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SepConv1d/2d for io_parallel with Latency strategy #1012

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@vloncar vloncar commented May 13, 2024

Description

This PR adds implementation of SeparableConv1D/2D for io_parallel and Latency strategy. It's based on existing code-generation for Conv1D/2D.

Type of change

  • New feature (non-breaking change which adds functionality)

Tests

Added tests in test_sepconv1d.py and test_sepconv2d.py. 1D tests had a nonsense test with a kernel size of 1, I changed that to 2 to be in line with the 2D tests.

Checklist

  • I have read the guidelines for contributing.
  • I have commented my code, particularly in hard-to-understand areas.
  • I have made corresponding changes to the documentation.
  • My changes generate no new warnings.
  • I have installed and run pre-commit on the files I edited or added.
  • I have added tests that prove my fix is effective or that my feature works.

@vloncar vloncar added the please test Trigger testing by creating local PR branch label May 13, 2024
@jmitrevs jmitrevs added please test Trigger testing by creating local PR branch and removed please test Trigger testing by creating local PR branch labels May 14, 2024
@jmitrevs jmitrevs added please test Trigger testing by creating local PR branch and removed please test Trigger testing by creating local PR branch labels May 23, 2024
@jmitrevs jmitrevs added please test Trigger testing by creating local PR branch and removed please test Trigger testing by creating local PR branch labels May 31, 2024
@jmitrevs jmitrevs added please test Trigger testing by creating local PR branch and removed please test Trigger testing by creating local PR branch labels Jun 3, 2024
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vloncar commented Jun 4, 2024

@dgburnette This could be followed up in Catapult. I noticed that you don't use the code-gen im2col implementations of conv1d/2d so I didn't want to mess with it

elif '2D' in node_class:
self._generate_im2col_2d(node)
node_class = node.class_name
if 'Separable' in node_class:
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If we just had DepthwiseConv2D (https://keras.io/api/layers/convolution_layers/depthwise_convolution2d/) would this work? We nominally support it.

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