refactor: remove redundant Tensor allocation in GaussianConditional.update()#343
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fracape merged 1 commit intoInterDigitalInc:masterfrom Oct 22, 2025
studyingeugene:studyingeugene-minor-suggestion
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refactor: remove redundant Tensor allocation in GaussianConditional.update()#343fracape merged 1 commit intoInterDigitalInc:masterfrom studyingeugene:studyingeugene-minor-suggestion
fracape merged 1 commit intoInterDigitalInc:masterfrom
studyingeugene:studyingeugene-minor-suggestion
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…pdate()
In GaussianConditional.update(), a temporary Tensor is allocated:
quantized_cdf = torch.Tensor(len(pmf_length), max_length + 2)
quantized_cdf = self._pmf_to_cdf(pmf, tail_mass, pmf_length, max_length)
The first line is a dead store: the variable is immediately overwritten by
the result of _pmf_to_cdf(). This removes the unnecessary allocation.
- No functional changes
- Slightly reduces heap traffic and avoids creating an uninitialized Tensor
- Keeps dtype/device fully defined by _pmf_to_cdf()
fracape
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Oct 22, 2025
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This PR removes a redundant one-line Tensor allocation in
GaussianConditional.update()withinentropy_models.py.Testing
Ran unit tests that exercise GaussianConditional.update() and dependent
compression/decompression paths; no regressions observed.
Verified that self._quantized_cdf, self._offset, and self._cdf_length
match previous values bit-for-bit for a fixed scale_table.
Why this trivial change matters
TorchDynamo or FX tracing can sometimes interpret redundant allocations as live ops, bloating the captured graph.
Removing this line avoids all of the above while keeping
_pmf_to_cdf()solely responsible for dtype and device consistency.No behavioral change is expected; this is a safe cleanup.