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Composing autoquant with compile #175
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@HDCharles - Could you also update the docs? |
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:
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* add dora kernels
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
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 * update
Install dev-requirements.txt --------- Co-authored-by: Mark Saroufim <[email protected]>
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:
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/175
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit e5d215f with merge base 63c5ac5 (): This comment was automatically generated by Dr. CI and updates every 15 minutes. |
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
Summary: att Test Plan: python test/quantization/test_quant_primitives.py -k test_raises Reviewers: Subscribers: Tasks: Tags:
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:
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
…abs/ao into 085_no_input_autoquant
dbyoung18
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Jul 31, 2024
* 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
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Dec 9, 2024
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Composing autoquant with compile
Summary:
this PR rewrites how torchao.autoquant works so that it works with
torch.compile. Previously you had to do:
now you can do
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:
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: