onnx export of per channel fake quantize functions (#42835) #52430
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Summary: Fixes #39502 This PR adds support for exporting **fake_quantize_per_channel_affine** to a pair of QuantizeLinear and DequantizeLinear. Per tensor support was added by PR #39738. `axis` attribute of QuantizeLinear and DequantizeLinear, which is required for per channel support, is added in opset13 added by onnx/onnx#2772. [update 1/20/2021]: opset13 is being supported on master, the added function is now properly tested. Code also rebased to new master. The function is also tested offline with the following code ```python import torch from torch import quantization from torchvision import models qat_resnet18 = models.resnet18(pretrained=True).eval().cuda() qat_resnet18.qconfig = quantization.QConfig( activation=quantization.default_fake_quant, weight=quantization.default_per_channel_weight_fake_quant) quantization.prepare_qat(qat_resnet18, inplace=True) qat_resnet18.apply(quantization.enable_observer) qat_resnet18.apply(quantization.enable_fake_quant) dummy_input = torch.randn(16, 3, 224, 224).cuda() _ = qat_resnet18(dummy_input) for module in qat_resnet18.modules(): if isinstance(module, quantization.FakeQuantize): module.calculate_qparams() qat_resnet18.apply(quantization.disable_observer) qat_resnet18.cuda() input_names = [ "actual_input_1" ] output_names = [ "output1" ] torch.onnx.export(qat_resnet18, dummy_input, "quant_model.onnx", verbose=True, opset_version=13) ``` It can generate the desired graph. Pull Request resolved: #42835 Reviewed By: houseroad Differential Revision: D26293823 Pulled By: SplitInfinity fbshipit-source-id: 300498a2e24b7731b12fa2fbdea4e73dde80e7ea
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Codecov Report
@@ Coverage Diff @@
## release/1.8 #52430 +/- ##
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+ Coverage 80.48% 80.82% +0.33%
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Files 1948 1948
Lines 213198 213229 +31
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+ Hits 171600 172335 +735
+ Misses 41598 40894 -704 |
malfet
approved these changes
Feb 18, 2021
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Summary:
Fixes #39502
This PR adds support for exporting fake_quantize_per_channel_affine to a pair of QuantizeLinear and DequantizeLinear. Per tensor support was added by PR #39738.
axisattribute of QuantizeLinear and DequantizeLinear, which is required for per channel support, is added in opset13 added by onnx/onnx#2772.[update 1/20/2021]: opset13 is being supported on master, the added function is now properly tested. Code also rebased to new master.
The function is also tested offline with the following code
It can generate the desired graph.
Pull Request resolved: #42835
Reviewed By: houseroad
Differential Revision: D26293823
Pulled By: SplitInfinity
fbshipit-source-id: 300498a2e24b7731b12fa2fbdea4e73dde80e7ea