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Implement Initializer.__eq__ #16680

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merged 2 commits into from
Nov 1, 2019
Merged

Implement Initializer.__eq__ #16680

merged 2 commits into from
Nov 1, 2019

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leezu
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@leezu leezu commented Oct 31, 2019

Description

When sharing parameters between Blocks, gluon checks that all attributes of to-be-shared parameters match. However, as mxnet initializers don't implement __eq__, the check will always fail. Thus it is currently impossible to share parameters between Blocks with custom initializers.

See #16049

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:
  • 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 my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Changes

  • Support sharing parameters between blocks with custom initializers

Comments

@szha @sxjscience

@leezu leezu requested a review from szha as a code owner October 31, 2019 09:05
python/mxnet/initializer.py Outdated Show resolved Hide resolved

bl = MyBlock()
bl2= MyBlock(params=bl.collect_params())
assert bl.param is bl2.param
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Thanks for your contribution!
Could you add the unittest 'b1.param is not bl3.param’ and the test for mx.init.Initializer?

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LGTM. The constant initializer still needs to deal with the type difference in input value and handle the equality.

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LGTM. Thank you : )

@leezu leezu merged commit 33d108b into apache:master Nov 1, 2019
@leezu leezu deleted the initializer__eq__ branch November 1, 2019 09:37
yajiedesign pushed a commit to yajiedesign/mxnet that referenced this pull request Nov 6, 2019
anirudh2290 pushed a commit to anirudh2290/mxnet that referenced this pull request Nov 14, 2019
ArmageddonKnight pushed a commit to UofT-EcoSystem/incubator-mxnet that referenced this pull request Feb 1, 2020
* Add cached op threadsafe version with corresponding C APIs, CPP Package changes, CI changes and tests

* Fix download cmd in runtime_functions

* Add CI changes

* Add stage

Fix indentation

* Fix lint

* Change to DEFAULT for C API

* Fix mxnet_unit_tests path

* export correct LD_LIBRARY_PATH

* Add cpp include dirs

* Build test with USE_CPP_PACKAGE

* Add cached op threadsafe version with corresponding C APIs, CPP Package changes, CI changes and tests

* Fix download cmd in runtime_functions

* Merge

* change mkldnn lib name

* Add static_alloc, static_Shape support

* Address review comments

* Make GetCachedOpThreadSafeState similar to cached_op

* Address review comments: comments for locking strategy

* multithreaded inference tutorial

* [Estimator] handle composite metrics in estimator (apache#16676)

* handle composite metrics in estimator

* fix composite metric case in handlers

* remove unused import

* [Estimator] refactor estimator to allow overriding evaluate/fit of a batch (apache#16678)

* refactor estimator to allow overriding evaluate/fit of a batch

* add doc to explain call structure and how to override

* fix and doc

* Pointwise fusion for GPU (apache#15167)

* Beginning of RTC of pointwise ops

* Code generation from the given JSON

* add initial simple_partition_pass and use it for pointwise fusion

* fix the fusion, use a symbol.Copy() at the beginning of binding function, use the name of input nodes in the cuda code

* Fixes

* Adding support for attribute inference for backward nodes when fusing

* keep proper input ordering for fused Op

* instantiate the indexed_graph before starting the subgraph replacement, return a new graph to reset the indexed_graph

* Fuse backward

* fix ordering of subgraph node inputs using subgraph topological ordering instead of main graph topological ordering, add tvm.patch

* excluse forward node fusion during the fusion of the nodes in the backward graph

* Dealing with fused backward nodes inferattr

* use subgraph.indexed_graph() instead of main for _FusedOpHelper nodes node_id, invert control_deps loop to modify topology of subgraph before calling its indexed_graph(), check that all node of the first DFSVisit are actually in the subgraph

* Adding support for other reqs in codegen

* Fix

* Cleaning

* Change the TVM submodule

* More cleaning

* Making linter happy

* Do fusion only if default context is GPU

* Fixes for tests
Add powerscalar and rpowerscalar, fix return type of zero and one
Cleaning, fixing lint
Go back to proper TVM submodule

* Fix the TVM commit

* Fix lint

* Guard fusion with MXNET_USE_CUDA

* Fix

* Fix clang-tidy

* Add erf and erfinv backward

* Gluon support for fusion

* Cleaning

* Cleaning and allow shape/type change in FusedOp

* Fixing Gluon bugs

* Fixing after rebase

* Fixing race condition and guarding against races when using NVRTC

* Cleaning and renaming FusedOp to _FusedOp

* Going easy on Windows compiler

* Disable fusion on Windows for now

* Refactor InferAttr and InferShapeAttr

* Added slice and half2 support to FusedOp

* Fix lint errors

* Added multiple types support for vector loading/storing

* add slice fusion when it's at the beginning of subgraphs

* Removed constant ndim assumption in fused op

* Fix memory alignment issue in slice for FusedOp

* Fixes

* Fix lint errors

* Do not include cuda_fp16.h

* Refactor fused op op lists

* Make linter happy

* Changes from review

* Fixes after rebase

* Expand FusedOp support for slice

* Fix for fp16 _zeros and _ones

* Fix

* Moving aux functions to unnamed namespace and detail namespace -> fusion
namespace

* Disabling fusion if it alters topological order of inputs

* Print code only when env variable is set

* Fix

* Fix lint and 2 tests that specify the same names for multiple inputs

* Fixes from review and disabling fusion of slice with non-default step

* Add amp_cast to fusion, fixes

* Add amp_multicast and its backward to the list of support ops

* Apply wording suggestions from code review

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Apply wording suggestions from code review

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Make clearer comment

* Adding punctuation and capitalization to \brief descriptions

* Fix

* Fix

* Add backward_cast to fusion

* Adding unittests for fusion. Fix for erfinv_grad

* Adding slice ops and add_n to tests

* Fixes from review

* Setting inplace option

* Fix lint

* Storing double in half

* Retrigger CI

* Slight relaxing of the relative tolerance in the test

* Move the env variable check to the end

* Fix a race condition between InferShape and scheduled Forward

* Fix flakey test_fusion test involving fp32 erfinv op.

* Fix from review

* Added broadcast_like and slice_like to fused op

* Minor fix and cleanup

* Added negative axis support in slice_axis, temporarily disabled fusion of slice_like and broadcast_like

* Added axes support to slice_like

* Added axis support to broadcast_like

* Add fast_load_slice function to fused op code

* Added runtime switch for choosing fast and slow slice kernel

* Fix lint and warning

* Going easy on Windows compiler (again)

* Fix slice_like

* Debug broadcast_like fusion

* Fix lint

* Fix lint

* Trigger CI

* Get rid of the initializer list

* Fix backward calls with different gradient type

* avoid cycle when adding node specific for inputs of subgraph for pointwise fusion

* Fix lint

* Add namespace to the fusion implementations

* Set launch bounds on the fused kernel

* Fix NumPy tests

* Test showcasing an issue fixed in PR apache#16553

* Cast scalarts to FP32 and perform (a*1.0/b) instead of (a/b)

Fix lint errors

Fix lint

* Fix a bug in cycle detection for inputs only op in pointwise fusion

* Add comments to simple_partition_pass.h file

* fix install dir (apache#16690)

* [numpy] add numpy operator : append (apache#16564)

* add operator : append ; fix op concatenate when axis = None

* pylint disable

remove mistake

disable pylint

* Initializer.__eq__ (apache#16680)

* fix binary dependencies in CD and nightly (apache#16693)

* [MKL-DNN] Add mxnet mkldnn cmake tutorial (apache#16688)

* add mxnet mkldnn cmake instruction

* imporve doc

* OMP->OpenMP

* Revert "[MKLDNN]Fix reorder2default (apache#16602)" (apache#16697)

This reverts commit dd4eaf5.

* [Estimator] refactor estimator and clarify docs (apache#16694)

* refactor estimator and clarify docs

* fix info message and test

* clean up after releasing logging handler

* Eliminate common expressions (apache#15657)

* Eliminate common expressions from a graph

* Guarding against optimizing out stateful ops and ops that require
resource

* Fix lint

* Added THasDeterministicOutput to multiple ops

* DDebug eliminate common expr

* Added test

* Expose get_optimized_symbol

* Fix

* Fix 2

* Add doc to the Python call

* Add env var MXNET_ELIMINATE_COMMON_EXPR, default true

* Add comments, improve readability of eliminate_common_expr_pass.cc

* Expand testing

* Lower priority of THasDeterministicOutput attr for equal Node test

* Change mx.gpu() to mx.cpu() in tests

* Skip CSE test on Windows (as env variable setting during test does not work there)

* Add missing import sys

* Add missing import logging

* Backport of apache#16711, apache#16737, apache#16408 to 1.6 branch (apache#16763)

* support mixed-precision true_divide (apache#16711)

* [MKLDNN] use dim_t instead of int in slice/transpose operators (apache#16737)

* use dim_t instead of int

* fix same issue in pooling

* rebase code

* trigger CI

* Add MXNet Ops for fast multihead attention (apache#16408)

* add MXNet Ops for fast multihead attention

* add cutlass as 3rdparty dependency

* add cutlass to compilation flags

* remove all cutlass stuff

* add better error message and description and remove cutlass from compilation flags

* change credit for the approach since the code have changed

* fix typos

* correct another typo

* Add all the cuda/cublas helper functions

* remove tests using kAddTo

* only use cublasStridedBatchedGemm if CUDA >= 9.1

* add equivalent mxnet code in description of mha ops

* remove a wrong copy-paste

* add _contrib for namespace and add GPU only on description

* add warning in bwd_ignore_zero_init description, also test with fp32

* add error return if bwd_ignore_zero_init is used without MXNET_EXEC_ENABLE_ADDTO

* remove std::move for clang

* remove bwd_ignore_zero_init flag

* remove bwd_ignore_zero_init in test_operator_gpu.py

* fix typo

* fix another typo

* Removed unrelated test

* Add example and documentation for multi threaded inference

* Add LICENSE

* Add get_model.py

* Add license for README

* Refactor cached op and cached op threadsafe

* Add limitation

* Add tests for naive engine

* Add latest test changes

* Thread Safety tests in NaiveEngine mode

* Thread Safety tests update

* Update thread safety tests, add unsupported use cases

* Changes to doc and refactor

* Fix todo owner, indentation and mx_float->float

* Refactor cached op code, remove num_threads arg from example

* Fix lint

* Fix warning

* Add back cython, required for unix-gpu build

* Fix for windows

* Add bulking support for thread safe cached op version

* Add support for subgraph testing

* import mxnet before calling get_backend_symbol

* Fix symbol json name

* Refactor DynamicForward

* Add comments

* Add DMLC_ATTRIBUTE_UNUSED

* Fix use_naive_run issue

* Fix lint

* Revert unittest_cpp to old test since it doesnt test thread safety

* Fix doc

Co-authored-by: Sheng Zha <szha@users.noreply.github.com>
Co-authored-by: Przemyslaw Tredak <ptrendx@gmail.com>
Co-authored-by: Tao Lv <tao.a.lv@intel.com>
Co-authored-by: JiangZhaoh <54654391+JiangZhaoh@users.noreply.github.com>
Co-authored-by: Leonard Lausen <leonard@lausen.nl>
Co-authored-by: Xinyu Chen <xinyu1.chen@intel.com>
Co-authored-by: Zhennan Qin <zhennan.qin@intel.com>
zheyuye pushed a commit to zheyuye/incubator-mxnet that referenced this pull request Feb 19, 2020
* Add cached op threadsafe version with corresponding C APIs, CPP Package changes, CI changes and tests

* Fix download cmd in runtime_functions

* Add CI changes

* Add stage

Fix indentation

* Fix lint

* Change to DEFAULT for C API

* Fix mxnet_unit_tests path

* export correct LD_LIBRARY_PATH

* Add cpp include dirs

* Build test with USE_CPP_PACKAGE

* Add cached op threadsafe version with corresponding C APIs, CPP Package changes, CI changes and tests

* Fix download cmd in runtime_functions

* Merge

* change mkldnn lib name

* Add static_alloc, static_Shape support

* Address review comments

* Make GetCachedOpThreadSafeState similar to cached_op

* Address review comments: comments for locking strategy

* multithreaded inference tutorial

* [Estimator] handle composite metrics in estimator (apache#16676)

* handle composite metrics in estimator

* fix composite metric case in handlers

* remove unused import

* [Estimator] refactor estimator to allow overriding evaluate/fit of a batch (apache#16678)

* refactor estimator to allow overriding evaluate/fit of a batch

* add doc to explain call structure and how to override

* fix and doc

* Pointwise fusion for GPU (apache#15167)

* Beginning of RTC of pointwise ops

* Code generation from the given JSON

* add initial simple_partition_pass and use it for pointwise fusion

* fix the fusion, use a symbol.Copy() at the beginning of binding function, use the name of input nodes in the cuda code

* Fixes

* Adding support for attribute inference for backward nodes when fusing

* keep proper input ordering for fused Op

* instantiate the indexed_graph before starting the subgraph replacement, return a new graph to reset the indexed_graph

* Fuse backward

* fix ordering of subgraph node inputs using subgraph topological ordering instead of main graph topological ordering, add tvm.patch

* excluse forward node fusion during the fusion of the nodes in the backward graph

* Dealing with fused backward nodes inferattr

* use subgraph.indexed_graph() instead of main for _FusedOpHelper nodes node_id, invert control_deps loop to modify topology of subgraph before calling its indexed_graph(), check that all node of the first DFSVisit are actually in the subgraph

* Adding support for other reqs in codegen

* Fix

* Cleaning

* Change the TVM submodule

* More cleaning

* Making linter happy

* Do fusion only if default context is GPU

* Fixes for tests
Add powerscalar and rpowerscalar, fix return type of zero and one
Cleaning, fixing lint
Go back to proper TVM submodule

* Fix the TVM commit

* Fix lint

* Guard fusion with MXNET_USE_CUDA

* Fix

* Fix clang-tidy

* Add erf and erfinv backward

* Gluon support for fusion

* Cleaning

* Cleaning and allow shape/type change in FusedOp

* Fixing Gluon bugs

* Fixing after rebase

* Fixing race condition and guarding against races when using NVRTC

* Cleaning and renaming FusedOp to _FusedOp

* Going easy on Windows compiler

* Disable fusion on Windows for now

* Refactor InferAttr and InferShapeAttr

* Added slice and half2 support to FusedOp

* Fix lint errors

* Added multiple types support for vector loading/storing

* add slice fusion when it's at the beginning of subgraphs

* Removed constant ndim assumption in fused op

* Fix memory alignment issue in slice for FusedOp

* Fixes

* Fix lint errors

* Do not include cuda_fp16.h

* Refactor fused op op lists

* Make linter happy

* Changes from review

* Fixes after rebase

* Expand FusedOp support for slice

* Fix for fp16 _zeros and _ones

* Fix

* Moving aux functions to unnamed namespace and detail namespace -> fusion
namespace

* Disabling fusion if it alters topological order of inputs

* Print code only when env variable is set

* Fix

* Fix lint and 2 tests that specify the same names for multiple inputs

* Fixes from review and disabling fusion of slice with non-default step

* Add amp_cast to fusion, fixes

* Add amp_multicast and its backward to the list of support ops

* Apply wording suggestions from code review

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Apply wording suggestions from code review

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Make clearer comment

* Adding punctuation and capitalization to \brief descriptions

* Fix

* Fix

* Add backward_cast to fusion

* Adding unittests for fusion. Fix for erfinv_grad

* Adding slice ops and add_n to tests

* Fixes from review

* Setting inplace option

* Fix lint

* Storing double in half

* Retrigger CI

* Slight relaxing of the relative tolerance in the test

* Move the env variable check to the end

* Fix a race condition between InferShape and scheduled Forward

* Fix flakey test_fusion test involving fp32 erfinv op.

* Fix from review

* Added broadcast_like and slice_like to fused op

* Minor fix and cleanup

* Added negative axis support in slice_axis, temporarily disabled fusion of slice_like and broadcast_like

* Added axes support to slice_like

* Added axis support to broadcast_like

* Add fast_load_slice function to fused op code

* Added runtime switch for choosing fast and slow slice kernel

* Fix lint and warning

* Going easy on Windows compiler (again)

* Fix slice_like

* Debug broadcast_like fusion

* Fix lint

* Fix lint

* Trigger CI

* Get rid of the initializer list

* Fix backward calls with different gradient type

* avoid cycle when adding node specific for inputs of subgraph for pointwise fusion

* Fix lint

* Add namespace to the fusion implementations

* Set launch bounds on the fused kernel

* Fix NumPy tests

* Test showcasing an issue fixed in PR apache#16553

* Cast scalarts to FP32 and perform (a*1.0/b) instead of (a/b)

Fix lint errors

Fix lint

* Fix a bug in cycle detection for inputs only op in pointwise fusion

* Add comments to simple_partition_pass.h file

* fix install dir (apache#16690)

* [numpy] add numpy operator : append (apache#16564)

* add operator : append ; fix op concatenate when axis = None

* pylint disable

remove mistake

disable pylint

* Initializer.__eq__ (apache#16680)

* fix binary dependencies in CD and nightly (apache#16693)

* [MKL-DNN] Add mxnet mkldnn cmake tutorial (apache#16688)

* add mxnet mkldnn cmake instruction

* imporve doc

* OMP->OpenMP

* Revert "[MKLDNN]Fix reorder2default (apache#16602)" (apache#16697)

This reverts commit dd4eaf5.

* [Estimator] refactor estimator and clarify docs (apache#16694)

* refactor estimator and clarify docs

* fix info message and test

* clean up after releasing logging handler

* Eliminate common expressions (apache#15657)

* Eliminate common expressions from a graph

* Guarding against optimizing out stateful ops and ops that require
resource

* Fix lint

* Added THasDeterministicOutput to multiple ops

* DDebug eliminate common expr

* Added test

* Expose get_optimized_symbol

* Fix

* Fix 2

* Add doc to the Python call

* Add env var MXNET_ELIMINATE_COMMON_EXPR, default true

* Add comments, improve readability of eliminate_common_expr_pass.cc

* Expand testing

* Lower priority of THasDeterministicOutput attr for equal Node test

* Change mx.gpu() to mx.cpu() in tests

* Skip CSE test on Windows (as env variable setting during test does not work there)

* Add missing import sys

* Add missing import logging

* Backport of apache#16711, apache#16737, apache#16408 to 1.6 branch (apache#16763)

* support mixed-precision true_divide (apache#16711)

* [MKLDNN] use dim_t instead of int in slice/transpose operators (apache#16737)

* use dim_t instead of int

* fix same issue in pooling

* rebase code

* trigger CI

* Add MXNet Ops for fast multihead attention (apache#16408)

* add MXNet Ops for fast multihead attention

* add cutlass as 3rdparty dependency

* add cutlass to compilation flags

* remove all cutlass stuff

* add better error message and description and remove cutlass from compilation flags

* change credit for the approach since the code have changed

* fix typos

* correct another typo

* Add all the cuda/cublas helper functions

* remove tests using kAddTo

* only use cublasStridedBatchedGemm if CUDA >= 9.1

* add equivalent mxnet code in description of mha ops

* remove a wrong copy-paste

* add _contrib for namespace and add GPU only on description

* add warning in bwd_ignore_zero_init description, also test with fp32

* add error return if bwd_ignore_zero_init is used without MXNET_EXEC_ENABLE_ADDTO

* remove std::move for clang

* remove bwd_ignore_zero_init flag

* remove bwd_ignore_zero_init in test_operator_gpu.py

* fix typo

* fix another typo

* Removed unrelated test

* Add example and documentation for multi threaded inference

* Add LICENSE

* Add get_model.py

* Add license for README

* Refactor cached op and cached op threadsafe

* Add limitation

* Add tests for naive engine

* Add latest test changes

* Thread Safety tests in NaiveEngine mode

* Thread Safety tests update

* Update thread safety tests, add unsupported use cases

* Changes to doc and refactor

* Fix todo owner, indentation and mx_float->float

* Refactor cached op code, remove num_threads arg from example

* Fix lint

* Fix warning

* Add back cython, required for unix-gpu build

* Fix for windows

* Add bulking support for thread safe cached op version

* Add support for subgraph testing

* import mxnet before calling get_backend_symbol

* Fix symbol json name

* Refactor DynamicForward

* Add comments

* Add DMLC_ATTRIBUTE_UNUSED

* Fix use_naive_run issue

* Fix lint

* Revert unittest_cpp to old test since it doesnt test thread safety

* Fix doc

Co-authored-by: Sheng Zha <szha@users.noreply.github.com>
Co-authored-by: Przemyslaw Tredak <ptrendx@gmail.com>
Co-authored-by: Tao Lv <tao.a.lv@intel.com>
Co-authored-by: JiangZhaoh <54654391+JiangZhaoh@users.noreply.github.com>
Co-authored-by: Leonard Lausen <leonard@lausen.nl>
Co-authored-by: Xinyu Chen <xinyu1.chen@intel.com>
Co-authored-by: Zhennan Qin <zhennan.qin@intel.com>
rondogency pushed a commit to rondogency/incubator-mxnet that referenced this pull request Jul 2, 2020
* Add cached op threadsafe version with corresponding C APIs, CPP Package changes, CI changes and tests

* Fix download cmd in runtime_functions

* Add CI changes

* Add stage

Fix indentation

* Fix lint

* Change to DEFAULT for C API

* Fix mxnet_unit_tests path

* export correct LD_LIBRARY_PATH

* Add cpp include dirs

* Build test with USE_CPP_PACKAGE

* Add cached op threadsafe version with corresponding C APIs, CPP Package changes, CI changes and tests

* Fix download cmd in runtime_functions

* Merge

* change mkldnn lib name

* Add static_alloc, static_Shape support

* Address review comments

* Make GetCachedOpThreadSafeState similar to cached_op

* Address review comments: comments for locking strategy

* multithreaded inference tutorial

* [Estimator] handle composite metrics in estimator (apache#16676)

* handle composite metrics in estimator

* fix composite metric case in handlers

* remove unused import

* [Estimator] refactor estimator to allow overriding evaluate/fit of a batch (apache#16678)

* refactor estimator to allow overriding evaluate/fit of a batch

* add doc to explain call structure and how to override

* fix and doc

* Pointwise fusion for GPU (apache#15167)

* Beginning of RTC of pointwise ops

* Code generation from the given JSON

* add initial simple_partition_pass and use it for pointwise fusion

* fix the fusion, use a symbol.Copy() at the beginning of binding function, use the name of input nodes in the cuda code

* Fixes

* Adding support for attribute inference for backward nodes when fusing

* keep proper input ordering for fused Op

* instantiate the indexed_graph before starting the subgraph replacement, return a new graph to reset the indexed_graph

* Fuse backward

* fix ordering of subgraph node inputs using subgraph topological ordering instead of main graph topological ordering, add tvm.patch

* excluse forward node fusion during the fusion of the nodes in the backward graph

* Dealing with fused backward nodes inferattr

* use subgraph.indexed_graph() instead of main for _FusedOpHelper nodes node_id, invert control_deps loop to modify topology of subgraph before calling its indexed_graph(), check that all node of the first DFSVisit are actually in the subgraph

* Adding support for other reqs in codegen

* Fix

* Cleaning

* Change the TVM submodule

* More cleaning

* Making linter happy

* Do fusion only if default context is GPU

* Fixes for tests
Add powerscalar and rpowerscalar, fix return type of zero and one
Cleaning, fixing lint
Go back to proper TVM submodule

* Fix the TVM commit

* Fix lint

* Guard fusion with MXNET_USE_CUDA

* Fix

* Fix clang-tidy

* Add erf and erfinv backward

* Gluon support for fusion

* Cleaning

* Cleaning and allow shape/type change in FusedOp

* Fixing Gluon bugs

* Fixing after rebase

* Fixing race condition and guarding against races when using NVRTC

* Cleaning and renaming FusedOp to _FusedOp

* Going easy on Windows compiler

* Disable fusion on Windows for now

* Refactor InferAttr and InferShapeAttr

* Added slice and half2 support to FusedOp

* Fix lint errors

* Added multiple types support for vector loading/storing

* add slice fusion when it's at the beginning of subgraphs

* Removed constant ndim assumption in fused op

* Fix memory alignment issue in slice for FusedOp

* Fixes

* Fix lint errors

* Do not include cuda_fp16.h

* Refactor fused op op lists

* Make linter happy

* Changes from review

* Fixes after rebase

* Expand FusedOp support for slice

* Fix for fp16 _zeros and _ones

* Fix

* Moving aux functions to unnamed namespace and detail namespace -> fusion
namespace

* Disabling fusion if it alters topological order of inputs

* Print code only when env variable is set

* Fix

* Fix lint and 2 tests that specify the same names for multiple inputs

* Fixes from review and disabling fusion of slice with non-default step

* Add amp_cast to fusion, fixes

* Add amp_multicast and its backward to the list of support ops

* Apply wording suggestions from code review

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Apply wording suggestions from code review

Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Make clearer comment

* Adding punctuation and capitalization to \brief descriptions

* Fix

* Fix

* Add backward_cast to fusion

* Adding unittests for fusion. Fix for erfinv_grad

* Adding slice ops and add_n to tests

* Fixes from review

* Setting inplace option

* Fix lint

* Storing double in half

* Retrigger CI

* Slight relaxing of the relative tolerance in the test

* Move the env variable check to the end

* Fix a race condition between InferShape and scheduled Forward

* Fix flakey test_fusion test involving fp32 erfinv op.

* Fix from review

* Added broadcast_like and slice_like to fused op

* Minor fix and cleanup

* Added negative axis support in slice_axis, temporarily disabled fusion of slice_like and broadcast_like

* Added axes support to slice_like

* Added axis support to broadcast_like

* Add fast_load_slice function to fused op code

* Added runtime switch for choosing fast and slow slice kernel

* Fix lint and warning

* Going easy on Windows compiler (again)

* Fix slice_like

* Debug broadcast_like fusion

* Fix lint

* Fix lint

* Trigger CI

* Get rid of the initializer list

* Fix backward calls with different gradient type

* avoid cycle when adding node specific for inputs of subgraph for pointwise fusion

* Fix lint

* Add namespace to the fusion implementations

* Set launch bounds on the fused kernel

* Fix NumPy tests

* Test showcasing an issue fixed in PR apache#16553

* Cast scalarts to FP32 and perform (a*1.0/b) instead of (a/b)

Fix lint errors

Fix lint

* Fix a bug in cycle detection for inputs only op in pointwise fusion

* Add comments to simple_partition_pass.h file

* fix install dir (apache#16690)

* [numpy] add numpy operator : append (apache#16564)

* add operator : append ; fix op concatenate when axis = None

* pylint disable

remove mistake

disable pylint

* Initializer.__eq__ (apache#16680)

* fix binary dependencies in CD and nightly (apache#16693)

* [MKL-DNN] Add mxnet mkldnn cmake tutorial (apache#16688)

* add mxnet mkldnn cmake instruction

* imporve doc

* OMP->OpenMP

* Revert "[MKLDNN]Fix reorder2default (apache#16602)" (apache#16697)

This reverts commit dd4eaf5.

* [Estimator] refactor estimator and clarify docs (apache#16694)

* refactor estimator and clarify docs

* fix info message and test

* clean up after releasing logging handler

* Eliminate common expressions (apache#15657)

* Eliminate common expressions from a graph

* Guarding against optimizing out stateful ops and ops that require
resource

* Fix lint

* Added THasDeterministicOutput to multiple ops

* DDebug eliminate common expr

* Added test

* Expose get_optimized_symbol

* Fix

* Fix 2

* Add doc to the Python call

* Add env var MXNET_ELIMINATE_COMMON_EXPR, default true

* Add comments, improve readability of eliminate_common_expr_pass.cc

* Expand testing

* Lower priority of THasDeterministicOutput attr for equal Node test

* Change mx.gpu() to mx.cpu() in tests

* Skip CSE test on Windows (as env variable setting during test does not work there)

* Add missing import sys

* Add missing import logging

* Backport of apache#16711, apache#16737, apache#16408 to 1.6 branch (apache#16763)

* support mixed-precision true_divide (apache#16711)

* [MKLDNN] use dim_t instead of int in slice/transpose operators (apache#16737)

* use dim_t instead of int

* fix same issue in pooling

* rebase code

* trigger CI

* Add MXNet Ops for fast multihead attention (apache#16408)

* add MXNet Ops for fast multihead attention

* add cutlass as 3rdparty dependency

* add cutlass to compilation flags

* remove all cutlass stuff

* add better error message and description and remove cutlass from compilation flags

* change credit for the approach since the code have changed

* fix typos

* correct another typo

* Add all the cuda/cublas helper functions

* remove tests using kAddTo

* only use cublasStridedBatchedGemm if CUDA >= 9.1

* add equivalent mxnet code in description of mha ops

* remove a wrong copy-paste

* add _contrib for namespace and add GPU only on description

* add warning in bwd_ignore_zero_init description, also test with fp32

* add error return if bwd_ignore_zero_init is used without MXNET_EXEC_ENABLE_ADDTO

* remove std::move for clang

* remove bwd_ignore_zero_init flag

* remove bwd_ignore_zero_init in test_operator_gpu.py

* fix typo

* fix another typo

* Removed unrelated test

* Add example and documentation for multi threaded inference

* Add LICENSE

* Add get_model.py

* Add license for README

* Refactor cached op and cached op threadsafe

* Add limitation

* Add tests for naive engine

* Add latest test changes

* Thread Safety tests in NaiveEngine mode

* Thread Safety tests update

* Update thread safety tests, add unsupported use cases

* Changes to doc and refactor

* Fix todo owner, indentation and mx_float->float

* Refactor cached op code, remove num_threads arg from example

* Fix lint

* Fix warning

* Add back cython, required for unix-gpu build

* Fix for windows

* Add bulking support for thread safe cached op version

* Add support for subgraph testing

* import mxnet before calling get_backend_symbol

* Fix symbol json name

* Refactor DynamicForward

* Add comments

* Add DMLC_ATTRIBUTE_UNUSED

* Fix use_naive_run issue

* Fix lint

* Revert unittest_cpp to old test since it doesnt test thread safety

* Fix doc

Co-authored-by: Sheng Zha <szha@users.noreply.github.com>
Co-authored-by: Przemyslaw Tredak <ptrendx@gmail.com>
Co-authored-by: Tao Lv <tao.a.lv@intel.com>
Co-authored-by: JiangZhaoh <54654391+JiangZhaoh@users.noreply.github.com>
Co-authored-by: Leonard Lausen <leonard@lausen.nl>
Co-authored-by: Xinyu Chen <xinyu1.chen@intel.com>
Co-authored-by: Zhennan Qin <zhennan.qin@intel.com>
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4 participants