Update

[ghstack-poisoned]

examples/sam2_amg_server/generate_data.py

@@ -60,6 +60,8 @@ def latencies_statistics(data):
     mean = np.mean(data_array)
     # Calculate the median
     median = np.median(data_array)
+    # Calculate the 90th percentile
+    p90 = np.percentile(data_array, 90)
     # Calculate the 95th percentile
     p95 = np.percentile(data_array, 95)
     # Calculate the 99th percentile
@@ -74,6 +76,7 @@ def latencies_statistics(data):
         {
             "mean": mean,
             "median": median,
+            "p90": p90,
             "p95": p95,
             "p99": p99,
             "p999": p999,

setup.py

@@ -215,10 +215,7 @@ def get_extensions():
     extra_link_args = []
     extra_compile_args = {
         "cxx": [f"-DPy_LIMITED_API={PY3_9_HEXCODE}"],
-        "nvcc": [
-            "-O3" if not debug_mode else "-O0",
-            "-t=0",
-        ],
+        "nvcc": ["-O3" if not debug_mode else "-O0", "-t=0", "-std=c++17"],
     }
 
     if not IS_WINDOWS:
@@ -257,12 +254,16 @@ def get_extensions():
         use_cutlass = True
         cutlass_dir = os.path.join(third_party_path, "cutlass")
         cutlass_include_dir = os.path.join(cutlass_dir, "include")
+        cutlass_tools_include_dir = os.path.join(
+            cutlass_dir, "tools", "util", "include"
+        )
         cutlass_extensions_include_dir = os.path.join(cwd, extensions_cuda_dir)
     if use_cutlass:
         extra_compile_args["nvcc"].extend(
             [
                 "-DTORCHAO_USE_CUTLASS",
                 "-I" + cutlass_include_dir,
+                "-I" + cutlass_tools_include_dir,
                 "-I" + cutlass_extensions_include_dir,
             ]
         )

test/prototype/mx_formats/test_mx_mm.py

@@ -0,0 +1,74 @@
+import pytest
+import torch
+
+from torchao.float8.float8_utils import compute_error
+from torchao.ops import mx_fp4_bf16, mx_fp8_bf16
+from torchao.prototype.mx_formats.mx_tensor import DTYPE_FP4, MXTensor
+from torchao.prototype.mx_formats.utils import to_blocked
+from torchao.utils import TORCH_VERSION_AT_LEAST_2_4, is_sm_at_least_100
+
+if not TORCH_VERSION_AT_LEAST_2_4:
+    pytest.skip("Unsupported PyTorch version", allow_module_level=True)
+
+
+def run_matrix_test(M: int, K: int, N: int, format) -> float:
+    dtype = torch.bfloat16
+    device = torch.device("cuda")
+
+    a = torch.rand((M, K), dtype=dtype, device=device)
+    b = torch.rand((N, K), dtype=dtype, device=device)
+
+    fmt = torch.float8_e4m3fn if format == "fp8" else DTYPE_FP4
+    mx_func = mx_fp8_bf16 if format == "fp8" else mx_fp4_bf16
+
+    a_mx = MXTensor.to_mx(a, fmt, 32)
+    b_mx = MXTensor.to_mx(b, fmt, 32)
+
+    a_data = a_mx._data
+    b_data = b_mx._data
+    assert b_data.is_contiguous()
+    b_data = b_data.transpose(-1, -2)
+
+    a_scale = a_mx._scale_e8m0.view(M, K // 32)
+    b_scale = b_mx._scale_e8m0.view(N, K // 32)
+
+    a_scale_block = to_blocked(a_scale)
+    b_scale_block = to_blocked(b_scale)
+
+    out_hp = a_mx.to_dtype(torch.bfloat16) @ b_mx.to_dtype(torch.bfloat16).transpose(
+        -1, -2
+    )
+    out = mx_func(a_data, b_data, a_scale_block, b_scale_block)
+
+    return compute_error(out_hp, out).item()
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(
+    not is_sm_at_least_100(), reason="CUDA capability >= 10.0 required for mxfloat8"
+)
+@pytest.mark.parametrize(
+    "size",
+    [
+        (128, 128, 128),
+        (256, 256, 256),
+        (384, 384, 384),  # Small
+        (512, 512, 512),
+        (768, 768, 768),  # Medium
+        (1024, 1024, 1024),
+        (8192, 8192, 8192),  # Large
+        (128, 256, 384),
+        (256, 384, 512),  # Non-square
+        (129, 256, 384),
+        (133, 512, 528),  # Non-aligned
+    ],
+    ids=lambda x: f"{x[0]}x{x[1]}x{x[2]}",
+)
+@pytest.mark.parametrize("format", ["fp8", "fp4"])
+def test_matrix_multiplication(size, format):
+    M, K, N = size
+    sqnr = run_matrix_test(M, K, N, format)
+    threshold = 80.0
+    assert (
+        sqnr >= threshold
+    ), f"{format} SQNR {sqnr} below threshold for dims {M}x{K}x{N}"