[quant] conv_transpose graph patterns#43199
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💊 CI failures summary and remediationsAs of commit ddfbf9e (more details on the Dr. CI page):
Extra GitHub checks: 1 failed
ci.pytorch.org: 1 failedThis comment was automatically generated by Dr. CI (expand for details).Follow this link to opt-out of these comments for your Pull Requests.Please report bugs/suggestions on the GitHub issue tracker or post in the (internal) Dr. CI Users group. This comment has been revised 86 times. |
jerryzh168
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could you add a test for this as well
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
| rtol=1e-2, | ||
| atol=1e-2) | ||
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@jerryzh168 For some reason there is no exact match -- 1e-2 works though :/
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can you show is it the traced model or scripted model that is failing?
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It's failing for both trace model and the script model
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Oh I think the graph is not even replaced with the quantized versions -- I might have missed something
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yeah, please print the graphs
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yeah, please print the graphs
Eager model AnnotatedConvModel(
(conv): QuantizedConvTranpose2d(3, 5, kernel_size=(3, 3), stride=(1, 1), scale=0.004468684550374746, zero_point=151)
(quant): Quantize(scale=tensor([0.0039]), zero_point=tensor([0]), dtype=torch.quint8)
(dequant): DeQuantize()
)
MuT graph graph(%self : __torch__.torch.testing._internal.common_quantization.___torch_mangle_3.ConvModel,
%x.1 : Tensor):
%4 : None = prim::Constant()
%2 : __torch__.torch.nn.modules.conv.___torch_mangle_2.ConvTranspose2d = prim::GetAttr[name="conv"](%self)
%x.3 : Tensor = prim::CallMethod[name="forward"](%2, %x.1, %4) # /home/zafar/Git/pytorch-dev/pytorch-convtranspose-new/torch/testing/_internal/common_quantization.py:594:12
return (%x.3)
quantized MuT graph graph(%self : __torch__.torch.testing._internal.common_quantization.___torch_mangle_18.ConvModel,
%x.1 : Tensor):
%5 : int = prim::Constant[value=1]() # /home/zafar/Git/pytorch-dev/pytorch-convtranspose-new/torch/nn/modules/conv.py:909:66
%4 : int[] = prim::Constant[value=[0, 0]]()
%3 : int[] = prim::Constant[value=[1, 1]]()
%self.conv.bias : None = prim::Constant()
%self.conv.weight : Float(3:45, 5:9, 3:3, 3:1, requires_grad=0, device=cpu) = prim::Constant[value=<Tensor>]()
%x.3 : Tensor = aten::conv_transpose2d(%x.1, %self.conv.weight, %self.conv.bias, %3, %4, %4, %5, %3) # /home/zafar/Git/pytorch-dev/pytorch-convtranspose-new/torch/nn/modules/conv.py:911:15
return (%x.3)
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
|
would be great to include some context and a test plan in the PR summary |
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Differential Revision: [D23228954](https://our.internmc.facebook.com/intern/diff/D23228954) [ghstack-poisoned]
Stack from ghstack:
Differential Revision: D23228954