Replace implementation for int8 dynamic quantization with call to `qu…

…antize`

Summary:
Previously we added `quantize` as a general API (#256) for
Affine Quantized tensor subclass, and also tensor subclass based dtype conversion in general.

The plan is to use this to replace existing quant APIs including int4 weight only, int8 weight only, int8 dynamic quant
and 8da4w (for executorch).

This PR we started replacing the implementation of int8 dynamic quant API with `quantize` API with affine quantized tensor
subclass. We'll make sure the performance does not regress for vit model.

Test Plan:
TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py

reference: elapsed_time:  1.4821058654785155  milliseconds
after refactor: elapsed_time:  1.4804757690429688  milliseconds

generated code diff: https://gist.github.com/jerryzh168/90c71107a5aaaa5d8dd2170c573e076d

Reviewers:

Subscribers:

Tasks:

Tags:

test/quantization/test_quant_api.py

@@ -118,6 +118,26 @@ def forward(self, x):
  x = self.linear2(x)
  return x
 
+
+def _ref_change_linear_weights_to_int8_dqtensors(model, filter_fn=None, **kwargs):
+ """
+ The deprecated implementation for int8 dynamic quant API, used as a reference for
+ numerics and performance
+ """
+ from torchao.quantization.quant_api import _in_features_greater_than_16
+ from torchao.quantization.quant_api import _is_linear
+ from torchao.quantization.quant_api import _get_subclass_inserter
+ from torchao.quantization.subclass import Int8DynamicallyQuantizedLinearWeight
+
+ if filter_fn is None:
+ filter_fn = lambda *args: _is_linear(*args) and _in_features_greater_than_16(
+ *args
+ )
+
+ _replace_with_custom_fn_if_matches_filter(
+ model, _get_subclass_inserter(Int8DynamicallyQuantizedLinearWeight, enable_parametrization=False, **kwargs), filter_fn
+ )
+
 class TestQuantFlow(unittest.TestCase):
  def test_dynamic_quant_gpu_singleline(self):
  m = ToyLinearModel().eval()
@@ -493,7 +513,7 @@ def test_quantized_tensor_subclass_int8(self):
  @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
  def test_quantized_tensor_subclass_int8_dyn_quant(self):
  # use 1024 so that we don't need padding
- m = ToyLinearModel(1024, 1024, 1024).eval().to(torch.bfloat16).to("cuda")
+ m = ToyLinearModel(1024, 1024, 2048).eval().to(torch.bfloat16).to("cuda")
  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")
@@ -525,6 +545,48 @@ def test_quantized_tensor_subclass_int8_dyn_quant(self):
  # make sure it compiles
  torch._export.aot_compile(m_unwrapped, example_inputs)
 
+ @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Test only enabled for 2.4+")
+ @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+ def test_quantized_tensor_subclass_int8_dyn_quant_perf(self):
+ # use 1024 so that we don't need padding
+ m = ToyLinearModel(1024, 1024, 1024).eval().to(torch.bfloat16).to("cuda")
+ m_ref = 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")
+
+ from torchao.quantization.quant_api import change_linear_weights_to_int8_dqtensors
+ change_linear_weights_to_int8_dqtensors(m)
+
+ # reference
+ _ref_change_linear_weights_to_int8_dqtensors(m_ref)
+
+ res = m(*example_inputs)
+ ref = m_ref(*example_inputs)
+
+ self.assertTrue(torch.equal(res, ref))
+
+ # perf comparison
+ from torchao.utils import benchmark_model
+ # warmup
+ WARMUP = 50
+ RUNS = 1000
+ input_tensor = example_inputs[0]
+ m = torch.compile(m, mode='max-autotune', fullgraph=True)
+
+ benchmark_model(m, WARMUP, input_tensor)
+ elapsed_time = benchmark_model(m, RUNS, input_tensor)
+
+ m_ref = torch.compile(m_ref, mode='max-autotune', fullgraph=True)
+ benchmark_model(m_ref, WARMUP, input_tensor)
+ ref_elapsed_time = benchmark_model(m_ref, RUNS, input_tensor)
+
+ # recent measurement result:
+ # elapsed time: 0.256736083984375, ref elapsed time: 0.2745975036621094
+ # elapsed time: 0.22768556213378907, ref elapsed time: 0.283327880859375
+ # elapsed time: 0.25300579833984377, ref elapsed time: 0.2877723083496094
+ print(f"elapsed time: {elapsed_time}, ref elapsed time: {ref_elapsed_time}")
+ self.assertTrue(elapsed_time < 1.05 * ref_elapsed_time)
+
 
 
 if __name__ == "__main__":

torchao/dtypes/aqt.py

@@ -177,6 +177,11 @@ def _apply_fn_to_data(self, fn):
  fn(self.zero_point),
  )
 
+ def _change_shape(self, shape):
+ return self.__class__(
+ self.int_data.view(shape), self.scale, self.zero_point
+ )
+
  @classmethod
  def __torch_dispatch__(cls, func, types, args, kwargs):
  kwargs = {} if kwargs is None else kwargs
@@ -245,6 +250,7 @@ def __tensor_unflatten__(
  cls, tensor_data_dict, tensor_attributes, outer_size, outer_stride
  ):
  packed_weight, scale_and_zero = tensor_data_dict["packed_weight"], tensor_data_dict["scale_and_zero"]
+ # TODO: fix the unflatten logic
  return cls(packed_weight, scale_and_zero)
 
  def to(self, *args, **kwargs):
@@ -470,6 +476,11 @@ def _apply_fn_to_data(self, fn):
  strides=self.stride(),
  )
 
+ def _change_shape(self, shape, block_size):
+ return self.__class__(
+ self.layout_tensor.view(shape), block_size, shape, self.quant_min, self.quant_max, self.zero_point_domain, dtype=self.dtype, strides=self.stride()
+ )
+
  @classmethod
  def __torch_dispatch__(cls, func, types, args, kwargs):
  # Note: we only added cpu path here for 8da4w, this is for executorch, in the future
@@ -491,13 +502,7 @@ def __torch_dispatch__(cls, func, types, args, kwargs):
  f"AffineQuantizedTensor dispatch: attempting to run {func}, this is not supported"
  )
 
-@implements_aqt_torch_function(torch.nn.functional.linear)
-def functional_linear(*args, **kwargs):
- input_tensor, weight_qtensor, bias = (
- args[0],
- args[1],
- args[2] if len(args) > 2 else None,
- )
+def _quantized_linear_op(input_tensor, weight_qtensor, bias):
  is_cuda = weight_qtensor.is_cuda
  is_cpu = weight_qtensor.device == torch.device("cpu")
  if isinstance(weight_qtensor, AffineQuantizedTensor):
@@ -508,14 +513,10 @@ def functional_linear(*args, **kwargs):
  # if input tensor is quantized, either dispatch to the int8 mm kernel
  # or just dequantize the input tensor
  input_is_int8 = _aqt_is_int8_reduced_range(input_tensor)
- input_tensor_dtype_is_expected = input_tensor.dtype in [
- torch.float,
- torch.bfloat16
- ]
  if (
  is_cuda and
  input_is_int8 and
- input_tensor_dtype_is_expected and
+ input_tensor.dtype == weight_qtensor.dtype and
  input_tensor.layout == "plain" and
  weight_qtensor.layout == "plain"
  ):
@@ -576,45 +577,77 @@ def functional_linear(*args, **kwargs):
  weight_qtensor.block_size[1] == weight_qtensor.shape[1] and
  weight_qtensor.layout == "plain"
  ):
- # TODO: enable mps path as well
+ # TODO: enable cpu and mps efficient path
  # per channel int8 weight only quantizated mm
- return torch.ops.aten._weight_int8pack_mm(input_tensor.contiguous(), weight_qtensor.layout_tensor.int_data, weight_qtensor.layout_tensor.scale)
- else:
- weight_tensor = weight_qtensor.dequantize()
- return torch.nn.functional.linear(input_tensor, weight_tensor, bias)
- else:
+ w_vals_int8_t = weight_qtensor.layout_tensor.int_data.t().contiguous()
+ orig_dtype = input_tensor.dtype
+ y = (
+ torch.mm(
+ input_tensor.reshape(-1, input_tensor.shape[-1]),
+ w_vals_int8_t.to(input_tensor.dtype),
+ )
+ * weight_qtensor.scale
+ )
+ y = y.reshape(*input_tensor.shape[:-1], y.shape[-1])
+ if bias is not None:
+ y += bias
+ return y.to(orig_dtype)
+
+ # is_cpu and is_mps only, some issue with is_contiguous() currently
+ # return torch.ops.aten._weight_int8pack_mm(input_tensor.contiguous(), w_vals_int8_t, weight_qtensor.layout_tensor.scale)
+
+ raise NotImplementedError("No specialized dispatch found for quantized linear op")
+
+
+@implements_aqt_torch_function(torch.nn.functional.linear)
+def functional_linear(*args, **kwargs):
+ input_tensor, weight_tensor, bias = (
+ args[0],
+ args[1],
+ args[2] if len(args) > 2 else None,
+ )
+ try:
+ return _quantized_linear_op(input_tensor, weight_tensor, bias)
+ except:
  if isinstance(input_tensor, AffineQuantizedTensor):
  input_tensor = input_tensor.dequantize()
+ if isinstance(weight_tensor, AffineQuantizedTensor):
+ weight_tensor = weight_tensor.dequantize()
  return torch.nn.functional.linear(input_tensor, weight_tensor, bias)
 
-
 @implements_aqt_aten_ops([aten.mm.default, aten.addmm.default])
 def aten_mm(func, *args, **kwargs):
  if not args[0].is_floating_point():
  raise NotImplementedError(f"{func} is not implemented for non floating point input")
 
  if func == aten.addmm.default:
- assert args[1].shape[-1] == args[2].shape[0], (
- f"need mat1 shape: {args[1].shape} final"
- f"dim to match mat2 shape: {args[2].shape} first dim "
- )
- input_tensor, weight_qtensor, bias = (
+ input_tensor, weight_tensor, bias = (
  args[1],
  args[2],
  args[0],
  )
+ try:
+ return _quantized_linear_op(input_tensor, weight_tensor, bias)
+ except:
+ if isinstance(input_tensor, AffineQuantizedTensor):
+ input_tensor = input_tensor.dequantize()
+ if isinstance(weight_tensor, AffineQuantizedTensor):
+ weight_tensor = weight_tensor.dequantize()
+ return func(bias, input_tensor, weight_tensor)
  else:
- assert args[0].shape[-1] == args[1].shape[0], (
- f"need mat1 shape: {args[0].shape} final dim"
- f"to match mat2 shape: {args[1].shape} first dim"
- )
- input_tensor, weight_qtensor, bias = (
+ input_tensor, weight_tensor, bias = (
  args[0],
  args[1],
- None if len(args) == 2 else args[2],
+ None
  )
- weight_tensor = weight_qtensor.dequantize()
- return func(input_tensor, weight_tensor, bias)
+ try:
+ return _quantized_linear_op(input_tensor, weight_tensor, bias)
+ except:
+ if isinstance(input_tensor, AffineQuantizedTensor):
+ input_tensor = input_tensor.dequantize()
+ if isinstance(weight_tensor, AffineQuantizedTensor):
+ weight_tensor = weight_tensor.dequantize()
+ return func(bias, input_tensor, weight_tensor)
 
 @implements_aqt_aten_ops([aten.detach.default])
 def detach(func, *args, **kwargs):
@@ -641,10 +674,10 @@ def _to_copy(func, *args, **kwargs):
 
 @implements_aqt_aten_ops([aten.t.default])
 def t(func, *args, **kwargs):
- # TODO: need to implement this
- # args[0].transposed = not args[0].transposed
- # new = args[0]._change_shape(args[0].shape[::-1])
- # return return_and_correct_aliasing(func, args, kwargs, new)
- raise Exception("transpose not implemented yet")
+ block_size = args[0].block_size
+ assert len(block_size) == 2
+ transposed_block_size = (block_size[1], block_size[0])
+ new = args[0]._change_shape(args[0].shape[::-1], transposed_block_size)
+ return return_and_correct_aliasing(func, args, kwargs, new)
 
 to_aq = AffineQuantizedTensor.from_float

torchao/quantization/quant_api.py

@@ -25,14 +25,19 @@
 from typing import Any, Callable
 
 from .dynamic_quant import DynamicallyPerAxisQuantizedLinear
-from .utils import TORCH_VERSION_AFTER_2_3, TORCH_VERSION_AFTER_2_4
+from .utils import (
+ TORCH_VERSION_AFTER_2_3,
+ TORCH_VERSION_AFTER_2_4,
+ unwrap_tensor_subclass,
+)
 
 from .subclass import (
  Int4WeightOnlyQuantizedLinearWeight,
  Int8DynamicallyQuantizedLinearWeight,
  Int8WeightOnlyQuantizedLinearWeight,
  QuantizedLinearWeightBase,
  to_laq,
+ LinearActQuantizedTensor,
 )
 
 from .quant_primitives import (
@@ -113,11 +118,15 @@ def _replace_with_custom_fn_if_matches_filter(
 
 
 def _is_linear(mod, *args):
+ from torchao.dtypes import AffineQuantizedTensor
+
  return (
  isinstance(mod, torch.nn.Linear)
  and hasattr(mod, "weight")
  and not isinstance(mod.weight, QuantizedLinearWeightBase)
  and not isinstance(mod.weight, AutoQuantizableLinearWeight)
+ and not isinstance(mod.weight, AffineQuantizedTensor)
+ and not isinstance(mod.weight, LinearActQuantizedTensor)
  )
 
 
@@ -187,9 +196,13 @@ def change_linear_weights_to_int8_dqtensors(model, filter_fn=None, **kwargs):
  *args
  )
 
- _replace_with_custom_fn_if_matches_filter(
- model, _get_subclass_inserter(Int8DynamicallyQuantizedLinearWeight, enable_parametrization=TORCH_VERSION_AFTER_2_4, **kwargs), filter_fn
- )
+ if TORCH_VERSION_AFTER_2_4:
+ quantize(model, get_apply_int8dyn_quant(), filter_fn)
+ unwrap_tensor_subclass(model, filter_fn)
+ else:
+ _replace_with_custom_fn_if_matches_filter(
+ model, _get_subclass_inserter(Int8DynamicallyQuantizedLinearWeight, enable_parametrization=False, **kwargs), filter_fn
+ )
 
 
 def change_linear_weights_to_int8_woqtensors(model, filter_fn=None, **kwargs):
@@ -282,7 +295,7 @@ def quantize(model: torch.nn.Module, apply_tensor_subclass: Callable[[torch.Tens
  zero_point_dtype = torch.bfloat16
  zero_point_domain = ZeroPointDomain.FLOAT
 
- apply_weight_quant = lambda x: to_aqt(x, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, zero_point_dtype=zero_point_dtype, preserve_zero=preserve_zero, zero_point_domain=zero_point_domain)
+ apply_weight_quant = lambda x: to_aq(x, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, zero_point_dtype=zero_point_dtype, preserve_zero=preserve_zero, zero_point_domain=zero_point_domain)
 
  # apply to modules under block0 submodule
  def filter_fn(module, fqn):