Add CUDA kernel for MXFP8 dim1 casting

benchmarks/mx_formats/cast_bench.py

@@ -4,12 +4,12 @@
 # This source code is licensed under the BSD 3-Clause license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Callable, Tuple
+from typing import Tuple
 
 import fire
 import torch
 import triton
-from torch._inductor.utils import do_bench_using_profiling
+from triton.testing import do_bench
 
 from torchao.prototype.mx_formats.kernels import (
     triton_to_mxfp8_dim1,
@@ -64,11 +64,8 @@ def to_mx_dim1_reference(x_hp, block_size):
     return data_d1.t(), scale_d1
 
 
-def benchmark_cuda_function_in_microseconds(func: Callable, *args, **kwargs) -> float:
-    """Thin wrapper around do_bench_using_profiling"""
-    no_args = lambda: func(*args, **kwargs)
-    time = do_bench_using_profiling(no_args)
-    return time * 1e3
+def benchmark_cuda_function_in_microseconds(f, *args):
+    return do_bench(lambda: f(*args), return_mode="median") * 1e3
 
 
 def run(
@@ -82,7 +79,16 @@ def run(
     print(f"torch version: {torch.__version__}")
     print(f"triton version: {triton.__version__}")
     print(f"mode: {mode}")
-    assert mode in ("dim0", "dim1", "dim0_dim1", "dim0_mx", "dim1_mx", "dim1_mx_triton")
+    assert mode in (
+        "dim0",
+        "dim1",
+        "dim0_dim1",
+        "dim0_mx_floor",
+        "dim1_mx_floor",
+        "dim1_mx_triton_floor",
+        "dim1_mx_cuda_floor",
+        "dim1_mx_cuda_rceil",
+    )
 
     x = torch.randn(M, K, dtype=torch.bfloat16, device="cuda") * 1000
 
@@ -141,7 +147,7 @@ def run(
         )
         bps = bytes_rw / (time_us / 1e6)
 
-    elif mode == "dim0_mx":
+    elif mode == "dim0_mx_floor":
         to_mx_dim0_reference_c = torch.compile(to_mx_dim0_reference)
         y_d0, s_d0 = to_mx_dim0_reference_c(x, BLOCK_SIZE)
 
@@ -159,7 +165,7 @@ def run(
         bytes_w = (y_d0.numel() + s_d0.numel()) * bytes_per_el_fp8
         bps = (bytes_r + bytes_w) / (time_us / 1e6)
 
-    elif mode == "dim1_mx":
+    elif mode == "dim1_mx_floor":
         to_mx_dim1_reference_c = torch.compile(to_mx_dim1_reference)
         y_d1, s_d1 = to_mx_dim1_reference_c(x, BLOCK_SIZE)
 
@@ -177,7 +183,7 @@ def run(
         bytes_w = (y_d1.numel() + s_d1.numel()) * bytes_per_el_fp8
         bps = (bytes_r + bytes_w) / (time_us / 1e6)
 
-    elif mode == "dim1_mx_triton":
+    elif mode == "dim1_mx_triton_floor":
         y_d1, s_d1 = triton_to_mxfp8_dim1(x, inner_block_size=BLOCK_SIZE)
 
         for _ in range(2):
@@ -194,6 +200,58 @@ def run(
         bytes_w = (y_d1.numel() + s_d1.numel()) * bytes_per_el_fp8
         bps = (bytes_r + bytes_w) / (time_us / 1e6)
 
+    elif mode == "dim1_mx_cuda_floor":
+        from torchao.prototype import mxfp8_cuda
+
+        _, y_d1, _, s_d1 = mxfp8_cuda.quantize(
+            x, rowwise=False, colwise=True, scaling_mode="floor"
+        )
+
+        for _ in range(2):
+            __ = mxfp8_cuda.quantize(
+                x, rowwise=False, colwise=True, scaling_mode="floor"
+            )
+
+        time_us = benchmark_cuda_function_in_microseconds(
+            lambda x: mxfp8_cuda.quantize(
+                x, rowwise=False, colwise=True, scaling_mode="floor"
+            ),
+            x,
+        )
+
+        assert y_d1.dtype == torch.float8_e4m3fn
+        assert s_d1.dtype == torch.float8_e8m0fnu
+
+        bytes_r = x.numel() * bytes_per_el_bf16
+        bytes_w = (y_d1.numel() + s_d1.numel()) * bytes_per_el_fp8
+        bps = (bytes_r + bytes_w) / (time_us / 1e6)
+
+    elif mode == "dim1_mx_cuda_rceil":
+        from torchao.prototype import mxfp8_cuda
+
+        _, y_d1, _, s_d1 = mxfp8_cuda.quantize(
+            x, rowwise=False, colwise=True, scaling_mode="rceil"
+        )
+
+        for _ in range(2):
+            __ = mxfp8_cuda.quantize(
+                x, rowwise=False, colwise=True, scaling_mode="rceil"
+            )
+
+        time_us = benchmark_cuda_function_in_microseconds(
+            lambda x: mxfp8_cuda.quantize(
+                x, rowwise=False, colwise=True, scaling_mode="rceil"
+            ),
+            x,
+        )
+
+        assert y_d1.dtype == torch.float8_e4m3fn
+        assert s_d1.dtype == torch.float8_e8m0fnu
+
+        bytes_r = x.numel() * bytes_per_el_bf16
+        bytes_w = (y_d1.numel() + s_d1.numel()) * bytes_per_el_fp8
+        bps = (bytes_r + bytes_w) / (time_us / 1e6)
+
     else:
         raise AssertionError(f"unknown mode {mode}")
 

setup.py

@@ -490,6 +490,13 @@ def get_extensions():
     if use_cuda:
         sources += cuda_sources
 
+    # Add MXFP8 cuda extension dir
+    mxfp8_extension_dir = os.path.join(extensions_dir, "cuda", "mx_kernels")
+    mxfp8_sources_to_exclude = list(
+        glob.glob(os.path.join(mxfp8_extension_dir, "**/*"), recursive=True)
+    )
+    sources = [s for s in sources if s not in mxfp8_sources_to_exclude]
+
     # TOOD: Remove this and use what CUDA has once we fix all the builds.
     if use_rocm:
         # Add ROCm GPU architecture check
@@ -610,6 +617,36 @@ def get_extensions():
             )
         )
 
+    # Add the mxfp8 casting CUDA extension
+    if use_cuda:
+        mxfp8_sources = [
+            os.path.join(mxfp8_extension_dir, "mxfp8_extension.cpp"),
+            os.path.join(mxfp8_extension_dir, "mxfp8_cuda.cu"),
+        ]
+
+        # Only add the extension if the source files exist AND we are building for sm100
+        mxfp8_src_files_exist = all(os.path.exists(f) for f in mxfp8_sources)
+        if mxfp8_src_files_exist and build_for_sm100a:
+            print("Building mxfp8_cuda extension")
+            ext_modules.append(
+                CUDAExtension(
+                    name="torchao.prototype.mxfp8_cuda",
+                    sources=mxfp8_sources,
+                    include_dirs=[
+                        mxfp8_extension_dir,  # For mxfp8_quantize.cuh, mxfp8_extension.cpp, and mxfp8_cuda.cu
+                        "/usr/local/cuda-12.8/include",  # CUDA 12.8 headers
+                    ],
+                    library_dirs=[
+                        "/usr/local/cuda-12.8/lib64",  # CUDA 12.8 libraries
+                    ],
+                    extra_compile_args={
+                        "cxx": ["-std=c++17", "-O3"],
+                        "nvcc": nvcc_args,
+                    },
+                    extra_link_args=["-lcuda", "-lcudart"],
+                ),
+            )
+
     # Only build the cutlass_90a extension if sm90a is in the architecture flags
     if (
         cutlass_90a_sources is not None

test/prototype/mx_formats/test_kernels.py

@@ -42,7 +42,7 @@
     triton_to_mxfp8_dim1_reference,
     unpack_uint4,
 )
-from torchao.prototype.mx_formats.mx_tensor import MXTensor
+from torchao.prototype.mx_formats.mx_tensor import MXTensor, ScaleCalculationMode, to_mx
 from torchao.prototype.mx_formats.utils import to_blocked
 from torchao.utils import (
     TORCH_VERSION_AT_LEAST_2_8,
@@ -56,6 +56,15 @@
     pytest.skip("Unsupported PyTorch version", allow_module_level=True)
 
 
+# TODO: shared utils file for benchmarking and testing
+def to_mx_dim1_reference(x_hp, block_size, scaling_mode):
+    x_hp = x_hp.t().contiguous()
+    scale_d1, data_d1 = to_mx(
+        x_hp, torch.float8_e4m3fn, block_size, scaling_mode=scaling_mode
+    )
+    return data_d1.t(), scale_d1
+
+
 @pytest.mark.skip(
     reason="TODO debug CI failure, low pri since this is not used in the MX code"  # noqa: E501
 )
@@ -488,3 +497,99 @@ def test_rearrange(shape):
     eager = to_blocked(scales, False)
     triton = to_blocked(scales, True)
     torch.testing.assert_close(eager, triton, atol=0, rtol=0)
+
+
+@pytest.mark.skipif(
+    not is_sm_at_least_100(),
+    reason="MXFP8 requires CUDA capability 10.0 or greater",
+)
+@pytest.mark.parametrize("M", (32, 64, 2048))
+@pytest.mark.parametrize("K", (32, 64, 2048))
+@pytest.mark.parametrize("input_dtype", (torch.float32, torch.bfloat16))
+@pytest.mark.parametrize(
+    "scaling_mode", (ScaleCalculationMode.FLOOR, ScaleCalculationMode.RCEIL)
+)
+def test_cuda_mx_dim1_numerics(M, K, input_dtype, scaling_mode):
+    from torchao.prototype import mxfp8_cuda
+
+    scaling_mode_str = (
+        "floor" if scaling_mode == ScaleCalculationMode.FLOOR else "rceil"
+    )
+    block_size = 32
+
+    # Use disinct incrementing values from 0 to M*K-1 to make debugging easier.
+    x = (
+        torch.arange(0, M * K, dtype=input_dtype, device="cuda")
+        .reshape(M, K)
+        .contiguous()
+    )
+
+    y_d1_ref, s_d1_ref = to_mx_dim1_reference(
+        x,
+        block_size=block_size,
+        scaling_mode=scaling_mode,
+    )
+
+    _, y_d1, _, s_d1 = mxfp8_cuda.quantize(
+        x,
+        rowwise=False,
+        colwise=True,
+        scaling_mode=scaling_mode_str,
+        scale_dim_x=1,
+        scale_dim_y=block_size,
+    )
+
+    # check scales
+    torch.testing.assert_close(s_d1, s_d1_ref, rtol=0, atol=0)
+
+    # check quantized values
+    torch.testing.assert_close(y_d1, y_d1_ref, rtol=0, atol=0)
+    assert y_d1.stride() == y_d1_ref.stride(), "quantized tensor strides do not match"
+
+
+@pytest.mark.skipif(
+    not is_sm_at_least_100(),
+    reason="MXFP8 requires CUDA capability 10.0 or greater",
+)
+def test_cuda_mx_dim0_not_supported():
+    from torchao.prototype import mxfp8_cuda
+
+    M, K = 64, 64
+    block_size = 32
+    x = (
+        torch.arange(0, M * K, dtype=torch.bfloat16, device="cuda")
+        .reshape(M, K)
+        .contiguous()
+    )
+    with pytest.raises(RuntimeError):
+        _, y_d1, _, s_d1 = mxfp8_cuda.quantize(
+            x,
+            rowwise=True,
+            colwise=False,
+            scale_dim_x=block_size,
+            scale_dim_y=1,
+        )
+
+
+@pytest.mark.skipif(
+    not is_sm_at_least_100(),
+    reason="MXFP8 requires CUDA capability 10.0 or greater",
+)
+def test_cuda_mx_dim1_invalid_block_size():
+    from torchao.prototype import mxfp8_cuda
+
+    M, K = 64, 64
+    x = (
+        torch.arange(0, M * K, dtype=torch.bfloat16, device="cuda")
+        .reshape(M, K)
+        .contiguous()
+    )
+    invalid_block_size = 4
+    with pytest.raises(RuntimeError):
+        _, y_d1, _, s_d1 = mxfp8_cuda.quantize(
+            x,
+            rowwise=False,
+            colwise=True,
+            scale_dim_x=1,
+            scale_dim_y=invalid_block_size,
+        )