[Quant] Add fused ConvAdd2d module for onednn backend #91152

leslie-fang-intel · 2022-12-20T03:54:56Z

Stack from ghstack (oldest at bottom):

Summary
Post op fusion can reduce data movement overhead and improve inference performance. This PR adds fused ConvAdd2d module for onednn backend, which will be used for int8 inference with onednn backend. Cannot call this module with other quantization backends otherwise an error is thrown.

Test plan

python -m pytest test_quantization.py -k test_conv2d_add

cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10

[ghstack-poisoned]

pytorch-bot · 2022-12-20T03:55:06Z

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cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

ghstack-source-id: 8271279 Pull Request resolved: pytorch#91152

cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

ghstack-source-id: 426bded Pull Request resolved: pytorch#91152

ghstack-source-id: 2dfaa66 Pull Request resolved: pytorch#91152

**Summary** Post op fusion can reduce data movement overhead and improve inference performance. This PR adds fused `ConvAdd2d` module for onednn backend, which will be used for int8 inference with onednn backend. Cannot call this module with other quantization backends otherwise an error is thrown. **Test plan** ``` python -m pytest test_quantization.py -k test_conv2d_add ``` cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

jerryzh168 · 2023-01-27T07:20:52Z

test/quantization/core/test_quantized_module.py

            batch_size, in_channels_per_group, input_feature_map_size,
            out_channels_per_group, groups, kernel_size, X_scale, X_zero_point,
            W_scale, W_zero_point, use_bias, use_channelwise)
+


there are a lot of if/else here in this function, I'm wondering if we can pass them as arguments,
e.g. can we pass
1). inputs to the module as arguments ([[X_q, X2_q]] if (post_op == "add") else [[X_q]])
2). separate the fused_conv_module and the individual conv_module as two arguments, to avoid branches in L296-303
3). any other refactors to remove branches

Thanks for the suggestions. I have removed most of the if/else branches in this function. Please help to take a look again @jerryzh168.

jerryzh168

I think we should remove the branches as much as we can to make the code clearer

**Summary** Post op fusion can reduce data movement overhead and improve inference performance. This PR adds fused `ConvAdd2d` module for onednn backend, which will be used for int8 inference with onednn backend. Cannot call this module with other quantization backends otherwise an error is thrown. **Test plan** ``` python -m pytest test_quantization.py -k test_conv2d_add ``` cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

leslie-fang-intel · 2023-01-28T06:04:08Z

I think we should remove the branches as much as we can to make the code clearer

Most of the branches has been removed.

**Summary** Post op fusion can reduce data movement overhead and improve inference performance. This PR adds fused `ConvAdd2d` module for onednn backend, which will be used for int8 inference with onednn backend. Cannot call this module with other quantization backends otherwise an error is thrown. **Test plan** ``` python -m pytest test_quantization.py -k test_conv2d_add ``` cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

leslie-fang-intel · 2023-01-31T01:20:42Z

Hi @jerryzh168, thanks for your review. the previous comments has been fixed. Could you kindly take a look of this PR again?

jerryzh168 · 2023-01-31T01:31:59Z

test/quantization/core/test_quantized_module.py

+            batch_size = 2
+            in_channels_per_group = 2
+            H = 8
+            W = 8
+            out_channels_per_group = 2
+            groups = 3
+            kernel_h = 3
+            kernel_w = 3
+            stride_h = 2
+            stride_w = 2
+            pad_h = 1
+            pad_w = 1
+            dilation = 1
+            # Tests the correctness of the conv2d module.
+            in_channels = in_channels_per_group * groups
+            out_channels = out_channels_per_group * groups
+            input_feature_map_size = (H, W)
+            kernel_size = (kernel_h, kernel_w)
+            stride = (stride_h, stride_w)
+            padding = (pad_h, pad_w)
+            dilation = (dilation, dilation)
+            X_scale = 1.3
+            X_zero_point = 2
+            W_scale = [0.5]
+            W_zero_point = [0] if qengine_is_onednn() else [3]
+            Y_scale = 5.0
+            Y_zero_point = 4
+            qconv_cls = nniq.ConvReLU2d
+            module_name = "QuantizedConvReLU2d"


nit: can you move these outside of the loop?

Thanks for the comments. Done.

jerryzh168 · 2023-01-31T01:32:17Z

test/quantization/core/test_quantized_module.py

+        for use_bias, use_channelwise in options:
+            if torch.backends.quantized.engine == "qnnpack":
+                use_channelwise = False
+            batch_size = 2


same for these

jerryzh168 · 2023-01-31T01:32:26Z

test/quantization/core/test_quantized_module.py

+                [True, False],  # use_channelwise
+            )
+            for pad_mode, use_bias, use_channelwise in options:
+                batch_size = 2


Also finished here.

jerryzh168 · 2023-01-31T01:39:23Z

torch/ao/nn/intrinsic/modules/fused.py

+class ConvAdd2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d modules with extra Add.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, add, conv):


nit: can we swap the order of conv and add, to be more consistent with the other ops

Good suggestion, changed the order to make it more consistent.

jerryzh168

looks good, had a few comments inline

jerryzh168 · 2023-01-31T01:41:40Z

torch/ao/nn/intrinsic/modules/fused.py

+    def forward(self, x1, x2):
+        return self.add(self[0](x1), x2)


also this feels a bit hacky, i'm wondering how we can extend _FusedModule to support this use case..

**Summary** Post op fusion can reduce data movement overhead and improve inference performance. This PR adds fused `ConvAdd2d` module for onednn backend, which will be used for int8 inference with onednn backend. Cannot call this module with other quantization backends otherwise an error is thrown. **Test plan** ``` python -m pytest test_quantization.py -k test_conv2d_add ``` cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

leslie-fang-intel · 2023-02-01T01:09:44Z

@pytorchbot merge

pytorchmergebot · 2023-02-01T01:11:21Z

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[Quant] Add fused ConvAdd2d module for onednn backend

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leslie-fang-intel requested review from jerryzh168 and z-a-f as code owners December 20, 2022 03:54

pytorch-bot bot added the release notes: quantization release notes category label Dec 20, 2022

leslie-fang-intel marked this pull request as draft December 20, 2022 03:55

leslie-fang-intel changed the title ~~[Quant] Add fused ConvAdd2d module for onednn backend~~ [WIP] [Quant] Add fused ConvAdd2d module for onednn backend Dec 20, 2022

leslie-fang-intel added ciflow/trunk Trigger trunk jobs on your pull request intel This tag is for PR from Intel labels Dec 20, 2022

leslie-fang-intel requested review from Xia-Weiwen and jgong5 December 20, 2022 03:56

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Update on "[WIP] [Quant] Add fused ConvAdd2d module for onednn backend"

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leslie-fang-intel added 2 commits December 20, 2022 15:11

Update on "[WIP] [Quant] Add fused ConvAdd2d module for onednn backend"

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cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

Update on "[WIP] [Quant] Add fused ConvAdd2d module for onednn backend"

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cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

leslie-fang-intel added a commit to leslie-fang-intel/pytorch that referenced this pull request Dec 20, 2022

[Quant] Add fused ConvAdd2d module for onednn backend

87dc5e1

ghstack-source-id: 8271279 Pull Request resolved: pytorch#91152

leslie-fang-intel added 2 commits December 21, 2022 13:28

Update on "[WIP] [Quant] Add fused ConvAdd2d module for onednn backend"

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cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

Update on "[WIP] [Quant] Add fused ConvAdd2d module for onednn backend"

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cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10 [ghstack-poisoned]

leslie-fang-intel changed the title ~~[WIP] [Quant] Add fused ConvAdd2d module for onednn backend~~ [Quant] Add fused ConvAdd2d module for onednn backend Dec 21, 2022

leslie-fang-intel added a commit to leslie-fang-intel/pytorch that referenced this pull request Dec 21, 2022

[Quant] Add fused ConvAdd2d module for onednn backend

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ghstack-source-id: 426bded Pull Request resolved: pytorch#91152

leslie-fang-intel added a commit to leslie-fang-intel/pytorch that referenced this pull request Jan 13, 2023

[Quant] Add fused ConvAdd2d module for onednn backend

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ghstack-source-id: 2dfaa66 Pull Request resolved: pytorch#91152

leslie-fang-intel added a commit to leslie-fang-intel/pytorch that referenced this pull request Jan 26, 2023

[Quant] Add fused ConvAdd2d module for onednn backend

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ghstack-source-id: 2dfaa66 Pull Request resolved: pytorch#91152

leslie-fang-intel added 3 commits January 26, 2023 10:14

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[Quant] Add fused ConvAdd2d module for onednn backend #91152

[Quant] Add fused ConvAdd2d module for onednn backend #91152

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