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from __future__ import annotations

import pytest
import torch

deep_gemm_testing = pytest.importorskip("deep_gemm.testing")
deep_gemm_utils = pytest.importorskip("deep_gemm.utils")

from tokenspeed_kernel.ops.gemm import deep_gemm as deep_gemm_ops
from tokenspeed_kernel.platform import current_platform

platform = current_platform()


@pytest.mark.skipif(not platform.is_nvidia, reason="Requires NVIDIA GPU")
def test_deep_gemm_mm_fp8_blockscale_matches_reference(device: str) -> None:
    if not torch.cuda.is_available():
        pytest.skip("CUDA is required for DeepGEMM verification")

    kernel = getattr(deep_gemm_ops, "deep_gemm_mm_fp8_blockscale", None)
    if kernel is None:
        pytest.skip("DeepGEMM kernel is not available")

    torch.manual_seed(0)
    m, n, k = 128, 128, 256
    use_ue8m0 = torch.cuda.get_device_capability()[0] >= 10

    a = torch.randn((m, k), device=device, dtype=torch.bfloat16)
    b = torch.randn((n, k), device=device, dtype=torch.bfloat16)
    expected = (a.float() @ b.float().T).to(torch.bfloat16)

    a_fp8, a_scales = deep_gemm_utils.per_token_cast_to_fp8(
        a,
        use_ue8m0=use_ue8m0,
        gran_k=128,
    )
    b_fp8, b_scales = deep_gemm_utils.per_block_cast_to_fp8(
        b,
        use_ue8m0=use_ue8m0,
        gran_k=128,
    )

    actual = kernel(
        a_fp8,
        b_fp8,
        a_scales,
        b_scales,
        torch.bfloat16,
        block_size=[128, 128],
    )

    torch.cuda.synchronize()
    assert deep_gemm_testing.calc_diff(actual, expected) < 0.001