- 10 5月, 2019 1 次提交
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由 qingqing01 提交于
* Add conv2d_grad_grad_op * Extracte the cuDNN conv algo searching code in conv_cudnn_helper.h. - Now use it in conv2d_grad_grad. - Will simply the searching code in conv2d and conv2d_grad in next PR. * Enhance and fix bug in unit testing of gradient_checker. * Support to fetch empty variables,return None in Python.
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- 23 4月, 2019 1 次提交
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由 Zeng Jinle 提交于
* make_conv_cudnn_ws_size_configurable, test=develop * change std::max to std::min test=develop
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- 15 4月, 2019 2 次提交
- 26 3月, 2019 1 次提交
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由 sneaxiy 提交于
fix ctest eager deletion disable bug test=develop
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- 19 3月, 2019 1 次提交
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由 zhhsplendid 提交于
test=develop
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- 18 3月, 2019 1 次提交
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由 Wojciech Uss 提交于
* Add cpu_quantize_pass for C-API quantization test=develop * add cpu_quantize_pass test * fix lint: add include memory unorderd_map and unordered_set test=develop * fuse_relu 1 test=develop * tuned 2 without squash * fixes test=develop * remove unused vars test=develop * refactored test=develop * fix lint c-style cast -> C++ style cast test=develop * remove QuantMax and c style casts test=develop * last usage of QuantMax removed test=develop * Fix Analysis Predictor UT Check if memory_optimize_pass has already been added to the analysis config before adding a new one, so that it is not added multiple times. test=develop * change map to unordered_map fix the forgotten part of cpu_quantize_pass_tester.cc test=develop * removed quantized attribute * fixed cpu_quantize_pass_tester and op attr comments test=develop * removed redundant line test=debug * removed gmock test=develop * fix after merge
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- 25 2月, 2019 1 次提交
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由 liangan1 提交于
test=develop
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- 21 2月, 2019 1 次提交
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由 Xin Pan 提交于
test=develop
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- 13 2月, 2019 1 次提交
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由 chengduo 提交于
test=develop
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- 21 1月, 2019 1 次提交
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由 Dun 提交于
* mem opt * test=develop * test=develop * test=develop * test=develop * test=develop * test=develop * test=develop * refine code test=develop * refine code test=develop * refine code test=develop * refine code test=develop * refine with cub test=develop * fix mkldnn test && remove comments && test=develop * polish code && test=develop * add only_forward test && test=develop
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- 04 1月, 2019 1 次提交
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由 xiaolil1 提交于
* Enable basic MKL-DNN INT8 Conv OP test=develop * Modify test case test=develop * Clean unittest code test=develop * Fix test test=develop * Modify test test=develop * Modify basic INT8 Conv test=develop
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- 25 12月, 2018 1 次提交
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由 sneaxiy 提交于
test=develop
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- 19 12月, 2018 1 次提交
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由 sneaxiy 提交于
test=develop
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- 14 12月, 2018 1 次提交
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由 Yan Chunwei 提交于
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- 12 12月, 2018 1 次提交
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由 Yu Yang 提交于
test=develop
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- 07 12月, 2018 1 次提交
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由 Yihua Xu 提交于
test=develop
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- 05 12月, 2018 2 次提交
- 03 12月, 2018 1 次提交
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由 Yihua Xu 提交于
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- 19 11月, 2018 1 次提交
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由 qingqing01 提交于
* Convolution fusion operator. * Clean code test=develop
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- 15 11月, 2018 1 次提交
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由 Sylwester Fraczek 提交于
* add is_test to pooling and activations add prop_kind support for layers activation. conv and pooling add a pass that sets is_test to true add transpiler version of is_test pass test=develop * patch test and pass test=develop * add pass to analyzer.h test=develop * add is_test attr description & pass only on mkldnn in: activation_op.cc batch_norm_op.cc conv_op.cc dropout_op.cc lrn_op.cc pool_op.cc sequence_pool_op.cc softmax_op.cc * fix is_test handling for activation pool and conv * change description of is_test for all layers again * remove GetAttr(use_mkldnn) from pass * rename correct_mkldnn_test_phase to is_test and remove dependency on MKLDNN test=develop * review fix magic number * two if(..)s into one * Check is_test once and pass mkldnn forward prop kind * dereference shared_ptr with * (without get()) test=develop * add is_test_pass back test=develop
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- 09 11月, 2018 2 次提交
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由 chengduo 提交于
* add_infer_var_type test=develop * InferVarTypeHelper-> VarTypeInferenceHelper test=develop * PassInputTypeAndDTypeOnOutput test=develop * follow comment test=develop
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由 qingqing01 提交于
* exhaustive search for cuDNN conv. * Refine code and add unit testing. * Fix model load in fluid/inference and unit testing in conv2d * Follow comments. * Fix compiling test=develop
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- 07 11月, 2018 2 次提交
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由 qingqing01 提交于
This reverts commit ce7d9b07.
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由 qingqing01 提交于
* exhaustive search for cuDNN conv. * Refine code and add unit testing. * Clean code * Fix model load in fluid/inference and unit testing in conv2d * Follow comments.
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- 02 11月, 2018 1 次提交
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由 dzhwinter 提交于
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- 22 10月, 2018 1 次提交
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由 Xin Pan 提交于
test=develop
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- 21 10月, 2018 4 次提交
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由 Tomasz Patejko 提交于
MKLDNN conv + elementwise_add fusion: skip connection attribute renamed. Comments about patterns added. test=develop
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由 Tomasz Patejko 提交于
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由 Tomasz Patejko 提交于
MKLDNN conv + elementwise_add fusion: output and elemwise param share data in conv primitive. Output is properly allocated
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由 Tomasz Patejko 提交于
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- 15 9月, 2018 1 次提交
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由 dzhwinter 提交于
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- 14 9月, 2018 1 次提交
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由 Michał Gallus 提交于
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- 11 9月, 2018 1 次提交
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由 Michal Gallus 提交于
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- 10 9月, 2018 1 次提交
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由 Krzysztof Binias 提交于
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- 21 8月, 2018 1 次提交
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由 Michał Gallus 提交于
* Fuse Convolution and Eltwise Add into Conv+Bias * Reduce bias branching at conv_mkldnn_op * Add MKLDNN build checks for Conv Bias * Conv-bias: check if bias input exist befor assignment * Conv-bias: Remove Bias dim check from infershape It was causing conv3d test to crash upon\ncalling HasInput(Bias)
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- 11 6月, 2018 2 次提交
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由 dzhwinter 提交于
* "add inplace attribute" * "register inplace attribute" * "change se-next model for memory-reuse" * "fix typo" * repick * fix merge conflict * "fix stupid error"
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由 mozga-intel 提交于
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- 07 6月, 2018 1 次提交
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由 mozga-intel 提交于
* Add MKLDNN layout support in Paddle Add MKLDNN layout in Paddle so that MKLDNN friendly memory layout can be used in MKLDNN enabled OP kernel. Before this commit, NCHW is hardcode to be used in all MKLDNN op kernels. As a result, non-optimized execution path is selected in MKLDNN primitive which bring worse performance. Besides framework change, three MKLDNN OP kernels were updated for using new MKLDNN layout. They are conv/pool2d/batch_norm. Other MKLDNN OP kernels need be also updated in similar way to achieve best performance. * Add MKLDNN layout support in activation OP * Don't populate layout from input to output when kMKLDNN in * Refine pool mkldnn op kernel * MKLDNN layout * Remove the inferitance from tensor file * MKLDNN layout: refactoring * Remove additional #define to register new operator * Prepare mkldnn tests to work with layout
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