[JIT] Separate GPU implementation of frozen_conv_add_relu_fusion.cpp #68149

davidberard98 · 2021-11-11T02:36:21Z

Stack from ghstack:

-> [JIT] Separate GPU implementation of frozen_conv_add_relu_fusion.cpp #68149

JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available)

Differential Revision: D32773666

JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)! [ghstack-poisoned]

facebook-github-bot · 2021-11-11T02:36:23Z

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JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)! ghstack-source-id: 143023620 Pull Request resolved: #68149

…fusion.cpp" JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)! [ghstack-poisoned]

Pull Request resolved: #68149 JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) ghstack-source-id: 143023620 Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)!

eellison · 2021-11-11T21:35:36Z

deferring to @malfet and @desertfire

torch/csrc/jit/passes/frozen_conv_add_relu_fusion_impl.cpp

malfet · 2021-11-12T02:44:07Z

To clarify on requesting changes - global initializer, as it is currently implemented, is wrong, isn't it?
Otherwise, LGTM, though again, #ifdef USE_CUDA guards are not needed for GPU-only code (though perhaps it would be better to add name cuda to the file that implements the pass.

…fusion.cpp" JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)! [ghstack-poisoned]

Pull Request resolved: #68149 JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) ghstack-source-id: 143245262 Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)!

…fusion.cpp" JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)! [ghstack-poisoned]

Pull Request resolved: #68149 JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) ghstack-source-id: 143292971 Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)!

…fusion.cpp" JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)! [ghstack-poisoned]

Pull Request resolved: #68149 JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) ghstack-source-id: 143314335 Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)!

…fusion.cpp" JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)! [ghstack-poisoned]

Pull Request resolved: #68149 JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) ghstack-source-id: 143676384 Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)!

eellison

seems like comments are addressed

desertfire · 2021-11-30T16:26:49Z

LGTM in general. One question is how extensible this solution is. Say if we want to add a similar fusion pass for MKLDNN (#64639), would the initializations (MKLDNN and CUDA) conflict with each other?

facebook-github-bot · 2021-11-30T17:34:01Z

This pull request has been merged in cfc75c2.

davidberard98 · 2021-11-30T17:57:14Z

@desertfire afaik this could be extended with other passes (maybe use a vector<function<>> instead) without issues

facebook-github-bot · 2021-12-01T20:56:41Z

This pull request has been reverted by cd043c3. To re-land this change, follow these steps.

…68149) JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) ghstack-source-id: 143676384 Test Plan: In the following script, conv_add_relu fusion is not observed without this change, but is observed when this change is added. ``` from typing import List, Optional import torch class Model(torch.nn.Module): def __init__(self): super().__init__() self.weight = torch.nn.Parameter(torch.rand((3, 3, 7, 7), device="cuda")) self.add_tensor = torch.nn.Parameter(torch.rand((3, 3, 7, 7), device="cuda")) def forward( self, inp: torch.Tensor, bias: Optional[torch.Tensor], stride: List[int], padding: List[int], dilation: List[int], groups: int, ): # weight = torch.zeros((3, 3, 7, 7), device="cuda") inp = inp.to("cuda") conv_result = torch.conv2d( inp, self.weight, bias, stride, padding, dilation, groups ) add_result = conv_result.add_(self.add_tensor) return add_result.relu_() torch.jit.export def make_prediction(self, inp: torch.Tensor): bias = None groups = 1 stride = (1, 1) padding = (0, 0) dilation = (1, 1) return self.forward(inp, bias, stride, padding, dilation, groups) if __name__ == "__main__": # generate some sample input groups = 1 channels_in = 3 channels_out = 3 kernel_size = (7, 7) stride = (1, 1) padding = (0, 0) dilation = (1, 1) inp = torch.rand((64, 3, 432, 432)) weight = torch.rand( (channels_out, channels_in, kernel_size[0], kernel_size[1]), device="cuda" ) bias = None model = Model() model.eval() script = torch.jit.script(model) script = torch.jit.freeze(script) script = torch.jit.optimize_for_inference(script) print("~~~~ FORWARD ~~~~") print(script.graph) print("with preserved_attrs") print(torch.sum(script.forward(inp, bias, stride, padding, dilation, groups))) ``` fbshipit-source-id: c0f10da4b9540c588819efe3ec540baa0fae4b35 [ghstack-poisoned]

…68149) JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) ghstack-source-id: 143676384 Test Plan: In the following script, conv_add_relu fusion is not observed without this change, but is observed when this change is added. ``` from typing import List, Optional import torch class Model(torch.nn.Module): def __init__(self): super().__init__() self.weight = torch.nn.Parameter(torch.rand((3, 3, 7, 7), device="cuda")) self.add_tensor = torch.nn.Parameter(torch.rand((3, 3, 7, 7), device="cuda")) def forward( self, inp: torch.Tensor, bias: Optional[torch.Tensor], stride: List[int], padding: List[int], dilation: List[int], groups: int, ): # weight = torch.zeros((3, 3, 7, 7), device="cuda") inp = inp.to("cuda") conv_result = torch.conv2d( inp, self.weight, bias, stride, padding, dilation, groups ) add_result = conv_result.add_(self.add_tensor) return add_result.relu_() torch.jit.export def make_prediction(self, inp: torch.Tensor): bias = None groups = 1 stride = (1, 1) padding = (0, 0) dilation = (1, 1) return self.forward(inp, bias, stride, padding, dilation, groups) if __name__ == "__main__": # generate some sample input groups = 1 channels_in = 3 channels_out = 3 kernel_size = (7, 7) stride = (1, 1) padding = (0, 0) dilation = (1, 1) inp = torch.rand((64, 3, 432, 432)) weight = torch.rand( (channels_out, channels_in, kernel_size[0], kernel_size[1]), device="cuda" ) bias = None model = Model() model.eval() script = torch.jit.script(model) script = torch.jit.freeze(script) script = torch.jit.optimize_for_inference(script) print("~~~~ FORWARD ~~~~") print(script.graph) print("with preserved_attrs") print(torch.sum(script.forward(inp, bias, stride, padding, dilation, groups))) ``` fbshipit-source-id: c0f10da4b9540c588819efe3ec540baa0fae4b35 ghstack-source-id: e921804 Pull Request resolved: #69253

…fusion.cpp" JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) Differential Revision: [D32329330](https://our.internmc.facebook.com/intern/diff/D32329330/) **NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D32329330/)! [ghstack-poisoned]

Pull Request resolved: #68149 JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) ghstack-source-id: 9c93680

davidberard98 · 2021-12-01T21:38:28Z

@davidberard98 has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator.

…68149) Summary: Pull Request resolved: #68149 JIT optimization passes are part of the CPU-only build (i.e. necessary GPU flags are not passed in). This separates the implementation of frozen_conv_add_relu_fusion so that the GPU-enabled implementation is registered at runtime (if it is available) Test Plan: Imported from OSS Reviewed By: H-Huang Differential Revision: D32773666 Pulled By: davidberard98 fbshipit-source-id: c83dbb88804bdef23dc60a6299acbfa76d5c1495

xsacha · 2022-02-07T02:05:17Z

In a CPU-only build of Torch, I get these warnings on every run of a new model:
[W C:\work\pytorch\torch\csrc\jit\passes\frozen_conv_add_relu_fusion.cpp:29] Warning: No definition of _fuseFrozenConvAddReluImpl found (function FuseFrozenConvAddRelu)

Is this normal? If it is normal to hit this path, should it not be silent instead?

davidberard98 · 2022-02-07T17:32:06Z

Removing the warning in #72441

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davidberard98 requested review from eellison and malfet November 11, 2021 18:02

eellison requested a review from desertfire November 11, 2021 21:35

malfet requested changes Nov 12, 2021

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[JIT] fix static variable initialization order issue #69185

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[JIT] Separate GPU implementation of frozen_conv_add_relu_fusion.cpp #68149

[JIT] Separate GPU implementation of frozen_conv_add_relu_fusion.cpp #68149

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