# !usr/bin/env python
# -*- coding:utf-8 -*-

'''
 Description  : 
 Version      : 1.0
 Author       : MrYXJ
 Github       : https://github.com/MrYxJ
 Date         : 2023-11-09 11:29:21
 LastEditTime : 2023-11-09 17:25:33
 Copyright (C) 2023 mryxj. All rights reserved.
'''

import math
import torch
import transformers


from einops import rearrange
from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
from flash_attn.bert_padding import unpad_input, pad_input
from torch import nn
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
from typing import Optional, Tuple


LLAMA2_MAX_LENGTH = 4096

def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
           Optional[Tuple[torch.Tensor]]]:
      
    """Input shape: Batch x Time x Channel

    attention_mask: [bsz, q_len]
    """
    bsz, q_len, _ = hidden_states.size()
    query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    # [bsz, q_len, nh, hd]
    # [bsz, nh, q_len, hd]
    
    kv_seq_len = key_states.shape[-2]
    # assert past_key_value is None, "past_key_value is not supported"
    if past_key_value is not None:
        kv_seq_len += past_key_value[0].shape[-2]

    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
    
    # [bsz, nh, t, hd]
    # assert not output_attentions, "output_attentions is not supported"
    # assert not use_cache, "use_cache is not supported"
    if past_key_value is not None:
        # reuse k, v, self_attention
        key_states = torch.cat([past_key_value[0], key_states], dim=2)
        value_states = torch.cat([past_key_value[1], value_states], dim=2)

    past_key_value = (key_states, value_states) if use_cache else None

    # Flash attention codes from
    # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py

    # transform the data into the format required by flash attention
    # import pdb; pdb.set_trace()
    
    if query_states.shape[-2] == 1 or query_states.shape[-2] != key_states.shape[-2]:
        # decode token-by-token, do not use flash attention
        # in incremental state, do not use flash attention
        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    qkv = torch.stack([query_states, key_states, value_states], dim=2)  # [bsz, nh, 3, q_len, hd]
    qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
    # We have disabled _prepare_decoder_attention_mask in LlamaModel
    # the attention_mask should be the same as the key_padding_mask
    key_padding_mask = attention_mask

    if key_padding_mask is None:
        qkv = rearrange(qkv, 'b s ... -> (b s) ...')
        max_s = q_len
        cu_q_lens = torch.arange(0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32,
                                 device=qkv.device)
        output = flash_attn_varlen_qkvpacked_func(
            qkv, cu_q_lens, max_s, 0.0,
            softmax_scale=None, causal=True
        )
        output = rearrange(output, '(b s) ... -> b s ...', b=bsz)
    else:
        nheads = qkv.shape[-2]
        x = rearrange(qkv, 'b s three h d -> b s (three h d)')
        x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
        x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
        output_unpad = flash_attn_varlen_qkvpacked_func(
            x_unpad, cu_q_lens, max_s, 0.0,
            softmax_scale=None, causal=True
        )
        output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
                                     indices, bsz, q_len),
                           'b s (h d) -> b s h d', h=nheads)
    attn_output = self.o_proj(rearrange(output, 'b s h d -> b s (h d)'))

    return attn_output, None, past_key_value

def forward_logn_train(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
           Optional[Tuple[torch.Tensor]]]:
      
    """Input shape: Batch x Time x Channel

    attention_mask: [bsz, q_len]
    """
    bsz, q_len, _ = hidden_states.size()
    query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    # [bsz, q_len, nh, hd]
    # [bsz, nh, q_len, hd]
    
    kv_seq_len = key_states.shape[-2]
    # assert past_key_value is None, "past_key_value is not supported"
    if past_key_value is not None:
        kv_seq_len += past_key_value[0].shape[-2]

    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
    
    logn_list = [
        math.log(i, q_len) for i in range(1, q_len+1)
    ]
    logn_tensor = torch.tensor(logn_list)[None, None, :, None].type_as(query_states).to(query_states.device)
    #self.register_buffer("logn_tensor", logn_tensor, persistent=False)
    query_states = query_states * logn_tensor.expand_as(query_states)
    
    # [bsz, nh, t, hd]
    # assert not output_attentions, "output_attentions is not supported"
    # assert not use_cache, "use_cache is not supported"
    if past_key_value is not None:
        # reuse k, v, self_attention
        key_states = torch.cat([past_key_value[0], key_states], dim=2)
        value_states = torch.cat([past_key_value[1], value_states], dim=2)

    past_key_value = (key_states, value_states) if use_cache else None

    # Flash attention codes from
    # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py

    # transform the data into the format required by flash attention
    # import pdb; pdb.set_trace()
    
    if query_states.shape[-2] == 1 or query_states.shape[-2] != key_states.shape[-2]:
        # decode token-by-token, do not use flash attention
        # in incremental state, do not use flash attention
        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    qkv = torch.stack([query_states, key_states, value_states], dim=2)  # [bsz, nh, 3, q_len, hd]
    qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
    # We have disabled _prepare_decoder_attention_mask in LlamaModel
    # the attention_mask should be the same as the key_padding_mask
    key_padding_mask = attention_mask

    if key_padding_mask is None:
        qkv = rearrange(qkv, 'b s ... -> (b s) ...')
        max_s = q_len
        cu_q_lens = torch.arange(0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32,
                                 device=qkv.device)
        output = flash_attn_varlen_qkvpacked_func(
            qkv, cu_q_lens, max_s, 0.0,
            softmax_scale=None, causal=True
        )
        output = rearrange(output, '(b s) ... -> b s ...', b=bsz)
    else:
        nheads = qkv.shape[-2]
        x = rearrange(qkv, 'b s three h d -> b s (three h d)')
        x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
        x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
        output_unpad = flash_attn_varlen_qkvpacked_func(
            x_unpad, cu_q_lens, max_s, 0.0,
            softmax_scale=None, causal=True
        )
        output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
                                     indices, bsz, q_len),
                           'b s (h d) -> b s h d', h=nheads)
    attn_output = self.o_proj(rearrange(output, 'b s h d -> b s (h d)'))

    return attn_output, None, past_key_value



def forward_logn_predict(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
           Optional[Tuple[torch.Tensor]]]:
      
    """Input shape: Batch x Time x Channel

    attention_mask: [bsz, q_len]
    """
    bsz, q_len, _ = hidden_states.size()
    query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    # [bsz, q_len, nh, hd]
    # [bsz, nh, q_len, hd]
    
    kv_seq_len = key_states.shape[-2]
    # assert past_key_value is None, "past_key_value is not supported"
    if past_key_value is not None:
        kv_seq_len += past_key_value[0].shape[-2]

    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
    
    logn_list = [
        math.log(i, LLAMA2_MAX_LENGTH) if i > LLAMA2_MAX_LENGTH else 1
        for i in range(1, q_len+1)
    ]
    logn_tensor = torch.tensor(logn_list)[None, None, :, None].type_as(query_states).to(query_states.device)
    #self.register_buffer("logn_tensor", logn_tensor, persistent=False)
    query_states = query_states * logn_tensor.expand_as(query_states)
    
    # [bsz, nh, t, hd]
    # assert not output_attentions, "output_attentions is not supported"
    # assert not use_cache, "use_cache is not supported"
    if past_key_value is not None:
        # reuse k, v, self_attention
        key_states = torch.cat([past_key_value[0], key_states], dim=2)
        value_states = torch.cat([past_key_value[1], value_states], dim=2)

    past_key_value = (key_states, value_states) if use_cache else None

    # Flash attention codes from
    # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py

    # transform the data into the format required by flash attention
    # import pdb; pdb.set_trace()
    
    if query_states.shape[-2] == 1 or query_states.shape[-2] != key_states.shape[-2]:
        # decode token-by-token, do not use flash attention
        # in incremental state, do not use flash attention
        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    qkv = torch.stack([query_states, key_states, value_states], dim=2)  # [bsz, nh, 3, q_len, hd]
    qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
    # We have disabled _prepare_decoder_attention_mask in LlamaModel
    # the attention_mask should be the same as the key_padding_mask
    key_padding_mask = attention_mask

    if key_padding_mask is None:
        qkv = rearrange(qkv, 'b s ... -> (b s) ...')
        max_s = q_len
        cu_q_lens = torch.arange(0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32,
                                 device=qkv.device)
        output = flash_attn_varlen_qkvpacked_func(
            qkv, cu_q_lens, max_s, 0.0,
            softmax_scale=None, causal=True
        )
        output = rearrange(output, '(b s) ... -> b s ...', b=bsz)
    else:
        nheads = qkv.shape[-2]
        x = rearrange(qkv, 'b s three h d -> b s (three h d)')
        x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
        x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
        output_unpad = flash_attn_varlen_qkvpacked_func(
            x_unpad, cu_q_lens, max_s, 0.0,
            softmax_scale=None, causal=True
        )
        output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
                                     indices, bsz, q_len),
                           'b s (h d) -> b s h d', h=nheads)
    attn_output = self.o_proj(rearrange(output, 'b s h d -> b s (h d)'))

    return attn_output, None, past_key_value


# Disable the transformation of the attention mask in LlamaModel as the flash attention
# requires the attention mask to be the same as the key_padding_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape,
                                    inputs_embeds, past_key_values_length):
    # [bsz, seq_len]
    if input_shape[-1] > 1 and past_key_values_length == 0:  # encode
        return attention_mask
    return transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask(self, attention_mask,
                                                                                              input_shape,
                                                                                              inputs_embeds,
                                                                                              past_key_values_length)


def replace_llama2_attn_with_flash_attn_attn():
    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
    transformers.models.llama.modeling_llama.LlamaAttention.forward = forward


def replace_llama2_attn_with_flash_attn_and_logn_attn_predict():
    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
    transformers.models.llama.modeling_llama.LlamaAttention.forward = forward_logn_predict

    
    
def replace_llama2_attn_with_flash_attn_and_logn_attn_train():
    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
    transformers.models.llama.modeling_llama.LlamaAttention.forward = forward_logn_train