################################################################################
# Copyright(c)2020-2025 Shanghai Biren Technology 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.
#
################################################################################

from typing import Optional, Tuple

import torch
import torch.nn.functional as F
import torch_br
import torch_br.supa._debug as supa_debug
from fastcore.basics import patch_to

import vllm
from vllm.distributed import tensor_model_parallel_all_reduce
from vllm.model_executor.layers.vocab_parallel_embedding import (
    UnquantizedEmbeddingMethod, VocabParallelEmbedding)
from vllm_br import envs


@patch_to(UnquantizedEmbeddingMethod)
def process_weights_after_loading(self, module):
    if envs.VLLM_BR_EMBEDDING_S0B:
        ori_weight = module.weight.data.cpu()
        module.weight.data = torch_br._empty_ut_only(module.weight.shape,
                                                     "colmajor",
                                                     False,
                                                     0,
                                                     dtype=module.weight.dtype,
                                                     sbp='SB')
        module.weight.data.copy_(ori_weight)


@patch_to(UnquantizedEmbeddingMethod)
def embedding(self, layer: torch.nn.Module,
              input_: torch.Tensor) -> torch.Tensor:
    if envs.VLLM_BR_EMBEDDING_S0B:
        y_supa = torch_br._empty_ut_only(
            [1, input_.shape[0], layer.weight.shape[-1]],
            is_numa=False,
            dtype=layer.weight.dtype,
            sbp='BB',
            tensor_type="colmajor",
        )
        torch_br.out_embedding(y_supa, layer.weight.data, input_, -1, -1)
        y_supa.squeeze_(0)
        return y_supa
    return F.embedding(input_, layer.weight)


@torch.jit.script
def get_masked_input_and_mask(
        input_: torch.Tensor, org_vocab_start_index: int,
        org_vocab_end_index: int, num_org_vocab_padding: int,
        added_vocab_start_index: int,
        added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]:
    # torch.jit.script will fuse all of the pointwise ops below
    # into a single kernel, making it very fast
    spc_num = envs.VLLM_BR_DEVICE_SPC_NUM
    if spc_num > 16:
        org_vocab_mask = (input_ >= org_vocab_start_index) & (
            input_ < org_vocab_end_index)
        added_vocab_mask = (input_ >= added_vocab_start_index) & (
            input_ < added_vocab_end_index)
        added_offset = added_vocab_start_index - (
            org_vocab_end_index -
            org_vocab_start_index) - num_org_vocab_padding
        valid_offset = (org_vocab_start_index *
                        org_vocab_mask) + (added_offset * added_vocab_mask)
        vocab_mask = org_vocab_mask | added_vocab_mask
        input_ = vocab_mask * (input_ - valid_offset)
        return input_, ~vocab_mask
    else:
        input_, inv_vocab_mask = torch_br.supa_embedding_mask_infer(
            input_, org_vocab_start_index, org_vocab_end_index,
            num_org_vocab_padding, added_vocab_start_index,
            added_vocab_end_index)
    return input_, inv_vocab_mask


vllm.model_executor.layers.vocab_parallel_embedding.get_masked_input_and_mask = get_masked_input_and_mask


def vocab_parallel_embedding_forward(self, input_) -> torch.Tensor:
    if self.tp_size > 1:
        # Build the mask.
        masked_input, input_mask = get_masked_input_and_mask(
            input_,
            self.shard_indices.org_vocab_start_index,
            self.shard_indices.org_vocab_end_index,
            self.shard_indices.num_org_vocab_padding,
            self.shard_indices.added_vocab_start_index,
            self.shard_indices.added_vocab_end_index,
        )
    else:
        masked_input = input_
    # Get the embeddings.
    output_parallel = self.quant_method.embedding(self, masked_input.long())
    # Mask the output embedding.
    if self.tp_size > 1:
        output_parallel.masked_fill_(input_mask.unsqueeze(-1),
                                     0)  # type: ignore
    # Reduce across all the model parallel GPUs.
    output = tensor_model_parallel_all_reduce(output_parallel)
    return output


def apply(self,
          layer: torch.nn.Module,
          x: torch.Tensor,
          bias: Optional[torch.Tensor] = None) -> torch.Tensor:
    # numa weight is 3-dims
    if len(layer.weight.shape) == 3:
        output_size = (layer.output_size_per_partition if hasattr(
            layer, "output_size_per_partition") else layer.output_size)
        return torch_br.br_fused_mlp_infer(
            x, [layer.weight],
            output_w=output_size,
            bias=[bias] if bias is not None else None)
    supa_debug.set_enable_sublas_api(True)
    output = F.linear(x, layer.weight, bias)
    supa_debug.set_enable_sublas_api(False)
    return output


UnquantizedEmbeddingMethod.apply = apply

VocabParallelEmbedding.forward = vocab_parallel_embedding_forward