# coding=utf-8
# Adapted from
# https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_phi.py
# Copyright 2023 The vLLM team.
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
#
# BSD 3-Clause License
#
# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
# All rights reserved.
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# modification, are permitted provided that the following conditions are met:
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"""Inference-only Phi-1.5 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 List, Optional, Tuple

import torch
from torch import nn
from transformers import PretrainedConfig

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 PagedAttentionWithRoPE
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               LinearMethodBase,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding, ParallelLMHead)
from vllm.model_executor.parallel_utils.parallel_state import (
    get_tensor_model_parallel_world_size)
from vllm.model_executor.weight_utils import (default_weight_loader,
                                              hf_model_weights_iterator)
from vllm.sequence import SamplerOutput

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


class PhiEmbedding(nn.Module):

    def __init__(self, config: PretrainedConfig):
        super().__init__()

        self.wte = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )

    def forward(self, input_ids: torch.LongTensor):
        return self.wte(input_ids)


class PhiAttention(nn.Module):

    def __init__(self,
                 config: PretrainedConfig,
                 linear_method: Optional[LinearMethodBase] = None):
        super().__init__()
        self.total_num_heads = config.num_attention_heads
        self.hidden_size = config.hidden_size
        self.head_size = self.hidden_size // self.total_num_heads

        tensor_model_parallel_world_size = (
            get_tensor_model_parallel_world_size())
        assert self.total_num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = (self.total_num_heads //
                          tensor_model_parallel_world_size)

        # pylint: disable=C0103
        self.Wqkv = QKVParallelLinear(
            self.hidden_size,
            self.head_size,
            self.total_num_heads,
            linear_method=linear_method,
        )
        self.qkv_proj = QKVParallelLinear(
            config.hidden_size,
            self.head_size,
            self.total_num_heads,
            bias=False,
            linear_method=linear_method,
        )
        self.out_proj = RowParallelLinear(
            self.hidden_size,
            self.hidden_size,
            linear_method=linear_method,
        )

        scaling = self.head_size**-0.5
        rotary_dim = config.rotary_dim
        assert rotary_dim % 2 == 0

        # pylint: disable=C0301
        # Refer to:
        # https://huggingface.co/microsoft/phi-1_5/blob/d212a789620c380ff32ca1d1ee9943a777360987/modeling_phi.py#L518
        rope_theta = 10000
        max_position_embeddings = getattr(config, "n_positions", 2048)
        self.attn = PagedAttentionWithRoPE(
            self.num_heads,
            self.head_size,
            scaling,
            rotary_dim,
            base=rope_theta,
            max_position=max_position_embeddings)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        qkv, _ = self.Wqkv(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        k_cache, v_cache = kv_cache
        attn_output = self.attn(position_ids, q, k, v, k_cache, v_cache,
                                input_metadata, cache_event)
        output, _ = self.out_proj(attn_output)
        return output


class PhiMLP(nn.Module):

    def __init__(self,
                 config: PretrainedConfig,
                 linear_method: Optional[LinearMethodBase] = None):
        super().__init__()

        n_inner = getattr(config, "n_inner", None)
        n_inner = n_inner if n_inner is not None else 4 * config.hidden_size

        self.fc1 = ColumnParallelLinear(
            config.hidden_size,
            n_inner,
            linear_method=linear_method,
        )
        self.fc2 = RowParallelLinear(
            n_inner,
            config.hidden_size,
            linear_method=linear_method,
        )
        quant_config = getattr(linear_method, "quant_config", None)
        self.act = get_act_fn(config.activation_function, quant_config,
                              n_inner)

    def forward(self, hidden_states):
        hidden_states, _ = self.fc1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.fc2(hidden_states)
        return hidden_states


class PhiLayer(nn.Module):

    def __init__(self,
                 config: PretrainedConfig,
                 linear_method: Optional[LinearMethodBase] = None):
        super().__init__()
        self.ln = nn.LayerNorm(config.hidden_size,
                               eps=config.layer_norm_epsilon)
        self.mixer = PhiAttention(config, linear_method)
        self.mlp = PhiMLP(config, linear_method)

    def forward(
        self,
        position_ids: torch.Tensor,
        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(hidden_states)
        attn_outputs = self.mixer(
            position_ids=position_ids,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            input_metadata=input_metadata,
            cache_event=cache_event,
        )
        feed_forward_hidden_states = self.mlp(hidden_states)
        hidden_states = attn_outputs + feed_forward_hidden_states + residual
        return hidden_states


class PhiCausalLMHead(nn.Module):

    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.ln = nn.LayerNorm(config.hidden_size,
                               eps=config.layer_norm_epsilon)
        self.linear = ParallelLMHead(config.vocab_size,
                                     config.hidden_size,
                                     bias=True)
        self.sampler = Sampler(config.vocab_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        input_metadata: InputMetadata,
    ):
        hidden_states = self.ln(hidden_states)
        next_tokens = self.sampler(self.linear.weight, hidden_states,
                                   input_metadata, self.linear.bias)
        return next_tokens


class PhiModel(nn.Module):

    def __init__(self,
                 config: PretrainedConfig,
                 linear_method: Optional[LinearMethodBase] = None):
        super().__init__()
        self.config = config
        self.linear_method = linear_method
        self.embd = PhiEmbedding(config)
        self.h = nn.ModuleList([
            PhiLayer(config, linear_method)
            for _ in range(config.num_hidden_layers)
        ])

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> SamplerOutput:
        hidden_states = self.embd(input_ids)
        for i in range(self.config.num_hidden_layers):
            if cache_events is None:
                cache_event = None
            else:
                cache_event = cache_events[i]
            layer = self.h[i]
            hidden_states = layer(
                positions,
                hidden_states,
                kv_caches[i],
                input_metadata,
                cache_event,
            )
        return hidden_states


class PhiForCausalLM(nn.Module):

    def __init__(self,
                 config: PretrainedConfig,
                 linear_method: Optional[LinearMethodBase] = None):
        super().__init__()
        self.config = config
        self.linear_method = linear_method

        self.transformer = PhiModel(config, linear_method)
        self.lm_head = PhiCausalLMHead(config)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> SamplerOutput:
        hidden_states = self.transformer(input_ids, positions, kv_caches,
                                         input_metadata, cache_events)
        lm_logits = self.lm_head(hidden_states, input_metadata)
        return lm_logits

    def load_weights(self,
                     model_name_or_path: str,
                     cache_dir: Optional[str] = None,
                     load_format: str = "auto",
                     revision: Optional[str] = None):
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in hf_model_weights_iterator(
                model_name_or_path, cache_dir, load_format, revision):
            if "rotary_emb.inv_freq" in name:
                continue

            # pylint: disable=E1136
            param = params_dict[name]
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)
