modify class MKLDNNActForward to new structure #9
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This commit may add some overhead of managing NDArray for each fallback.
Conflicts: src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
2. Add memory into signature; 3. Try to split BatchNorm into .h file and .cc file. Will finish it after backward code is refactored.
Caching primitive for BatchNorm forward computation
TaoLv
reviewed
Jan 10, 2018
src/operator/nn/mkldnn/mkldnn_act.cc
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| auto out_mem = const_cast<NDArray &>(out_data).CreateMKLDNNData( | ||
| fwd_pd.dst_primitive_desc()); | ||
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| if (this->data == nullptr) { |
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I think it would be better to add initialization and first creation in the constructive function the MKLDNNActForward class. So there is no need to chech nullptr for every iteration.
Besides, please follow the coding style and naming methods we discussed in previous email.
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OK move to constructed function;
src/operator/nn/mkldnn/mkldnn_act.cc
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| @@ -107,36 +107,46 @@ class MKLDNNActForward { | |||
| const NDArray &data, const mkldnn::memory &mem): fwd_pd( | |||
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There is no need to input both data and mem. You can get mem from data easily.
src/operator/nn/mkldnn/mkldnn_act.cc
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| CHECK(fwd_pd.dst_primitive_desc() == output.get_primitive_desc()); | ||
| if (this->out == nullptr) | ||
| CHECK(fwd_pd.dst_primitive_desc() == out_mem->get_primitive_desc()); |
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Why check this? out_mem is just created in L116 with the dst_primitive_desc. Do you think mkldnn will fail in doing that?
rongzha1
commented
Jan 11, 2018
src/operator/nn/mkldnn/mkldnn_act.cc
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| @@ -107,36 +107,46 @@ class MKLDNNActForward { | |||
| const NDArray &data, const mkldnn::memory &mem): fwd_pd( | |||
src/operator/nn/mkldnn/mkldnn_act.cc
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| auto out_mem = const_cast<NDArray &>(out_data).CreateMKLDNNData( | ||
| fwd_pd.dst_primitive_desc()); | ||
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| if (this->data == nullptr) { |
There was a problem hiding this comment.
OK move to constructed function;
src/operator/nn/mkldnn/mkldnn_act.cc
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| CHECK(fwd_pd.dst_primitive_desc() == output.get_primitive_desc()); | ||
| if (this->out == nullptr) | ||
| CHECK(fwd_pd.dst_primitive_desc() == out_mem->get_primitive_desc()); |
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* [Quantization] 8bit Quantization and GPU Support [Quantization] CuDNN 8bit quantized relu v0.1 [Quantization] CuDNN 8bit quantized max_pool v0.1 [Quantization] CuDNN 8bit quantized lrn v0.1 [Quantization] CuDNN 8bit quantized convolution v0.1 [Quantization] CuDNN 8bit quantized fully connected v0.1 [Quantization] Small fix [Quantization] Implement backward method [Quantization] Convolution backward method [Quantization] Add range for matmul and conv [Quantization] New types in ndarray.py [Quantization] 8bit conv works [Quantization] conv support multiple type [Quantization] matmul works now [Quantization] matmul works well [Quantization] efactor quantization operators [Quantization] Op: quantize_down_and_shrink_range [Quantization] Complete quantize_graph_pass [Quantization] Add example [Quantization] Take zero-center quantize, accuracy fixed [Quantization] Multiple layers MLP pass [Quantization] Make quantized_conv same as Convolution [Quantization] quantized_conv works [Quantization] Fix bug [Quantization] lenet works now [Quantization] Add quantized_flatten [Quantization] Quantized max pool works well [Quantization] Make quantized_conv support NHWC [Quantization] add max_pool [Quantization] add ignore_symbols [Quantization] Save change [Quantization] Reorganize tests, 8 layers resnet works on cifar [Quantization] Support for 'NHWC' max pool [Quantization] Support for 'NHWC' quantized max pool [Quantization] Fix speed of quantize_down_and_shrink_range [Quantization] script for resnet on imagenet [Quantization] refactor for quantize offline [Quantization] Fix infershape [Quantization] Update test [Quantization] Update example [Quantization] Fix build error * [Quantization] Add calibration flow and refactor code Rebase with dmlc/master Add quantize_down_and_shrink by threshold Don't assign resource when threshold is available for quantize_down_and_shrink Fix quantize_down_and_shrink saturation Implement pass for setting calib table to node attrs Rebase with upstream master Change threshold to min/max quantized params Add c-api for setting calib table to graph Add calibration front end function Bug fixes and add unit test Add data iter type to calibration Fix bug in calibrate_quantized_model Bug fix and add example Add the second calibration approach and benchmark Fix Fix infer error and add benchmark for conv Add benchmark script Change output names and argument names Remove commented out code Change name Add layout to benchmark_convolution Remove redundant comment Remove common and add soft link More fix and benchmark Add scripts to plot images Minor fix More fix More fix and util tools Tools and support bias in quantized_conv2d Add script for getting the optimal thresholds using kl divergence Add kl divergence for optimizing thresholds Add benchmark scripts Fix compile after rebasing on master Allocate temp space only once for quantized_conv2d Change quantize_down_and_shrink_range to allocate temp space once No temp space for calib model Refactor quantize_down_and_shrink_range into requantize Refactor quantized convolution using nnvm interfaces Fix quantized_conv bug Use ConvolutionParam for QuantizedCuDNNConvOp Refactor quantized fc using nnvm interfaces Change TQuantizationNeedShrink to FNeedRequantize Refactor quantized_pooling Simplify FQuantizedOp interface Better naming Fix shape and type inference for quantized_flatten Clean up quantization frontend APIs and examples Delete quantized lrn and relu Add python script for generating quantized models Add script for running inference Add inference example Remove redundant files from example/quantization Simplify user-level python APIs Add logger Improve user-level python api Fix coding style Add unit test for quantized_conv Fix bugs in quantized_fully_connected and add unit test Add unit test for requantize Fix a bug and add python api unit tests Import test_quantization in test_operator_gpu.py Rebase with master Remove redundant files Fix test case for python3 and fix doc Fix unit tests Fix unit tests for python3 Release used ndarrays in calibration for saving memory usage Simplify releasing memory of used ndarrays for calibration Fix a bug Revert "Fix a bug" This reverts commit f7853f2. Revert "Simplify releasing memory of used ndarrays for calibration" This reverts commit 70b9e38. Clean up benchmark script and improve example Add API and example documentation and fix bugs Remove redundant test file and improve error message Merge quantize and dequantize with master impl Remove commented code Hide monitor interface from users Remove interface from Module Add license header Move quantization unittests to a separate folder so that it can be only run on P3 instances Remove quantization unittests from test_operator_gpu.py Move quantization to contrib Fix lint Add mxnetlinux-gpu-p3 to jenkins Fix jenkins Fix CI build Fix CI Update jenkins file Use cudnn7 for ci Add docker file for quantization unit test only Correctly skip build with cudnn < 6 Add doc for quantize symbol api Fix lint Fix python3 and add doc Try to fix cudnn build problem * Fix compile error * Fix CI * Remove tests that should not run on P3 * Remove unnecessary docker file * Fix registering quantized nn ops * Reformat Jenkinsfile and switch quantization to CUDA 9 (#9) * Address interface change cr * Address comments and fix bugs * Make unit test stable * Improve unit test * Address cr * Address cr * Fix flaky unit test layer_norm * Fix doc
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Mar 30, 2018
* [Quantization] 8bit Quantization and GPU Support [Quantization] CuDNN 8bit quantized relu v0.1 [Quantization] CuDNN 8bit quantized max_pool v0.1 [Quantization] CuDNN 8bit quantized lrn v0.1 [Quantization] CuDNN 8bit quantized convolution v0.1 [Quantization] CuDNN 8bit quantized fully connected v0.1 [Quantization] Small fix [Quantization] Implement backward method [Quantization] Convolution backward method [Quantization] Add range for matmul and conv [Quantization] New types in ndarray.py [Quantization] 8bit conv works [Quantization] conv support multiple type [Quantization] matmul works now [Quantization] matmul works well [Quantization] efactor quantization operators [Quantization] Op: quantize_down_and_shrink_range [Quantization] Complete quantize_graph_pass [Quantization] Add example [Quantization] Take zero-center quantize, accuracy fixed [Quantization] Multiple layers MLP pass [Quantization] Make quantized_conv same as Convolution [Quantization] quantized_conv works [Quantization] Fix bug [Quantization] lenet works now [Quantization] Add quantized_flatten [Quantization] Quantized max pool works well [Quantization] Make quantized_conv support NHWC [Quantization] add max_pool [Quantization] add ignore_symbols [Quantization] Save change [Quantization] Reorganize tests, 8 layers resnet works on cifar [Quantization] Support for 'NHWC' max pool [Quantization] Support for 'NHWC' quantized max pool [Quantization] Fix speed of quantize_down_and_shrink_range [Quantization] script for resnet on imagenet [Quantization] refactor for quantize offline [Quantization] Fix infershape [Quantization] Update test [Quantization] Update example [Quantization] Fix build error * [Quantization] Add calibration flow and refactor code Rebase with dmlc/master Add quantize_down_and_shrink by threshold Don't assign resource when threshold is available for quantize_down_and_shrink Fix quantize_down_and_shrink saturation Implement pass for setting calib table to node attrs Rebase with upstream master Change threshold to min/max quantized params Add c-api for setting calib table to graph Add calibration front end function Bug fixes and add unit test Add data iter type to calibration Fix bug in calibrate_quantized_model Bug fix and add example Add the second calibration approach and benchmark Fix Fix infer error and add benchmark for conv Add benchmark script Change output names and argument names Remove commented out code Change name Add layout to benchmark_convolution Remove redundant comment Remove common and add soft link More fix and benchmark Add scripts to plot images Minor fix More fix More fix and util tools Tools and support bias in quantized_conv2d Add script for getting the optimal thresholds using kl divergence Add kl divergence for optimizing thresholds Add benchmark scripts Fix compile after rebasing on master Allocate temp space only once for quantized_conv2d Change quantize_down_and_shrink_range to allocate temp space once No temp space for calib model Refactor quantize_down_and_shrink_range into requantize Refactor quantized convolution using nnvm interfaces Fix quantized_conv bug Use ConvolutionParam for QuantizedCuDNNConvOp Refactor quantized fc using nnvm interfaces Change TQuantizationNeedShrink to FNeedRequantize Refactor quantized_pooling Simplify FQuantizedOp interface Better naming Fix shape and type inference for quantized_flatten Clean up quantization frontend APIs and examples Delete quantized lrn and relu Add python script for generating quantized models Add script for running inference Add inference example Remove redundant files from example/quantization Simplify user-level python APIs Add logger Improve user-level python api Fix coding style Add unit test for quantized_conv Fix bugs in quantized_fully_connected and add unit test Add unit test for requantize Fix a bug and add python api unit tests Import test_quantization in test_operator_gpu.py Rebase with master Remove redundant files Fix test case for python3 and fix doc Fix unit tests Fix unit tests for python3 Release used ndarrays in calibration for saving memory usage Simplify releasing memory of used ndarrays for calibration Fix a bug Revert "Fix a bug" This reverts commit f7853f2. Revert "Simplify releasing memory of used ndarrays for calibration" This reverts commit 70b9e38. Clean up benchmark script and improve example Add API and example documentation and fix bugs Remove redundant test file and improve error message Merge quantize and dequantize with master impl Remove commented code Hide monitor interface from users Remove interface from Module Add license header Move quantization unittests to a separate folder so that it can be only run on P3 instances Remove quantization unittests from test_operator_gpu.py Move quantization to contrib Fix lint Add mxnetlinux-gpu-p3 to jenkins Fix jenkins Fix CI build Fix CI Update jenkins file Use cudnn7 for ci Add docker file for quantization unit test only Correctly skip build with cudnn < 6 Add doc for quantize symbol api Fix lint Fix python3 and add doc Try to fix cudnn build problem * Fix compile error * Fix CI * Remove tests that should not run on P3 * Remove unnecessary docker file * Fix registering quantized nn ops * Reformat Jenkinsfile and switch quantization to CUDA 9 (zheng-da#9) * Address interface change cr * Address comments and fix bugs * Make unit test stable * Improve unit test * Address cr * Address cr * Fix flaky unit test layer_norm * Fix doc
zheng-da
pushed a commit
that referenced
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Jun 28, 2018
* [Quantization] 8bit Quantization and GPU Support [Quantization] CuDNN 8bit quantized relu v0.1 [Quantization] CuDNN 8bit quantized max_pool v0.1 [Quantization] CuDNN 8bit quantized lrn v0.1 [Quantization] CuDNN 8bit quantized convolution v0.1 [Quantization] CuDNN 8bit quantized fully connected v0.1 [Quantization] Small fix [Quantization] Implement backward method [Quantization] Convolution backward method [Quantization] Add range for matmul and conv [Quantization] New types in ndarray.py [Quantization] 8bit conv works [Quantization] conv support multiple type [Quantization] matmul works now [Quantization] matmul works well [Quantization] efactor quantization operators [Quantization] Op: quantize_down_and_shrink_range [Quantization] Complete quantize_graph_pass [Quantization] Add example [Quantization] Take zero-center quantize, accuracy fixed [Quantization] Multiple layers MLP pass [Quantization] Make quantized_conv same as Convolution [Quantization] quantized_conv works [Quantization] Fix bug [Quantization] lenet works now [Quantization] Add quantized_flatten [Quantization] Quantized max pool works well [Quantization] Make quantized_conv support NHWC [Quantization] add max_pool [Quantization] add ignore_symbols [Quantization] Save change [Quantization] Reorganize tests, 8 layers resnet works on cifar [Quantization] Support for 'NHWC' max pool [Quantization] Support for 'NHWC' quantized max pool [Quantization] Fix speed of quantize_down_and_shrink_range [Quantization] script for resnet on imagenet [Quantization] refactor for quantize offline [Quantization] Fix infershape [Quantization] Update test [Quantization] Update example [Quantization] Fix build error * [Quantization] Add calibration flow and refactor code Rebase with dmlc/master Add quantize_down_and_shrink by threshold Don't assign resource when threshold is available for quantize_down_and_shrink Fix quantize_down_and_shrink saturation Implement pass for setting calib table to node attrs Rebase with upstream master Change threshold to min/max quantized params Add c-api for setting calib table to graph Add calibration front end function Bug fixes and add unit test Add data iter type to calibration Fix bug in calibrate_quantized_model Bug fix and add example Add the second calibration approach and benchmark Fix Fix infer error and add benchmark for conv Add benchmark script Change output names and argument names Remove commented out code Change name Add layout to benchmark_convolution Remove redundant comment Remove common and add soft link More fix and benchmark Add scripts to plot images Minor fix More fix More fix and util tools Tools and support bias in quantized_conv2d Add script for getting the optimal thresholds using kl divergence Add kl divergence for optimizing thresholds Add benchmark scripts Fix compile after rebasing on master Allocate temp space only once for quantized_conv2d Change quantize_down_and_shrink_range to allocate temp space once No temp space for calib model Refactor quantize_down_and_shrink_range into requantize Refactor quantized convolution using nnvm interfaces Fix quantized_conv bug Use ConvolutionParam for QuantizedCuDNNConvOp Refactor quantized fc using nnvm interfaces Change TQuantizationNeedShrink to FNeedRequantize Refactor quantized_pooling Simplify FQuantizedOp interface Better naming Fix shape and type inference for quantized_flatten Clean up quantization frontend APIs and examples Delete quantized lrn and relu Add python script for generating quantized models Add script for running inference Add inference example Remove redundant files from example/quantization Simplify user-level python APIs Add logger Improve user-level python api Fix coding style Add unit test for quantized_conv Fix bugs in quantized_fully_connected and add unit test Add unit test for requantize Fix a bug and add python api unit tests Import test_quantization in test_operator_gpu.py Rebase with master Remove redundant files Fix test case for python3 and fix doc Fix unit tests Fix unit tests for python3 Release used ndarrays in calibration for saving memory usage Simplify releasing memory of used ndarrays for calibration Fix a bug Revert "Fix a bug" This reverts commit f7853f2. Revert "Simplify releasing memory of used ndarrays for calibration" This reverts commit 70b9e38. Clean up benchmark script and improve example Add API and example documentation and fix bugs Remove redundant test file and improve error message Merge quantize and dequantize with master impl Remove commented code Hide monitor interface from users Remove interface from Module Add license header Move quantization unittests to a separate folder so that it can be only run on P3 instances Remove quantization unittests from test_operator_gpu.py Move quantization to contrib Fix lint Add mxnetlinux-gpu-p3 to jenkins Fix jenkins Fix CI build Fix CI Update jenkins file Use cudnn7 for ci Add docker file for quantization unit test only Correctly skip build with cudnn < 6 Add doc for quantize symbol api Fix lint Fix python3 and add doc Try to fix cudnn build problem * Fix compile error * Fix CI * Remove tests that should not run on P3 * Remove unnecessary docker file * Fix registering quantized nn ops * Reformat Jenkinsfile and switch quantization to CUDA 9 (#9) * Address interface change cr * Address comments and fix bugs * Make unit test stable * Improve unit test * Address cr * Address cr * Fix flaky unit test layer_norm * Fix doc
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Description
change class Act to new structure.
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Essentials
make lint)Changes
modify class MKLDNNActForward to new structure
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