix
* remove huber_loss in functional/__init__.py, test=document_fix
* add multinomial cpu kernel
* fix C++ notype error
* fix windows ci array len error
* let array len be const
* change array to vector
* add cuda kernrl with num_distribution is 1, and not support replacement=False
* add multinomial python api
* support num_distribution different multinomial distributions
* add categorical class
* fix test_distribution enable_static error
* add unittest for different setting of Categorical
* optimize format
* little change
* little change
* add raise error if shape not match, optimize format
* fix windows CI dtype error in concat
* little changes
* little changes2
* change values type to int64
* change values type to int64
* change values type to int64
* remove input requirment in dygraph Model
* correct unittest
* upadte save inference model in dygraph without input
* fix unittets for test_model.py
* solve conflicts
* solve conflicts
* delete http.log
* fix test_model.py bug, correct initialization of MyModel
* fix unittests bugs
* set paddle manual seed for unittest
* fix Model bugs, because inputs can be list or dict when it is provided.
* add random seed for test_export_deploy_model
* delete redundant codes, because calls
* Code optimization, error information optimization
* add multinomial cpu kernel
* fix C++ notype error
* fix windows ci array len error
* let array len be const
* change array to vector
* add cuda kernrl with num_distribution is 1, and not support replacement=False
* add multinomial python api
* support num_distribution different multinomial distributions
* add multinomial python api unittest
* change output dtype to int64
* fix coverage prob
* optimize format
* fix dtype of output error, should be int64_t
* increase tolerance
* increase the difference between low and high
* change tolerance of Normal log_prob method
* change probs tolerance to 1e-4
* change tolerance of Normal kl method
* polish Program api doc & example
* polish CompiledProgram api doc & example
* polish ParallelEnv api doc & examples
* polish details, test=document_fix
* polish program doc details, test=document_fix
* polish details, test=document_fix
* fix note format error, test=document_fix
* add lost example, test=document_fix
* fix lost example, test=document_fix
Refine Dy2stat APIs to 2.0rc
After discussion, we accepted 3 key points from reviewers:
1. In 2.0rc we changed dygraph_to_static folder to dy2static
2. Keep the three files: convert_call_func.py, convert_operators.py, variable_trans_func.py
3. Remove convert_operators path when users import convert_xxx.
After this PR, users can import convert_xxx APIs by:
`import paddle.jit.dy2static.convert_xxx`
The file structure will be:
```
jit
dy2static
convert_operators.py
convert_func_call.py
variable_trans_func.py
```
Detail changed API in files:
In python/paddle/jit/dygraph_to_static/convert_call_func.py:
from ...fluid.dygraph.dygraph_to_static.convert_call_func import convert_call #DEFINE_ALIAS
In python/paddle/jit/dygraph_to_static/convert_operators.py:
from ...fluid.dygraph.dygraph_to_static.convert_operators import cast_bool_if_necessary #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_assert #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_ifelse #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_len #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_logical_and #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_logical_not #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_logical_or #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_print #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_var_dtype #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_var_shape #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_while_loop #DEFINE_ALIAS
In python/paddle/jit/dygraph_to_static/variable_trans_func.py:
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import create_fill_constant_node #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import create_static_variable_gast_node #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import data_layer_not_check #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import to_static_variable #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import to_static_variable_gast_node #DEFINE_ALIAS
This PR fixed two bugs when converting LSTM in dy2stat:
is_unsupported has a condition can trigger Python syntax error
LSTM API's implementation in _rnn_static_graph doesn't include parameter initialization, which can cause dy2stat error.
We decreased the batch size on CPU so that it can run correctly on Win/Mac machine, this may cause the delta to be larger. So I set larger delta value.
* modified sample code of add_position_encoding to 2.0, test=document_fix
* use core.op in add_position_encoding API.
* add test for add_position_encoding in dygraph mode
* add unittests and op version register for tensorrt_subgraph_pass
* rename to test_trt_subgraph_pass.py
* fix softmax converter diff when padding dim=1
* Support assignment to a Variable in dynamic mode. Note: not deal with backward.
* Rewrite VarBase __setitem__ for high-performance.
* try to test 3 means to do __setitem__ and test the performance of 3 means.
* Retain the means of the highest performance: C++ code and don't trace op.
* add float64 input to ctc_loss
* modified error message of warpctc
* update repo and tag of warpctc
* add test for warpctc with float64 input
* modified warpctc.cmake to make sure build always
* resolved sample code bug of warpctc
* add core.ops in warpctc dygraph
* fix a bug of test
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