multiscale_retention.py

# -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang

from __future__ import annotations

from typing import TYPE_CHECKING, Dict, Optional, Tuple

import torch
import torch.nn as nn
from einops import rearrange, repeat
from transformers.activations import ACT2FN

from fla.layers.utils import get_unpad_data, index_first_axis, pad_input
from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution
from fla.modules.rotary import RotaryEmbedding
from fla.ops.retention import chunk_retention, fused_chunk_retention, fused_recurrent_retention, parallel_retention
from fla.ops.utils.index import prepare_lens_from_mask

if TYPE_CHECKING:
    from transformers.processing_utils import Unpack

    from fla.models.utils import Cache


class MultiScaleRetention(nn.Module):
    r"""
    The layer implementaion for [Retentive Network: A Successor to Transformer for Large Language Models](https://arxiv.org/pdf/2307.08621.pdf).  # noqa

    Args:
        mode (str, Optional):
            Which Retention kernel to use.
            Currently available: `chunk`, `fused_recurrent`, `parallel`, and `fused_chunk`.
            Default: `chunk`.
        hidden_size (int, Optional):
            The hidden size of the input. Default: 1024.
        expand_k (float, Optional):
            The expansion ratio for the key dim. Default: 1.0.
        expand_v (float, Optional):
            The expansion ratio for the value dim. Default: 2.0.
        num_heads (int, Optional):
            The number of heads. Default: 8.
        num_kv_heads (int, Optional):
            The number of key/value heads, used for MQA. Default: None.
        feature_map (str, Optional):
            Feature map function applied to queries/keys. Default: None.
        use_short_conv (bool, Optional):
            Whether to use short convolutions. Default: `False`.
        conv_size (int, Optional):
            The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4.
        conv_bias (bool, Optional):
            Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`.
        use_output_gate (bool, Optional):
            Whether to use output gate. Default: `True`.
        gate_fn (str, Optional):
            The activation function for the output gate. Default: `swish`.
        elementwise_affine (bool, Optional):
            If `True`, applies elementwise affine to LayerNorm with learnable parameters. Default: `True`.
        norm_eps (float, Optional):
            The epsilon value for the layernorm/rmsnorm layer. Default: 1e-5.
        fuse_norm (bool, Optional):
            Whether to fuse the norm and the output gate for better memory footprint. Default: `True`.
        layer_idx (int, Optional):
            The index of the layer. Default: None.
    """

    def __init__(
        self,
        mode: str = 'chunk',
        hidden_size: int = 1024,
        expand_k: float = 1.0,
        expand_v: float = 2.0,
        num_heads: int = 8,
        num_kv_heads: Optional[int] = None,
        feature_map: Optional[str] = None,
        use_short_conv: bool = False,
        conv_size: int = 4,
        conv_bias: bool = False,
        use_output_gate: bool = True,
        gate_fn: str = 'swish',
        elementwise_affine: Optional[bool] = True,
        norm_eps: float = 1e-5,
        fuse_norm: bool = True,
        layer_idx: int = None,
        **kwargs
    ) -> MultiScaleRetention:
        super().__init__()

        self.mode = mode
        self.hidden_size = hidden_size
        self.expand_k = expand_k
        self.expand_v = expand_v
        self.num_heads = num_heads
        self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads
        self.num_kv_groups = self.num_heads // self.num_kv_heads
        self.feature_map_fn = ACT2FN[feature_map] if feature_map is not None else None

        self.use_short_conv = use_short_conv
        self.conv_size = conv_size
        self.conv_bias = conv_bias
        self.use_output_gate = use_output_gate

        self.key_dim = int(hidden_size * expand_k)
        self.value_dim = int(hidden_size * expand_v)
        self.key_dim_per_group = self.key_dim // self.num_kv_groups
        self.value_dim_per_group = self.value_dim // self.num_kv_groups
        self.layer_idx = layer_idx

        assert mode in ['chunk', 'fused_chunk', 'parallel', 'fused_recurrent'], f"Not suppoerted mode `{mode}`."
        assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}"
        assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}"

        self.head_k_dim = self.key_dim // num_heads
        self.head_v_dim = self.value_dim // num_heads

        self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
        self.k_proj = nn.Linear(hidden_size, self.key_dim_per_group, bias=False)
        self.v_proj = nn.Linear(hidden_size, self.value_dim_per_group, bias=False)
        if self.use_output_gate:
            self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False)

        if use_short_conv:
            self.conv_size = conv_size
            self.q_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu')
            self.k_conv1d = ShortConvolution(self.key_dim_per_group, conv_size, activation='silu')
            self.v_conv1d = ShortConvolution(self.value_dim_per_group, conv_size, activation='silu')

        self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False)

        if gate_fn == 'swish' and fuse_norm and use_output_gate:
            self.g_norm_swish_gate = FusedRMSNormGated(
                hidden_size=self.head_v_dim,
                elementwise_affine=elementwise_affine,
                eps=norm_eps
            )
            self.fuse_norm_and_gate = True
        else:
            self.fuse_norm_and_gate = False
            self.g_norm = RMSNorm(
                hidden_size=self.head_v_dim,
                elementwise_affine=elementwise_affine,
                eps=norm_eps
            )
            self.gate_fn = ACT2FN[gate_fn]

        # TODO: fix this issue
        # https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/ops/triton/rotary.py#L180
        # Ideally, we would want to support arbitrary d_head_qk
        assert self.head_k_dim <= 256, "head_k_dim must be less than or equal to 256"
        self.rotary = RotaryEmbedding(dim=self.head_k_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Cache] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
        **kwargs: Unpack[Dict]
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
        if attention_mask is not None:
            assert len(attention_mask.shape) == 2, (
                "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] "
                "for padding purposes (0 indicating padding). "
                "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed."
            )

        batch_size, q_len, _ = hidden_states.shape
        mode = 'fused_recurrent' if q_len <= 64 else self.mode

        last_state = None
        if past_key_values is not None and len(past_key_values) > self.layer_idx:
            last_state = past_key_values[self.layer_idx]

        cu_seqlens = kwargs.get('cu_seqlens', None)
        if attention_mask is not None:
            indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:])
            hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0)

        if self.use_short_conv:
            conv_state_q, conv_state_k, conv_state_v = None, None, None
            if last_state is not None:
                conv_state_q, conv_state_k, conv_state_v = last_state['conv_state']
            q, conv_state_q = self.q_conv1d(
                x=self.q_proj(hidden_states),
                cache=conv_state_q,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens
            )
            k, conv_state_k = self.k_conv1d(
                x=self.k_proj(hidden_states),
                cache=conv_state_k,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens
            )
            v, conv_state_v = self.v_conv1d(
                x=self.v_proj(hidden_states),
                cache=conv_state_v,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens
            )
        else:
            q = self.q_proj(hidden_states)
            k = self.k_proj(hidden_states)
            v = self.v_proj(hidden_states)

        q = rearrange(q, '... (h d) -> ... h d', d=self.head_k_dim)
        k = rearrange(k, '... (h d) -> ... h d', d=self.head_k_dim)
        if self.feature_map_fn is not None:
            q, k = map(self.feature_map_fn, (q, k))

        seqlen_offset, max_seqlen = 0, q_len
        if past_key_values is not None:
            seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
            max_seqlen = q_len + seqlen_offset

            if attention_mask is not None and seqlen_offset > 0:
                # to deliminate the offsets of padding tokens
                seqlen_offset = prepare_lens_from_mask(attention_mask) - q_len
                max_seqlen = q_len + seqlen_offset.max().item()

        q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens)

        if self.num_kv_groups > 1:
            k = repeat(k, '... h d -> ... (h g) d', g=self.num_kv_groups)
            v = repeat(v, '... (h d) -> ... (h g) d', d=self.head_v_dim, g=self.num_kv_groups)
        else:
            v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim)

        recurrent_state = last_state['recurrent_state'] if last_state is not None else None
        if mode == 'chunk':
            o, recurrent_state = chunk_retention(
                q=q,
                k=k,
                v=v,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens,
            )
        elif mode == 'fused_chunk':
            o, recurrent_state = fused_chunk_retention(
                q=q,
                k=k,
                v=v,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens,
            )
        elif mode == 'parallel':
            o, recurrent_state = parallel_retention(
                q=q,
                k=k,
                v=v,
                cu_seqlens=cu_seqlens,
            )
        elif mode == 'fused_recurrent':
            o, recurrent_state = fused_recurrent_retention(
                q=q,
                k=k,
                v=v,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens,
            )
        else:
            raise NotImplementedError(f"Not supported mode `{mode}`.")

        if past_key_values is not None:
            past_key_values.update(
                recurrent_state=recurrent_state,
                conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None,
                layer_idx=self.layer_idx,
                offset=q_len
            )

        if self.use_output_gate:
            g = self.g_proj(hidden_states)
            if self.fuse_norm_and_gate:
                g = rearrange(g, '... (h d) -> ... h d', d=self.head_v_dim)
                o = self.g_norm_swish_gate(o, g)
                o = rearrange(o, '... h d -> ... (h d)')
            else:
                o = rearrange(self.g_norm(o), '... h d -> ... (h d)')
                o = o * self.gate_fn(g)
        else:
            o = rearrange(self.g_norm(o), '... h d -> ... (h d)')
        o = self.o_proj(o)
        if attention_mask is not None:
            o = pad_input(o.squeeze(0), indices, batch_size, q_len)

        return o, None, past_key_values