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chore: Upgrade TensorRT version to TRT 10 EA (#2699) #2774

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merged 5 commits into from
Apr 25, 2024
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@peri044 peri044 commented Apr 23, 2024

Description

Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.

Fixes # (issue)

Type of change

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  • Bug fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to not work as expected)
  • This change requires a documentation update

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  • My code follows the style guidelines of this project (You can use the linters)
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Co-authored-by: Evan Li <zewenl@nvidia.com>
@github-actions github-actions bot added component: tests Issues re: Tests component: lowering Issues re: The lowering / preprocessing passes component: conversion Issues re: Conversion stage component: core Issues re: The core compiler component: converters Issues re: Specific op converters component: build system Issues re: Build system component: api [Python] Issues re: Python API component: api [C++] Issues re: C++ API component: evaluators Issues re: Specific op evaluators component: runtime component: fx component: dynamo Issues relating to the `torch.compile` or `torch._dynamo.export` paths labels Apr 23, 2024
@github-actions github-actions bot requested a review from zewenli98 April 23, 2024 17:55
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@peri044 peri044 merged commit 1a11f86 into main Apr 25, 2024
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ctx.net, lhs_val, rhs_val, f"{name}_lhs", f"{name}_rhs"
)
else:
lhs_val_shape = lhs_val.shape
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@peri044

I followed the code changes to address the error caused by broadcasting, and it seems to be related to this issue.

If we perform element-wise operations on two inputs with shapes lhs (2, 2, 14, 1, 5) and rhs (2, 1, 3, 5), the output of the element-wise operation layer should be (2, 2, 14, 3, 5).
However, with the current code, the output of the element-wise operation layer follows the larger rank of lhs_val and becomes (2, 2, 14, 1, 5).

As a result, an error that did not occur on the local machine was found during CI/CD (#2726)

Is it necessary to make adjustments to fix this code?

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Yes, it is necessary to fix this code as any converter that uses this would likely have that failure. It sounds like the fix would be broadcasting across mismatched dimensions instead of using rank heuristic. @zewenli98 probably you have an idea about these specific changes while fixing test_binary_ops ?

laikhtewari pushed a commit that referenced this pull request May 24, 2024
Co-authored-by: Evan Li <zewenl@nvidia.com>
Co-authored-by: George S <113141689+gs-olive@users.noreply.github.com>
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4 participants