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Name change AOPermod -> ModuleFqn (#38456)

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Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
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Driss Guessous 2025-06-03 11:43:31 -04:00 committed by GitHub
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20
docs/source/en/quantization/torchao.md
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@ -62,7 +62,7 @@ Install torchao from PyPi or the PyTorch index with the following commands.
# Stable release from Pypi which will default to CUDA 12.6 # Stable release from Pypi which will default to CUDA 12.6
pip install --upgrade torchao transformers pip install --upgrade torchao transformers
``` ```
</hfoption> </hfoption>
<hfoption id="PyTorch Index"> <hfoption id="PyTorch Index">
Stable Release from the PyTorch index Stable Release from the PyTorch index
```bash ```bash
@ -276,18 +276,18 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
### Per Module Quantization ### Per Module Quantization
#### 1. Skip quantization for certain layers #### 1. Skip quantization for certain layers
With `AOPerModuleConfig` we can specify a default configuration for all layers while skipping quantization for certain layers. With `ModuleFqnToConfig` we can specify a default configuration for all layers while skipping quantization for certain layers.
```py ```py
import torch import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
model_id = "meta-llama/Llama-3.1-8B-Instruct" model_id = "meta-llama/Llama-3.1-8B-Instruct"
from torchao.quantization import Int4WeightOnlyConfig, AOPerModuleConfig from torchao.quantization import Int4WeightOnlyConfig, ModuleFqnToConfig
config = Int4WeightOnlyConfig(group_size=128) config = Int4WeightOnlyConfig(group_size=128)
# set default to int4 (for linears), and skip quantizing `model.layers.0.self_attn.q_proj` # set default to int4 (for linears), and skip quantizing `model.layers.0.self_attn.q_proj`
quant_config = AOPerModuleConfig({"_default": config, "model.layers.0.self_attn.q_proj": None}) quant_config = ModuleFqnToConfig({"_default": config, "model.layers.0.self_attn.q_proj": None})
quantization_config = TorchAoConfig(quant_type=quant_config) quantization_config = TorchAoConfig(quant_type=quant_config)
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config) quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
# lm_head is not quantized and model.layers.0.self_attn.q_proj is not quantized # lm_head is not quantized and model.layers.0.self_attn.q_proj is not quantized
@ -311,7 +311,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
model_id = "facebook/opt-125m" model_id = "facebook/opt-125m"
from torchao.quantization import Int4WeightOnlyConfig, AOPerModuleConfig, Int8DynamicActivationInt4WeightConfig, IntxWeightOnlyConfig, PerAxis, MappingType from torchao.quantization import Int4WeightOnlyConfig, ModuleFqnToConfig, Int8DynamicActivationInt4WeightConfig, IntxWeightOnlyConfig, PerAxis, MappingType
weight_dtype = torch.int8 weight_dtype = torch.int8
granularity = PerAxis(0) granularity = PerAxis(0)
@ -322,7 +322,7 @@ embedding_config = IntxWeightOnlyConfig(
mapping_type=mapping_type, mapping_type=mapping_type,
) )
linear_config = Int8DynamicActivationInt4WeightConfig(group_size=128) linear_config = Int8DynamicActivationInt4WeightConfig(group_size=128)
quant_config = AOPerModuleConfig({"_default": linear_config, "model.decoder.embed_tokens": embedding_config, "model.decoder.embed_positions": None}) quant_config = ModuleFqnToConfig({"_default": linear_config, "model.decoder.embed_tokens": embedding_config, "model.decoder.embed_positions": None})
# set `include_embedding` to True in order to include embedding in quantization # set `include_embedding` to True in order to include embedding in quantization
# when `include_embedding` is True, we'll remove input embedding from `modules_not_to_convert` as well # when `include_embedding` is True, we'll remove input embedding from `modules_not_to_convert` as well
quantization_config = TorchAoConfig(quant_type=quant_config, include_embedding=True) quantization_config = TorchAoConfig(quant_type=quant_config, include_embedding=True)
@ -427,8 +427,8 @@ quantized_model.save_pretrained(output_dir, safe_serialization=False)
# reload the quantized model # reload the quantized model
reloaded_model = AutoModelForCausalLM.from_pretrained( reloaded_model = AutoModelForCausalLM.from_pretrained(
output_dir, output_dir,
device_map="auto", device_map="auto",
torch_dtype=torch.bfloat16 torch_dtype=torch.bfloat16
) )
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
@ -463,8 +463,8 @@ quantized_model.save_pretrained(output_dir, safe_serialization=False)
# reload the quantized model # reload the quantized model
reloaded_model = AutoModelForCausalLM.from_pretrained( reloaded_model = AutoModelForCausalLM.from_pretrained(
output_dir, output_dir,
device_map="cpu", device_map="cpu",
torch_dtype=torch.bfloat16 torch_dtype=torch.bfloat16
) )
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")

8
src/transformers/quantizers/quantizer_torchao.py
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@ -261,12 +261,12 @@ class TorchAoHfQuantizer(HfQuantizer):
model.tie_weights() model.tie_weights()
setattr(model.config.get_text_config(decoder=True), "tie_word_embeddings", False) setattr(model.config.get_text_config(decoder=True), "tie_word_embeddings", False)
# handle AOPerModuleConfig, introduced in torchao 0.11.0+ # handle ModuleFqnToConfig, introduced in torchao 0.12.0+
if self.quantization_config._get_ao_version() > version.Version("0.10.0"): if self.quantization_config._get_ao_version() >= version.Version("0.12.0"):
from torchao.quantization import AOPerModuleConfig from torchao.quantization import ModuleFqnToConfig
config = self.quantization_config.get_apply_tensor_subclass() config = self.quantization_config.get_apply_tensor_subclass()
if isinstance(config, AOPerModuleConfig): if isinstance(config, ModuleFqnToConfig):
module_fqn, _ = param_name.rsplit(".", 1) module_fqn, _ = param_name.rsplit(".", 1)
c = None c = None
if module_fqn in config.module_fqn_to_config: if module_fqn in config.module_fqn_to_config:

6
tests/quantization/torchao_integration/test_torchao.py
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@ -43,10 +43,10 @@ if is_torchao_available():
TensorCoreTiledLayout, TensorCoreTiledLayout,
) )
from torchao.quantization import ( from torchao.quantization import (
AOPerModuleConfig,
Int8WeightOnlyConfig, Int8WeightOnlyConfig,
IntxWeightOnlyConfig, IntxWeightOnlyConfig,
MappingType, MappingType,
ModuleFqnToConfig,
PerAxis, PerAxis,
) )
from torchao.quantization.autoquant import AQMixin from torchao.quantization.autoquant import AQMixin
@ -226,7 +226,7 @@ class TorchAoTest(unittest.TestCase):
granularity=granularity, granularity=granularity,
mapping_type=mapping_type, mapping_type=mapping_type,
) )
config = AOPerModuleConfig( config = ModuleFqnToConfig(
{"_default": None, "model.embed_tokens": embedding_config, "lm_head": embedding_config} {"_default": None, "model.embed_tokens": embedding_config, "lm_head": embedding_config}
) )
# need set `include_input_output_embeddings` to True # need set `include_input_output_embeddings` to True
@ -253,7 +253,7 @@ class TorchAoTest(unittest.TestCase):
@require_torchao_version_greater_or_equal("0.11.0") @require_torchao_version_greater_or_equal("0.11.0")
def test_per_module_config_skip(self): def test_per_module_config_skip(self):
linear_config = Int8WeightOnlyConfig() linear_config = Int8WeightOnlyConfig()
config = AOPerModuleConfig({"_default": linear_config, "model.layers.0.self_attn.q_proj": None}) config = ModuleFqnToConfig({"_default": linear_config, "model.layers.0.self_attn.q_proj": None})
quant_config = TorchAoConfig(quant_type=config) quant_config = TorchAoConfig(quant_type=config)
quantized_model = AutoModelForCausalLM.from_pretrained( quantized_model = AutoModelForCausalLM.from_pretrained(
self.model_name, self.model_name,