Add developer guide code to tutorials

Summary:
Moved notebook: https://colab.research.google.com/drive/1jqC53MwiW9dSiPS-a6hng_yo0ywdc3nH#scrollTo=Aj9ii4darSRA from pytorch#391 to `tutorials` folder
so that code can be executed while we develop new APIs/utils and being kept up to date

Test Plan:
python

Reviewers:
python tutorials/developer_api_guide.py

regression tests:
python test/quantization/test_quant_api.py
python test/integration/test_integraton.py

Subscribers:

Tasks:

Tags:

torchao/dtypes/affine_quantized_tensor.py

@@ -22,71 +22,17 @@
     _register_layout_cls,
     _get_layout_tensor_constructor,
     LayoutType,
+    PlainLayoutType,
     is_device,
 )
-from typing import ClassVar
 from dataclasses import dataclass
 from torchao.utils import TORCH_VERSION_AFTER_2_5
 
 aten = torch.ops.aten
 
-@dataclass(frozen=True)
-class PlainLayoutType(LayoutType):
-    pass
-
-@dataclass(frozen=True)
-class SemiSparseLayoutType(LayoutType):
-
-    def pre_process(self, input: torch.Tensor) -> torch.Tensor:
-        # prune to 2:4 if not already
-        temp = input.detach()
-        pruning_inds = temp.abs().view(-1, 4).argsort(dim=1)[:, :2]
-        temp.view(-1, 4).scatter_(1, pruning_inds, value=0)
-        return temp
-
-
-@dataclass(frozen=True)
-class TensorCoreTiledLayoutType(LayoutType):
-    inner_k_tiles: int = 8
-
-    def pre_process(self, input: torch.Tensor) -> torch.Tensor:
-        orig_out_features, orig_in_features = input.shape
-        in_features = find_multiple(orig_in_features, 1024)
-        out_features = find_multiple(orig_out_features, 8)
-        input = torch.nn.functional.pad(
-            input,
-            (0, in_features - orig_in_features, 0, out_features - orig_out_features),
-        )
-        return input
-
-    def extra_repr(self):
-        return f"inner_k_tiles={self.inner_k_tiles}"
-
-
-def _aqt_is_int8(aqt):
-    """Check if an AffineQuantizedTensor is int8 quantized Tensor"""
-    return (
-        aqt.layout_tensor.dtype == torch.int8 and
-        aqt.quant_min is None or aqt.quant_min == -128 and
-        aqt.quant_max is None or aqt.quant_max == 127
-    )
-
-def _aqt_is_int8_reduced_range(aqt):
-    return (
-        aqt.layout_tensor.dtype == torch.int8 and
-        aqt.quant_min == -127 and
-        aqt.quant_max is None or aqt.quant_max == 127
-    )
-
-def _aqt_is_uint4(aqt):
-    """Check if an AffineQuantizedTensor is uint4 quantized Tensor"""
-    # TODO: use torch.uint4
-    return (
-        aqt.layout_tensor.dtype == torch.int32 and
-        aqt.quant_min is None or aqt.quant_min == 0 and
-        aqt.quant_max is None or aqt.quant_max == 15
-    )
-
+###############################
+# Base Layout Tensor Subclass #
+###############################
 class AQTLayout(torch.Tensor):
     """
     Base class for the layout tensor for `AffineQuantizedTensor`
@@ -126,6 +72,10 @@ def _get_to_kwargs(self, *args, **kwargs):
         }
         return kwargs
 
+##############################
+# Tensor Subclass Definition #
+##############################
+
 class AffineQuantizedTensor(torch.Tensor):
     """
     Affine quantized tensor subclass. Affine quantization means we quantize the floating point tensor with an affine transformation:
@@ -337,7 +287,6 @@ def _apply_fn_to_data(self, fn):
             strides=self.stride(),
         )
 
-
     implements = classmethod(_implements)
     # Note: we only added cpu path here for 8da4w, this is for executorch, in the future
     # 1. we'll add cpu/cuda version (int4mm etc.)
@@ -353,14 +302,46 @@ def _apply_fn_to_data(self, fn):
     __torch_dispatch__ = classmethod(_dispatch__torch_dispatch__)
     __torch_function__ = classmethod(_dispatch__torch_function__)
 
-implements = AffineQuantizedTensor.implements
+
+######################################################
+# LayoutType and Layout Tensor Subclass Registration #
+######################################################
 
 def register_layout_cls(layout_type_class: type(LayoutType)):
     return _register_layout_cls(AffineQuantizedTensor, layout_type_class)
 
 def get_layout_tensor_constructor(layout_type_class: type(LayoutType)):
     return _get_layout_tensor_constructor(AffineQuantizedTensor, layout_type_class)
 
+@dataclass(frozen=True)
+class SemiSparseLayoutType(LayoutType):
+
+    def pre_process(self, input: torch.Tensor) -> torch.Tensor:
+        # prune to 2:4 if not already
+        temp = input.detach()
+        pruning_inds = temp.abs().view(-1, 4).argsort(dim=1)[:, :2]
+        temp.view(-1, 4).scatter_(1, pruning_inds, value=0)
+        return temp
+
+
+@dataclass(frozen=True)
+class TensorCoreTiledLayoutType(LayoutType):
+    inner_k_tiles: int = 8
+
+    def pre_process(self, input: torch.Tensor) -> torch.Tensor:
+        orig_out_features, orig_in_features = input.shape
+        in_features = find_multiple(orig_in_features, 1024)
+        out_features = find_multiple(orig_out_features, 8)
+        input = torch.nn.functional.pad(
+            input,
+            (0, in_features - orig_in_features, 0, out_features - orig_out_features),
+        )
+        return input
+
+    def extra_repr(self):
+        return f"inner_k_tiles={self.inner_k_tiles}"
+
+
 @register_layout_cls(PlainLayoutType)
 class PlainAQTLayout(AQTLayout):
     """
@@ -487,7 +468,7 @@ def __torch_dispatch__(cls, func, types, args, kwargs):
         )
 
     def get_plain(self):
-        # Currently we don't have cuSPARSELt expansion routines, so we matmul by 
+        # Currently we don't have cuSPARSELt expansion routines, so we matmul by
         # the identity matrix to get the original dense matrix. This is slow though.
         cols = self.int_data.numel() * 16 // (10 * self.scale.shape[0])
         int_data_expanded = torch._cslt_sparse_mm(self.int_data,
@@ -507,7 +488,7 @@ def from_plain(
         assert isinstance(layout_type, SemiSparseLayoutType)
         int_data_compressed = torch._cslt_compress(int_data)
         return cls(int_data_compressed, scale, zero_point, layout_type)
-    
+
 
 @register_layout_cls(TensorCoreTiledLayoutType)
 class TensorCoreTiledAQTLayout(AQTLayout):
@@ -654,6 +635,34 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     def get_layout_type(self) -> LayoutType:
         return self.layout_type
 
+#####################################################
+# torch functional and aten operator implementation #
+#####################################################
+
+def _aqt_is_int8(aqt):
+    """Check if an AffineQuantizedTensor is int8 quantized Tensor"""
+    return (
+        aqt.layout_tensor.dtype == torch.int8 and
+        aqt.quant_min is None or aqt.quant_min == -128 and
+        aqt.quant_max is None or aqt.quant_max == 127
+    )
+
+def _aqt_is_int8_reduced_range(aqt):
+    return (
+        aqt.layout_tensor.dtype == torch.int8 and
+        aqt.quant_min == -127 and
+        aqt.quant_max is None or aqt.quant_max == 127
+    )
+
+def _aqt_is_uint4(aqt):
+    """Check if an AffineQuantizedTensor is uint4 quantized Tensor"""
+    # TODO: use torch.uint4
+    return (
+        aqt.layout_tensor.dtype == torch.int32 and
+        aqt.quant_min is None or aqt.quant_min == 0 and
+        aqt.quant_max is None or aqt.quant_max == 15
+    )
+
 def _quantized_linear_op(input_tensor, weight_qtensor, bias):
     """
     Quantized version of F.linear operator
@@ -811,8 +820,10 @@ def _quantized_linear_op(input_tensor, weight_qtensor, bias):
     raise NotImplementedError("No specialized dispatch found for quantized linear op")
 
 
+implements = AffineQuantizedTensor.implements
+
 @implements(torch.nn.functional.linear)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     input_tensor, weight_tensor, bias = (
         args[0],
         args[1],
@@ -831,7 +842,7 @@ def _(func, types, *args, **kwargs):
         return torch.nn.functional.linear(input_tensor, weight_tensor, bias)
 
 @implements([aten.mm.default, aten.addmm.default])
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     if not args[0].is_floating_point():
         raise NotImplementedError(f"{func} is not implemented for non floating point input")
 
@@ -870,21 +881,21 @@ def _(func, types, *args, **kwargs):
             return func(input_tensor, weight_tensor)
 
 @implements([aten.detach.default])
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     return return_and_correct_aliasing(
         func, args, kwargs, args[0]._apply_fn_to_data(torch.detach)
     )
 
 
 @implements([aten.clone.default])
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     return return_and_correct_aliasing(
         func, args, kwargs, args[0]._apply_fn_to_data(torch.clone)
     )
 
 
 @implements([aten._to_copy.default])
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     return return_and_correct_aliasing(
         func,
         args,
@@ -893,7 +904,7 @@ def _(func, types, *args, **kwargs):
     )
 
 @implements([aten.t.default])
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     block_size = args[0].block_size
     assert len(block_size) == 2
     transposed_block_size = (block_size[1], block_size[0])

torchao/dtypes/utils.py

@@ -32,8 +32,8 @@ def _(func, types, args, kwargs):
     def decorator(func):
         for op in aten_ops_or_torch_fns:
             @functools.wraps(op)
-            def wrapper(*args, **kwargs):
-                return func(*args, **kwargs)
+            def wrapper(f, types, args, kwargs):
+                return func(f, types, args, kwargs)
 
             cls._ATEN_OP_OR_TORCH_FN_TABLE[op] = wrapper
         return func
@@ -50,7 +50,7 @@ class MyTensor(torch.Tensor):
     kwargs = {} if kwargs is None else kwargs
     if hasattr(cls, "_ATEN_OP_OR_TORCH_FN_TABLE") and \
        func in cls._ATEN_OP_OR_TORCH_FN_TABLE:
-        return cls._ATEN_OP_OR_TORCH_FN_TABLE[func](func, types, *args, **kwargs)
+        return cls._ATEN_OP_OR_TORCH_FN_TABLE[func](func, types, args, kwargs)
 
     with torch._C.DisableTorchFunctionSubclass():
         return func(*args, **kwargs)
@@ -65,7 +65,7 @@ class MyTensor(torch.Tensor):
     """
     if hasattr(cls, "_ATEN_OP_OR_TORCH_FN_TABLE") and \
        func in cls._ATEN_OP_OR_TORCH_FN_TABLE:
-        return cls._ATEN_OP_OR_TORCH_FN_TABLE[func](func, types, *args, **kwargs)
+        return cls._ATEN_OP_OR_TORCH_FN_TABLE[func](func, types, args, kwargs)
 
     raise NotImplementedError(f"{cls.__name__} dispatch: attempting to run unimplemented operator/function: {func}")
 
@@ -87,6 +87,14 @@ def __repr__(self):
     def extra_repr(self) -> str:
         return ""
 
+"""
+Plain LayoutType, the most basic LayoutType, also has no extra metadata, will typically be the default
+"""
+@dataclass(frozen=True)
+class PlainLayoutType(LayoutType):
+    pass
+
+
 """
 layout tensor constructor registration for different tensor subclassesa
 

torchao/prototype/low_bit_optim/subclass_4bit.py

@@ -89,7 +89,7 @@ def __repr__(self):
 
 
 @OptimState4bit.implements(aten.copy_.default)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     dst = args[0]
     src = args[1]
 
@@ -116,14 +116,14 @@ def _(func, types, *args, **kwargs):
 
 
 @OptimState4bit.implements(aten.lerp.Scalar)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     args = [x.dequantize() if isinstance(x, OptimState4bit) else x for x in args]
     return func(*args, **kwargs)
 
 
 # this is needed for DTensor.from_local() and for flattening tensor
 @OptimState4bit.implements(aten.view.default)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     x, shape = args
 
     if tuple(x.shape) == tuple(shape):
@@ -142,7 +142,7 @@ def _(func, types, *args, **kwargs):
     c10d_functional.wait_tensor.default,
     _c10d_functional.wait_tensor.default,
 ])
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     x = args[0]
     if not isinstance(x, OptimState4bit):
         raise ValueError(f"expecting a OptimState4bit but found {type(x)}")

torchao/prototype/low_bit_optim/subclass_8bit.py

@@ -75,7 +75,7 @@ def __repr__(self):
 
 
 @OptimState8bit.implements(aten.copy_.default)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     dst = args[0]
     src = args[1]
 
@@ -98,14 +98,14 @@ def _(func, types, *args, **kwargs):
 
 
 @OptimState8bit.implements(aten.lerp.Scalar)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     args = [x.dequantize() if isinstance(x, OptimState8bit) else x for x in args]
     return func(*args, **kwargs)
 
 
 # this is needed for DTensor.from_local()
 @OptimState8bit.implements(aten.view.default)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     x, shape = args
     return OptimState8bit(x.codes.view(shape), x.scale, x.qmap, x.signed)
 
@@ -117,7 +117,7 @@ def _(func, types, *args, **kwargs):
     c10d_functional.wait_tensor.default,
     _c10d_functional.wait_tensor.default,
 ])
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     x = args[0]
     if not isinstance(x, OptimState8bit):
         raise ValueError(f"expecting a OptimState8bit but found {type(x)}")

torchao/prototype/low_bit_optim/subclass_fp8.py

@@ -81,7 +81,7 @@ def __repr__(self):
 
 
 @OptimStateFp8.implements(aten.copy_.default)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     dst = args[0]
     src = args[1]
 
@@ -102,14 +102,14 @@ def _(func, types, *args, **kwargs):
 
 
 @OptimStateFp8.implements(aten.lerp.Scalar)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     args = [x.dequantize() if isinstance(x, OptimStateFp8) else x for x in args]
     return func(*args, **kwargs)
 
 
 # this is needed for DTensor.from_local()
 @OptimStateFp8.implements(aten.view.default)
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     x, shape = args
     return OptimStateFp8(x.codes.view(shape), x.scale)
 
@@ -121,7 +121,7 @@ def _(func, types, *args, **kwargs):
     c10d_functional.wait_tensor.default,
     _c10d_functional.wait_tensor.default,
 ])
-def _(func, types, *args, **kwargs):
+def _(func, types, args, kwargs):
     x = args[0]
     if not isinstance(x, OptimStateFp8):
         raise ValueError(f"expecting a OptimStateFp8 but found {type(x)}")