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Merged
merged 26 commits into from
Aug 5, 2025
Merged

Deprecate old QAT APIs #2641

merged 26 commits into from
Aug 5, 2025

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@andrewor14
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@andrewor14 andrewor14 commented Jul 30, 2025 •
edited
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Stack from ghstack (oldest at bottom):

Summary: Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

Test Plan:

python test/quantization/test_qat.py -k test_qat_api_deprecation

Also manual testing:

>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see https://github.com/pytorch/ao/issues/2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)

@andrewor14
[bc-breaking] Generalize FakeQuantizeConfig beyond intx
c358b1b 
 **Summary:** The existing `FakeQuantizeConfig` performs only
intx quantization, but we plan to extend QAT to other dtypes
such as fp8 and nvfp4 in the near future. This is the necessary
refactor before that. Specifically:

```
# New abstract class
FakeQuantizeConfigBase
# Rename
FakeQuantizeConfig -> IntxFakeQuantizeConfig
```

In the future, we will have other types of `FakeQuantizeConfigBase`
for float dtypes that users can pass in instead of the existing
Intx one.

**BC-breaking notes:** For BC, we keep around the old names to
reference the new ones. However, this commit is still BC-breaking
in the sense that a few APIs now accept the abstract
`FakeQuantizeConfigBase` instead. For the most part, this abstract
class will be hidden from the user.

Before:
```
activation_config = FakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = FakeQuantizeConfig(torch.int4, group_size=32)
```

After:
```
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
```

**Test Plan:**
python test/quantization/test_qat.py

[ghstack-poisoned]
@andrewor14
New multi-step QAT API
d076264 
**Summary:** This commit adds a new multi-step QAT API with the
main goal of simplifying the existing UX. The new API uses the
same `QATConfig` for both the prepare and convert steps, and
automatically infers the fake quantization configs based on
a PTQ base config provided by the user:

```
from torchao.quantization import (
    quantize_,
    Int8DynamicActivationInt4WeightConfig
)
from torchao.quantization.qat import QATConfig

\# prepare
base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
qat_config = QATConfig(base_config, step="prepare")
quantize_(m, qat_config)

\# train (not shown)

\# convert
quantize_(m, QATConfig(base_config, step="convert"))
```

The main improvements include:
- A single config for both prepare and convert steps
- A single quantize_ for convert (instead of 2)
- No chance for incompatible prepare vs convert configs
- Much less boilerplate code for most common use case
- Simpler config names

For less common use cases such as experimentation, users can
still specify arbitrary fake quantization configs for
activations and/or weights as before. This is still important
since there may not always be a corresponding PTQ base config.
For example:

```
from torchao.quantization import quantize_
from torchao.quantization.qat import IntxFakeQuantizeConfig, QATConfig

activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = QATConfig(
    activation_config=activation_config,
    weight_config=weight_config,
    step="prepare",
)
quantize_(model, qat_config)

\# train and convert same as above (not shown)
```

**BC-breaking notes:** This change by itself is technically not
BC-breaking since we keep around the old path, but will become
so when we deprecate and remove the old path in the future.

Before:
```
\# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = IntXQuantizationAwareTrainingConfig(activation_config, weight_config),
quantize_(model, qat_config)

\# train (not shown)

\# convert
quantize_(model, FromIntXQuantizationAwareTrainingConfig())
quantize_(model, Int8DynamicActivationInt4WeightConfig(group_size=32))
```

After: (see above)

**Test Plan:**
```
python test/quantization/test_qat.py
```

[ghstack-poisoned]
@andrewor14
Update on "New multi-step QAT API"
8f56651 
**Summary:** This commit adds a new multi-step QAT API with the
main goal of simplifying the existing UX. The new API uses the
same `QATConfig` for both the prepare and convert steps, and
automatically infers the fake quantization configs based on
a PTQ base config provided by the user:

```
from torchao.quantization import (
    quantize_,
    Int8DynamicActivationInt4WeightConfig
)
from torchao.quantization.qat import QATConfig

# prepare
base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
quantize_(m, QATConfig(base_config, step="prepare"))

# train (not shown)

# convert
quantize_(m, QATConfig(base_config, step="convert"))
```

The main improvements include:
- A single config for both prepare and convert steps
- A single quantize_ for convert (instead of 2)
- No chance for incompatible prepare vs convert configs
- Much less boilerplate code for most common use case
- Simpler config names

For less common use cases such as experimentation, users can
still specify arbitrary fake quantization configs for
activations and/or weights as before. This is still important
since there may not always be a corresponding PTQ base config.
For example:

```
from torchao.quantization import quantize_
from torchao.quantization.qat import IntxFakeQuantizeConfig, QATConfig

# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = QATConfig(
    activation_config=activation_config,
    weight_config=weight_config,
    step="prepare",
)
quantize_(model, qat_config)

# train and convert same as above (not shown)
```

**BC-breaking notes:** This change by itself is technically not
BC-breaking since we keep around the old path, but will become
so when we deprecate and remove the old path in the future.

Before:
```
# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = IntXQuantizationAwareTrainingConfig(activation_config, weight_config),
quantize_(model, qat_config)

# train (not shown)

# convert
quantize_(model, FromIntXQuantizationAwareTrainingConfig())
quantize_(model, Int8DynamicActivationInt4WeightConfig(group_size=32))
```

After: (see above)

**Test Plan:**
```
python test/quantization/test_qat.py
```

[ghstack-poisoned]
@andrewor14
Update on "New multi-step QAT API"
7a9fe90 
**Summary:** This commit adds a new multi-step QAT API with the
main goal of simplifying the existing UX. The new API uses the
same `QATConfig` for both the prepare and convert steps, and
automatically infers the fake quantization configs based on
a PTQ base config provided by the user:

```
from torchao.quantization import (
    quantize_,
    Int8DynamicActivationInt4WeightConfig
)
from torchao.quantization.qat import QATConfig

# prepare
base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
quantize_(m, QATConfig(base_config, step="prepare"))

# train (not shown)

# convert
quantize_(m, QATConfig(base_config, step="convert"))
```

The main improvements include:
- A single config for both prepare and convert steps
- A single quantize_ for convert (instead of 2)
- No chance for incompatible prepare vs convert configs
- Much less boilerplate code for most common use case
- Simpler config names

For less common use cases such as experimentation, users can
still specify arbitrary fake quantization configs for
activations and/or weights as before. This is still important
since there may not always be a corresponding PTQ base config.
For example:

```
from torchao.quantization import quantize_
from torchao.quantization.qat import IntxFakeQuantizeConfig, QATConfig

# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = QATConfig(
    activation_config=activation_config,
    weight_config=weight_config,
    step="prepare",
)
quantize_(model, qat_config)

# train and convert same as above (not shown)
```

**BC-breaking notes:** This change by itself is technically not
BC-breaking since we keep around the old path, but will become
so when we deprecate and remove the old path in the future.

Before:
```
# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = IntXQuantizationAwareTrainingConfig(activation_config, weight_config),
quantize_(model, qat_config)

# train (not shown)

# convert
quantize_(model, FromIntXQuantizationAwareTrainingConfig())
quantize_(model, Int8DynamicActivationInt4WeightConfig(group_size=32))
```

After: (see above)

**Test Plan:**
```
python test/quantization/test_qat.py
```

[ghstack-poisoned]
@andrewor14
Deprecate old QAT APIs
1e88ebf 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

[ghstack-poisoned]
This was referenced Jul 30, 2025
Merged
Merged
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pytorch-bot bot commented Jul 30, 2025 •
edited
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As of commit 3f06429 with merge base 5f3ab63 (image):

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andrewor14 added a commit that referenced this pull request Jul 30, 2025
@andrewor14
Deprecate old QAT APIs
46e4783 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

ghstack-source-id: f6988d7
Pull Request resolved: #2641
@meta-cla meta-cla 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 Jul 30, 2025
@andrewor14 andrewor14 added the topic: improvement Use this tag if this PR is an improvement (doesn't fit into any of the other categories) label Jul 30, 2025
andrewor14 added a commit that referenced this pull request Jul 30, 2025
@andrewor14
Deprecate old QAT APIs
39da9d0 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

ghstack-source-id: f6988d7
Pull Request resolved: #2641
@andrewor14
Update base for Update on "Deprecate old QAT APIs"
12e8c3f 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

[ghstack-poisoned]
@andrewor14
Update on "Deprecate old QAT APIs"
2ed5e50 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Jul 30, 2025
@andrewor14
Deprecate old QAT APIs
cf23da7 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

ghstack-source-id: f6988d7
Pull Request resolved: #2641
@andrewor14
Update base for Update on "Deprecate old QAT APIs"
019b665 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]
@andrewor14
Update on "Deprecate old QAT APIs"
b0c4721 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Jul 30, 2025
@andrewor14
Deprecate old QAT APIs
27cfa3b 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

ghstack-source-id: f6988d7
Pull Request resolved: #2641
@andrewor14
Update base for Update on "Deprecate old QAT APIs"
0eb0983 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]
@andrewor14
Update on "Deprecate old QAT APIs"
c91b218 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Jul 30, 2025
@andrewor14
Deprecate old QAT APIs
9103367 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

ghstack-source-id: ac1b30e
Pull Request resolved: #2641
@andrewor14
Update base for Update on "Deprecate old QAT APIs"
affc74e 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]
@andrewor14
Update on "Deprecate old QAT APIs"
3069075 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Jul 30, 2025
@andrewor14
Deprecate old QAT APIs
41c4876 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

ghstack-source-id: bb3fc80
Pull Request resolved: #2641
andrewor14 added a commit that referenced this pull request Jul 31, 2025
@andrewor14
Deprecate old QAT APIs
0e6ac9a 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

ghstack-source-id: bb3fc80
Pull Request resolved: #2641
@andrewor14
Update base for Update on "Deprecate old QAT APIs"
b41f4e7 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]
@andrewor14
Update on "Deprecate old QAT APIs"
12d920b 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]
@andrewor14 andrewor14 changed the base branch from gh/andrewor14/15/base to main August 4, 2025 16:59
@andrewor14 andrewor14 merged commit eb3f4aa into main Aug 5, 2025
33 of 38 checks passed
andrewor14 added a commit that referenced this pull request Aug 18, 2025
@andrewor14
Deprecate old QAT APIs
3126e8d 
**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

ghstack-source-id: 33a6f38
Pull Request resolved: #2641
liangel-02 pushed a commit that referenced this pull request Aug 25, 2025
@andrewor14 @liangel-02
Deprecate old QAT APIs (#2641)
3122aa4 
* [bc-breaking] Generalize FakeQuantizeConfig beyond intx

 **Summary:** The existing `FakeQuantizeConfig` performs only
intx quantization, but we plan to extend QAT to other dtypes
such as fp8 and nvfp4 in the near future. This is the necessary
refactor before that. Specifically:

```
# New abstract class
FakeQuantizeConfigBase
# Rename
FakeQuantizeConfig -> IntxFakeQuantizeConfig
```

In the future, we will have other types of `FakeQuantizeConfigBase`
for float dtypes that users can pass in instead of the existing
Intx one.

**BC-breaking notes:** For BC, we keep around the old names to
reference the new ones. However, this commit is still BC-breaking
in the sense that a few APIs now accept the abstract
`FakeQuantizeConfigBase` instead. For the most part, this abstract
class will be hidden from the user.

Before:
```
activation_config = FakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = FakeQuantizeConfig(torch.int4, group_size=32)
```

After:
```
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
```

**Test Plan:**
python test/quantization/test_qat.py

[ghstack-poisoned]

* New multi-step QAT API

**Summary:** This commit adds a new multi-step QAT API with the
main goal of simplifying the existing UX. The new API uses the
same `QATConfig` for both the prepare and convert steps, and
automatically infers the fake quantization configs based on
a PTQ base config provided by the user:

```
from torchao.quantization import (
    quantize_,
    Int8DynamicActivationInt4WeightConfig
)
from torchao.quantization.qat import QATConfig

\# prepare
base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
qat_config = QATConfig(base_config, step="prepare")
quantize_(m, qat_config)

\# train (not shown)

\# convert
quantize_(m, QATConfig(base_config, step="convert"))
```

The main improvements include:
- A single config for both prepare and convert steps
- A single quantize_ for convert (instead of 2)
- No chance for incompatible prepare vs convert configs
- Much less boilerplate code for most common use case
- Simpler config names

For less common use cases such as experimentation, users can
still specify arbitrary fake quantization configs for
activations and/or weights as before. This is still important
since there may not always be a corresponding PTQ base config.
For example:

```
from torchao.quantization import quantize_
from torchao.quantization.qat import IntxFakeQuantizeConfig, QATConfig

activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = QATConfig(
    activation_config=activation_config,
    weight_config=weight_config,
    step="prepare",
)
quantize_(model, qat_config)

\# train and convert same as above (not shown)
```

**BC-breaking notes:** This change by itself is technically not
BC-breaking since we keep around the old path, but will become
so when we deprecate and remove the old path in the future.

Before:
```
\# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = IntXQuantizationAwareTrainingConfig(activation_config, weight_config),
quantize_(model, qat_config)

\# train (not shown)

\# convert
quantize_(model, FromIntXQuantizationAwareTrainingConfig())
quantize_(model, Int8DynamicActivationInt4WeightConfig(group_size=32))
```

After: (see above)

**Test Plan:**
```
python test/quantization/test_qat.py
```

[ghstack-poisoned]

* Update on "New multi-step QAT API"


**Summary:** This commit adds a new multi-step QAT API with the
main goal of simplifying the existing UX. The new API uses the
same `QATConfig` for both the prepare and convert steps, and
automatically infers the fake quantization configs based on
a PTQ base config provided by the user:

```
from torchao.quantization import (
    quantize_,
    Int8DynamicActivationInt4WeightConfig
)
from torchao.quantization.qat import QATConfig

# prepare
base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
quantize_(m, QATConfig(base_config, step="prepare"))

# train (not shown)

# convert
quantize_(m, QATConfig(base_config, step="convert"))
```

The main improvements include:
- A single config for both prepare and convert steps
- A single quantize_ for convert (instead of 2)
- No chance for incompatible prepare vs convert configs
- Much less boilerplate code for most common use case
- Simpler config names

For less common use cases such as experimentation, users can
still specify arbitrary fake quantization configs for
activations and/or weights as before. This is still important
since there may not always be a corresponding PTQ base config.
For example:

```
from torchao.quantization import quantize_
from torchao.quantization.qat import IntxFakeQuantizeConfig, QATConfig

# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = QATConfig(
    activation_config=activation_config,
    weight_config=weight_config,
    step="prepare",
)
quantize_(model, qat_config)

# train and convert same as above (not shown)
```

**BC-breaking notes:** This change by itself is technically not
BC-breaking since we keep around the old path, but will become
so when we deprecate and remove the old path in the future.

Before:
```
# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = IntXQuantizationAwareTrainingConfig(activation_config, weight_config),
quantize_(model, qat_config)

# train (not shown)

# convert
quantize_(model, FromIntXQuantizationAwareTrainingConfig())
quantize_(model, Int8DynamicActivationInt4WeightConfig(group_size=32))
```

After: (see above)

**Test Plan:**
```
python test/quantization/test_qat.py
```

[ghstack-poisoned]

* Update on "New multi-step QAT API"


**Summary:** This commit adds a new multi-step QAT API with the
main goal of simplifying the existing UX. The new API uses the
same `QATConfig` for both the prepare and convert steps, and
automatically infers the fake quantization configs based on
a PTQ base config provided by the user:

```
from torchao.quantization import (
    quantize_,
    Int8DynamicActivationInt4WeightConfig
)
from torchao.quantization.qat import QATConfig

# prepare
base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
quantize_(m, QATConfig(base_config, step="prepare"))

# train (not shown)

# convert
quantize_(m, QATConfig(base_config, step="convert"))
```

The main improvements include:
- A single config for both prepare and convert steps
- A single quantize_ for convert (instead of 2)
- No chance for incompatible prepare vs convert configs
- Much less boilerplate code for most common use case
- Simpler config names

For less common use cases such as experimentation, users can
still specify arbitrary fake quantization configs for
activations and/or weights as before. This is still important
since there may not always be a corresponding PTQ base config.
For example:

```
from torchao.quantization import quantize_
from torchao.quantization.qat import IntxFakeQuantizeConfig, QATConfig

# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = QATConfig(
    activation_config=activation_config,
    weight_config=weight_config,
    step="prepare",
)
quantize_(model, qat_config)

# train and convert same as above (not shown)
```

**BC-breaking notes:** This change by itself is technically not
BC-breaking since we keep around the old path, but will become
so when we deprecate and remove the old path in the future.

Before:
```
# prepare
activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
qat_config = IntXQuantizationAwareTrainingConfig(activation_config, weight_config),
quantize_(model, qat_config)

# train (not shown)

# convert
quantize_(model, FromIntXQuantizationAwareTrainingConfig())
quantize_(model, Int8DynamicActivationInt4WeightConfig(group_size=32))
```

After: (see above)

**Test Plan:**
```
python test/quantization/test_qat.py
```

[ghstack-poisoned]

* Deprecate old QAT APIs

**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

[ghstack-poisoned]

* Update base for Update on "Deprecate old QAT APIs"


**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

[ghstack-poisoned]

* Update base for Update on "Deprecate old QAT APIs"


**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]

* Update base for Update on "Deprecate old QAT APIs"


**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]

* Update base for Update on "Deprecate old QAT APIs"


**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]

* Update base for Update on "Deprecate old QAT APIs"


**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]

* Update base for Update on "Deprecate old QAT APIs"


**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]

* Update base for Update on "Deprecate old QAT APIs"


**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]

* Update base for Update on "Deprecate old QAT APIs"


**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]

* Update base for Update on "Deprecate old QAT APIs"


**Summary:** Deprecates QAT APIs that should no longer be used.
Print helpful deprecation warning to help users migrate.

**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_api_deprecation
```

Also manual testing:
```
>>> from torchao.quantization.qat import IntXQuantizationAwareTrainingConfig
>>> IntXQuantizationAwareTrainingConfig()
'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead:

    base_config = Int8DynamicActivationInt4WeightConfig(group_size=32)
    quantize_(model, QATConfig(base_config, step="prepare"))
    # train (not shown)
    quantize_(model, QATConfig(base_config, step="convert"))

Alternatively, if you prefer to pass in fake quantization configs:

    activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
    weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32)
    qat_config = QATConfig(
        activation_config=activation_config,
        weight_config=weight_config,
        step="prepare",
    )
    quantize_(model, qat_config)

Please see #2630 for more details.

IntXQuantizationAwareTrainingConfig(activation_config=None, weight_config=None)
```

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Oct 10, 2025
@andrewor14
Remove old QAT APIs
d1bae41 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
@andrewor14 andrewor14 mentioned this pull request Oct 10, 2025
Closed
andrewor14 added a commit that referenced this pull request Oct 10, 2025
@andrewor14
Remove old QAT APIs
c26a816 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

ghstack-source-id: b22f940
Pull Request resolved: #3147
andrewor14 added a commit that referenced this pull request Oct 13, 2025
@andrewor14
Remove old QAT APIs
8658c68 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

ghstack-source-id: b22f940
Pull Request resolved: #3147
andrewor14 added a commit that referenced this pull request Oct 13, 2025
@andrewor14
Update base for Update on "Remove old QAT APIs"
f321317 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Oct 13, 2025
@andrewor14
Remove old QAT APIs
f347e35 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

ghstack-source-id: de20afc
Pull Request resolved: #3147
andrewor14 added a commit that referenced this pull request Oct 13, 2025
@andrewor14
Update on "Remove old QAT APIs"
0907263 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Oct 13, 2025
@andrewor14
Remove old QAT APIs
e033343 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

ghstack-source-id: de20afc
Pull Request resolved: #3147
andrewor14 added a commit that referenced this pull request Oct 13, 2025
@andrewor14
Update base for Update on "Remove old QAT APIs"
5faaa32 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Oct 13, 2025
@andrewor14
Update on "Remove old QAT APIs"
1328245 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Oct 13, 2025
@andrewor14
Remove old QAT APIs
f3400a0 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

ghstack-source-id: d0abb07
Pull Request resolved: #3147
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Remove old QAT APIs
775d87b 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

ghstack-source-id: 8ddff9e
Pull Request resolved: #3147
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Update base for Update on "Remove old QAT APIs"
799dbd6 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Update on "Remove old QAT APIs"
9483ff0 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Update base for Update on "Remove old QAT APIs"
d624214 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Update on "Remove old QAT APIs"
b7f32ae 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Remove old QAT APIs
1472a64 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

ghstack-source-id: dd9015a
Pull Request resolved: #3147
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Update base for Update on "Remove old QAT APIs"
deb8bb9 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Update on "Remove old QAT APIs"
f9a769c 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Remove old QAT APIs
ea3e37f 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

ghstack-source-id: 722a3f6
Pull Request resolved: #3147
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Update base for Update on "Remove old QAT APIs"
bf11281 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Update on "Remove old QAT APIs"
d719f46 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

[ghstack-poisoned]
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Remove old QAT APIs
f028efd 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI

ghstack-source-id: 1cdef82
Pull Request resolved: #3147
andrewor14 added a commit that referenced this pull request Nov 7, 2025
@andrewor14
Remove old QAT APIs
1c56500 
**Summary:** As a follow-up to #2641,
which deprecated the old QAT APIs in 0.13.0, we remove them now
in the next release 0.15.0.

Fixes #2630.

**Test Plan:** CI
@andrewor14 andrewor14 mentioned this pull request Nov 7, 2025
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@jerryzh168 jerryzh168 jerryzh168 approved these changes

@vkuzo vkuzo Awaiting requested review from vkuzo

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