Allow batching in AmplitudeEmbedding with Tensorflow
#4818
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## master #4818 +/- ##
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- Coverage 99.64% 99.64% -0.01%
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Files 381 381
Lines 34382 34117 -265
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- Hits 34260 33995 -265
Misses 122 122
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Confirmed to fix issue posted in discussion forum: https://discuss.pennylane.ai/t/importerror-cannot-import-name-shape/1383/13?u=isaacdevlugt |
albi3ro
approved these changes
Nov 10, 2023
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Context:
The padding and normalization step in
AmplitudeEmbeddingpreviously used a manual for loop, making it incompatible with Tensorflowtf.function(jit_compile=True).Description of the Change:
The preprocessing now process the whole batch of features simultaneously, using numpy-like broadcasting instead.
(This also should be faster, usually).
Also, the tests have been complemented to test Tensorflow JITting with padding, and padding/non-padding tests have been combined into a single test function.
Benefits:
Support of Tensorflow,including JIT compilation in Tensorflow.
(probably) a speedup.
Possible Drawbacks:
Strictly speaking, the previous implementation allowed for different feature vector lengths across the batching dimension (because the padding happens before we rely on the input being a non-ragged array/tensor).
I think it is fine to constrain the input to the preprocessing already to non-ragged arrays.
Related GitHub Issues:
closes #2976