Summary:

Context: the existing implementation of `quantized_input_idxs` is convert-only.
Therefore, observers are inserted between the input and the first
quantized node.  This is a problem during QAT, because the initial
input is a fake_quant, and it starts with scale=1 and zp=0.  This does
not match the quantization parameters of the graph input, which can
lead to incorrect numerics.

Fix: do not insert observer for a quantized input.

Test Plan:

```
python test/test_quantization.py TestQuantizeFx
```

Reviewers:

Subscribers:

Tasks:

Tags:

[ghstack-poisoned]

Summary:

Context: the existing implementation of `quantized_input_idxs` is convert-only.
Therefore, observers are inserted between the input and the first
quantized node.  This is a problem during QAT, because the initial
input is a fake_quant, and it starts with scale=1 and zp=0.  This does
not match the quantization parameters of the graph input, which can
lead to incorrect numerics.

Fix: do not insert observer for a quantized input.

Test Plan:

```
python test/test_quantization.py TestQuantizeFx
```

Reviewers:

Subscribers:

Tasks:

Tags:

Differential Revision: [D25499486](https://our.internmc.facebook.com/intern/diff/D25499486)

[ghstack-poisoned]

Summary:

Context: the existing implementation of `quantized_input_idxs` is convert-only.
Therefore, observers are inserted between the input and the first
quantized node.  This is a problem during QAT, because the initial
input is a fake_quant, and it starts with scale=1 and zp=0.  This does
not match the quantization parameters of the graph input, which can
lead to incorrect numerics.

Fix: do not insert observer for a quantized input.

Test Plan:

```
python test/test_quantization.py TestQuantizeFx
```

Reviewers:

Subscribers:

Tasks:

Tags:

Differential Revision: [D25499486](https://our.internmc.facebook.com/intern/diff/D25499486)

[ghstack-poisoned]

Summary:

Context: the existing implementation of `quantized_input_idxs` is convert-only.
Therefore, observers are inserted between the input and the first
quantized node.  This is a problem during QAT, because the initial
input is a fake_quant, and it starts with scale=1 and zp=0.  This does
not match the quantization parameters of the graph input, which can
lead to incorrect numerics.

Fix: do not insert observer for a quantized input.

Test Plan:

```
python test/test_quantization.py TestQuantizeFx
```

Reviewers:

Subscribers:

Tasks:

Tags:

Differential Revision: [D25499486](https://our.internmc.facebook.com/intern/diff/D25499486)

[ghstack-poisoned]