[mxfp8 moe training] refactor all var names with suffix _mx to _data for clarity

torchao/prototype/moe_training/scaled_grouped_mm.py

@@ -122,7 +122,7 @@ def forward(
             round_scales_to_power_of_2=True,
         )
         A_scaled = A.to(torch.float32) * A_scales
-        A_fp8_row_major = to_fp8_saturated(A_scaled, torch.float8_e4m3fn)
+        A_data_row_major = to_fp8_saturated(A_scaled, torch.float8_e4m3fn)
 
         # Convert B to float8, column-major for right operand of grouped GEMM.
         # B_t shape: (E, K, N)
@@ -136,18 +136,18 @@ def forward(
             round_scales_to_power_of_2=True,
         )
         B_t_scaled = B_t.to(torch.float32) * B_t_scales
-        B_t_fp8_col_major = to_fp8_saturated(B_t_scaled, torch.float8_e4m3fn)
+        B_t_data_col_major = to_fp8_saturated(B_t_scaled, torch.float8_e4m3fn)
 
         # Store what we need for backward.
         ctx.save_for_backward(A, B_t, offs)
         ctx.out_dtype = out_dtype
 
         # Perform scaled grouped GEMM and return result.
         # output shape: scaled grouped mm of (M,K) @ (B,K,N) = (M,N)
-        assert not _is_column_major(A_fp8_row_major), (
+        assert not _is_column_major(A_data_row_major), (
             "A must be row-major for output = A @ B"
         )
-        assert _is_column_major(B_t_fp8_col_major), (
+        assert _is_column_major(B_t_data_col_major), (
             "B must be column-major for output = A @ B"
         )
 
@@ -157,8 +157,8 @@ def forward(
         A_scales = A_scales.squeeze(-1)
         B_t_scales = B_t_scales.squeeze(1)
         return torch._scaled_grouped_mm(
-            A_fp8_row_major,
-            B_t_fp8_col_major,
+            A_data_row_major,
+            B_t_data_col_major,
             A_scales.reciprocal(),  # Reciprocals are needed for rescaling the output.
             B_t_scales.reciprocal(),
             offs,
@@ -184,13 +184,13 @@ def backward(ctx, grad_output: torch.Tensor):
             round_scales_to_power_of_2=True,
         )
         grad_output_scaled = grad_output.to(torch.float32) * grad_output_scales
-        grad_output_fp8_row_major = to_fp8_saturated(
+        grad_output_data_row_major = to_fp8_saturated(
             grad_output_scaled, torch.float8_e4m3fn
         )
 
         # Compute B fp8 column-major for right operand of grouped GEMM:
         # grad_A = grad_output @ B.
-        B_fp8_col_major, B_scales = triton_fp8_rowwise_3d_transpose_rhs(
+        B_data_col_major, B_scales = triton_fp8_rowwise_3d_transpose_rhs(
             B_t._data if hasattr(B_t, "_data") else B_t,
             output_dtype=torch.float8_e4m3fn,
             round_scales_to_power_of_2=True,
@@ -199,10 +199,10 @@ def backward(ctx, grad_output: torch.Tensor):
         # Compute grad_A.
         # grad_A = grad_output @ B
         # grad_A = scaled grouped mm of (M,N) @ (B,N,K) = (M,K)
-        assert not _is_column_major(grad_output_fp8_row_major), (
+        assert not _is_column_major(grad_output_data_row_major), (
             "grad_output must be row-major for grad_A = grad_output @ B"
         )
-        assert _is_column_major(B_fp8_col_major), (
+        assert _is_column_major(B_data_col_major), (
             "B must be column-major for grad_A = grad_output @ B"
         )
 
@@ -212,8 +212,8 @@ def backward(ctx, grad_output: torch.Tensor):
         grad_output_scales = grad_output_scales.squeeze(-1)
         B_scales = B_scales.squeeze(1)
         grad_A = torch._scaled_grouped_mm(
-            grad_output_fp8_row_major,
-            B_fp8_col_major,
+            grad_output_data_row_major,
+            B_data_col_major,
             grad_output_scales.reciprocal(),
             B_scales.reciprocal(),
             offs,
@@ -227,18 +227,18 @@ def backward(ctx, grad_output: torch.Tensor):
         # Convert transpose of grad_output to float8, row-major for left operand of grouped GEMM
         # needed for grad_B: grad_output_t @ A
         # Use transpose method to avoid uncoalesced memory accesses.
-        grad_out_fp8_colwise, grad_out_scales = triton_fp8_per_group_colwise_scales(
+        grad_out_data_colwise, grad_out_scales = triton_fp8_per_group_colwise_scales(
             grad_output.t()
             .contiguous()
             .t(),  # Quantization is over 2x faster when input is col major, even with this transformation
             offs,
             torch.float8_e4m3fn,
             round_scales_to_power_of_2=True,
         )
-        grad_output_t_fp8_row_major = grad_out_fp8_colwise.t()
+        grad_output_t_data_row_major = grad_out_data_colwise.t()
         grad_output_t_scales = grad_out_scales.t()
 
-        A_fp8_col_major, A_scales = triton_fp8_per_group_colwise_scales(
+        A_data_col_major, A_scales = triton_fp8_per_group_colwise_scales(
             A.t()
             .contiguous()
             .t(),  # Quantization is over 2x faster when input is col major, even with this transformation
@@ -249,19 +249,19 @@ def backward(ctx, grad_output: torch.Tensor):
 
         # Compute grad_B = grad_output_t @ A.
         # grad_B = grad_output_t @ A
-        assert not _is_column_major(grad_output_t_fp8_row_major), (
+        assert not _is_column_major(grad_output_t_data_row_major), (
             "grad_output_t must be row-major for grad_B = grad_output_t @ A"
         )
-        assert _is_column_major(A_fp8_col_major), (
+        assert _is_column_major(A_data_col_major), (
             "A must be column-major for grad_B = grad_output_t @ A"
         )
 
         # Per-token group scales computed via triton kernels above do not have
         # the empty dim like the scales computed via tensor_to_scale, so we need
         # don't need to squeeze here.
         grad_B = torch._scaled_grouped_mm(
-            grad_output_t_fp8_row_major,
-            A_fp8_col_major,
+            grad_output_t_data_row_major,
+            A_data_col_major,
             grad_output_t_scales.reciprocal(),
             A_scales.reciprocal(),
             offs,
@@ -295,13 +295,15 @@ def forward(
         ctx.out_dtype = out_dtype
         ctx.emulated = emulated
 
-        # A_mx shape: (M, K)
+        # A_data shape: (M, K)
         # A_scale shape: (M, K//block_size)
-        A_scale, A_mx = to_mx(A, elem_dtype=torch.float8_e4m3fn, block_size=block_size)
+        A_scale, A_data = to_mx(
+            A, elem_dtype=torch.float8_e4m3fn, block_size=block_size
+        )
 
-        # B_mx shape: (E, N, K)
+        # B_data shape: (E, N, K)
         # B_scale shape: (E, N, K//block_size)
-        B_scales, B_mx = to_mx(
+        B_scales, B_data = to_mx(
             B_t.transpose(-2, -1),
             elem_dtype=torch.float8_e4m3fn,
             block_size=block_size,
@@ -315,9 +317,9 @@ def forward(
             else fbgemm_mxfp8_grouped_mm_2d_3d
         )
         out = mxfp8_2d_3d_grouped_mm(
-            A_mx,
+            A_data,
             A_scale,
-            B_mx,
+            B_data,
             B_scales,
             offs=offs,
             block_size=block_size,
@@ -332,15 +334,15 @@ def backward(ctx, grad_out: torch.Tensor):
         out_dtype = ctx.out_dtype
         emulated = ctx.emulated
 
-        # grad_out_mx shape: (M, N)
+        # grad_out_data shape: (M, N)
         # grad_out_scale shape: (M, N//block_size)
-        grad_out_scale, grad_out_mx = to_mx(
+        grad_out_scale, grad_out_data = to_mx(
             grad_out, elem_dtype=torch.float8_e4m3fn, block_size=block_size
         )
 
-        # B_mx shape: (E, K, N)
+        # B_data shape: (E, K, N)
         # B_scale shape: (E, K, N//block_size)
-        B_scales, B_mx = to_mx(
+        B_scales, B_data = to_mx(
             # TODO: can we support non-contiguous input tensor in to_mx to eliminate this inefficiency?
             B_t.contiguous(),
             elem_dtype=torch.float8_e4m3fn,
@@ -354,43 +356,43 @@ def backward(ctx, grad_out: torch.Tensor):
             else fbgemm_mxfp8_grouped_mm_2d_3d
         )
         grad_A = mxfp8_2d_3d_grouped_mm(
-            grad_out_mx,
+            grad_out_data,
             grad_out_scale,
-            B_mx,
+            B_data,
             B_scales,
             offs=offs,
             out_dtype=out_dtype,
         )
 
-        # grad_out_t_mx shape: (N, M)
+        # grad_out_t_data shape: (N, M)
         # grad_out_t_scales shape: (N, M//block_size)
-        grad_out_t_scales, grad_out_t_mx = to_mx(
+        grad_out_t_scales, grad_out_t_data = to_mx(
             # TODO: can we support non-contiguous input tensor in to_mx to eliminate this inefficiency?
             grad_out.transpose(-2, -1).contiguous(),
             elem_dtype=torch.float8_e4m3fn,
             block_size=block_size,
         )
 
         # Transpose A so we can scale along the M dimension, then un-transpose.
-        # A_t_mx shape: (K, M)
+        # A_t_data shape: (K, M)
         # A_t_scales shape: (K, M//block_size)
-        A_t_scales, A_t_mx = to_mx(
+        A_t_scales, A_t_data = to_mx(
             A.transpose(-2, -1).contiguous(),
             elem_dtype=torch.float8_e4m3fn,
             block_size=block_size,
         )
 
-        # A_mx shape = (M, K)
-        A_mx = A_t_mx.transpose(-2, -1)
+        # A_data shape = (M, K)
+        A_data = A_t_data.transpose(-2, -1)
 
         # A_scales shape = (M//block_size, K)
         A_scales = A_t_scales.transpose(-2, -1)
 
         # grad_B_t = scaled grouped mm of (N,M) @ (M,K) = (E,N,K)
         grad_B = _emulated_mxfp8_scaled_grouped_mm_2d_2d(
-            grad_out_t_mx,
+            grad_out_t_data,
             grad_out_t_scales,
-            A_mx,
+            A_data,
             A_scales,
             offs=offs,
         )
@@ -402,64 +404,68 @@ def backward(ctx, grad_out: torch.Tensor):
 
 
 def _emulated_mxfp8_scaled_grouped_mm_2d_3d(
-    A_mx: torch.Tensor,
+    A_data: torch.Tensor,
     A_scale: torch.Tensor,
-    B_mx: torch.Tensor,
+    B_data: torch.Tensor,
     B_scale: torch.Tensor,
     offs: Optional[torch.Tensor] = None,
     out_dtype: Optional[torch.dtype] = torch.bfloat16,
     block_size: int = 32,
 ) -> torch.Tensor:
-    assert A_mx.ndim == 2, f"A must be 2D, got {A_mx.ndim}"
-    assert B_mx.ndim == 3, f"B must be 3D, got {B_mx.ndim}"
-    assert A_scale.shape[0] == A_mx.shape[0], (
-        f"A_scale must have same M dim as A_mx, got A={A_mx.shape} and A_scale={A_scale.shape}"
+    assert A_data.ndim == 2, f"A must be 2D, got {A_data.ndim}"
+    assert B_data.ndim == 3, f"B must be 3D, got {B_data.ndim}"
+    assert A_scale.shape[0] == A_data.shape[0], (
+        f"A_scale must have same M dim as A_data, got A={A_data.shape} and A_scale={A_scale.shape}"
     )
-    assert A_scale.shape[1] == A_mx.shape[1] // block_size, (
-        f"A_scale dim1 should be size K//block_size, got A={A_mx.shape} and A_scale={A_scale.shape}"
+    assert A_scale.shape[1] == A_data.shape[1] // block_size, (
+        f"A_scale dim1 should be size K//block_size, got A={A_data.shape} and A_scale={A_scale.shape}"
     )
-    assert B_scale.shape[0] == B_mx.shape[0], (
-        f"B_scale must have same E dim as B_mx, got B={B_mx.shape} and B_scale={B_scale.shape}"
+    assert B_scale.shape[0] == B_data.shape[0], (
+        f"B_scale must have same E dim as B_data, got B={B_data.shape} and B_scale={B_scale.shape}"
     )
-    assert B_scale.shape[1] == B_mx.shape[1], (
-        f"B_scale must have same N dim as B_mx, got B={B_mx.shape} and B_scale={B_scale.shape}"
+    assert B_scale.shape[1] == B_data.shape[1], (
+        f"B_scale must have same N dim as B_data, got B={B_data.shape} and B_scale={B_scale.shape}"
     )
-    assert B_scale.shape[2] == B_mx.shape[2] // block_size, (
-        f"B_scale dim2 should be size K//block_size, got B={B_mx.shape} and B_scale={B_scale.shape}"
+    assert B_scale.shape[2] == B_data.shape[2] // block_size, (
+        f"B_scale dim2 should be size K//block_size, got B={B_data.shape} and B_scale={B_scale.shape}"
     )
 
     # Dequantize input
-    # A_mx shape: (M, K)
+    # A_data shape: (M, K)
     # A_scale shape: (M, K//block_size)
-    A_orig_shape = A_mx.shape
+    A_orig_shape = A_data.shape
 
     # Reshape to be able to do per-scaling group multiplication
-    # A_mx shape: (M, K//block_size, block_size)
+    # A_data shape: (M, K//block_size, block_size)
     # A_scale shape: (M, K//block_size, 1)
-    A_mx = A_mx.reshape(*A_mx.shape[:-1], A_mx.shape[-1] // block_size, block_size)
+    A_data = A_data.reshape(
+        *A_data.shape[:-1], A_data.shape[-1] // block_size, block_size
+    )
     A_scale = A_scale.unsqueeze(-1)
 
     # Rescale and cast to bfloat16
-    A = A_mx.to(torch.bfloat16) * A_scale.to(torch.bfloat16)
+    A = A_data.to(torch.bfloat16) * A_scale.to(torch.bfloat16)
 
     # Reshape back to original shape
     # A shape: (M, K)
     A = A.reshape(A_orig_shape)
 
     # Dequantize weights
     # Tranpose to get block_size on rightmost dim
-    # B_mx shape: (E, N, K)
+    # B_data shape: (E, N, K)
     # B_scale shape: (E, N, K//block_size)
-    E, N, K = B_mx.shape
+    E, N, K = B_data.shape
 
     # Reshape to be able to do per-scaling group multiplication
-    # B_mx shape: (E, N, K//block_size, block_size)
+    # B_data shape: (E, N, K//block_size, block_size)
     # B_scale shape: (E, N, K//block_size, 1)
-    B_mx = B_mx.reshape(*B_mx.shape[:-1], B_mx.shape[-1] // block_size, block_size)
+    B_data = B_data.reshape(
+        *B_data.shape[:-1], B_data.shape[-1] // block_size, block_size
+    )
     B_scale = B_scale.unsqueeze(-1)
 
     # Rescale and cast to bfloat16
-    B = B_mx.to(torch.bfloat16) * B_scale.to(torch.bfloat16)
+    B = B_data.to(torch.bfloat16) * B_scale.to(torch.bfloat16)
 
     # Reshape back to original shape
     # B shape: (E, K, N)
@@ -471,27 +477,27 @@ def _emulated_mxfp8_scaled_grouped_mm_2d_3d(
 
 
 def _emulated_mxfp8_scaled_grouped_mm_2d_2d(
-    A_mx: torch.Tensor,  # (M, K)
+    A_data: torch.Tensor,  # (M, K)
     A_scale: torch.Tensor,  # (M, K//block_size)
-    B_mx: torch.Tensor,  # (K, N)
+    B_data: torch.Tensor,  # (K, N)
     B_scale: torch.Tensor,  # (K//block_size, N)
     offs: torch.Tensor,
     out_dtype: Optional[torch.dtype] = torch.bfloat16,
     block_size: int = 32,
 ) -> torch.Tensor:
-    assert A_mx.ndim == 2, "A must be 2D"
-    assert B_mx.ndim == 2, "B must be 2D"
+    assert A_data.ndim == 2, "A must be 2D"
+    assert B_data.ndim == 2, "B must be 2D"
     A = torch.zeros(
-        A_mx.shape,
+        A_data.shape,
         dtype=torch.bfloat16,
-        device=A_mx.device,
-        requires_grad=A_mx.requires_grad,
+        device=A_data.device,
+        requires_grad=A_data.requires_grad,
     )
     B = torch.zeros(
-        B_mx.shape,
+        B_data.shape,
         dtype=torch.bfloat16,
-        device=B_mx.device,
-        requires_grad=B_mx.requires_grad,
+        device=B_data.device,
+        requires_grad=B_data.requires_grad,
     )
 
     # Dequantize input per each scaling group
@@ -507,7 +513,7 @@ def _emulated_mxfp8_scaled_grouped_mm_2d_2d(
         # -- Dequantize A tensor
         # A_group shape: (M, group_size)
         # A_scale shape: (M, group_size//block_size)
-        A_group = A_mx[:, group_start_idx:group_end_idx]
+        A_group = A_data[:, group_start_idx:group_end_idx]
         A_group_shape = A_group.shape
 
         # Get scales for this group.
@@ -532,7 +538,7 @@ def _emulated_mxfp8_scaled_grouped_mm_2d_2d(
 
         # -- Dequantize B tensor
         # B_group shape is (group_size, N)
-        B_group = B_mx[group_start_idx:group_end_idx, :]
+        B_group = B_data[group_start_idx:group_end_idx, :]
         B_group_shape = B_group.shape
 
         # Scales shape is (group_size//block_size, N)