[pt][quant] Avoid the misleading zero_point and scale [2/2] #28827
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When we print the `DynamicLinear` module, we don't want to print the scale and zero points as they are not needed for the dynamic quantization.
Let's take the output of RoBERTa model as an example:
Before this PR:
```
(19): TransformerEncoderLayer(
(dropout): Dropout(p=0.1, inplace=False)
(attention): MultiheadAttention(
(dropout): Dropout(p=0.1, inplace=False)
(input_projection): DynamicQuantizedLinear(in_features=1024, out_features=3072, scale=1.0, zero_point=0)
(output_projection): DynamicQuantizedLinear(in_features=1024, out_features=1024, scale=1.0, zero_point=0)
)
(residual_mlp): ResidualMLP(
(mlp): Sequential(
(0): DynamicQuantizedLinear(in_features=1024, out_features=4096, scale=1.0, zero_point=0)
(1): GeLU()
(2): Dropout(p=0.1, inplace=False)
(3): DynamicQuantizedLinear(in_features=4096, out_features=1024, scale=1.0, zero_point=0)
(4): Dropout(p=0.1, inplace=False)
)
)
(attention_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(20): TransformerEncoderLayer(
(dropout): Dropout(p=0.1, inplace=False)
(attention): MultiheadAttention(
(dropout): Dropout(p=0.1, inplace=False)
(input_projection): DynamicQuantizedLinear(in_features=1024, out_features=3072, scale=1.0, zero_point=0)
(output_projection): DynamicQuantizedLinear(in_features=1024, out_features=1024, scale=1.0, zero_point=0)
)
(residual_mlp): ResidualMLP(
(mlp): Sequential(
(0): DynamicQuantizedLinear(in_features=1024, out_features=4096, scale=1.0, zero_point=0)
(1): GeLU()
(2): Dropout(p=0.1, inplace=False)
(3): DynamicQuantizedLinear(in_features=4096, out_features=1024, scale=1.0, zero_point=0)
(4): Dropout(p=0.1, inplace=False)
)
)
(attention_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
```
After this PR:
```
(19): TransformerEncoderLayer(
(dropout): Dropout(p=0.1, inplace=False)
(attention): MultiheadAttention(
(dropout): Dropout(p=0.1, inplace=False)
(input_projection): DynamicQuantizedLinear(in_features=1024, out_features=3072)
(output_projection): DynamicQuantizedLinear(in_features=1024, out_features=1024)
)
(residual_mlp): ResidualMLP(
(mlp): Sequential(
(0): DynamicQuantizedLinear(in_features=1024, out_features=4096)
(1): GeLU()
(2): Dropout(p=0.1, inplace=False)
(3): DynamicQuantizedLinear(in_features=4096, out_features=1024)
(4): Dropout(p=0.1, inplace=False)
)
)
(attention_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(20): TransformerEncoderLayer(
(dropout): Dropout(p=0.1, inplace=False)
(attention): MultiheadAttention(
(dropout): Dropout(p=0.1, inplace=False)
(input_projection): DynamicQuantizedLinear(in_features=1024, out_features=3072)
(output_projection): DynamicQuantizedLinear(in_features=1024, out_features=1024)
)
(residual_mlp): ResidualMLP(
(mlp): Sequential(
(0): DynamicQuantizedLinear(in_features=1024, out_features=4096)
(1): GeLU()
(2): Dropout(p=0.1, inplace=False)
(3): DynamicQuantizedLinear(in_features=4096, out_features=1024)
(4): Dropout(p=0.1, inplace=False)
)
)
(attention_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
```
Differential Revision: [D18197022](https://our.internmc.facebook.com/intern/diff/D18197022/)
[ghstack-poisoned]
jianyuh
added a commit
that referenced
this pull request
Oct 29, 2019
When we print the `DynamicLinear` module, we don't want to print the scale and zero points as they are not needed for the dynamic quantization.
Let's take the output of RoBERTa model as an example:
Before this PR:
```
(19): TransformerEncoderLayer(
(dropout): Dropout(p=0.1, inplace=False)
(attention): MultiheadAttention(
(dropout): Dropout(p=0.1, inplace=False)
(input_projection): DynamicQuantizedLinear(in_features=1024, out_features=3072, scale=1.0, zero_point=0)
(output_projection): DynamicQuantizedLinear(in_features=1024, out_features=1024, scale=1.0, zero_point=0)
)
(residual_mlp): ResidualMLP(
(mlp): Sequential(
(0): DynamicQuantizedLinear(in_features=1024, out_features=4096, scale=1.0, zero_point=0)
(1): GeLU()
(2): Dropout(p=0.1, inplace=False)
(3): DynamicQuantizedLinear(in_features=4096, out_features=1024, scale=1.0, zero_point=0)
(4): Dropout(p=0.1, inplace=False)
)
)
(attention_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(20): TransformerEncoderLayer(
(dropout): Dropout(p=0.1, inplace=False)
(attention): MultiheadAttention(
(dropout): Dropout(p=0.1, inplace=False)
(input_projection): DynamicQuantizedLinear(in_features=1024, out_features=3072, scale=1.0, zero_point=0)
(output_projection): DynamicQuantizedLinear(in_features=1024, out_features=1024, scale=1.0, zero_point=0)
)
(residual_mlp): ResidualMLP(
(mlp): Sequential(
(0): DynamicQuantizedLinear(in_features=1024, out_features=4096, scale=1.0, zero_point=0)
(1): GeLU()
(2): Dropout(p=0.1, inplace=False)
(3): DynamicQuantizedLinear(in_features=4096, out_features=1024, scale=1.0, zero_point=0)
(4): Dropout(p=0.1, inplace=False)
)
)
(attention_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
```
After this PR:
```
(19): TransformerEncoderLayer(
(dropout): Dropout(p=0.1, inplace=False)
(attention): MultiheadAttention(
(dropout): Dropout(p=0.1, inplace=False)
(input_projection): DynamicQuantizedLinear(in_features=1024, out_features=3072)
(output_projection): DynamicQuantizedLinear(in_features=1024, out_features=1024)
)
(residual_mlp): ResidualMLP(
(mlp): Sequential(
(0): DynamicQuantizedLinear(in_features=1024, out_features=4096)
(1): GeLU()
(2): Dropout(p=0.1, inplace=False)
(3): DynamicQuantizedLinear(in_features=4096, out_features=1024)
(4): Dropout(p=0.1, inplace=False)
)
)
(attention_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(20): TransformerEncoderLayer(
(dropout): Dropout(p=0.1, inplace=False)
(attention): MultiheadAttention(
(dropout): Dropout(p=0.1, inplace=False)
(input_projection): DynamicQuantizedLinear(in_features=1024, out_features=3072)
(output_projection): DynamicQuantizedLinear(in_features=1024, out_features=1024)
)
(residual_mlp): ResidualMLP(
(mlp): Sequential(
(0): DynamicQuantizedLinear(in_features=1024, out_features=4096)
(1): GeLU()
(2): Dropout(p=0.1, inplace=False)
(3): DynamicQuantizedLinear(in_features=4096, out_features=1024)
(4): Dropout(p=0.1, inplace=False)
)
)
(attention_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
```
Differential Revision: [D18197022](https://our.internmc.facebook.com/intern/diff/D18197022/)
ghstack-source-id: 92807317
Pull Request resolved: #28827
Contributor
|
This pull request has been merged in ef5a6b2. |
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Stack from ghstack:
When we print the
DynamicLinearmodule, we don't want to print the scale and zero points as they are not needed for the dynamic quantization.Let's take the output of RoBERTa model as an example:
Before this PR:
After this PR:
Differential Revision: D18197022