Renaming quantize to quantize_

Summary:
Addressing feedback for `quantize` API from #391 (comment)

this is an API that changes model inplace, so we want to change the name to reflect that. inplace model quantization is important especially for LLM since it will be hard to load the model to memory. we typically load the model to meta device and then load the quantized weight.

Test Plan:
python test/quantization/test_quant_api.py
python test/integration/test_integration.py

Reviewers:

Subscribers:

Tasks:

Tags:

test/integration/test_integration.py

@@ -23,7 +23,7 @@
  int4_weight_only,
  int8_weight_only,
  int8_dynamic_activation_int8_weight,
- quantize,
+ quantize_,
  _replace_with_custom_fn_if_matches_filter,
 )
 # APIs to be deprecated (used for torch 2.2.2 and 2.3)
@@ -98,21 +98,21 @@
 
 def _int8wo_api(mod):
  if TORCH_VERSION_AFTER_2_4:
- quantize(mod, int8_weight_only(), set_inductor_config=False)
+ quantize_(mod, int8_weight_only(), set_inductor_config=False)
  unwrap_tensor_subclass(mod)
  else:
  change_linear_weights_to_int8_woqtensors(mod)
 
 def _int8da_int8w_api(mod):
  if TORCH_VERSION_AFTER_2_4:
- quantize(mod, int8_dynamic_activation_int8_weight(), set_inductor_config=False)
+ quantize_(mod, int8_dynamic_activation_int8_weight(), set_inductor_config=False)
  unwrap_tensor_subclass(mod)
  else:
  change_linear_weights_to_int8_dqtensors(mod)
 
 def _int4wo_api(mod):
  if TORCH_VERSION_AFTER_2_4:
- quantize(mod, int4_weight_only(), set_inductor_config=False)
+ quantize_(mod, int4_weight_only(), set_inductor_config=False)
  unwrap_tensor_subclass(mod)
  else:
  change_linear_weights_to_int4_woqtensors(mod)
@@ -127,8 +127,8 @@ def _int4wo_api(mod):
 def undo_recommended_configs():
  torch._inductor.config.coordinate_descent_tuning = False
  torch._inductor.config.coordinate_descent_check_all_directions = False
- torch._inductor.config.force_fuse_int_mm_with_mul = False 
- torch._inductor.config.fx_graph_cache = False 
+ torch._inductor.config.force_fuse_int_mm_with_mul = False
+ torch._inductor.config.fx_graph_cache = False
  torch._inductor.config.triton.unique_kernel_names = False
  torch.set_float32_matmul_precision("highest")
 
@@ -844,7 +844,7 @@ def api(mod):
  kwargs_copy = kwargs.copy()
  kwargs_copy["group_size"] = groupsize
  del kwargs_copy["groupsize"]
- quantize(mod, int4_weight_only(**kwargs_copy))
+ quantize_(mod, int4_weight_only(**kwargs_copy))
  unwrap_tensor_subclass(mod)
  else:
  change_linear_weights_to_int4_woqtensors(mod, **kwargs)
@@ -865,7 +865,7 @@ def test_dynamic_quant(self):
  m = nn.Sequential(nn.Linear(K, N))
 
  y_ref = m(x)
- quantize(m, int8_dynamic_activation_int8_weight())
+ quantize_(m, int8_dynamic_activation_int8_weight())
  y_test = m(x)
 
  sqnr = compute_error(y_ref, y_test)
@@ -1259,7 +1259,7 @@ def test_autoquant_manual(self, device, dtype):
  out3 = mod(example_input)
  sqnr2 = SQNR(out, out3)
  self.assertTrue(sqnr2 >= 30)
- 
+
 
  @parameterized.expand(combine_parameters(COMMON_DEVICE_DTYPE,
  [

test/prototype/test_quant_llm.py

@@ -16,7 +16,7 @@
 )
 from torchao.prototype.quant_llm.quant_llm import _pack_tc_fpx, _pack_tc_fp6
 from torchao.prototype.custom_fp_utils import _f32_to_fpx_unpacked, _fpx_unpacked_to_f32
-from torchao.quantization.quant_api import quantize
+from torchao.quantization.quant_api import quantize_
 
 
 _DEVICES = ["cpu"] + (["cuda"] if torch.cuda.is_available() else [])
@@ -91,7 +91,7 @@ def test_quant_llm_quantize(self, ebits, mbits, bias):
 
  linear = torch.nn.Linear(IC, OC, bias=bias, device=device)
  fpx_linear = copy.deepcopy(linear)
- quantize(fpx_linear, quant_llm_fpx_weight_only(ebits, mbits))
+ quantize_(fpx_linear, quant_llm_fpx_weight_only(ebits, mbits))
 
  x = torch.randn(N, IC, device=device, dtype=torch.half)
  expected = fpx_linear(x)

test/quantization/test_quant_api.py

@@ -31,7 +31,7 @@
  Int8WeightOnlyQuantizedLinearWeight,
  Int4WeightOnlyQuantizedLinearWeight,
 )
-from torchao import quantize
+from torchao import quantize_
 from torchao.quantization.quant_api import (
  _replace_with_custom_fn_if_matches_filter,
  Quantizer,
@@ -89,7 +89,7 @@ def convert(self, model: torch.nn.Module) -> torch.nn.Module:
 
 class TorchCompileDynamicQuantizer(Quantizer):
  def quantize(self, model: torch.nn.Module) -> torch.nn.Module:
- quantize(model, int8_dynamic_activation_int8_weight())
+ quantize_(model, int8_dynamic_activation_int8_weight())
  return model
 
 class ToyLinearModel(torch.nn.Module):
@@ -152,7 +152,7 @@ class TestQuantFlow(TestCase):
  def test_dynamic_quant_gpu_singleline(self):
  m = ToyLinearModel().eval()
  example_inputs = m.example_inputs()
- m = quantize(m, int8_dynamic_activation_int8_weight())
+ quantize_(m, int8_dynamic_activation_int8_weight())
  quantized = m(*example_inputs)
  # AssertionError: Expecting input to have dtype torch.float32, but got dtype: torch.float64
  # While executing %choose_qparams_tensor_1 : [num_users=2] = call_function[target=torch.ops.quantized_decomposed.choose_qparams.tensor](args = (%arg0_3, -128, 127, 0.000244140625, torch.int8), kwargs = {})
@@ -195,7 +195,7 @@ def test_int8_wo_quant_save_load(self):
  )
  m = ToyLinearModel().eval().cpu()
  def api(model):
- model = quantize(model, int8_weight_only())
+ quantize_(model, int8_weight_only())
  unwrap_tensor_subclass(model)
 
  api(m)
@@ -501,7 +501,7 @@ def test_quantized_tensor_subclass_8da4w(self):
  m = ToyLinearModel().eval()
  m_copy = copy.deepcopy(m)
  example_inputs = m.example_inputs()
- m = quantize(m, int8_dynamic_activation_int4_weight(group_size=group_size))
+ quantize_(m, int8_dynamic_activation_int4_weight(group_size=group_size))
 
  assert isinstance(m.linear1.weight, LinearActQuantizedTensor)
  assert isinstance(m.linear2.weight, LinearActQuantizedTensor)
@@ -530,7 +530,7 @@ def test_quantized_tensor_subclass_int4(self):
  example_inputs = m.example_inputs(dtype=torch.bfloat16, device="cuda")
 
  group_size = 32
- m = quantize(m, int4_weight_only(group_size=group_size))
+ quantize_(m, int4_weight_only(group_size=group_size))
  assert isinstance(m.linear1.weight, AffineQuantizedTensor)
  assert isinstance(m.linear2.weight, AffineQuantizedTensor)
 
@@ -550,7 +550,7 @@ def test_quantized_tensor_subclass_int8_wo(self):
  m_copy = copy.deepcopy(m)
  example_inputs = tuple(map(lambda x: x.to(torch.bfloat16), m.example_inputs()))
 
- m = quantize(m, int8_weight_only())
+ quantize_(m, int8_weight_only())
 
  assert isinstance(m.linear1.weight, AffineQuantizedTensor)
  assert isinstance(m.linear2.weight, AffineQuantizedTensor)
@@ -573,7 +573,7 @@ def test_quantized_tensor_subclass_int8_dyn_quant(self):
  m_copy = copy.deepcopy(m)
  # setting batch_size to 20 to be compatible with the kernel
  example_inputs = m.example_inputs(batch_size=20, dtype=torch.bfloat16, device="cuda")
- m = quantize(m, int8_dynamic_activation_int8_weight())
+ quantize_(m, int8_dynamic_activation_int8_weight())
 
  assert isinstance(m.linear1.weight, LinearActQuantizedTensor)
  assert isinstance(m.linear2.weight, LinearActQuantizedTensor)
@@ -607,7 +607,7 @@ def test_quantized_tensor_subclass_save_load(self):
  m_copy = copy.deepcopy(m)
  example_inputs = m.example_inputs(dtype=torch.bfloat16)
 
- m = quantize(m, int8_weight_only())
+ quantize_(m, int8_weight_only())
  ref = m(*example_inputs)
  with tempfile.NamedTemporaryFile() as f:
  torch.save(m.state_dict(), f)

torchao/__init__.py

@@ -30,14 +30,14 @@
 
 from torchao.quantization import (
  autoquant,
- quantize,
+ quantize_,
 )
 from . import dtypes
 
 __all__ = [
  "dtypes",
  "autoquant",
- "quantize",
+ "quantize_",
 ]
 
 # test-pytorchbot

torchao/quantization/README.md

@@ -74,7 +74,7 @@ from torchao.quantization.quant_primitives import MappingType, ZeroPointDomain
 from torchao.dtypes import to_affine_quantized
 import copy
 from torchao.quantization.quant_api import (
- quantize,
+ quantize_,
  int4_weight_only,
 )
 
@@ -101,7 +101,7 @@ m_bf16 = torch.compile(m_bf16, mode='max-autotune')
 # apply int4 weight only quant (compatible with tinygemm int4 weight only quant mm kernel in torchao)
 group_size = 32
 # only works for torch 2.4+
-m = quantize(m, int4_weight_only(group_size=group_size))
+quantize_(m, int4_weight_only(group_size=group_size))
 
 # temporary workaround for tensor subclass + torch.compile
 from torchao.utils import unwrap_tensor_subclass
@@ -168,7 +168,7 @@ torch._inductor.config.force_fuse_int_mm_with_mul = True
 
 # for torch 2.4+
 from torchao.quantization import quantize, int8_dynamic_activation_int8_weight
-quantize(model, int8_dynamic_activation_int8_weight())
+quantize_(model, int8_dynamic_activation_int8_weight())
 
 # for torch 2.2.2 and 2.3
 from torchao.quantization.quant_api import change_linear_weights_to_int8_dqtensors
@@ -180,7 +180,7 @@ change_linear_weights_to_int8_dqtensors(model)
 ```python
 # for torch 2.4+
 from torchao.quantization import quantize, int8_weight_only
-quantize(model, int8_weight_only())
+quantize_(model, int8_weight_only())
 
 # for torch 2.2.2 and 2.3
 from torchao.quantization.quant_api import change_linear_weights_to_int8_woqtensors
@@ -195,7 +195,7 @@ This technique works best when the torch._inductor.config.use_mixed_mm option is
 ```python
 # for torch 2.4+
 from torchao.quantization import quantize, int4_weight_only
-quantize(model, int4_weight_only())
+quantize_(model, int4_weight_only())
 
 # for torch 2.2.2 and 2.3
 from torchao.quantization.quant_api import change_linear_weights_to_int4_woqtensors

torchao/quantization/__init__.py

@@ -29,7 +29,7 @@
  "quantize_affine",
  "dequantize_affine",
  "choose_qprams_affine",
- "quantize",
+ "quantize_",
  "int8_dynamic_activation_int4_weight",
  "int8_dynamic_activation_int8_weight",
  "int4_weight_only",

torchao/quantization/quant_api.py

@@ -54,7 +54,7 @@
  "Int4WeightOnlyQuantizer",
  "autoquant",
  "_get_subclass_inserter",
- "quantize",
+ "quantize_",
  "int8_dynamic_activation_int4_weight",
  "int8_dynamic_activation_int8_weight",
  "int4_weight_only",
@@ -259,8 +259,8 @@ def insert_subclass(lin):
 
  return insert_subclass
 
-def quantize(model: torch.nn.Module, apply_tensor_subclass: Callable[[torch.Tensor], torch.Tensor], filter_fn: Optional[Callable[[torch.nn.Module, str], bool]]=None, set_inductor_config: bool=True) -> torch.nn.Module:
- """Convert the weight of linear modules in the model with `apply_tensor_subclass`
+def quantize_(model: torch.nn.Module, apply_tensor_subclass: Callable[[torch.Tensor], torch.Tensor], filter_fn: Optional[Callable[[torch.nn.Module, str], bool]]=None, set_inductor_config: bool=True):
+ """Convert the weight of linear modules in the model with `apply_tensor_subclass`, model is modified inplace
 
  Args:
  model (torch.nn.Module): input model
@@ -273,7 +273,7 @@ def quantize(model: torch.nn.Module, apply_tensor_subclass: Callable[[torch.Tens
 
  import torch
  import torch.nn as nn
- from torchao import quantize
+ from torchao import quantize_
 
  # 1. quantize with some predefined `apply_tensor_subclass` method that corresponds to
  # optimized execution paths or kernels (e.g. int4 tinygemm kernel)
@@ -286,7 +286,7 @@ def quantize(model: torch.nn.Module, apply_tensor_subclass: Callable[[torch.Tens
  from torchao.quantization.quant_api import int4_weight_only
 
  m = nn.Sequential(nn.Linear(32, 1024), nn.Linear(1024, 32))
- m = quantize(m, int4_weight_only(group_size=32))
+ quantize_(m, int4_weight_only(group_size=32))
 
  # 2. write your own new apply_tensor_subclass
  # You can also add your own apply_tensor_subclass by manually calling tensor subclass constructor
@@ -305,7 +305,7 @@ def filter_fn(module: nn.Module, fqn: str) -> bool:
  return isinstance(module, nn.Linear)
 
  m = nn.Sequential(nn.Linear(32, 1024), nn.Linear(1024, 32))
- m = quantize(m, apply_weight_quant, filter_fn)
+ quantize_(m, apply_weight_quant, filter_fn)
 
  """
  if set_inductor_config:
@@ -315,7 +315,7 @@ def filter_fn(module: nn.Module, fqn: str) -> bool:
  _get_linear_subclass_inserter(apply_tensor_subclass),
  _is_linear if filter_fn is None else filter_fn,
  )
- return model
+
 
 def int8_dynamic_activation_int4_weight(group_size=32):
  """Applies int8 dynamic per token asymmetric activation quantization and int4 per group weight symmetric quantization to linear

tutorials/quantize_vit/run_vit_b_quant.py

@@ -19,9 +19,9 @@
 # for APIs for earlier torch version and other quantization techniques
 
 # for torch 2.4+
-from torchao.quantization.quant_api import quantize
+from torchao.quantization.quant_api import quantize_
 from torchao.quantization.quant_api import int8_dynamic_activation_int8_weight
-quantize(model, int8_dynamic_activation_int8_weight())
+quantize_(model, int8_dynamic_activation_int8_weight())
 ## Quantization code - end
 
 ## compilation configs