Add NVFP4 QAT #2666
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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 FailureAs of commit 1ff5de1 with merge base 8079abc ( NEW FAILURE - The following job has failed:
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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
**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]
andrewor14
added a commit
that referenced
this pull request
Aug 18, 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
```
ghstack-source-id: 8f3c0d9
Pull Request resolved: #2666
andrewor14
added a commit
that referenced
this pull request
Aug 18, 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
```
ghstack-source-id: 8f3c0d9
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
```
[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]
andrewor14
added a commit
that referenced
this pull request
Aug 18, 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
```
ghstack-source-id: ab61a74
Pull Request resolved: #2666
andrewor14
added a commit
that referenced
this pull request
Aug 18, 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
```
ghstack-source-id: ab61a74
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
```
[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]
andrewor14
added a commit
that referenced
this pull request
Aug 18, 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
```
ghstack-source-id: 5548756
Pull Request resolved: #2666
andrewor14
added a commit
that referenced
this pull request
Aug 19, 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
```
ghstack-source-id: 5548756
Pull Request resolved: #2666
andrewor14
added a commit
that referenced
this pull request
Aug 21, 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
```
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
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
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
|
|
||
| @unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available") | ||
| @parametrize("use_per_tensor_scale", [True, False]) | ||
| def test_qat_nvfp4(self, use_per_tensor_scale: bool): |
There was a problem hiding this comment.
can we add a test where we compare the outputs of the converted qat model to the ptq only model with the same config and assert they are identicallly equal
drisspg
reviewed
Aug 22, 2025
| per_tensor_scale = None | ||
|
|
||
| # quantize | ||
| scale, q = _nvfp4_quantize( |
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]) |
There was a problem hiding this comment.
why does this happen here? can this happen in quant primtive ops?
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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 oasst1 for 3 epochs: