[wip] add axiswise granularity to Float8Tensor

Summary:

This PR adds the axiswise scaling granularity to `Float8Tensor` and
ensures that basic ops like transpose and `torch._scaled_mm` work as
expected.

A future PR will add integration with `Float8Linear`.

Test Plan:

TODO

Reviewers:

Subscribers:

Tasks:

Tags:

ghstack-source-id: 23f0f0c79d47ddeb8c929dc13583d04436b34b35
Pull Request resolved: #351

float8_experimental/config.py

@@ -21,6 +21,18 @@ def short_str(self):
             return "dyn"
 
 
+class ScalingGranularity(enum.Enum):
+    """
+    Defines the granularity of scaling strategies for casting to float8
+    """
+
+    # A single scaling factor for the entire tensor
+    TENSORWISE = "tensorwise"
+    # Scaling factors computed along one axis of the tensor, reducing it to
+    # size 1.
+    AXISWISE = "axiswise"
+
+
 @dataclass(frozen=True)
 class CastConfig:
     """

float8_experimental/float8_ops.py

@@ -19,6 +19,15 @@
 FLOAT8_OPS_TABLE: Dict[Any, Any] = {}
 
 
+def _assert_tensorwise_scale(aten_op, scale):
+    assert (
+        # TODO(future PR): figure out why tensorwise scaling can have
+        # both rank 0 and rank 1
+        len(scale.shape)
+        in (0, 1)
+    ), f"{aten_op} with axiswise scaling is not supported yet"
+
+
 def implements(aten_ops):
     """Register aten ops to the float8 op table"""
 
@@ -34,16 +43,15 @@ def decorator(func):
     [
         aten.view.default,
         aten._unsafe_view.default,
-        aten.t.default,
         aten.as_strided.default,
         aten.clone.default,
         aten.detach.default,
         aten.slice.Tensor,
-        aten.transpose.int,
         aten.fill_.Scalar,
     ]
 )
 def float8_desugar_op(aten_op, args, kwargs=None):
+    _assert_tensorwise_scale(aten_op, args[0]._scale)
     new_data = aten_op(args[0]._data, *args[1:], **kwargs)
     return Float8Tensor(
         new_data,
@@ -54,8 +62,27 @@ def float8_desugar_op(aten_op, args, kwargs=None):
     )
 
 
+@implements(
+    [
+        aten.t.default,
+        aten.transpose.int,
+    ]
+)
+def float8_desugar_data_and_scale(aten_op, args, kwargs=None):
+    new_data = aten_op(args[0]._data, *args[1:], **kwargs)
+    new_scale = aten_op(args[0]._scale, *args[1:], **kwargs)
+    return Float8Tensor(
+        new_data,
+        new_scale,
+        args[0]._orig_dtype,
+        args[0]._linear_mm_config,
+        args[0]._gemm_input_role,
+    )
+
+
 @implements([aten.split.Tensor])
 def float8_split(aten_op, args, kwargs=None):
+    _assert_tensorwise_scale(aten_op, args[0]._scale)
     new_data_tensors = aten_op(args[0]._data, *args[1:], **kwargs)
 
     def make_float8(data):
@@ -101,6 +128,7 @@ def float8_cat(aten_op, args, kwargs=None):
         assert (
             chunk._gemm_input_role is gemm_input_role
         ), "Expecting all chunks to have the same gemm_input_role as a result of a split"
+        _assert_tensorwise_scale(aten_op, chunk._scale)
         chunk_data.append(chunk._data.view(torch.uint8))
 
     new_data = aten_op(chunk_data, *args[1:], **kwargs)
@@ -117,6 +145,7 @@ def float8_cast_up_op(aten_op, args, kwargs=None):
     "addmm" -> out
     "hp_gradBias" <-"sum" <- "identity" <- gradOut <- "hp_gradOut"
     """
+    _assert_tensorwise_scale(aten_op, args[0]._scale)
 
     def unwrap(x):
         if isinstance(x, Float8Tensor):
@@ -229,6 +258,7 @@ def float8_addmm(aten_op, args, kwargs=None):
 
 @implements([aten.is_same_size.default])
 def float8_is_same_size(aten_op, args, kwargs=None):
+    _assert_tensorwise_scale(aten_op, args[0]._scale)
     return args[0].shape == args[1].shape
 
 
@@ -238,6 +268,7 @@ def autocast_to_copy(aten_op, args, kwargs=None):
     when the input is a Float8Tensor, presenting as a fp32
     tensor.
     """
+    _assert_tensorwise_scale(aten_op, args[0]._scale)
     assert isinstance(args[0], Float8Tensor)
     assert (
         len(kwargs) == 1 and "dtype" in kwargs
@@ -265,6 +296,7 @@ def allgather_fp8(aten_op, args, kwargs=None):
     """
     override funcol with FP8 handling
     """
+    _assert_tensorwise_scale(aten_op, args[0]._scale)
     fp8_input = args[0]
     assert isinstance(
         fp8_input, Float8Tensor
@@ -284,6 +316,7 @@ def allgather_fp8(aten_op, args, kwargs=None):
 
 @implements([c10d_functional.wait_tensor.default, _c10d_functional.wait_tensor.default])
 def wait_tensor_fp8(aten_op, args, kwargs=None):
+    _assert_tensorwise_scale(aten_op, args[0]._scale)
     fp8_input = args[0]
     assert isinstance(fp8_input, Float8Tensor)
 
@@ -304,6 +337,7 @@ def index_put_fp8(aten_op, args, kwargs=None):
     fp8_values = args[2]
     assert isinstance(fp8_self, Float8Tensor)
     assert isinstance(fp8_values, Float8Tensor)
+    _assert_tensorwise_scale(fp8_self, args[0]._scale)
     assert fp8_self._scale == fp8_values._scale
     assert fp8_self.dtype == fp8_values.dtype
     assert fp8_self._orig_dtype == fp8_values._orig_dtype
@@ -334,8 +368,10 @@ def copy_fp8(aten_op, args, kwargs=None):
 
     if not isinstance(self, Float8Tensor) and isinstance(src, Float8Tensor):
         src_hp = src.to_original_precision()
+        _assert_tensorwise_scale(aten_op, src._scale)
         return aten_op(self, src_hp, *args[2:], **kwargs)
     elif isinstance(self, Float8Tensor) and isinstance(src, Float8Tensor):
+        _assert_tensorwise_scale(aten_op, src._scale)
         assert (
             self._orig_dtype == src._orig_dtype
         ), "Expecting both Float8Tensors to be of the same dtype"

float8_experimental/float8_python_api.py

@@ -38,6 +38,14 @@ def addmm_float8_unwrapped(
     """
     a_inverse_scale = a_scale.reciprocal()
     b_inverse_scale = b_scale.reciprocal()
+
+    # TODO: should we change torch._scaled_mm?
+    # torch._scaled_mm expects rowwise scaled scales to be of rank 1, not rank
+    # 2.  Translate to this format.
+    # TODO: audit if we need to make this more generic for various shapes.
+    a_inverse_scale = a_inverse_scale.squeeze()
+    b_inverse_scale = b_inverse_scale.squeeze()
+
     if output_dtype == torch.float32 and bias is not None:
         # Bias is not supported by _scaled_mm when output is fp32
         output = torch._scaled_mm(

float8_experimental/float8_scaling_utils.py

@@ -12,6 +12,8 @@
 
 import torch
 
+from float8_experimental.config import ScalingGranularity
+
 from float8_experimental.float8_tensor import (
     Float8Tensor,
     GemmInputRole,
@@ -36,6 +38,8 @@ def hp_tensor_to_float8_dynamic(
     linear_mm_config: LinearMMConfig,
     reduce_amax: bool = False,
     gemm_input_role: GemmInputRole = GemmInputRole.INPUT,
+    scaling_granularity: ScalingGranularity = ScalingGranularity.TENSORWISE,
+    axiswise_dim: Optional[int] = None,
 ) -> Float8Tensor:
     """
     Given a high precision tensor `hp_tensor`,
@@ -49,10 +53,18 @@ def hp_tensor_to_float8_dynamic(
         reduce_amax: whether to reduce the max(abs(hp_tensor)) value across distributed ranks
         gemm_input_role: Defines the role of this tensor (input, weight or grad_output) in
           the 3 fwd/bwd gemms of linear
+        scaling_granularity: Defines the scaling granularity
+        axiswise_dim: if axiswise granularity is used, defines the dim to scale across
     """
     if tensor_already_casted_to_fp8(hp_tensor):
         return hp_tensor
-    scale = tensor_to_scale(hp_tensor, float8_dtype, reduce_amax)
+    scale = tensor_to_scale(
+        hp_tensor,
+        float8_dtype,
+        reduce_amax,
+        scaling_granularity,
+        axiswise_dim,
+    )
     return hp_tensor_and_scale_to_float8(
         hp_tensor,
         scale,

float8_experimental/float8_tensor.py

@@ -250,7 +250,12 @@ class Float8Tensor(torch.Tensor):
     * `_data`: the underlying e4m3 or e5m2 data
     * `_scale`: the scale used to scale the original fp32 tensor. We multiply
       by scale to go from fp32 range to fp8 range, and divide by scale to go
-      from fp8 range to fp32 range.
+      from fp8 range to fp32 range. Scale is guaranteed to have a shape compatible
+      with `_data`. For example:
+      - if scaling is tensorwise, `_scale` is a scalar tensor
+      - if scaling is axiswise and _data.shape is [3, 5], `_scale` could have
+        shape [1, 5] or [5, 1]. The dim of the non-one entry defines the scaling
+        axis.
     * `_orig_dtype`: the original dtype of the tensor used to create this
       tensor.
     * `_emulate`: if true using fp32 emulation for the matmuls, helpful
@@ -279,12 +284,6 @@ def __new__(
         linear_mm_config: Optional[LinearMMConfig],
         gemm_input_role: Optional[GemmInputRole] = GemmInputRole.INPUT,
     ):
-        assert (
-            scale.numel() == 1
-        ), "Scale should contain a single value, but got: {} elements".format(
-            scale.numel()
-        )
-
         self = torch.Tensor._make_wrapper_subclass(
             cls,
             data.size(),

float8_experimental/float8_utils.py

@@ -4,12 +4,13 @@
 # This source code is licensed under the BSD 3-Clause license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Iterable, Literal, Tuple, Union
+from typing import Iterable, Literal, Optional, Tuple, Union
 
 import float8_experimental.config as config
 
 import torch
 import torch.distributed as dist
+from float8_experimental.config import ScalingGranularity
 
 # Helpful visualizer for debugging (only supports fp32):
 # https://www.h-schmidt.net/FloatConverter/IEEE754.html
@@ -100,8 +101,23 @@ def amax_history_to_scale_stack(
 
 
 @torch.no_grad()
-def tensor_to_amax(x: torch.Tensor, reduce_amax: bool = False) -> torch.Tensor:
-    amax = torch.max(torch.abs(x))
+def tensor_to_amax(
+    x: torch.Tensor,
+    reduce_amax: bool = False,
+    scaling_granularity: ScalingGranularity = ScalingGranularity.TENSORWISE,
+    axiswise_dim: Optional[int] = None,
+) -> torch.Tensor:
+    if scaling_granularity is ScalingGranularity.TENSORWISE:
+        amax = torch.max(torch.abs(x))
+    else:
+        assert scaling_granularity is ScalingGranularity.AXISWISE, "unsupported"
+        assert axiswise_dim is not None, "unsupported"
+
+        # convert from axiswise_dim (dim to keep) to
+        # dim as the input to the `torch.amax` function (tuple of dims to reduce)
+        dim_to_reduce = tuple(d for d in range(len(x.shape)) if d != axiswise_dim)
+
+        amax = torch.amax(torch.abs(x), dim=dim_to_reduce, keepdim=True)
 
     # If the user asked for distributed reduction, do it.
     # If the user did not ask for it, assume that it will
@@ -114,9 +130,13 @@ def tensor_to_amax(x: torch.Tensor, reduce_amax: bool = False) -> torch.Tensor:
 
 @torch.no_grad()
 def tensor_to_scale(
-    x: torch.Tensor, float8_dtype: torch.dtype, reduce_amax: bool = False
+    x: torch.Tensor,
+    float8_dtype: torch.dtype,
+    reduce_amax: bool = False,
+    scaling_granularity: ScalingGranularity = ScalingGranularity.TENSORWISE,
+    axiswise_dim: Optional[int] = None,
 ) -> torch.Tensor:
-    amax = tensor_to_amax(x, reduce_amax=reduce_amax)
+    amax = tensor_to_amax(x, reduce_amax, scaling_granularity, axiswise_dim)
     return amax_to_scale(amax, float8_dtype, x.dtype)
 
 

test/test_base.py

@@ -16,14 +16,20 @@
 import torch
 import torch.nn as nn
 
-from float8_experimental.config import CastConfig, Float8LinearConfig, ScalingType
+from float8_experimental.config import (
+    CastConfig,
+    Float8LinearConfig,
+    ScalingGranularity,
+    ScalingType,
+)
 from float8_experimental.float8_linear import Float8Linear
 from float8_experimental.float8_linear_utils import (
     convert_to_float8_training,
     linear_requires_sync,
     sync_float8_amax_and_scale_history,
 )
 from float8_experimental.float8_python_api import addmm_float8_unwrapped
+from float8_experimental.float8_scaling_utils import hp_tensor_to_float8_dynamic
 from float8_experimental.float8_tensor import (
     Float8Tensor,
     GemmInputRole,
@@ -143,6 +149,48 @@ def test_weights_only_load(self):
         buffer.seek(0)
         _ = torch.load(buffer, weights_only=True)
 
+    def test_axiswise_dynamic_cast(self):
+        a = torch.randn(16, 32, dtype=torch.bfloat16)
+        linear_mm_config = LinearMMConfig()
+        a_fp8 = hp_tensor_to_float8_dynamic(
+            a,
+            e4m3_dtype,
+            linear_mm_config,
+            scaling_granularity=ScalingGranularity.AXISWISE,
+            axiswise_dim=0,
+        )
+        # print(a_fp8)
+        # print(a_fp8.to_original_precision())
+        # print(a_fp8.t())
+        b = a_fp8.t()
+        # TODO check numerical accuracy
+
+    def test_axiswise_gemm(self):
+        a = torch.randn(16, 32, dtype=torch.bfloat16, device="cuda")
+        b = torch.randn(64, 32, dtype=torch.bfloat16, device="cuda")
+
+        linear_mm_config = LinearMMConfig()
+
+        a_fp8 = hp_tensor_to_float8_dynamic(
+            a,
+            e4m3_dtype,
+            linear_mm_config,
+            gemm_input_role=GemmInputRole.INPUT,
+            scaling_granularity=ScalingGranularity.AXISWISE,
+            axiswise_dim=0,
+        )
+        b_fp8 = hp_tensor_to_float8_dynamic(
+            b,
+            e4m3_dtype,
+            linear_mm_config,
+            gemm_input_role=GemmInputRole.WEIGHT,
+            scaling_granularity=ScalingGranularity.AXISWISE,
+            axiswise_dim=0,
+        )
+        c = torch.mm(a_fp8, b_fp8.t())
+        print(c)
+        # TODO check numerical accuracy
+
 
 class TestFloat8Linear:
     def _test_linear_impl(