# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
# Copyright 2023 The vLLM team.
# Copyright 2023 CTranslate2, and Michael Feil
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
"""Inference-only GPTBigCode model compatible with HuggingFace weights.

The input of the model is flattened to a 1D tensor of tokens. The model uses
InputMetadata to extract the original 2D shape of the input.
"""
from typing import Dict, List, Optional, Tuple

import torch
from torch import nn
from transformers import GPTBigCodeConfig

from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.attention import PagedAttention
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
                                              load_tensor_parallel_weights)
from vllm.model_executor.parallel_utils.parallel_state import (
    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
from vllm.model_executor.parallel_utils.tensor_parallel import (
    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
from vllm.sequence import SequenceOutputs

KVCache = Tuple[torch.Tensor, torch.Tensor]


class GPTBigCodeAttention(nn.Module):

    def __init__(self, config: GPTBigCodeConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        total_num_heads = config.num_attention_heads
        tensor_model_parallel_world_size = (
            get_tensor_model_parallel_world_size())
        assert total_num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = total_num_heads // tensor_model_parallel_world_size
        self.head_dim = self.hidden_size // total_num_heads
        self.num_kv_heads = 1 if config.multi_query else self.num_heads
        self.kv_dim = self.num_kv_heads * self.head_dim
        self.scale = self.head_dim**-0.5

        self.c_attn = ColumnParallelLinear(self.hidden_size,
                                           self.hidden_size + 2 * self.kv_dim,
                                           bias=True,
                                           gather_output=False,
                                           perform_initialization=False)
        self.c_proj = RowParallelLinear(self.hidden_size,
                                        self.hidden_size,
                                        bias=True,
                                        input_is_parallel=True,
                                        perform_initialization=False)
        self.attn = PagedAttention(self.num_heads,
                                   self.head_dim,
                                   scale=self.scale,
                                   num_kv_heads=self.num_kv_heads)

    def forward(
        self,
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        qkv, _ = self.c_attn(hidden_states)
        q, k, v = qkv.split([self.hidden_size, self.kv_dim, self.kv_dim],
                            dim=-1)
        key_cache, value_cache = kv_cache
        attn_output = self.attn(q, k, v, key_cache, value_cache,
                                input_metadata, cache_event)
        attn_output, _ = self.c_proj(attn_output)
        return attn_output


class GPTBigMLP(nn.Module):

    def __init__(
        self,
        intermediate_size: int,
        config: GPTBigCodeConfig,
    ):
        super().__init__()
        hidden_size = config.hidden_size
        self.c_fc = ColumnParallelLinear(hidden_size,
                                         intermediate_size,
                                         bias=True,
                                         gather_output=False,
                                         perform_initialization=False)
        self.c_proj = RowParallelLinear(intermediate_size,
                                        hidden_size,
                                        bias=True,
                                        input_is_parallel=True,
                                        perform_initialization=False)
        self.act = get_act_fn(config.activation_function)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states, _ = self.c_fc(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.c_proj(hidden_states)
        return hidden_states


class GPTBigCodeBlock(nn.Module):

    def __init__(self, config: GPTBigCodeConfig):
        super().__init__()
        hidden_size = config.hidden_size
        inner_dim = (config.n_inner if config.n_inner is not None else 4 *
                     hidden_size)

        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.attn = GPTBigCodeAttention(config)
        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.mlp = GPTBigMLP(inner_dim, config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_output = self.attn(
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            input_metadata=input_metadata,
            cache_event=cache_event,
        )
        # residual connection
        hidden_states = attn_output + residual

        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        # residual connection
        hidden_states = residual + feed_forward_hidden_states
        return hidden_states


class GPTBigCodeModel(nn.Module):

    def __init__(self, config: GPTBigCodeConfig):
        super().__init__()
        self.config = config
        assert not config.add_cross_attention

        self.embed_dim = config.hidden_size

        # Optimization: While the vocab size of GPT-2 is 50257, we extend it
        # to 50304 in order to make it divisible by 64.
        # This improves performance since GPUs are faster if the dimension
        # is divisible by 64. In addition, it allows us to shard the embedding
        # layer across 2, 4, 8, or more GPUs.
        vocab_size = ((config.vocab_size + 63) // 64) * 64
        self.wte = VocabParallelEmbedding(vocab_size, self.embed_dim)
        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
        self.h = nn.ModuleList(
            [GPTBigCodeBlock(config) for _ in range(config.num_hidden_layers)])
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> torch.Tensor:
        inputs_embeds = self.wte(input_ids)
        position_embeds = self.wpe(position_ids)
        hidden_states = inputs_embeds + position_embeds

        for i in range(len(self.h)):
            if cache_events is None:
                cache_event = None
            else:
                cache_event = cache_events[i]
            layer = self.h[i]
            hidden_states = layer(hidden_states, kv_caches[i], input_metadata,
                                  cache_event)

        hidden_states = self.ln_f(hidden_states)
        return hidden_states


class GPTBigCodeForCausalLM(nn.Module):

    def __init__(self, config: GPTBigCodeConfig):
        super().__init__()
        self.config = config
        self.transformer = GPTBigCodeModel(config)
        # TODO(zhuohan): create a new weight after implementing pipeline
        #                parallelism
        self.lm_head_weight = self.transformer.wte.weight
        self.sampler = Sampler(config.vocab_size)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> Dict[int, SequenceOutputs]:
        hidden_states = self.transformer(input_ids, positions, kv_caches,
                                         input_metadata, cache_events)
        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
                                   input_metadata)
        return next_tokens

    _column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
    _row_parallel_weights = ["c_proj.weight"]

    def load_weights(self,
                     model_name_or_path: str,
                     cache_dir: Optional[str] = None,
                     use_np_cache: bool = False):
        tensor_model_parallel_world_size = (
            get_tensor_model_parallel_world_size())
        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
        state_dict = self.state_dict()

        for name, loaded_weight in hf_model_weights_iterator(
                model_name_or_path, cache_dir, use_np_cache):
            if "lm_head.weight" in name:
                # GPT-2 ties the weights of the embedding layer and the final
                # linear layer.
                continue
            if ".attn.bias" in name:
                # Skip attention mask.
                # NOTE: "c_attn.bias" should not be skipped.
                continue

            param = state_dict[name]

            if not name.startswith("transformer."):
                name = "transformer." + name

            if name == "transformer.wte.weight":
                # Consider padding in the vocab size.
                padded_vocab_size = param.shape[
                    0] * tensor_model_parallel_world_size
                num_extra_rows = padded_vocab_size - self.config.vocab_size
                extra_rows = torch.empty(num_extra_rows,
                                         loaded_weight.shape[1])
                extra_rows = extra_rows.to(loaded_weight)
                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)

            # For the fused QKV linear layer, manually shard the weights.
            if "c_attn" in name:
                # GPT-2's fused QKV has the shape of
                # [3 * num_heads * head_size, hidden_size].
                # When tensor parallelism is used, we shard the weights along
                # the head dimension.
                total_num_heads = self.config.num_attention_heads
                total_num_kv_heads = (1 if self.config.multi_query else
                                      total_num_heads)
                hidden_size = self.config.hidden_size
                head_size = hidden_size // total_num_heads
                total_kv_size = head_size * total_num_kv_heads
                num_heads = total_num_heads // tensor_model_parallel_world_size
                head_start = tensor_model_parallel_rank * num_heads
                head_end = (tensor_model_parallel_rank + 1) * num_heads

                wq, wk, wv = torch.split(
                    loaded_weight, [hidden_size, total_kv_size, total_kv_size],
                    dim=0)

                wq = wq[head_size * head_start:head_size * head_end]
                if not self.config.multi_query:
                    # Split the heads when using normal multi-head attention
                    wk = wk[head_size * head_start:head_size * head_end]
                    wv = wv[head_size * head_start:head_size * head_end]
                    # Else, keep the weights as is for multi-query attention

                loaded_weight = torch.cat([wq, wk, wv], dim=0)

            load_tensor_parallel_weights(param, loaded_weight, name,
                                         self._column_parallel_weights,
                                         self._row_parallel_weights,
                                         tensor_model_parallel_rank)
