attention.py

"""Attention layers."""
import math
import warnings
from typing import Optional, Dict, Any, NamedTuple, Protocol, Tuple, Union
import torch
import torch.nn as nn
from einops import rearrange
from packaging import version
from torch import nn
from torch.utils.checkpoint import checkpoint
from .norm import LPLayerNorm
from .is_torch_version import is_torch_version

class PastKeyValue(NamedTuple):
    key: torch.Tensor
    value: torch.Tensor

class AttnFnOutput(NamedTuple):
    attns: torch.Tensor
    attn_probs: Optional[torch.Tensor]
    past_key_value: Union[PastKeyValue, Tuple, None]

class AttnFn(Protocol):
    def __call__(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        n_heads: int,
        softmax_scale: Optional[float] = None,
        attn_bias: Optional[torch.Tensor] = None,
        key_padding_mask: Optional[torch.ByteTensor] = None,
        is_causal = False,
        dropout_p = 0.0,
        training = False,
        needs_weights = False,
        multiquery = False,
    ) -> AttnFnOutput: ...

class AttnFnCheckpointed(Protocol):
    def __call__(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        n_heads: int,
        softmax_scale: Optional[float],
        attn_bias: Optional[torch.Tensor],
        key_padding_mask: Optional[torch.ByteTensor],
        is_causal: bool,
        dropout_p: float,
        training: bool,
        needs_weights: bool,
    ) -> AttnFnOutput: ...

class AttnOutput(NamedTuple):
    projected_context: torch.Tensor
    attn_weights: Optional[torch.Tensor]
    past_key_value: Union[PastKeyValue, Tuple, None]

class Attn(Protocol):
    def __call__(
        self,
        x: torch.Tensor,
        past_key_value: Union[PastKeyValue, Tuple, None] = None,
        attn_bias: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.ByteTensor] = None,
        is_causal = True,
        needs_weights = False,
    ) -> AttnOutput: ...

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: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    n_heads: int,
    past_key_value=None,
    softmax_scale: Optional[float] = None,
    attn_bias: Optional[torch.Tensor] = None,
    key_padding_mask: Optional[torch.ByteTensor] = None,
    is_causal = False,
    dropout_p = 0.0,
    training = False,
    needs_weights = False,
    multiquery = False,
) -> AttnFnOutput:
    q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads)
    kv_n_heads = 1 if multiquery else n_heads
    k = rearrange(key, 'b s (h d) -> b h d s', h=kv_n_heads)
    v = rearrange(value, 'b s (h d) -> b h s d', h=kv_n_heads)

    if past_key_value is not None:
        # attn_impl: flash & triton use kernels which expect input shape [b, s, h, d_head].
        # kv_cache is therefore stored using that shape.
        # attn_impl: torch stores the kv_cache in the ordering which is most advantageous
        # for its attn computation ie
        # keys are stored as tensors with shape [b, h, d_head, s] and
        # values are stored as tensors with shape [b, h, s, d_head]
        if len(past_key_value) != 0:
            k = torch.cat([past_key_value[0], k], dim=3)
            v = torch.cat([past_key_value[1], v], dim=2)

        past_key_value = (k, v)
    (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:
        # clamp to 0 necessary for torch 2.0 compile()
        _s_q = max(0, attn_bias.size(2) - s_q)
        _s_k = max(0, attn_bias.size(3) - s_k)
        attn_bias = attn_bias[:, :, _s_q:, _s_k:]
        if attn_bias.size(-1) != 1 and attn_bias.size(-1) != s_k or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != 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
    min_val = torch.finfo(q.dtype).min
    if key_padding_mask is not None:
        if attn_bias is not None:
            warnings.warn('Propagating 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 and (not q.size(2) == 1):
        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)')
    if needs_weights:
        return AttnFnOutput(out, attn_weight, past_key_value)
    return AttnFnOutput(out, None, past_key_value)

def check_valid_inputs(*tensors, 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: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    n_heads: int,
    past_key_value=None,
    softmax_scale: Optional[float] = None,
    attn_bias: Optional[torch.Tensor] = None,
    key_padding_mask: Optional[torch.ByteTensor] = None,
    is_causal = False,
    dropout_p = 0.0,
    training = False,
    needs_weights = False,
    multiquery = False,
) -> AttnFnOutput:
    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 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:
        # clamp to 0 necessary for torch 2.0 compile()
        _s_q = max(0, attn_bias.size(2) - query.size(1))
        _s_k = max(0, attn_bias.size(3) - key.size(1))
        attn_bias = attn_bias[:, :, _s_q:, _s_k:]
    if attn_bias is not None:
        raise NotImplementedError(f'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 AttnFnOutput(output, None, past_key_value)

def triton_flash_attn_fn(
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    n_heads: int,
    past_key_value=None,
    softmax_scale: Optional[float] = None,
    attn_bias: Optional[torch.Tensor] = None,
    key_padding_mask: Optional[torch.ByteTensor] = None,
    is_causal = False,
    dropout_p = 0.0,
    training = False,
    needs_weights = False,
    multiquery = False,
) -> AttnFnOutput:
    try:
        from .flash_attn_triton import flash_attn_func
    except:
        _installed = False
        if version.parse(torch.__version__) < version.parse('2.0.0'):
            _installed = True
            try:
                from flash_attn.flash_attn_triton import flash_attn_func
            except:
                _installed = False
        if not _installed:
            raise RuntimeError('Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU and `pip install .[gpu]` if installing from llm-foundry source or `pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). Note: (1) requires you have CMake and PyTorch already installed.')
    check_valid_inputs(query, key, value)
    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:
        # clamp to 0 necessary for torch 2.0 compile()
        _s_q = max(0, attn_bias.size(2) - query.size(1))
        _s_k = max(0, attn_bias.size(3) - key.size(1))
        attn_bias = attn_bias[:, :, _s_q:, _s_k:]
    if dropout_p:
        raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.')
    if needs_weights:
        raise NotImplementedError(f'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_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
    output = attn_output.view(*attn_output.shape[:2], -1)
    return AttnFnOutput(output, None, past_key_value)

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

    Using torch or triton attention implemetation enables user to also use
    additive bias.
    """
    gradient_checkpointing = False
    attn_fn: AttnFn

    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: torch.Tensor,
        past_key_value: Union[PastKeyValue, Tuple, None] = None,
        attn_bias: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.ByteTensor] = None,
        is_causal = True,
        needs_weights = False,
    ) -> AttnOutput:
        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 self.training and self.gradient_checkpointing:
            ckpt_kwargs: Dict[str, Any] = {'use_reentrant': False} if is_torch_version('>=', '1.11.0') else {}
            def create_custom_forward(attn_fn: AttnFn) -> AttnFnCheckpointed:
                def custom_forward(
                    query: torch.Tensor,
                    key: torch.Tensor,
                    value: torch.Tensor,
                    n_heads: int,
                    softmax_scale: Optional[float],
                    attn_bias: Optional[torch.Tensor],
                    key_padding_mask: Optional[torch.ByteTensor],
                    is_causal: bool,
                    dropout_p: float,
                    training: bool,
                    needs_weights: bool,
                ):
                    return attn_fn(
                        query,
                        key,
                        value,
                        n_heads,
                        softmax_scale,
                        attn_bias,
                        key_padding_mask,
                        is_causal,
                        dropout_p,
                        training,
                        needs_weights,
                        False, # multiquery
                    )
                return custom_forward
            attn_fn_out: AttnFnOutput = checkpoint(
                create_custom_forward(self.attn_fn),
                query,
                key,
                value,
                self.n_heads,
                self.softmax_scale,
                attn_bias,
                key_padding_mask,
                is_causal,
                self.attn_dropout_p,
                self.training,
                needs_weights,
                **ckpt_kwargs,
            )
        else:
            attn_fn_out: AttnFnOutput = self.attn_fn(
                query,
                key,
                value,
                self.n_heads,
                past_key_value=past_key_value,
                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,
            )
        context, attn_weights, past_key_value = attn_fn_out
        return AttnOutput(self.out_proj(context), attn_weights, past_key_value)

class MultiQueryAttention(nn.Module, Attn):
    """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: torch.Tensor,
        past_key_value: Union[PastKeyValue, Tuple, None] = None,
        attn_bias: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.ByteTensor] = None,
        is_causal = True,
        needs_weights = False,
    ) -> AttnOutput:
        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 = PastKeyValue(key, value)
        if attn_bias is not None:
            attn_bias = attn_bias[:, :, -query.size(1):, -key.size(1):]
        if self.training and self.gradient_checkpointing:
            ckpt_kwargs: Dict[str, Any] = {'use_reentrant': False} if is_torch_version('>=', '1.11.0') else {}
            def create_custom_forward(attn_fn: AttnFn) -> AttnFnCheckpointed:
                def custom_forward(
                    query: torch.Tensor,
                    key: torch.Tensor,
                    value: torch.Tensor,
                    n_heads: int,
                    softmax_scale: Optional[float],
                    attn_bias: Optional[torch.Tensor],
                    key_padding_mask: Optional[torch.ByteTensor],
                    is_causal: bool,
                    dropout_p: float,
                    training: bool,
                    needs_weights: bool,
                ):
                    return attn_fn(
                        query,
                        key,
                        value,
                        n_heads,
                        softmax_scale,
                        attn_bias,
                        key_padding_mask,
                        is_causal,
                        dropout_p,
                        training,
                        needs_weights,
                        True, # multiquery
                    )
                return custom_forward
            attn_fn_out: AttnFnOutput = checkpoint(
                create_custom_forward(self.attn_fn),
                query,
                key,
                value,
                self.n_heads,
                self.softmax_scale,
                attn_bias,
                key_padding_mask,
                is_causal,
                self.attn_dropout_p,
                self.training,
                needs_weights,
                **ckpt_kwargs,
            )
        else:
            attn_fn_out: AttnFnOutput = self.attn_fn(
                query,
                key,
                value,
                self.n_heads,
                past_key_value=past_key_value,
                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,
            )
        context, attn_weights = attn_fn_out
        return AttnOutput(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}