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[NumPy] turn on set_np#18631

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szha wants to merge 2 commits intoapache:masterfrom
szha:np_default
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[NumPy] turn on set_np#18631
szha wants to merge 2 commits intoapache:masterfrom
szha:np_default

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@szha szha commented Jun 28, 2020

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  • The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant JIRA issue created (except PRs with tiny changes)
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  • To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

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  • Feature1, tests, (and when applicable, API doc)
  • Feature2, tests, (and when applicable, API doc)

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  • If this change is a backward incompatible change, why must this change be made.
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mxnet-bot commented Jun 28, 2020

Hey @szha , Thanks for submitting the PR
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@leezu leezu mentioned this pull request Jul 13, 2020
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@szha szha mentioned this pull request Apr 20, 2021
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@barry-jin barry-jin mentioned this pull request May 20, 2021
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@szha szha closed this May 21, 2021
@szha szha deleted the np_default branch May 21, 2021 20:10
leezu pushed a commit that referenced this pull request Jun 21, 2021
@barry-jin
Switch all HybridBlocks to use forward interface (#20262)
7152685 
Changes

Adopt packed_func based ffi on npx.group_norm
Gluon2.0 upgrade: use forward interface in blocks

    gluon/data/vision/*
    gluon/loss.py
    gluon/model_zoo/*
    gluon/nn/*
    gluon/rnn/*

use np/npx interface in gluon/metric.py
Implement infer_shape method

    gluon/nn/basic_layers.py::Dense
    gluon/nn/basic_layers.py::BatchNorm
    gluon/nn/basic_layers.py::InstanceNorm
    gluon/nn/basic_layers.py::LayerNorm
    gluon/nn/basic_layers.py::GroupNorm
    gluon/nn/conv_layers.py::_Conv
    gluon/nn/conv_layers.py::DeformableConvolution
    gluon/nn/conv_layers.py::ModulatedDeformableConvolution

Remove hybrid mode with F in gluon/probability/*
gluon/rnn/*

    conv_rnn_cell.py: implement forward, infer_shape, np/npx
    rnn_cell.py: implement forward, np/npx; use special infer_shape method based on layer, input_size and if it's bidirectional.
    rnn_layer.py: implement forward, infer_shape, np/npx

fix issue np.average return ADT type; npx.pooling
Copy control flow ops(loop_while, cond, foreach) from ndarray.contrib to mx.npx.control_flow
Register some legacy ops in npx

    stes_op
    sync_batch_norm
    legacy pad (np.pad doesn't have backward computation for 'reflect" mode)
    Some rnn related: sequence_last, sequence_reverse, slice_channel, broadcast_greater, softsign

Use forward, np/npx for all the gluon related tests; remove gluon tests with symbol inputs

    remove test_gluon_data_vision.py and test_gluon_probability_v1.py as related tests are covered in test_numpy_gluon_data_vision.py and test_gluon_probability_v2.py
    Test test_numpy_op.py::test_np_nan_to_num only for copy argument is set to True, since Inplace operations are not supported when recording in deferred compute mode

Remove hybrid_block interface in gluon/block.py
Remove hybrid_block interface in documentation and docstring

    update docs/python_docs/python/tutorials/packages/gluon/blocks/custom_layers
    remove python_tutorials/packages/gluon/blocks/custom_layers_beginners.md as it's duplicate to custom_layers
    remove docs/python_docs/python/docstutorials/packages/legacy/ndarray/sparse/train_gluon as gluon2.0 do not support sparse
    update docs/python_docs/python/tutorials/packages/gluon/blocks/custom_loss
    update docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize

Turn on NumPy mode by default (#18631)
Fix gluon2.0 reference leak.
Migrate control flow operators to npx namespace

    Foreach
    while_loop
    cond
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@szha @mxnet-bot