[3/x] clean up casting functions: delete to_fp8_no_autograd

float8_experimental/float8_scaling_utils.py

@@ -18,7 +18,6 @@
     LinearMMConfig,
     ScaledMMConfig,
     tensor_already_casted_to_fp8,
-    to_fp8_no_autograd,
     ToFloat8ConstrFunc,
 )
 
@@ -146,12 +145,12 @@ def backward(ctx, go):
 
         fp8_amax_grad_output.fill_(tensor_to_amax(go))
 
-        res = to_fp8_no_autograd(
+        res = ToFloat8ConstrFunc.apply(
             go,
             fp8_scale_grad_output,
             e5m2_dtype,
-            linear_mm_config=ctx.linear_mm_config,
-            gemm_input_role=GemmInputRole.GRAD_OUTPUT,
+            ctx.linear_mm_config,
+            GemmInputRole.GRAD_OUTPUT,
         )
         empty_grads = None, None, None, None, None, None
         return res, *empty_grads
@@ -178,11 +177,11 @@ def backward(ctx, gradY):
         if tensor_already_casted_to_fp8(gradY):
             return gradY, None
         gradY_scale = tensor_to_scale(gradY, e5m2_dtype)
-        fp8_tensor = to_fp8_no_autograd(
+        fp8_tensor = ToFloat8ConstrFunc.apply(
             gradY,
             gradY_scale,
             e5m2_dtype,
-            linear_mm_config=ctx.linear_mm_config,
-            gemm_input_role=GemmInputRole.GRAD_OUTPUT,
+            ctx.linear_mm_config,
+            GemmInputRole.GRAD_OUTPUT,
         )
         return fp8_tensor, None

float8_experimental/float8_tensor.py

@@ -128,71 +128,6 @@ def tensor_already_casted_to_fp8(tensor: torch.Tensor) -> bool:
     return False
 
 
-# TODO: rename to hp_tensor_and_scale_to_float8_tensor
-def to_fp8_no_autograd(
-    x: torch.Tensor,
-    x_scale: torch.Tensor,
-    float8_dtype: torch.dtype,
-    linear_mm_config: Optional[LinearMMConfig],
-    gemm_input_role: Optional[GemmInputRole],
-) -> "Float8Tensor":
-    """Convert a tensor to float8 without autograd
-    This is used in multiple places in the codebase to convert a tensor to float8
-
-    This function will apply the scaling, and then convert to a Float8Tensor
-
-    Note:
-    We will call this function with a DTensor subclass. Ideally this would be an aten OP
-    that DTensor could overload to ensure proper semantics. There are some techincal issues
-    with that composing with FakeTensor, so we special case here.
-
-    DTensor Invariant: DTensor must always be the outer most tensor subclass
-
-    Args:
-        x: the tensor to convert
-        scale: the scale to use to convert the tensor
-        float8_dtype: the float8 dtype to use
-        linear_mm_config: Defines the configuration for the scaled_mm for
-          the 3 fwd/bwd gemms of linear
-        gemm_input_role: Defines the role of this tensor (x, w or dL_dY) in
-          the 3 fwd/bwd gemms of linear
-    """
-    x_scaled = x * x_scale
-    bits_fp8 = to_fp8_saturated(x_scaled, float8_dtype)
-
-    if isinstance(bits_fp8, DTensor):
-        assert isinstance(
-            x, DTensor
-        ), "Expected Float8 scale to be a DTensor if bits_fp8 is a DTensor"
-        bits_mesh = bits_fp8.device_mesh
-        bits_placements = bits_fp8.placements
-        local_bits = bits_fp8.to_local()
-        local_scale = x_scale.to_local()
-        inner_float8_tensor = Float8Tensor(
-            local_bits,
-            local_scale,
-            x.dtype,
-            linear_mm_config=linear_mm_config,
-            gemm_input_role=gemm_input_role,
-        )
-        return DTensor.from_local(
-            inner_float8_tensor,
-            bits_mesh,
-            bits_placements,
-            run_check=False,
-            shape=bits_fp8.size(),
-            stride=bits_fp8.stride(),
-        )
-
-    return Float8Tensor(
-        bits_fp8,
-        x_scale,
-        x.dtype,
-        linear_mm_config=linear_mm_config,
-        gemm_input_role=gemm_input_role,
-    )
-
-
 @torch._dynamo.allow_in_graph
 class ToFloat8ConstrFunc(torch.autograd.Function):
     """
@@ -210,18 +145,56 @@ def forward(
         linear_mm_config: Optional[LinearMMConfig] = None,
         gemm_input_role: Optional[GemmInputRole] = GemmInputRole.INPUT,
     ):
-        """Autograd enabled wrapper around to_fp8_no_autograd that will also populate the amax buffer.
-        Args
+        """
+        This function will apply the scaling, and then convert to a Float8Tensor
+
+        Note:
+        We will call this function with a DTensor subclass. Ideally this would be an aten OP
+        that DTensor could overload to ensure proper semantics. There are some techincal issues
+        with that composing with FakeTensor, so we special case here.
+
+        DTensor Invariant: DTensor must always be the outer most tensor subclass
+
+        Args:
             tensor: the tensor to convert
             scale: the scale to use to convert the tensor
-            float8_dtype: the float8 dtype either, torch.float8_e4m3fn or torch.float8_e5m2fn
-            emulate: whether to emulate the matmuls in fp32
+            float8_dtype: the float8 dtype to use
+            linear_mm_config: Defines the configuration for the scaled_mm for
+              the 3 fwd/bwd gemms of linear
+            gemm_input_role: Defines the role of this tensor (input, weight or grad_output) in
+              the 3 fwd/bwd gemms of linear
         """
+        tensor_scaled = tensor * scale
+        bits_fp8 = to_fp8_saturated(tensor_scaled, float8_dtype)
+
+        if isinstance(bits_fp8, DTensor):
+            assert isinstance(
+                scale, DTensor
+            ), "Expected Float8 scale to be a DTensor if bits_fp8 is a DTensor"
+            bits_mesh = bits_fp8.device_mesh
+            bits_placements = bits_fp8.placements
+            local_bits = bits_fp8.to_local()
+            local_scale = scale.to_local()
+            inner_float8_tensor = Float8Tensor(
+                local_bits,
+                local_scale,
+                tensor.dtype,
+                linear_mm_config=linear_mm_config,
+                gemm_input_role=gemm_input_role,
+            )
+            return DTensor.from_local(
+                inner_float8_tensor,
+                bits_mesh,
+                bits_placements,
+                run_check=False,
+                shape=bits_fp8.size(),
+                stride=bits_fp8.stride(),
+            )
 
-        return to_fp8_no_autograd(
-            tensor,
+        return Float8Tensor(
+            bits_fp8,
             scale,
-            float8_dtype,
+            tensor.dtype,
             linear_mm_config=linear_mm_config,
             gemm_input_role=gemm_input_role,
         )