# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from nvfp4_utils import (
    FLOAT4_E2M1_MAX,
    FLOAT8_E4M3_MAX,
    convert_swizzled_to_linear,
    dequantize_nvfp4_to_dtype,
)

from vllm import _custom_ops as ops
from vllm.platforms import current_platform
from vllm.utils.flashinfer import (
    flashinfer_scaled_fp4_mm,
)
from vllm.utils.torch_utils import set_random_seed

if not current_platform.has_device_capability(100):
    pytest.skip(
        reason="Nvfp4 Requires compute capability of 10 or above.",
        allow_module_level=True,
    )

DTYPES = [torch.float16, torch.bfloat16]
# m, n, k
SHAPES = [
    (128, 128, 64),
    (128, 128, 128),
    (256, 128, 64),
    (128, 256, 128),
    (1, 128, 128),
]
PAD_SHAPES = [(150, 128, 64), (128, 128, 96), (2, 128, 64), (3, 128, 96)]
SHAPES.extend(PAD_SHAPES)

SEEDS = [42]
CUDA_DEVICES = ["cuda:0"]


def get_ref_results(
    a_fp4,
    b_fp4,
    a_sf,
    b_sf,
    a_global_scale,
    b_global_scale,
    m,
    n,
    dtype,
    block_size,
    device,
    is_sf_128x4_layout,
):
    _, m_k = a_fp4.shape
    _, n_k = b_fp4.shape
    assert m_k == n_k
    a_in_dtype = dequantize_nvfp4_to_dtype(
        a_fp4,
        a_sf,
        a_global_scale,
        dtype=dtype,
        device=device,
        block_size=block_size,
        is_sf_128x4_layout=is_sf_128x4_layout,
    )
    b_in_dtype = dequantize_nvfp4_to_dtype(
        b_fp4, b_sf, b_global_scale, dtype=dtype, device=device, block_size=block_size
    )
    return torch.matmul(a_in_dtype, b_in_dtype.t())


@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("shape", SHAPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("backend", ["cutlass", "trtllm"])
@pytest.mark.parametrize("autotune", [False, True])
@torch.inference_mode()
def test_flashinfer_nvfp4_gemm(
    dtype: torch.dtype,
    shape: tuple[int, int, int],
    seed: int,
    device: str,
    backend: str,
    autotune: bool,
) -> None:
    if "trtllm" in backend and dtype == torch.float16:
        pytest.skip("Only torch.bfloat16 is supported for TRTLLM FP4 GEMM operations")

    set_random_seed(seed)
    m, n, packed_k = shape
    k = packed_k * 2
    block_size = 16
    a_dtype = torch.randn((m, k), dtype=dtype, device=device)
    b_dtype = torch.randn((n, k), dtype=dtype, device=device)

    a_global_scale = (
        (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(a_dtype.flatten(), dim=-1)
    ).to(torch.float32)
    b_global_scale = (
        (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(b_dtype.flatten(), dim=-1)
    ).to(torch.float32)
    alpha = 1.0 / (a_global_scale * b_global_scale)

    # ops.scaled_fp4_quant returns swizzled scales, while weights
    # from checkpoints are in linear scales.
    # So instead of needing to swizzle for cutlass as in modelopt.py,
    # we need to unswizzle for trtllm here.
    a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(
        a_dtype, a_global_scale, is_sf_swizzled_layout=True, backend=backend
    )
    is_sf_128x4_layout = not (backend == "trtllm" and m <= 32)

    b_fp4, b_scale_interleaved = ops.scaled_fp4_quant(
        b_dtype, b_global_scale, is_sf_swizzled_layout=True
    )

    # get_ref_results unswizzles the scales internally.
    expected_out = get_ref_results(
        a_fp4,
        b_fp4,
        a_scale_interleaved,
        b_scale_interleaved,
        a_global_scale,
        b_global_scale,
        m,
        n,
        dtype,
        block_size,
        device,
        is_sf_128x4_layout,
    )

    import flashinfer

    if "trtllm" in backend:
        epilogue_tile_m = 128
        b_fp4 = flashinfer.shuffle_matrix_a(b_fp4.view(torch.uint8), epilogue_tile_m)
        b_scale_interleaved = convert_swizzled_to_linear(
            b_scale_interleaved, n, k, block_size
        )
        b_scale_interleaved = (
            flashinfer.shuffle_matrix_sf_a(
                b_scale_interleaved.view(torch.uint8), epilogue_tile_m
            )
            .reshape(b_scale_interleaved.shape)
            .view(torch.float8_e4m3fn)
        )

    with flashinfer.autotune(autotune):
        out = flashinfer_scaled_fp4_mm(
            a_fp4,
            b_fp4,
            a_scale_interleaved,
            b_scale_interleaved,
            alpha,
            dtype,
            backend=backend,
        )

    torch.testing.assert_close(out, expected_out.to(dtype=dtype), atol=1e-1, rtol=1e-1)
