- 27 2月, 2019 1 次提交
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由 xiaolil1 提交于
* Optimize key creation of INT8 pool kernel to improve the peformance of ResNet-50 and MobileNet, especially for latency. test=develop * Optimize key creation of pool fp32 grad. test=develop
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- 22 2月, 2019 1 次提交
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由 Sylwester Fraczek 提交于
reason: dereferencing smart pointer is the same as the underlying pointer test=develop
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- 29 1月, 2019 1 次提交
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由 Krzysztof Binias 提交于
test=develop
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- 10 1月, 2019 1 次提交
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由 xiaoli.liu@intel.com 提交于
test=develop
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- 02 1月, 2019 1 次提交
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由 xiaolil1 提交于
* Enable INT8 pool OP test=develop * fix unittest test=develop * Clean unittest code. test=develop
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- 25 12月, 2018 1 次提交
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由 xiaoli.liu@intel.com 提交于
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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- 25 9月, 2018 2 次提交
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由 Michal Gallus 提交于
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由 Michal Gallus 提交于
Also fix MKL-DNN pooling integration for ceil mode
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- 11 6月, 2018 1 次提交
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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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- 17 5月, 2018 1 次提交
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由 Jacek Czaja 提交于
- Finished draft of pooling reusing of operators - Using gethash in PoolGrad added - Removed diagnostic - Added pool mkldnn grad reusing of primitives - Added diagnostic - Removed diagnostic - added dependency to mkldnn data type for pooling mkldnn - Added mkldnn memory data type determining based on template type of op - Compilation warning fix - codying style fixes
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- 13 4月, 2018 1 次提交
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由 Abhinav Arora 提交于
* Fix CPPLint errors in operators * Fix prior box op * Fix Prior Box op * Fix top_k_op.cu * Fix pool mkmldnn * Fix pool mkmldnn
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- 10 3月, 2018 1 次提交
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由 pzelazko-intel 提交于
* MKLDNN pool2d OP kernel added * conv2d and pool2d MKLDNN kernels renamed * MKLDNN conv2d kernel refactoring
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