[NCCL] Changed FutureNCCL's then callback logic for better efficiency. #42869

sinannasir · 2020-08-11T18:40:49Z

Stack from ghstack:

DDP communication hook examples #43310 DDP communication hook examples
[NCCL] Changed FutureNCCL's then callback logic for better efficiency. #42869 [NCCL] Changed FutureNCCL's then callback logic for better efficiency.

We realized that when we invoke a simple callback that divides the tensors by world_size after allreduce, the performance was almost 50% lower in terms of QPS compared to the case where a simple allreduce hook is used with no then callback.

The main problem was as we call work.wait() before invoking then callback, we were synchronizing work's stream with the default PyTorch stream inside runHook and stalling the backward computation.

In that PR, we ensure that FutureNCCL's then callback is not stalling the backward computation. Assuming single-process single-device, FutureNCCL gets a new stream from device's pool using at::cuda::getStreamFromPool to run callback and before invoking the callback inline it synchronizes WorkNCCL's stream by callback's stream not the default stream.

Differential Revision: D23055807

@pritam

We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritam realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

@pritamdamania87

… efficiency." We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritamdamania87 realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

@pritam

Pull Request resolved: #42869 We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritam realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. ghstack-source-id: 109675502 Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/)

rohan-varma

Nice! This overall looks great, have a few comments/questions inline.

torch/csrc/distributed/c10d/init.cpp

torch/lib/c10d/ProcessGroupNCCL.hpp

rohan-varma · 2020-08-11T21:13:36Z

torch/lib/c10d/ProcessGroupNCCL.hpp

  // wrapper to synchronize streams appropriately and it mostly enables
  // the async programming model of CUDA while trying to adhere to the
-  // Future interface.
+  // Future interface. FutureNCCL does not support NCCL_BLOCKING_WAIT flag


Can you clarify this a bit? If I do run with NCCL_BLOCKING_WAIT, does this mean that if I call .wait() on a future returned by get_future() it still won't block? Would we want to add this support later on (seems like we would), if so can we file a GH issue? Same goes for barrier() - what wouldn't work if we did barrier() with FutureNCCL?

I think in case of NCCL_BLOCKING_WAIT=0 it will just wait until streams are synchronized and it will return before the whole NCCL kernel completed on the GPU.
My understanding is that in case of barrier() and `NCCL_BLOCKING_WAIT, then callback will become even more complicated. Any thoughts @pritamdamania87, do we wanna support those later?

rohan-varma · 2020-08-11T21:17:45Z

Can we describe the perf issue with the previous solution and why this commit fixes it (basically no longer blocking default stream on allreduce in then(), which eliminated overlap)? Would be useful context for those who come across this in the future.

dr-ci · 2020-08-11T22:57:38Z

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This comment has been revised 33 times.

@pritamdamania87

… efficiency." We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritamdamania87 realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

@pritam

Pull Request resolved: #42869 We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritam realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. ghstack-source-id: 109805861 Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/)

pritamdamania87 · 2020-08-14T00:35:24Z

torch/lib/c10d/ProcessGroupNCCL.cpp

+  std::shared_ptr<at::cuda::CUDAEvent> cudaEvent = {cudaEvents_,
+                                                    &(*cudaEvents_)[0]};


Why are we doing this and is this even correct? Shouldn't we just pass cudaEvents_ to FutureNCCL?

I was trying to address, @rohan-varma's comment #42869 (comment).

We just have one event, so probably using a vector isn't needed. I was just trying to create a pointer that shares the ownership with cudaEvents_ and points to its first element. Since at::cuda::CUDAEvent's constructor is deleted, I need to be careful here, but probably that's not an elegant solution and linter gives warning.

If using a vector is okay, I can just revert this and keep using vector inside FutureNCCL.

I think let's just pass a vector since I'm not sure if it's possible to create a shared_ptr pointing to one element of a shared_ptr vector.

pritamdamania87 · 2020-08-14T01:08:02Z

torch/csrc/distributed/c10d/init.cpp

+                Note that ``fut.done()`` returns if work's NCCL streams were synchronized with PyTorch's
+                default device streams.


This doesn't seem to be right? In completed() we actually check if the work has completed on the GPU using cudaEventQuery.

pritamdamania87 · 2020-08-14T01:10:47Z

torch/lib/c10d/ProcessGroupNCCL.cpp

+  std::shared_ptr<at::cuda::CUDAEvent> cudaEvent = {cudaEvents_,
+                                                    &(*cudaEvents_)[0]};


I think let's just pass a vector since I'm not sure if it's possible to create a shared_ptr pointing to one element of a shared_ptr vector.

torch/lib/c10d/ProcessGroupNCCL.hpp

@pritamdamania87

… efficiency." We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritamdamania87 realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

@pritamdamania87

… efficiency." We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritamdamania87 realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

sinannasir · 2020-08-15T00:30:00Z

Can we describe the perf issue with the previous solution and why this commit fixes it (basically no longer blocking default stream on allreduce in then(), which eliminated overlap)? Would be useful context for those who come across this in the future.

I agree. I included additional small description in the documentation. I'll update the summary accordingly and give more details.

@pritamdamania87

… efficiency." We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritamdamania87 realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

@pritam

Pull Request resolved: #42869 We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritam realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. ghstack-source-id: 109993778 Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/)

torch/lib/c10d/ProcessGroupNCCL.hpp

@pritamdamania87

… efficiency." We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. @pritamdamania87 realized that we should run the callback in `then` on a different stream and synchronize the NCCL stream to that stream instead and not the default device stream. Now a new `FutureNCCL` is returned from `then` hold the return value of the callback and new cudaEvents that recorded the stream that guards this callback.. This new FutureNCCL actually synchronizes on the new stream we used for the callback when its wait() is called. **Ongoing discussions:** * Calling `fut.wait()` (instead of `fut.value()`) inside the callback of the allreduce then divide hook is okay. * Calling `fut.wait()` if a new futureNCCL object is defined inside then callback is essential to get correct results. It could be fine in terms of performance since within the scope of then callback the default stream becomes the stream that guards the callback but not PyTorch's default device stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

… efficiency." We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. The main problem was as we call `work.wait()` before invoking `then` callback, we were synchronizing `work`'s stream with the default PyTorch stream inside [`runHook`](https://github.com/pytorch/pytorch/blob/master/torch/csrc/distributed/c10d/reducer.cpp#L609) and stalling the backward computation. In that PR, we ensure that FutureNCCL's `then` callback is not stalling the backward computation. Assuming single-process single-device, `FutureNCCL` gets a new stream from device's pool using `at::cuda::getStreamFromPool` to run `callback` and before invoking the `callback` inline it synchronizes `WorkNCCL`'s stream by callback's stream not the default stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

Pull Request resolved: #42869 We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. The main problem was as we call `work.wait()` before invoking `then` callback, we were synchronizing `work`'s stream with the default PyTorch stream inside [`runHook`](https://github.com/pytorch/pytorch/blob/master/torch/csrc/distributed/c10d/reducer.cpp#L609) and stalling the backward computation. In that PR, we ensure that FutureNCCL's `then` callback is not stalling the backward computation. Assuming single-process single-device, `FutureNCCL` gets a new stream from device's pool using `at::cuda::getStreamFromPool` to run `callback` and before invoking the `callback` inline it synchronizes `WorkNCCL`'s stream by callback's stream not the default stream. ghstack-source-id: 110091730 Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/)

… efficiency." We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. The main problem was as we call `work.wait()` before invoking `then` callback, we were synchronizing `work`'s stream with the default PyTorch stream inside [`runHook`](https://github.com/pytorch/pytorch/blob/master/torch/csrc/distributed/c10d/reducer.cpp#L609) and stalling the backward computation. In that PR, we ensure that FutureNCCL's `then` callback is not stalling the backward computation. Assuming single-process single-device, `FutureNCCL` gets a new stream from device's pool using `at::cuda::getStreamFromPool` to run `callback` and before invoking the `callback` inline it synchronizes `WorkNCCL`'s stream by callback's stream not the default stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

Pull Request resolved: #42869 We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. The main problem was as we call `work.wait()` before invoking `then` callback, we were synchronizing `work`'s stream with the default PyTorch stream inside [`runHook`](https://github.com/pytorch/pytorch/blob/master/torch/csrc/distributed/c10d/reducer.cpp#L609) and stalling the backward computation. In that PR, we ensure that FutureNCCL's `then` callback is not stalling the backward computation. Assuming single-process single-device, `FutureNCCL` gets a new stream from device's pool using `at::cuda::getStreamFromPool` to run `callback` and before invoking the `callback` inline it synchronizes `WorkNCCL`'s stream by callback's stream not the default stream. ghstack-source-id: 110175728 Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/)

… efficiency." We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. The main problem was as we call `work.wait()` before invoking `then` callback, we were synchronizing `work`'s stream with the default PyTorch stream inside [`runHook`](https://github.com/pytorch/pytorch/blob/master/torch/csrc/distributed/c10d/reducer.cpp#L609) and stalling the backward computation. In that PR, we ensure that FutureNCCL's `then` callback is not stalling the backward computation. Assuming single-process single-device, `FutureNCCL` gets a new stream from device's pool using `at::cuda::getStreamFromPool` to run `callback` and before invoking the `callback` inline it synchronizes `WorkNCCL`'s stream by callback's stream not the default stream. Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/) [ghstack-poisoned]

Pull Request resolved: #42869 We realized that when we invoke a simple callback that divides the tensors by `world_size` after `allreduce`, the performance was almost 50% lower in terms of QPS compared to the case where a simple `allreduce` hook is used with no `then` callback. The main problem was as we call `work.wait()` before invoking `then` callback, we were synchronizing `work`'s stream with the default PyTorch stream inside [`runHook`](https://github.com/pytorch/pytorch/blob/master/torch/csrc/distributed/c10d/reducer.cpp#L609) and stalling the backward computation. In that PR, we ensure that FutureNCCL's `then` callback is not stalling the backward computation. Assuming single-process single-device, `FutureNCCL` gets a new stream from device's pool using `at::cuda::getStreamFromPool` to run `callback` and before invoking the `callback` inline it synchronizes `WorkNCCL`'s stream by callback's stream not the default stream. ghstack-source-id: 110208431 Differential Revision: [D23055807](https://our.internmc.facebook.com/intern/diff/D23055807/)

facebook-github-bot · 2020-08-20T04:12:16Z

This pull request has been merged in 6e1127e.

sinannasir requested review from apaszke, mrshenli, pietern and zhaojuanmao as code owners August 11, 2020 18:40

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[jit] Can not pickle torch.futures.Future #43190

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DDP communication hook examples #43309

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DDP communication hook examples #43310

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mruberry added the Merged label Oct 28, 2020

		std::shared_ptr<at::cuda::CUDAEvent> cudaEvent = {cudaEvents_,
		&(*cudaEvents_)[0]};

		Note that ``fut.done()`` returns if work's NCCL streams were synchronized with PyTorch's
		default device streams.

[NCCL] Changed FutureNCCL's then callback logic for better efficiency. #42869

[NCCL] Changed FutureNCCL's then callback logic for better efficiency. #42869

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