[wip] triton kernel to cast to mx and write in col-major

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ghstack-source-id: c44a9db
ghstack-comment-id: 2743450537
Pull Request resolved: #1932

Author: vkuzo

benchmarks/mx_formats/cast_bench.py

@@ -5,6 +5,9 @@
 import triton
 from torch._inductor.utils import do_bench_using_profiling
 
+from torchao.prototype.mx_formats.custom_cast import (
+    to_mxfp8_dim1,
+)
 from torchao.prototype.mx_formats.mx_tensor import to_mx
 
 torch.manual_seed(0)
@@ -49,6 +52,12 @@ def to_mx_dim0_reference(x_hp, block_size):
     return data_d0, scale_d0
 
 
+def to_mx_dim1_reference(x_hp, block_size):
+    x_hp = x_hp.t().contiguous()
+    scale_d1, data_d1 = to_mx(x_hp, torch.float8_e4m3fn, 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)
@@ -67,7 +76,7 @@ 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")
+    assert mode in ("dim0", "dim1", "dim0_dim1", "dim0_mx", "dim1_mx", "dim1_mx_triton")
 
     x = torch.randn(M, K, dtype=torch.bfloat16, device="cuda") * 1000
 
@@ -144,6 +153,41 @@ 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":
+        to_mx_dim1_reference_c = torch.compile(to_mx_dim1_reference)
+        y_d1, s_d1 = to_mx_dim1_reference_c(x, BLOCK_SIZE)
+
+        for _ in range(2):
+            __ = to_mx_dim1_reference_c(x, BLOCK_SIZE)
+        time_us = benchmark_cuda_function_in_microseconds(
+            lambda x, b: to_mx_dim1_reference_c(x, BLOCK_SIZE),
+            x,
+            BLOCK_SIZE,
+        )
+
+        assert y_d1.dtype == torch.float8_e4m3fn
+        assert s_d1.dtype == torch.uint8
+        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_triton":
+        y_d1, s_d1 = to_mxfp8_dim1(x, inner_block_size=BLOCK_SIZE)
+
+        for _ in range(2):
+            __ = to_mxfp8_dim1(x, inner_block_size=BLOCK_SIZE)
+        time_us = benchmark_cuda_function_in_microseconds(
+            lambda x, b: to_mxfp8_dim1(x, inner_block_size=BLOCK_SIZE),
+            x,
+            BLOCK_SIZE,
+        )
+
+        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}")
 

test/prototype/mx_formats/test_custom_cast.py

@@ -26,6 +26,9 @@
     get_bits,
     pack_uint4,
     pack_uint6,
+    # TODO(before land): better name?
+    to_mxfp8_dim1,
+    to_mxfp8_dim1_reference,
     triton_f4_to_bf16,
     triton_f6_e2m3_to_bf16,
     triton_f6_e3m2_to_bf16,
@@ -42,7 +45,11 @@
     sem_vals_to_f32,
 )
 from torchao.prototype.mx_formats.mx_tensor import MXTensor
-from torchao.utils import TORCH_VERSION_AT_LEAST_2_8, is_sm_at_least_100
+from torchao.utils import (
+    TORCH_VERSION_AT_LEAST_2_8,
+    is_sm_at_least_89,
+    is_sm_at_least_100,
+)
 
 torch.manual_seed(0)
 
@@ -444,3 +451,18 @@ def test_fp6_e3m2_pack_unpack():
         torch.float32
     )
     assert torch.all(orig_vals_f6_packed_unpacked == orig_vals)
+
+
+@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))
+@pytest.mark.parametrize("K", (256, 2048))
+def test_triton_mxfp8_dim1(M, K):
+    x = torch.randn(M, K, dtype=torch.bfloat16, device="cuda")
+    x_mx_ref, x_s_ref = to_mxfp8_dim1_reference(x, block_size=32)
+    x_mx_t, x_s_t = to_mxfp8_dim1(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)