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add expecttest to requirements.txt #225

Merged
merged 2 commits into from
May 7, 2024
Merged

add expecttest to requirements.txt #225

merged 2 commits into from
May 7, 2024

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msaroufim
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@msaroufim msaroufim commented May 7, 2024 •
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Fixes #222

Tested locally

(new) [marksaroufim@devvm17057.vll0 ~]$ pip uninstall expecttest
Found existing installation: expecttest 0.2.1
Uninstalling expecttest-0.2.1:
  Would remove:
    /home/marksaroufim/.local/lib/python3.10/site-packages/expecttest-0.2.1.dist-info/*
    /home/marksaroufim/.local/lib/python3.10/site-packages/expecttest/*
Proceed (Y/n)? Y
  Successfully uninstalled expecttest-0.2.1
(new) [marksaroufim@devvm17057.vll0 ~]$ python -c "import torchao"
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/marksaroufim/.local/lib/python3.10/site-packages/torchao/__init__.py", line 8, in <module>
    from torch.testing._internal.common_utils import IS_FBCODE
  File "/home/marksaroufim/.local/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 62, in <module>
    import expecttest

Which is fixed if you pip install expecttest but then you get a warning for hypothesis on every import

new) [marksaroufim@devvm17057.vll0 ~/ao (msaroufim/expectest)]$ python -c "import torchao"
Fail to import hypothesis in common_utils, tests are not derandomized
(new) [marksaroufim@devvm17057.vll0 ~/ao (msaroufim/expectest)]$ pip install hypothesis
Collecting hypothesis
  Downloading hypothesis-6.100.5-py3-none-any.whl.metadata (6.3 kB)
Collecting attrs>=22.2.0 (from hypothesis)
  Using cached attrs-23.2.0-py3-none-any.whl.metadata (9.5 kB)
Collecting sortedcontainers<3.0.0,>=2.1.0 (from hypothesis)
  Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl.metadata (10 kB)
Requirement already satisfied: exceptiongroup>=1.0.0 in /home/marksaroufim/.local/lib/python3.10/site-packages (from hypothesis) (1.2.0)
Downloading hypothesis-6.100.5-py3-none-any.whl (459 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 459.1/459.1 kB 6.1 MB/s eta 0:00:00
Using cached attrs-23.2.0-py3-none-any.whl (60 kB)
Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)
Installing collected packages: sortedcontainers, attrs, hypothesis
Successfully installed attrs-23.2.0 hypothesis-6.100.5 sortedcontainers-2.4.0
(new) [marksaroufim@devvm17057.vll0 ~/ao (msaroufim/expectest)]$ python -c "import torchao"
(new) [marksaroufim@devvm17057.vll0 ~/ao (msaroufim/expectest)]$

@facebook-github-bot facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label May 7, 2024
@msaroufim msaroufim requested a review from jerryzh168 May 7, 2024 16:55
@svekars
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svekars commented May 7, 2024

I added pip install dev-requirements.txt to the doc job here.

@msaroufim msaroufim mentioned this pull request May 7, 2024
Merged
@msaroufim msaroufim merged commit cce5960 into main May 7, 2024
15 checks passed
@msaroufim msaroufim deleted the msaroufim/expectest branch May 7, 2024 17:18
HDCharles added a commit that referenced this pull request May 8, 2024
@HDCharles @jeromeku
@jerryzh168 @msaroufim @svekars
Composing autoquant with compile (#175)
f6d56ca 
* Composing autoquant with compile

Summary:

this PR rewrites how torchao.autoquant works so that it works with
torch.compile. Previously you had to do:

torchao.autoquant(model, input)
mod=torch.compile(model)
mod(input)

now you can do
torchao.autoquant(torch.compile(model))
model(input)

The new method works with/without compile. Also this is BC so the old
path also works.

We use a forward_prehook to intercept the model call before
torch.compile tracing occurs at which point we do the autoquantization
and clean up all remaining hooks before passing things off to the
normal torch.compile tracing functionality.

note: in the case of multiple inputs, you can also do:

model.forward_log_only(input) to run the model forward with autoquant
shape logging and prevent the torch.compile tracing/autoquant
quantization from occuring.

Test Plan: python test/integration/test_integration.py -k "autoquant"

Reviewers:

Subscribers:

Tasks:

Tags:

* Fused DoRA kernels (#216)

* add dora kernels

* allowing error_on_unseen in autoquant func

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Unified AffineQuantizedTensor subclass (#214)

Summary:
Creatd a `AffineQuantizedTensor` subclass that works for both weight and input (for dynamic quantization), for all granularities (levering the recently added choose_qparams_affine, quantize_affine
and dequantize_affine ops)

only verified for 8da4w right now, we can make it work for other types of quantization (mostly the operator dispatching part) later

Test Plan:
python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_8da4w

Reviewers:

Subscribers:

Tasks:

Tags:

Co-authored-by: Mark Saroufim <marksaroufim@meta.com>

* add expecttest to requirements.txt (#225)

* add expecttest to requirements.txt

* update

* Install dev-requirements.txt in doc build (#224)

Install dev-requirements.txt

---------

Co-authored-by: Mark Saroufim <marksaroufim@meta.com>

* Fix an error in subclass impl (#226)

Summary:
Accidently changed the device check code for old subclass instead of the new one, forgot to fix before landing

Test Plan:
CI

Reviewers:

Subscribers:

Tasks:

Tags:

* update readme.md

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* trying to fix the error in CI on cleanup hooks

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* correct docs

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Some follow up fixes for quant primitives (#220)

Summary:
att

Test Plan:
python test/quantization/test_quant_primitives.py -k test_raises

Reviewers:

Subscribers:

Tasks:

Tags:

* Composing autoquant with compile

Summary:

this PR rewrites how torchao.autoquant works so that it works with
torch.compile. Previously you had to do:

torchao.autoquant(model, input)
mod=torch.compile(model)
mod(input)

now you can do
torchao.autoquant(torch.compile(model))
model(input)

The new method works with/without compile. Also this is BC so the old
path also works.

We use a forward_prehook to intercept the model call before
torch.compile tracing occurs at which point we do the autoquantization
and clean up all remaining hooks before passing things off to the
normal torch.compile tracing functionality.

note: in the case of multiple inputs, you can also do:

model.forward_log_only(input) to run the model forward with autoquant
shape logging and prevent the torch.compile tracing/autoquant
quantization from occuring.

Test Plan: python test/integration/test_integration.py -k "autoquant"

Reviewers:

Subscribers:

Tasks:

Tags:

* allowing error_on_unseen in autoquant func

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* update readme.md

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* trying to fix the error in CI on cleanup hooks

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* correct docs

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

---------

Co-authored-by: jeromeku <jerome.ku@gmail.com>
Co-authored-by: Jerry Zhang <jerryzh168@gmail.com>
Co-authored-by: Mark Saroufim <marksaroufim@meta.com>
Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
dbyoung18 pushed a commit to dbyoung18/ao that referenced this pull request Jul 31, 2024
@msaroufim
add expecttest to requirements.txt (pytorch#225)
ac14b4b 
* add expecttest to requirements.txt

* update
dbyoung18 pushed a commit to dbyoung18/ao that referenced this pull request Jul 31, 2024
@HDCharles @jeromeku
@jerryzh168 @msaroufim @svekars
Composing autoquant with compile (pytorch#175)
32b6f0a 
* Composing autoquant with compile

Summary:

this PR rewrites how torchao.autoquant works so that it works with
torch.compile. Previously you had to do:

torchao.autoquant(model, input)
mod=torch.compile(model)
mod(input)

now you can do
torchao.autoquant(torch.compile(model))
model(input)

The new method works with/without compile. Also this is BC so the old
path also works.

We use a forward_prehook to intercept the model call before
torch.compile tracing occurs at which point we do the autoquantization
and clean up all remaining hooks before passing things off to the
normal torch.compile tracing functionality.

note: in the case of multiple inputs, you can also do:

model.forward_log_only(input) to run the model forward with autoquant
shape logging and prevent the torch.compile tracing/autoquant
quantization from occuring.

Test Plan: python test/integration/test_integration.py -k "autoquant"

Reviewers:

Subscribers:

Tasks:

Tags:

* Fused DoRA kernels (pytorch#216)

* add dora kernels

* allowing error_on_unseen in autoquant func

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Unified AffineQuantizedTensor subclass (pytorch#214)

Summary:
Creatd a `AffineQuantizedTensor` subclass that works for both weight and input (for dynamic quantization), for all granularities (levering the recently added choose_qparams_affine, quantize_affine
and dequantize_affine ops)

only verified for 8da4w right now, we can make it work for other types of quantization (mostly the operator dispatching part) later

Test Plan:
python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_8da4w

Reviewers:

Subscribers:

Tasks:

Tags:

Co-authored-by: Mark Saroufim <marksaroufim@meta.com>

* add expecttest to requirements.txt (pytorch#225)

* add expecttest to requirements.txt

* update

* Install dev-requirements.txt in doc build (pytorch#224)

Install dev-requirements.txt

---------

Co-authored-by: Mark Saroufim <marksaroufim@meta.com>

* Fix an error in subclass impl (pytorch#226)

Summary:
Accidently changed the device check code for old subclass instead of the new one, forgot to fix before landing

Test Plan:
CI

Reviewers:

Subscribers:

Tasks:

Tags:

* update readme.md

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* trying to fix the error in CI on cleanup hooks

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* correct docs

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Some follow up fixes for quant primitives (pytorch#220)

Summary:
att

Test Plan:
python test/quantization/test_quant_primitives.py -k test_raises

Reviewers:

Subscribers:

Tasks:

Tags:

* Composing autoquant with compile

Summary:

this PR rewrites how torchao.autoquant works so that it works with
torch.compile. Previously you had to do:

torchao.autoquant(model, input)
mod=torch.compile(model)
mod(input)

now you can do
torchao.autoquant(torch.compile(model))
model(input)

The new method works with/without compile. Also this is BC so the old
path also works.

We use a forward_prehook to intercept the model call before
torch.compile tracing occurs at which point we do the autoquantization
and clean up all remaining hooks before passing things off to the
normal torch.compile tracing functionality.

note: in the case of multiple inputs, you can also do:

model.forward_log_only(input) to run the model forward with autoquant
shape logging and prevent the torch.compile tracing/autoquant
quantization from occuring.

Test Plan: python test/integration/test_integration.py -k "autoquant"

Reviewers:

Subscribers:

Tasks:

Tags:

* allowing error_on_unseen in autoquant func

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* update readme.md

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* trying to fix the error in CI on cleanup hooks

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* correct docs

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

---------

Co-authored-by: jeromeku <jerome.ku@gmail.com>
Co-authored-by: Jerry Zhang <jerryzh168@gmail.com>
Co-authored-by: Mark Saroufim <marksaroufim@meta.com>
Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
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[BUG] No module named 'expecttest' when import torchao
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