[quant] Input-Weight Equalization - ConvReLU support #61350
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Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
This was referenced Jul 7, 2021
💊 CI failures summary and remediationsAs of commit e1e28e8 (more details on the Dr. CI page and at hud.pytorch.org/pr/61350):
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Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
Summary: Applied changes in convert to allow for ConvReLU2d layers
Initial Model: `x -> conv1 -> relu`
After fusion: `x -> convRelu2d`
After prepare: `x -> input_quant_obs -> input_eq_obs1 -> convRelu2d -> output_quant_obs1`
After equalization functions: `x -> mul -> input_quant_obs (scaled) -> convRelu2d -> output_quant_obs`
After convert: `x -> mul -> quantize_per_tensor -> quantized::convRelu2d -> dequantize`
Test Plan: `python test/test_quantization.py TestEqualizeFx`
Initial Model:
```
ConvReluModel(
(fc): Conv2d(3, 5, kernel_size=(3, 3), stride=(1, 1))
(relu): ReLU()
)
```
After prepare:
```
GraphModule(
(x_activation_post_process_0): MinMaxObserver(min_val=5.960464477539063e-08, max_val=0.9999999403953552)
(x_activation_post_process_0_equalization_process_0): _InputEqualizationObserver(
(input_obs): PerChannelMinMaxObserver(min_val=tensor([1.1921e-07, 3.3379e-06, 5.9605e-08]), max_val=tensor([1.0000, 1.0000, 1.0000]))
)
(fc): ConvReLU2d(
(0): Conv2d(3, 5, kernel_size=(3, 3), stride=(1, 1))
(1): ReLU()
)
(fc_activation_post_process_0): MinMaxObserver(min_val=0.0, max_val=1.2341605424880981)
)
graph():
%x : [#users=1] = placeholder[target=x]
%x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_0](args = (%x,), kwargs = {})
%x_activation_post_process_0_equalization_process_0 : [#users=1] = call_module[target=x_activation_post_process_0_equalization_process_0](args = (%x_activation_post_process_0,), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%x_activation_post_process_0_equalization_process_0,), kwargs = {})
%fc_activation_post_process_0 : [#users=1] = call_module[target=fc_activation_post_process_0](args = (%fc,), kwargs = {})
return fc_activation_post_process_0
```
After equalization functions:
```
graph():
%x : [#users=1] = placeholder[target=x]
%x_equalization_scale0 : [#users=1] = get_attr[target=x_equalization_scale0]
%mul : [#users=1] = call_function[target=torch.mul](args = (%x, %x_equalization_scale0), kwargs = {})
%x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_0](args = (%mul,), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%x_activation_post_process_0,), kwargs = {})
%fc_activation_post_process_0 : [#users=1] = call_module[target=fc_activation_post_process_0](args = (%fc,), kwargs = {})
return fc_activation_post_process_0
```
After convert:
```
graph():
%x : [#users=1] = placeholder[target=x]
%x_equalization_scale0 : [#users=1] = get_attr[target=x_equalization_scale0]
%mul : [#users=1] = call_function[target=torch.mul](args = (%x, %x_equalization_scale0), kwargs = {})
%fc_input_scale_0 : [#users=1] = get_attr[target=fc_input_scale_0]
%fc_input_zero_point_0 : [#users=1] = get_attr[target=fc_input_zero_point_0]
%quantize_per_tensor : [#users=1] = call_function[target=torch.quantize_per_tensor](args = (%mul, %fc_input_scale_0, %fc_input_zero_point_0, torch.quint8), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%quantize_per_tensor,), kwargs = {})
%dequantize : [#users=1] = call_method[target=dequantize](args = (%fc,), kwargs = {})
return dequantize
```
Reviewers:
Subscribers:
Tasks:
Tags:
[ghstack-poisoned]
|
@angelayi has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator. |
jerryzh168
reviewed
Jul 9, 2021
jerryzh168
reviewed
Jul 9, 2021
jerryzh168
approved these changes
Jul 9, 2021
Summary: Applied changes in convert to allow for ConvReLU2d layers
Initial Model: `x -> conv1 -> relu`
After fusion: `x -> convRelu2d`
After prepare: `x -> input_quant_obs -> input_eq_obs1 -> convRelu2d -> output_quant_obs1`
After equalization functions: `x -> mul -> input_quant_obs (scaled) -> convRelu2d -> output_quant_obs`
After convert: `x -> mul -> quantize_per_tensor -> quantized::convRelu2d -> dequantize`
Test Plan: `python test/test_quantization.py TestEqualizeFx`
Initial Model:
```
ConvReluModel(
(fc): Conv2d(3, 5, kernel_size=(3, 3), stride=(1, 1))
(relu): ReLU()
)
```
After prepare:
```
GraphModule(
(x_activation_post_process_0): MinMaxObserver(min_val=5.960464477539063e-08, max_val=0.9999999403953552)
(x_activation_post_process_0_equalization_process_0): _InputEqualizationObserver(
(input_obs): PerChannelMinMaxObserver(min_val=tensor([1.1921e-07, 3.3379e-06, 5.9605e-08]), max_val=tensor([1.0000, 1.0000, 1.0000]))
)
(fc): ConvReLU2d(
(0): Conv2d(3, 5, kernel_size=(3, 3), stride=(1, 1))
(1): ReLU()
)
(fc_activation_post_process_0): MinMaxObserver(min_val=0.0, max_val=1.2341605424880981)
)
graph():
%x : [#users=1] = placeholder[target=x]
%x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_0](args = (%x,), kwargs = {})
%x_activation_post_process_0_equalization_process_0 : [#users=1] = call_module[target=x_activation_post_process_0_equalization_process_0](args = (%x_activation_post_process_0,), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%x_activation_post_process_0_equalization_process_0,), kwargs = {})
%fc_activation_post_process_0 : [#users=1] = call_module[target=fc_activation_post_process_0](args = (%fc,), kwargs = {})
return fc_activation_post_process_0
```
After equalization functions:
```
graph():
%x : [#users=1] = placeholder[target=x]
%x_equalization_scale0 : [#users=1] = get_attr[target=x_equalization_scale0]
%mul : [#users=1] = call_function[target=torch.mul](args = (%x, %x_equalization_scale0), kwargs = {})
%x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_0](args = (%mul,), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%x_activation_post_process_0,), kwargs = {})
%fc_activation_post_process_0 : [#users=1] = call_module[target=fc_activation_post_process_0](args = (%fc,), kwargs = {})
return fc_activation_post_process_0
```
After convert:
```
graph():
%x : [#users=1] = placeholder[target=x]
%x_equalization_scale0 : [#users=1] = get_attr[target=x_equalization_scale0]
%mul : [#users=1] = call_function[target=torch.mul](args = (%x, %x_equalization_scale0), kwargs = {})
%fc_input_scale_0 : [#users=1] = get_attr[target=fc_input_scale_0]
%fc_input_zero_point_0 : [#users=1] = get_attr[target=fc_input_zero_point_0]
%quantize_per_tensor : [#users=1] = call_function[target=torch.quantize_per_tensor](args = (%mul, %fc_input_scale_0, %fc_input_zero_point_0, torch.quint8), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%quantize_per_tensor,), kwargs = {})
%dequantize : [#users=1] = call_method[target=dequantize](args = (%fc,), kwargs = {})
return dequantize
```
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D29638275](https://our.internmc.facebook.com/intern/diff/D29638275)
[ghstack-poisoned]
Summary: Applied changes in convert to allow for ConvReLU2d layers
Initial Model: `x -> conv1 -> relu`
After fusion: `x -> convRelu2d`
After prepare: `x -> input_quant_obs -> input_eq_obs1 -> convRelu2d -> output_quant_obs1`
After equalization functions: `x -> mul -> input_quant_obs (scaled) -> convRelu2d -> output_quant_obs`
After convert: `x -> mul -> quantize_per_tensor -> quantized::convRelu2d -> dequantize`
Test Plan: `python test/test_quantization.py TestEqualizeFx`
Initial Model:
```
ConvReluModel(
(fc): Conv2d(3, 5, kernel_size=(3, 3), stride=(1, 1))
(relu): ReLU()
)
```
After prepare:
```
GraphModule(
(x_activation_post_process_0): MinMaxObserver(min_val=5.960464477539063e-08, max_val=0.9999999403953552)
(x_activation_post_process_0_equalization_process_0): _InputEqualizationObserver(
(input_obs): PerChannelMinMaxObserver(min_val=tensor([1.1921e-07, 3.3379e-06, 5.9605e-08]), max_val=tensor([1.0000, 1.0000, 1.0000]))
)
(fc): ConvReLU2d(
(0): Conv2d(3, 5, kernel_size=(3, 3), stride=(1, 1))
(1): ReLU()
)
(fc_activation_post_process_0): MinMaxObserver(min_val=0.0, max_val=1.2341605424880981)
)
graph():
%x : [#users=1] = placeholder[target=x]
%x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_0](args = (%x,), kwargs = {})
%x_activation_post_process_0_equalization_process_0 : [#users=1] = call_module[target=x_activation_post_process_0_equalization_process_0](args = (%x_activation_post_process_0,), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%x_activation_post_process_0_equalization_process_0,), kwargs = {})
%fc_activation_post_process_0 : [#users=1] = call_module[target=fc_activation_post_process_0](args = (%fc,), kwargs = {})
return fc_activation_post_process_0
```
After equalization functions:
```
graph():
%x : [#users=1] = placeholder[target=x]
%x_equalization_scale0 : [#users=1] = get_attr[target=x_equalization_scale0]
%mul : [#users=1] = call_function[target=torch.mul](args = (%x, %x_equalization_scale0), kwargs = {})
%x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_0](args = (%mul,), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%x_activation_post_process_0,), kwargs = {})
%fc_activation_post_process_0 : [#users=1] = call_module[target=fc_activation_post_process_0](args = (%fc,), kwargs = {})
return fc_activation_post_process_0
```
After convert:
```
graph():
%x : [#users=1] = placeholder[target=x]
%x_equalization_scale0 : [#users=1] = get_attr[target=x_equalization_scale0]
%mul : [#users=1] = call_function[target=torch.mul](args = (%x, %x_equalization_scale0), kwargs = {})
%fc_input_scale_0 : [#users=1] = get_attr[target=fc_input_scale_0]
%fc_input_zero_point_0 : [#users=1] = get_attr[target=fc_input_zero_point_0]
%quantize_per_tensor : [#users=1] = call_function[target=torch.quantize_per_tensor](args = (%mul, %fc_input_scale_0, %fc_input_zero_point_0, torch.quint8), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%quantize_per_tensor,), kwargs = {})
%dequantize : [#users=1] = call_method[target=dequantize](args = (%fc,), kwargs = {})
return dequantize
```
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D29638275](https://our.internmc.facebook.com/intern/diff/D29638275)
[ghstack-poisoned]
Summary: Applied changes in convert to allow for ConvReLU2d layers
Initial Model: `x -> conv1 -> relu`
After fusion: `x -> convRelu2d`
After prepare: `x -> input_quant_obs -> input_eq_obs1 -> convRelu2d -> output_quant_obs1`
After equalization functions: `x -> mul -> input_quant_obs (scaled) -> convRelu2d -> output_quant_obs`
After convert: `x -> mul -> quantize_per_tensor -> quantized::convRelu2d -> dequantize`
Test Plan: `python test/test_quantization.py TestEqualizeFx`
Initial Model:
```
ConvReluModel(
(fc): Conv2d(3, 5, kernel_size=(3, 3), stride=(1, 1))
(relu): ReLU()
)
```
After prepare:
```
GraphModule(
(x_activation_post_process_0): MinMaxObserver(min_val=5.960464477539063e-08, max_val=0.9999999403953552)
(x_activation_post_process_0_equalization_process_0): _InputEqualizationObserver(
(input_obs): PerChannelMinMaxObserver(min_val=tensor([1.1921e-07, 3.3379e-06, 5.9605e-08]), max_val=tensor([1.0000, 1.0000, 1.0000]))
)
(fc): ConvReLU2d(
(0): Conv2d(3, 5, kernel_size=(3, 3), stride=(1, 1))
(1): ReLU()
)
(fc_activation_post_process_0): MinMaxObserver(min_val=0.0, max_val=1.2341605424880981)
)
graph():
%x : [#users=1] = placeholder[target=x]
%x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_0](args = (%x,), kwargs = {})
%x_activation_post_process_0_equalization_process_0 : [#users=1] = call_module[target=x_activation_post_process_0_equalization_process_0](args = (%x_activation_post_process_0,), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%x_activation_post_process_0_equalization_process_0,), kwargs = {})
%fc_activation_post_process_0 : [#users=1] = call_module[target=fc_activation_post_process_0](args = (%fc,), kwargs = {})
return fc_activation_post_process_0
```
After equalization functions:
```
graph():
%x : [#users=1] = placeholder[target=x]
%x_equalization_scale0 : [#users=1] = get_attr[target=x_equalization_scale0]
%mul : [#users=1] = call_function[target=torch.mul](args = (%x, %x_equalization_scale0), kwargs = {})
%x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_0](args = (%mul,), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%x_activation_post_process_0,), kwargs = {})
%fc_activation_post_process_0 : [#users=1] = call_module[target=fc_activation_post_process_0](args = (%fc,), kwargs = {})
return fc_activation_post_process_0
```
After convert:
```
graph():
%x : [#users=1] = placeholder[target=x]
%x_equalization_scale0 : [#users=1] = get_attr[target=x_equalization_scale0]
%mul : [#users=1] = call_function[target=torch.mul](args = (%x, %x_equalization_scale0), kwargs = {})
%fc_input_scale_0 : [#users=1] = get_attr[target=fc_input_scale_0]
%fc_input_zero_point_0 : [#users=1] = get_attr[target=fc_input_zero_point_0]
%quantize_per_tensor : [#users=1] = call_function[target=torch.quantize_per_tensor](args = (%mul, %fc_input_scale_0, %fc_input_zero_point_0, torch.quint8), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%quantize_per_tensor,), kwargs = {})
%dequantize : [#users=1] = call_method[target=dequantize](args = (%fc,), kwargs = {})
return dequantize
```
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D29638275](https://our.internmc.facebook.com/intern/diff/D29638275)
[ghstack-poisoned]
|
@angelayi has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator. |
|
This pull request has been merged in 0751a41. |
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Stack from ghstack:
Summary: Applied changes in convert to allow for ConvReLU2d layers
Initial Model:
x -> conv1 -> reluAfter fusion:
x -> convRelu2dAfter prepare:
x -> input_quant_obs -> input_eq_obs1 -> convRelu2d -> output_quant_obs1After equalization functions:
x -> mul -> input_quant_obs (scaled) -> convRelu2d -> output_quant_obsAfter convert:
x -> mul -> quantize_per_tensor -> quantized::convRelu2d -> dequantizeTest Plan:
python test/test_quantization.py TestEqualizeFxInitial Model:
After prepare:
After equalization functions:
After convert:
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: D29638275