"""
Full definition of a GPT Language Model, all of it in this single file.
Includes:
- cheap Q gate
- Q-MLP gate (128 hidden)
- Q-MLP gate (192 hidden) -- param-matched control for Q+A MLP
- full-width Q gate
- Q head-shared elementwise gate
- full-width X gate
- bottleneck X gate
- paper-faithful X variants:
  - head-specific elementwise G1
  - head-specific headwise G1
  - head-shared elementwise G1
- Q+A dual-signal variants:
  - cheap Q+A gate (shared Linear 128->64 applied per head/token)
  - Q+A MLP gate (shared MLP 128->128->64 applied per head/token)
  - legacy Q+A head-shared elementwise gate (shared Linear 128->64; kept for compatibility)
  - bilinear diagonal Q+A gate (learned elementwise q*y interaction)
  - normed Q+A gate (RMSNorm on q and y before shared 128->64 mix)
  - lowrank Q+A gate (shared 128->16->64 bottleneck mix)
  - soft-QA gate (independent learned soft blends for q and y)
- per-dimension soft-QA gate (independent 64-dim learned blend scales for q and y)
"""

import math
import inspect
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F

class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
    def forward(self, x):
        rms = torch.sqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
        return x / rms * self.weight

class LayerNorm(nn.Module):
    def __init__(self, ndim, bias):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(ndim))
        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
    def forward(self, input):
        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)

class CausalSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)

        self.qv_variant = config.qv_variant
        hs = config.n_embd // config.n_head

        # --------------------------------------------------
        # Q-conditioned gates
        # --------------------------------------------------
        if self.qv_variant in ['dynamic', 'dynamic_swiglu']:
            self.qv_gate_proj = nn.Linear(hs, hs, bias=False)

        if self.qv_variant == 'dynamic_qconditioned_mlp128':
            self.q_gate_fc1 = nn.Linear(hs, 128, bias=False)
            self.q_gate_fc2 = nn.Linear(128, hs, bias=False)

        if self.qv_variant == 'dynamic_qconditioned_mlp192':
            self.q_gate_fc1_192 = nn.Linear(hs, 192, bias=False)
            self.q_gate_fc2_192 = nn.Linear(192, hs, bias=False)

        if self.qv_variant == 'dynamic_qconditioned_fullwidth':
            self.q_gate_proj_full = nn.Linear(config.n_embd, config.n_embd, bias=False)

        if self.qv_variant == 'dynamic_qconditioned_fullwidth_headspecific':
            # full-width Q source (768) with separate 768->64 map for each head
            self.q_gate_proj_full_headspecific = nn.Parameter(torch.empty(config.n_head, config.n_embd, hs))
            nn.init.normal_(self.q_gate_proj_full_headspecific, mean=0.0, std=0.02)

        if self.qv_variant == 'dynamic_q_headshared_elementwise':
            self.q_gate_headshared = nn.Linear(hs, hs, bias=False)

        # --------------------------------------------------
        # X-conditioned gates
        # --------------------------------------------------
        if self.qv_variant == 'dynamic_xconditioned_g1':
            self.x_gate_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)

        if self.qv_variant == 'dynamic_xconditioned_fullwidth_headspecific':
            # full-width X source (768) with separate 768->64 map for each head
            self.x_gate_proj_full_headspecific = nn.Parameter(torch.empty(config.n_head, config.n_embd, hs))
            nn.init.normal_(self.x_gate_proj_full_headspecific, mean=0.0, std=0.02)

        if self.qv_variant == 'dynamic_xconditioned_bottleneck':
            self.x_gate_bottleneck = nn.Linear(config.n_embd, hs, bias=False)

        if self.qv_variant == 'dynamic_x_g1_headspecific_elementwise':
            self.x_gate_headspecific_elementwise = nn.Parameter(torch.empty(config.n_head, hs, hs))
            nn.init.normal_(self.x_gate_headspecific_elementwise, mean=0.0, std=0.02)

        if self.qv_variant == 'dynamic_x_g1_headspecific_headwise':
            self.x_gate_headspecific_headwise = nn.Parameter(torch.empty(config.n_head, hs, 1))
            nn.init.normal_(self.x_gate_headspecific_headwise, mean=0.0, std=0.02)

        if self.qv_variant == 'dynamic_x_g1_headshared_elementwise':
            self.x_gate_headshared_elementwise = nn.Linear(hs, hs, bias=False)

        # --------------------------------------------------
        # Q+A dual-signal gates
        # --------------------------------------------------
        # random gates have no learned parameters — nothing to init here
        # dot product gates also have no learned parameters — nothing to init here

        if self.qv_variant == 'dynamic_a_conditioned':
            # A-only: per-head Linear(64->64), conditioned on y only
            self.a_gate_proj = nn.Linear(hs, hs, bias=False)

        if self.qv_variant == 'dynamic_qa_conditioned':
            # cheap Q+A: shared Linear(128->64) applied to each head/token
            self.qa_gate_proj = nn.Linear(hs * 2, hs, bias=False)

        if self.qv_variant == 'dynamic_qa_conditioned_headspecific':
            # true head-specific Q+A: separate 128->64 matrix for each head
            self.qa_gate_proj_headspecific = nn.Parameter(torch.empty(config.n_head, hs * 2, hs))
            nn.init.normal_(self.qa_gate_proj_headspecific, mean=0.0, std=0.02)

        if self.qv_variant == 'dynamic_qa_conditioned_mlp128':
            # main Q+A MLP: per-head MLP(128->128->64)
            self.qa_gate_fc1 = nn.Linear(hs * 2, 128, bias=False)
            self.qa_gate_fc2 = nn.Linear(128, hs, bias=False)

        if self.qv_variant == 'dynamic_qa_headshared_elementwise':
            # legacy compatibility variant: shared Linear(128->64) applied per head/token
            self.qa_gate_headshared = nn.Linear(hs * 2, hs, bias=False)

        if self.qv_variant == 'dynamic_qa_bilinear_diag':
            # learned elementwise interaction on q*y; 64 params per layer
            self.qa_bilinear_diag = nn.Parameter(torch.ones(hs))

        if self.qv_variant == 'dynamic_qa_conditioned_normed':
            # RMSNorm q and y separately before shared Linear(128->64)
            self.qa_q_norm = RMSNorm(hs)
            self.qa_y_norm = RMSNorm(hs)
            self.qa_gate_proj_normed = nn.Linear(hs * 2, hs, bias=False)

        if self.qv_variant == 'dynamic_qa_conditioned_normed_yonly':
            # RMSNorm y only, raw q, before shared Linear(128->64)
            self.qa_y_norm_yonly = RMSNorm(hs)
            self.qa_gate_proj_normed_yonly = nn.Linear(hs * 2, hs, bias=False)

        if self.qv_variant == 'dynamic_qa_conditioned_normed_qonly':
            # RMSNorm q only, raw y, before shared Linear(128->64)
            self.qa_q_norm_qonly = RMSNorm(hs)
            self.qa_gate_proj_normed_qonly = nn.Linear(hs * 2, hs, bias=False)

        if self.qv_variant == 'dynamic_qa_conditioned_softq':
            # Learnable blend between RMSNorm(q) and raw q, with raw y
            self.qa_q_norm_soft = RMSNorm(hs)
            self.qa_gate_proj_softq = nn.Linear(hs * 2, hs, bias=False)
            self.qa_q_blend_alpha = nn.Parameter(torch.tensor(0.5))

        if self.qv_variant == 'dynamic_qa_conditioned_softqa':
            # Independent learnable blends for RMSNorm(q)/raw q and RMSNorm(y)/raw y
            self.qa_q_norm_softqa = RMSNorm(hs)
            self.qa_y_norm_softqa = RMSNorm(hs)
            self.qa_gate_proj_softqa = nn.Linear(hs * 2, hs, bias=False)
            self.qa_q_blend_alpha = nn.Parameter(torch.tensor(0.5))
            self.qa_y_blend_alpha = nn.Parameter(torch.tensor(0.5))

        if self.qv_variant == 'dynamic_qa_conditioned_softqa_perlayer':
            # Per-layer independent learnable blends for RMSNorm(q)/raw q and RMSNorm(y)/raw y
            self.qa_q_norm_softqa_pl = RMSNorm(hs)
            self.qa_y_norm_softqa_pl = RMSNorm(hs)
            self.qa_gate_proj_softqa_pl = nn.Linear(hs * 2, hs, bias=False)
            self.qa_q_blend_alpha = nn.Parameter(torch.tensor(0.0))
            self.qa_y_blend_alpha = nn.Parameter(torch.tensor(0.0))

        if self.qv_variant == 'dynamic_qa_conditioned_softqa_perdim':
            # Per-dimension independent learnable blends for RMSNorm(q)/raw q and RMSNorm(y)/raw y
            self.qa_q_norm_softqa_pd = RMSNorm(hs)
            self.qa_y_norm_softqa_pd = RMSNorm(hs)
            self.qa_gate_proj_softqa_pd = nn.Linear(hs * 2, hs, bias=False)
            self.qa_q_dim_scale = nn.Parameter(torch.zeros(hs))
            self.qa_y_dim_scale = nn.Parameter(torch.zeros(hs))

        if self.qv_variant == 'dynamic_qa_conditioned_softqa_perdim_informed':
            # Per-dimension independent learnable blends with informed initialization:
            # 1.0459 = sigmoid^-1(0.74), matching the Phase 17 converged scalar value.
            self.qa_q_norm_softqa_pd = RMSNorm(hs)
            self.qa_y_norm_softqa_pd = RMSNorm(hs)
            self.qa_gate_proj_softqa_pd = nn.Linear(hs * 2, hs, bias=False)
            self.qa_q_dim_scale = nn.Parameter(torch.full((hs,), 1.0459))
            self.qa_y_dim_scale = nn.Parameter(torch.full((hs,), 1.0459))

        if self.qv_variant == 'dynamic_qa_conditioned_softqa_perdim_free':
            # Per-dimension independent learnable blends with tiny random symmetry breaking:
            # start near sigmoid(scale)=0.5, but each dimension differs slightly.
            self.qa_q_norm_softqa_pd = RMSNorm(hs)
            self.qa_y_norm_softqa_pd = RMSNorm(hs)
            self.qa_gate_proj_softqa_pd = nn.Linear(hs * 2, hs, bias=False)
            self.qa_q_dim_scale = nn.Parameter(torch.empty(hs))
            self.qa_y_dim_scale = nn.Parameter(torch.empty(hs))
            nn.init.normal_(self.qa_q_dim_scale, mean=0.0, std=0.01)
            nn.init.normal_(self.qa_y_dim_scale, mean=0.0, std=0.01)

        if self.qv_variant == 'dynamic_qa_conditioned_lowrank16':
            # shared low-rank Q+A mixer: 128->16->64 per head/token
            self.qa_gate_lowrank_fc1 = nn.Linear(hs * 2, 16, bias=False)
            self.qa_gate_lowrank_fc2 = nn.Linear(16, hs, bias=False)

        # --------------------------------------------------
        # Static / normalization controls
        # --------------------------------------------------
        if self.qv_variant == 'post_rmsnorm_y':
            self.post_attn_norm = RMSNorm(hs)
        elif self.qv_variant == 'static_gate':
            self.static_gate_param = nn.Parameter(torch.zeros(config.n_embd))
        elif self.qv_variant == 'static_gate_prehead':
            self.static_gate_prehead_param = nn.Parameter(torch.zeros(config.n_head, hs))

        self.q_norm = None
        self.v_norm = None
        self.qv_modulator = None
        variant = getattr(config, 'qv_variant', 'none')
        if variant in ['qvnorm']:
            self.q_norm = RMSNorm(hs)
        if variant in ['vnorm', 'qvnorm']:
            self.v_norm = RMSNorm(hs)

        self.attn_dropout = nn.Dropout(config.dropout)
        self.resid_dropout = nn.Dropout(config.dropout)
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.dropout = config.dropout
        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
        if not self.flash:
            self.register_buffer(
                "bias",
                torch.tril(torch.ones(config.block_size, config.block_size)).view(
                    1, 1, config.block_size, config.block_size
                ),
            )

    def forward(self, x, x_prenorm=None):
        B, T, C = x.size()
        hs = C // self.n_head

        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
        k = k.view(B, T, self.n_head, hs).transpose(1, 2)
        q = q.view(B, T, self.n_head, hs).transpose(1, 2)
        v = v.view(B, T, self.n_head, hs).transpose(1, 2)

        if self.q_norm is not None:
            q = self.q_norm(q)
        if self.v_norm is not None:
            v = self.v_norm(v)

        if self.flash:
            y = torch.nn.functional.scaled_dot_product_attention(
                q, k, v, is_causal=True, dropout_p=self.dropout if self.training else 0
            )
        else:
            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
            att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
            att = F.softmax(att, dim=-1)
            att = self.attn_dropout(att)
            y = att @ v

        # --------------------------------------------------
        # Gate application — y shape: (B, n_head, T, hs)
        # --------------------------------------------------

        # Q-conditioned gates
        if self.qv_variant in ['dynamic', 'dynamic_swiglu']:
            gate_logit = self.qv_gate_proj(q)
            gate = torch.sigmoid(gate_logit) if self.qv_variant == 'dynamic' else gate_logit * torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qconditioned_mlp128':
            gate_hidden = F.silu(self.q_gate_fc1(q))
            gate_logit = self.q_gate_fc2(gate_hidden)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qconditioned_mlp192':
            gate_hidden = F.silu(self.q_gate_fc1_192(q))
            gate_logit = self.q_gate_fc2_192(gate_hidden)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qconditioned_fullwidth':
            q_full = q.transpose(1, 2).contiguous().view(B, T, C)
            gate_logit = self.q_gate_proj_full(q_full)
            gate = torch.sigmoid(gate_logit)
            gate = gate.view(B, T, self.n_head, hs).transpose(1, 2)
            y = y * gate

        elif self.qv_variant == 'dynamic_qconditioned_fullwidth_headspecific':
            q_full = q.transpose(1, 2).contiguous().view(B, T, C)
            gate_logit = torch.einsum('btc,hce->bhte', q_full, self.q_gate_proj_full_headspecific)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_q_headshared_elementwise':
            gate_logit = self.q_gate_headshared(q)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        # Static / normalization controls
        elif self.qv_variant == 'post_rmsnorm_y':
            y = self.post_attn_norm(y)

        elif self.qv_variant == 'static_gate':
            gate = torch.sigmoid(self.static_gate_param)
            y = y.transpose(1, 2).contiguous().view(B, T, C)
            y = y * gate[None, None, :]

        elif self.qv_variant == 'static_gate_prehead':
            gate = torch.sigmoid(self.static_gate_prehead_param)
            y = y * gate[None, :, None, :]

        # X-conditioned gates
        elif self.qv_variant == 'dynamic_xconditioned_g1':
            gate_logit = self.x_gate_proj(x_prenorm)
            gate = torch.sigmoid(gate_logit)
            gate = gate.view(B, T, self.n_head, hs).transpose(1, 2)
            y = y * gate

        elif self.qv_variant == 'dynamic_xconditioned_fullwidth_headspecific':
            gate_logit = torch.einsum('btc,hce->bhte', x_prenorm, self.x_gate_proj_full_headspecific)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_xconditioned_bottleneck':
            z = self.x_gate_bottleneck(x_prenorm)
            gate = torch.sigmoid(z)
            gate = gate.unsqueeze(1)
            y = y * gate

        elif self.qv_variant == 'dynamic_x_g1_headspecific_elementwise':
            xh = x_prenorm.view(B, T, self.n_head, hs).transpose(1, 2)
            gate_logit = torch.einsum('bhtd,hde->bhte', xh, self.x_gate_headspecific_elementwise)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_x_g1_headspecific_headwise':
            xh = x_prenorm.view(B, T, self.n_head, hs).transpose(1, 2)
            gate_logit = torch.einsum('bhtd,hde->bhte', xh, self.x_gate_headspecific_headwise)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_x_g1_headshared_elementwise':
            xh = x_prenorm.view(B, T, self.n_head, hs).transpose(1, 2)
            gate_logit = self.x_gate_headshared_elementwise(xh)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        # Random / ablation gates — no learned params
        elif self.qv_variant == 'dynamic_random_gate':
            # Uniform random [0,1] gate — tests if location matters at all
            gate = torch.rand_like(y)
            y = y * gate

        elif self.qv_variant == 'dynamic_ones_gate':
            # Always 1.0 — pure identity, sanity check (should == baseline)
            pass  # y unchanged

        elif self.qv_variant == 'dynamic_random_normal':
            # Normal(0.5, 0.2) gate clamped to [0,1] — different noise shape
            gate = torch.randn_like(y) * 0.2 + 0.5
            gate = gate.clamp(0.0, 1.0)
            y = y * gate

        elif self.qv_variant == 'dynamic_bernoulli_gate':
            # Binary gate: randomly zeros 50% of head dims — spicy dropout variant
            gate = torch.bernoulli(torch.full_like(y, 0.5))
            # Scale by 2.0 to preserve expected value (like standard dropout)
            y = y * gate * 2.0

        # A-only gate
        elif self.qv_variant == 'dynamic_a_conditioned':
            gate_logit = self.a_gate_proj(y)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        # Q+A dual-signal gates
        elif self.qv_variant == 'dynamic_qa_conditioned':
            # cheap Q+A: cat(q, y) -> shared Linear(128->64) -> sigmoid
            gate_in = torch.cat([q, y], dim=-1)   # (B, n_head, T, 128)
            gate_logit = self.qa_gate_proj(gate_in)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_conditioned_headspecific':
            # true head-specific Q+A: per-head (128->64) -> sigmoid
            gate_in = torch.cat([q, y], dim=-1)   # (B, n_head, T, 128)
            gate_logit = torch.einsum('bhtd,hde->bhte', gate_in, self.qa_gate_proj_headspecific)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_conditioned_mlp128':
            # main Q+A MLP: cat(q, y) -> Linear(128->128) -> SiLU -> Linear(128->64) -> sigmoid
            gate_in = torch.cat([q, y], dim=-1)   # (B, n_head, T, 128)
            gate_hidden = F.silu(self.qa_gate_fc1(gate_in))
            gate_logit = self.qa_gate_fc2(gate_hidden)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_headshared_elementwise':
            # legacy compatibility variant: cat(q, y) -> shared Linear(128->64) -> sigmoid
            gate_in = torch.cat([q, y], dim=-1)   # (B, n_head, T, 128)
            gate_logit = self.qa_gate_headshared(gate_in)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_bilinear_diag':
            # learned diagonal bilinear interaction: sigmoid((q * y * w) / sqrt(d_head))
            gate_logit = (q * y * self.qa_bilinear_diag[None, None, None, :]) / math.sqrt(hs)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_conditioned_normed':
            qn = self.qa_q_norm(q)
            yn = self.qa_y_norm(y)
            gate_in = torch.cat([qn, yn], dim=-1)
            gate_logit = self.qa_gate_proj_normed(gate_in)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_conditioned_normed_yonly':
            yn = self.qa_y_norm_yonly(y)
            gate_in = torch.cat([q, yn], dim=-1)
            gate_logit = self.qa_gate_proj_normed_yonly(gate_in)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_conditioned_normed_qonly':
            qn = self.qa_q_norm_qonly(q)
            gate_in = torch.cat([qn, y], dim=-1)
            gate_logit = self.qa_gate_proj_normed_qonly(gate_in)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_conditioned_softq':
            qn = self.qa_q_norm_soft(q)
            alpha = torch.sigmoid(self.qa_q_blend_alpha)
            q_blend = alpha * qn + (1.0 - alpha) * q
            gate_in = torch.cat([q_blend, y], dim=-1)
            gate_logit = self.qa_gate_proj_softq(gate_in)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_conditioned_softqa':
            qn = self.qa_q_norm_softqa(q)
            yn = self.qa_y_norm_softqa(y)
            alpha_q = torch.sigmoid(self.qa_q_blend_alpha)
            alpha_y = torch.sigmoid(self.qa_y_blend_alpha)
            q_blend = alpha_q * qn + (1.0 - alpha_q) * q
            y_blend = alpha_y * yn + (1.0 - alpha_y) * y
            gate_in = torch.cat([q_blend, y_blend], dim=-1)
            gate_logit = self.qa_gate_proj_softqa(gate_in)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_conditioned_softqa_perlayer':
            qn = self.qa_q_norm_softqa_pl(q)
            yn = self.qa_y_norm_softqa_pl(y)
            alpha_q = torch.sigmoid(self.qa_q_blend_alpha)
            alpha_y = torch.sigmoid(self.qa_y_blend_alpha)
            q_blend = alpha_q * qn + (1.0 - alpha_q) * q
            y_blend = alpha_y * yn + (1.0 - alpha_y) * y
            gate_in = torch.cat([q_blend, y_blend], dim=-1)
            gate_logit = self.qa_gate_proj_softqa_pl(gate_in)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant in ['dynamic_qa_conditioned_softqa_perdim', 'dynamic_qa_conditioned_softqa_perdim_informed', 'dynamic_qa_conditioned_softqa_perdim_free']:
            qn = self.qa_q_norm_softqa_pd(q)
            yn = self.qa_y_norm_softqa_pd(y)
            scale_q = torch.sigmoid(self.qa_q_dim_scale).to(dtype=q.dtype, device=q.device)
            scale_y = torch.sigmoid(self.qa_y_dim_scale).to(dtype=y.dtype, device=y.device)
            q_blend = scale_q * qn + (1.0 - scale_q) * q
            y_blend = scale_y * yn + (1.0 - scale_y) * y
            gate_in = torch.cat([q_blend, y_blend], dim=-1)
            gate_logit = self.qa_gate_proj_softqa_pd(gate_in)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        elif self.qv_variant == 'dynamic_qa_conditioned_lowrank16':
            gate_in = torch.cat([q, y], dim=-1)
            gate_hidden = F.silu(self.qa_gate_lowrank_fc1(gate_in))
            gate_logit = self.qa_gate_lowrank_fc2(gate_hidden)
            gate = torch.sigmoid(gate_logit)
            y = y * gate

        # Zero-parameter dot product gates — raw Q·y geometry
        elif self.qv_variant == 'dynamic_dot_scalar':
            # scalar gate: sigmoid(sum(q*y) / sqrt(d_head))
            # one scalar per head per token, zero params
            hs = q.shape[-1]
            dot = (q * y).sum(dim=-1, keepdim=True)          # (B, n_head, T, 1)
            gate = torch.sigmoid(dot / math.sqrt(hs))         # scalar broadcast
            y = y * gate

        elif self.qv_variant == 'dynamic_dot_elementwise':
            # elementwise gate: sigmoid(q*y / sqrt(d_head))
            # same shape as cheap_qa gate but zero params
            hs = q.shape[-1]
            gate = torch.sigmoid((q * y) / math.sqrt(hs))    # (B, n_head, T, 64)
            y = y * gate

        if self.qv_variant != 'static_gate':
            y = y.transpose(1, 2).contiguous().view(B, T, C)

        y = self.resid_dropout(self.c_proj(y))
        return y

class MLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
        self.gelu = nn.GELU()
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
        self.dropout = nn.Dropout(config.dropout)
    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x

class Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
        self.mlp = MLP(config)
    def forward(self, x):
        x_norm = self.ln_1(x)
        attn_out = self.attn(x_norm, x_norm)
        x = x + attn_out
        x = x + self.mlp(self.ln_2(x))
        return x

@dataclass
class GPTConfig:
    block_size: int = 1024
    vocab_size: int = 50304
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768
    dropout: float = 0.0
    bias: bool = True
    qv_variant: str = 'none'

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        assert config.vocab_size is not None
        assert config.block_size is not None
        self.config = config
        self.transformer = nn.ModuleDict(dict(
            wte = nn.Embedding(config.vocab_size, config.n_embd),
            wpe = nn.Embedding(config.block_size, config.n_embd),
            drop = nn.Dropout(config.dropout),
            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
            ln_f = LayerNorm(config.n_embd, bias=config.bias),
        ))
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.transformer.wte.weight = self.lm_head.weight
        self.apply(self._init_weights)
        for pn, p in self.named_parameters():
            if pn.endswith('c_proj.weight'):
                torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))
        print("number of parameters: %.2fM" % (self.get_num_params()/1e6,))

    def get_num_params(self, non_embedding=True):
        n_params = sum(p.numel() for p in self.parameters())
        if non_embedding:
            n_params -= self.transformer.wpe.weight.numel()
        return n_params

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, idx, targets=None):
        device = idx.device
        b, t = idx.size()
        assert t <= self.config.block_size
        pos = torch.arange(0, t, dtype=torch.long, device=device)
        tok_emb = self.transformer.wte(idx)
        pos_emb = self.transformer.wpe(pos)
        x = self.transformer.drop(tok_emb + pos_emb)
        for block in self.transformer.h:
            x = block(x)
        x = self.transformer.ln_f(x)
        if targets is not None:
            logits = self.lm_head(x)
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
        else:
            logits = self.lm_head(x[:, [-1], :])
            loss = None
        return logits, loss

    def crop_block_size(self, block_size):
        assert block_size <= self.config.block_size
        self.config.block_size = block_size
        self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
        for block in self.transformer.h:
            if hasattr(block.attn, 'bias'):
                block.attn.bias = block.attn.bias[:, :, :block_size, :block_size]

    @classmethod
    def from_pretrained(cls, model_type, override_args=None):
        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
        override_args = override_args or {}
        assert all(k == 'dropout' for k in override_args)
        from transformers import GPT2LMHeadModel
        print("loading weights from pretrained gpt: %s" % model_type)
        config_args = {
            'gpt2':       dict(n_layer=12, n_head=12, n_embd=768),
            'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024),
            'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280),
            'gpt2-xl':    dict(n_layer=48, n_head=25, n_embd=1600),
        }[model_type]
        config_args['vocab_size'] = 50257
        config_args['block_size'] = 1024
        config_args['bias'] = True
        if 'dropout' in override_args:
            config_args['dropout'] = override_args['dropout']
        config = GPTConfig(**config_args)
        model = GPT(config)
        sd = model.state_dict()
        sd_keys = [k for k in sd.keys() if not k.endswith('.attn.bias')]
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()
        sd_keys_hf = [k for k in sd_hf.keys() if not k.endswith('.attn.masked_bias') and not k.endswith('.attn.bias')]
        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
        assert len(sd_keys_hf) == len(sd_keys)
        for k in sd_keys_hf:
            if any(k.endswith(w) for w in transposed):
                assert sd_hf[k].shape[::-1] == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k].t())
            else:
                assert sd_hf[k].shape == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k])
        return model

    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
        param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
        optim_groups = [
            {'params': decay_params, 'weight_decay': weight_decay},
            {'params': nodecay_params, 'weight_decay': 0.0}
        ]
        num_decay_params = sum(p.numel() for p in decay_params)
        num_nodecay_params = sum(p.numel() for p in nodecay_params)
        print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
        print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
        use_fused = fused_available and device_type == 'cuda'
        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, fused=use_fused)
        print(f"using fused AdamW: {use_fused}")
        return optimizer

    def estimate_mfu(self, fwdbwd_per_iter, dt):
        N = self.get_num_params()
        cfg = self.config
        L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd // cfg.n_head, cfg.block_size
        flops_per_token = 6 * N + 12 * L * H * Q * T
        flops_per_fwdbwd = flops_per_token * T
        flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
        flops_achieved = flops_per_iter * (1.0 / dt)
        flops_promised = 312e12
        mfu = flops_achieved / flops_promised
        return mfu

    @torch.no_grad()
    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
        for _ in range(max_new_tokens):
            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :] / temperature
            if top_k is not None:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, [-1]]] = -float('Inf')
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
        return idx