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Install dev-requirements.txt in doc build #224

Merged
merged 4 commits into from
May 7, 2024
Merged

Install dev-requirements.txt in doc build #224

merged 4 commits into from
May 7, 2024

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svekars
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@svekars svekars commented May 7, 2024

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@svekars svekars requested a review from msaroufim May 7, 2024 16:06
@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 reviewed May 7, 2024
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docs/source/api_ref_sparsity.rst Outdated
@@ -1,14 +0,0 @@
.. _api_sparsity:
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This is one of the few things we do have docs for though, wondering what the root cause is still

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I added pip install dev-requirements to the docs_build.yml - I think it should work now - it was missing expecttest.

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Ahhh I'm fixing that upstream #225

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We should probably still install dev-requirements.txt in the doc build for consistency?

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Huh this indeed did fix the doc build though, 😕

@svekars svekars changed the title Fix build by removing the sparsity docs Install dev-requirements.txt in doc build May 7, 2024
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huydhn commented May 7, 2024

@pytorchbot drci

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huydhn commented May 7, 2024

@pytorchbot drci

@msaroufim msaroufim self-requested a review May 7, 2024 17:58
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Thanks! Feel free to merge whenever you like

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pytorch-bot bot commented May 7, 2024

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/224

Note: Links to docs will display an error until the docs builds have been completed.

✅ No Failures

As of commit 77e019b with merge base cce5960 (image):
💚 Looks good so far! There are no failures yet. 💚

This comment was automatically generated by Dr. CI and updates every 15 minutes.

@svekars svekars merged commit b34d1ac into main May 7, 2024
13 checks passed
@svekars svekars deleted the remove-sparsity-from-docs branch May 7, 2024 18:06
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 <[email protected]>

* 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 <[email protected]>

* 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 <[email protected]>
Co-authored-by: Jerry Zhang <[email protected]>
Co-authored-by: Mark Saroufim <[email protected]>
Co-authored-by: Svetlana Karslioglu <[email protected]>
dbyoung18 pushed a commit to dbyoung18/ao that referenced this pull request Jul 31, 2024
@svekars @msaroufim
Install dev-requirements.txt in doc build (pytorch#224)
4b12b6b 
Install dev-requirements.txt

---------

Co-authored-by: Mark Saroufim <[email protected]>
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 <[email protected]>

* 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 <[email protected]>

* 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 <[email protected]>
Co-authored-by: Jerry Zhang <[email protected]>
Co-authored-by: Mark Saroufim <[email protected]>
Co-authored-by: Svetlana Karslioglu <[email protected]>
yanbing-j pushed a commit to yanbing-j/ao that referenced this pull request Dec 9, 2024
@mergennachin @malfet
Enable CLANG-FORMAT (pytorch#224)
2c76deb
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