replace to_affine_quantized_floatx with to_affine_quantized_float8 in…

… quantization APIs

ghstack-source-id: 75b010de13f2a6627d542965d6a9fa6f60b86bbb
ghstack-comment-id: 2608105249
Pull Request resolved: #1599

docs/source/api_ref_dtypes.rst

@@ -13,7 +13,7 @@ torchao.dtypes
     to_nf4
     to_affine_quantized_intx
     to_affine_quantized_intx_static
-    to_affine_quantized_floatx
+    to_affine_quantized_float8
     to_affine_quantized_floatx_static
     to_affine_quantized_fpx
     NF4Tensor

torchao/dtypes/__init__.py

@@ -1,16 +1,13 @@
 from . import affine_quantized_tensor_ops
 from .affine_quantized_tensor import (
     AffineQuantizedTensor,
-    to_affine_quantized_floatx,
     to_affine_quantized_floatx_static,
     # experimental, will be merged into floatx in the future
     to_affine_quantized_fpx,
     to_affine_quantized_intx,
     to_affine_quantized_intx_static,
 )
-from .floatx import (
-    Float8Layout,
-)
+from .float8 import Float8Layout, to_affine_quantized_float8
 from .nf4tensor import NF4Tensor, to_nf4
 from .uintx import (
     BlockSparseLayout,
@@ -21,13 +18,10 @@
     MarlinSparseLayout,
     SemiSparseLayout,
     TensorCoreTiledLayout,
-    UintxLayout,
     to_marlinqqq_quantized_intx,
+    UintxLayout,
 )
-from .utils import (
-    Layout,
-    PlainLayout,
-)
+from .utils import Layout, PlainLayout
 
 __all__ = [
     "NF4Tensor",
@@ -36,8 +30,8 @@
     "to_affine_quantized_intx",
     "to_affine_quantized_intx_static",
     "to_affine_quantized_fpx",
-    "to_affine_quantized_floatx",
     "to_affine_quantized_floatx_static",
+    "to_affine_quantized_float8",
     "to_marlinqqq_quantized_intx",
     "Layout",
     "PlainLayout",

torchao/dtypes/affine_quantized_tensor.py

@@ -10,13 +10,10 @@
     MappingType,
     ZeroPointDomain,
     choose_qparams_affine,
-    choose_qparams_affine_float8,
     choose_qparams_affine_floatx,
     choose_qparams_and_quantize_affine_hqq,
     dequantize_affine,
-    dequantize_affine_floatx,
     quantize_affine,
-    quantize_affine_float8,
     quantize_affine_floatx,
 )
 from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, TorchAOBaseTensor
@@ -28,7 +25,6 @@
     "AffineQuantizedTensor",
     "register_layout",
     "to_affine_quantized_intx",
-    "to_affine_quantized_floatx",
     "to_affine_quantized_intx_static",
     "to_affine_quantized_floatx_static",
     "to_affine_quantized_fpx",
@@ -121,40 +117,28 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor
         if output_dtype is None:
             output_dtype = self.dtype
 
-        from torchao.dtypes.floatx import FloatxTensorCoreLayout
-
-        if isinstance(self._layout, FloatxTensorCoreLayout):
-            int_data, scale = self.tensor_impl.get_plain()
-            return dequantize_affine_floatx(
-                int_data,
-                scale,
-                self._layout.ebits,
-                self._layout.mbits,
-                output_dtype=output_dtype,
-            )
-        else:
-            data, scale, zero_point = self.tensor_impl.get_plain()
-            dq = dequantize_affine(
-                data,
-                self.block_size,
-                scale,
-                zero_point,
-                data.dtype,
-                self.quant_min,
-                self.quant_max,
-                self.zero_point_domain,
-                output_dtype=output_dtype,
-            )
-            from torchao.dtypes.uintx import TensorCoreTiledLayout
+        data, scale, zero_point = self.tensor_impl.get_plain()
+        dq = dequantize_affine(
+            data,
+            self.block_size,
+            scale,
+            zero_point,
+            data.dtype,
+            self.quant_min,
+            self.quant_max,
+            self.zero_point_domain,
+            output_dtype=output_dtype,
+        )
+        from torchao.dtypes.uintx import TensorCoreTiledLayout
 
-            if isinstance(self._layout, TensorCoreTiledLayout):
-                # need to return to original shape if tensor was padded
-                # in preprocessing
-                # TODO: we could add an API for this if there are more use cases
-                # (e.g. dequant_post_process) in TensorImpl or Layout
-                for dim, dim_size in enumerate(self.shape):
-                    dq = dq.narrow(dim, 0, dim_size)
-            return dq
+        if isinstance(self._layout, TensorCoreTiledLayout):
+            # need to return to original shape if tensor was padded
+            # in preprocessing
+            # TODO: we could add an API for this if there are more use cases
+            # (e.g. dequant_post_process) in TensorImpl or Layout
+            for dim, dim_size in enumerate(self.shape):
+                dq = dq.narrow(dim, 0, dim_size)
+        return dq
 
     def __tensor_flatten__(self):
         return ["tensor_impl"], [
@@ -272,7 +256,7 @@ def from_hp_to_intx(
             # Note: output will be uint8 tensor for sub byte tensors for now
 
         data = _layout.post_process(data)
-        tensor_impl_ctr = get_tensor_impl_constructor(type(_layout))
+        tensor_impl_ctr = cls.get_tensor_impl_constructor(type(_layout))
         tensor_impl = tensor_impl_ctr(data, scale, zero_point, _layout)
         return cls(
             tensor_impl,
@@ -417,36 +401,6 @@ def from_hp_to_fpx(
         tensor_impl = tensor_impl_ctr(floatx_packed, scale, None, _layout)
         return cls(tensor_impl, block_size, original_shape, dtype=input_float.dtype)
 
-    @classmethod
-    def from_hp_to_float8(
-        cls,
-        input_float: torch.Tensor,
-        target_dtype: torch.dtype,
-        block_size: Tuple[int, ...],
-        _layout: Layout = PlainLayout(),
-    ):
-        assert target_dtype in FP8_TYPES, f"Unsupported dtype {target_dtype} for float8"
-        original_shape = input_float.shape
-        scale = choose_qparams_affine_float8(
-            input_float,
-            target_dtype,
-            target_dtype,
-        )
-        fp8_data = quantize_affine_float8(
-            input_float,
-            scale,
-            target_dtype,
-        )
-        fp8_data = _layout.post_process(fp8_data)
-        tensor_impl_ctr = get_tensor_impl_constructor(type(_layout))
-        tensor_impl = tensor_impl_ctr(fp8_data, scale, None, _layout)
-        return cls(
-            tensor_impl,
-            block_size,
-            original_shape,
-            dtype=input_float.dtype,
-        )
-
     @property
     def _layout(self) -> Layout:
         return self.tensor_impl._layout
@@ -500,9 +454,7 @@ def _apply_fn_to_data(self, fn):
 
 to_affine_quantized_intx = AffineQuantizedTensor.from_hp_to_intx
 to_affine_quantized_intx_static = AffineQuantizedTensor.from_hp_to_intx_static
-to_affine_quantized_floatx = AffineQuantizedTensor.from_hp_to_floatx
 to_affine_quantized_floatx_static = AffineQuantizedTensor.from_hp_to_floatx_static
-to_affine_quantized_float8 = AffineQuantizedTensor.from_hp_to_float8
 # experimental will be merged in to floatx
 to_affine_quantized_fpx = AffineQuantizedTensor.from_hp_to_fpx
 

torchao/dtypes/affine_quantized_tensor_ops.py

@@ -3,10 +3,8 @@
 import torch
 from torch.utils._python_dispatch import return_and_correct_aliasing
 
-from torchao.dtypes.affine_quantized_tensor import (
-    AffineQuantizedTensor,
-)
-from torchao.dtypes.floatx.float8_layout import (
+from torchao.dtypes.affine_quantized_tensor import AffineQuantizedTensor
+from torchao.dtypes.float8.float8_layout import (
     _linear_fp8_act_fp8_weight_check,
     _linear_fp8_act_fp8_weight_impl,
     _linear_fp_act_fp8_weight_check,
@@ -37,11 +35,11 @@
     _linear_fp_act_int4_weight_sparse_marlin_impl,
 )
 from torchao.dtypes.uintx.plain_layout import (
-    PlainAQTTensorImpl,
     _linear_fp_act_int8_weight_check,
     _linear_fp_act_int8_weight_impl,
     _linear_int8_act_int8_weight_check,
     _linear_int8_act_int8_weight_impl,
+    PlainAQTTensorImpl,
 )
 from torchao.dtypes.uintx.semi_sparse_layout import (
     _linear_int8_act_int8_weight_semi_structured_sparse_check,
@@ -52,9 +50,7 @@
     _linear_bf16_act_uint4_weight_impl,
 )
 from torchao.quantization.quant_primitives import dequantize_affine
-from torchao.utils import (
-    fill_defaults,
-)
+from torchao.utils import fill_defaults
 
 logger = logging.getLogger(__name__)
 

torchao/dtypes/float8/__init__.py

@@ -0,0 +1,20 @@
+from .float8_layout import (
+    _linear_fp8_act_fp8_weight_check,
+    _linear_fp8_act_fp8_weight_impl,
+    _linear_fp_act_fp8_weight_check,
+    _linear_fp_act_fp8_weight_impl,
+    Float8Layout,
+    Float8QuantizedTensor,
+    to_affine_quantized_float8,
+)
+
+
+__all__ = [
+    "Float8Layout",
+    "to_affine_quantized_float8",
+    "Float8QuantizedTensor",
+    "_linear_fp8_act_fp8_weight_check",
+    "_linear_fp8_act_fp8_weight_impl",
+    "_linear_fp_act_fp8_weight_check",
+    "_linear_fp_act_fp8_weight_impl",
+]

torchao/dtypes/floatx/float8_layout.py → torchao/dtypes/float8/float8_layout.py

@@ -11,13 +11,19 @@
     AffineQuantizedTensor,
     register_layout,
 )
-from torchao.dtypes.utils import AQTTensorImpl, Layout, get_out_shape
+from torchao.dtypes.utils import AQTTensorImpl, get_out_shape, Layout
 from torchao.float8.inference import (
-    Float8MMConfig,
     _is_rowwise_scaled,
     addmm_float8_unwrapped_inference,
+    Float8MMConfig,
     preprocess_data,
 )
+from torchao.quantization.quant_primitives import (
+    choose_qparams_affine_float8,
+    dequantize_affine_float8,
+    FP8_TYPES,
+    quantize_affine_float8,
+)
 from torchao.utils import _is_float8_type, fill_defaults
 
 aten = torch.ops.aten
@@ -209,19 +215,64 @@ def __repr__(self):
         )
 
 
+class Float8QuantizedTensor(AffineQuantizedTensor):
+    """
+    Float8 quantized tensor subclass which inherits Float8QuantizedTensor class.
+    """
+
+    def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor:
+        if output_dtype is None:
+            output_dtype = self.dtype
+        int_data, scale = self.tensor_impl.get_plain()
+        return dequantize_affine_float8(
+            int_data,
+            scale,
+            output_dtype=output_dtype,
+        )
+
+    @classmethod
+    def from_hp_to_float8(
+        cls,
+        input_float: torch.Tensor,
+        target_dtype: torch.dtype,
+        block_size: Tuple[int, ...],
+        _layout: Layout = Float8Layout(),
+    ):
+        assert target_dtype in FP8_TYPES, f"Unsupported dtype {target_dtype} for float8"
+        original_shape = input_float.shape
+        scale = choose_qparams_affine_float8(
+            input_float,
+            target_dtype,
+        )
+        fp8_data = quantize_affine_float8(
+            input_float,
+            scale,
+            target_dtype,
+        )
+        fp8_data = _layout.post_process(fp8_data)
+        tensor_impl_ctr = cls.get_tensor_impl_constructor(type(_layout))
+        tensor_impl = tensor_impl_ctr(fp8_data, scale, None, _layout)
+        return cls(
+            tensor_impl,
+            block_size,
+            original_shape,
+            dtype=input_float.dtype,
+        )
+
+
 ##########################
 # Float8 Dispatch Kernels
 ##########################
 
 
 def _linear_fp8_act_fp8_weight_check(
-    input_tensor: Union[torch.Tensor, "AffineQuantizedTensor"],
-    weight_tensor: Union[torch.Tensor, "AffineQuantizedTensor"],
+    input_tensor: Union[torch.Tensor, "Float8QuantizedTensor"],
+    weight_tensor: Union[torch.Tensor, "Float8QuantizedTensor"],
     bias: Optional[torch.Tensor],
 ) -> bool:
-    def check_aqt(aqt: Union[torch.Tensor, AffineQuantizedTensor]) -> bool:
+    def check_aqt(aqt: Union[torch.Tensor, Float8QuantizedTensor]) -> bool:
         return (
-            isinstance(aqt, AffineQuantizedTensor)
+            isinstance(aqt, Float8QuantizedTensor)
             and isinstance(aqt._layout, Float8Layout)
             and aqt.tensor_impl.dtype in [torch.float8_e4m3fn, torch.float8_e5m2]
             and (aqt.shape == aqt.block_size or _is_rowwise_scaled(aqt))
@@ -241,8 +292,8 @@ def preprocess_scale(input_scale: torch.Tensor, input_shape: Tuple[int]):
 
 
 def _linear_fp8_act_fp8_weight_impl(
-    input_tensor: "AffineQuantizedTensor",
-    weight_tensor: "AffineQuantizedTensor",
+    input_tensor: "Float8QuantizedTensor",
+    weight_tensor: "Float8QuantizedTensor",
     bias: Optional[torch.Tensor],
 ):
     """Implements matmul between FP8 input and FP8 weight with compute using _scaled_mm"""
@@ -285,8 +336,8 @@ def _linear_fp8_act_fp8_weight_impl(
 
 
 def _linear_fp_act_fp8_weight_check(
-    input_tensor: Union[torch.Tensor, "AffineQuantizedTensor"],
-    weight_tensor: Union[torch.Tensor, "AffineQuantizedTensor"],
+    input_tensor: Union[torch.Tensor, "Float8QuantizedTensor"],
+    weight_tensor: Union[torch.Tensor, "Float8QuantizedTensor"],
     bias: Optional[torch.Tensor],
 ) -> bool:
     return (
@@ -295,7 +346,7 @@ def _linear_fp_act_fp8_weight_check(
         and input_tensor.is_floating_point()
         and
         # weight is float8 quantized affine quantized tensor
-        isinstance(weight_tensor, AffineQuantizedTensor)
+        isinstance(weight_tensor, Float8QuantizedTensor)
         and isinstance(weight_tensor._layout, Float8Layout)
         and weight_tensor.tensor_impl.dtype in [torch.float8_e4m3fn, torch.float8_e5m2]
         and (
@@ -307,7 +358,10 @@ def _linear_fp_act_fp8_weight_check(
 
 def _linear_fp_act_fp8_weight_impl(
     input_tensor: torch.Tensor,
-    weight_tensor: "AffineQuantizedTensor",
+    weight_tensor: "Float8QuantizedTensor",
     bias: Optional[torch.Tensor],
 ):
     return torch.nn.functional.linear(input_tensor, weight_tensor.dequantize(), bias)
+
+
+to_affine_quantized_float8 = Float8QuantizedTensor.from_hp_to_float8

torchao/dtypes/floatx/__init__.py

@@ -1,4 +1,3 @@
-from .float8_layout import Float8Layout
 from .floatx_tensor_core_layout import (
     FloatxTensorCoreLayout,
     from_scaled_tc_floatx,
@@ -9,5 +8,4 @@
     "FloatxTensorCoreLayout",
     "to_scaled_tc_floatx",
     "from_scaled_tc_floatx",
-    "Float8Layout",
 ]