tests/test_triton_scaled_mm.py

# SPDX-License-Identifier: Apache-2.0
"""Tests for the triton_scaled_mm kernel

Run `pytest tests/kernels/test_triton_scaled_mm.py`.
"""
from typing import Optional

import pytest
import torch

from triton_scaled_mm import triton_scaled_mm

device = "cuda"


def scaled_mm_torch(
    a: torch.Tensor,
    b: torch.Tensor,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    out_dtype: type[torch.dtype],
    bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    out = torch.mm(a.to(torch.float32), b.to(torch.float32))
    out = scale_a * out
    out = scale_b.T * out
    out = out.to(out_dtype)
    if bias is not None:
        out = out + bias

    return out


def get_8bit_types():
    types = [torch.int8]
    minor, major = torch.cuda.get_device_capability()
    capability = major * 10 + minor
    supports_fp8 = capability >= 89

    if supports_fp8 and torch.version.hip is not None:
        types.append(torch.float8_e4m3fnuz)
    elif supports_fp8 and torch.version.cuda is not None and torch.cuda.is_available():
        types.append(torch.float8_e4m3fn)
    return types


@pytest.mark.parametrize("M", [1, 33, 64, 512])
@pytest.mark.parametrize("N", [256, 971, 20486])
@pytest.mark.parametrize("K", [128, 496, 1024])
@pytest.mark.parametrize("out_dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("in_dtype", get_8bit_types())
@pytest.mark.parametrize("use_scalar_scale_a", [True, False])
@pytest.mark.parametrize("use_scalar_scale_b", [True, False])
@pytest.mark.parametrize("use_bias", [True, False])
def test_scaled_mm(
    M, N, K, in_dtype, out_dtype, use_scalar_scale_a, use_scalar_scale_b, use_bias
):
    is_floating_point_type = lambda t: torch.tensor([1, 1], dtype=t).is_floating_point()

    torch.manual_seed(0)

    # NOTE: There are cases, where if the matrix is large enough, an output
    # like 65504.4 can be produced, and can easily turn into inf when
    # multiplied when using float16/bfloat16.  This means one function, e.g.,
    # testing function, and another function, e.g. golden function, can
    # produce a non-inf value while the other produces an inf value, and
    # will cause assert_close/allclose to fail, even though if overflow
    # wouldn't have occurred, the values would have been "close."
    #
    # So, the values here are kept small enough to avoid this situation.
    if is_floating_point_type(in_dtype):
        a = (0.25 * torch.rand((M, K), dtype=torch.float32, device=device)).to(in_dtype)
        b = (0.25 * torch.rand((K, N), dtype=torch.float32, device=device)).to(in_dtype)
    else:
        a = torch.randint(-32, 32, (M, K), dtype=in_dtype, device=device)
        b = torch.randint(-32, 32, (K, N), dtype=in_dtype, device=device)

    if use_scalar_scale_a:
        scale_a = torch.rand((1, 1), device=device)
    else:
        scale_a = 0.25 * torch.rand((M, 1), device=device)

    if use_scalar_scale_b:
        scale_b = torch.rand((1, 1), device=device)
    else:
        scale_b = 0.25 * torch.rand((N, 1), device=device)

    bias = None
    if use_bias:
        bias = torch.rand((N,), device=device, dtype=out_dtype)

    c_check = triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)

    a_cpu = a.cpu()
    b_cpu = b.cpu()
    scale_a_cpu = scale_a.cpu()
    scale_b_cpu = scale_b.cpu()
    bias_cpu = None if bias is None else bias.cpu()

    c_actual = scaled_mm_torch(
        a_cpu, b_cpu, scale_a_cpu, scale_b_cpu, out_dtype, bias_cpu
    )

    c_check_cpu = c_check.cpu()
    torch.testing.assert_close(c_check_cpu, c_actual, rtol=1e-1, atol=1e-1)