"""
Copyright (c) 2025 by FlashInfer team.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

  http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import math

import pytest
import torch
from einops import einsum

from flashinfer.gemm import (
    batch_deepgemm_fp8_nt_groupwise,
    gemm_fp8_nt_blockscaled,
    gemm_fp8_nt_groupwise,
    group_deepgemm_fp8_nt_groupwise,
    group_gemm_fp8_nt_groupwise,
)
from flashinfer.testing.utils import dequantize_fp8, quantize_fp8


@pytest.mark.parametrize("m", [128, 256, 512, 4096, 8192])
@pytest.mark.parametrize("n", [128, 256, 512, 4096, 8192])
@pytest.mark.parametrize("k", [128, 256, 512, 4096, 8192])
@pytest.mark.parametrize("scale_major_mode", ["MN", "K"])
@pytest.mark.parametrize("out_dtype", [torch.bfloat16])
def test_fp8_blockscale_gemm(
    m,
    n,
    k,
    scale_major_mode,
    out_dtype,
):
    torch.random.manual_seed(0)
    tile_size = 128

    a_val = torch.randn((m, k), dtype=torch.float, device="cuda")
    b_val = torch.randn((n, k), dtype=torch.float, device="cuda") / math.sqrt(k)

    if scale_major_mode == "K":
        a_scale_shape = (m // tile_size, k // tile_size)
        b_scale_shape = (n // tile_size, k // tile_size)
    else:
        a_scale_shape = (k // tile_size, m // tile_size)
        b_scale_shape = (k // tile_size, n // tile_size)
    a_tile_shape = (tile_size, tile_size)
    b_tile_shape = (tile_size, tile_size)

    a_fp8, a_scale = quantize_fp8(a_val, a_scale_shape, a_tile_shape, scale_major_mode)
    b_fp8, b_scale = quantize_fp8(b_val, b_scale_shape, b_tile_shape, scale_major_mode)

    a_dequant = dequantize_fp8(a_fp8, a_scale, scale_major_mode)
    b_dequant = dequantize_fp8(b_fp8, b_scale, scale_major_mode)
    ref_c = einsum(a_dequant, b_dequant, "m k, n k -> m n").to(out_dtype)

    c = gemm_fp8_nt_blockscaled(
        a_fp8, b_fp8, a_scale, b_scale, scale_major_mode, out_dtype=out_dtype
    )
    torch.testing.assert_close(c, ref_c, atol=1e-2, rtol=1e-2)


@pytest.mark.parametrize("m", [128, 256, 512, 4096, 8192])
@pytest.mark.parametrize("n", [128, 256, 512, 4096, 8192])
@pytest.mark.parametrize("k", [128, 256, 512, 4096, 8192])
@pytest.mark.parametrize("scale_major_mode", ["MN", "K"])
@pytest.mark.parametrize("backend", ["cutlass", "trtllm"])
def test_fp8_groupwise_gemm(
    m,
    n,
    k,
    scale_major_mode,
    backend,
):
    if backend == "trtllm":
        if scale_major_mode != "MN":
            pytest.skip("trtllm only supports MN scale_major_mode")
        if k < 256:
            pytest.skip("k < 256")

    torch.random.manual_seed(0)
    tile_size = 128
    out_dtype = torch.bfloat16

    a_val = torch.randn((m, k), dtype=torch.float, device="cuda")
    b_val = torch.randn((n, k), dtype=torch.float, device="cuda") / math.sqrt(k)

    if scale_major_mode == "K":
        a_scale_shape = (m, k // tile_size)
        b_scale_shape = (n // tile_size, k // tile_size)
    else:
        a_scale_shape = (k // tile_size, m)
        b_scale_shape = (k // tile_size, n // tile_size)
    a_tile_shape = (1, tile_size)
    b_tile_shape = (tile_size, tile_size)

    a_fp8, a_scale = quantize_fp8(a_val, a_scale_shape, a_tile_shape, scale_major_mode)
    b_fp8, b_scale = quantize_fp8(b_val, b_scale_shape, b_tile_shape, scale_major_mode)

    a_dequant = dequantize_fp8(a_fp8, a_scale, scale_major_mode)
    b_dequant = dequantize_fp8(b_fp8, b_scale, scale_major_mode)
    ref_c = einsum(a_dequant, b_dequant, "m k, n k -> m n").to(out_dtype)

    if backend == "trtllm":
        b_scale = b_scale.t().contiguous()

    c = gemm_fp8_nt_groupwise(
        a_fp8,
        b_fp8,
        a_scale,
        b_scale,
        scale_major_mode,
        out_dtype=out_dtype,
        backend=backend,
    )
    torch.testing.assert_close(c, ref_c, atol=1e-2, rtol=1e-2)


@pytest.mark.parametrize("m", [4, 128, 256, 512, 4096, 8192])
@pytest.mark.parametrize("n", [128, 256, 512, 4096, 8192])
@pytest.mark.parametrize("k", [128, 256, 512, 4096, 8192])
@pytest.mark.parametrize("group_size", [1, 2, 4, 8])
@pytest.mark.parametrize("scale_major_mode", ["MN", "K"])
@pytest.mark.parametrize("out_dtype", [torch.bfloat16])
def test_fp8_groupwise_group_gemm(
    m,
    n,
    k,
    group_size,
    scale_major_mode,
    out_dtype,
):
    torch.random.manual_seed(0)
    tile_size = 128

    a_val = torch.randn((group_size * m, k), dtype=torch.float, device="cuda")
    b_val = torch.randn(
        (group_size, n, k), dtype=torch.float, device="cuda"
    ) / math.sqrt(k)

    if scale_major_mode == "K":
        a_scale_shape = (group_size * m, k // tile_size)
        b_scale_shape = (group_size, n // tile_size, k // tile_size)
    else:
        a_scale_shape = (k // tile_size, m * group_size)
        b_scale_shape = (group_size, k // tile_size, n // tile_size)
    a_tile_shape = (1, tile_size)
    b_tile_shape = (1, tile_size, tile_size)

    a_fp8, a_scale = quantize_fp8(a_val, a_scale_shape, a_tile_shape, scale_major_mode)
    b_fp8, b_scale = quantize_fp8(b_val, b_scale_shape, b_tile_shape, scale_major_mode)

    a_dequant = dequantize_fp8(a_fp8, a_scale, scale_major_mode)
    b_dequant = dequantize_fp8(b_fp8, b_scale, scale_major_mode)

    m_indptr = torch.arange(0, group_size + 1, dtype=torch.int32, device="cuda") * m

    out = group_gemm_fp8_nt_groupwise(
        a_fp8,
        b_fp8,
        a_scale,
        b_scale,
        m_indptr,
        scale_major_mode=scale_major_mode,
        out_dtype=out_dtype,
    )
    ref_c = (
        einsum(
            a_dequant.view((group_size, m, k)),
            b_dequant,
            "b m k, b n k -> b m n",
        )
        .view((group_size * m, n))
        .to(out_dtype)
    )
    torch.testing.assert_close(out, ref_c, atol=1e-2, rtol=1e-2)


@pytest.mark.xfail(reason="Expected failures for deepgemm tests on SM > 100")
@pytest.mark.parametrize("m", [128, 256, 512, 1024])
@pytest.mark.parametrize("nk", [(128, 512), (512, 128), (4096, 7168), (7168, 2048)])
@pytest.mark.parametrize("group_size", [1, 4, 8, 64, 128, 256])
@pytest.mark.parametrize("out_dtype", [torch.bfloat16])
def test_fp8_groupwise_group_deepgemm(
    m,
    nk,
    group_size,
    out_dtype,
):
    torch.random.manual_seed(0)
    m_per_group = m // group_size
    if m_per_group < 128:
        return
    n, k = nk
    a = torch.randn((m, k), device="cuda", dtype=torch.float32)
    b = torch.randn((group_size, n, k), device="cuda", dtype=torch.float32)
    m_indptr = torch.empty((m,), device="cuda", dtype=torch.int32)
    a_fp8, a_scale = quantize_fp8(a, (m, k // 128), (1, 128), "K")
    b_fp8, b_scale = quantize_fp8(
        b, (group_size, n // 128, k // 128), (1, 128, 128), "K"
    )
    a_dequant = dequantize_fp8(a_fp8, a_scale, "K")
    b_dequant = dequantize_fp8(b_fp8, b_scale, "K")

    ref = torch.empty((m, n), device="cuda", dtype=out_dtype)

    for i in range(group_size):
        r = slice(i * m_per_group, (i + 1) * m_per_group)
        m_indptr[r] = i
        ref[r] = a_dequant[r] @ b_dequant[i].t()

    out = group_deepgemm_fp8_nt_groupwise(
        a_fp8,
        b_fp8,
        a_scale,
        b_scale,
        m_indptr,
        out_dtype=out_dtype,
    )
    torch.testing.assert_close(out, ref, atol=3e-2, rtol=3e-2)


@pytest.mark.xfail(reason="Expected failures for deepgemm tests on SM > 100")
@pytest.mark.parametrize("m", [128, 256, 512, 1024])
@pytest.mark.parametrize("nk", [(128, 512), (512, 128), (4096, 7168), (7168, 2048)])
@pytest.mark.parametrize("group_size", [1, 4, 8, 64, 128, 256])
@pytest.mark.parametrize("out_dtype", [torch.bfloat16])
def test_fp8_groupwise_batch_deepgemm_masked(
    m,
    nk,
    group_size,
    out_dtype,
):
    torch.random.manual_seed(0)
    n, k = nk
    a = torch.randn((group_size, m, k), device="cuda", dtype=torch.float32)
    b = torch.randn((group_size, n, k), device="cuda", dtype=torch.float32)
    masked_m = torch.randint(0, m, (group_size,), device="cuda", dtype=torch.int32)

    a_fp8, a_scale = quantize_fp8(a, (group_size, m, k // 128), (1, 1, 128), "K")
    b_fp8, b_scale = quantize_fp8(
        b, (group_size, n // 128, k // 128), (1, 128, 128), "K"
    )

    a_dequant = dequantize_fp8(a_fp8, a_scale, "K")
    b_dequant = dequantize_fp8(b_fp8, b_scale, "K")
    ref = torch.einsum("bmk,bnk->bmn", a_dequant, b_dequant).to(out_dtype)

    expected_m = min(int(masked_m.float().mean()) + 1, m)

    out = batch_deepgemm_fp8_nt_groupwise(
        a_fp8,
        b_fp8,
        a_scale,
        b_scale,
        masked_m,
        expected_m,
        out_dtype=out_dtype,
    )
    for i in range(group_size):
        torch.testing.assert_close(
            out[i][: masked_m[i]], ref[i][: masked_m[i]], atol=3e-2, rtol=3e-2
        )


if __name__ == "__main__":
    test_fp8_blockscale_gemm(8192, 8192, 8192, "MN", torch.bfloat16)
    test_fp8_groupwise_gemm(8192, 8192, 8192, "K", backend="cutlass")
    test_fp8_groupwise_group_gemm(4, 128, 256, 2, "MN", torch.bfloat16)
    test_fp8_groupwise_group_deepgemm(256, (128, 512), 4, torch.bfloat16)
    test_fp8_groupwise_batch_deepgemm_masked(256, (128, 512), 8, torch.bfloat16)