Rewrite lerp operator to use TensorIterator and support compile-time vectorization. #22038
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cpuhrsch
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Jun 20, 2019
cpuhrsch
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Jun 20, 2019
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@pytorchbot retest this please |
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cpuhrsch
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Jun 21, 2019
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@VitalyFedyunin merged this pull request in fe580e8. |
iotamudelta
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Jun 21, 2019
…vectorization. (pytorch#22038) Summary: Get benefit from the compile time vectorization and multi-threading. Before: ```python In [1]: import torch In [2]: x = torch.randn(1000000) In [3]: y = torch.randn(1000000) In [4]: w = 0.7 In [5]: timeit torch.lerp(x, y, w) 2.29 ms ± 23.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) ``` After: ```python In [1]: import torch In [2]: x = torch.randn(1000000) In [3]: y = torch.randn(1000000) In [4]: w = 0.7 In [5]: timeit torch.lerp(x, y, w) 452 µs ± 1.81 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) ``` After with multi-processing: ```python In [1]: import torch In [2]: x = torch.randn(1000000) In [3]: y = torch.randn(1000000) In [4]: w = 0.7 In [5]: timeit torch.lerp(x, y, w) 167 µs ± 48.8 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) ``` Pull Request resolved: pytorch#22038 Differential Revision: D15941468 Pulled By: VitalyFedyunin fbshipit-source-id: fa8a5126187df4e6c849452e035b00b22be25739
zdevito
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…vectorization. (#22038) Summary: Get benefit from the compile time vectorization and multi-threading. Before: ```python In [1]: import torch In [2]: x = torch.randn(1000000) In [3]: y = torch.randn(1000000) In [4]: w = 0.7 In [5]: timeit torch.lerp(x, y, w) 2.29 ms ± 23.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) ``` After: ```python In [1]: import torch In [2]: x = torch.randn(1000000) In [3]: y = torch.randn(1000000) In [4]: w = 0.7 In [5]: timeit torch.lerp(x, y, w) 452 µs ± 1.81 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) ``` After with multi-processing: ```python In [1]: import torch In [2]: x = torch.randn(1000000) In [3]: y = torch.randn(1000000) In [4]: w = 0.7 In [5]: timeit torch.lerp(x, y, w) 167 µs ± 48.8 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) ``` Pull Request resolved: pytorch/pytorch#22038 Differential Revision: D15941468 Pulled By: VitalyFedyunin fbshipit-source-id: fa8a5126187df4e6c849452e035b00b22be25739
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Get benefit from the compile time vectorization and multi-threading.
Before:
After:
After with multi-processing: