rename all variables to use input/weight/grad_output notation

Summary:

In #323 we
changed the user facing variable notation from `x/w/dL_dY` to
`input/weight/grad_output`.

This PR follows up by changing most of the internal variables to also match
the new notation, to reduce confusion.

Test Plan:

```
./test/test_everything.sh
```

Reviewers:

Subscribers:

Tasks:

Tags:

[ghstack-poisoned]

float8_experimental/float8_dynamic_utils.py

@@ -42,7 +42,7 @@ def backward(ctx, gradY):
             gradY_scale,
             e5m2_dtype,
             linear_mm_config=ctx.linear_mm_config,
-            gemm_input_role=GemmInputRole.DL_DY,
+            gemm_input_role=GemmInputRole.GRAD_OUTPUT,
         )
         return fp8_tensor, None
 
@@ -51,7 +51,7 @@ def cast_to_float8_e4m3_dynamic(
     inpt_tensor: torch.Tensor,
     linear_mm_config: LinearMMConfig,
     reduce_amax: bool = False,
-    gemm_input_role: GemmInputRole = GemmInputRole.X,
+    gemm_input_role: GemmInputRole = GemmInputRole.INPUT,
 ) -> Float8Tensor:
     if tensor_already_casted_to_fp8(inpt_tensor):
         return inpt_tensor

float8_experimental/float8_linear.py

@@ -125,7 +125,7 @@ def backward(ctx, go):
             fp8_scale_grad_output,
             e5m2_dtype,
             linear_mm_config=ctx.linear_mm_config,
-            gemm_input_role=GemmInputRole.DL_DY,
+            gemm_input_role=GemmInputRole.GRAD_OUTPUT,
         )
         empty_grads = None, None, None, None, None, None
         return res, *empty_grads
@@ -273,21 +273,21 @@ def convert_amax_buffer_to_float32(self):
             if self._buffers[key] is not None:
                 self._buffers[key] = self._buffers[key].to(torch.float32)
 
-    def cast_x_to_float8(
-        self, x: torch.Tensor, is_amax_initialized: bool
+    def cast_input_to_float8(
+        self, input: torch.Tensor, is_amax_initialized: bool
     ) -> torch.Tensor:
         # Duplicate the autocast logic for F.linear, so that the output
         # of our module has the right original precision
         if torch.is_autocast_enabled():
             # For now, hardcode to GPU's autocast dtype
             # if we need CPU support in the future, we can add it
             autocast_dtype = torch.get_autocast_gpu_dtype()
-            x = x.to(autocast_dtype)
+            input = input.to(autocast_dtype)
 
         if self.scaling_type_input is TensorScalingType.DELAYED:
             scale_fn_name = self.config.delayed_scaling_config.scale_fn_name
             _maybe_initialize_amaxes_scales_for_float8_cast(
-                x,
+                input,
                 self.fp8_amax_input,
                 self.fp8_amax_history_input,
                 self.fp8_scale_input,
@@ -296,29 +296,29 @@ def cast_x_to_float8(
                 is_amax_initialized,
                 reduce_amax=True,
             )
-            x_fp8 = Float8Tensor.to_float8(
-                x,
+            input_fp8 = Float8Tensor.to_float8(
+                input,
                 self.fp8_scale_input,
                 e4m3_dtype,
                 self.fp8_amax_input,
                 linear_mm_config=self.linear_mm_config,
-                gemm_input_role=GemmInputRole.X,
+                gemm_input_role=GemmInputRole.INPUT,
             )
         else:
             assert self.scaling_type_input is TensorScalingType.DYNAMIC
-            x_fp8 = cast_to_float8_e4m3_dynamic(x, self.linear_mm_config)
-        return x_fp8
+            input_fp8 = cast_to_float8_e4m3_dynamic(input, self.linear_mm_config)
+        return input_fp8
 
-    def cast_w_to_float8(
-        self, w: torch.Tensor, is_amax_initialized: bool
+    def cast_weight_to_float8(
+        self, weight: torch.Tensor, is_amax_initialized: bool
     ) -> torch.Tensor:
         if self.scaling_type_weight is TensorScalingType.DELAYED:
             if isinstance(self.weight, Float8Tensor):  # cast by FSDP
-                w_fp8 = self.weight
+                weight_fp8 = self.weight
             else:
                 scale_fn_name = self.config.delayed_scaling_config.scale_fn_name
                 _maybe_initialize_amaxes_scales_for_float8_cast(
-                    w,
+                    weight,
                     self.fp8_amax_weight,
                     self.fp8_amax_history_weight,
                     self.fp8_scale_weight,
@@ -328,29 +328,31 @@ def cast_w_to_float8(
                     reduce_amax=False,
                 )
 
-                w_fp8 = Float8Tensor.to_float8(
-                    w,
+                weight_fp8 = Float8Tensor.to_float8(
+                    weight,
                     self.fp8_scale_weight,
                     e4m3_dtype,
                     self.fp8_amax_weight,
                     linear_mm_config=self.linear_mm_config,
-                    gemm_input_role=GemmInputRole.W,
+                    gemm_input_role=GemmInputRole.WEIGHT,
                 )
         else:
             assert self.scaling_type_weight is TensorScalingType.DYNAMIC
             if isinstance(self.weight, Float8Tensor):  # cast by FSDP
-                w_fp8 = self.weight
+                weight_fp8 = self.weight
             else:
-                w_fp8 = cast_to_float8_e4m3_dynamic(
-                    self.weight, self.linear_mm_config, gemm_input_role=GemmInputRole.W
+                weight_fp8 = cast_to_float8_e4m3_dynamic(
+                    self.weight,
+                    self.linear_mm_config,
+                    gemm_input_role=GemmInputRole.WEIGHT,
                 )
-        return w_fp8
+        return weight_fp8
 
-    def cast_y_to_float8_in_bw(self, y: torch.Tensor) -> torch.Tensor:
+    def cast_output_to_float8_in_bw(self, output: torch.Tensor) -> torch.Tensor:
         if self.scaling_type_grad_output is TensorScalingType.DELAYED:
             scale_fn_name = self.config.delayed_scaling_config.scale_fn_name
-            y = NoopFwToFloat8E5M2Bw.apply(
-                y,
+            output = NoopFwToFloat8E5M2Bw.apply(
+                output,
                 self.fp8_amax_grad_output,
                 self.fp8_amax_history_grad_output,
                 self.fp8_scale_grad_output,
@@ -360,10 +362,10 @@ def cast_y_to_float8_in_bw(self, y: torch.Tensor) -> torch.Tensor:
             )
         else:
             assert self.scaling_type_grad_output is TensorScalingType.DYNAMIC
-            y = cast_to_float8_e5m2_dynamic_bw(y, self.linear_mm_config)
-        return y
+            output = cast_to_float8_e5m2_dynamic_bw(output, self.linear_mm_config)
+        return output
 
-    def float8_pre_forward(self, x):
+    def float8_pre_forward(self, input):
         if not self.enable_pre_and_post_forward:
             return
         if (
@@ -374,7 +376,7 @@ def float8_pre_forward(self, x):
             raise AssertionError(
                 "amaxes and scales not synced, please call `sync_float8_amax_and_scale_history` before forward"
             )
-        self.last_seen_input_dtype = x.dtype
+        self.last_seen_input_dtype = input.dtype
 
     def float8_post_forward(self):
         if not self.enable_pre_and_post_forward:
@@ -388,25 +390,25 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
         if self.has_any_delayed_scaling:
             self.float8_pre_forward(input)
 
-        x_fp8 = self.cast_x_to_float8(input, self.is_amax_initialized)
-        w_fp8 = self.cast_w_to_float8(self.weight, self.is_amax_initialized)
+        input_fp8 = self.cast_input_to_float8(input, self.is_amax_initialized)
+        weight_fp8 = self.cast_weight_to_float8(self.weight, self.is_amax_initialized)
 
-        y = torch.matmul(x_fp8, w_fp8.t())
+        output = torch.matmul(input_fp8, weight_fp8.t())
 
-        # Cast gradY to float8_e5m2 during backward
-        y = self.cast_y_to_float8_in_bw(y)
+        # Cast grad_output to float8_e5m2 during backward
+        output = self.cast_output_to_float8_in_bw(output)
 
         if self.bias is not None:
-            y = y + self.bias.to(y.dtype)
+            output = output + self.bias.to(output.dtype)
 
         if self.has_any_delayed_scaling:
             self.float8_post_forward()
-        return y
+        return output
 
     def scaling_repr(self):
         # add scaling settings without using too many characters
-        # example: "x:del,w:del,dldy:dyn"
-        return f"x:{self.scaling_type_input.short_str()},w:{self.scaling_type_weight.short_str()},dldy:{self.scaling_type_grad_output.short_str()}"
+        # example: "i:del,w:del,go:dyn"
+        return f"i:{self.scaling_type_input.short_str()},w:{self.scaling_type_weight.short_str()},go:{self.scaling_type_grad_output.short_str()}"
 
     def extra_repr(self):
         s = f'{super().extra_repr()}, scaling="{self.scaling_repr()}"'

float8_experimental/float8_tensor.py

@@ -27,21 +27,21 @@
 #
 # There are three gemms in a forward + backward of a Linear layer:
 #
-# 1.     x @ w_t   = y     (forward pass)
-# 2. dL_dY @ w     = dL_dX (backward pass)
-# 3.   x_t @ dL_dY = dL_dW (backward pass)
+# 1.       input @ weight_t    = output     (forward pass)
+# 2. grad_output @ weight      = grad_input (backward pass)
+# 3.     input_t @ grad_output = grad_weight (backward pass)
 #
 # In the formulas above, there are:
-# A. six input tensors (x, x_t, w, w_t, dL_dY, dL_dY_t).
-#    - Note that dL_dY_t is implied because of memory format requirements
+# A. six input tensors (input, input_t, weight, weight_t, grad_output, grad_output_t).
+#    - Note that grad_output_t is implied because of memory format requirements
 #      of float8 gemms
-# B. three output tensors (y, dL_dX, dL_dW)
+# B. three output tensors (output, grad_input, grad_weight)
 #
 # We want each input tensor, gemm, and output tensor to be configurable.
 # The state of this configuration today is:
 #
 # i. pairs of input tensors (non-t and t variants) have their scaling
-#    configurable via the scaling_type_{x_w_dL_dY} arguments to Float8Linear
+#    configurable via the scaling_type_* arguments to Float8Linear
 # ii. each gemm + output is configurable via ScaledMMConfig, which is not user facing
 # iii. LinearMMConfig is a container for the three ScaledMMConfig objects needed
 #    to configure all three gemms, also not user facing
@@ -60,11 +60,12 @@
 
 # The object below is not user facing and exists for convenience,
 # to allow Float8Tensor to use
-# the right config based on which gemm from `y`, `dL_dX`, `dL_dW` is
+# the right config based on which gemm from gemms with outputs
+# `output`, `grad_input`, `grad_weight` is
 # being called.
 LinearMMConfig = namedtuple(
     "LinearMMConfig",
-    ["y", "dL_dX", "dL_dW"],
+    ["output", "grad_input", "grad_weight"],
     defaults=[
         ScaledMMConfig(False, True, False, False),
         ScaledMMConfig(False, False, False, False),
@@ -81,9 +82,9 @@ class GemmInputRole(enum.Enum):
     gemm is performed.
     """
 
-    X = "x"
-    W = "w"
-    DL_DY = "dL_dY"
+    INPUT = "input"
+    WEIGHT = "weight"
+    GRAD_OUTPUT = "grad_output"
 
 
 # choose which scaled_mm_config to use based on gemm inputs
@@ -93,21 +94,21 @@ def choose_scaled_mm_config(
     b_role: GemmInputRole,
     b_linear_mm_config: LinearMMConfig,
 ):
-    if a_role is GemmInputRole.X and b_role is GemmInputRole.W:
+    if a_role is GemmInputRole.INPUT and b_role is GemmInputRole.WEIGHT:
         assert (
-            a_linear_mm_config.y == b_linear_mm_config.y
-        ), f"linear_mm_config.y mismatch: {a_linear_mm_config.y} vs {b_linear_mm_config.y}"
-        return a_linear_mm_config.y
-    elif a_role is GemmInputRole.DL_DY and b_role is GemmInputRole.W:
+            a_linear_mm_config.output == b_linear_mm_config.output
+        ), f"linear_mm_config.output mismatch: {a_linear_mm_config.output} vs {b_linear_mm_config.output}"
+        return a_linear_mm_config.output
+    elif a_role is GemmInputRole.GRAD_OUTPUT and b_role is GemmInputRole.WEIGHT:
         assert (
-            a_linear_mm_config.dL_dX == b_linear_mm_config.dL_dX
-        ), f"linear_mm_config.dL_dX mismatch: {a_linear_mm_config.dL_dX} vs {b_linear_mm_config.dL_dX}"
-        return a_linear_mm_config.dL_dX
-    elif a_role is GemmInputRole.DL_DY and b_role is GemmInputRole.X:
+            a_linear_mm_config.grad_input == b_linear_mm_config.grad_input
+        ), f"linear_mm_config.grad_input mismatch: {a_linear_mm_config.grad_input} vs {b_linear_mm_config.grad_input}"
+        return a_linear_mm_config.grad_input
+    elif a_role is GemmInputRole.GRAD_OUTPUT and b_role is GemmInputRole.INPUT:
         assert (
-            a_linear_mm_config.dL_dW == b_linear_mm_config.dL_dW
-        ), f"linear_mm_config.dL_dW mismatch: {a_linear_mm_config.dL_dW} vs {b_linear_mm_config.dL_dW}"
-        return a_linear_mm_config.dL_dW
+            a_linear_mm_config.grad_weight == b_linear_mm_config.grad_weight
+        ), f"linear_mm_config.grad_weight mismatch: {a_linear_mm_config.grad_weight} vs {b_linear_mm_config.grad_weight}"
+        return a_linear_mm_config.grad_weight
     else:
         raise AssertionError(f"unexpected a_role {a_role} and b_role {b_role}")
 
@@ -207,7 +208,7 @@ def forward(
         float8_dtype=e4m3_dtype,
         amax_buffer: Optional[torch.Tensor] = None,
         linear_mm_config: Optional[LinearMMConfig] = None,
-        gemm_input_role: Optional[GemmInputRole] = GemmInputRole.X,
+        gemm_input_role: Optional[GemmInputRole] = GemmInputRole.INPUT,
     ):
         """Autograd enabled wrapper around to_fp8_no_autograd that will also populate the amax buffer.
         Args
@@ -287,7 +288,7 @@ def __new__(
         scale: torch.Tensor,
         orig_dtype: torch.dtype,
         linear_mm_config: Optional[LinearMMConfig],
-        gemm_input_role: Optional[GemmInputRole] = GemmInputRole.X,
+        gemm_input_role: Optional[GemmInputRole] = GemmInputRole.INPUT,
     ):
         assert (
             scale.numel() == 1
@@ -348,7 +349,7 @@ def to_float8(
         float8_dtype: torch.dtype,
         amax_buffer: Optional[torch.Tensor] = None,
         linear_mm_config: Optional[LinearMMConfig] = None,
-        gemm_input_role: Optional[GemmInputRole] = GemmInputRole.X,
+        gemm_input_role: Optional[GemmInputRole] = GemmInputRole.INPUT,
     ):
         """Converts a higher precision tensor to float8 in a differentiable way.
 

float8_experimental/float8_tensor_parallel.py

@@ -48,7 +48,7 @@ def _prepare_input_fn(
         input_tensor = cast_to_float8_e4m3_dynamic(
             input_tensor,
             mod.linear_mm_config,
-            gemm_input_role=GemmInputRole.X,
+            gemm_input_role=GemmInputRole.INPUT,
         )  # DTensor(Float8Tensor)
 
         # transform the input layouts to the desired layouts of ColwiseParallel
@@ -101,7 +101,7 @@ def _prepare_input_fn(
         input_tensor = cast_to_float8_e4m3_dynamic(
             input_tensor,
             mod.linear_mm_config,
-            gemm_input_role=GemmInputRole.X,
+            gemm_input_role=GemmInputRole.INPUT,
         )  # DTensor(Float8Tensor)
 
         if input_layouts != desired_input_layouts:
@@ -199,7 +199,7 @@ def _prepare_input_arg(self, input, mesh, input_layout, desired_layout):
             dt_inp = cast_to_float8_e4m3_dynamic(
                 dt_inp,
                 self.linear_mm_config,
-                gemm_input_role=GemmInputRole.X,
+                gemm_input_role=GemmInputRole.INPUT,
             )  # DTensor(Float8Tensor)
             if desired_layout is not None and input_layout != desired_layout:
                 dt_inp = dt_inp.redistribute(placements=(desired_layout,))

float8_experimental/fsdp_utils.py

@@ -171,14 +171,14 @@ def fsdp_pre_all_gather(self, mesh):
                 self._precomputed_scale,
                 torch.float8_e4m3fn,
                 linear_mm_config=self._linear_mm_config,
-                gemm_input_role=GemmInputRole.W,
+                gemm_input_role=GemmInputRole.WEIGHT,
             )
         else:
             float8_tensor = cast_to_float8_e4m3_dynamic(
                 self._tensor,
                 self._linear_mm_config,
                 reduce_amax=True,
-                gemm_input_role=GemmInputRole.W,
+                gemm_input_role=GemmInputRole.WEIGHT,
             )
         return (float8_tensor._data,), (float8_tensor._scale,)
 
@@ -201,7 +201,7 @@ def fsdp_post_all_gather(
             scale,
             param_dtype,
             self._linear_mm_config,
-            gemm_input_role=GemmInputRole.W,
+            gemm_input_role=GemmInputRole.WEIGHT,
         ), (data,)
 
 
@@ -364,7 +364,7 @@ def fsdp_pre_all_gather(self, mesh):
             e4m3_dtype,
             self._amax_buffer,
             self._linear_mm_config,
-            gemm_input_role=GemmInputRole.W,
+            gemm_input_role=GemmInputRole.WEIGHT,
         )
         return (float8_tensor._data,), (float8_tensor._scale,)
 
@@ -387,5 +387,5 @@ def fsdp_post_all_gather(
             scale,
             param_dtype,
             self._linear_mm_config,
-            gemm_input_role=GemmInputRole.W,
+            gemm_input_role=GemmInputRole.WEIGHT,
         ), (data,)

float8_experimental/inference.py

@@ -132,7 +132,7 @@ def quantize_weight(self, dtype: torch.dtype = e4m3_dtype) -> None:
             scale,
             dtype,
             self.linear_mm_config,
-            gemm_input_role=GemmInputRole.W,
+            gemm_input_role=GemmInputRole.WEIGHT,
         )
         self.weight = nn.Parameter(quantized_weight)
         self.weight.requires_grad = False
@@ -205,7 +205,7 @@ def cast_to_float8_e4m3_inference(
         scale,
         e4m3_dtype,
         linear_mm_config=linear_mm_config,
-        gemm_input_role=GemmInputRole.X,
+        gemm_input_role=GemmInputRole.INPUT,
     )