# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/sample/logprob.py.
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# 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.
# This file is a part of the vllm-ascend project.

import torch
from vllm.triton_utils import tl, triton


@triton.jit
def _topk_log_softmax_kernel(
    output_ptr,
    logits_ptr,
    logits_stride,
    topk_ids_ptr,
    topk,
    vocab_size,
    BLOCK_SIZE: tl.constexpr,
    PADDED_TOPK: tl.constexpr,
):
    req_idx = tl.program_id(0)
    row_ptr = logits_ptr + req_idx * logits_stride

    max_val = float("-inf")
    for i in range(0, vocab_size, BLOCK_SIZE):
        block = i + tl.arange(0, BLOCK_SIZE)
        logits = tl.load(row_ptr + block, mask=block < vocab_size, other=float("-inf"))
        max_val = tl.max(tl.maximum(logits, max_val))
    max_val = max_val.to(tl.float32)  # type: ignore

    se = 0.0
    for i in range(0, vocab_size, BLOCK_SIZE):
        block = i + tl.arange(0, BLOCK_SIZE)
        logits = tl.load(row_ptr + block, mask=block < vocab_size, other=0.0)
        # NOTE(woosuk): Make sure that logits and all following operations use FP32.
        logits = logits.to(tl.float32)
        # NOTE(wangx700): tl.where does not support int64 so we cast it to float32.
        block = block.to(tl.float32)
        e = tl.exp(logits - max_val)
        e = tl.where(block < vocab_size, e, 0.0)
        se += tl.sum(e)
    lse = tl.log(se)

    k_offset = tl.arange(0, PADDED_TOPK)
    k_mask = k_offset < topk
    topk_ids = tl.load(topk_ids_ptr + req_idx * topk + k_offset, mask=k_mask, other=0)

    logits = tl.load(row_ptr + topk_ids, mask=k_mask)
    logits = logits.to(tl.float32)
    o = logits - max_val - lse
    tl.store(output_ptr + req_idx * topk + k_offset, o, mask=k_mask)


def compute_token_logprobs(logits: torch.Tensor, token_ids: torch.Tensor) -> torch.Tensor:
    batch_size, vocab_size = logits.shape
    token_ids = token_ids.to(torch.int64)
    num_logprobs = token_ids.shape[1]
    logprobs = logits.new_empty((batch_size, num_logprobs), dtype=torch.float32)
    _topk_log_softmax_kernel[(batch_size,)](
        logprobs,
        logits,
        logits.stride(0),
        token_ids,
        num_logprobs,
        vocab_size,
        BLOCK_SIZE=1024,  # type: ignore
        # NOTE(wangx700): PADDED_TOPK must be at least 2 to avoid
        # num_logprobs=1 getting wrong results.
        PADDED_TOPK=max(triton.next_power_of_2(num_logprobs), 2),
    )
    return logprobs