"""Attention layers."""
import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
from einops import rearrange
from torch import nn
from .norm import LPLayerNorm

def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool):
    if original_is_causal and num_query_tokens != num_key_tokens:
        if num_query_tokens != 1:
            raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.')
        else:
            return False
    return original_is_causal

def scaled_multihead_dot_product_attention(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
    q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads)
    k = rearrange(key, 'b s (h d) -> b h d s', h=1 if multiquery else n_heads)
    v = rearrange(value, 'b s (h d) -> b h s d', h=1 if multiquery else n_heads)
    min_val = torch.finfo(q.dtype).min
    (b, _, s_q, d) = q.shape
    s_k = k.size(-1)
    if softmax_scale is None:
        softmax_scale = 1 / math.sqrt(d)
    attn_weight = q.matmul(k) * softmax_scale
    if attn_bias is not None:
        if attn_bias.size(-1) not in [1, s_k] or attn_bias.size(-2) not in [
            1,
            s_q,
        ]:
            raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.')
        attn_weight = attn_weight + attn_bias
    if key_padding_mask is not None:
        if attn_bias is not None:
            warnings.warn('Propogating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unneccessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')
        attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val)
    if is_causal:
        s = max(s_q, s_k)
        causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16)
        causal_mask = causal_mask.tril()
        causal_mask = causal_mask.to(torch.bool)
        causal_mask = ~causal_mask
        causal_mask = causal_mask[-s_q:, -s_k:]
        attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val)
    attn_weight = torch.softmax(attn_weight, dim=-1)
    if dropout_p:
        attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True)
    out = attn_weight.matmul(v)
    out = rearrange(out, 'b h s d -> b s (h d)')
    return (out, attn_weight) if needs_weights else (out, None)

def check_valid_inputs(*tensors, valid_dtypes=None):
    if valid_dtypes is None:
        valid_dtypes = [torch.float16, torch.bfloat16]
    for tensor in tensors:
        if tensor.dtype not in valid_dtypes:
            raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.')
        if not tensor.is_cuda:
            raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).')

def flash_attn_fn(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
    try:
        from flash_attn import bert_padding, flash_attn_interface
    except:
        raise RuntimeError('Please install flash-attn==1.0.3.post0')
    check_valid_inputs(query, key, value)
    if attn_bias is not None:
        raise NotImplementedError('attn_bias not implemented for flash attn.')
    (batch_size, seqlen) = query.shape[:2]
    if key_padding_mask is None:
        key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)
    query_padding_mask = key_padding_mask[:, -query.size(1):]
    (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask)
    query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads)
    (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask)
    key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
    (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask)
    value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
    if multiquery:
        key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))
        value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1))
    dropout_p = dropout_p if training else 0.0
    reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
    output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights)
    output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen)
    return (output, None)

def triton_flash_attn_fn(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
    try:
        from flash_attn import flash_attn_triton
    except:
        raise RuntimeError('Please install flash-attn==1.0.3.post0 and triton==2.0.0.dev20221202')
    check_valid_inputs(query, key, value)
    if dropout_p:
        raise NotImplementedError('Dropout not implemented for attn_impl: triton.')
    if needs_weights:
        raise NotImplementedError('attn_impl: triton cannot return attn weights.')
    if key_padding_mask is not None:
        warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')
        (b_size, s_k) = key_padding_mask.shape[:2]
        if attn_bias is None:
            attn_bias = query.new_zeros(b_size, 1, 1, s_k)
        attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min)
    query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads)
    key = rearrange(key, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
    value = rearrange(value, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
    if multiquery:
        key = key.expand(*key.shape[:2], n_heads, key.size(-1))
        value = value.expand(*value.shape[:2], n_heads, value.size(-1))
    reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
    attn_output = flash_attn_triton.flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
    output = attn_output.view(*attn_output.shape[:2], -1)
    return (output, None)

class MultiheadAttention(nn.Module):
    """Multi-head self attention.

    Using torch or triton attention implemetation enables user to also use
    additive bias.
    """

    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, device: Optional[str]=None):
        super().__init__()
        self.attn_impl = attn_impl
        self.clip_qkv = clip_qkv
        self.qk_ln = qk_ln
        self.d_model = d_model
        self.n_heads = n_heads
        self.softmax_scale = softmax_scale
        if self.softmax_scale is None:
            self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
        self.attn_dropout_p = attn_pdrop
        self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device)
        fuse_splits = (d_model, 2 * d_model)
        self.Wqkv._fused = (0, fuse_splits)
        if self.qk_ln:
            layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
            self.q_ln = layernorm_class(self.d_model, device=device)
            self.k_ln = layernorm_class(self.d_model, device=device)
        if self.attn_impl == 'flash':
            self.attn_fn = flash_attn_fn
        elif self.attn_impl == 'triton':
            self.attn_fn = triton_flash_attn_fn
            warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
        elif self.attn_impl == 'torch':
            self.attn_fn = scaled_multihead_dot_product_attention
            if torch.cuda.is_available():
                warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
        else:
            raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
        self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
        self.out_proj._is_residual = True

    def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
        qkv = self.Wqkv(x)
        if self.clip_qkv:
            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
        (query, key, value) = qkv.chunk(3, dim=2)
        key_padding_mask = attention_mask
        if self.qk_ln:
            dtype = query.dtype
            query = self.q_ln(query).to(dtype)
            key = self.k_ln(key).to(dtype)
        if past_key_value is not None:
            if len(past_key_value) != 0:
                key = torch.cat([past_key_value[0], key], dim=1)
                value = torch.cat([past_key_value[1], value], dim=1)
            past_key_value = (key, value)
        if attn_bias is not None:
            attn_bias = attn_bias[:, :, -query.size(1):, -key.size(1):]
        (context, attn_weights) = self.attn_fn(query, key, value, self.n_heads, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights)
        return (self.out_proj(context), attn_weights, past_key_value)

class MultiQueryAttention(nn.Module):
    """Multi-Query self attention.

    Using torch or triton attention implemetation enables user to also use
    additive bias.
    """

    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, device: Optional[str]=None):
        super().__init__()
        self.attn_impl = attn_impl
        self.clip_qkv = clip_qkv
        self.qk_ln = qk_ln
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.softmax_scale = softmax_scale
        if self.softmax_scale is None:
            self.softmax_scale = 1 / math.sqrt(self.head_dim)
        self.attn_dropout_p = attn_pdrop
        self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device)
        fuse_splits = (d_model, d_model + self.head_dim)
        self.Wqkv._fused = (0, fuse_splits)
        if self.qk_ln:
            layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
            self.q_ln = layernorm_class(d_model, device=device)
            self.k_ln = layernorm_class(self.head_dim, device=device)
        if self.attn_impl == 'flash':
            self.attn_fn = flash_attn_fn
        elif self.attn_impl == 'triton':
            self.attn_fn = triton_flash_attn_fn
            warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
        elif self.attn_impl == 'torch':
            self.attn_fn = scaled_multihead_dot_product_attention
            if torch.cuda.is_available():
                warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
        else:
            raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
        self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
        self.out_proj._is_residual = True

    def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
        qkv = self.Wqkv(x)
        if self.clip_qkv:
            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
        (query, key, value) = qkv.split([self.d_model, self.head_dim, self.head_dim], dim=2)
        key_padding_mask = attention_mask
        if self.qk_ln:
            dtype = query.dtype
            query = self.q_ln(query).to(dtype)
            key = self.k_ln(key).to(dtype)
        if past_key_value is not None:
            if len(past_key_value) != 0:
                key = torch.cat([past_key_value[0], key], dim=1)
                value = torch.cat([past_key_value[1], value], dim=1)
            past_key_value = (key, value)
        if attn_bias is not None:
            attn_bias = attn_bias[:, :, -query.size(1):, -key.size(1):]
        (context, attn_weights) = self.attn_fn(query, key, value, self.n_heads, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights, multiquery=True)
        return (self.out_proj(context), attn_weights, past_key_value)

def attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id):
    if attn_impl == 'flash':
        return None
    elif attn_impl in ['torch', 'triton']:
        if alibi:
            if (prefix_lm or not causal) or use_sequence_id:
                return (1, n_heads, seq_len, seq_len)
            return (1, n_heads, 1, seq_len)
        elif prefix_lm or use_sequence_id:
            return (1, 1, seq_len, seq_len)
        return None
    else:
        raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')

def build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8):
    if attn_impl == 'flash':
        return None
    elif attn_impl in ['torch', 'triton']:
        if alibi:
            (device, dtype) = (attn_bias.device, attn_bias.dtype)
            attn_bias = attn_bias.add(build_alibi_bias(n_heads, seq_len, full=not causal, alibi_bias_max=alibi_bias_max, device=device, dtype=dtype))
        return attn_bias
    else:
        raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')

def gen_slopes(n_heads, alibi_bias_max=8, device=None):
    _n_heads = 2 ** math.ceil(math.log2(n_heads))
    m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device)
    m = m.mul(alibi_bias_max / _n_heads)
    slopes = 1.0 / torch.pow(2, m)
    if _n_heads != n_heads:
        slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]
    return slopes.view(1, n_heads, 1, 1)

def build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None):
    alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len)
    if full:
        alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1)
        alibi_bias = alibi_bias.abs().mul(-1)
    slopes = gen_slopes(n_heads, alibi_bias_max, device=device)
    alibi_bias = alibi_bias * slopes
    return alibi_bias.to(dtype=dtype)
ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention}