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jjsjann123 · 2019-01-22T05:48:50Z

Summary:
Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible
block dimension to improve efficiency for reduction cases with small fast
dimension.

Previously TensorIterator launches blocks with fixed 32x16 threads.
For cases like:

import torch
torch.randn(2**20, 4, device='cuda').sum(0)

The fixed launch config does handle coalesced memory access efficiently.

Updated launch configure enables flexible block dimension. Combining with
improved reduction scheme (using flexible vertical / horizontal reduction
instead of limited warp / block reduction in the old code), it ensures optimal
memory access pattern even with reduction on dimension with small stride.

Possible future improvements:

Precise dynamic shared memory allocation.
Using warp shuffle for vertical (block_y) reduction.

Summary: Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible block dimension to improve efficiency for reduction cases with small fast dimension. Previously TensorIterator launches blocks with fixed 32x16 threads. For cases like: import torch torch.randn(2**20, 4, device='cuda').sum(0) The fixed launch config does handle coalesced memory access efficiently. Updated launch configure enables flexible block dimension. Combining with improved reduction scheme (using flexible vertical / horizontal reduction instead of limited warp / block reduction in the old code), it ensures optimal memory access pattern even with reduction on dimension with small stride. Possible future improvements: 1. Precise dynamic shared memory allocation. 2. Using warp shuffle for vertical (block_y) reduction.

jjsjann123 · 2019-01-22T06:11:24Z

This is my monkey benchmarking. Notice the perf gain for small fast changing dimensions.

jjsjann123 · 2019-01-22T06:12:18Z

For visibility @ngimel @umanwizard @colesbury

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umanwizard · 2019-01-25T01:05:49Z

We are getting this error on some internal builds:

aten/src/ATen/native/cuda/Reduce.cuh:34:12: error: right shift count >= width of type [-Werror=shift-count-overflow]
   n |= (n >> 32);

umanwizard

see above comment

jjsjann123 · 2019-01-25T01:16:17Z

My bad.
I thought I updated that two commits ago when I was changing the data type from int64 to int. Guess it didn't happen.

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apaszke · 2019-01-31T18:36:14Z

I'm somewhat worried about the slowdowns. Why are we getting rid of warp reductions? Aren't those supposed to be fast?

jjsjann123 · 2019-01-31T19:05:58Z

I'm not getting rid of warp reduction. Keeping them as necessary for the old launch config where block dimension as 32x16: https://github.com/pytorch/pytorch/pull/16224/files#diff-662693ef7b7f32fa32d7179b6614fc16R379

Renaming warp_reduce to block_x_reduce because as we have flexible block dimension. Given the cases where blockDim.x > 32, it requires a hybrid of shared memory reduction and warp reduction, because not all threads are in the same warp.

jjsjann123 · 2019-02-06T22:24:10Z

Test failure doesn't seem to be relevant. Merging ToT to see if it goes away

addressed

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@soumith is landing this pull request. If you are a Facebook employee, you can view this diff on Phabricator.

Summary: Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible block dimension to improve efficiency for reduction cases with small fast dimension. Previously TensorIterator launches blocks with fixed 32x16 threads. For cases like: import torch torch.randn(2**20, 4, device='cuda').sum(0) The fixed launch config does handle coalesced memory access efficiently. Updated launch configure enables flexible block dimension. Combining with improved reduction scheme (using flexible vertical / horizontal reduction instead of limited warp / block reduction in the old code), it ensures optimal memory access pattern even with reduction on dimension with small stride. Possible future improvements: 1. Precise dynamic shared memory allocation. 2. Using warp shuffle for vertical (block_y) reduction. Pull Request resolved: pytorch/pytorch#16224 Differential Revision: D13806753 Pulled By: soumith fbshipit-source-id: 37e45c7767b5748cf9ecf894fad306e040e2f79f

soumith · 2019-02-08T19:41:20Z

just fyi @jjsjann123 this is being reverted, we are seeing "illegal memory exception" and RuntimeError: Creating MTGP constants failed. at caffe2/aten/src/THC/THCTensorRandom.cu:33 (when using torch.manual_seed) after this PR was landed in hard-to-repro internal workloads.

jjsjann123 · 2019-02-12T02:37:52Z

I think I figured out the issue. It's me overlooking the old heuristics for inter-block vs inter-warp reduction. Surprised this doesn't get caught by CI tests.

Working on a fix. Will also add tests that covers it.

update: 1. global_reduce check for should_block_y_reduce first. This avoids the enabling global_reduce without block_y_reduce. Leading to accessing shared memory during global reduce without allocation. 2. updating block_y_reduce heuristics. Improves perf on tiny tensors 3. adding test case covering old cases where illegal memory access might occur TensorIterator cuda launch configs update (pytorch#16224) Summary: Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible block dimension to improve efficiency for reduction cases with small fast dimension. Previously TensorIterator launches blocks with fixed 32x16 threads. For cases like: import torch torch.randn(2**20, 4, device='cuda').sum(0) The fixed launch config does handle coalesced memory access efficiently. Updated launch configure enables flexible block dimension. Combining with improved reduction scheme (using flexible vertical / horizontal reduction instead of limited warp / block reduction in the old code), it ensures optimal memory access pattern even with reduction on dimension with small stride. Possible future improvements: 1. Precise dynamic shared memory allocation. 2. Using warp shuffle for vertical (block_y) reduction. Pull Request resolved: pytorch#16224

Summary: update: 1. global_reduce check for should_block_y_reduce first. This avoids the enabling global_reduce without block_y_reduce. Leading to accessing shared memory during global reduce without allocation. 2. updating block_y_reduce heuristics. Improves perf on tiny tensors 3. adding test case covering old cases where illegal memory access might occur TensorIterator cuda launch configs update (#16224) Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible block dimension to improve efficiency for reduction cases with small fast dimension. Previously TensorIterator launches blocks with fixed 32x16 threads. For cases like: import torch torch.randn(2**20, 4, device='cuda').sum(0) The fixed launch config does handle coalesced memory access efficiently. Updated launch configure enables flexible block dimension. Combining with improved reduction scheme (using flexible vertical / horizontal reduction instead of limited warp / block reduction in the old code), it ensures optimal memory access pattern even with reduction on dimension with small stride. Possible future improvements: 1. Precise dynamic shared memory allocation. 2. Using warp shuffle for vertical (block_y) reduction. Pull Request resolved: #16224 Pull Request resolved: #17040 Differential Revision: D14078295 Pulled By: umanwizard fbshipit-source-id: ecc55054a5a4035e731f0196d633412225c3b06c

Summary: update: 1. global_reduce check for should_block_y_reduce first. This avoids the enabling global_reduce without block_y_reduce. Leading to accessing shared memory during global reduce without allocation. 2. updating block_y_reduce heuristics. Improves perf on tiny tensors 3. adding test case covering old cases where illegal memory access might occur TensorIterator cuda launch configs update (#16224) Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible block dimension to improve efficiency for reduction cases with small fast dimension. Previously TensorIterator launches blocks with fixed 32x16 threads. For cases like: import torch torch.randn(2**20, 4, device='cuda').sum(0) The fixed launch config does handle coalesced memory access efficiently. Updated launch configure enables flexible block dimension. Combining with improved reduction scheme (using flexible vertical / horizontal reduction instead of limited warp / block reduction in the old code), it ensures optimal memory access pattern even with reduction on dimension with small stride. Possible future improvements: 1. Precise dynamic shared memory allocation. 2. Using warp shuffle for vertical (block_y) reduction. Pull Request resolved: pytorch/pytorch#16224 Pull Request resolved: pytorch/pytorch#17040 Differential Revision: D14078295 Pulled By: umanwizard fbshipit-source-id: ecc55054a5a4035e731f0196d633412225c3b06c

Summary: update: 1. global_reduce check for should_block_y_reduce first. This avoids the enabling global_reduce without block_y_reduce. Leading to accessing shared memory during global reduce without allocation. 2. updating block_y_reduce heuristics. Improves perf on tiny tensors 3. adding test case covering old cases where illegal memory access might occur TensorIterator cuda launch configs update (pytorch#16224) Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible block dimension to improve efficiency for reduction cases with small fast dimension. Previously TensorIterator launches blocks with fixed 32x16 threads. For cases like: import torch torch.randn(2**20, 4, device='cuda').sum(0) The fixed launch config does handle coalesced memory access efficiently. Updated launch configure enables flexible block dimension. Combining with improved reduction scheme (using flexible vertical / horizontal reduction instead of limited warp / block reduction in the old code), it ensures optimal memory access pattern even with reduction on dimension with small stride. Possible future improvements: 1. Precise dynamic shared memory allocation. 2. Using warp shuffle for vertical (block_y) reduction. Pull Request resolved: pytorch#16224 Pull Request resolved: pytorch#17040 Differential Revision: D14078295 Pulled By: umanwizard fbshipit-source-id: ecc55054a5a4035e731f0196d633412225c3b06c

Summary: Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible block dimension to improve efficiency for reduction cases with small fast dimension. Previously TensorIterator launches blocks with fixed 32x16 threads. For cases like: import torch torch.randn(2**20, 4, device='cuda').sum(0) The fixed launch config does handle coalesced memory access efficiently. Updated launch configure enables flexible block dimension. Combining with improved reduction scheme (using flexible vertical / horizontal reduction instead of limited warp / block reduction in the old code), it ensures optimal memory access pattern even with reduction on dimension with small stride. Possible future improvements: 1. Precise dynamic shared memory allocation. 2. Using warp shuffle for vertical (block_y) reduction. Pull Request resolved: pytorch#16224 Differential Revision: D13806753 Pulled By: soumith fbshipit-source-id: 37e45c7767b5748cf9ecf894fad306e040e2f79f

Summary: update: 1. global_reduce check for should_block_y_reduce first. This avoids the enabling global_reduce without block_y_reduce. Leading to accessing shared memory during global reduce without allocation. 2. updating block_y_reduce heuristics. Improves perf on tiny tensors 3. adding test case covering old cases where illegal memory access might occur TensorIterator cuda launch configs update (pytorch#16224) Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible block dimension to improve efficiency for reduction cases with small fast dimension. Previously TensorIterator launches blocks with fixed 32x16 threads. For cases like: import torch torch.randn(2**20, 4, device='cuda').sum(0) The fixed launch config does handle coalesced memory access efficiently. Updated launch configure enables flexible block dimension. Combining with improved reduction scheme (using flexible vertical / horizontal reduction instead of limited warp / block reduction in the old code), it ensures optimal memory access pattern even with reduction on dimension with small stride. Possible future improvements: 1. Precise dynamic shared memory allocation. 2. Using warp shuffle for vertical (block_y) reduction. Pull Request resolved: pytorch#16224 Pull Request resolved: pytorch#17040 Differential Revision: D14078295 Pulled By: umanwizard fbshipit-source-id: ecc55054a5a4035e731f0196d633412225c3b06c

jjsjann123 added 2 commits January 23, 2019 00:04

setting min block dimension to 1x1 to avoid division by 0

da61a93

fixing diverged __syncthreads

73b15ac

umanwizard self-requested a review January 24, 2019 21:09

facebook-github-bot reviewed Jan 24, 2019

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umanwizard previously requested changes Jan 25, 2019

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removing 32 bit shift as previously changed data type from int64 to int

4b78cd8

facebook-github-bot reviewed Jan 25, 2019

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fmassa mentioned this pull request Feb 4, 2019

PyTorch stable version facebookresearch/maskrcnn-benchmark#261

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iotamudelta reviewed Feb 5, 2019

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Comment thread aten/src/ATen/native/cuda/Reduce.cuh Outdated

jjsjann123 added 2 commits February 5, 2019 16:04

removing hard coded warp size of 32 with warpSize

51f487d

Merge remote-tracking branch 'origin/master' into TI_launch_config_PR

3d61f44

facebook-github-bot reviewed Feb 7, 2019

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facebook-github-bot closed this in 49443d4 Feb 7, 2019

jjsjann123 mentioned this pull request Feb 13, 2019

Second PR to restore reverted commit (#16224) #17040

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zou3519 mentioned this pull request Mar 12, 2019

Second PR to restore reverted commit (#16224) (#17040) zou3519/pytorch#7

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ezyang added open source merged labels Jun 24, 2019

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