add axiswise granularity to Float8Tensor

test/float8/test_base.py

@@ -22,14 +22,20 @@
     pytest.skip("Unsupported PyTorch version", allow_module_level=True)
 
 
-from torchao.float8.config import CastConfig, Float8LinearConfig, ScalingType
+from torchao.float8.config import (
+    CastConfig, 
+    Float8LinearConfig, 
+    ScalingGranularity,
+    ScalingType,
+)
 from torchao.float8.float8_linear import Float8Linear
 from torchao.float8.float8_linear_utils import (
     convert_to_float8_training,
     linear_requires_sync,
     sync_float8_amax_and_scale_history,
 )
 from torchao.float8.float8_python_api import addmm_float8_unwrapped
+from torchao.float8.float8_scaling_utils import hp_tensor_to_float8_dynamic
 from torchao.float8.float8_tensor import (
     Float8Tensor,
     GemmInputRole,
@@ -51,14 +57,15 @@
 
 
 is_cuda_8_9 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 9)
+is_cuda_9_0 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (9, 0)
 
 def bitwise_identical(a: Float8Tensor, b: Float8Tensor) -> bool:
     assert torch.all(a._scale == b._scale).item(), "scales are not identical"
     assert torch.all(a._data == b._data).item(), "data is not identical"
     return True
 
 
-class TestFloat8Tensor(unittest.TestCase):
+class TestFloat8Tensor:
     def test_preserves_dtype(self) -> None:
         # hp means high precision, lp means low precision
         hp_dtypes = (torch.float32, torch.float16, torch.bfloat16)
@@ -68,7 +75,7 @@ def test_preserves_dtype(self) -> None:
             x1_s = tensor_to_scale(x1_hp, lp_dtype)
             x2_lp = hp_tensor_and_scale_to_float8(x1_hp, x1_s, lp_dtype)
             x3_hp = x2_lp.to_original_precision()
-            self.assertTrue(x3_hp.dtype == hp_dtype)
+            assert x3_hp.dtype == hp_dtype
 
     def test_differentiable_casts(self) -> None:
         lp_dtypes = (e4m3_dtype, e5m2_dtype)
@@ -103,7 +110,7 @@ def test_index_put(self):
         fp8_b = hp_tensor_and_scale_to_float8(b, scale_a, torch.float8_e4m3fn)
         fp8_b_bad = hp_tensor_and_scale_to_float8(b, scale_b, torch.float8_e4m3fn)
 
-        with self.assertRaises(AssertionError):
+        with pytest.raises(AssertionError):
             b[index] = fp8_a
             fp8_b[index] = a
             fp8_b_bad[index] = fp8_a
@@ -117,7 +124,7 @@ def test_copy_(self):
         b = torch.empty(16, dtype=torch.bfloat16)
         b.copy_(fp8_a)  # Should work
         torch.testing.assert_close(b, fp8_a.to_original_precision())
-        with self.assertRaises(RuntimeError):
+        with pytest.raises(RuntimeError):
             fp8_a.copy_(b)  # Should fail
 
         fp8_b = Float8Tensor(
@@ -129,6 +136,111 @@ def test_copy_(self):
         fp8_b.copy_(fp8_a)
         torch.testing.assert_close(fp8_a._data, fp8_b._data)
 
+    @pytest.mark.parametrize("shape", [(8, 16), (4, 8, 16), (2, 4, 8, 16)])
+    @pytest.mark.parametrize("dim_name", ["first", "last"])
+    def test_axiswise_dynamic_cast(self, shape, dim_name):
+        a = torch.randn(*shape, dtype=torch.bfloat16)
+
+        if dim_name == "first":
+            dim = 0
+        elif dim_name == "last":
+            dim = len(a.shape) - 1
+
+        linear_mm_config = LinearMMConfig()
+        a_fp8 = hp_tensor_to_float8_dynamic(
+            a,
+            e4m3_dtype,
+            linear_mm_config,
+            scaling_granularity=ScalingGranularity.AXISWISE,
+            axiswise_dim=dim,
+        )
+        a_dq = a_fp8.to_original_precision()
+        sqnr = compute_error(a, a_dq)
+        assert sqnr >= 25.0
+
+    def test_axiswise_reshape(self):
+        a = torch.randn(3, 5, 7, dtype=torch.bfloat16)
+        linear_mm_config = LinearMMConfig()
+
+        # if we scale across dim0, we can only reshape to [3, -1]
+        a_fp8_d0 = hp_tensor_to_float8_dynamic(
+            a,
+            e4m3_dtype,
+            linear_mm_config,
+            scaling_granularity=ScalingGranularity.AXISWISE,
+            axiswise_dim=0,
+        )
+        assert list(a_fp8_d0._data.shape) == [3, 5, 7]
+        assert list(a_fp8_d0._scale.shape) == [1, 5, 7]
+
+        a_fp8_d0_r = a_fp8_d0.reshape(3, -1)
+        assert list(a_fp8_d0_r.shape) == [3, 5 * 7]
+        assert list(a_fp8_d0_r._scale.shape) == [1, 5 * 7]
+        # verify numerics did not change
+        assert torch.allclose(
+            a_fp8_d0.to_original_precision(),
+            a_fp8_d0_r.to_original_precision().reshape(3, 5, 7),
+            atol=0,
+            rtol=0,
+        )
+        with pytest.raises(RuntimeError):
+            a_fp8_d0_r2 = a_fp8_d0.reshape(-1, 7)
+
+        # if we scale across dim2, we can only reshape to [-1, 7]
+        a_fp8_d2 = hp_tensor_to_float8_dynamic(
+            a,
+            e4m3_dtype,
+            linear_mm_config,
+            scaling_granularity=ScalingGranularity.AXISWISE,
+            axiswise_dim=2,
+        )
+        assert list(a_fp8_d2._data.shape) == [3, 5, 7]
+        assert list(a_fp8_d2._scale.shape) == [3, 5, 1]
+
+        a_fp8_d2_r = a_fp8_d2.reshape(-1, 7)
+        assert list(a_fp8_d2_r.shape) == [3 * 5, 7]
+        assert list(a_fp8_d2_r._scale.shape) == [3 * 5, 1]
+        # verify numerics did not change
+        assert torch.allclose(
+            a_fp8_d2.to_original_precision(),
+            a_fp8_d2_r.to_original_precision().reshape(3, 5, 7),
+            atol=0,
+            rtol=0,
+        )
+        with pytest.raises(RuntimeError):
+            a_fp8_d2_r2 = a_fp8_d2.reshape(3, -1)
+
+    @pytest.mark.parametrize("a_shape", [(16, 32), (2, 16, 32), (1, 2, 16, 32)])
+    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
+    @unittest.skipIf(not is_cuda_9_0, "Requires CUDA capability >= 9.0")
+    def test_axiswise_gemm(self, a_shape):
+        a = torch.randn(*a_shape, 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=-1,
+        )
+        a_fp8 = a_fp8.reshape(-1, a_shape[-1])
+        b_fp8 = hp_tensor_to_float8_dynamic(
+            b,
+            e4m3_dtype,
+            linear_mm_config,
+            gemm_input_role=GemmInputRole.WEIGHT,
+            scaling_granularity=ScalingGranularity.AXISWISE,
+            axiswise_dim=1,  # will be transposed
+        )
+        c_fp8_compute = torch.mm(a_fp8, b_fp8.t())
+        a = a.reshape(-1, a_shape[-1])
+        c_ref = torch.mm(a, b.t())
+        sqnr = compute_error(c_ref, c_fp8_compute)
+        assert sqnr >= 25.0
 
 
 class TestFloat8Linear:

torchao/float8/config.py

@@ -26,6 +26,18 @@ def short_str(self):
             return "sta"
 
 
+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:
     """

torchao/float8/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"""
 
@@ -45,6 +54,7 @@ def decorator(func):
     ]
 )
 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,
@@ -55,10 +65,82 @@ 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)
+
+    if aten_op == aten.transpose.int:
+        _assert_tensorwise_scale(aten_op, args[0]._scale)
+
+    old_axiswise_dim = args[0]._axiswise_dim
+    new_axiswise_dim = old_axiswise_dim
+    if old_axiswise_dim is not None:
+        if old_axiswise_dim == 0:
+            new_axiswise_dim == -1
+        else:
+            new_axiswise_dim == 0
+
+    return Float8Tensor(
+        new_data,
+        new_scale,
+        args[0]._orig_dtype,
+        args[0]._linear_mm_config,
+        args[0]._gemm_input_role,
+        new_axiswise_dim,
+    )
+
+
+@implements([aten.view.default])
+def float8_view(aten_op, args, kwargs=None):
+    if len(args[0]._scale.shape) < 2:
+        # tensorwise scaling
+        return float8_desugar_op(aten_op, args, kwargs)
+
+    t, new_shape = args[0], args[1]
+    # for now, only support reshaping to [-1, dim] or [dim, -1]
+    axiswise_dim = t._axiswise_dim
+    if len(new_shape) == 2:
+
+        if axiswise_dim == 0:
+            new_data = aten_op(t._data, new_shape, **kwargs)
+            new_scale_shape = [1, new_shape[-1]]
+            new_scale = aten_op(t._scale, new_scale_shape, **kwargs)
+            return Float8Tensor(
+                new_data,
+                new_scale,
+                t._orig_dtype,
+                t._linear_mm_config,
+                t._gemm_input_role,
+                t._axiswise_dim,
+            )
+        elif axiswise_dim == -1 or axiswise_dim == (len(t.shape) - 1):
+            new_data = aten_op(t._data, new_shape, **kwargs)
+            new_scale_shape = [new_shape[0], 1]
+            new_scale = aten_op(t._scale, new_scale_shape, **kwargs)
+            new_axiswise_dim = -1
+            return Float8Tensor(
+                new_data,
+                new_scale,
+                t._orig_dtype,
+                t._linear_mm_config,
+                t._gemm_input_role,
+                new_axiswise_dim,
+            )
+    raise AssertionError(
+        f"{aten_op} with axiswise scaling and t.shape {t.shape} t._scale.shape {t._scale.shape} t._axiswise_dim {t._axiswise_dim} new_shape {new_shape} is not supported yet."
+    )
+
+
 @implements([aten.split.Tensor])
 def float8_split(aten_op, args, kwargs=None):
     new_data_tensors = aten_op(args[0]._data, *args[1:], **kwargs)
-
+    _assert_tensorwise_scale(aten_op, args[0]._scale)
     def make_float8(data):
         return Float8Tensor(
             data,
@@ -102,6 +184,7 @@ 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)
@@ -118,6 +201,7 @@ 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):
@@ -230,6 +314,7 @@ 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
 
 
@@ -239,6 +324,7 @@ 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
@@ -266,6 +352,7 @@ 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
@@ -285,6 +372,7 @@ 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)
 
@@ -305,6 +393,7 @@ 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
@@ -335,8 +424,10 @@ 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"