Automatic fixes that replaces certain list comprehensions with generator ones where appropriate so that they are immediately consumed. This is preview functionality in ruff for rule C419 and it was automatically applied.
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123960
Approved by: https://github.com/malfet
Summary:
This PR modifies `benchmarks/tensorexpr`. It follows up[ https://github.com/pytorch/pytorch/issues/44101](https://github.com/pytorch/pytorch/pull/44101) and further supports characterizing fusers with dynamic shape benchmarks. Dynamic shape condition models the use case when the input tensor shape changes in each call to the graph.
Changes include:
Added an auxiliary class `DynamicShape `that provides a simple API for enabling dynamic shapes in existing test cases, example can be found with `DynamicSimpleElementBench`
Created new bench_cls: `DynamicSimpleElementBench`, `DynamicReduce2DInnerBench`, `DynamicReduce2DOuterBench`, and `DynamicLSTM`. They are all dynamic shaped versions of existing benchmarks and examples of enabling dynamic shape with `DynamicShape`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46107
Reviewed By: glaringlee
Differential Revision: D24229400
Pulled By: bertmaher
fbshipit-source-id: 889fece5ea87d0f6f6374d31dbe11b1cd1380683
Summary:
Modified files in `benchmarks/tensorexpr` to add support for NVIDIA's Fuser for the jit compiler.
This support has some modifications besides adding an option to support the NVIDIA fuser:
* Adds FP16 Datatype support
* Fixes SOL/Algo calculations to generally use the data type instead of being fixed to 4 bytes
* Adds IR printing and kernel printing knobs
* Adds a knob `input_iter` to create ranges of inputs currently only for reductions
* Adds further reduction support for Inner and Outer dimension reductions that are compatible with the `input_iter` knob.
* Added `simple_element`, `reduce2d_inner`, and `reduce2d_outer` to isolate performance on elementwise and reduction operations in the most minimal fashion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44101
Reviewed By: ngimel
Differential Revision: D23713658
Pulled By: bertmaher
fbshipit-source-id: d6b83cfab559aefe107c23b3c0f2df9923b3adc1
