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8 Commits

Author SHA1 Message Date
Edward Z. Yang
3bf922a6ce Apply UFMT to low traffic torch modules (#106249) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/106249
Approved by: https://github.com/Skylion007
 2023-07-29 23:37:30 +00:00
HDCharles
f286cbebce [ao][fx] fixing public v private graph_module.py (#88395) 
Summary: made _is_observed_module, _is_observed_standalone_module
private

Test Plan: python test/test_public_bindings.py

Reviewers:

Subscribers:

Tasks:

Tags:

Differential Revision: [D41015545](https://our.internmc.facebook.com/intern/diff/D41015545)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/88395
Approved by: https://github.com/jcaip
 2022-12-15 02:15:04 +00:00
Jerry Zhang
508845f2b5 [quant] AO migration of the torch/quantization/quantize_fx.py and torch/quantization/fx/* (#65033) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65033

1. Move the file:
```
hg mv caffe2/torch/quantization/fx caffe2/torch/ao/quantization/fx
hg mv caffe2/torch/quantization/quantize_fx.py caffe2/torch/ao/quantization/quantize_fx.py
```
2. Create new files
```
touch caffe2/torch/quantization/quantize_fx.py
touch caffe2/torch/quantization/fx/__init__.py
```
3. import things in the new files
4. add tests to test/quantization/ao_migration/test_quantization_fx.py
this is because we have some fx import in quantize_fx and fx/*.py

Test Plan: buck test mode/dev //caffe2/test:quantization

Reviewed By: vkuzo, z-a-f

Differential Revision: D30949749

fbshipit-source-id: 9e5d4d039c8a0a0820bc9040e224f0d2c26886d3
 2021-09-22 09:29:15 -07:00
Angela Yi
45c31cabb5 [quant] Input Weight Equalization - prepare modifications (#59747) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59747

Modifies prepare_fx for input-weight equalization. If a current
node is being equalized (there exists a EqualizationQConfig), then the
EqualizationObserver will be inserted before its quantization observer.

For a singular linear layer, the general flow looks like:
Original graph: `x0 -> linear -> x1`, `w -> linear`
After prepare: `x0 -> InpEqObs -> MinMaxObs -> linear1 -> MinMaxObs -> x1`
  `w -> WeightEqObs -> MinMaxObs -> linear1`

For two connected linear layers, the general flow looks like:
Original graph: `x0 -> linear1 -> linear2 -> x1`,
  `w1 -> linear1`, `w2 -> linear2`
After prepare: `x0 -> InpEqObs -> MinMaxObs -> linear1 -> MinMaxObs -> InpEqObs -> linear2 -> MinMaxObs -> x1`
  `w1 -> WeightEqObs -> MinMaxObs -> linear1`, `w2 -> WeightEqObs -> MinMaxObs -> linear2

Test Plan:
`python test/test_quantization.py
TestEqualizeFx.test_input_equalization_prepare`

Original model with one `nn.Linear` layer
```
LinearModule(
  (linear): Linear(in_features=1, out_features=1, bias=True)
)
```

Graph after `prepare_fx`:
```
graph():
    %x : [#users=1] = placeholder[target=x]
    %x_equalization_process_0 : [#users=1] = call_module[target=x_equalization_process_0](args = (%x,), kwargs = {})
    %x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_00](args = (%x_equalization_process_0,), kwargs = {})
    %linear : [#users=1] = call_module[target=linear](args = (%x_activation_post_process_0,), kwargs = {})
    %linear_activation_post_process_0 : [#users=1] = call_module[target=linear_activation_post_process_0](args = (%linear,), kwargs = {})
    return linear_activation_post_process_0
```
--------------------------------------

Original model with two connected functional linear layers
```
FunctionalLinearModule(
  (linear1): Linear()
  (linear2): Linear()
)
```

Graph after `prepare_fx`:
```
graph():
    %x : [#users=1] = placeholder[target=x]
    %x_equalization_process_0 : [#users=1] = call_module[target=x_equalization_process_0](args = (%x,), kwargs = {})
    %x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_00](args = (%x_equalization_process_0,), kwargs = {})
    %linear1_w : [#users=1] = get_attr[target=linear1.w]
    %linear1_w_equalization_process_0 : [#users=1] = call_module[target=linear1_w_equalization_process_0](args = (%linear1_w,), kwargs = {})
    %linear1_w_activation_post_process_0 : [#users=1] = call_module[target=linear1_w_activation_post_process_00](args = (%linear1_w_equalization_process_0,), kwargs = {})
    %linear1_b : [#users=1] = get_attr[target=linear1.b]
    %linear : [#users=1] = call_function[target=torch.nn.functional.linear](args = (%x_activation_post_process_0, %linear1_w_activation_post_process_0), kwargs = {bias: %linear1_b})
    %linear_activation_post_process_0 : [#users=1] = call_module[target=linear_activation_post_process_0](args = (%linear,), kwargs = {})
    %linear_activation_post_process_0_equalization_process_0 : [#users=1] = call_module[target=linear_activation_post_process_0_equalization_process_0](args = (%linear_activation_post_process_0,), kwargs = {})
    %linear2_w : [#users=1] = get_attr[target=linear2.w]
    %linear2_w_equalization_process_0 : [#users=1] = call_module[target=linear2_w_equalization_process_0](args = (%linear2_w,), kwargs = {})
    %linear2_w_activation_post_process_0 : [#users=1] = call_module[target=linear2_w_activation_post_process_00](args = (%linear2_w_equalization_process_0,), kwargs = {})
    %linear2_b : [#users=1] = get_attr[target=linear2.b]
    %linear_1 : [#users=1] = call_function[target=torch.nn.functional.linear](args = (%linear_activation_post_process_0_equalization_process_0, %linear2_w_activation_post_process_0), kwargs = {bias: %linear2_b})
    %linear_1_activation_post_process_0 : [#users=1] = call_module[target=linear_1_activation_post_process_0](args = (%linear_1,), kwargs = {})
    return linear_1_activation_post_process_0
```

Imported from OSS

Reviewed By: jerryzh168

Differential Revision: D29135316

fbshipit-source-id: 91697e805ede254dbb2a42ee4c23eb1c1c64590e
 2021-06-16 22:32:28 -07:00
Jerry Zhang
1719cb82f3 [quant][graphmode][fx] Support preserving attributes in deepcopy of observed/quantized graphmodule (#56550) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56550

Add support for preserving a list of attributes on observed/quantized GraphModule

Test Plan:
python test/test_quantization.py TestQuantizeFx.test_deepcopy_preserve_attributes

Imported from OSS

Reviewed By: vkuzo, kazhang

Differential Revision: D27899317

fbshipit-source-id: ebf21334715e5ab764aaa27eed534cc0cdf9f2b5
 2021-04-22 15:02:44 -07:00
Sam Estep
75024e228c Add lint for unqualified type: ignore (#56290) 
Summary:
The other half of https://github.com/pytorch/pytorch/issues/56272.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/56290

Test Plan:
CI should pass on the tip of this PR, and we know that the lint works because the following CI runs (before this PR was finished) failed:

- https://github.com/pytorch/pytorch/runs/2384511062
- https://github.com/pytorch/pytorch/actions/runs/765036024

Reviewed By: seemethere

Differential Revision: D27867219

Pulled By: samestep

fbshipit-source-id: e648f07b6822867e70833e23ddafe7fb7eaca235
 2021-04-21 08:07:23 -07:00
Jerry Zhang
55544cb13a [quant][graphmode][fx] Add support for one value being quantized with different qconfigs (#53586) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53586

Previously one value can only be quantized to one dtype, this PR adds the support for quantizing one value
in the fx graph with multiple dtypes, e.g. first quantize to int8 and then float16

might do some followup PRs to clean up the hacks and refactor the code.

Test Plan:
python test/test_quantization.py TestQuantizeFx.test_multiple_qconfigs_single_value

Imported from OSS

Reviewed By: vkuzo

Differential Revision: D26912676

fbshipit-source-id: ae3653fd67f05870a3a9e808f491871826c555d5
 2021-03-31 17:48:50 -07:00
Jerry Zhang
cb6b65699f [quant][graphmode][fx] Add support for packed params in state_dict (#51639) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/51639

Test Plan: Imported from OSS

Reviewed By: z-a-f

Differential Revision: D26228185

fbshipit-source-id: 6cf8b4106fec9c6900521a2afe0de6f3d29cc896
 2021-02-26 15:13:50 -08:00