"""
This script is mainly used to tests various hidden_sizes. We have collected the
hidden_sizes included in the LoRA models currently supported by vLLM. It tests
whether the corresponding Triton kernel can run normally when tensor parallelism
is set to [1, 2, 4, 8, 16, 32, 64].
"""
import pytest
import torch

from vllm.lora.ops.bgmv_expand import bgmv_expand
from vllm.lora.ops.bgmv_expand_slice import bgmv_expand_slice
from vllm.lora.ops.bgmv_shrink import bgmv_shrink
from vllm.lora.ops.sgmv_expand import sgmv_expand
from vllm.lora.ops.sgmv_expand_slice import sgmv_expand_slice
from vllm.lora.ops.sgmv_shrink import sgmv_shrink
from vllm.platforms import current_platform

from .utils import (generate_data, generate_data_for_expand_nslices,
                    ref_torch_groupgemm)

HIDDEN_SIZES = [
    128,
    256,
    512,
    896,
    1024,
    1152,
    1216,
    1280,
    1536,
    1664,
    2048,
    2240,
    2304,
    2368,
    2432,
    2560,
    2752,
    3072,
    3328,
    3456,
    3584,
    3712,
    4096,
    4480,
    4608,
    4736,
    4864,
    5120,
    5504,
    5632,
    5888,
    6144,
    6400,
    6848,
    6912,
    7168,
    7424,
    8192,
    8960,
    9216,
    9472,
    10240,
    11008,
    11264,
    13824,
    14336,
    14784,
    14848,
    15360,
    18944,
    22016,
    22528,
    24576,
    27392,
    27648,
    29568,
    29696,
    32000,
    32256,
    32512,
    32768,
    33024,
    36864,
    43264,
    49152,
    49408,
    60544,
    60672,
    64000,
    64256,
    102400,
    102656,
    128000,
    128256,
]
#The size of TP
divisibility = [1, 2, 8, 16, 64]

all_hidden_size = []
for div in divisibility:
    for hidden_size in HIDDEN_SIZES:
        all_hidden_size.append(hidden_size // div)

HIDDEN_SIZES = list(set(all_hidden_size))

BATCHES = [4]
NUM_LORA = [4]
DTYPES = [torch.float16, torch.bfloat16]
MAX_RANKS = [32]
SCALES = [0.5]
SEED = [0]
CUDA_DEVICES = [f"cuda:{0}"]


def assert_close(a, b):
    rtol, atol = {
        torch.float16: (6e-2, 6e-2),
        torch.bfloat16: (6e-2, 6e-2),
        torch.float32: (1e-2, 1e-2),
    }[a.dtype]
    torch.testing.assert_close(a, b, rtol=rtol, atol=atol)


@pytest.mark.parametrize("batches", BATCHES)
@pytest.mark.parametrize("num_loras", NUM_LORA)
@pytest.mark.parametrize("rank", MAX_RANKS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("scaling", SCALES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("op_type", ["shrink", "expand"])
@pytest.mark.parametrize("seed", SEED)
@pytest.mark.parametrize("device", CUDA_DEVICES)
def test_punica_sgmv(
    batches: int,
    num_loras: int,
    rank: int,
    hidden_size: int,
    scaling: float,
    dtype: torch.dtype,
    op_type: str,
    seed: int,
    device: str,
):
    torch.set_default_device(device)
    current_platform.seed_everything(seed)

    seq_length = 128
    (
        inputs_tensor,
        lora_weights,
        our_out_tensor,
        ref_out_tensor,
        b_seq_start_loc,
        lora_indices_tensor,
        seq_len_tensor,
        indices,
    ) = generate_data(
        batches,
        hidden_size,
        num_loras,
        rank,
        seq_length,
        dtype,
        op_type,
        device,
    )
    max_seq_length = seq_len_tensor.max()
    token_nums = seq_len_tensor.sum().item()
    if isinstance(max_seq_length, tuple):
        max_seq_length = max_seq_length[0].item()
    else:
        max_seq_length = max_seq_length.item()
    if op_type == "shrink":
        sgmv_shrink(
            inputs_tensor,
            lora_weights,
            our_out_tensor,
            b_seq_start_loc,
            seq_len_tensor,
            lora_indices_tensor,
            batches,
            max_seq_length,
            token_nums,
            scaling,
        )
    else:
        sgmv_expand(
            inputs_tensor,
            lora_weights,
            our_out_tensor,
            b_seq_start_loc,
            seq_len_tensor,
            lora_indices_tensor,
            batches,
            max_seq_length,
            token_nums,
            add_inputs=True,
        )
    ref_torch_groupgemm(
        ref_out_tensor,
        inputs_tensor,
        lora_weights,
        lora_indices_tensor,
        seq_len_tensor,
        batches,
        scaling if op_type == "shrink" else 1.0,
        op_type,
    )
    if op_type == "shrink":
        ref_out_tensor = ref_out_tensor.to(torch.float32)
    assert_close(our_out_tensor, ref_out_tensor)


@pytest.mark.parametrize("batches", BATCHES)
@pytest.mark.parametrize("num_loras", NUM_LORA)
@pytest.mark.parametrize("rank", MAX_RANKS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("scaling", SCALES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("op_type", ["shrink", "expand"])
@pytest.mark.parametrize("seed", SEED)
@pytest.mark.parametrize("device", CUDA_DEVICES)
def test_punica_bgmv(
    batches: int,
    num_loras: int,
    rank: int,
    hidden_size: int,
    scaling: float,
    dtype: torch.dtype,
    op_type: str,
    seed: int,
    device: str,
):
    torch.set_default_device(device)
    current_platform.seed_everything(seed)

    seq_length = 1
    (
        inputs_tensor,
        lora_weights,
        our_out_tensor,
        ref_out_tensor,
        b_seq_start_loc,
        lora_indices_tensor,
        seq_len_tensor,
        indices,
    ) = generate_data(
        batches,
        hidden_size,
        num_loras,
        rank,
        seq_length,
        dtype,
        op_type,
        device,
    )
    if op_type == "shrink":
        bgmv_shrink(
            inputs_tensor,
            lora_weights,
            our_out_tensor,
            indices,
            scaling,
        )
    else:
        bgmv_expand(
            inputs_tensor,
            lora_weights,
            our_out_tensor,
            indices,
            add_inputs=True,
        )
    ref_torch_groupgemm(
        ref_out_tensor,
        inputs_tensor,
        lora_weights,
        lora_indices_tensor,
        seq_len_tensor,
        batches,
        scaling if op_type == "shrink" else 1.0,
        op_type,
    )
    if op_type == "shrink":
        ref_out_tensor = ref_out_tensor.to(torch.float32)
    assert_close(our_out_tensor, ref_out_tensor)


@pytest.mark.parametrize("batches", BATCHES)
@pytest.mark.parametrize("num_loras", NUM_LORA)
@pytest.mark.parametrize("rank", MAX_RANKS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("nslices", [2, 3])
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("op_type", ["sgmv", "bgmv"])
@pytest.mark.parametrize("seed", SEED)
@pytest.mark.parametrize("device", CUDA_DEVICES)
def test_punica_expand_nslices(
    batches: int,
    num_loras: int,
    rank: int,
    hidden_size: int,
    nslices: int,
    dtype: torch.dtype,
    op_type: str,
    seed: int,
    device: str,
):

    torch.set_default_device(device)
    current_platform.seed_everything(seed)

    seq_length = 128 if op_type == "sgmv" else 1
    (
        inputs_tensor,
        lora_weights_lst,
        our_outputs,
        ref_outputs,
        b_seq_start_loc,
        lora_indices_tensor,
        seq_len_tensor,
        indices,
    ) = generate_data_for_expand_nslices(
        batches,
        hidden_size,
        num_loras,
        rank,
        seq_length,
        dtype,
        nslices,
        device,
    )
    max_seq_length = seq_len_tensor.max()
    token_nums = seq_len_tensor.sum().item()
    if isinstance(max_seq_length, tuple):
        max_seq_length = max_seq_length[0].item()
    else:
        max_seq_length = max_seq_length.item()
    slice_offset = 0
    for index in range(nslices):
        lora_weights = lora_weights_lst[index]
        if op_type == "sgmv":
            sgmv_expand_slice(
                inputs_tensor,
                lora_weights,
                our_outputs,
                b_seq_start_loc,
                seq_len_tensor,
                lora_indices_tensor,
                batches,
                max_seq_length,
                token_nums,
                slice_offset,
                hidden_size,
                add_inputs=True,
            )
        else:

            bgmv_expand_slice(
                inputs_tensor,
                lora_weights,
                our_outputs,
                indices,
                slice_offset,
                slice_size=hidden_size,
                add_inputs=True,
            )
        ref_torch_groupgemm(
            ref_outputs[:, slice_offset:slice_offset + hidden_size],
            inputs_tensor,
            lora_weights,
            lora_indices_tensor,
            seq_len_tensor,
            batches,
            1.0,
            op_type="expand",
        )

        slice_offset += hidden_size
    assert_close(our_outputs, ref_outputs)
