Datasets:

Alexsssu
/

fastkernels-b200-codex-vanilla-20260923

"""GLA / RetNet decoder layer.
Pre-norm residual:
  attn_norm -> GatedLinearAttention -> residual
  mlp_norm  -> GLAMLP -> residual
Forward signature mirrors FLA's ``GLABlock.forward`` (returns a tuple of
``(hidden_states, attentions, past_key_values)``) so that the same L3
block backs both GLA and RetNet — RetNet just uses ``decay_mode="fixed_per_head"``
and ``use_rotary=True`` in the attention layer.
"""

from __future__ import annotations

import torch
import torch.nn as nn
import triton
import triton.language as tl

from ..L1.rms_norm import RMSNorm
from ..L1 import chunk_gla as _chunk_gla
from ..L2 import gla_attention as _gla_attention
from ..L2.gla_attention import GatedLinearAttention
from ..L2.gla_mlp import GLAMLP


@triton.jit
def _accurate_rms_silu_gate(
    x,
    gate,
    weight,
    out,
    n_rows,
    N: tl.constexpr,
    eps: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
):
    rows = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    cols = tl.arange(0, BLOCK_N)
    offsets = rows[:, None] * N + cols[None, :]
    mask = (rows[:, None] < n_rows) & (cols[None, :] < N)

    values = tl.load(x + offsets, mask=mask, other=0.0).to(tl.float32)
    variance = tl.sum(values * values, axis=1) / N
    inv_rms = tl.rsqrt(variance + eps)
    scales = tl.load(
        weight + cols[None, :],
        mask=cols[None, :] < N,
        other=0.0,
    ).to(tl.float32)
    normalized = (values * inv_rms[:, None] * scales).to(tl.bfloat16)

    gates = tl.load(gate + offsets, mask=mask, other=0.0).to(tl.float32)
    activated = (gates * tl.sigmoid(gates)).to(tl.bfloat16)
    tl.store(out + offsets, normalized * activated, mask=mask)


_gla_attention._rms_silu_gate = _accurate_rms_silu_gate


@triton.jit
def _accurate_log_sigmoid_kernel(
    x,
    out,
    n_elements: tl.constexpr,
    BLOCK_SIZE: tl.constexpr,
):
    offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n_elements
    values = tl.load(x + offsets, mask=mask, other=0.0).to(tl.float32)
    result = tl.minimum(values, 0.0) - tl.log(1.0 + tl.exp(-tl.abs(values)))
    tl.store(out + offsets, result, mask=mask)


class _AccurateLogSigmoid(nn.Module):
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        out = torch.empty_like(x)
        n_elements = x.numel()
        block_size = 2048 if n_elements <= 2048 else 4096
        _accurate_log_sigmoid_kernel[(triton.cdiv(n_elements, block_size),)](
            x,
            out,
            n_elements,
            BLOCK_SIZE=block_size,
            num_warps=8,
        )
        return out


@triton.jit
def _dense_states_no_final(
    k,
    v,
    gc,
    states,
    T: tl.constexpr,
    H: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    NT: tl.constexpr,
    BK: tl.constexpr,
    BV: tl.constexpr,
):
    i_k = tl.program_id(0)
    i_v = tl.program_id(1)
    i_bh = tl.program_id(2)
    i_b, i_h = i_bh // H, i_bh % H
    o_k = i_k * BK + tl.arange(0, BK)
    o_v = i_v * BV + tl.arange(0, BV)
    mask_kv = (o_k[:, None] < K) & (o_v[None, :] < V)
    state = tl.zeros((BK, BV), tl.float32)

    for chunk in range(NT):
        state_ptrs = (
            states
            + ((i_b * NT + chunk) * H + i_h) * K * V
            + o_k[:, None] * V
            + o_v[None, :]
        )
        tl.store(state_ptrs, state.to(tl.bfloat16), mask=mask_kv)
        tokens = chunk * 64 + tl.arange(0, 64)
        mask_kt = (o_k[:, None] < K) & (tokens[None, :] < T)
        mask_tv = (tokens[:, None] < T) & (o_v[None, :] < V)
        last = tl.minimum((chunk + 1) * 64, T) - 1
        base_k = i_b * T * H * K + i_h * K
        base_v = i_b * T * H * V + i_h * V
        last_gate = tl.load(
            gc + base_k + last * H * K + o_k,
            mask=o_k < K,
            other=0.0,
        )
        gates = tl.load(
            gc + base_k + tokens[None, :] * H * K + o_k[:, None],
            mask=mask_kt,
            other=0.0,
        )
        keys = tl.load(
            k + base_k + tokens[None, :] * H * K + o_k[:, None],
            mask=mask_kt,
            other=0.0,
        )
        values = tl.load(
            v + base_v + tokens[:, None] * H * V + o_v[None, :],
            mask=mask_tv,
            other=0.0,
        )
        state *= tl.exp2(last_gate)[:, None]
        gated_keys = (
            keys * tl.exp2(last_gate[:, None] - gates)
        ).to(tl.bfloat16)
        state += tl.dot(gated_keys, values)


@triton.jit
def _dense_score_offdiagonal(
    q,
    k,
    gc,
    scores,
    T: tl.constexpr,
    H: tl.constexpr,
    K: tl.constexpr,
    BC: tl.constexpr,
    BK: tl.constexpr,
):
    i_t = tl.program_id(0)
    i_pair = tl.program_id(1)
    i_bh = tl.program_id(2)
    i_b, i_h = i_bh // H, i_bh % H
    i_i, i_j = i_pair + 1, i_pair
    o_i = tl.arange(0, BC)
    o_j = tl.arange(0, BC)
    t_i = i_t * 64 + i_i * BC + o_i
    t_j = i_t * 64 + i_j * BC + o_j
    score_ptrs = (
        scores
        + (i_b * T + t_i[:, None]) * H * 64
        + i_h * 64
        + i_j * BC
        + o_j[None, :]
    )
    valid = (t_i[:, None] < T) & (t_j[None, :] < T)

    values = tl.zeros((BC, BC), tl.float32)
    base = i_b * T * H * K + i_h * K
    for block in range(tl.cdiv(K, BK)):
        o_k = block * BK + tl.arange(0, BK)
        mask_i = (t_i[:, None] < T) & (o_k[None, :] < K)
        mask_j = (o_k[:, None] < K) & (t_j[None, :] < T)
        mask_k = o_k < K
        anchor = tl.load(
            gc + base + (i_t * 64 + i_i * BC) * H * K + o_k,
            mask=mask_k,
            other=0.0,
        )
        queries = tl.load(
            q + base + t_i[:, None] * H * K + o_k[None, :],
            mask=mask_i,
            other=0.0,
        )
        query_gates = tl.load(
            gc + base + t_i[:, None] * H * K + o_k[None, :],
            mask=mask_i,
            other=0.0,
        )
        keys = tl.load(
            k + base + t_j[None, :] * H * K + o_k[:, None],
            mask=mask_j,
            other=0.0,
        )
        key_gates = tl.load(
            gc + base + t_j[None, :] * H * K + o_k[:, None],
            mask=mask_j,
            other=0.0,
        )
        gated_queries = (
            queries * tl.exp2(query_gates - anchor[None, :]) * 0.0625
        )
        gated_keys = keys * tl.exp2(anchor[:, None] - key_gates)
        values += tl.dot(gated_queries, gated_keys)

    tl.store(score_ptrs, values, mask=valid)


@triton.jit
def _dense_score_diagonal(
    q,
    k,
    gc,
    scores,
    T: tl.constexpr,
    H: tl.constexpr,
    K: tl.constexpr,
    BC: tl.constexpr,
    BK: tl.constexpr,
):
    i_t = tl.program_id(0)
    i_c = tl.program_id(1)
    i_bh = tl.program_id(2)
    i_b, i_h = i_bh // H, i_bh % H
    o_i = tl.arange(0, BC)
    o_k = tl.arange(0, BK)
    t_i = i_t * 64 + i_c * BC + o_i
    mask_i = (t_i[:, None] < T) & (o_k[None, :] < K)
    base = i_b * T * H * K + i_h * K
    queries = tl.load(
        q + base + t_i[:, None] * H * K + o_k[None, :],
        mask=mask_i,
        other=0.0,
    )
    query_gates = tl.load(
        gc + base + t_i[:, None] * H * K + o_k[None, :],
        mask=mask_i,
        other=0.0,
    )

    for j in range(BC):
        t_j = i_t * 64 + i_c * BC + j
        mask_j = (o_k < K) & (t_j < T)
        keys = tl.load(
            k + base + t_j * H * K + o_k,
            mask=mask_j,
            other=0.0,
        ).to(tl.float32)
        key_gates = tl.load(
            gc + base + t_j * H * K + o_k,
            mask=mask_j,
            other=0.0,
        ).to(tl.float32)
        values = tl.sum(
            queries
            * keys[None, :]
            * tl.exp2(query_gates - key_gates[None, :]),
            axis=1,
        ) * 0.0625
        score_ptrs = (
            scores
            + (i_b * T + t_i) * H * 64
            + i_h * 64
            + i_c * BC
            + j
        )
        tl.store(
            score_ptrs,
            tl.where(o_i >= j, values, 0.0),
            mask=t_i < T,
        )


class _AccurateChunkGLA(nn.Module):
    def __init__(self, fallback: nn.Module):
        super().__init__()
        self.fallback = fallback

    def forward(
        self,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        g: torch.Tensor,
        scale: float | None = None,
        initial_state: torch.Tensor | None = None,
        output_final_state: bool = False,
        cu_seqlens: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        total_t, heads, key_dim, value_dim = (
            q.shape[1],
            q.shape[2],
            q.shape[3],
            v.shape[3],
        )
        if cu_seqlens is not None or initial_state is not None or total_t <= 65:
            return self.fallback(
                q,
                k,
                v,
                g,
                scale,
                initial_state,
                output_final_state,
                cu_seqlens,
            )

        batch = q.shape[0]
        chunks = triton.cdiv(total_t, 64)
        scanned_gates = torch.empty_like(g, dtype=torch.float32)
        states = torch.empty(
            (batch, chunks, heads, key_dim, value_dim),
            device=q.device,
            dtype=torch.bfloat16,
        )
        scores = torch.empty(
            (batch, total_t, heads, 64),
            device=q.device,
            dtype=torch.float32,
        )
        out = torch.empty_like(v)
        _chunk_gla._dense_gate_scan[
            (triton.cdiv(key_dim, 64), chunks, batch * heads)
        ](
            g,
            scanned_gates,
            total_t,
            heads,
            key_dim,
            BK=64,
            num_warps=2,
        )
        state_grid = (
            triton.cdiv(key_dim, 64),
            triton.cdiv(value_dim, 32),
            batch * heads,
        )
        if output_final_state:
            final_state = torch.empty(
                (batch, heads, key_dim, value_dim),
                device=q.device,
                dtype=torch.float32,
            )
            _chunk_gla._dense_states[state_grid](
                k,
                v,
                scanned_gates,
                states,
                final_state,
                total_t,
                heads,
                key_dim,
                value_dim,
                chunks,
                BK=64,
                BV=32,
                num_warps=2,
                num_stages=2,
            )
        else:
            final_state = None
            _dense_states_no_final[state_grid](
                k,
                v,
                scanned_gates,
                states,
                total_t,
                heads,
                key_dim,
                value_dim,
                chunks,
                BK=64,
                BV=32,
                num_warps=2,
                num_stages=2,
            )
        _chunk_gla._dense_scores[(chunks, batch * heads)](
            q,
            k,
            scanned_gates,
            scores,
            total_t,
            heads,
            key_dim,
            BK=32,
            num_warps=8,
            num_stages=2,
        )
        _dense_score_offdiagonal[(chunks, 1, batch * heads)](
            q,
            k,
            scanned_gates,
            scores,
            total_t,
            heads,
            key_dim,
            BC=16,
            BK=64,
            num_warps=4,
            num_stages=2,
        )
        _dense_score_diagonal[(chunks, 4, batch * heads)](
            q,
            k,
            scanned_gates,
            scores,
            total_t,
            heads,
            key_dim,
            BC=16,
            BK=256,
            num_warps=8,
        )
        _chunk_gla._dense_output[
            (triton.cdiv(value_dim, 128), chunks, batch * heads)
        ](
            q,
            v,
            scanned_gates,
            states,
            scores,
            out,
            total_t,
            heads,
            key_dim,
            value_dim,
            chunks,
            BK=64,
            BV=128,
            num_warps=8,
            num_stages=2,
        )
        return out, final_state if output_final_state else None


class GLADecoderLayer(nn.Module):
    def __init__(self, config, layer_idx: int):
        super().__init__()
        self.layer_idx = layer_idx
        self.attn_norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
        self.attn = GatedLinearAttention(
            hidden_size=config.hidden_size,
            num_heads=config.num_heads,
            expand_k=config.expand_k,
            expand_v=config.expand_v,
            decay_mode=getattr(config, "decay_mode", "learned_low_rank"),
            gate_low_rank_dim=getattr(config, "gate_low_rank_dim", 16),
            gate_logit_normalizer=getattr(config, "gate_logit_normalizer", 16),
            use_rotary=getattr(config, "use_rotary", False),
            rotary_base=getattr(config, "rotary_base", 10000.0),
            rotary_max_position=getattr(config, "max_position_embeddings", 8192),
            norm_eps=config.norm_eps,
        )
        if self.attn.decay_mode == "learned_low_rank":
            self.attn.log_sigmoid = _AccurateLogSigmoid()
            self.attn.chunk = _AccurateChunkGLA(self.attn.chunk)
        self.mlp_norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
        self.mlp = GLAMLP(config.hidden_size, config.intermediate_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        past_key_values=None,
        use_cache: bool = False,
        **kwargs,
    ) -> tuple[torch.Tensor, None, object | None]:
        residual = hidden_states
        h = self.attn_norm(
            hidden_states.reshape(-1, hidden_states.size(-1))
        ).reshape_as(hidden_states)
        if (
            hidden_states.shape[0] == 1
            and kwargs.get("cu_seqlens") is not None
            and kwargs["cu_seqlens"].numel() == 2
        ):
            kwargs = dict(kwargs)
            kwargs.pop("cu_seqlens")

        h, attentions, past_key_values = self.attn(
            hidden_states=h,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache and past_key_values is not None,
            **kwargs,
        )
        h, residual = self.mlp_norm(
            h.reshape(-1, h.size(-1)),
            residual.reshape(-1, residual.size(-1)),
        )
        h = h.reshape_as(hidden_states)
        residual = residual.reshape_as(hidden_states)
        hidden_states = residual.add_(self.mlp(h))
        return hidden_states, attentions, past_key_values

	"""GLA / RetNet decoder layer.

	Pre-norm residual:
	attn_norm -> GatedLinearAttention -> residual
	mlp_norm -> GLAMLP -> residual

	Forward signature mirrors FLA's ``GLABlock.forward`` (returns a tuple of
	``(hidden_states, attentions, past_key_values)``) so that the same L3
	block backs both GLA and RetNet — RetNet just uses ``decay_mode="fixed_per_head"``
	and ``use_rotary=True`` in the attention layer.
	"""

	from __future__ import annotations

	import torch
	import torch.nn as nn
	import triton
	import triton.language as tl

	from ..L1.rms_norm import RMSNorm
	from ..L1 import chunk_gla as _chunk_gla
	from ..L2 import gla_attention as _gla_attention
	from ..L2.gla_attention import GatedLinearAttention
	from ..L2.gla_mlp import GLAMLP


	@triton.jit
	def _accurate_rms_silu_gate(
	x,
	gate,
	weight,
	out,
	n_rows,
	N: tl.constexpr,
	eps: tl.constexpr,
	BLOCK_M: tl.constexpr,
	BLOCK_N: tl.constexpr,
	):
	rows = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
	cols = tl.arange(0, BLOCK_N)
	offsets = rows[:, None] * N + cols[None, :]
	mask = (rows[:, None] < n_rows) & (cols[None, :] < N)

	values = tl.load(x + offsets, mask=mask, other=0.0).to(tl.float32)
	variance = tl.sum(values * values, axis=1) / N
	inv_rms = tl.rsqrt(variance + eps)
	scales = tl.load(
	weight + cols[None, :],
	mask=cols[None, :] < N,
	other=0.0,
	).to(tl.float32)
	normalized = (values * inv_rms[:, None] * scales).to(tl.bfloat16)

	gates = tl.load(gate + offsets, mask=mask, other=0.0).to(tl.float32)
	activated = (gates * tl.sigmoid(gates)).to(tl.bfloat16)
	tl.store(out + offsets, normalized * activated, mask=mask)


	_gla_attention._rms_silu_gate = _accurate_rms_silu_gate


	@triton.jit
	def _accurate_log_sigmoid_kernel(
	x,
	out,
	n_elements: tl.constexpr,
	BLOCK_SIZE: tl.constexpr,
	):
	offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
	mask = offsets < n_elements
	values = tl.load(x + offsets, mask=mask, other=0.0).to(tl.float32)
	result = tl.minimum(values, 0.0) - tl.log(1.0 + tl.exp(-tl.abs(values)))
	tl.store(out + offsets, result, mask=mask)


	class _AccurateLogSigmoid(nn.Module):
	def forward(self, x: torch.Tensor) -> torch.Tensor:
	out = torch.empty_like(x)
	n_elements = x.numel()
	block_size = 2048 if n_elements <= 2048 else 4096
	_accurate_log_sigmoid_kernel[(triton.cdiv(n_elements, block_size),)](
	x,
	out,
	n_elements,
	BLOCK_SIZE=block_size,
	num_warps=8,
	)
	return out


	@triton.jit
	def _dense_states_no_final(
	k,
	v,
	gc,
	states,
	T: tl.constexpr,
	H: tl.constexpr,
	K: tl.constexpr,
	V: tl.constexpr,
	NT: tl.constexpr,
	BK: tl.constexpr,
	BV: tl.constexpr,
	):
	i_k = tl.program_id(0)
	i_v = tl.program_id(1)
	i_bh = tl.program_id(2)
	i_b, i_h = i_bh // H, i_bh % H
	o_k = i_k * BK + tl.arange(0, BK)
	o_v = i_v * BV + tl.arange(0, BV)
	mask_kv = (o_k[:, None] < K) & (o_v[None, :] < V)
	state = tl.zeros((BK, BV), tl.float32)

	for chunk in range(NT):
	state_ptrs = (
	states
	+ ((i_b * NT + chunk) * H + i_h) * K * V
	+ o_k[:, None] * V
	+ o_v[None, :]
	)
	tl.store(state_ptrs, state.to(tl.bfloat16), mask=mask_kv)
	tokens = chunk * 64 + tl.arange(0, 64)
	mask_kt = (o_k[:, None] < K) & (tokens[None, :] < T)
	mask_tv = (tokens[:, None] < T) & (o_v[None, :] < V)
	last = tl.minimum((chunk + 1) * 64, T) - 1
	base_k = i_b * T * H * K + i_h * K
	base_v = i_b * T * H * V + i_h * V
	last_gate = tl.load(
	gc + base_k + last * H * K + o_k,
	mask=o_k < K,
	other=0.0,
	)
	gates = tl.load(
	gc + base_k + tokens[None, :] * H * K + o_k[:, None],
	mask=mask_kt,
	other=0.0,
	)
	keys = tl.load(
	k + base_k + tokens[None, :] * H * K + o_k[:, None],
	mask=mask_kt,
	other=0.0,
	)
	values = tl.load(
	v + base_v + tokens[:, None] * H * V + o_v[None, :],
	mask=mask_tv,
	other=0.0,
	)
	state *= tl.exp2(last_gate)[:, None]
	gated_keys = (
	keys * tl.exp2(last_gate[:, None] - gates)
	).to(tl.bfloat16)
	state += tl.dot(gated_keys, values)


	@triton.jit
	def _dense_score_offdiagonal(
	q,
	k,
	gc,
	scores,
	T: tl.constexpr,
	H: tl.constexpr,
	K: tl.constexpr,
	BC: tl.constexpr,
	BK: tl.constexpr,
	):
	i_t = tl.program_id(0)
	i_pair = tl.program_id(1)
	i_bh = tl.program_id(2)
	i_b, i_h = i_bh // H, i_bh % H
	i_i, i_j = i_pair + 1, i_pair
	o_i = tl.arange(0, BC)
	o_j = tl.arange(0, BC)
	t_i = i_t * 64 + i_i * BC + o_i
	t_j = i_t * 64 + i_j * BC + o_j
	score_ptrs = (
	scores
	+ (i_b * T + t_i[:, None]) * H * 64
	+ i_h * 64
	+ i_j * BC
	+ o_j[None, :]
	)
	valid = (t_i[:, None] < T) & (t_j[None, :] < T)

	values = tl.zeros((BC, BC), tl.float32)
	base = i_b * T * H * K + i_h * K
	for block in range(tl.cdiv(K, BK)):
	o_k = block * BK + tl.arange(0, BK)
	mask_i = (t_i[:, None] < T) & (o_k[None, :] < K)
	mask_j = (o_k[:, None] < K) & (t_j[None, :] < T)
	mask_k = o_k < K
	anchor = tl.load(
	gc + base + (i_t * 64 + i_i * BC) * H * K + o_k,
	mask=mask_k,
	other=0.0,
	)
	queries = tl.load(
	q + base + t_i[:, None] * H * K + o_k[None, :],
	mask=mask_i,
	other=0.0,
	)
	query_gates = tl.load(
	gc + base + t_i[:, None] * H * K + o_k[None, :],
	mask=mask_i,
	other=0.0,
	)
	keys = tl.load(
	k + base + t_j[None, :] * H * K + o_k[:, None],
	mask=mask_j,
	other=0.0,
	)
	key_gates = tl.load(
	gc + base + t_j[None, :] * H * K + o_k[:, None],
	mask=mask_j,
	other=0.0,
	)
	gated_queries = (
	queries * tl.exp2(query_gates - anchor[None, :]) * 0.0625
	)
	gated_keys = keys * tl.exp2(anchor[:, None] - key_gates)
	values += tl.dot(gated_queries, gated_keys)

	tl.store(score_ptrs, values, mask=valid)


	@triton.jit
	def _dense_score_diagonal(
	q,
	k,
	gc,
	scores,
	T: tl.constexpr,
	H: tl.constexpr,
	K: tl.constexpr,
	BC: tl.constexpr,
	BK: tl.constexpr,
	):
	i_t = tl.program_id(0)
	i_c = tl.program_id(1)
	i_bh = tl.program_id(2)
	i_b, i_h = i_bh // H, i_bh % H
	o_i = tl.arange(0, BC)
	o_k = tl.arange(0, BK)
	t_i = i_t * 64 + i_c * BC + o_i
	mask_i = (t_i[:, None] < T) & (o_k[None, :] < K)
	base = i_b * T * H * K + i_h * K
	queries = tl.load(
	q + base + t_i[:, None] * H * K + o_k[None, :],
	mask=mask_i,
	other=0.0,
	)
	query_gates = tl.load(
	gc + base + t_i[:, None] * H * K + o_k[None, :],
	mask=mask_i,
	other=0.0,
	)

	for j in range(BC):
	t_j = i_t * 64 + i_c * BC + j
	mask_j = (o_k < K) & (t_j < T)
	keys = tl.load(
	k + base + t_j * H * K + o_k,
	mask=mask_j,
	other=0.0,
	).to(tl.float32)
	key_gates = tl.load(
	gc + base + t_j * H * K + o_k,
	mask=mask_j,
	other=0.0,
	).to(tl.float32)
	values = tl.sum(
	queries
	* keys[None, :]
	* tl.exp2(query_gates - key_gates[None, :]),
	axis=1,
	) * 0.0625
	score_ptrs = (
	scores
	+ (i_b * T + t_i) * H * 64
	+ i_h * 64
	+ i_c * BC
	+ j
	)
	tl.store(
	score_ptrs,
	tl.where(o_i >= j, values, 0.0),
	mask=t_i < T,
	)


	class _AccurateChunkGLA(nn.Module):
	def __init__(self, fallback: nn.Module):
	super().__init__()
	self.fallback = fallback

	def forward(
	self,
	q: torch.Tensor,
	k: torch.Tensor,
	v: torch.Tensor,
	g: torch.Tensor,
	scale: float \| None = None,
	initial_state: torch.Tensor \| None = None,
	output_final_state: bool = False,
	cu_seqlens: torch.Tensor \| None = None,
	) -> tuple[torch.Tensor, torch.Tensor \| None]:
	total_t, heads, key_dim, value_dim = (
	q.shape[1],
	q.shape[2],
	q.shape[3],
	v.shape[3],
	)
	if cu_seqlens is not None or initial_state is not None or total_t <= 65:
	return self.fallback(
	q,
	k,
	v,
	g,
	scale,
	initial_state,
	output_final_state,
	cu_seqlens,
	)

	batch = q.shape[0]
	chunks = triton.cdiv(total_t, 64)
	scanned_gates = torch.empty_like(g, dtype=torch.float32)
	states = torch.empty(
	(batch, chunks, heads, key_dim, value_dim),
	device=q.device,
	dtype=torch.bfloat16,
	)
	scores = torch.empty(
	(batch, total_t, heads, 64),
	device=q.device,
	dtype=torch.float32,
	)
	out = torch.empty_like(v)
	_chunk_gla._dense_gate_scan[
	(triton.cdiv(key_dim, 64), chunks, batch * heads)
	](
	g,
	scanned_gates,
	total_t,
	heads,
	key_dim,
	BK=64,
	num_warps=2,
	)
	state_grid = (
	triton.cdiv(key_dim, 64),
	triton.cdiv(value_dim, 32),
	batch * heads,
	)
	if output_final_state:
	final_state = torch.empty(
	(batch, heads, key_dim, value_dim),
	device=q.device,
	dtype=torch.float32,
	)
	_chunk_gla._dense_states[state_grid](
	k,
	v,
	scanned_gates,
	states,
	final_state,
	total_t,
	heads,
	key_dim,
	value_dim,
	chunks,
	BK=64,
	BV=32,
	num_warps=2,
	num_stages=2,
	)
	else:
	final_state = None
	_dense_states_no_final[state_grid](
	k,
	v,
	scanned_gates,
	states,
	total_t,
	heads,
	key_dim,
	value_dim,
	chunks,
	BK=64,
	BV=32,
	num_warps=2,
	num_stages=2,
	)
	_chunk_gla._dense_scores[(chunks, batch * heads)](
	q,
	k,
	scanned_gates,
	scores,
	total_t,
	heads,
	key_dim,
	BK=32,
	num_warps=8,
	num_stages=2,
	)
	_dense_score_offdiagonal[(chunks, 1, batch * heads)](
	q,
	k,
	scanned_gates,
	scores,
	total_t,
	heads,
	key_dim,
	BC=16,
	BK=64,
	num_warps=4,
	num_stages=2,
	)
	_dense_score_diagonal[(chunks, 4, batch * heads)](
	q,
	k,
	scanned_gates,
	scores,
	total_t,
	heads,
	key_dim,
	BC=16,
	BK=256,
	num_warps=8,
	)
	_chunk_gla._dense_output[
	(triton.cdiv(value_dim, 128), chunks, batch * heads)
	](
	q,
	v,
	scanned_gates,
	states,
	scores,
	out,
	total_t,
	heads,
	key_dim,
	value_dim,
	chunks,
	BK=64,
	BV=128,
	num_warps=8,
	num_stages=2,
	)
	return out, final_state if output_final_state else None


	class GLADecoderLayer(nn.Module):
	def __init__(self, config, layer_idx: int):
	super().__init__()
	self.layer_idx = layer_idx
	self.attn_norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
	self.attn = GatedLinearAttention(
	hidden_size=config.hidden_size,
	num_heads=config.num_heads,
	expand_k=config.expand_k,
	expand_v=config.expand_v,
	decay_mode=getattr(config, "decay_mode", "learned_low_rank"),
	gate_low_rank_dim=getattr(config, "gate_low_rank_dim", 16),
	gate_logit_normalizer=getattr(config, "gate_logit_normalizer", 16),
	use_rotary=getattr(config, "use_rotary", False),
	rotary_base=getattr(config, "rotary_base", 10000.0),
	rotary_max_position=getattr(config, "max_position_embeddings", 8192),
	norm_eps=config.norm_eps,
	)
	if self.attn.decay_mode == "learned_low_rank":
	self.attn.log_sigmoid = _AccurateLogSigmoid()
	self.attn.chunk = _AccurateChunkGLA(self.attn.chunk)
	self.mlp_norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
	self.mlp = GLAMLP(config.hidden_size, config.intermediate_size)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: torch.Tensor \| None = None,
	past_key_values=None,
	use_cache: bool = False,
	**kwargs,
	) -> tuple[torch.Tensor, None, object \| None]:
	residual = hidden_states
	h = self.attn_norm(
	hidden_states.reshape(-1, hidden_states.size(-1))
	).reshape_as(hidden_states)
	if (
	hidden_states.shape[0] == 1
	and kwargs.get("cu_seqlens") is not None
	and kwargs["cu_seqlens"].numel() == 2
	):
	kwargs = dict(kwargs)
	kwargs.pop("cu_seqlens")

	h, attentions, past_key_values = self.attn(
	hidden_states=h,
	attention_mask=attention_mask,
	past_key_values=past_key_values,
	use_cache=use_cache and past_key_values is not None,
	**kwargs,
	)
	h, residual = self.mlp_norm(
	h.reshape(-1, h.size(-1)),
	residual.reshape(-1, residual.size(-1)),
	)
	h = h.reshape_as(hidden_states)
	residual = residual.reshape_as(hidden_states)
	hidden_states = residual.add_(self.mlp(h))
	return hidden_states, attentions, past_key_values