# SPDX-License-Identifier: Apache-2.0
from threading import Lock

import pytest
import torch

import vllm.lora.ops.torch_ops as torch_ops
import vllm.lora.ops.triton_ops as triton_ops
from vllm.lora.ops.triton_ops import LoRAKernelMeta
from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
from vllm.platforms import current_platform

from .utils import PunicaTensors, assert_close, generate_data_for_nslices


# Utility shrink and expand operations used as reference implementations.
def sgmv_shrink_for_nslices(
        nslices: int, inputs_tensor: torch.Tensor,
        lora_weights_lst: list[torch.Tensor], out_tensor: torch.Tensor,
        b_seq_start_loc: torch.Tensor, seq_len_tensor: torch.Tensor,
        prompt_lora_mapping: torch.Tensor, batches: int, max_seq_length: int,
        num_tokens: int, scaling: float):
    """
    Wrapper around torch_ops.sgmv_shrink that handles any nslices.
    """
    for index in range(nslices):
        torch_ops.sgmv_shrink(
            inputs_tensor,
            lora_weights_lst[index],
            out_tensor[index],
            b_seq_start_loc,
            seq_len_tensor,
            prompt_lora_mapping,
            batches,
            max_seq_length,
            num_tokens,
            scaling,
        )


def sgmv_expand_for_nslices(nslices: int, hidden_size: int,
                            inputs_tensor: torch.Tensor,
                            lora_weights_lst: list[torch.Tensor],
                            out_tensor: torch.Tensor,
                            b_seq_start_loc: torch.Tensor,
                            seq_len_tensor: torch.Tensor,
                            prompt_lora_mapping: torch.Tensor, batches: int,
                            max_seq_length: int, num_tokens: int,
                            add_inputs: bool) -> None:
    """
    Wrapper around torch_ops.sgmv_expand that handles any nslices.
    """
    if nslices == 1:
        # Verify the torch's sgmv_expand op
        torch_ops.sgmv_expand(
            inputs_tensor[0],
            lora_weights_lst[0],
            out_tensor,
            b_seq_start_loc,
            seq_len_tensor,
            prompt_lora_mapping,
            batches,
            max_seq_length,
            num_tokens,
            add_inputs=add_inputs,
        )
    else:
        slice_offset = 0
        for index in range(nslices):
            lora_weights = lora_weights_lst[index]
            torch_ops.sgmv_expand_slice(
                inputs_tensor[index],
                lora_weights,
                out_tensor,
                b_seq_start_loc,
                seq_len_tensor,
                prompt_lora_mapping,
                batches,
                max_seq_length,
                num_tokens,
                slice_offset,
                hidden_size,
                add_inputs=add_inputs,
            )
            slice_offset += hidden_size


_dict_lock = Lock()


def check_lora_shrink_kernel(batches: int, num_loras: int, rank: int,
                             hidden_size: int, nslices: int,
                             dtype: torch.dtype, device: str, seq_length: int,
                             scaling: float):
    """
    Compare outputs of torch_ops.sgmv_shrink and triton_ops.lora_shrink
    kernels.
    """
    data: PunicaTensors = generate_data_for_nslices(
        batches,
        hidden_size,
        num_loras,
        rank,
        seq_length,
        nslices,
        dtype,
        "shrink",
        device,
    )
    max_seq_length, token_nums = data.meta()

    # Setup metadata information for SGMV and reference kernels
    sgmv_meta_args = (data.b_seq_start_loc, data.seq_len_tensor,
                      data.prompt_lora_mapping, batches, max_seq_length,
                      token_nums)

    # Setup metadata information for the LoRA kernel.
    lora_meta = LoRAKernelMeta.make(max_loras=num_loras,
                                    max_num_tokens=token_nums,
                                    device='cuda')
    lora_meta.prepare_tensors(data.token_lora_mapping)

    ref_out_tensor = data.ref_out_tensor
    out_tensor = data.our_out_tensor.clone()

    # Preventing cache error pointer.
    with _dict_lock:
        # lora_shrink kernel
        _LORA_A_PTR_DICT.clear()
        triton_ops.lora_shrink(
            data.inputs_tensor,
            data.lora_weights,
            out_tensor,
            *lora_meta.meta_args(token_nums=token_nums),
            scaling,
        )

    # Reference
    sgmv_shrink_for_nslices(
        nslices,
        data.inputs_tensor,
        data.lora_weights,
        ref_out_tensor,
        *sgmv_meta_args,
        scaling,
    )

    assert_close(out_tensor, ref_out_tensor)


def check_lora_expand_kernel(batches: int, num_loras: int, rank: int,
                             hidden_size: int, nslices: int,
                             dtype: torch.dtype, device: str, seq_length: int,
                             add_inputs: bool):
    """
    Compare outputs of torch_ops.sgmv_expand and triton_ops.lora_expand
    kernels.
    """
    data: PunicaTensors = generate_data_for_nslices(
        batches,
        hidden_size,
        num_loras,
        rank,
        seq_length,
        nslices,
        dtype,
        "expand",
        device,
    )

    max_seq_length, token_nums = data.meta()

    # Setup metadata information for SGMV and reference kernels
    sgmv_meta_args = (data.b_seq_start_loc, data.seq_len_tensor,
                      data.prompt_lora_mapping, batches, max_seq_length,
                      token_nums)

    # Setup metadata information for the LoRA kernel.
    lora_meta = LoRAKernelMeta.make(max_loras=num_loras,
                                    max_num_tokens=token_nums,
                                    device='cuda')
    lora_meta.prepare_tensors(data.token_lora_mapping)

    # Setup output tensors
    ref_out_tensor = data.ref_out_tensor
    out_tensor = data.our_out_tensor.clone()

    with _dict_lock:
        # lora_expand kernel
        _LORA_B_PTR_DICT.clear()
        triton_ops.lora_expand(data.inputs_tensor,
                               data.lora_weights,
                               out_tensor,
                               *lora_meta.meta_args(token_nums=token_nums),
                               offset_start=0,
                               add_inputs=add_inputs)

    # Reference
    sgmv_expand_for_nslices(nslices,
                            hidden_size,
                            data.inputs_tensor,
                            data.lora_weights,
                            ref_out_tensor,
                            *sgmv_meta_args,
                            add_inputs=add_inputs)

    assert_close(out_tensor, ref_out_tensor)


# Tests
# We test the punica kernels along 2 verticals mainly.
# 1. Variations in hidden_dim size
# 2. Variations in all other parameters like (batch_size, max_rank, num_loras
#  etc.)

# 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].
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))

# Test params that focuses on hidden_size variation.
hs_test_params = {
    "hidden_sizes": HIDDEN_SIZES,
    "batches": [4],
    "num_loras": [4],
    "max_ranks": [32],
}

# General tests params that tests for variations in all dimensions
# except hidden_size.
test_params = {
    "hidden_sizes": [2049],
    "batches": [1, 4, 16, 32],
    "num_loras": [1, 8, 32, 128],
    "max_ranks": [1, 4, 8, 16, 32, 64, 128, 256],
}

DTYPES = [torch.float16, torch.bfloat16]
DEVICES = [f"cuda:{0}"]
SEED = [0]


@pytest.mark.parametrize("batches", test_params['batches'])
@pytest.mark.parametrize("num_loras", test_params['num_loras'])
@pytest.mark.parametrize("rank", test_params['max_ranks'])
@pytest.mark.parametrize("hidden_size", test_params['hidden_sizes'])
@pytest.mark.parametrize("nslices", [1, 2, 3])
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("seed", SEED)
@pytest.mark.parametrize("op_type", ["shrink", "expand"])
def test_kernels(
    batches: int,
    num_loras: int,
    rank: int,
    hidden_size: int,
    nslices: int,
    dtype: torch.dtype,
    device: str,
    seed: int,
    op_type: str,
):
    """
    Tests LoRA kernels.
    """
    torch.set_default_device(device)
    current_platform.seed_everything(seed)

    if op_type == "shrink":
        check_lora_shrink_kernel(batches=batches,
                                 num_loras=num_loras,
                                 rank=rank,
                                 hidden_size=hidden_size,
                                 nslices=nslices,
                                 dtype=dtype,
                                 device=device,
                                 seq_length=128,
                                 scaling=0.5)
    else:
        check_lora_expand_kernel(batches=batches,
                                 num_loras=num_loras,
                                 rank=rank,
                                 hidden_size=hidden_size,
                                 nslices=nslices,
                                 dtype=dtype,
                                 device=device,
                                 seq_length=128,
                                 add_inputs=True)


@pytest.mark.parametrize("batches", hs_test_params['batches'])
@pytest.mark.parametrize("num_loras", hs_test_params['num_loras'])
@pytest.mark.parametrize("rank", hs_test_params['max_ranks'])
@pytest.mark.parametrize("hidden_size", hs_test_params['hidden_sizes'])
@pytest.mark.parametrize("nslices", [1, 2, 3])
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("seed", SEED)
@pytest.mark.parametrize("op_type", ["shrink", "expand"])
def test_kernels_hidden_size(
    batches: int,
    num_loras: int,
    rank: int,
    hidden_size: int,
    nslices: int,
    dtype: torch.dtype,
    device: str,
    seed: int,
    op_type: str,
):
    """
    Tests SGMV and LoRA kernels.
    """
    torch.set_default_device(device)
    current_platform.seed_everything(seed)

    if op_type == "shrink":
        check_lora_shrink_kernel(batches=batches,
                                 num_loras=num_loras,
                                 rank=rank,
                                 hidden_size=hidden_size,
                                 nslices=nslices,
                                 dtype=dtype,
                                 device=device,
                                 seq_length=128,
                                 scaling=0.5)
    else:
        check_lora_expand_kernel(batches=batches,
                                 num_loras=num_loras,
                                 rank=rank,
                                 hidden_size=hidden_size,
                                 nslices=nslices,
                                 dtype=dtype,
                                 device=device,
                                 seq_length=128,
                                 add_inputs=True)