[mxfp8 moe training] wrap 3d quantize tensor in custom ops and integrate it

stack-info: PR: #3003, branch: danielvegamyhre/stack/70

Author: danielvegamyhre

benchmarks/prototype/moe_training/bench_2d_3d_grouped_gemm.py

@@ -17,7 +17,7 @@
 from benchmarks.utils import benchmark_cuda_function_in_microseconds
 from torchao.float8.config import ScalingGranularity
 from torchao.float8.float8_utils import tensor_to_scale, to_fp8_saturated
-from torchao.prototype.moe_training.kernels.mxfp8_blocked_scales import (
+from torchao.prototype.moe_training.kernels.mxfp8 import (
     torch_to_blocked_2d_M_groups,
     torch_to_blocked_per_group_3d,
 )

benchmarks/prototype/moe_training/benchmark_scaled_grouped_mm_dq.py

@@ -62,28 +62,28 @@ def get_configs() -> List[ExperimentConfig]:
         (16640, 8192, 5120, 2),
         (16640, 8192, 5120, 4),
         (16640, 8192, 5120, 8),
-        (16640, 5120, 8192, 1),
-        (16640, 5120, 8192, 2),
-        (16640, 5120, 8192, 4),
-        (16640, 5120, 8192, 8),
-        # DSV3 236B with various experts per device (i.e., different EP degrees)
-        (16640, 5120, 1536, 1),
-        (16640, 5120, 1536, 2),
-        (16640, 5120, 1536, 4),
-        (16640, 5120, 1536, 8),
-        (16640, 1536, 5120, 1),
-        (16640, 1536, 5120, 2),
-        (16640, 1536, 5120, 4),
-        (16640, 1536, 5120, 8),
-        # DSV3 671B with various experts per device (i.e., different EP degrees)
-        (16640, 7168, 2048, 1),
-        (16640, 7168, 2048, 2),
-        (16640, 7168, 2048, 4),
-        (16640, 7168, 2048, 8),
-        (16640, 2048, 7168, 1),
-        (16640, 2048, 7168, 2),
-        (16640, 2048, 7168, 4),
-        (16640, 2048, 7168, 8),
+        # (16640, 5120, 8192, 1),
+        # (16640, 5120, 8192, 2),
+        # (16640, 5120, 8192, 4),
+        # (16640, 5120, 8192, 8),
+        # # DSV3 236B with various experts per device (i.e., different EP degrees)
+        # (16640, 5120, 1536, 1),
+        # (16640, 5120, 1536, 2),
+        # (16640, 5120, 1536, 4),
+        # (16640, 5120, 1536, 8),
+        # (16640, 1536, 5120, 1),
+        # (16640, 1536, 5120, 2),
+        # (16640, 1536, 5120, 4),
+        # (16640, 1536, 5120, 8),
+        # # DSV3 671B with various experts per device (i.e., different EP degrees)
+        # (16640, 7168, 2048, 1),
+        # (16640, 7168, 2048, 2),
+        # (16640, 7168, 2048, 4),
+        # (16640, 7168, 2048, 8),
+        # (16640, 2048, 7168, 1),
+        # (16640, 2048, 7168, 2),
+        # (16640, 2048, 7168, 4),
+        # (16640, 2048, 7168, 8),
     ]
     recipes = [MoEScalingType.FP8_ROWWISE, MoEScalingType.MXFP8]
     high_precision_dtypes = [torch.bfloat16]

benchmarks/prototype/moe_training/mxfp8/bench_quantize_3d.py

@@ -13,7 +13,7 @@
 from tqdm import tqdm
 
 from benchmarks.utils import benchmark_cuda_function_in_microseconds
-from torchao.prototype import mxfp8_cuda
+from torchao.prototype.moe_training.kernels.mxfp8 import mxfp8_quantize_cuda_3d
 from torchao.prototype.moe_training.scaled_grouped_mm import (
     _to_mxfp8_dim1_3d,
 )
@@ -110,9 +110,9 @@ def using_to_mx(x: torch.Tensor) -> torch.Tensor:
     )
 
     # bench 3d cuda kernel
-    data_cuda_3d, scales_cuda_3d = mxfp8_cuda.quantize_3d(input_tensor)
+    data_cuda_3d, scales_cuda_3d = mxfp8_quantize_cuda_3d(input_tensor)
     time_cuda_3d_us = benchmark_cuda_function_in_microseconds(
-        mxfp8_cuda.quantize_3d,
+        mxfp8_quantize_cuda_3d,
         input_tensor,
     )
 

benchmarks/prototype/moe_training/mxfp8/bench_triton_mx_block_rearrange_2d_M_groups.py

@@ -14,7 +14,7 @@
 from tqdm import tqdm
 
 from benchmarks.utils import benchmark_cuda_function_in_microseconds
-from torchao.prototype.moe_training.kernels.mxfp8_blocked_scales import (
+from torchao.prototype.moe_training.kernels.mxfp8 import (
     compute_blocked_scale_offsets_for_M_groups,
     torch_to_blocked_2d_M_groups,
     triton_mx_block_rearrange_2d_M_groups,

benchmarks/prototype/moe_training/mxfp8/bench_triton_mx_block_rearrange_per_group_3d.py

@@ -13,7 +13,7 @@
 from tqdm import tqdm
 
 from benchmarks.utils import benchmark_cuda_function_in_microseconds
-from torchao.prototype.moe_training.kernels.mxfp8_blocked_scales import (
+from torchao.prototype.moe_training.kernels.mxfp8 import (
     torch_to_blocked_per_group_3d,
     triton_mx_block_rearrange_per_group_3d,
 )

test/prototype/moe_training/test_kernels.py

@@ -20,7 +20,7 @@
     triton_fp8_per_group_colwise_scales,
     triton_fp8_per_group_rowwise_scales,
 )
-from torchao.prototype.moe_training.kernels.mxfp8_blocked_scales import (
+from torchao.prototype.moe_training.kernels.mxfp8 import (
     compute_blocked_scale_offsets_for_K_groups,
     compute_blocked_scale_offsets_for_M_groups,
     torch_to_blocked_2d_K_groups,

torchao/prototype/moe_training/kernels/mxfp8_blocked_scales.py renamed to torchao/prototype/moe_training/kernels/mxfp8.py

@@ -1,3 +1,4 @@
+import logging
 from typing import Tuple
 
 import torch
@@ -7,7 +8,10 @@
 from torch.library import triton_op, wrap_triton
 
 from torchao.prototype.mx_formats.utils import to_blocked
-from torchao.utils import ceil_div
+from torchao.utils import (
+    ceil_div,
+    is_sm_at_least_100,
+)
 
 
 def torch_to_blocked_2d_M_groups(
@@ -620,3 +624,77 @@ def _dest_indices_for_block(
     # Flatten
     dest_indices_flat = tl.reshape(dest_indices, (BLOCK_ROWS * BLOCK_COLS))
     return dest_indices_flat
+
+
+mxfp8_cuda_extension_available = False
+if is_sm_at_least_100():
+    try:
+        # MXFP8 CUDA kernel is only built on SM100+. Furthermore,
+        # currently our CI runners are not SM100+, so the user needs to build
+        # from source.
+        # TODO(#2932): improve this
+        from torchao.prototype import mxfp8_cuda
+
+        mxfp8_cuda_extension_available = True
+    except ImportError:
+        logging.debug("Skipping import of torchao.prototype.mxfp8_cuda")
+
+if mxfp8_cuda_extension_available:
+    # TODO: Make `scaling_mode` a choice (enum-like) rather than arbitrary string.
+    # Currently we have to use an arbitrary string because custom ops don't support enum
+    # params.
+    @torch.library.custom_op("torchao::mxfp8_quantize_cuda_3d", mutates_args=())
+    def mxfp8_quantize_cuda_3d(
+        x: torch.Tensor,
+        block_size: int = 32,
+        scaling_mode: str = "floor",
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Quantizes a 3D tensor of shape (E,N,K) to MXFP8 format, scaling along N.
+
+        Args:
+            x (torch.Tensor): Input tensor to be quantized.
+            block_size (int, optional): Block size for quantization. Defaults to 32.
+            scaling_mode (str, optional): Scaling mode for quantization. Defaults to "floor".
+
+        Returns:
+            torch.Tensor: quantized tensor
+            torch.Tensor: scales tensor
+        """
+        assert x.ndim == 3, "Input tensor must be 3D"
+        assert x.dtype in (torch.float32, torch.bfloat16), (
+            "Input tensor must be float32 or bfloat16"
+        )
+        q_data, scales = mxfp8_cuda.quantize_3d(
+            x, scale_dim_n=block_size, scaling_mode=scaling_mode
+        )
+        return q_data, scales
+
+    @mxfp8_quantize_cuda_3d.register_fake
+    def _fake_mxfp8_quantize_cuda_3d(
+        x: torch.Tensor,
+        block_size: int = 32,
+        scaling_mode: str = "floor",
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        assert x.ndim == 3, "Input tensor must be 3D"
+        assert x.dtype in (torch.float32, torch.bfloat16), (
+            "Input tensor must be float32 or bfloat16"
+        )
+        E, N, K = x.shape
+        # Quantized tensor is in column major layouts
+        q_data = x.new_empty(x.shape, dtype=torch.float8_e4m3fn).as_strided(
+            x.shape, (N * K, 1, N)
+        )
+        scales = x.new_empty((E, N // block_size, K), dtype=torch.float8_e8m0fnu)
+        return q_data, scales
+
+else:
+
+    def mxfp8_quantize_cuda_3d(
+        x: torch.Tensor,
+        block_size: int = 32,
+        scaling_mode: str = "floor",
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        raise NotImplementedError(
+            "mxfp8_quantize_cuda_3d is not implemented on this device"
+        )

torchao/prototype/moe_training/kernels/mxfp8_gemms.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from torchao.prototype.moe_training.kernels.mxfp8_blocked_scales import (
+from torchao.prototype.moe_training.kernels.mxfp8 import (
     torch_to_blocked_2d_M_groups,
     torch_to_blocked_per_group_3d,
 )

torchao/prototype/moe_training/scaled_grouped_mm.py

@@ -16,9 +16,10 @@
     triton_fp8_per_group_colwise_scales,
     triton_fp8_rowwise_3d_transpose_rhs,
 )
-from torchao.prototype.moe_training.kernels.mxfp8_blocked_scales import (
+from torchao.prototype.moe_training.kernels.mxfp8 import (
     compute_blocked_scale_offsets_for_K_groups,
     compute_blocked_scale_offsets_for_M_groups,
+    mxfp8_quantize_cuda_3d,
     triton_mx_block_rearrange_2d_K_groups,
     triton_mx_block_rearrange_2d_M_groups,
     triton_mx_block_rearrange_per_group_3d,
@@ -354,18 +355,15 @@ def backward(ctx, grad_out: torch.Tensor):
             grad_out, elem_dtype=torch.float8_e4m3fn, block_size=block_size
         )
 
-        # B_data shape: (E, K, N)
-        # B_scale shape: (E, K, N//block_size)
-        B_scales_ref, B_data_ref = 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,
-            block_size=block_size,
+        # Quantize 3d expert weights along N (contraction dimension for next grouped gemm)
+        # (E, K, N) -> (E, N, K)
+        B = B_t.transpose(-2, -1)
+        B_data, B_scales = mxfp8_quantize_cuda_3d(
+            B._data if hasattr(B, "_data") else B, block_size=block_size
         )
 
-        # Experiment with cuda kernel
-        B = B_t.transpose(-2, -1)
-        B_scales, B_data = _to_mxfp8_dim1_3d(B, block_size=block_size)
+        # (E, N//block_size, K) -> (E, K, N//block_size)
+        B_scales = B_scales.transpose(-2, -1)
 
         # Convert scales to blocked format for 2d-3d grouped mm
         grad_out_scales_blocked = triton_mx_block_rearrange_2d_M_groups(
@@ -400,6 +398,7 @@ def backward(ctx, grad_out: torch.Tensor):
         grad_out_t_scales = grad_out_t_mx._scale_e8m0
 
         # Transpose A so we can scale along the M dimension, then un-transpose.
+        # A shape: (M, K)
         # A_t_data shape: (K, M)
         # A_t_scales shape: (K, M//block_size)
         A_t_mx = _to_mxfp8_dim1_kernel_wrapper(