attention.py

"""Attention layers."""

import math
import warnings
from typing import Any, Optional
import torch
import torch.nn as nn
import transformers
from einops import rearrange
from packaging import version
from torch import nn
from .fc import FC_CLASS_REGISTRY
from .norm import NORM_CLASS_REGISTRY


def is_flash_v2_installed(v2_version: str = "2.0.0"):
    assert version.parse(v2_version) >= version.parse("2.0.0")
    try:
        import flash_attn as flash_attn
    except:
        return False
    return version.parse(flash_attn.__version__) >= version.parse(v2_version)


def is_flash_v1_installed():
    try:
        import flash_attn as flash_attn
    except:
        return False
    return version.parse(flash_attn.__version__) < version.parse("2.0.0")


def is_transformers_version_gte(hf_version: str) -> bool:
    return version.parse(transformers.__version__) >= version.parse(hf_version)


def check_alibi_support(attention_impl: str) -> bool:
    return attention_impl != "flash" or is_flash_v2_installed(v2_version="v2.4.2")


if is_flash_v1_installed():
    import transformers

    transformers.utils.is_flash_attn_available = lambda: False
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb


def _reset_is_causal(
    num_query_tokens: int, num_key_tokens: int, original_is_causal: bool
) -> 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 repeat_kv_for_gqa(hidden: torch.Tensor, n_rep: int) -> torch.Tensor:
    """Perform repeat of kv heads along a particular dimension.

    hidden.shape expected to be: (batch size, seq len, kv_n_heads, head_dim)
    n_rep: amount of repetitions of kv_n_heads
    Unlike torch.repeat_interleave, this function avoids allocating new memory.
    """
    if n_rep == 1:
        return hidden
    (b, s, kv_n_heads, d) = hidden.shape
    hidden = hidden[:, :, :, None, :].expand(b, s, kv_n_heads, n_rep, d)
    return hidden.reshape(b, s, kv_n_heads * n_rep, d)


def scaled_multihead_dot_product_attention(
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    n_heads: int,
    kv_n_heads: int,
    past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
    softmax_scale: Optional[float] = None,
    attn_bias: Optional[torch.Tensor] = None,
    key_padding_mask: Optional[torch.Tensor] = None,
    is_causal: bool = False,
    dropout_p: float = 0.0,
    training: bool = False,
    needs_weights: bool = False,
) -> tuple[
    torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]
]:
    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=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:
        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 kv_n_heads > 1 and kv_n_heads < n_heads:
        k = repeat_kv_for_gqa(k.transpose(1, 2), n_heads // kv_n_heads).transpose(1, 2)
        v = repeat_kv_for_gqa(v.transpose(1, 2), n_heads // kv_n_heads).transpose(1, 2)
    if softmax_scale is None:
        softmax_scale = 1 / math.sqrt(d)
    attn_weight = q.matmul(k) * softmax_scale
    if attn_bias is not None:
        _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 "
                + "unnecessary 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.float32)
        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.to(v.dtype).matmul(v)
    out = rearrange(out, "b h s d -> b s (h d)")
    if needs_weights:
        return (out, attn_weight, past_key_value)
    return (out, None, past_key_value)


def check_valid_inputs(
    *tensors: torch.Tensor, valid_dtypes: Optional[list[torch.dtype]] = 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: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    n_heads: int,
    kv_n_heads: int,
    past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
    softmax_scale: Optional[float] = None,
    attn_bias: Optional[torch.Tensor] = None,
    key_padding_mask: Optional[torch.Tensor] = None,
    is_causal: bool = False,
    dropout_p: float = 0.0,
    training: bool = False,
    needs_weights: bool = False,
    multiquery: bool = False,
    should_repeat_kv_for_gqa: Optional[bool] = True,
    sliding_window_size: int = -1,
    alibi_slopes: Optional[torch.Tensor] = None,
    flash_attn_padding_info: Optional[dict[str, torch.Tensor]] = None,
) -> tuple[
    torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]
]:
    if key_padding_mask is not None:
        raise ValueError("key_padding_mask should be None for flash attn.")
    del key_padding_mask
    if flash_attn_padding_info is None:
        raise ValueError("flash_attn_padding_info is required for flash attn.")
    try:
        from flash_attn import bert_padding, flash_attn_interface
    except:
        raise RuntimeError("Please install flash-attn==1.0.9 or flash-attn==2.3.6")
    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:
        raise NotImplementedError(f"attn_bias not implemented for flash attn.")
    (batch_size, seqlen) = query.shape[:2]
    indices_q = flash_attn_padding_info["indices_q"]
    indices_k = flash_attn_padding_info["indices_k"]
    indices_v = flash_attn_padding_info["indices_v"]
    cu_seqlens_q = flash_attn_padding_info["cu_seqlens_q"]
    cu_seqlens_k = flash_attn_padding_info["cu_seqlens_k"]
    max_seqlen_q = flash_attn_padding_info["max_seqlen_q"]
    max_seqlen_k = flash_attn_padding_info["max_seqlen_k"]
    query_unpad = bert_padding.index_first_axis(
        rearrange(query, "b s ... -> (b s) ..."), indices_q
    )
    query_unpad = rearrange(query_unpad, "nnz (h d) -> nnz h d", h=n_heads)
    key_unpad = bert_padding.index_first_axis(
        rearrange(key, "b s ... -> (b s) ..."), indices_k
    )
    key_unpad = rearrange(key_unpad, "nnz (h d) -> nnz h d", h=kv_n_heads)
    value_unpad = bert_padding.index_first_axis(
        rearrange(value, "b s ... -> (b s) ..."), indices_v
    )
    value_unpad = rearrange(value_unpad, "nnz (h d) -> nnz h d", h=kv_n_heads)
    if (
        kv_n_heads < n_heads
        and (not is_flash_v2_installed())
        and (not should_repeat_kv_for_gqa)
    ):
        raise ValueError(
            "For Grouped Query Attention or Multi Query Attention, should_repeat_kv_for_gqa should be set to True if not using Flash Attention v2."
        )
    if should_repeat_kv_for_gqa:
        if kv_n_heads == 1:
            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)
            )
        elif kv_n_heads < n_heads:
            key_unpad = repeat_kv_for_gqa(
                key_unpad.view(1, key_unpad.size(0), kv_n_heads, -1),
                n_heads // kv_n_heads,
            ).view(key_unpad.size(0), n_heads, -1)
            value_unpad = repeat_kv_for_gqa(
                value_unpad.view(1, value_unpad.size(0), kv_n_heads, -1),
                n_heads // kv_n_heads,
            ).view(value_unpad.size(0), n_heads, -1)
    dropout_p = dropout_p if training else 0.0
    reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
    if is_flash_v1_installed():
        output_unpad = flash_attn_interface.flash_attn_unpadded_func(
            q=query_unpad,
            k=key_unpad,
            v=value_unpad,
            cu_seqlens_q=cu_seqlens_q,
            cu_seqlens_k=cu_seqlens_k,
            max_seqlen_q=max_seqlen_q,
            max_seqlen_k=max_seqlen_k,
            dropout_p=dropout_p,
            softmax_scale=softmax_scale,
            causal=reset_is_causal,
            return_attn_probs=needs_weights,
        )
    elif is_flash_v2_installed():
        alibi_kwargs = {}
        if check_alibi_support("flash"):
            alibi_kwargs = {"alibi_slopes": alibi_slopes}
        elif alibi_slopes is not None:
            raise ValueError("alibi_slopes is only supported for flash-attn>=2.4.2")
        output_unpad = flash_attn_interface.flash_attn_varlen_func(
            q=query_unpad,
            k=key_unpad,
            v=value_unpad,
            cu_seqlens_q=cu_seqlens_q,
            cu_seqlens_k=cu_seqlens_k,
            max_seqlen_q=max_seqlen_q,
            max_seqlen_k=max_seqlen_k,
            dropout_p=dropout_p,
            softmax_scale=softmax_scale,
            causal=reset_is_causal,
            return_attn_probs=needs_weights,
            window_size=(sliding_window_size, sliding_window_size),
            **alibi_kwargs,
        )
    else:
        raise RuntimeError("flash-attn==1.0.9 or flash-attn==2.4.2 is required.")
    output = bert_padding.pad_input(
        rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices_q, batch_size, seqlen
    )
    return (output, None, past_key_value)


def triton_flash_attn_fn(
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    n_heads: int,
    kv_n_heads: int,
    past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
    softmax_scale: Optional[float] = None,
    attn_bias: Optional[torch.Tensor] = None,
    key_padding_mask: Optional[torch.Tensor] = None,
    is_causal: bool = False,
    dropout_p: float = 0.0,
    training: bool = False,
    needs_weights: bool = False,
) -> tuple[
    torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]
]:
    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:
        _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.")
    dropout_p = dropout_p if training else 0.0
    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=kv_n_heads)
    value = rearrange(value, "b s (h d) -> b s h d", h=kv_n_heads)
    if kv_n_heads == 1:
        key = key.repeat(1, 1, n_heads, 1)
        value = value.repeat(1, 1, n_heads, 1)
    elif kv_n_heads < n_heads:
        key = repeat_kv_for_gqa(key, n_heads // kv_n_heads)
        value = repeat_kv_for_gqa(value, n_heads // kv_n_heads)
    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 (output, None, past_key_value)


class GroupedQueryAttention(nn.Module):
    """Grouped Query Attention (GQA) is a generalization of Multi-head (MHA).

    and Multi-query attention (MQA).

    This allows the user to set a variable of number of kv_n_heads, rather than
    just n_heads or 1, as in MHA and MQA. Using torch or triton attention
    implementation enables user to also use additive bias.
    """

    def __init__(
        self,
        d_model: int,
        n_heads: int,
        kv_n_heads: int,
        attn_impl: str = "triton",
        clip_qkv: Optional[float] = None,
        qk_ln: bool = False,
        qk_gn: bool = False,
        softmax_scale: Optional[float] = None,
        attn_pdrop: float = 0.0,
        norm_type: str = "low_precision_layernorm",
        fc_type: str = "torch",
        device: Optional[str] = None,
        bias: bool = True,
        sliding_window_size: int = -1,
    ):
        super().__init__()
        self.attn_impl = attn_impl
        self.clip_qkv = clip_qkv
        self.qk_ln = qk_ln
        self.qk_gn = qk_gn
        self.d_model = d_model
        self.n_heads = n_heads
        self.kv_n_heads = kv_n_heads
        self.sliding_window_size = sliding_window_size
        self.head_dim = d_model // n_heads
        if self.kv_n_heads <= 0:
            raise ValueError("kv_n_heads should be greater than zero.")
        if self.kv_n_heads > self.n_heads:
            raise ValueError(
                "The number of KV heads should be less than or equal to Q heads."
            )
        if self.n_heads % self.kv_n_heads != 0:
            raise ValueError(
                "Each Q head should get the same number of KV heads, so n_heads must be divisible by kv_n_heads."
            )
        if qk_ln and qk_gn:
            raise ValueError("Only one of qk_ln and qk_gn can be set to True.")
        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
        fc_kwargs: dict[str, Any] = {"bias": bias}
        if fc_type != "te":
            fc_kwargs["device"] = device
        self.Wqkv = FC_CLASS_REGISTRY[fc_type](
            self.d_model,
            self.d_model + 2 * self.kv_n_heads * self.head_dim,
            **fc_kwargs,
        )
        fuse_splits = [
            i * self.head_dim for i in range(1, self.n_heads + 2 * self.kv_n_heads)
        ]
        self.Wqkv._fused = (0, fuse_splits)
        if self.qk_ln or self.qk_gn:
            norm_class = NORM_CLASS_REGISTRY[norm_type.lower()]
            norm_size = self.head_dim if qk_gn else d_model
            self.q_ln = norm_class(norm_size, device=device)
            if qk_ln:
                norm_size = self.head_dim * kv_n_heads
            self.k_ln = norm_class(norm_size, 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
        elif self.attn_impl == "torch":
            self.attn_fn = scaled_multihead_dot_product_attention
        else:
            raise ValueError(f"attn_impl={attn_impl!r} is an invalid setting.")
        self.out_proj = FC_CLASS_REGISTRY[fc_type](
            self.d_model, self.d_model, **fc_kwargs
        )
        self.out_proj._is_residual = True

    def forward(
        self,
        x: torch.Tensor,
        past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
        attn_bias: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        rotary_emb_w_meta_info: Optional[dict] = None,
        is_causal: bool = True,
        needs_weights: bool = False,
        alibi_slopes: Optional[torch.Tensor] = None,
        flash_attn_padding_info: Optional[dict[str, torch.Tensor]] = None,
    ) -> tuple[
        torch.Tensor,
        Optional[torch.Tensor],
        Optional[tuple[torch.Tensor, torch.Tensor]],
    ]:
        qkv = self.Wqkv(x)
        if self.clip_qkv:
            qkv = qkv.clamp(min=-self.clip_qkv, max=self.clip_qkv)
        (query, key, value) = qkv.split(
            [
                self.d_model,
                self.kv_n_heads * self.head_dim,
                self.kv_n_heads * self.head_dim,
            ],
            dim=2,
        )
        key_padding_mask = attention_mask
        if self.qk_ln or self.qk_gn:
            (q_shape, k_shape) = (query.shape, key.shape)
            if self.qk_gn:
                (b, s) = query.shape[:2]
                query = query.view(b, s, self.n_heads, -1)
                key = key.view(b, s, self.kv_n_heads, -1)
            dtype = query.dtype
            query = self.q_ln(query).to(dtype).view(q_shape)
            key = self.k_ln(key).to(dtype).view(k_shape)
        if rotary_emb_w_meta_info is not None:
            rotary_emb = rotary_emb_w_meta_info["rotary_emb"]
            seq_len = rotary_emb_w_meta_info["seq_len"]
            offset_info = rotary_emb_w_meta_info["offset_info"]
            (bsz, seqlen) = query.shape[:2]
            query = query.view(bsz, seqlen, -1, self.head_dim)
            key = key.view(bsz, seqlen, -1, self.head_dim)
            if rotary_emb_w_meta_info["impl"] == "dail":
                value = value.view(bsz, seqlen, -1, self.head_dim)
                kv = torch.stack([key, value], dim=2)
                (query, kv) = rotary_emb(
                    query, kv, seqlen_offset=offset_info, max_seqlen=seq_len
                )
                [key, value] = torch.unbind(kv, dim=2)
                value = value.view(bsz, seqlen, self.kv_n_heads * self.head_dim)
            elif rotary_emb_w_meta_info["impl"] == "hf":
                (cos, sin) = rotary_emb(value, seq_len)
                if is_transformers_version_gte("4.36"):
                    (query, key) = apply_rotary_pos_emb(
                        query, key, cos, sin, offset_info, unsqueeze_dim=2
                    )
                else:
                    query = query.transpose(1, 2)
                    key = key.transpose(1, 2)
                    (query, key) = apply_rotary_pos_emb(
                        query, key, cos, sin, offset_info
                    )
                    query = query.transpose(1, 2)
                    key = key.transpose(1, 2)
            query = query.view(bsz, seqlen, self.d_model)
            key = key.view(bsz, seqlen, self.kv_n_heads * self.head_dim)
        extra_attn_kwargs = {}
        if self.attn_impl == "flash":
            key_padding_mask = None
            extra_attn_kwargs = {
                "should_repeat_kv_for_gqa": not is_flash_v2_installed(),
                "sliding_window_size": self.sliding_window_size,
                "alibi_slopes": alibi_slopes,
                "flash_attn_padding_info": flash_attn_padding_info,
            }
        (context, attn_weights, past_key_value) = self.attn_fn(
            query,
            key,
            value,
            self.n_heads,
            self.kv_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,
            **extra_attn_kwargs,
        )
        return (self.out_proj(context), attn_weights, past_key_value)


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

    Using torch or triton attention implementation 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,
        qk_gn: bool = False,
        softmax_scale: Optional[float] = None,
        attn_pdrop: float = 0.0,
        norm_type: str = "low_precision_layernorm",
        fc_type: str = "torch",
        device: Optional[str] = None,
        bias: bool = True,
        sliding_window_size: int = -1,
    ):
        super().__init__(
            d_model=d_model,
            n_heads=n_heads,
            kv_n_heads=n_heads,
            attn_impl=attn_impl,
            clip_qkv=clip_qkv,
            qk_ln=qk_ln,
            qk_gn=qk_gn,
            softmax_scale=softmax_scale,
            attn_pdrop=attn_pdrop,
            norm_type=norm_type,
            fc_type=fc_type,
            device=device,
            bias=bias,
            sliding_window_size=sliding_window_size,
        )


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

    Using torch or triton attention implementation 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,
        qk_gn: bool = False,
        softmax_scale: Optional[float] = None,
        attn_pdrop: float = 0.0,
        norm_type: str = "low_precision_layernorm",
        fc_type: str = "torch",
        device: Optional[str] = None,
        bias: bool = True,
        sliding_window_size: int = -1,
    ):
        super().__init__(
            d_model=d_model,
            n_heads=n_heads,
            kv_n_heads=1,
            attn_impl=attn_impl,
            clip_qkv=clip_qkv,
            qk_ln=qk_ln,
            qk_gn=qk_gn,
            softmax_scale=softmax_scale,
            attn_pdrop=attn_pdrop,
            norm_type=norm_type,
            fc_type=fc_type,
            device=device,
            bias=bias,
            sliding_window_size=sliding_window_size,
        )


def attn_bias_shape(
    attn_impl: str,
    n_heads: int,
    seq_len: int,
    alibi: bool,
    prefix_lm: bool,
    causal: bool,
    use_sequence_id: bool,
) -> Optional[tuple[int, int, int, int]]:
    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: str,
    attn_bias: torch.Tensor,
    n_heads: int,
    seq_len: int,
    causal: bool = False,
    alibi: bool = False,
    alibi_bias_max: int = 8,
) -> Optional[torch.Tensor]:
    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: int,
    alibi_bias_max: int = 8,
    device: Optional[torch.device] = None,
    return_1d: bool = False,
) -> torch.Tensor:
    _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]
    if return_1d:
        return slopes
    return slopes.view(1, n_heads, 1, 1)


def build_alibi_bias(
    n_heads: int,
    seq_len: int,
    full: bool = False,
    alibi_bias_max: int = 8,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
) -> torch.Tensor:
    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,
    "grouped_query_attention": GroupedQueryAttention,
}