[GitHub] [incubator-mxnet] TaoLv commented on issue #16735: Use single-bit for mask in dropout operator
TaoLv commented on issue #16735: Use single-bit for mask in dropout operator URL: https://github.com/apache/incubator-mxnet/pull/16735#issuecomment-586564858 > Does the slow down come from more computation in the new algorithm or the sub-optimal implementation? The new implementation increases both memory load and additional bit-wise operations. So performance slow down is expected. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (d352673 -> 149975c)
This is an automated email from the ASF dual-hosted git repository.
haoj pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.
from d352673 Fix transformer.cu interleaved matmul for cuda arch < 5
(#17596)
add 149975c [numpy][Do Not Review]add op insert (#16865)
No new revisions were added by this update.
Summary of changes:
python/mxnet/ndarray/numpy/_op.py | 110 +-
python/mxnet/numpy/multiarray.py | 85 -
python/mxnet/numpy_dispatch_protocol.py| 1 +
python/mxnet/symbol/numpy/_symbol.py | 65 +++-
src/operator/numpy/np_insert_op-inl.h | 371 +
src/operator/numpy/np_insert_op_scalar-inl.h | 160 +
src/operator/numpy/np_insert_op_scalar.cc | 135
.../multi_lars.cu => numpy/np_insert_op_scalar.cu} | 16 +-
src/operator/numpy/np_insert_op_slice-inl.h| 200 +++
src/operator/numpy/np_insert_op_slice.cc | 160 +
.../multi_lars.cu => numpy/np_insert_op_slice.cu} | 16 +-
src/operator/numpy/np_insert_op_tensor-inl.h | 229 +
src/operator/numpy/np_insert_op_tensor.cc | 152 +
.../multi_lars.cu => numpy/np_insert_op_tensor.cu} | 16 +-
.../python/unittest/test_numpy_interoperability.py | 12 +
tests/python/unittest/test_numpy_op.py | 113 +++
16 files changed, 1814 insertions(+), 27 deletions(-)
create mode 100644 src/operator/numpy/np_insert_op-inl.h
create mode 100644 src/operator/numpy/np_insert_op_scalar-inl.h
create mode 100644 src/operator/numpy/np_insert_op_scalar.cc
copy src/operator/{contrib/multi_lars.cu => numpy/np_insert_op_scalar.cu} (75%)
create mode 100644 src/operator/numpy/np_insert_op_slice-inl.h
create mode 100644 src/operator/numpy/np_insert_op_slice.cc
copy src/operator/{contrib/multi_lars.cu => numpy/np_insert_op_slice.cu} (75%)
create mode 100644 src/operator/numpy/np_insert_op_tensor-inl.h
create mode 100644 src/operator/numpy/np_insert_op_tensor.cc
copy src/operator/{contrib/multi_lars.cu => numpy/np_insert_op_tensor.cu} (75%)
[incubator-mxnet] branch master updated (d352673 -> 149975c)
This is an automated email from the ASF dual-hosted git repository.
haoj pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.
from d352673 Fix transformer.cu interleaved matmul for cuda arch < 5
(#17596)
add 149975c [numpy][Do Not Review]add op insert (#16865)
No new revisions were added by this update.
Summary of changes:
python/mxnet/ndarray/numpy/_op.py | 110 +-
python/mxnet/numpy/multiarray.py | 85 -
python/mxnet/numpy_dispatch_protocol.py| 1 +
python/mxnet/symbol/numpy/_symbol.py | 65 +++-
src/operator/numpy/np_insert_op-inl.h | 371 +
src/operator/numpy/np_insert_op_scalar-inl.h | 160 +
src/operator/numpy/np_insert_op_scalar.cc | 135
.../multi_lars.cu => numpy/np_insert_op_scalar.cu} | 16 +-
src/operator/numpy/np_insert_op_slice-inl.h| 200 +++
src/operator/numpy/np_insert_op_slice.cc | 160 +
.../multi_lars.cu => numpy/np_insert_op_slice.cu} | 16 +-
src/operator/numpy/np_insert_op_tensor-inl.h | 229 +
src/operator/numpy/np_insert_op_tensor.cc | 152 +
.../multi_lars.cu => numpy/np_insert_op_tensor.cu} | 16 +-
.../python/unittest/test_numpy_interoperability.py | 12 +
tests/python/unittest/test_numpy_op.py | 113 +++
16 files changed, 1814 insertions(+), 27 deletions(-)
create mode 100644 src/operator/numpy/np_insert_op-inl.h
create mode 100644 src/operator/numpy/np_insert_op_scalar-inl.h
create mode 100644 src/operator/numpy/np_insert_op_scalar.cc
copy src/operator/{contrib/multi_lars.cu => numpy/np_insert_op_scalar.cu} (75%)
create mode 100644 src/operator/numpy/np_insert_op_slice-inl.h
create mode 100644 src/operator/numpy/np_insert_op_slice.cc
copy src/operator/{contrib/multi_lars.cu => numpy/np_insert_op_slice.cu} (75%)
create mode 100644 src/operator/numpy/np_insert_op_tensor-inl.h
create mode 100644 src/operator/numpy/np_insert_op_tensor.cc
copy src/operator/{contrib/multi_lars.cu => numpy/np_insert_op_tensor.cu} (75%)
[GitHub] [incubator-mxnet] haojin2 merged pull request #16865: [numpy][Do Not Review]add op insert
haojin2 merged pull request #16865: [numpy][Do Not Review]add op insert URL: https://github.com/apache/incubator-mxnet/pull/16865 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet-site] branch asf-site updated: Bump the publish timestamp.
This is an automated email from the ASF dual-hosted git repository. aaronmarkham pushed a commit to branch asf-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git The following commit(s) were added to refs/heads/asf-site by this push: new b73b3ea Bump the publish timestamp. b73b3ea is described below commit b73b3eab845bd1a0f9c35f8402fd3b65402a0879 Author: mxnet-ci AuthorDate: Sat Feb 15 06:45:42 2020 + Bump the publish timestamp. --- date.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/date.txt b/date.txt new file mode 100644 index 000..00774e1 --- /dev/null +++ b/date.txt @@ -0,0 +1 @@ +Sat Feb 15 06:45:42 UTC 2020
[GitHub] [incubator-mxnet] leezu opened a new pull request #17603: Backport #17596
leezu opened a new pull request #17603: Backport #17596 URL: https://github.com/apache/incubator-mxnet/pull/17603 Backport #17596 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu commented on issue #17017: USE_BLAS=apple broken on OSX 10.15
leezu commented on issue #17017: USE_BLAS=apple broken on OSX 10.15 URL: https://github.com/apache/incubator-mxnet/issues/17017#issuecomment-586560692 Fixed by https://github.com/apache/incubator-mxnet/pull/17602 for cmake build This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] apeforest commented on a change in pull request #17599: Fixed Embedding op for LT input
apeforest commented on a change in pull request #17599: Fixed Embedding op for
LT input
URL: https://github.com/apache/incubator-mxnet/pull/17599#discussion_r379766442
##
File path: src/operator/tensor/indexing_op.h
##
@@ -66,7 +66,7 @@ enum QuantizedEmbeddingOpResource {kTempSpace};
struct SparseEmbeddingParam: public dmlc::Parameter {
- int input_dim;
+ index_t input_dim;
int output_dim;
Review comment:
Would there be a case where output_dim also exceeds 2^32?
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[GitHub] [incubator-mxnet] apeforest commented on a change in pull request #16735: Use single-bit for mask in dropout operator
apeforest commented on a change in pull request #16735: Use single-bit for mask
in dropout operator
URL: https://github.com/apache/incubator-mxnet/pull/16735#discussion_r379763372
##
File path: src/operator/nn/dropout-inl.h
##
@@ -152,15 +181,20 @@ class DropoutOp {
const std::vector _grad) {
Stream *s = ctx.get_stream();
Tensor grad = out_grad[dropout::kOut].FlatTo2D(s);
-Tensor mask = out_data[dropout::kMask].FlatTo2D(s);
+Tensor mask = out_data[dropout::kMask].FlatTo1D(s);
Tensor gdata = in_grad[dropout::kData].FlatTo2D(s);
DType *ingradptr = gdata.dptr_;
const DType *outgradptr = grad.dptr_;
-const DType *maskptr = mask.dptr_;
-const int count = mask.shape_[0] * mask.shape_[1];
-#pragma omp parallel for
num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
-for (int i = 0; i < count; ++i) {
- ingradptr[i] = outgradptr[i] * maskptr[i];
+const uint8_t *maskptr = mask.dptr_;
+const index_t count = grad.shape_[0] * grad.shape_[1];
+const float pk_1 = 1.0f / this->pkeep_;
+const int nthr = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+#pragma omp parallel for num_threads(nthr) schedule(static, 8)
+for (index_t i = 0; i < count; ++i) {
+ auto mask_idx = i >> 3; // div 8;
+ uint8_t mask_offset = i & 7; // mod 8
+ bool mask_val = maskptr[mask_idx] & (1U << mask_offset);
+ ingradptr[i] = outgradptr[i] * mask_val * pk_1;
Review comment:
However there is no read from `ingradptr`, therefore this is not a case of
the false sharing, right? I tried this block and didn't noticed any noticeable
performance gain.
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[GitHub] [incubator-mxnet] leezu opened a new pull request #17602: Fix OS X staticbuild and add tests
leezu opened a new pull request #17602: Fix OS X staticbuild and add tests URL: https://github.com/apache/incubator-mxnet/pull/17602 ## Description ## Fix OS X staticbuild and add tests based on Github Actions. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (9ee4f04 -> d352673)
This is an automated email from the ASF dual-hosted git repository. lausen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 9ee4f04 Support broadcast assign for `npi_boolean_mask_assign_tensor` (#17131) add d352673 Fix transformer.cu interleaved matmul for cuda arch < 5 (#17596) No new revisions were added by this update. Summary of changes: src/operator/contrib/transformer.cu | 71 ++--- 1 file changed, 59 insertions(+), 12 deletions(-)
[incubator-mxnet] branch master updated (9ee4f04 -> d352673)
This is an automated email from the ASF dual-hosted git repository. lausen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 9ee4f04 Support broadcast assign for `npi_boolean_mask_assign_tensor` (#17131) add d352673 Fix transformer.cu interleaved matmul for cuda arch < 5 (#17596) No new revisions were added by this update. Summary of changes: src/operator/contrib/transformer.cu | 71 ++--- 1 file changed, 59 insertions(+), 12 deletions(-)
[GitHub] [incubator-mxnet] leezu merged pull request #17596: Fix transformer.cu interleaved matmul for cuda arch < 5
leezu merged pull request #17596: Fix transformer.cu interleaved matmul for cuda arch < 5 URL: https://github.com/apache/incubator-mxnet/pull/17596 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] reminisce opened a new pull request #17601: Port conv3d dilate > 1 fix to 1.5.x
reminisce opened a new pull request #17601: Port conv3d dilate > 1 fix to 1.5.x URL: https://github.com/apache/incubator-mxnet/pull/17601 Port https://github.com/apache/incubator-mxnet/pull/17491 to 1.5.x. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] TaoLv commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data
TaoLv commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data URL: https://github.com/apache/incubator-mxnet/issues/17595#issuecomment-586552974 I can reproduce the crash. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (f619c52 -> 9ee4f04)
This is an automated email from the ASF dual-hosted git repository. reminisce pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from f619c52 Python 2 cleanup (#17583) add 9ee4f04 Support broadcast assign for `npi_boolean_mask_assign_tensor` (#17131) No new revisions were added by this update. Summary of changes: python/mxnet/ndarray/ndarray.py | 5 +- python/mxnet/numpy/multiarray.py | 4 +- src/operator/numpy/np_boolean_mask_assign.cc | 68 +--- src/operator/numpy/np_boolean_mask_assign.cu | 36 ++- src/operator/tensor/indexing_op.cc | 4 +- src/operator/tensor/indexing_op.cu | 4 +- src/operator/tensor/init_op.h| 2 +- tests/python/unittest/test_numpy_op.py | 21 ++--- 8 files changed, 92 insertions(+), 52 deletions(-)
[GitHub] [incubator-mxnet] reminisce merged pull request #17131: Support broadcast assign for `npi_boolean_mask_assign_tensor`
reminisce merged pull request #17131: Support broadcast assign for `npi_boolean_mask_assign_tensor` URL: https://github.com/apache/incubator-mxnet/pull/17131 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] reminisce opened a new pull request #17600: Fix Non-ASCII character in docstring
reminisce opened a new pull request #17600: Fix Non-ASCII character in docstring URL: https://github.com/apache/incubator-mxnet/pull/17600 ## Description ## As title. Expected to complement https://github.com/apache/incubator-mxnet/pull/17593 for fixing https://github.com/apache/incubator-mxnet/issues/17562. ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes) - [ ] Changes are complete (i.e. I finished coding on this PR) - [ ] All changes have test coverage: - Unit tests are added for small changes to verify correctness (e.g. adding a new operator) - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore) - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL) - [ ] Code is well-documented: - For user-facing API changes, API doc string has been updated. - For new C++ functions in header files, their functionalities and arguments are documented. - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable - Check the API doc at https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html - [ ] To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### - [ ] Feature1, tests, (and when applicable, API doc) - [ ] Feature2, tests, (and when applicable, API doc) ## Comments ## - If this change is a backward incompatible change, why must this change be made. - Interesting edge cases to note here This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] TaoLv commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data
TaoLv commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data URL: https://github.com/apache/incubator-mxnet/issues/17595#issuecomment-586548992 Thank you for reporting the issue. I will take a look at this. But my initial thought is that MKL-DNN itself already supports int64 shape since the v1.0 upgrading, while I don't think the current integration of MKL/openblas supports int64 GEMM. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] TaoLv commented on issue #17576: Fix storage type infer of softmax backward
TaoLv commented on issue #17576: Fix storage type infer of softmax backward URL: https://github.com/apache/incubator-mxnet/pull/17576#issuecomment-586548359 @leeze, Got it. Thank you for the merging. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] TaoLv commented on a change in pull request #16735: Use single-bit for mask in dropout operator
TaoLv commented on a change in pull request #16735: Use single-bit for mask in
dropout operator
URL: https://github.com/apache/incubator-mxnet/pull/16735#discussion_r379713840
##
File path: src/operator/nn/dropout-inl.h
##
@@ -152,15 +181,20 @@ class DropoutOp {
const std::vector _grad) {
Stream *s = ctx.get_stream();
Tensor grad = out_grad[dropout::kOut].FlatTo2D(s);
-Tensor mask = out_data[dropout::kMask].FlatTo2D(s);
+Tensor mask = out_data[dropout::kMask].FlatTo1D(s);
Tensor gdata = in_grad[dropout::kData].FlatTo2D(s);
DType *ingradptr = gdata.dptr_;
const DType *outgradptr = grad.dptr_;
-const DType *maskptr = mask.dptr_;
-const int count = mask.shape_[0] * mask.shape_[1];
-#pragma omp parallel for
num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
-for (int i = 0; i < count; ++i) {
- ingradptr[i] = outgradptr[i] * maskptr[i];
+const uint8_t *maskptr = mask.dptr_;
+const index_t count = grad.shape_[0] * grad.shape_[1];
+const float pk_1 = 1.0f / this->pkeep_;
+const int nthr = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+#pragma omp parallel for num_threads(nthr) schedule(static, 8)
+for (index_t i = 0; i < count; ++i) {
+ auto mask_idx = i >> 3; // div 8;
+ uint8_t mask_offset = i & 7; // mod 8
+ bool mask_val = maskptr[mask_idx] & (1U << mask_offset);
+ ingradptr[i] = outgradptr[i] * mask_val * pk_1;
Review comment:
We're writing to `ingradptr`. We also hope the elements in one cache line
will be handled by one openmp thread. With the original parallelization, one
cache line is loaded and only one element in it is handled by the current
thread. For the next thread, it need load the same cache line, and handle the
next element.
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[GitHub] [incubator-mxnet] rongzha1 commented on a change in pull request #17265: Add bfloat16 floating-point format support based on AMP
rongzha1 commented on a change in pull request #17265: Add bfloat16 floating-point format support based on AMP URL: https://github.com/apache/incubator-mxnet/pull/17265#discussion_r379713720 ## File path: 3rdparty/mshadow/mshadow/bfloat.h ## @@ -0,0 +1,167 @@ +/*! Review comment: done thanks This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] connorgoggins commented on issue #17599: Fixed Embedding op for LT input
connorgoggins commented on issue #17599: Fixed Embedding op for LT input URL: https://github.com/apache/incubator-mxnet/pull/17599#issuecomment-586543671 @access2rohit great question. Here is an example of when sparseEmbedding params are used without my fix, and the resulting error (same error as with Embedding): ``` >>> mx.contrib.nd.SparseEmbedding(data=mx.nd.random_normal(shape=(2**32,1)), weight=mx.nd.random_normal(shape=(2**32, 1)), input_dim=2**32, output_dim=1) mxnet.base.MXNetError: MXNetError: Invalid Parameter format for input_dim expect int but value='4294967296', in operator _contrib_SparseEmbedding(name="", output_dim="1", input_dim="4294967296") ``` With my fix, the call above passes without any issues - output below: ``` [[[-0.5190417]] [[-1.4388928]] [[ 1.1367434]] ... [[-1.4388928]] [[-1.4388928]] [[-0.5190417]]] ``` This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] zhreshold commented on issue #17265: Add bfloat16 floating-point format support based on AMP
zhreshold commented on issue #17265: Add bfloat16 floating-point format support based on AMP URL: https://github.com/apache/incubator-mxnet/pull/17265#issuecomment-586543482 @larroy Do you have idea how to display more logs for the edge tests? It consistently fail at this stage. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu commented on issue #17592: Add cmake build support for macOS static build.
leezu commented on issue #17592: Add cmake build support for macOS static build. URL: https://github.com/apache/incubator-mxnet/pull/17592#issuecomment-586543059 As per https://github.com/leezu/mxnet/commit/0bbd44c45f34c37bf70c90635e29a5fcf2e6f0cc/checks/447232316/logs libtiff 4.1 does not work either. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] access2rohit edited a comment on issue #17599: Fixed Embedding op for LT input
access2rohit edited a comment on issue #17599: Fixed Embedding op for LT input URL: https://github.com/apache/incubator-mxnet/pull/17599#issuecomment-586540131 @connorgoggins can you paste output of a case where sparseEmbedding params are used ? Are both used in the same example use case presented here ? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] access2rohit commented on issue #17599: Fixed Embedding op for LT input
access2rohit commented on issue #17599: Fixed Embedding op for LT input URL: https://github.com/apache/incubator-mxnet/pull/17599#issuecomment-586540131 @connorgoggins can you paste output of a case where sparseEmbedding params are used ? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (40c0c54 -> f619c52)
This is an automated email from the ASF dual-hosted git repository. lausen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 40c0c54 Fix storage type infer of softmax backward (#17576) add f619c52 Python 2 cleanup (#17583) No new revisions were added by this update. Summary of changes: CMakeLists.txt | 2 +- amalgamation/python/mxnet_predict.py | 50 +++- ci/docker/runtime_functions.sh | 7 +- ci/jenkins/Jenkins_steps.groovy| 10 +- .../python/tutorials/packages/gluon/text/gnmt.rst | 7 +- python/mxnet/__init__.py | 1 - python/mxnet/_ctypes/ndarray.py| 1 - python/mxnet/_ctypes/symbol.py | 1 - python/mxnet/_cy2/README | 1 - python/mxnet/_cy2/__init__.py | 18 --- python/mxnet/attribute.py | 1 - python/mxnet/autograd.py | 2 - python/mxnet/base.py | 137 ++--- python/mxnet/callback.py | 1 - python/mxnet/context.py| 1 - python/mxnet/contrib/autograd.py | 2 - python/mxnet/contrib/io.py | 1 - python/mxnet/contrib/onnx/mx2onnx/__init__.py | 1 - .../mxnet/contrib/onnx/mx2onnx/_op_translations.py | 4 - python/mxnet/contrib/onnx/mx2onnx/export_model.py | 4 - python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | 4 - .../contrib/onnx/onnx2mx/_translation_utils.py | 1 - python/mxnet/contrib/onnx/onnx2mx/import_onnx.py | 1 - python/mxnet/contrib/quantization.py | 1 - python/mxnet/contrib/tensorboard.py| 1 - python/mxnet/contrib/text/_constants.py| 2 - python/mxnet/contrib/text/embedding.py | 5 +- python/mxnet/contrib/text/utils.py | 2 - python/mxnet/contrib/text/vocab.py | 2 - python/mxnet/cython/ndarray.pyx| 1 - python/mxnet/cython/symbol.pyx | 1 - python/mxnet/engine.py | 1 - python/mxnet/executor.py | 1 - python/mxnet/executor_manager.py | 1 - python/mxnet/gluon/contrib/data/text.py| 3 +- python/mxnet/gluon/data/dataloader.py | 35 ++ python/mxnet/gluon/loss.py | 1 - python/mxnet/gluon/model_zoo/model_store.py| 5 +- python/mxnet/gluon/model_zoo/vision/resnet.py | 1 - python/mxnet/gluon/model_zoo/vision/vgg.py | 1 - python/mxnet/gluon/parameter.py| 1 - python/mxnet/gluon/rnn/rnn_layer.py| 1 - python/mxnet/gluon/utils.py| 9 +- python/mxnet/image/detection.py| 1 - python/mxnet/image/image.py| 3 +- python/mxnet/initializer.py| 1 - python/mxnet/io/__init__.py| 1 - python/mxnet/io/io.py | 1 - python/mxnet/kvstore/base.py | 1 - python/mxnet/kvstore/kvstore.py| 1 - python/mxnet/kvstore/kvstore_server.py | 1 - python/mxnet/libinfo.py| 1 - python/mxnet/library.py| 1 - python/mxnet/log.py| 8 +- python/mxnet/metric.py | 1 - python/mxnet/model.py | 1 - python/mxnet/monitor.py| 1 - python/mxnet/name.py | 1 - python/mxnet/ndarray/_internal.py | 5 +- python/mxnet/ndarray/contrib.py| 1 - python/mxnet/ndarray/ndarray.py| 16 ++- python/mxnet/ndarray/numpy/_op.py | 1 - python/mxnet/ndarray/numpy/linalg.py | 1 - python/mxnet/ndarray/numpy/random.py | 1 - python/mxnet/ndarray/numpy_extension/random.py | 1 - python/mxnet/ndarray/register.py | 1 - python/mxnet/ndarray/sparse.py | 2 - python/mxnet/ndarray_doc.py| 1 - python/mxnet/numpy/__init__.py | 1 - python/mxnet/numpy/_register.py| 1 - python/mxnet/numpy/arrayprint.py | 1 - python/mxnet/numpy/fallback.py | 1 - python/mxnet/numpy/fallback_linalg.py | 1 - python/mxnet/numpy/function_base.py| 1 - python/mxnet/numpy/io.py | 1 - python/mxnet/numpy/linalg.py | 1 -
[incubator-mxnet] branch master updated (40c0c54 -> f619c52)
This is an automated email from the ASF dual-hosted git repository. lausen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 40c0c54 Fix storage type infer of softmax backward (#17576) add f619c52 Python 2 cleanup (#17583) No new revisions were added by this update. Summary of changes: CMakeLists.txt | 2 +- amalgamation/python/mxnet_predict.py | 50 +++- ci/docker/runtime_functions.sh | 7 +- ci/jenkins/Jenkins_steps.groovy| 10 +- .../python/tutorials/packages/gluon/text/gnmt.rst | 7 +- python/mxnet/__init__.py | 1 - python/mxnet/_ctypes/ndarray.py| 1 - python/mxnet/_ctypes/symbol.py | 1 - python/mxnet/_cy2/README | 1 - python/mxnet/_cy2/__init__.py | 18 --- python/mxnet/attribute.py | 1 - python/mxnet/autograd.py | 2 - python/mxnet/base.py | 137 ++--- python/mxnet/callback.py | 1 - python/mxnet/context.py| 1 - python/mxnet/contrib/autograd.py | 2 - python/mxnet/contrib/io.py | 1 - python/mxnet/contrib/onnx/mx2onnx/__init__.py | 1 - .../mxnet/contrib/onnx/mx2onnx/_op_translations.py | 4 - python/mxnet/contrib/onnx/mx2onnx/export_model.py | 4 - python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | 4 - .../contrib/onnx/onnx2mx/_translation_utils.py | 1 - python/mxnet/contrib/onnx/onnx2mx/import_onnx.py | 1 - python/mxnet/contrib/quantization.py | 1 - python/mxnet/contrib/tensorboard.py| 1 - python/mxnet/contrib/text/_constants.py| 2 - python/mxnet/contrib/text/embedding.py | 5 +- python/mxnet/contrib/text/utils.py | 2 - python/mxnet/contrib/text/vocab.py | 2 - python/mxnet/cython/ndarray.pyx| 1 - python/mxnet/cython/symbol.pyx | 1 - python/mxnet/engine.py | 1 - python/mxnet/executor.py | 1 - python/mxnet/executor_manager.py | 1 - python/mxnet/gluon/contrib/data/text.py| 3 +- python/mxnet/gluon/data/dataloader.py | 35 ++ python/mxnet/gluon/loss.py | 1 - python/mxnet/gluon/model_zoo/model_store.py| 5 +- python/mxnet/gluon/model_zoo/vision/resnet.py | 1 - python/mxnet/gluon/model_zoo/vision/vgg.py | 1 - python/mxnet/gluon/parameter.py| 1 - python/mxnet/gluon/rnn/rnn_layer.py| 1 - python/mxnet/gluon/utils.py| 9 +- python/mxnet/image/detection.py| 1 - python/mxnet/image/image.py| 3 +- python/mxnet/initializer.py| 1 - python/mxnet/io/__init__.py| 1 - python/mxnet/io/io.py | 1 - python/mxnet/kvstore/base.py | 1 - python/mxnet/kvstore/kvstore.py| 1 - python/mxnet/kvstore/kvstore_server.py | 1 - python/mxnet/libinfo.py| 1 - python/mxnet/library.py| 1 - python/mxnet/log.py| 8 +- python/mxnet/metric.py | 1 - python/mxnet/model.py | 1 - python/mxnet/monitor.py| 1 - python/mxnet/name.py | 1 - python/mxnet/ndarray/_internal.py | 5 +- python/mxnet/ndarray/contrib.py| 1 - python/mxnet/ndarray/ndarray.py| 16 ++- python/mxnet/ndarray/numpy/_op.py | 1 - python/mxnet/ndarray/numpy/linalg.py | 1 - python/mxnet/ndarray/numpy/random.py | 1 - python/mxnet/ndarray/numpy_extension/random.py | 1 - python/mxnet/ndarray/register.py | 1 - python/mxnet/ndarray/sparse.py | 2 - python/mxnet/ndarray_doc.py| 1 - python/mxnet/numpy/__init__.py | 1 - python/mxnet/numpy/_register.py| 1 - python/mxnet/numpy/arrayprint.py | 1 - python/mxnet/numpy/fallback.py | 1 - python/mxnet/numpy/fallback_linalg.py | 1 - python/mxnet/numpy/function_base.py| 1 - python/mxnet/numpy/io.py | 1 - python/mxnet/numpy/linalg.py | 1 -
[GitHub] [incubator-mxnet] leezu merged pull request #17583: Python 2 cleanup
leezu merged pull request #17583: Python 2 cleanup URL: https://github.com/apache/incubator-mxnet/pull/17583 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu commented on issue #17596: Fix transformer.cu interleaved matmul for cuda arch < 5
leezu commented on issue #17596: Fix transformer.cu interleaved matmul for cuda arch < 5 URL: https://github.com/apache/incubator-mxnet/pull/17596#issuecomment-586539281 Verified this patch by finetuning Bert on P2 instance. Verification was initially blocked / delayed by https://github.com/apache/incubator-mxnet/pull/17576 ... ``` % python finetune_classifier.py --task_name RTE --batch_size 32 --epochs 3 --gpu 0 --lr 2e-5 INFO:root:01:21:10 Namespace(accumulate=None, batch_size=32, bert_dataset='book_corpus_wiki_en_uncased', bert_model='bert_12_768_12', calib_mode='customize', deploy=False, dev_batch_size=8, dtype='float32', early_stop=None, epochs=3, epsilon=1e-06, gpu=0, log_interval=10, lr=2e-05, max_len=128, model_parameters=None, model_prefix=None, num_calib_batches=5, only_calibration=False, only_inference=False, optimizer='bertadam', output_dir='./output_dir', pretrained_bert_parameters=None, quantized_dtype='auto', round_to=None, seed=2, task_name='RTE', training_steps=None, warmup_ratio=0.1) [01:21:12] ../src/base.cc:84: Upgrade advisory: this mxnet has been built against cuDNN lib version 7501, which is older than the oldest version tested by CI (7600). Set MXNET_CUDNN_LIB_CHECKING=0 to quiet this warning. INFO:root:01:21:26 processing dataset... INFO:root:01:21:35 Now we are doing BERT classification training on gpu(0)! INFO:root:01:21:35 training steps=233 INFO:root:01:21:45 [Epoch 1 Batch 10/82] loss=0.7479, lr=0.078, metrics:accuracy:0.5507 INFO:root:01:21:54 [Epoch 1 Batch 20/82] loss=0.7263, lr=0.165, metrics:accuracy:0.5235 INFO:root:01:22:02 [Epoch 1 Batch 30/82] loss=0.6821, lr=0.194, metrics:accuracy:0.5306 INFO:root:01:22:12 [Epoch 1 Batch 40/82] loss=0.6718, lr=0.185, metrics:accuracy:0.5370 INFO:root:01:22:21 [Epoch 1 Batch 50/82] loss=0.6743, lr=0.175, metrics:accuracy:0.5518 INFO:root:01:22:31 [Epoch 1 Batch 60/82] loss=0.6894, lr=0.166, metrics:accuracy:0.5551 INFO:root:01:22:39 [Epoch 1 Batch 70/82] loss=0.6872, lr=0.156, metrics:accuracy:0.5587 INFO:root:01:22:48 [Epoch 1 Batch 80/82] loss=0.6626, lr=0.147, metrics:accuracy:0.5693 INFO:root:01:22:50 Now we are doing evaluation on dev with gpu(0). INFO:root:01:22:51 [Batch 10/35] loss=0.6449, metrics:accuracy:0.6750 INFO:root:01:22:52 [Batch 20/35] loss=0.6266, metrics:accuracy:0.6813 INFO:root:01:22:54 [Batch 30/35] loss=0.6930, metrics:accuracy:0.6625 INFO:root:01:22:54 validation metrics:accuracy:0.6715 INFO:root:01:22:54 Time cost=4.00s, throughput=69.97 samples/s INFO:root:01:22:55 params saved in: ./output_dir/model_bert_RTE_0.params INFO:root:01:22:55 Time cost=79.30s INFO:root:01:23:03 [Epoch 2 Batch 10/82] loss=0.5310, lr=0.135, metrics:accuracy:0.7719 INFO:root:01:23:12 [Epoch 2 Batch 20/82] loss=0.5022, lr=0.126, metrics:accuracy:0.7650 INFO:root:01:23:22 [Epoch 2 Batch 30/82] loss=0.4835, lr=0.116, metrics:accuracy:0.7733 INFO:root:01:23:31 [Epoch 2 Batch 40/82] loss=0.4762, lr=0.107, metrics:accuracy:0.7754 INFO:root:01:23:40 [Epoch 2 Batch 50/82] loss=0.4412, lr=0.097, metrics:accuracy:0.7728 INFO:root:01:23:48 [Epoch 2 Batch 60/82] loss=0.4915, lr=0.088, metrics:accuracy:0.7741 INFO:root:01:23:57 [Epoch 2 Batch 70/82] loss=0.4512, lr=0.078, metrics:accuracy:0.7767 INFO:root:01:24:05 [Epoch 2 Batch 80/82] loss=0.3897, lr=0.069, metrics:accuracy:0.7832 INFO:root:01:24:06 Now we are doing evaluation on dev with gpu(0). INFO:root:01:24:08 [Batch 10/35] loss=0.6482, metrics:accuracy:0.7125 INFO:root:01:24:09 [Batch 20/35] loss=0.6311, metrics:accuracy:0.7125 INFO:root:01:24:10 [Batch 30/35] loss=0.7034, metrics:accuracy:0.7042 INFO:root:01:24:10 validation metrics:accuracy:0.7076 INFO:root:01:24:10 Time cost=4.00s, throughput=70.06 samples/s INFO:root:01:24:11 params saved in: ./output_dir/model_bert_RTE_1.params INFO:root:01:24:11 Time cost=76.11s INFO:root:01:24:21 [Epoch 3 Batch 10/82] loss=0.2911, lr=0.057, metrics:accuracy:0.9125 INFO:root:01:24:30 [Epoch 3 Batch 20/82] loss=0.2762, lr=0.048, metrics:accuracy:0.9092 INFO:root:01:24:39 [Epoch 3 Batch 30/82] loss=0.2438, lr=0.038, metrics:accuracy:0.9121 INFO:root:01:24:47 [Epoch 3 Batch 40/82] loss=0.2719, lr=0.029, metrics:accuracy:0.9077 INFO:root:01:24:56 [Epoch 3 Batch 50/82] loss=0.2787, lr=0.019, metrics:accuracy:0.9054 INFO:root:01:25:05 [Epoch 3 Batch 60/82] loss=0.3279, lr=0.010, metrics:accuracy:0.9049 INFO:root:01:25:12 Finish training step: 233 INFO:root:01:25:12 Now we are doing evaluation on dev with gpu(0). INFO:root:01:25:14 [Batch 10/35] loss=0.7463, metrics:accuracy:0.7125 INFO:root:01:25:15 [Batch 20/35] loss=0.6660, metrics:accuracy:0.7250 INFO:root:01:25:16 [Batch 30/35] loss=0.7802, metrics:accuracy:0.7125 INFO:root:01:25:16 validation
[incubator-mxnet] branch master updated (39b158f -> 40c0c54)
This is an automated email from the ASF dual-hosted git repository. lausen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 39b158f quantile_scalar (#17572) add 40c0c54 Fix storage type infer of softmax backward (#17576) No new revisions were added by this update. Summary of changes: src/operator/nn/softmax.cc | 16 +++- 1 file changed, 7 insertions(+), 9 deletions(-)
[incubator-mxnet] branch master updated (39b158f -> 40c0c54)
This is an automated email from the ASF dual-hosted git repository. lausen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 39b158f quantile_scalar (#17572) add 40c0c54 Fix storage type infer of softmax backward (#17576) No new revisions were added by this update. Summary of changes: src/operator/nn/softmax.cc | 16 +++- 1 file changed, 7 insertions(+), 9 deletions(-)
[GitHub] [incubator-mxnet] leezu merged pull request #17576: Fix storage type infer of softmax backward
leezu merged pull request #17576: Fix storage type infer of softmax backward URL: https://github.com/apache/incubator-mxnet/pull/17576 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] D-Roberts commented on issue #17590: [numpy] Implement Weibull backward
D-Roberts commented on issue #17590: [numpy] Implement Weibull backward URL: https://github.com/apache/incubator-mxnet/pull/17590#issuecomment-586538103 @haojin2 @xidulu Ready for review. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu commented on issue #17576: Fix storage type infer of softmax backward
leezu commented on issue #17576: Fix storage type infer of softmax backward URL: https://github.com/apache/incubator-mxnet/pull/17576#issuecomment-586537272 @TaoLv could you next time revert the buggy commit? I spent some time today to track down the bug, which could have been avoided. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] connorgoggins commented on issue #17599: Fixed Embedding op for LT input
connorgoggins commented on issue #17599: Fixed Embedding op for LT input URL: https://github.com/apache/incubator-mxnet/pull/17599#issuecomment-586535371 @mxnet-label-bot add [pr-awaiting-review] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] connorgoggins opened a new pull request #17599: Fixed Embedding op for LT input
connorgoggins opened a new pull request #17599: Fixed Embedding op for LT input URL: https://github.com/apache/incubator-mxnet/pull/17599 ## Description ## The Embedding op was previously breaking on large tensor (dimension >= 2^32) data. With the following input: ``` nd.Embedding(data=nd.random_normal(shape=(2**32,1)), weight=nd.random_normal(shape=(2**32,1)), input_dim=2**32, output_dim=1) ``` the following error was thrown: ``` mxnet.base.MXNetError: MXNetError: Invalid Parameter format for input_dim expect int but value='4294967296', in operator Embedding(name="", output_dim="1", input_dim="4294967296") ``` To fix this issue, I modified `indexing_op.h` to switch from storing input_dim as an `int` to storing it as an `index_t`. After implementing my fix and rebuilding, the previous input command displayed the correct output: ``` [[[-0.5190417]] [[-1.4388928]] [[ 1.1367434]] ... [[-1.4388928]] [[-1.4388928]] [[-0.5190417]]] ``` ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [x] Changes are complete (i.e. I finished coding on this PR) - [x] All changes have test coverage - [x] Code is well-documented - [x] To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### - M src/operator/tensor/indexing_op.h ## Comments ## Tested on r5dn.24xl-ubuntu 16.04 and p2.16xl-ubuntu 16.04 with 1. Individual op run 2. Full OpPerf run ## Results ## [Single operator test - Embedding op (GPU)](https://gist.github.com/connorgoggins/2ead27ac3b142e458ddba43eb4093bf7) [Single operator test - Embedding op (CPU)](https://gist.github.com/connorgoggins/cdd5659abd5d2152a29f44a1f1ee03c7) [Full OpPerf test (GPU)](https://gist.github.com/connorgoggins/09131cdbbdcbba1dc39a93099dccaad4) [Full OpPerf test (CPU)](https://gist.github.com/connorgoggins/3fc3c7a40dae8b5cea7f82d9ca4b1a72) @apeforest @access2rohit This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu commented on a change in pull request #17592: Add cmake build support for macOS static build.
leezu commented on a change in pull request #17592: Add cmake build support for macOS static build. URL: https://github.com/apache/incubator-mxnet/pull/17592#discussion_r379702140 ## File path: tools/dependencies/curl.sh ## @@ -35,6 +35,7 @@ if [[ ! -f $DEPS_PATH/lib/libcurl.a ]]; then CONFIG_FLAG="--with-darwinssl" fi ./configure $CONFIG_FLAG \ +--without-libidn2 \ Review comment: > No, there's no known security reason to avoid enabling libidn2 in curl builds. For generic curl builds I would recommend building with it so that users can use international domain names in URLs https://curl.haxx.se/mail/lib-2017-10/0158.html Why disable this? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet-site] branch asf-site updated: Bump the publish timestamp.
This is an automated email from the ASF dual-hosted git repository. aaronmarkham pushed a commit to branch asf-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git The following commit(s) were added to refs/heads/asf-site by this push: new fe0b694 Bump the publish timestamp. fe0b694 is described below commit fe0b6944b48982ffe156a315b98397116cd592ce Author: mxnet-ci AuthorDate: Sat Feb 15 00:43:03 2020 + Bump the publish timestamp. --- date.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/date.txt b/date.txt new file mode 100644 index 000..c84af6f --- /dev/null +++ b/date.txt @@ -0,0 +1 @@ +Sat Feb 15 00:43:03 UTC 2020
[GitHub] [incubator-mxnet] apeforest commented on a change in pull request #16735: Use single-bit for mask in dropout operator
apeforest commented on a change in pull request #16735: Use single-bit for mask
in dropout operator
URL: https://github.com/apache/incubator-mxnet/pull/16735#discussion_r379688573
##
File path: src/operator/nn/dropout-inl.h
##
@@ -152,15 +181,20 @@ class DropoutOp {
const std::vector _grad) {
Stream *s = ctx.get_stream();
Tensor grad = out_grad[dropout::kOut].FlatTo2D(s);
-Tensor mask = out_data[dropout::kMask].FlatTo2D(s);
+Tensor mask = out_data[dropout::kMask].FlatTo1D(s);
Tensor gdata = in_grad[dropout::kData].FlatTo2D(s);
DType *ingradptr = gdata.dptr_;
const DType *outgradptr = grad.dptr_;
-const DType *maskptr = mask.dptr_;
-const int count = mask.shape_[0] * mask.shape_[1];
-#pragma omp parallel for
num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
-for (int i = 0; i < count; ++i) {
- ingradptr[i] = outgradptr[i] * maskptr[i];
+const uint8_t *maskptr = mask.dptr_;
+const index_t count = grad.shape_[0] * grad.shape_[1];
+const float pk_1 = 1.0f / this->pkeep_;
+const int nthr = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+#pragma omp parallel for num_threads(nthr) schedule(static, 8)
+for (index_t i = 0; i < count; ++i) {
+ auto mask_idx = i >> 3; // div 8;
+ uint8_t mask_offset = i & 7; // mod 8
+ bool mask_val = maskptr[mask_idx] & (1U << mask_offset);
+ ingradptr[i] = outgradptr[i] * mask_val * pk_1;
Review comment:
After more thoughts, I think we actually don't need to do blocking in the
backward pass as there is no write to maskptr and hence no cache eviction nor
race condition.
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[GitHub] [incubator-mxnet] apeforest edited a comment on issue #16735: Use single-bit for mask in dropout operator
apeforest edited a comment on issue #16735: Use single-bit for mask in dropout operator URL: https://github.com/apache/incubator-mxnet/pull/16735#issuecomment-586519348 > Does the slow down come from more computation in the new algorithm or the sub-optimal implementation? @PatricZhao The slowdown comes from extra computation in the new algorithm when Dropout uses MKL implementation. MKL already computed the mask but stored each mask as integer. The new algorithm simply repackage this int32 based mask into bit-based mask and therefore introduced extra runtime. In the ideal case, it would be to enhance MKL dropout to store mask using bits. But it requires modification of the VSL APIs. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] apeforest commented on issue #16735: Use single-bit for mask in dropout operator
apeforest commented on issue #16735: Use single-bit for mask in dropout operator URL: https://github.com/apache/incubator-mxnet/pull/16735#issuecomment-586519348 > Does the slow down come from more computation in the new algorithm or the sub-optimal implementation? @PatricZhao The slowdown comes from extra computation in the new algorithm when dropout uses MKL. MKL already does the masking but stores each mask as integer. The new algorithm simply repackage this int32 based mask into bit-based mask and therefore introduced extra runtime. In the ideal case, it would be to enhance MKL dropout to store mask using bits. But it requires modification of the VSL APIs. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] apeforest commented on a change in pull request #16735: Use single-bit for mask in dropout operator
apeforest commented on a change in pull request #16735: Use single-bit for mask
in dropout operator
URL: https://github.com/apache/incubator-mxnet/pull/16735#discussion_r379688573
##
File path: src/operator/nn/dropout-inl.h
##
@@ -152,15 +181,20 @@ class DropoutOp {
const std::vector _grad) {
Stream *s = ctx.get_stream();
Tensor grad = out_grad[dropout::kOut].FlatTo2D(s);
-Tensor mask = out_data[dropout::kMask].FlatTo2D(s);
+Tensor mask = out_data[dropout::kMask].FlatTo1D(s);
Tensor gdata = in_grad[dropout::kData].FlatTo2D(s);
DType *ingradptr = gdata.dptr_;
const DType *outgradptr = grad.dptr_;
-const DType *maskptr = mask.dptr_;
-const int count = mask.shape_[0] * mask.shape_[1];
-#pragma omp parallel for
num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
-for (int i = 0; i < count; ++i) {
- ingradptr[i] = outgradptr[i] * maskptr[i];
+const uint8_t *maskptr = mask.dptr_;
+const index_t count = grad.shape_[0] * grad.shape_[1];
+const float pk_1 = 1.0f / this->pkeep_;
+const int nthr = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+#pragma omp parallel for num_threads(nthr) schedule(static, 8)
+for (index_t i = 0; i < count; ++i) {
+ auto mask_idx = i >> 3; // div 8;
+ uint8_t mask_offset = i & 7; // mod 8
+ bool mask_val = maskptr[mask_idx] & (1U << mask_offset);
+ ingradptr[i] = outgradptr[i] * mask_val * pk_1;
Review comment:
After more thoughts, I think we actually don't need to do blocking in the
backward pass as there is no write to maskptr and hence no race condition
either.
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add op insert
haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add
op insert
URL: https://github.com/apache/incubator-mxnet/pull/16865#discussion_r379682542
##
File path: src/operator/numpy/np_insert_op_tensor-inl.h
##
@@ -0,0 +1,229 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_insert_op-inl.h
+ * \brief Function definition of insert operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_INSERT_OP_TENSOR_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_INSERT_OP_TENSOR_INL_H_
+
+#include
+#include
+#include
+#include "../../common/utils.h"
+#include "../tensor/sort_op.h"
+#include "../tensor/init_op.h"
+#include "../operator_common.h"
+#include "../mxnet_op.h"
+#include "./np_delete_op-inl.h"
+#include "./np_insert_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+/*
+ * Only support tensor indices (the type of param 'obj' is tensor).
+ */
+template
+void NumpyInsertTensorCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const NumpyInsertParam& param = nnvm::get(attrs.parsed);
+ int input_count = param.val.has_value() ? 1 : 2;
+ int insize = input_count + 1;
+ CHECK_EQ(inputs.size(), insize);
+ CHECK_EQ(outputs.size(), 1);
+ CHECK_EQ(req.size(), 1);
+ mshadow::Stream *s = ctx.get_stream();
+ const int arr_pos = 0;
+ const int val_pos = param.val.has_value() ? 0 : 1;
+ const int obj_pos = val_pos + 1;
+ const int out_pos = 0;
+ int ndim = inputs[arr_pos].shape_.ndim();
+ int axis = param.axis.has_value() ? param.axis.value() : 0;
+ TBlob arr;
+ TBlob values = param.val.has_value() ?
+ TBlob(nullptr, mxnet::TShape(0, 1), xpu::kDevMask,
outputs[out_pos].type_flag_) :
+ inputs[val_pos];
+ if (!param.axis.has_value()) {
+arr = inputs[arr_pos].reshape(Shape1(inputs[arr_pos].shape_.Size()));
+ndim = 1;
+ } else if (ndim == 0) {
+if (param.val.has_value()) {
+ CHECK_EQ(inputs[val_pos].shape_.ndim(), 0)
+<< "'arr' is a 0-d array, 'values' can not assign to it. "
+<< "alueError: assignment to 0-d array.";
+ mxnet_op::copy(s, outputs[out_pos], inputs[val_pos]);
+} else {
+ MSHADOW_TYPE_SWITCH(outputs[out_pos].type_flag_, DType, {
+Fill(s, outputs[out_pos], req[0],
static_cast(param.val.value()));
+ });
+}
+return;
+ } else {
+arr = inputs[arr_pos];
+CHECK(axis >= -1 * arr.shape_.ndim() && axis < arr.shape_.ndim())
+ << "Axis should be in the range of [-r, r-1] where r is the rank of
input tensor";
+axis += (axis < 0) ? arr.shape_.ndim() : 0;
+ }
+
+ int N = arr.shape_[axis];
+ size_t indices_len = inputs[obj_pos].shape_.Size(); // indices amount
+
+ // get and check indices from tensor
+ int numnew = 0; // numnew = output.shape[axis] - arr.shape[axis]
+ mxnet::TShape val_newshape(arr.shape_.ndim(), -1);
+ // modify values's ndim to arr's ndim, for broadcast easily later
+ // e.g. value shape: (2,) arr shape: (3, 2) => value shape: (1, 2)
+ for (int i = values.shape_.ndim() - 1, j = arr.shape_.ndim() - 1;
+i >= 0 || j >= 0;
+--i, --j) {
+if (i >= 0 && j >= 0) {
+ val_newshape[j] = values.shape_[i];
+} else if (i >= 0) {
+ CHECK_EQ(values.shape_[i], 1) << "index exceed limits.";
+} else {
+ val_newshape[j] = 1;
+}
+ }
+ values.shape_.assign(val_newshape.begin(), val_newshape.end());
+
+ // get numnew
+ mxnet::TShape old_valshape(values.shape_);
+ if (inputs[obj_pos].shape_.ndim() == 0) { // scaler
+// values = moveaxis(values, 0, axis), will change values's shape
+numnew = values.shape_[0];
+mxnet::TShape axes(values.ndim(), -1); // moved axes
+mxnet::TShape val_newshape(values.ndim(), -1);
+int axes_id = 0;
+for (int i = 1; i <= axis; ++i) {
+ axes[axes_id++] = i;
+}
+axes[axes_id++] = 0;
+for (int i = axis + 1; i < values.ndim(); ++i) {
+
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add op insert
haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add
op insert
URL: https://github.com/apache/incubator-mxnet/pull/16865#discussion_r379682473
##
File path: src/operator/numpy/np_insert_op_tensor-inl.h
##
@@ -0,0 +1,229 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_insert_op-inl.h
+ * \brief Function definition of insert operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_INSERT_OP_TENSOR_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_INSERT_OP_TENSOR_INL_H_
+
+#include
+#include
+#include
+#include "../../common/utils.h"
+#include "../tensor/sort_op.h"
+#include "../tensor/init_op.h"
+#include "../operator_common.h"
+#include "../mxnet_op.h"
+#include "./np_delete_op-inl.h"
+#include "./np_insert_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+/*
+ * Only support tensor indices (the type of param 'obj' is tensor).
+ */
+template
+void NumpyInsertTensorCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const NumpyInsertParam& param = nnvm::get(attrs.parsed);
+ int input_count = param.val.has_value() ? 1 : 2;
+ int insize = input_count + 1;
+ CHECK_EQ(inputs.size(), insize);
+ CHECK_EQ(outputs.size(), 1);
+ CHECK_EQ(req.size(), 1);
+ mshadow::Stream *s = ctx.get_stream();
+ const int arr_pos = 0;
+ const int val_pos = param.val.has_value() ? 0 : 1;
+ const int obj_pos = val_pos + 1;
+ const int out_pos = 0;
+ int ndim = inputs[arr_pos].shape_.ndim();
+ int axis = param.axis.has_value() ? param.axis.value() : 0;
+ TBlob arr;
+ TBlob values = param.val.has_value() ?
+ TBlob(nullptr, mxnet::TShape(0, 1), xpu::kDevMask,
outputs[out_pos].type_flag_) :
+ inputs[val_pos];
+ if (!param.axis.has_value()) {
+arr = inputs[arr_pos].reshape(Shape1(inputs[arr_pos].shape_.Size()));
+ndim = 1;
+ } else if (ndim == 0) {
+if (param.val.has_value()) {
+ CHECK_EQ(inputs[val_pos].shape_.ndim(), 0)
+<< "'arr' is a 0-d array, 'values' can not assign to it. "
+<< "alueError: assignment to 0-d array.";
+ mxnet_op::copy(s, outputs[out_pos], inputs[val_pos]);
+} else {
+ MSHADOW_TYPE_SWITCH(outputs[out_pos].type_flag_, DType, {
+Fill(s, outputs[out_pos], req[0],
static_cast(param.val.value()));
+ });
+}
+return;
+ } else {
+arr = inputs[arr_pos];
+CHECK(axis >= -1 * arr.shape_.ndim() && axis < arr.shape_.ndim())
+ << "Axis should be in the range of [-r, r-1] where r is the rank of
input tensor";
+axis += (axis < 0) ? arr.shape_.ndim() : 0;
+ }
+
+ int N = arr.shape_[axis];
+ size_t indices_len = inputs[obj_pos].shape_.Size(); // indices amount
+
+ // get and check indices from tensor
+ int numnew = 0; // numnew = output.shape[axis] - arr.shape[axis]
+ mxnet::TShape val_newshape(arr.shape_.ndim(), -1);
+ // modify values's ndim to arr's ndim, for broadcast easily later
+ // e.g. value shape: (2,) arr shape: (3, 2) => value shape: (1, 2)
+ for (int i = values.shape_.ndim() - 1, j = arr.shape_.ndim() - 1;
+i >= 0 || j >= 0;
+--i, --j) {
+if (i >= 0 && j >= 0) {
+ val_newshape[j] = values.shape_[i];
+} else if (i >= 0) {
+ CHECK_EQ(values.shape_[i], 1) << "index exceed limits.";
+} else {
+ val_newshape[j] = 1;
+}
+ }
+ values.shape_.assign(val_newshape.begin(), val_newshape.end());
+
+ // get numnew
+ mxnet::TShape old_valshape(values.shape_);
+ if (inputs[obj_pos].shape_.ndim() == 0) { // scaler
+// values = moveaxis(values, 0, axis), will change values's shape
+numnew = values.shape_[0];
+mxnet::TShape axes(values.ndim(), -1); // moved axes
+mxnet::TShape val_newshape(values.ndim(), -1);
+int axes_id = 0;
+for (int i = 1; i <= axis; ++i) {
+ axes[axes_id++] = i;
+}
+axes[axes_id++] = 0;
+for (int i = axis + 1; i < values.ndim(); ++i) {
+
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add op insert
haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add
op insert
URL: https://github.com/apache/incubator-mxnet/pull/16865#discussion_r379682391
##
File path: src/operator/numpy/np_insert_op_slice-inl.h
##
@@ -0,0 +1,200 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_insert_op_slice-inl.h
+ * \brief Function definition of insert operators (insert by slice index)
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_INSERT_OP_SLICE_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_INSERT_OP_SLICE_INL_H_
+
+#include
+#include
+#include "./np_insert_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+/*
+ * Only support slice index (the type of param 'obj' is slice).
+ */
+template
+void NumpyInsertSliceCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const NumpyInsertParam& param = nnvm::get(attrs.parsed);
+ CHECK_EQ(inputs.size(), (param.val.has_value() ? 1 : 2));
+ CHECK_EQ(outputs.size(), 1);
+ CHECK_EQ(req.size(), 1);
+ mshadow::Stream *s = ctx.get_stream();
+ const int arr_pos = 0;
+ const int val_pos = param.val.has_value() ? 0 : 1;
+ const int out_pos = 0;
+ int ndim = inputs[arr_pos].shape_.ndim();
+ int axis = param.axis.has_value() ? param.axis.value() : 0;
+ TBlob arr;
+ TBlob values = param.val.has_value() ?
+ TBlob(nullptr, mxnet::TShape(0, 1), xpu::kDevMask,
outputs[out_pos].type_flag_) :
+ inputs[val_pos];
+ if (!param.axis.has_value()) {
+arr = inputs[arr_pos].reshape(Shape1(inputs[arr_pos].shape_.Size()));
+ndim = 1;
+ } else if (ndim == 0) {
+if (param.val.has_value()) {
+ CHECK_EQ(inputs[val_pos].shape_.ndim(), 0)
+<< "'arr' is a 0-d array, 'values' can not assign to it. "
+<< "alueError: assignment to 0-d array.";
+ mxnet_op::copy(s, outputs[out_pos], inputs[val_pos]);
+} else {
+ MSHADOW_TYPE_SWITCH(outputs[out_pos].type_flag_, DType, {
+Fill(s, outputs[out_pos], req[0],
static_cast(param.val.value()));
+ });
+}
+return;
+ } else {
+arr = inputs[arr_pos];
+CHECK(axis >= -1 * arr.shape_.ndim() && axis < arr.shape_.ndim())
+ << "Axis should be in the range of [-r, r-1] where r is the rank of
input tensor";
+axis += (axis < 0) ? arr.shape_.ndim() : 0;
+ }
+
+ int N = arr.shape_[axis];
+ size_t indices_len = 0; // indices amount
+ int start = 0, stop = 0, step = 0; // arguments from 'obj' when it's 'slice'
+
+ // get and check indices from slice or sequence of ints
+ SliceIndices(param.start, param.stop, param.step,
+N, , , , _len);
+
+ int numnew = 0; // numnew = output.shape[axis] - arr.shape[axis]
+ int index = 0; // save modified index, because index may be negative integer
+ mxnet::TShape val_newshape(arr.shape_.ndim(), -1);
+ // modify values's ndim to arr's ndim, for broadcast easily later
+ // e.g. value shape: (2,) arr shape: (3, 2) => value shape: (1, 2)
+ for (int i = values.shape_.ndim() - 1, j = arr.shape_.ndim() - 1;
+i >= 0 || j >= 0;
+--i, --j) {
+if (i >= 0 && j >= 0) {
+ val_newshape[j] = values.shape_[i];
+} else if (i >= 0) {
+ CHECK_EQ(values.shape_[i], 1) << "index exceed limits.";
+} else {
+ val_newshape[j] = 1;
+}
+ }
+ values.shape_.assign(val_newshape.begin(), val_newshape.end());
+
+ // get numnew
+ mxnet::TShape old_valshape(values.shape_);
+ if (indices_len == 1) { // tensor with only one element
+numnew = values.shape_[axis];
+index = start;
+CHECK(index >= -1 * N && index <= N)
+ << "Index should be in the range of [-r, r-1] where r is the dim size in
'axis'";
+if (index < 0) {
+ index += N;
+}
+ } else {
+numnew = static_cast(indices_len);
+ }
+
+ const mxnet::TShape& outshape = outputs[out_pos].shape_;
+ int dtype = outputs[out_pos].type_flag_;
+ int vtype = param.val.has_value() ?
+
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add op insert
haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add
op insert
URL: https://github.com/apache/incubator-mxnet/pull/16865#discussion_r379682345
##
File path: src/operator/numpy/np_insert_op_slice-inl.h
##
@@ -0,0 +1,200 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_insert_op_slice-inl.h
+ * \brief Function definition of insert operators (insert by slice index)
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_INSERT_OP_SLICE_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_INSERT_OP_SLICE_INL_H_
+
+#include
+#include
+#include "./np_insert_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+/*
+ * Only support slice index (the type of param 'obj' is slice).
+ */
+template
+void NumpyInsertSliceCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const NumpyInsertParam& param = nnvm::get(attrs.parsed);
+ CHECK_EQ(inputs.size(), (param.val.has_value() ? 1 : 2));
+ CHECK_EQ(outputs.size(), 1);
+ CHECK_EQ(req.size(), 1);
+ mshadow::Stream *s = ctx.get_stream();
+ const int arr_pos = 0;
+ const int val_pos = param.val.has_value() ? 0 : 1;
+ const int out_pos = 0;
+ int ndim = inputs[arr_pos].shape_.ndim();
+ int axis = param.axis.has_value() ? param.axis.value() : 0;
+ TBlob arr;
+ TBlob values = param.val.has_value() ?
+ TBlob(nullptr, mxnet::TShape(0, 1), xpu::kDevMask,
outputs[out_pos].type_flag_) :
+ inputs[val_pos];
+ if (!param.axis.has_value()) {
+arr = inputs[arr_pos].reshape(Shape1(inputs[arr_pos].shape_.Size()));
+ndim = 1;
+ } else if (ndim == 0) {
+if (param.val.has_value()) {
+ CHECK_EQ(inputs[val_pos].shape_.ndim(), 0)
+<< "'arr' is a 0-d array, 'values' can not assign to it. "
+<< "alueError: assignment to 0-d array.";
+ mxnet_op::copy(s, outputs[out_pos], inputs[val_pos]);
+} else {
+ MSHADOW_TYPE_SWITCH(outputs[out_pos].type_flag_, DType, {
+Fill(s, outputs[out_pos], req[0],
static_cast(param.val.value()));
+ });
+}
+return;
+ } else {
+arr = inputs[arr_pos];
+CHECK(axis >= -1 * arr.shape_.ndim() && axis < arr.shape_.ndim())
+ << "Axis should be in the range of [-r, r-1] where r is the rank of
input tensor";
+axis += (axis < 0) ? arr.shape_.ndim() : 0;
+ }
+
+ int N = arr.shape_[axis];
+ size_t indices_len = 0; // indices amount
+ int start = 0, stop = 0, step = 0; // arguments from 'obj' when it's 'slice'
+
+ // get and check indices from slice or sequence of ints
+ SliceIndices(param.start, param.stop, param.step,
+N, , , , _len);
+
+ int numnew = 0; // numnew = output.shape[axis] - arr.shape[axis]
+ int index = 0; // save modified index, because index may be negative integer
+ mxnet::TShape val_newshape(arr.shape_.ndim(), -1);
+ // modify values's ndim to arr's ndim, for broadcast easily later
+ // e.g. value shape: (2,) arr shape: (3, 2) => value shape: (1, 2)
+ for (int i = values.shape_.ndim() - 1, j = arr.shape_.ndim() - 1;
+i >= 0 || j >= 0;
+--i, --j) {
+if (i >= 0 && j >= 0) {
+ val_newshape[j] = values.shape_[i];
+} else if (i >= 0) {
+ CHECK_EQ(values.shape_[i], 1) << "index exceed limits.";
+} else {
+ val_newshape[j] = 1;
+}
+ }
+ values.shape_.assign(val_newshape.begin(), val_newshape.end());
+
+ // get numnew
+ mxnet::TShape old_valshape(values.shape_);
+ if (indices_len == 1) { // tensor with only one element
+numnew = values.shape_[axis];
+index = start;
+CHECK(index >= -1 * N && index <= N)
+ << "Index should be in the range of [-r, r-1] where r is the dim size in
'axis'";
+if (index < 0) {
+ index += N;
+}
+ } else {
+numnew = static_cast(indices_len);
+ }
+
+ const mxnet::TShape& outshape = outputs[out_pos].shape_;
+ int dtype = outputs[out_pos].type_flag_;
+ int vtype = param.val.has_value() ?
+
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add op insert
haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add
op insert
URL: https://github.com/apache/incubator-mxnet/pull/16865#discussion_r379682486
##
File path: src/operator/numpy/np_insert_op_tensor-inl.h
##
@@ -0,0 +1,229 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_insert_op-inl.h
+ * \brief Function definition of insert operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_INSERT_OP_TENSOR_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_INSERT_OP_TENSOR_INL_H_
+
+#include
+#include
+#include
+#include "../../common/utils.h"
+#include "../tensor/sort_op.h"
+#include "../tensor/init_op.h"
+#include "../operator_common.h"
+#include "../mxnet_op.h"
+#include "./np_delete_op-inl.h"
+#include "./np_insert_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+/*
+ * Only support tensor indices (the type of param 'obj' is tensor).
+ */
+template
+void NumpyInsertTensorCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const NumpyInsertParam& param = nnvm::get(attrs.parsed);
+ int input_count = param.val.has_value() ? 1 : 2;
+ int insize = input_count + 1;
+ CHECK_EQ(inputs.size(), insize);
+ CHECK_EQ(outputs.size(), 1);
+ CHECK_EQ(req.size(), 1);
+ mshadow::Stream *s = ctx.get_stream();
+ const int arr_pos = 0;
+ const int val_pos = param.val.has_value() ? 0 : 1;
+ const int obj_pos = val_pos + 1;
+ const int out_pos = 0;
+ int ndim = inputs[arr_pos].shape_.ndim();
+ int axis = param.axis.has_value() ? param.axis.value() : 0;
+ TBlob arr;
+ TBlob values = param.val.has_value() ?
+ TBlob(nullptr, mxnet::TShape(0, 1), xpu::kDevMask,
outputs[out_pos].type_flag_) :
+ inputs[val_pos];
+ if (!param.axis.has_value()) {
+arr = inputs[arr_pos].reshape(Shape1(inputs[arr_pos].shape_.Size()));
+ndim = 1;
+ } else if (ndim == 0) {
+if (param.val.has_value()) {
+ CHECK_EQ(inputs[val_pos].shape_.ndim(), 0)
+<< "'arr' is a 0-d array, 'values' can not assign to it. "
+<< "alueError: assignment to 0-d array.";
+ mxnet_op::copy(s, outputs[out_pos], inputs[val_pos]);
+} else {
+ MSHADOW_TYPE_SWITCH(outputs[out_pos].type_flag_, DType, {
+Fill(s, outputs[out_pos], req[0],
static_cast(param.val.value()));
+ });
+}
+return;
+ } else {
+arr = inputs[arr_pos];
+CHECK(axis >= -1 * arr.shape_.ndim() && axis < arr.shape_.ndim())
+ << "Axis should be in the range of [-r, r-1] where r is the rank of
input tensor";
+axis += (axis < 0) ? arr.shape_.ndim() : 0;
+ }
+
+ int N = arr.shape_[axis];
+ size_t indices_len = inputs[obj_pos].shape_.Size(); // indices amount
+
+ // get and check indices from tensor
+ int numnew = 0; // numnew = output.shape[axis] - arr.shape[axis]
+ mxnet::TShape val_newshape(arr.shape_.ndim(), -1);
+ // modify values's ndim to arr's ndim, for broadcast easily later
+ // e.g. value shape: (2,) arr shape: (3, 2) => value shape: (1, 2)
+ for (int i = values.shape_.ndim() - 1, j = arr.shape_.ndim() - 1;
+i >= 0 || j >= 0;
+--i, --j) {
+if (i >= 0 && j >= 0) {
+ val_newshape[j] = values.shape_[i];
+} else if (i >= 0) {
+ CHECK_EQ(values.shape_[i], 1) << "index exceed limits.";
+} else {
+ val_newshape[j] = 1;
+}
+ }
+ values.shape_.assign(val_newshape.begin(), val_newshape.end());
+
+ // get numnew
+ mxnet::TShape old_valshape(values.shape_);
+ if (inputs[obj_pos].shape_.ndim() == 0) { // scaler
+// values = moveaxis(values, 0, axis), will change values's shape
+numnew = values.shape_[0];
+mxnet::TShape axes(values.ndim(), -1); // moved axes
+mxnet::TShape val_newshape(values.ndim(), -1);
+int axes_id = 0;
+for (int i = 1; i <= axis; ++i) {
+ axes[axes_id++] = i;
+}
+axes[axes_id++] = 0;
+for (int i = axis + 1; i < values.ndim(); ++i) {
+
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add op insert
haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add
op insert
URL: https://github.com/apache/incubator-mxnet/pull/16865#discussion_r379682089
##
File path: src/operator/numpy/np_insert_op-inl.h
##
@@ -0,0 +1,371 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_insert_op-inl.h
+ * \brief Function definition of insert operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_INSERT_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_INSERT_OP_INL_H_
+
+#include
+#include
+#include
+#include "../../common/utils.h"
+#include "../tensor/sort_op.h"
+#include "../tensor/init_op.h"
+#include "../operator_common.h"
+#include "../mxnet_op.h"
+#include "./np_delete_op-inl.h"
+namespace mxnet {
+namespace op {
+
+struct NumpyInsertParam : public dmlc::Parameter {
+ dmlc::optional val;
+ dmlc::optional start;
+ dmlc::optional stop;
+ dmlc::optional step;
+ dmlc::optional int_ind;
+ dmlc::optional axis;
+ DMLC_DECLARE_PARAMETER(NumpyInsertParam) {
+DMLC_DECLARE_FIELD(val)
+.set_default(dmlc::optional())
+.describe("A scaler to be inserted into 'array'");
+DMLC_DECLARE_FIELD(start)
+.set_default(dmlc::optional())
+.describe("If 'obj' is slice, 'start' is one of it's arguments.");
+DMLC_DECLARE_FIELD(stop)
+.set_default(dmlc::optional())
+.describe("If 'obj' is slice, 'stop' is one of it's arguments.");
+DMLC_DECLARE_FIELD(step)
+.set_default(dmlc::optional())
+.describe("If 'obj' is slice, 'step' is one of it's arguments.");
+DMLC_DECLARE_FIELD(int_ind)
+.set_default(dmlc::optional())
+.describe("If 'obj' is int, 'int_ind' is the index before which"
+ "'values' is inserted");
+DMLC_DECLARE_FIELD(axis)
+.set_default(dmlc::optional())
+.describe("Axis along which to insert 'values'.");
+ }
+};
+
+/*!
+ * \brief insert when obj is 'scaler' or a 'slice' with only one element.
+ * \tparam ndim - both 'in_arr', 'in_val' and 'out_data' have same ndim before
call this.
+ * \param out_data - output: insert 'value' to 'arr' according to 'index'.
+ * \param in_arr - input: 'arr', original array.
+ * \param index - input(only for first Map): it's the only element in 'obj'
indicats insert position.
+ * \param in_obj - input(only for second Map): It indicats insert position,
it's ndim may equals to 0.
+ * \param in_val - input: 'value', insert to 'arr' according to 'index'.
+ * \param N - (only for first Map) arr.shape_[axis]
+ * \param numnew - extra dim size in 'out_data' compared with 'arr' in 'axis'.
+ * \param axis - insert 'value' to 'arr' in 'axis'.
+ * \param moveaxis - If 'obj' is a scaler, moveaxis is true;
+ If 'obj' is a slice with one element, moveaxis is false.
+ * \note Different between the two Map:
+ The first one use a scaler index;
+ The second one use a sequence of indecies which only has one index.
+ */
+template
+struct InsertSingleIndexKernel {
+ template
+ MSHADOW_XINLINE static void Map(int i, DType* out_data,
+ const VType* in_val, const DType* in_arr,
+ const mshadow::Shape outshape,
+ const mshadow::Shape valshape,
+ const int index, const int numnew,
+ const mshadow::Shape val_stride,
+ const mshadow::Shape old_val_stride,
+ const mshadow::Shape arr_stride,
+ const mshadow::Shape out_stride,
+ const int axis, bool moveaxis, const int
req) {
+// i is the global flattened index in the output
+// out_idx: i -> position in output's shape
+mshadow::Shape out_idx = mxnet_op::unravel(i, outshape);
+int64_t dest_idx;
+if (out_idx[axis] >= index && out_idx[axis] < index + numnew) { // from
'value'
+ int idx_val = out_idx[axis] - index;
+ // val_idx: i -> position in values's shape
+ mshadow::Shape val_idx(out_idx);
+ val_idx[axis] = idx_val;
+ for (int j = ndim - 1; j >= 0; --j) {
+
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add op insert
haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add
op insert
URL: https://github.com/apache/incubator-mxnet/pull/16865#discussion_r379682109
##
File path: src/operator/numpy/np_insert_op-inl.h
##
@@ -0,0 +1,371 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_insert_op-inl.h
+ * \brief Function definition of insert operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_INSERT_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_INSERT_OP_INL_H_
+
+#include
+#include
+#include
+#include "../../common/utils.h"
+#include "../tensor/sort_op.h"
+#include "../tensor/init_op.h"
+#include "../operator_common.h"
+#include "../mxnet_op.h"
+#include "./np_delete_op-inl.h"
+namespace mxnet {
+namespace op {
+
+struct NumpyInsertParam : public dmlc::Parameter {
+ dmlc::optional val;
+ dmlc::optional start;
+ dmlc::optional stop;
+ dmlc::optional step;
+ dmlc::optional int_ind;
+ dmlc::optional axis;
+ DMLC_DECLARE_PARAMETER(NumpyInsertParam) {
+DMLC_DECLARE_FIELD(val)
+.set_default(dmlc::optional())
+.describe("A scaler to be inserted into 'array'");
+DMLC_DECLARE_FIELD(start)
+.set_default(dmlc::optional())
+.describe("If 'obj' is slice, 'start' is one of it's arguments.");
+DMLC_DECLARE_FIELD(stop)
+.set_default(dmlc::optional())
+.describe("If 'obj' is slice, 'stop' is one of it's arguments.");
+DMLC_DECLARE_FIELD(step)
+.set_default(dmlc::optional())
+.describe("If 'obj' is slice, 'step' is one of it's arguments.");
+DMLC_DECLARE_FIELD(int_ind)
+.set_default(dmlc::optional())
+.describe("If 'obj' is int, 'int_ind' is the index before which"
+ "'values' is inserted");
+DMLC_DECLARE_FIELD(axis)
+.set_default(dmlc::optional())
+.describe("Axis along which to insert 'values'.");
+ }
+};
+
+/*!
+ * \brief insert when obj is 'scaler' or a 'slice' with only one element.
+ * \tparam ndim - both 'in_arr', 'in_val' and 'out_data' have same ndim before
call this.
+ * \param out_data - output: insert 'value' to 'arr' according to 'index'.
+ * \param in_arr - input: 'arr', original array.
+ * \param index - input(only for first Map): it's the only element in 'obj'
indicats insert position.
+ * \param in_obj - input(only for second Map): It indicats insert position,
it's ndim may equals to 0.
+ * \param in_val - input: 'value', insert to 'arr' according to 'index'.
+ * \param N - (only for first Map) arr.shape_[axis]
+ * \param numnew - extra dim size in 'out_data' compared with 'arr' in 'axis'.
+ * \param axis - insert 'value' to 'arr' in 'axis'.
+ * \param moveaxis - If 'obj' is a scaler, moveaxis is true;
+ If 'obj' is a slice with one element, moveaxis is false.
+ * \note Different between the two Map:
+ The first one use a scaler index;
+ The second one use a sequence of indecies which only has one index.
+ */
+template
+struct InsertSingleIndexKernel {
+ template
+ MSHADOW_XINLINE static void Map(int i, DType* out_data,
+ const VType* in_val, const DType* in_arr,
+ const mshadow::Shape outshape,
+ const mshadow::Shape valshape,
+ const int index, const int numnew,
+ const mshadow::Shape val_stride,
+ const mshadow::Shape old_val_stride,
+ const mshadow::Shape arr_stride,
+ const mshadow::Shape out_stride,
+ const int axis, bool moveaxis, const int
req) {
+// i is the global flattened index in the output
+// out_idx: i -> position in output's shape
+mshadow::Shape out_idx = mxnet_op::unravel(i, outshape);
+int64_t dest_idx;
+if (out_idx[axis] >= index && out_idx[axis] < index + numnew) { // from
'value'
+ int idx_val = out_idx[axis] - index;
+ // val_idx: i -> position in values's shape
+ mshadow::Shape val_idx(out_idx);
+ val_idx[axis] = idx_val;
+ for (int j = ndim - 1; j >= 0; --j) {
+
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add op insert
haojin2 commented on a change in pull request #16865: [numpy][Do Not Review]add
op insert
URL: https://github.com/apache/incubator-mxnet/pull/16865#discussion_r379681197
##
File path: src/operator/numpy/np_insert_op_tensor.cc
##
@@ -0,0 +1,152 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_insert_op_tensor.cc
+ * \brief CPU Implementation of numpy insert operations
+ */
+
+#include
+#include "./np_insert_op-inl.h"
+#include "./np_insert_op_tensor-inl.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyInsertParam);
+
+bool NumpyInsertTensorType(const nnvm::NodeAttrs& attrs,
+ std::vector *in_type,
Review comment:
alignment.
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[GitHub] [incubator-mxnet] haojin2 commented on issue #17590: [WIP][numpy] Implement Weibull backward
haojin2 commented on issue #17590: [WIP][numpy] Implement Weibull backward URL: https://github.com/apache/incubator-mxnet/pull/17590#issuecomment-586505702 @D-Roberts Thanks for your contribution! Please let @xidulu and/or me know when you think this PR is ready for a review. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (8d887ca -> 39b158f)
This is an automated email from the ASF dual-hosted git repository. haoj pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 8d887ca add isposinf isneginf isfinite (#17563) add 39b158f quantile_scalar (#17572) No new revisions were added by this update. Summary of changes: python/mxnet/ndarray/numpy/_op.py | 10 +++-- python/mxnet/numpy/multiarray.py | 4 ++-- python/mxnet/symbol/numpy/_symbol.py | 10 +++-- src/operator/numpy/np_percentile_op-inl.h | 25 ++ src/operator/numpy/np_percentile_op.cc | 15 - .../python/unittest/test_numpy_interoperability.py | 13 +++ tests/python/unittest/test_numpy_op.py | 22 +-- 7 files changed, 73 insertions(+), 26 deletions(-)
[GitHub] [incubator-mxnet] haojin2 merged pull request #17572: [Numpy] Quantile/Percentile-scalar support
haojin2 merged pull request #17572: [Numpy] Quantile/Percentile-scalar support URL: https://github.com/apache/incubator-mxnet/pull/17572 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (1c07771 -> 8d887ca)
This is an automated email from the ASF dual-hosted git repository. haoj pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 1c07771 fix CD and remove leftover from #15990 (#17551) add 8d887ca add isposinf isneginf isfinite (#17563) No new revisions were added by this update. Summary of changes: python/mxnet/ndarray/numpy/_op.py | 152 - python/mxnet/numpy/multiarray.py | 152 - python/mxnet/numpy_dispatch_protocol.py| 3 + python/mxnet/symbol/numpy/_symbol.py | 104 +- src/operator/mshadow_op.h | 24 src/operator/numpy/np_elemwise_unary_op_basic.cc | 35 +++-- src/operator/numpy/np_elemwise_unary_op_basic.cu | 9 ++ src/operator/operator_tune.cc | 3 + .../python/unittest/test_numpy_interoperability.py | 39 +++--- tests/python/unittest/test_numpy_op.py | 49 ++- 10 files changed, 529 insertions(+), 41 deletions(-)
[GitHub] [incubator-mxnet] haojin2 merged pull request #17563: [Numpy] add isposinf isneginf isfinite
haojin2 merged pull request #17563: [Numpy] add isposinf isneginf isfinite URL: https://github.com/apache/incubator-mxnet/pull/17563 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu closed pull request #17598: Add OS X Staticbuild CI based on Github Actions
leezu closed pull request #17598: Add OS X Staticbuild CI based on Github Actions URL: https://github.com/apache/incubator-mxnet/pull/17598 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu commented on issue #17598: Add OS X Staticbuild CI based on Github Actions
leezu commented on issue #17598: Add OS X Staticbuild CI based on Github Actions URL: https://github.com/apache/incubator-mxnet/pull/17598#issuecomment-586497521 Closing to avoid trigger Jenkins on every push. You can track status at above link This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu commented on issue #17598: Add OS X Staticbuild CI based on Github Actions
leezu commented on issue #17598: Add OS X Staticbuild CI based on Github Actions URL: https://github.com/apache/incubator-mxnet/pull/17598#issuecomment-586496421 WIP: See build status at https://github.com/leezu/mxnet/actions This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu opened a new pull request #17598: Add OS X Staticbuild CI based on Github Actions
leezu opened a new pull request #17598: Add OS X Staticbuild CI based on Github Actions URL: https://github.com/apache/incubator-mxnet/pull/17598 ## Description ## Add OS X Staticbuild CI based on Github Actions This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ChaiBapchya commented on issue #17597: move stacktrance print to console rather than system.err
ChaiBapchya commented on issue #17597: move stacktrance print to console rather than system.err URL: https://github.com/apache/incubator-mxnet/pull/17597#issuecomment-586492046 @anirudh2290 @access2rohit @marcoabreu Since you reviewed the previous PR, thought you could take a look at this one. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ChaiBapchya commented on issue #17597: move stacktrance print to console rather than system.err
ChaiBapchya commented on issue #17597: move stacktrance print to console rather than system.err URL: https://github.com/apache/incubator-mxnet/pull/17597#issuecomment-586491889 @mxnet-label-bot add [pr-awaiting-review] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ChaiBapchya opened a new pull request #17597: move stacktrance print to console rather than system.err
ChaiBapchya opened a new pull request #17597: move stacktrance print to console
rather than system.err
URL: https://github.com/apache/incubator-mxnet/pull/17597
## Description ##
Right now with #17465 if the build fails, we print the stacktrace using
`error.printStackTrace()` function
a. It is an insecure function that needs to be approved by administrators in
Jenkins -> Manage Jenkins -> In-process Script approval
Without the approval it gives SandBox error
```
`org.jenkinsci.plugins.scriptsecurity.sandbox.RejectedAccessException:
Scripts not permitted to use method java.lang.Throwable getStackTrace`
```
b. However, the `error.printStackTrace()` function sends the report to
System.err
It doesn't get printed on the console.
To print on console, it has to be passed as a parameter.
Verified it works by adding a failure case in the try (so as to enter the
"catch")
```
try {
update_github_commit_status('PENDING', 'Job has been enqueued')
System.out.println(12 / 0)
}
```
It enters catch and then prints the stack trace for divide by zero exception
as expected.
Refer :
http://jenkins.mxnet-ci-dev.amazon-ml.com/job/mxnet-validation/job/apache-unix-cpu/job/remove_print_stacktrace/lastBuild/console
## Checklist ##
### Essentials ###
Please feel free to remove inapplicable items for your PR.
- [ ] Changes are complete (i.e. I finished coding on this PR)
- [ ] All changes have test coverage:
- [ ] Code is well-documented:
- [ ] To the best of my knowledge, examples are either not affected by this
change, or have been fixed to be compatible with this change
## Comments ##
Read more about Jenkins Script Security :
https://plugins.jenkins.io/script-security/
How to handle printStackTrace -
https://stackoverflow.com/questions/12095378/difference-between-e-printstacktrace-and-system-out-printlne
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[GitHub] [incubator-mxnet] access2rohit edited a comment on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data
access2rohit edited a comment on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data URL: https://github.com/apache/incubator-mxnet/issues/17595#issuecomment-586451995 @connorgoggins thanks for bringing this up @PatricZhao @TaoLv looks like blas=MKL/openblas/none(mnative mxnet) and MKLDNN=OFF are supporting gemm on int64 but with MKLDNN its not. If its not a known issue with MKLDNN can you guys please take a look This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu commented on issue #16899: Enable MKL-DNN in pip packages
leezu commented on issue #16899: Enable MKL-DNN in pip packages URL: https://github.com/apache/incubator-mxnet/pull/16899#issuecomment-586477909 @TaoLv Sheng just merged fixes for the CD pipeline. Let's wait 24 hours to verify CD works on master branch. Then we can merge this PR. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (0f35489 -> 1c07771)
This is an automated email from the ASF dual-hosted git repository. zhasheng pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 0f35489 use py3 for kvstore tests (#17593) add 1c07771 fix CD and remove leftover from #15990 (#17551) No new revisions were added by this update. Summary of changes: cd/mxnet_lib/mxnet_lib_pipeline.groovy | 38 -- cd/python/pypi/Jenkins_pipeline.groovy | 1 - cd/python/pypi/pypi_package.sh | 8 +++ ci/docker/runtime_functions.sh | 10 + 4 files changed, 5 insertions(+), 52 deletions(-)
[incubator-mxnet] branch master updated (0f35489 -> 1c07771)
This is an automated email from the ASF dual-hosted git repository. zhasheng pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 0f35489 use py3 for kvstore tests (#17593) add 1c07771 fix CD and remove leftover from #15990 (#17551) No new revisions were added by this update. Summary of changes: cd/mxnet_lib/mxnet_lib_pipeline.groovy | 38 -- cd/python/pypi/Jenkins_pipeline.groovy | 1 - cd/python/pypi/pypi_package.sh | 8 +++ ci/docker/runtime_functions.sh | 10 + 4 files changed, 5 insertions(+), 52 deletions(-)
[GitHub] [incubator-mxnet] szha merged pull request #17551: [CD] fix CD and remove leftover from #15990
szha merged pull request #17551: [CD] fix CD and remove leftover from #15990 URL: https://github.com/apache/incubator-mxnet/pull/17551 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu opened a new pull request #17596: Fix transformer.cu interleaved matmul for cuda arch < 5
leezu opened a new pull request #17596: Fix transformer.cu interleaved matmul for cuda arch < 5 URL: https://github.com/apache/incubator-mxnet/pull/17596 ## Description ## `cublasGemmBatchedEx` is only supported for GPU with architecture capabilities equal or greater than 5.0. Fixes a bug in https://github.com/apache/incubator-mxnet/pull/16408 ### Changes ### - [X] Fix transformer.cu interleaved matmul for cuda arch < 5 ## Comments ## CC @Caenorst This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] eric-haibin-lin commented on issue #1137: cublas error when using like mx.nd.***
eric-haibin-lin commented on issue #1137: cublas error when using like mx.nd.*** URL: https://github.com/apache/incubator-mxnet/issues/1137#issuecomment-586459620 or just put a `mx.nd.waitall()` This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] access2rohit edited a comment on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data
access2rohit edited a comment on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data URL: https://github.com/apache/incubator-mxnet/issues/17595#issuecomment-586451995 @connorgoggins thanks for bringing this up @PatricZhao @TaoLv looks like blas=MKL/openblas/none(mnative mxnet) and MKLDNN=OFF is supporting gemm on int64 but with MKLDNN its not. If its not a known issue with MKLDNN can you guys please take a look This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] access2rohit commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data
access2rohit commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data URL: https://github.com/apache/incubator-mxnet/issues/17595#issuecomment-586451995 @PatricZhao @TaoLv looks like blas=MKL/openblas/none(mnative mxnet) and MKLDNN=OFF is supporting gemm on int64 but with MKLDNN its not. If its not a known issue with MKLDNN can you guys please take a look This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] apeforest commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data
apeforest commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data URL: https://github.com/apache/incubator-mxnet/issues/17595#issuecomment-586443098 @PatricZhao Could your team please take a look at this? Thanks! This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] access2rohit commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data
access2rohit commented on issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data URL: https://github.com/apache/incubator-mxnet/issues/17595#issuecomment-586439922 @mxnet-label-bot add [MKLDNN] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] connorgoggins opened a new issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data
connorgoggins opened a new issue #17595: MKLDNN incompatibility with large tensor (dim >= 2^32) data URL: https://github.com/apache/incubator-mxnet/issues/17595 ## Description While testing individual ops for large tensor (dimension >= 2^32) input functionality, I found an error in MKLDNN. Within `3rdparty/mkldnn/src/cpu/gemm/gemm.cpp` on line 43 there is a function which takes in several parameters, including `M` (the variable used to accept the data dimension in the input). `M` is designated as an `int`, so when the value 2^32 is passed in as the first dimension of the input data the > 0 assertion on the next line fails (since the `int` dtype in C++ interprets 2^32 as 0). Note that this error occurs whenever MKLDNN is enabled - whether the BLAS engine is MKL, OpenBLAS, or none. When MKLDNN is disabled, the error does not occur. ## Failing Environments ### BLAS = None ✖ CUDA, ✖ CUDNN, ✖ NCCL, ✖ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3, ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE, ✔ F16C, ✖ JEMALLOC, ✖ BLAS_OPEN, ✖ BLAS_ATLAS, ✖ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK, ✔ MKLDNN, ✖ OPENCV, ✖ CAFFE, ✖ PROFILER, ✖ DIST_KVSTORE, ✖ CXX14, ✔ INT64_TENSOR_SIZE, ✖ SIGNAL_HANDLER, ✔ DEBUG, ✖ TVM_OP ### BLAS = MKL ✖ CUDA, ✖ CUDNN, ✖ NCCL, ✖ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3, ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE, ✔ F16C, ✖ JEMALLOC, ✖ BLAS_OPEN, ✖ BLAS_ATLAS, ✔ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK, ✔ MKLDNN, ✖ OPENCV, ✖ CAFFE, ✖ PROFILER, ✖ DIST_KVSTORE, ✖ CXX14, ✔ INT64_TENSOR_SIZE, ✖ SIGNAL_HANDLER, ✔ DEBUG, ✖ TVM_OP ### BLAS = OpenBLAS ✖ CUDA, ✖ CUDNN, ✖ NCCL, ✖ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3, ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE, ✔ F16C, ✖ JEMALLOC, ✔ BLAS_OPEN, ✖ BLAS_ATLAS, ✖ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK, ✔ MKLDNN, ✖ OPENCV, ✖ CAFFE, ✖ PROFILER, ✖ DIST_KVSTORE, ✖ CXX14, ✔ INT64_TENSOR_SIZE, ✖ SIGNAL_HANDLER, ✔ DEBUG, ✖ TVM_OP ## Successful Environments ### BLAS = None ✖ CUDA, ✖ CUDNN, ✖ NCCL, ✖ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3, ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE, ✔ F16C, ✖ JEMALLOC, ✖ BLAS_OPEN, ✖ BLAS_ATLAS, ✖ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK, ✖ MKLDNN, ✖ OPENCV, ✖ CAFFE, ✖ PROFILER, ✖ DIST_KVSTORE, ✖ CXX14, ✔ INT64_TENSOR_SIZE, ✖ SIGNAL_HANDLER, ✔ DEBUG, ✖ TVM_OP ### BLAS = MKL ✖ CUDA, ✖ CUDNN, ✖ NCCL, ✖ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3, ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE, ✔ F16C, ✖ JEMALLOC, ✖ BLAS_OPEN, ✖ BLAS_ATLAS, ✔ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK, ✖ MKLDNN, ✖ OPENCV, ✖ CAFFE, ✖ PROFILER, ✖ DIST_KVSTORE, ✖ CXX14, ✔ INT64_TENSOR_SIZE, ✖ SIGNAL_HANDLER, ✔ DEBUG, ✖ TVM_OP ### BLAS = OpenBLAS ✖ CUDA, ✖ CUDNN, ✖ NCCL, ✖ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3, ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE, ✔ F16C, ✖ JEMALLOC, ✔ BLAS_OPEN, ✖ BLAS_ATLAS, ✖ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK, ✖ MKLDNN, ✖ OPENCV, ✖ CAFFE, ✖ PROFILER, ✖ DIST_KVSTORE, ✖ CXX14, ✔ INT64_TENSOR_SIZE, ✖ SIGNAL_HANDLER, ✔ DEBUG, ✖ TVM_OP ### Steps to reproduce Run `mx.nd.FullyConnected(data=mx.nd.random_normal(shape=(2**32,1)), weight=mx.nd.random_normal(shape=(1,1)), bias=mx.nd.random_normal(shape=(1,)), flatten=False, num_hidden=1` ### Error Message ```python3: /home/ubuntu/mxnet/3rdparty/mkldnn/src/cpu/gemm/gemm.cpp:43: void dnnl::impl::cpu::msan_unpoison_matrix(void*, int, int, int, size_t): Assertion `C != nullptr && M > 0 && N > 0 && LDC >= M && typesize' failed. Aborted (core dumped) ``` This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] zhreshold commented on a change in pull request #17566: [WIP] Discussion on merging BMXNet 2 contributions
zhreshold commented on a change in pull request #17566: [WIP] Discussion on merging BMXNet 2 contributions URL: https://github.com/apache/incubator-mxnet/pull/17566#discussion_r379594847 ## File path: .gitlab-ci.yml ## @@ -0,0 +1,71 @@ +stages: Review comment: this file should not go in to master This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] zhreshold commented on a change in pull request #17566: [WIP] Discussion on merging BMXNet 2 contributions
zhreshold commented on a change in pull request #17566: [WIP] Discussion on merging BMXNet 2 contributions URL: https://github.com/apache/incubator-mxnet/pull/17566#discussion_r379595075 ## File path: .gitmodules ## @@ -29,3 +29,6 @@ [submodule "3rdparty/nvidia_cub"] path = 3rdparty/nvidia_cub url = https://github.com/NVlabs/cub.git +[submodule "example/bmxnet-examples"] Review comment: These examples should copy to examples instead of submodule This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] zhreshold commented on a change in pull request #17566: [WIP] Discussion on merging BMXNet 2 contributions
zhreshold commented on a change in pull request #17566: [WIP] Discussion on merging BMXNet 2 contributions URL: https://github.com/apache/incubator-mxnet/pull/17566#discussion_r379595336 ## File path: README.md ## @@ -1,116 +1,136 @@ - - - - - - - - - - - - - - - - - - - https://mxnet.incubator.apache.org/;>https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet_logo_2.png;> - - -Apache MXNet (incubating) for Deep Learning -= -| Master | Docs | License | -| :-:|:-:|::| -| [](http://jenkins.mxnet-ci.amazon-ml.com/job/incubator-mxnet/job/master/) | [](https://mxnet.incubator.apache.org/) | [](./LICENSE) | - - - -Apache MXNet (incubating) is a deep learning framework designed for both *efficiency* and *flexibility*. -It allows you to ***mix*** [symbolic and imperative programming](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts) -to ***maximize*** efficiency and productivity. -At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. -A graph optimization layer on top of that makes symbolic execution fast and memory efficient. -MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. - -MXNet is more than a deep learning project. It is a collection of -[blue prints and guidelines](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts) for building -deep learning systems, and interesting insights of DL systems for hackers. - -Ask Questions -- -* Please use [discuss.mxnet.io](https://discuss.mxnet.io/) for asking questions. -* Please use [mxnet/issues](https://github.com/apache/incubator-mxnet/issues) for reporting bugs. -* [Frequent Asked Questions](https://mxnet.incubator.apache.org/faq/faq.html) - -How to Contribute -- -* [Contribute to MXNet](https://mxnet.incubator.apache.org/community/contribute.html) - -What's New --- -* [Version 1.4.1 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.4.1) - MXNet 1.4.1 Patch Release. -* [Version 1.4.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.4.0) - MXNet 1.4.0 Release. -* [Version 1.3.1 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.3.1) - MXNet 1.3.1 Patch Release. -* [Version 1.3.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.3.0) - MXNet 1.3.0 Release. -* [Version 1.2.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.2.0) - MXNet 1.2.0 Release. -* [Version 1.1.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.1.0) - MXNet 1.1.0 Release. -* [Version 1.0.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/1.0.0) - MXNet 1.0.0 Release. -* [Version 0.12.1 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.12.1) - MXNet 0.12.1 Patch Release. -* [Version 0.12.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.12.0) - MXNet 0.12.0 Release. -* [Version 0.11.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.11.0) - MXNet 0.11.0 Release. -* [Apache Incubator](http://incubator.apache.org/projects/mxnet.html) - We are now an Apache Incubator project. -* [Version 0.10.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.10.0) - MXNet 0.10.0 Release. -* [Version 0.9.3 Release](./docs/architecture/release_note_0_9.md) - First 0.9 official release. -* [Version 0.9.1 Release (NNVM refactor)](./docs/architecture/release_note_0_9.md) - NNVM branch is merged into master now. An official release will be made soon. -* [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0) -* [Updated Image Classification with new Pre-trained Models](./example/image-classification) -* [Notebooks How to Use MXNet](https://github.com/d2l-ai/d2l-en) -* [MKLDNN for Faster CPU Performance](./docs/tutorials/mkldnn/MKLDNN_README.md) -* [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost](https://github.com/dmlc/mxnet-memonger) -* [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial) -* [Embedding Torch layers and functions in MXNet](https://mxnet.incubator.apache.org/faq/torch.html) -* [MXNet.js: Javascript Package for Deep Learning in Browser (without server) -](https://github.com/dmlc/mxnet.js/) -* [Design Note: Design Efficient Deep Learning Data Loading Module](https://mxnet.incubator.apache.org/architecture/note_data_loading.html) -* [MXNet on
[GitHub] [incubator-mxnet] aaronmarkham opened a new pull request #17594: upgrade sphinx and autodocsumm
aaronmarkham opened a new pull request #17594: upgrade sphinx and autodocsumm URL: https://github.com/apache/incubator-mxnet/pull/17594 ## Description ## Rollback of #17561 A new version of autodocsumm came out. It supports the most recent version of Sphinx. This reverts the emergency patch to rollback these packages, but also pins the versions for both packages. Tested the build and it passes. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] mjdenkowski commented on issue #17559: [MXNET-1446] Quantization: intgemm matrix multiply wrappers
mjdenkowski commented on issue #17559: [MXNET-1446] Quantization: intgemm matrix multiply wrappers URL: https://github.com/apache/incubator-mxnet/pull/17559#issuecomment-586425627 Hi @TaoLv, thanks for taking a look at this! We understand your comments about MXNet already having multiple GEMM libraries. We're particularly interested in Kenneth's (@kpuatamazon) intgemm because it provides functionality we weren't able to find in the existing libraries. As he mentioned in the PR, we're seeing a roughly 3X inference speedup on an already significantly optimized transformer implementation. Like many other Gluon users, our inference model is not currently expressible as a static graph. We would like to work out the best way to make this functionality available to the larger community. Are there particular concerns we can address about adding intgemm as a third party library? Is there another path to using intgemm with MXNet that you recommend? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] apeforest edited a comment on issue #16735: Use single-bit for mask in dropout operator
apeforest edited a comment on issue #16735: Use single-bit for mask in dropout
operator
URL: https://github.com/apache/incubator-mxnet/pull/16735#issuecomment-586062120
@roywei Using the test script in
https://github.com/apache/incubator-mxnet/pull/13896
Build | runtime (before) | runtime (after)
---|---|---
CPU w/ MKL | 262 ms ± 1.2 ms | 337 ms ± 12.5 ms
CPU w/o MKL |359 ms ± 241 µs | 426 ms ± 222 µs
GPU w/ cuDNN | 25.9 ms ± 202 µs | 25.8 ms ± 183 µs
GPU w/o cuDNNN |1.34 s ± 5.83 ms | 1.35 s ± 13.1 ms
Using python timer to measure CPU performance with MKL:
This PR:
```
[{'Dropout': [{'avg_time_Dropout': 1.1714265774935484, 'p50_time_Dropout':
1.1715246364474297, 'p90_time_Dropout': 1.190436165779829, 'p99_time_Dropout':
1.2154309218749404, 'inputs': {'data': (1024, 1024)}}]}]
```
Master:
```
[{'Dropout': [{'avg_time_Dropout': 0.6394564639776945, 'p50_time_Dropout':
0.6996351294219494, 'p90_time_Dropout': 1.045508868992329, 'p99_time_Dropout':
1.59036863129586, 'inputs': {'data': (1024, 1024)}}]}]
```
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[GitHub] [incubator-mxnet] leezu edited a comment on issue #16408: Add MXNet Ops for fast multihead attention
leezu edited a comment on issue #16408: Add MXNet Ops for fast multihead attention URL: https://github.com/apache/incubator-mxnet/pull/16408#issuecomment-586418793 There are two separate problems. `cublasGemmStridedBatchedEx` is buggy and has fixes in 10.2. But `cublasGemmStridedBatchedEx` is not supported in the first place on 3.5 arch and that's a bug in MXNet. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leezu commented on issue #16408: Add MXNet Ops for fast multihead attention
leezu commented on issue #16408: Add MXNet Ops for fast multihead attention URL: https://github.com/apache/incubator-mxnet/pull/16408#issuecomment-586418793 No. My comment was wrong This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet-site] branch asf-site updated: Bump the publish timestamp.
This is an automated email from the ASF dual-hosted git repository. aaronmarkham pushed a commit to branch asf-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git The following commit(s) were added to refs/heads/asf-site by this push: new 978b5ea Bump the publish timestamp. 978b5ea is described below commit 978b5eadea8b579e71d663b18498e5f29ad8adf3 Author: mxnet-ci AuthorDate: Fri Feb 14 18:42:36 2020 + Bump the publish timestamp. --- date.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/date.txt b/date.txt new file mode 100644 index 000..f224188 --- /dev/null +++ b/date.txt @@ -0,0 +1 @@ +Fri Feb 14 18:42:36 UTC 2020
[GitHub] [incubator-mxnet] larroy commented on issue #16408: Add MXNet Ops for fast multihead attention
larroy commented on issue #16408: Add MXNet Ops for fast multihead attention URL: https://github.com/apache/incubator-mxnet/pull/16408#issuecomment-586415593 So it's related to cuda and not the arch? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (8438d98 -> 0f35489)
This is an automated email from the ASF dual-hosted git repository. zhasheng pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 8438d98 Implement all miscellaneous ops (#17511) add 0f35489 use py3 for kvstore tests (#17593) No new revisions were added by this update. Summary of changes: ci/docker/runtime_functions.sh | 26 +- 1 file changed, 13 insertions(+), 13 deletions(-)
[incubator-mxnet] branch master updated (8438d98 -> 0f35489)
This is an automated email from the ASF dual-hosted git repository. zhasheng pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 8438d98 Implement all miscellaneous ops (#17511) add 0f35489 use py3 for kvstore tests (#17593) No new revisions were added by this update. Summary of changes: ci/docker/runtime_functions.sh | 26 +- 1 file changed, 13 insertions(+), 13 deletions(-)
[GitHub] [incubator-mxnet] szha merged pull request #17593: [CI] use py3 for kvstore tests
szha merged pull request #17593: [CI] use py3 for kvstore tests URL: https://github.com/apache/incubator-mxnet/pull/17593 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] szha commented on a change in pull request #17265: Add bfloat16 floating-point format support based on AMP
szha commented on a change in pull request #17265: Add bfloat16 floating-point format support based on AMP URL: https://github.com/apache/incubator-mxnet/pull/17265#discussion_r379517986 ## File path: 3rdparty/mshadow/mshadow/bfloat.h ## @@ -0,0 +1,167 @@ +/*! Review comment: yes This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] TaoLv commented on a change in pull request #17265: Add bfloat16 floating-point format support based on AMP
TaoLv commented on a change in pull request #17265: Add bfloat16 floating-point format support based on AMP URL: https://github.com/apache/incubator-mxnet/pull/17265#discussion_r379484516 ## File path: 3rdparty/mshadow/mshadow/bfloat.h ## @@ -0,0 +1,167 @@ +/*! Review comment: @szha Do we need Apache license header for this new file? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] TaoLv commented on issue #17265: Add bfloat16 floating-point format support based on AMP
TaoLv commented on issue #17265: Add bfloat16 floating-point format support based on AMP URL: https://github.com/apache/incubator-mxnet/pull/17265#issuecomment-586329605 Hi @leezu, @larroy, this PR can pass the builds for ARM but always hit the time out of the test. http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fedge/detail/PR-17265/29/pipeline We don't have any environment to reproduce. Could you please take a look or have any suggestion for further debugging? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] TaoLv commented on a change in pull request #17318: Enable MKL-DNN FullyConnected backward
TaoLv commented on a change in pull request #17318: Enable MKL-DNN
FullyConnected backward
URL: https://github.com/apache/incubator-mxnet/pull/17318#discussion_r379465641
##
File path: tests/cpp/include/test_mkldnn.h
##
@@ -330,7 +330,7 @@ inline void PrintVerifyMsg(const NDArrayAttrs , const
NDArrayAttrs ) {
*/
inline std::vector GetTestInputArrays(
int types = ArrayTypes::All, bool rand = false,
-std::vector scale = {1}, bool spatial_data_format = false) {
+std::vector scale = {1}, bool spatial_data_format = false, int max
= 50) {
Review comment:
See
https://github.com/apache/incubator-mxnet/pull/17318#discussion_r379465354.
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[GitHub] [incubator-mxnet] TaoLv commented on a change in pull request #17318: Enable MKL-DNN FullyConnected backward
TaoLv commented on a change in pull request #17318: Enable MKL-DNN
FullyConnected backward
URL: https://github.com/apache/incubator-mxnet/pull/17318#discussion_r379465354
##
File path: tests/cpp/include/test_mkldnn.h
##
@@ -63,24 +63,24 @@ struct TestArrayShapes {
};
// Init arrays with the default layout.
-inline static void InitDefaultArray(NDArray *arr, bool is_rand = false) {
+inline static void InitDefaultArray(NDArray *arr, bool is_rand = false, int
max = 50) {
const TBlob = arr->data();
mshadow::default_real_t *data = blob.dptr();
int size = blob.Size();
for (int i = 0; i < size; i++)
if (is_rand) {
- data[i] = (std::rand() % 100) - 50;
+ data[i] = (std::rand() % (max * 2)) - max;
Review comment:
Because I don't want to affect other test case which still use the default
max=50 to generate integers in [50, 50). But For the FullyConnectedOp, I want
to generate relative small numbers. With the given code, the elements will be
-1 and 0. Any suggestion?
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[GitHub] [incubator-mxnet] sl1pkn07 commented on issue #17514: C++-Package examples broken
sl1pkn07 commented on issue #17514: C++-Package examples broken URL: https://github.com/apache/incubator-mxnet/issues/17514#issuecomment-586274460 try with ~~~ -DCUDA_HOST_COMPILER=/usr/bin/cc-8 \ -DCMAKE_C_COMPILER=/usr/bin/cc-8 \ -DCMAKE_C_COMPILER_AR=/usr/bin/gcc-ar-8 \ -DCMAKE_C_COMPILER_RANLIB=/usr/bin/gcc-ranlib-8 \ -DCMAKE_CXX_COMPILER=/usr/bin/c++-8 \ -DCMAKE_CXX_COMPILER_AR=/usr/bin/gcc-ar-8 \ -DCMAKE_CXX_COMPILER_RANLIB=/usr/bin/gcc-ranlib-8 ~~~ This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet-site] branch asf-site updated: Bump the publish timestamp.
This is an automated email from the ASF dual-hosted git repository. aaronmarkham pushed a commit to branch asf-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git The following commit(s) were added to refs/heads/asf-site by this push: new 3fca7d8 Bump the publish timestamp. 3fca7d8 is described below commit 3fca7d8d02a9751ea8cb2d5e2927f530bb247bbd Author: mxnet-ci AuthorDate: Fri Feb 14 12:42:49 2020 + Bump the publish timestamp. --- date.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/date.txt b/date.txt new file mode 100644 index 000..642a3ea --- /dev/null +++ b/date.txt @@ -0,0 +1 @@ +Fri Feb 14 12:42:49 UTC 2020
[GitHub] [incubator-mxnet] ciyongch commented on a change in pull request #17318: Enable MKL-DNN FullyConnected backward
ciyongch commented on a change in pull request #17318: Enable MKL-DNN
FullyConnected backward
URL: https://github.com/apache/incubator-mxnet/pull/17318#discussion_r379387996
##
File path: tests/cpp/include/test_mkldnn.h
##
@@ -63,24 +63,24 @@ struct TestArrayShapes {
};
// Init arrays with the default layout.
-inline static void InitDefaultArray(NDArray *arr, bool is_rand = false) {
+inline static void InitDefaultArray(NDArray *arr, bool is_rand = false, int
max = 50) {
const TBlob = arr->data();
mshadow::default_real_t *data = blob.dptr();
int size = blob.Size();
for (int i = 0; i < size; i++)
if (is_rand) {
- data[i] = (std::rand() % 100) - 50;
+ data[i] = (std::rand() % (max * 2)) - max;
Review comment:
How about change to `data[i] = std::rand() * 1.0 / RAND_MAX - 0.5;`? As `max
= 1` will only generate two values: -1.0and 0.0 .
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[GitHub] [incubator-mxnet] ciyongch commented on a change in pull request #17318: Enable MKL-DNN FullyConnected backward
ciyongch commented on a change in pull request #17318: Enable MKL-DNN
FullyConnected backward
URL: https://github.com/apache/incubator-mxnet/pull/17318#discussion_r379389375
##
File path: tests/cpp/include/test_mkldnn.h
##
@@ -63,24 +63,24 @@ struct TestArrayShapes {
};
// Init arrays with the default layout.
-inline static void InitDefaultArray(NDArray *arr, bool is_rand = false) {
+inline static void InitDefaultArray(NDArray *arr, bool is_rand = false, int
max = 50) {
const TBlob = arr->data();
mshadow::default_real_t *data = blob.dptr();
int size = blob.Size();
for (int i = 0; i < size; i++)
if (is_rand) {
- data[i] = (std::rand() % 100) - 50;
+ data[i] = (std::rand() % (max * 2)) - max;
} else {
- data[i] = i % 100 - 50;
+ data[i] = i % (max * 2) - max;
Review comment:
Same as above, how about change to something like `data[i] = i * 2.0 / size
- 1.0` to generate [-1.0, 1.0)?
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[GitHub] [incubator-mxnet] ChiaraXian commented on issue #8148: How do I interpret the JSON compute graph?
ChiaraXian commented on issue #8148: How do I interpret the JSON compute graph? URL: https://github.com/apache/incubator-mxnet/issues/8148#issuecomment-586195655 @aidan-plenert-macdonald Thanks a lot!!! Thank you for your help!!! This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] pengzhao-intel commented on a change in pull request #17318: Enable MKL-DNN FullyConnected backward
pengzhao-intel commented on a change in pull request #17318: Enable MKL-DNN
FullyConnected backward
URL: https://github.com/apache/incubator-mxnet/pull/17318#discussion_r379349568
##
File path: tests/cpp/include/test_mkldnn.h
##
@@ -330,7 +330,7 @@ inline void PrintVerifyMsg(const NDArrayAttrs , const
NDArrayAttrs ) {
*/
inline std::vector GetTestInputArrays(
int types = ArrayTypes::All, bool rand = false,
-std::vector scale = {1}, bool spatial_data_format = false) {
+std::vector scale = {1}, bool spatial_data_format = false, int max
= 50) {
Review comment:
Is the new parameter for better usability?
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