# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from unittest.mock import patch

import pytest
import torch

from vllm.model_executor.layers.quantization.utils import fp8_utils, int8_utils


@pytest.mark.parametrize(
    "shape", [(31, 128), (32, 128), (63, 256), (64, 256), (16, 512)]
)
@pytest.mark.parametrize("column_major", [False, True])
@pytest.mark.parametrize("tma_aligned", [False, True])
@pytest.mark.parametrize("scale_ue8m0", [False, True])
@pytest.mark.parametrize("group_size", [64, 128])
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
def test_per_token_group_quant_fp8(
    shape, column_major: bool, tma_aligned: bool, scale_ue8m0: bool, group_size: int
):
    device = "cuda"

    torch.manual_seed(42)
    num_tokens, hidden_dim = shape

    x = torch.randn((num_tokens, hidden_dim), device=device, dtype=torch.bfloat16) * 8

    # cuda path
    out_q, scale = fp8_utils.per_token_group_quant_fp8(
        x,
        group_size,
        column_major_scales=column_major,
        tma_aligned_scales=tma_aligned,
        use_ue8m0=scale_ue8m0,
    )

    # triton ref
    with patch("vllm.platforms.current_platform.is_cuda", return_value=False):
        ref_q, ref_s = fp8_utils.per_token_group_quant_fp8(
            x,
            group_size,
            column_major_scales=column_major,
            use_ue8m0=scale_ue8m0,
        )

    assert torch.allclose(out_q.float(), ref_q.float(), atol=0.15, rtol=0.15)
    assert torch.allclose(scale, ref_s, atol=0.01, rtol=0.01)


@pytest.mark.parametrize("shape", [(32, 128), (64, 256), (16, 512)])
@pytest.mark.parametrize("group_size", [64, 128])
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
def test_per_token_group_quant_int8(shape, group_size: int):
    device = "cuda"

    torch.manual_seed(42)
    num_tokens, hidden_dim = shape

    x = torch.randn((num_tokens, hidden_dim), device=device, dtype=torch.bfloat16) * 8

    # cuda path
    out_q, scale = int8_utils.per_token_group_quant_int8(
        x,
        group_size,
    )

    # triton ref
    with patch("vllm.platforms.current_platform.is_cuda", return_value=False):
        ref_q, ref_s = int8_utils.per_token_group_quant_int8(
            x,
            group_size,
        )

    assert torch.allclose(out_q.float(), ref_q.float(), atol=0.15, rtol=0.15)
    assert torch.allclose(scale, ref_s, atol=0.01, rtol=0.01)
