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[torchao] Add regex support for ModuleFqnToConfig #41242

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[torchao] Add regex support for ModuleFqnToConfig #41242

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13 changes: 12 additions & 1 deletion src/transformers/quantizers/quantizer_torchao.py
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Original file line number Diff line number Diff line change
Expand Up @@ -297,6 +297,8 @@ def create_quantized_param(

# handle ModuleFqnToConfig, introduced in torchao 0.12.0+
if self.quantization_config._get_ao_version() >= version.Version("0.12.0"):
import re

from torchao.quantization import ModuleFqnToConfig

config = self.quantization_config.get_apply_tensor_subclass()
Expand All @@ -306,7 +308,16 @@ def create_quantized_param(
if module_fqn in config.module_fqn_to_config:
c = config.module_fqn_to_config[module_fqn]
else:
c = config.module_fqn_to_config.get("_default", None)
for maybe_module_fqn_pattern in config.module_fqn_to_config:
if not maybe_module_fqn_pattern.startswith("re:"):
continue
elif re.fullmatch(maybe_module_fqn_pattern[3:], module_fqn):
# we'll apply the config for first fully matched pattern
c = config.module_fqn_to_config[maybe_module_fqn_pattern]
break
else:
c = config.module_fqn_to_config.get("_default", None)

if c is not None:
# filter_fn: not filtering out any modules
quantize_(module, c, filter_fn=lambda x, fqn: True)
Expand Down
85 changes: 85 additions & 0 deletions tests/quantization/torchao_integration/test_torchao.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,6 +46,8 @@
TensorCoreTiledLayout,
)
from torchao.quantization import (
Float8Tensor,
Float8WeightOnlyConfig,
Int8WeightOnlyConfig,
IntxWeightOnlyConfig,
MappingType,
Expand Down Expand Up @@ -278,6 +280,89 @@ def test_per_module_config_skip(self):
self.assertTrue(tokenizer.decode(output[0], skip_special_tokens=True) in EXPECTED_OUTPUT)


@require_torchao_version_greater_or_equal("0.13.0")
def test_module_fqn_to_config_regex_basic(self):
linear_config = Int8WeightOnlyConfig()
config = ModuleFqnToConfig({"_default": linear_config, r"re:model\.layers\..+\.self_attn\.q_proj": None})
quant_config = TorchAoConfig(quant_type=config)
quantized_model = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=self.device,
quantization_config=quant_config,
)
# making sure `model.layers.0.self_attn.q_proj` is skipped
self.assertTrue(not isinstance(quantized_model.model.layers[0].self_attn.q_proj.weight, AffineQuantizedTensor))
tokenizer = AutoTokenizer.from_pretrained(self.model_name)

input_ids = tokenizer(self.input_text, return_tensors="pt").to(self.device)

output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
EXPECTED_OUTPUT = [
"What are we having for dinner?\n\nJessica: (smiling)",
"What are we having for dinner?\n\nJess: (smiling) I",
]
self.assertTrue(tokenizer.decode(output[0], skip_special_tokens=True) in EXPECTED_OUTPUT)

@require_torchao_version_greater_or_equal("0.13.0")
def test_module_fqn_to_config_regex_fullmatch(self):
"""Testing that we will only match the fqns that fully
matches the regex
"""
linear1_config = Int8WeightOnlyConfig()
linear2_config = Float8WeightOnlyConfig()
# intentially removing `j` after `q_proj` so it's not a full match
config = ModuleFqnToConfig({r"re:model\.layers\.+\.self_attn\.q_pro": linear1_config, "model.layers.3.self_attn.q_proj": linear2_config})
quant_config = TorchAoConfig(quant_type=config)
quantized_model = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=self.device,
quantization_config=quant_config,
)
# highest precedence is fully specified module fqn
self.assertTrue(isinstance(quantized_model.model.layers[3].self_attn.q_proj.weight, Float8Tensor))
# because regex `model\.layers\.+*\.self_attn\.q_pro` didin't fully match `model.layers.1.self_attn.q_proj` (missing last `j`)
# this layer is not expected to be quantized to int8
self.assertTrue(not isinstance(quantized_model.model.layers[1].self_attn.q_proj.weight, AffineQuantizedTensor))
tokenizer = AutoTokenizer.from_pretrained(self.model_name)

input_ids = tokenizer(self.input_text, return_tensors="pt").to(self.device)

output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
EXPECTED_OUTPUT = [
"What are we having for dinner?\n\nJessica: (smiling)",
"What are we having for dinner?\n\nJess: (smiling) I",
]
self.assertTrue(tokenizer.decode(output[0], skip_special_tokens=True) in EXPECTED_OUTPUT)

@require_torchao_version_greater_or_equal("0.13.0")
def test_module_fqn_to_config_regex_precedence(self):
linear1_config = Int8WeightOnlyConfig()
linear2_config = Float8WeightOnlyConfig()
config = ModuleFqnToConfig({r"re:model\.layers\..+\.self_attn\.q_proj": None, "model.layers.3.self_attn.q_proj": linear2_config, "_default": linear1_config})
quant_config = TorchAoConfig(quant_type=config)
quantized_model = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=self.device,
quantization_config=quant_config,
)
# highest precedence is fully specified module fqn
self.assertTrue(isinstance(quantized_model.model.layers[3].self_attn.q_proj.weight, Float8Tensor))
# second precedence: regex
self.assertTrue(not isinstance(quantized_model.model.layers[1].self_attn.q_proj.weight, AffineQuantizedTensor))
# last precedence: _default
self.assertTrue(isinstance(quantized_model.model.layers[1].self_attn.k_proj.weight, AffineQuantizedTensor))
tokenizer = AutoTokenizer.from_pretrained(self.model_name)

input_ids = tokenizer(self.input_text, return_tensors="pt").to(self.device)

output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
EXPECTED_OUTPUT = [
"What are we having for dinner?\n\nJessica: (smiling)",
"What are we having for dinner?\n\nJess: (smiling) I",
]
self.assertTrue(tokenizer.decode(output[0], skip_special_tokens=True) in EXPECTED_OUTPUT)


@require_torch_accelerator
class TorchAoAcceleratorTest(TorchAoTest):
device = torch_device
Expand Down