delta_net.py

# -*- coding: utf-8 -*-
# Copyright (c) 2024, Songlin Yang, Yu Zhang

from __future__ import annotations
from typing import TYPE_CHECKING, Optional, Tuple
import torch
import torch.nn as nn
from einops import rearrange
from torch.nn import functional as F
from fla.modules import FusedRMSNormSwishGate, RMSNorm, ShortConvolution
from fla.modules.l2norm import l2_norm
from fla.ops.delta_rule import (chunk_delta_rule, fused_chunk_delta_rule,
                                fused_recurrent_delta_rule)

if TYPE_CHECKING:
    from fla.models.utils import Cache


def elu_p1(x):
    return (F.elu(x, 1., False) + 1.).to(x)


def sum_norm(x):
    return (x / x.sum(-1, keepdim=True)).to(x)



class DeltaNet(nn.Module):
    r"""
    The layer implementaion for [Parallelizing Linear Transformers with the Delta Rule over Sequence Length](https://arxiv.org/abs/2406.06484).  # noqa: 
    DeltaNet was originally proposed in [Linear Transformers Are Secretly Fast Weight Programmers](https://arxiv.org/abs/2102.11174). # noqa

    Args:
        mode (str, Optional):
            Which DeltaNet kernel to use.
            Currently available: `chunk`, `fused_recurrent`, 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: 1.0.
        num_heads (int, Optional):
            The number of heads. Default: 4.
        use_beta (bool, Optional):
            Whether to use beta. Default: `True`.
        use_gate (bool, Optional):
            Whether to use output gate. Default: `False`.
        use_short_conv (bool, Optional):
            Whether to use short convolutions. Default: `True`.
        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`.
        allow_neg_eigval (bool, Optional):
            Allow negative eigenvalues. Default: `False`. If set to `True`, the beta will be multiplied by 2. 
            See reference: [Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues](https://arxiv.org/abs/2411.12537)
        layer_idx (int, Optional):
            The index of the layer. Default: None.
        norm_eps (float, Optional):
            The epsilon value for the layernorm/rmsnorm layer. Default: 1e-5.
        qk_activation (str, Optional):
            The activation function for the query and key. Default: `silu`.
        qk_norm (str, Optional):
            The normalization method for the query and key. Default: `l2`.
    """

    def __init__(
        self,
        mode: str = 'chunk',
        d_model: int = None,
        hidden_size: int = 1024,
        expand_k: float = 1.0,
        expand_v: float = 1.0,
        num_heads: int = 4,
        use_beta: bool = True,
        use_gate: bool = False,
        use_short_conv: bool = True,
        conv_size: int = 4,
        conv_bias: bool = False,
        allow_neg_eigval: bool = False,
        layer_idx: int = None,
        qk_activation: str = 'silu',
        qk_norm: str = 'l2',
        norm_eps: float = 1e-5,
        **kwargs
    ) -> DeltaNet:
        super().__init__()

        self.mode = mode
        self.qk_activation = qk_activation
        self.qk_norm = qk_norm

        assert self.qk_activation in ['silu', 'relu', 'elu', 'identity']
        assert self.qk_norm in ['l2', 'sum']

        if d_model is not None:
            hidden_size = d_model
        self.hidden_size = hidden_size
        self.expand_k = expand_k
        self.expand_v = expand_v
        self.num_heads = num_heads
        self.use_gate = use_gate
        self.use_short_conv = use_short_conv
        self.conv_size = conv_size
        self.conv_bias = conv_bias
        self.allow_neg_eigval = allow_neg_eigval

        self.key_dim = int(hidden_size * expand_k)
        self.value_dim = int(hidden_size * expand_v)
        self.head_qk_dim = self.key_dim // num_heads
        self.head_v_dim = self.value_dim // num_heads
        self.layer_idx = layer_idx

        self.silu = nn.SiLU()

        assert mode in ['chunk', 'fused_chunk', '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.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
        self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
        self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False)

        self.use_beta = use_beta
        if self.use_beta:
            self.b_proj = nn.Linear(hidden_size, self.num_heads, bias=False)
        if use_short_conv:
            self.conv_size = conv_size
            self.q_conv1d = ShortConvolution(
                hidden_size=self.key_dim,
                kernel_size=conv_size,
                activation='silu' if qk_activation == 'silu' else None
            )
            self.k_conv1d = ShortConvolution(
                hidden_size=self.key_dim,
                kernel_size=conv_size,
                activation='silu' if qk_activation == 'silu' else None
            )
            self.v_conv1d = ShortConvolution(
                hidden_size=self.value_dim,
                kernel_size=conv_size,
                activation='silu'
            )
        else:
            raise UserWarning(
                "ShortConvolution is crucial to the performance. "
                "Do not turn it off, i.e., setting `use_short_conv=False` unless you know what you are doing."
            )
        if use_gate:
            self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
            self.o_norm = FusedRMSNormSwishGate(self.head_v_dim, eps=norm_eps)
        else:
            self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps)

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

        self.apply(self._initialize_weights)

    def _initialize_weights(self, module: nn.Module):
        if getattr(module, "_is_hf_initialized", False):
            return
        if isinstance(module, nn.Linear):
            nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        module._is_hf_initialized = True

    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
    ) -> 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."
            )

        # change to inference mode.
        mode = 'fused_recurrent' if hidden_states.shape[1] <= 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]

        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']
            conv_mask = attention_mask[:, -hidden_states.shape[1]:] if attention_mask is not None else None
            q, conv_state_q = self.q_conv1d(x=self.q_proj(hidden_states),
                                            mask=conv_mask,
                                            cache=conv_state_q,
                                            output_final_state=use_cache)
            k, conv_state_k = self.k_conv1d(x=self.k_proj(hidden_states),
                                            mask=conv_mask,
                                            cache=conv_state_k,
                                            output_final_state=use_cache)
            v, conv_state_v = self.v_conv1d(x=self.v_proj(hidden_states),
                                            mask=conv_mask,
                                            cache=conv_state_v,
                                            output_final_state=use_cache)
        else:
            q = self.q_proj(hidden_states)
            k = self.k_proj(hidden_states)
            if self.qk_activation == 'silu':
                q, k = self.silu(q), self.silu(k)
            v = self.silu(self.v_proj(hidden_states))

        q, k, v = map(lambda x: rearrange(x, '... (h d) -> ... h d', h=self.num_heads), (q, k, v))
        if self.qk_activation != 'silu':
            if self.qk_activation == 'relu':
                q, k = q.relu(), k.relu()
            elif self.qk_activation == 'elu':
                q, k = elu_p1(q), elu_p1(k)
            elif self.qk_activation == 'identity':
                pass
            else:
                raise NotImplementedError

        if self.qk_norm is not None:
            if self.qk_norm == 'l2':
                q = l2_norm(q)
                k = l2_norm(k)
            elif self.qk_norm == 'sum':
                q = sum_norm(q).to(q)
                k = sum_norm(k).to(k)

        if self.use_beta:
            beta = self.b_proj(hidden_states).sigmoid()
        else:
            beta = q.new_ones(q.shape[0], q.shape[1], q.shape[2])
        
        if self.allow_neg_eigval:
            beta = beta * 2. 

        # dealing with padding
        if attention_mask is not None:
            beta = beta.mul(attention_mask[:, -beta.shape[-2]:, None])
        recurrent_state = last_state['recurrent_state'] if last_state is not None else None
        if mode == 'fused_recurrent':
            o, recurrent_state = fused_recurrent_delta_rule(
                q=q,
                k=k,
                v=v,
                beta=beta,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                head_first=False
            )
        elif mode == 'fused_chunk':
            o, recurrent_state = fused_chunk_delta_rule(
                q=q,
                k=k,
                v=v,
                beta=beta,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                head_first=False
            )
        elif mode == 'chunk':
            o, recurrent_state = chunk_delta_rule(
                q=q,
                k=k,
                v=v,
                beta=beta,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                head_first=False
            )
        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.shape[2]
            )

        if self.use_gate:
            g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', h=self.num_heads)
            o = self.o_norm(o, g)
        else:
            o = self.o_norm(o)
        o = rearrange(o, 'b t h d -> b t (h d)')
        o = self.o_proj(o)

        return o, None, past_key_values