[mxfp8 moe training] add triton kernel for mxfp8 quantization along dim0

stack-info: PR: #3128, branch: danielvegamyhre/stack/75

Author: danielvegamyhre

benchmarks/mx_formats/cast_bench.py

@@ -13,6 +13,7 @@
 
 from torchao.prototype.mx_formats.config import ScaleCalculationMode
 from torchao.prototype.mx_formats.kernels import (
+    triton_to_mxfp8_dim0,
     triton_to_mxfp8_dim1,
 )
 from torchao.prototype.mx_formats.mx_tensor import to_mx
@@ -97,6 +98,7 @@ def run(
         "dim0_mxfp8_floor",
         "dim0_mxfp4_floor",
         "dim0_mxfp8_rceil",
+        "dim0_mxfp8_triton_floor",
         "dim1_mxfp8_floor",
         "dim1_mxfp8_rceil",
         "dim1_mxfp8_triton_floor",
@@ -222,6 +224,22 @@ def run(
         bytes_w = (y_d0.numel() + s_d0.numel()) * bytes_per_el_fp8
         bps = (bytes_r + bytes_w) / (time_us / 1e6)
 
+    elif mode == "dim0_mxfp8_triton_floor":
+        y_d0, s_d0 = triton_to_mxfp8_dim0(x, inner_block_size=BLOCK_SIZE)
+
+        for _ in range(2):
+            __ = triton_to_mxfp8_dim0(x, inner_block_size=BLOCK_SIZE)
+        time_us = benchmark_cuda_function_in_microseconds(
+            lambda x, b: triton_to_mxfp8_dim0(x, inner_block_size=BLOCK_SIZE),
+            x,
+            BLOCK_SIZE,
+        )
+        assert y_d0.dtype == torch.float8_e4m3fn
+        assert s_d0.dtype == torch.float8_e8m0fnu
+        bytes_r = x.numel() * bytes_per_el_bf16
+        bytes_w = (y_d0.numel() + s_d0.numel()) * bytes_per_el_fp8
+        bps = (bytes_r + bytes_w) / (time_us / 1e6)
+
     elif mode == "dim1_mxfp8_floor":
         to_mx_dim1_reference_c = torch.compile(to_mx_dim1_reference)
         y_d1, s_d1 = to_mx_dim1_reference_c(x, BLOCK_SIZE)

test/prototype/mx_formats/test_kernels.py

@@ -37,6 +37,7 @@
     pack_uint6,
     triton_f6_e2m3_to_bf16,
     triton_f6_e3m2_to_bf16,
+    triton_to_mxfp8_dim0,
     triton_to_mxfp8_dim1,
     triton_to_mxfp8_dim1_reference,
     unpack_uint4,
@@ -431,6 +432,23 @@ def test_fp6_e3m2_pack_unpack():
     assert torch.all(orig_vals_f6_packed_unpacked == orig_vals)
 
 
+def triton_to_mxfp8_dim0_reference(
+    x_hp: torch.Tensor, block_size
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    A reference version of `triton_to_mxfp8_dim0` for rowwise quantization.
+    """
+    from torchao.prototype.mx_formats.mx_tensor import to_mx
+
+    # cast across dim0 (rowwise) - no transpose needed
+    scale_e8m0_dim0, x_hp_d0_normalized = to_mx(x_hp, torch.float8_e4m3fn, block_size)
+    scale_e8m0_dim0 = scale_e8m0_dim0.view(torch.float8_e8m0fnu)
+    return (
+        x_hp_d0_normalized,
+        scale_e8m0_dim0.unsqueeze(-1),
+    )
+
+
 @pytest.mark.skipif(not has_triton(), reason="unsupported without triton")
 @pytest.mark.skipif(
     not is_sm_at_least_89(),
@@ -446,6 +464,21 @@ def test_triton_mxfp8_dim1_randn(M, K):
     torch.testing.assert_close(x_s_t, x_s_ref, rtol=0, atol=0)
 
 
+@pytest.mark.skipif(not has_triton(), reason="unsupported without triton")
+@pytest.mark.skipif(
+    not is_sm_at_least_89(),
+    reason="float8 in triton requires CUDA capability 8.9 or greater",
+)
+@pytest.mark.parametrize("M", (256, 2048, 131072))
+@pytest.mark.parametrize("K", (256, 5120, 7168))
+def test_triton_mxfp8_dim0_randn(M, K):
+    x = torch.randn(M, K, dtype=torch.bfloat16, device="cuda")
+    x_mx_ref, x_s_ref = triton_to_mxfp8_dim0_reference(x, block_size=32)
+    x_mx_t, x_s_t = triton_to_mxfp8_dim0(x, inner_block_size=32)
+    torch.testing.assert_close(x_mx_t, x_mx_ref, rtol=0, atol=0)
+    torch.testing.assert_close(x_s_t, x_s_ref, rtol=0, atol=0)
+
+
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
 @pytest.mark.parametrize(
     "shape",

torchao/prototype/mx_formats/kernels.py

@@ -829,6 +829,8 @@ def _(uint8_data):
     import triton.language as tl
     from torch.library import triton_op, wrap_triton
 
+    print("importing triton ops")
+
     @triton.jit
     def _triton_calculate_scale(x, axis):
         # There is no good support for accessing globals from a jit'ed triton
@@ -891,13 +893,13 @@ def _get_mxfp8_dim1_kernel_autotune_configs():
 
     @triton.autotune(
         configs=_get_mxfp8_dim1_kernel_autotune_configs(),
-        key=["n_rows", "n_cols", "INNER_BLOCK_SIZE"],
+        key=["n_cols", "INNER_BLOCK_SIZE"],
     )
     @triton.jit
     def to_mxfp8_dim1_kernel(
         x_ptr,  # pointer to input tensor
         output_col_major_ptr,  # pointer to column-major output tensor (column-normalized)
-        col_scale_ptr,  # pointer to store column-wise maximum absolute values
+        col_scale_ptr,  # pointer to store scales
         n_rows,  # number of rows in the tensor
         n_cols,  # number of columns in the tensor
         ROW_TILE_SIZE: tl.constexpr,
@@ -1038,6 +1040,175 @@ def to_mxfp8_dim1_kernel(
         # TODO(future): mask this store
         tl.store(col_scale_start_ptr + col_scale_indices, col_scale_e8m0)
 
+    @triton.autotune(
+        configs=_get_mxfp8_dim1_kernel_autotune_configs(),
+        key=["n_rows", "n_cols", "INNER_BLOCK_SIZE"],
+    )
+    @triton.jit
+    def to_mxfp8_dim0_kernel(
+        x_ptr,
+        output_ptr,
+        row_scale_ptr,
+        n_rows,
+        n_cols,
+        ROW_TILE_SIZE: tl.constexpr,
+        COL_TILE_SIZE: tl.constexpr,
+        INNER_BLOCK_SIZE: tl.constexpr,  # should be 32 for MX
+    ):
+        """
+        Quantizes a high precision tensor to mxfp8 rowwise (1x32 scaling granularity).
+
+        This is the counterpart to to_mxfp8_dim1_kernel which does columnwise quantization.
+        Instead of transposing and scaling across columns, this kernel scales across rows.
+        """
+
+        BLOCKS_PER_COL_TILE: tl.constexpr = COL_TILE_SIZE // INNER_BLOCK_SIZE
+
+        # Get program ID
+        pid_row = tl.program_id(0)
+        pid_col = tl.program_id(1)
+
+        # Calculate starting row and column for this tile
+        start_row = pid_row * ROW_TILE_SIZE
+        start_col = pid_col * COL_TILE_SIZE
+
+        # Create offsets for the block
+        row_offsets = tl.arange(0, ROW_TILE_SIZE)
+        col_offsets = tl.arange(0, COL_TILE_SIZE)
+
+        # Compute global row/col positions
+        rows = start_row + row_offsets[:, None]
+        cols = start_col + col_offsets[None, :]
+
+        # Create masks for out-of-bounds accesses
+        row_mask = rows < n_rows
+        col_mask = cols < n_cols
+        mask = row_mask & col_mask
+
+        # Compute memory offsets for row-major layout (rows, cols)
+        row_major_offsets = (rows * n_cols + cols).to(tl.int32)
+
+        # Load the entire block in a single operation
+        # shape: (ROW_TILE_SIZE, COL_TILE_SIZE)
+        x_block = tl.load(x_ptr + row_major_offsets, mask=mask)
+
+        # Reshape to inner tile size for rowwise scaling
+        # shape: (ROW_TILE_SIZE, COL_TILE_SIZE) -> (ROW_TILE_SIZE * BLOCKS_PER_COL_TILE, INNER_BLOCK_SIZE)
+        x_block_r = x_block.reshape(
+            ROW_TILE_SIZE * BLOCKS_PER_COL_TILE, INNER_BLOCK_SIZE
+        )
+
+        # Calculate the absolute values of elements in the block
+        x_block_abs_r = tl.abs(x_block_r)
+
+        # Find the maximum absolute value for each row (across columns)
+        # shape: (ROW_TILE_SIZE * BLOCKS_PER_COL_TILE,)
+        row_scale_r, row_scale_e8m0_r = _triton_calculate_scale(x_block_abs_r, axis=1)
+
+        # Divide each row by scale
+        # Broadcasting row_scale to match x_block's shape
+        # x_block_r shape (ROW_TILE_SIZE * BLOCKS_PER_COL_TILE, INNER_BLOCK_SIZE)
+        # row_scale shape (ROW_TILE_SIZE * BLOCKS_PER_COL_TILE,) -> (ROW_TILE_SIZE * BLOCKS_PER_COL_TILE, 1)
+        row_normalized_r = x_block_r / row_scale_r[:, None]
+
+        # Reshape back to original tile size
+        row_normalized = tl.reshape(row_normalized_r, ROW_TILE_SIZE, COL_TILE_SIZE)
+
+        # Quantize to float8
+        row_normalized = row_normalized.to(tl.float8e4nv)
+
+        # Store the row-normalized result in row-major format
+        tl.store(output_ptr + row_major_offsets, row_normalized, mask=mask)
+
+        # reshape row_scale_e8m0_r for proper storage
+        # shape: (ROW_TILE_SIZE * BLOCKS_PER_COL_TILE,)
+        row_scale_e8m0 = row_scale_e8m0_r.reshape(ROW_TILE_SIZE * BLOCKS_PER_COL_TILE)
+
+        row_scale_start_offsets = (
+            (pid_row * ROW_TILE_SIZE * (n_cols // COL_TILE_SIZE))
+            * BLOCKS_PER_COL_TILE  # number of blocks seen so far
+            + pid_col * BLOCKS_PER_COL_TILE  # increment BLOCKS_PER_COL_TILE
+        )
+
+        row_scale_start_ptr = row_scale_ptr + row_scale_start_offsets
+
+        # calculate row_scale_indices
+        row_scale_indices = tl.arange(0, ROW_TILE_SIZE * BLOCKS_PER_COL_TILE)
+
+        # How many values are in all the other rows for this col_pid, need to jump
+        # over them for every BLOCKS_PER_COL_TILE values
+        jump_vals_per_row = (n_cols - COL_TILE_SIZE) // INNER_BLOCK_SIZE
+
+        # example transformation (specifics depend on tile sizes):
+        # [0, 1, 2, 3, 4, 5, 6, 7] -> [0, 1, 4, 5, 8, 9, 12, 13]
+        row_scale_indices = row_scale_indices + (
+            (row_scale_indices // BLOCKS_PER_COL_TILE) * jump_vals_per_row
+        )
+
+        # Store the scales
+        tl.store(row_scale_start_ptr + row_scale_indices, row_scale_e8m0)
+
+    @triton_op("torchao::triton_to_mxfp8_dim0", mutates_args={})
+    def triton_to_mxfp8_dim0(
+        x: torch.Tensor, inner_block_size: int = 32
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Input:
+        * `x` - input tensor, in row major memory layout
+        * `inner_block_size` - size of tiles to scale across, default is 32 for MX recipes
+
+        Output:
+        * `output`: the `float8_e4m3fn` values of `x` cast to mxfp8 across dim0 (rowwise)
+        * `row_scale`: the `e8m0` values of `x_scale` used to cast `x` to mxfp8 across dim0
+        """
+        assert x.is_contiguous(), "`x` must be contiguous"
+        assert inner_block_size <= 32
+
+        # Get tensor shape
+        n_rows, n_cols = x.shape
+
+        # Masking of loads and stores is not well tested yet, so for now enforce
+        # shapes which do not need masking. Note that this condition depends on max values of
+        # ROW_TILE_SIZE and COL_TILE_SIZE, which are autotuned above.
+        # TODO(future): implement and test masking and remove this restriction
+        max_row_tile_size = 128
+        max_col_tile_size = 128
+        assert n_rows % max_row_tile_size == 0, "unsupported"
+        assert n_cols % max_col_tile_size == 0, "unsupported"
+
+        # Create output tensors
+        output = torch.empty(
+            (n_rows, n_cols), dtype=torch.float8_e4m3fn, device=x.device
+        )
+
+        # Create scale tensors for rowwise scaling
+        row_scale = torch.empty(
+            (n_rows, n_cols // inner_block_size, 1),
+            dtype=torch.uint8,
+            device=x.device,
+        )
+
+        # Calculate grid dimensions based on tile size
+        grid = lambda META: (
+            triton.cdiv(n_rows, META["ROW_TILE_SIZE"]),
+            triton.cdiv(n_cols, META["COL_TILE_SIZE"]),
+        )
+
+        # Launch the kernel
+        wrap_triton(to_mxfp8_dim0_kernel)[grid](
+            x_ptr=x,
+            output_ptr=output,
+            row_scale_ptr=row_scale,
+            n_rows=n_rows,
+            n_cols=n_cols,
+            INNER_BLOCK_SIZE=inner_block_size,
+        )
+
+        return (
+            output,
+            row_scale.view(torch.float8_e8m0fnu),
+        )
+
     @triton_op("torchao::triton_to_mxfp8_dim1", mutates_args={})
     def triton_to_mxfp8_dim1(
         x: torch.Tensor, inner_block_size: int = 32
@@ -1459,6 +1630,12 @@ def _(scale_tensor):
         return scale_tensor.new_empty((padded_rows, padded_cols))
 else:
 
+    def triton_to_mxfp8_dim0(
+        x: torch.Tensor,
+        inner_block_size=32,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        raise AssertionError("needs torch version 2.8+ and triton")
+
     def triton_to_mxfp8_dim1(
         x, inner_block_size=32
     ) -> Tuple[torch.Tensor, torch.Tensor]: