- 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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- 21 5月, 2018 1 次提交
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由 Krzysztof Binias 提交于
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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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- 17 4月, 2018 1 次提交
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由 mozga-intel 提交于
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- 10 4月, 2018 1 次提交
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由 Yi Wang 提交于
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- 23 3月, 2018 2 次提交
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由 Krzysztof Binias 提交于
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由 Krzysztof Binias 提交于
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- 07 3月, 2018 1 次提交
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由 pzelazko-intel 提交于
* MKLDNN conv2 OP kernel added * TODOs added * mkldnn conv2d OP refactor * CanCUDNNBeUsed and CanMKLDNNBeUsed moved
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- 12 2月, 2018 1 次提交
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由 qingqing01 提交于
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- 10 2月, 2018 1 次提交
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由 Yi Wang 提交于
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- 05 1月, 2018 1 次提交
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由 tensor-tang 提交于
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- 03 1月, 2018 2 次提交
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由 tensor-tang 提交于
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由 tensor-tang 提交于
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- 04 7月, 2017 1 次提交
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由 liaogang 提交于
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- 29 6月, 2017 2 次提交
- 25 5月, 2017 1 次提交
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由 Yu Yang 提交于
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- 09 12月, 2016 1 次提交
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由 Yi Wang 提交于
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- 29 8月, 2016 1 次提交
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由 zhangjinchao01 提交于
ISSUE=4586495 git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1408 1ad973e4-5ce8-4261-8a94-b56d1f490c56
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