gated_deltaproduct.py

# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
#   https://github.com/fla-org/flash-linear-attention/graphs/contributors

from __future__ import annotations

import math
import warnings
from typing import TYPE_CHECKING

import torch
import torch.nn as nn
from einops import rearrange, repeat
from torch.nn import functional as F

from fla.layers.utils import get_layer_cache, repad_hidden_states, unpad_hidden_states, update_layer_cache
from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution
from fla.ops.gated_delta_product import chunk_gated_delta_product
from fla.ops.gated_delta_rule import fused_recurrent_gated_delta_rule

if TYPE_CHECKING:
    from transformers.processing_utils import Unpack

    from fla.models.utils import Cache


class GatedDeltaProduct(nn.Module):
    """
    Generalized version of GatedDoubleDeltaNet that supports arbitrary number of householder transformations.
    """

    def __init__(
        self,
        hidden_size: int = 2048,
        expand_v: float = 2,
        head_dim: int = 256,
        num_heads: int = 6,
        num_v_heads: int = None,
        mode: str = 'chunk',
        use_output_gate: bool = True,
        use_short_conv: bool = True,
        conv_size: int = 4,
        conv_bias: bool = False,
        layer_idx: int = None,
        norm_eps: float = 1e-5,
        use_forget_gate: bool = True,
        allow_neg_eigval: bool = True,
        num_householder: int = 2,
        **kwargs,
    ) -> GatedDeltaProduct:
        super().__init__()

        self.mode = mode

        self.hidden_size = hidden_size
        self.expand_v = expand_v

        self.use_forget_gate = use_forget_gate
        self.allow_neg_eigval = allow_neg_eigval
        self.num_householder = num_householder
        self.use_output_gate = use_output_gate
        self.use_short_conv = use_short_conv
        self.conv_size = conv_size
        self.conv_bias = conv_bias

        self.head_dim = head_dim
        self.num_heads = num_heads
        self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads

        self.head_k_dim = head_dim
        self.head_v_dim = int(self.head_dim * self.expand_v)
        self.key_dim = int(self.num_heads * self.head_k_dim)
        self.value_dim = int(self.num_v_heads * self.head_v_dim)
        self.layer_idx = layer_idx

        # Consistency check: Ensure expand_v produces integer values
        if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5):
            raise ValueError(
                f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. "
                f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.",
            )
        if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0:
            raise ValueError(
                f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.",
            )

        if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5):
            raise ValueError(
                f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. "
                f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.",
            )
        assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`."

        self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
        self.k_proj = nn.Linear(hidden_size, self.key_dim * num_householder, bias=False)
        self.v_proj = nn.Linear(hidden_size, self.value_dim * num_householder, bias=False)
        self.b_proj = nn.Linear(hidden_size, self.num_v_heads * num_householder, bias=False)

        if self.use_forget_gate:
            self.a_proj = nn.Linear(hidden_size, self.num_v_heads, bias=False)
            A = torch.empty(self.num_v_heads, dtype=torch.float32).uniform_(0, 16)
            self.A_log = nn.Parameter(torch.log(A))
            self.A_log._no_weight_decay = True
            # hard coded for now
            dt_min = 0.001
            dt_max = 0.1
            dt_init_floor = 1e-4
            dt = torch.exp(
                torch.rand(self.num_v_heads) * (math.log(dt_max) - math.log(dt_min))
                + math.log(dt_min),
            )
            dt = torch.clamp(dt, min=dt_init_floor)
            # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
            inv_dt = dt + torch.log(-torch.expm1(-dt))
            self.dt_bias = nn.Parameter(inv_dt)
            # Just to be explicit. Without this we already don't put wd on dt_bias because of the check
            # name.endswith("bias") in param_grouping.py
            self.dt_bias._no_weight_decay = True

        if use_short_conv:
            self.conv_size = conv_size
            self.q_conv1d = ShortConvolution(
                hidden_size=self.key_dim,
                kernel_size=conv_size,
                bias=conv_bias,
                activation='silu',
            )
            self.k_conv1d = ShortConvolution(
                hidden_size=self.key_dim * num_householder,
                kernel_size=conv_size,
                bias=conv_bias,
                activation='silu',
            )
            self.v_conv1d = ShortConvolution(
                hidden_size=self.value_dim * num_householder,
                kernel_size=conv_size,
                bias=conv_bias,
                activation='silu',
            )
        else:
            warnings.warn(
                "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_output_gate:
            self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
            self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps)
        else:
            self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps, dtype=torch.float32)
        self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False)

    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: torch.Tensor | None = None,
        past_key_values: Cache | None = None,
        use_cache: bool | None = False,
        output_attentions: bool | None = False,
        **kwargs: Unpack[dict],
    ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]:
        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
        if torch.is_grad_enabled():
            mode = 'chunk'
        elif q_len <= 64 and not self.training:
            mode = 'fused_recurrent'
        else:
            mode = self.mode
        if self.training:
            assert mode == 'chunk', "Only chunk mode is supported in training."

        last_state = get_layer_cache(self, past_key_values)

        cu_seqlens = kwargs.get('cu_seqlens')
        hidden_states, indices, cu_seqlens = unpad_hidden_states(hidden_states, cu_seqlens, attention_mask, q_len)

        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 = F.silu(self.q_proj(hidden_states))
            k = F.silu(self.k_proj(hidden_states))
            v = F.silu(self.v_proj(hidden_states))

        q = rearrange(q, '... (h d) -> ... h d', d=self.head_k_dim)
        k = rearrange(k, '... t (n h d) -> ... (t n) h d', n=self.num_householder, d=self.head_k_dim)
        v = rearrange(v, '... t (n h d) -> ... (t n) h d', n=self.num_householder, d=self.head_v_dim)

        if self.num_v_heads > self.num_heads:
            q, k = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q, k))

        beta = self.b_proj(hidden_states).sigmoid()
        if self.allow_neg_eigval:
            beta = beta * 2.

        beta = rearrange(beta, '... t (n h) -> ... (t n) h', n=self.num_householder)
        if self.use_forget_gate:
            g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias)
        else:
            g = None

        recurrent_state = last_state['recurrent_state'] if last_state is not None else None
        if mode == 'chunk':
            o, recurrent_state = chunk_gated_delta_product(
                q=q,
                k=k,
                v=v,
                g=g,
                beta=beta,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens,
                num_householder=self.num_householder,
                use_qk_l2norm_in_kernel=True,
            )

        elif mode == 'fused_recurrent':
            if self.use_forget_gate:
                g_new = g.new_zeros(g.shape[0], g.shape[1], self.num_householder, g.shape[2])
                g_new[:, :, 0] = g
                g = rearrange(g_new, '... t n h -> ... (t n) h')

            q_new = q.new_zeros(q.shape[0], q.shape[1], self.num_householder, q.shape[2], q.shape[3])
            q_new[:, :, -1] = q
            q = rearrange(q_new, '... t n h d-> ... (t n) h d')
            o, recurrent_state = fused_recurrent_gated_delta_rule(
                q=q,
                k=k,
                v=v,
                g=g,
                beta=beta,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens * self.num_householder if cu_seqlens is not None else None,
                use_qk_l2norm_in_kernel=True,
            )
            o = rearrange(o, '... (t n) h d -> ... t n h d', n=self.num_householder)[..., -1, :, :].contiguous()

        update_layer_cache(
            self,
            past_key_values,
            recurrent_state=recurrent_state,
            conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None,
            offset=q_len,
        )

        if self.use_output_gate:
            g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim)
            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)
        o = repad_hidden_states(o, indices, batch_size, q_len)

        return o, None, past_key_values