import pytest
import torch
from sgl_kernel import int8_scaled_mm


def to_int8(tensor: torch.Tensor) -> torch.Tensor:
    return torch.round(tensor.clamp(min=-128, max=127)).to(dtype=torch.int8)


def torch_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias):
    o = torch.matmul(a.to(torch.float32), b.to(torch.float32))
    if bias is not None:
        o = o.to(torch.float32) * scale_a.view(-1, 1) * scale_b.view(1, -1) + bias
    else:
        o = o.to(torch.float32) * scale_a.view(-1, 1) * scale_b.view(1, -1)
    return o.to(out_dtype)


def _test_accuracy_once(M, N, K, with_bias, out_dtype, device):
    a = to_int8(torch.randn((M, K), device=device) * 5)
    b = to_int8(torch.randn((N, K), device=device).t() * 5)
    scale_a = torch.randn((M,), device="cuda", dtype=torch.float32)
    scale_b = torch.randn((N,), device="cuda", dtype=torch.float32)
    if with_bias:
        bias = torch.randn((N,), device="cuda", dtype=out_dtype) * 10
    else:
        bias = None
    o = int8_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
    o1 = torch_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
    torch.testing.assert_close(o, o1)


@pytest.mark.parametrize("M", [1, 16, 32, 64, 128, 512, 1024, 4096, 8192])
@pytest.mark.parametrize("N", [16, 128, 512, 1024, 4096, 8192, 16384])
@pytest.mark.parametrize("K", [512, 1024, 4096, 8192, 16384])
@pytest.mark.parametrize("with_bias", [True, False])
@pytest.mark.parametrize("out_dtype", [torch.float16, torch.bfloat16])
def test_accuracy(M, N, K, with_bias, out_dtype):
    _test_accuracy_once(M, N, K, with_bias, out_dtype, "cuda")


if __name__ == "__main__":
    pytest.main([__file__])