Add NVFP4 QAT#2666
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**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**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]
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
[ghstack-poisoned]
This was referenced Aug 1, 2025
Merged
Merged
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/2666
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New Failure, 1 Cancelled JobAs of commit a024e29 with merge base bc2c83e ( NEW FAILURE - The following job has failed:
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andrewor14
added a commit
that referenced
this pull request
Aug 1, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
ghstack-source-id: fe592ca
Pull Request resolved: #2666
andrewor14
added a commit
that referenced
this pull request
Aug 22, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
ghstack-source-id: 5548756
Pull Request resolved: #2666
andrewor14
added a commit
that referenced
this pull request
Aug 22, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
ghstack-source-id: 5548756
Pull Request resolved: #2666
andrewor14
added a commit
that referenced
this pull request
Aug 22, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
ghstack-source-id: 5548756
Pull Request resolved: #2666
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
[ghstack-poisoned]
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
[ghstack-poisoned]
andrewor14
added a commit
that referenced
this pull request
Aug 22, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
ghstack-source-id: 8c7d051
Pull Request resolved: #2666
drisspg
reviewed
Aug 22, 2025
drisspg
reviewed
Aug 22, 2025
| per_tensor_scale = None | ||
|
|
||
| # quantize | ||
| scale, q = _nvfp4_quantize( |
Contributor
Author
There was a problem hiding this comment.
added STE instead (see _Float8Round)
drisspg
reviewed
Aug 22, 2025
andrewor14
added a commit
that referenced
this pull request
Aug 22, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
ghstack-source-id: 5548756
Pull Request resolved: #2666
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
[ghstack-poisoned]
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
[ghstack-poisoned]
andrewor14
added a commit
that referenced
this pull request
Aug 22, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on oasst1 for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7927|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7323|± | N/A|
| | |none |None |word_perplexity|↓ |18.8815|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.7921|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7316|± | N/A|
| | |none |None |word_perplexity|↓ |18.8409|± | N/A|
```
ghstack-source-id: 512c7c2
Pull Request resolved: #2666
jerryzh168
reviewed
Aug 23, 2025
Comment on lines
+116
to
+117
| if x.dim() == 3: | ||
| x = x.view(-1, x.shape[-1]) |
Contributor
There was a problem hiding this comment.
why does this happen here? can this happen in quant primtive ops?
Contributor
Author
There was a problem hiding this comment.
I found that this was necessary for activations during training (for the batch size). Not sure if it was necessary for inference? @drisspg
andrewor14
added a commit
that referenced
this pull request
Aug 25, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
ghstack-source-id: 5548756
Pull Request resolved: #2666
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
[ghstack-poisoned]
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
[ghstack-poisoned]
andrewor14
added a commit
that referenced
this pull request
Aug 25, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
ghstack-source-id: 5ddda63
Pull Request resolved: #2666
andrewor14
added a commit
that referenced
this pull request
Aug 25, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
ghstack-source-id: 5ddda63
Pull Request resolved: #2666
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
[ghstack-poisoned]
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
[ghstack-poisoned]
andrewor14
added a commit
that referenced
this pull request
Aug 25, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
ghstack-source-id: fb5c617
Pull Request resolved: #2666
andrewor14
added a commit
that referenced
this pull request
Aug 25, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
ghstack-source-id: fb5c617
Pull Request resolved: #2666
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
[ghstack-poisoned]
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
activation_config=NVFP4FakeQuantizeConfig(),
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
[ghstack-poisoned]
andrewor14
added a commit
that referenced
this pull request
Aug 25, 2025
**Summary:** This commit adds a QAT flow for NVFP4, following the
numerics in `NVFP4Tensor` closely but without the dtyping casting,
swizzling, and the packing/unpacking. Users can call this flow as follows:
```
from torchao.quantization import quantize_
from torchao.quantization.qat import NVFP4FakeQuantizeConfig, QATConfig
qat_config = QATConfig(
weight_config=NVFP4FakeQuantizeConfig(),
step="prepare",
)
quantize_(model, qat_config)
```
**Test Plan:**
```
python test/quantization/test_qat.py -k test_qat_nvfp4
```
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs:
```
# Without QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8322|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7804|± | N/A|
| | |none |None |word_perplexity|↓ |21.8611|± | N/A|
# With QAT
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext| 2|none |None |bits_per_byte |↓ | 0.8271|± | N/A|
| | |none |None |byte_perplexity|↓ | 1.7741|± | N/A|
| | |none |None |word_perplexity|↓ |21.4467|± | N/A|
```
ghstack-source-id: 3e1b617
Pull Request resolved: #2666
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Stack from ghstack (oldest at bottom):
Summary: This commit adds a QAT flow for NVFP4, following the
numerics in
NVFP4Tensorclosely but without the dtyping casting,swizzling, and the packing/unpacking. Users can call this flow as follows:
Test Plan:
Initial benchmarks on fine-tuning Qwen3-1.7B on alpaca for 3 epochs: