Initial commit: ept — backprop-free equilibrium transformer (EP)

Code (ep_run/), organized docs (docs/{method,campaign,hardware,outreach,paper}),
analysis scripts (scripts/), ONBOARDING.md entry point. Large data/checkpoints
git-ignored (share separately).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn

1 files changed, 156 insertions, 0 deletions

diff --git a/ep_run/model.py b/ep_run/model.py
new file mode 100644
index 0000000..149724b
--- /dev/null
+++ b/ep_run/model.py
@@ -0,0 +1,156 @@
+"""Tiny GPT with switchable softmax/sigmoid causal self-attention.
+
+Architecture follows nanoGPT (Karpathy), trimmed to a single file for this
+Sigmoid Attention reproduction experiment (Ramapuram et al. 2024).
+"""
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+@dataclass
+class GPTConfig:
+    block_size: int = 256
+    vocab_size: int = 65
+    n_layer: int = 6
+    n_head: int = 6
+    n_embd: int = 384
+    dropout: float = 0.2
+    bias: bool = False  # bias in linear layers
+    attn_mode: str = "softmax"  # "softmax" or "sigmoid"
+    sigmoid_bias_mode: str = "neg_log_n"  # "zero" | "neg_log_n" | "learned"
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.head_dim = config.n_embd // config.n_head
+        self.block_size = config.block_size
+        self.attn_mode = config.attn_mode
+        self.sigmoid_bias_mode = config.sigmoid_bias_mode
+
+        self.qkv = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        self.proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        self.attn_drop = nn.Dropout(config.dropout)
+        self.resid_drop = nn.Dropout(config.dropout)
+
+        causal = torch.tril(torch.ones(config.block_size, config.block_size, dtype=torch.bool))
+        self.register_buffer("causal_mask", causal, persistent=False)
+
+        if config.attn_mode == "sigmoid":
+            if config.sigmoid_bias_mode == "zero":
+                init_b = 0.0
+            else:
+                init_b = -math.log(config.block_size)
+            if config.sigmoid_bias_mode == "learned":
+                self.sig_bias = nn.Parameter(torch.tensor(init_b))
+            else:
+                self.register_buffer("sig_bias", torch.tensor(init_b), persistent=False)
+
+    def forward(self, x):
+        B, T, C = x.shape
+        q, k, v = self.qkv(x).split(self.n_embd, dim=-1)
+        q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+
+        scores = (q @ k.transpose(-2, -1)) * (self.head_dim ** -0.5)
+        mask = self.causal_mask[:T, :T]
+        scores = scores.masked_fill(~mask, float("-inf"))
+
+        if self.attn_mode == "softmax":
+            attn = F.softmax(scores, dim=-1)
+        else:
+            # sigmoid(scores + b). masked -> sigmoid(-inf) = 0 naturally.
+            attn = torch.sigmoid(scores + self.sig_bias)
+
+        attn = self.attn_drop(attn)
+        out = (attn @ v).transpose(1, 2).contiguous().view(B, T, C)
+        return self.resid_drop(self.proj(out))
+
+
+class MLP(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        self.fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
+        self.proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.drop = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        return self.drop(self.proj(F.gelu(self.fc(x))))
+
+
+class Block(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        self.config = config
+        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
+        self.pos_emb = nn.Embedding(config.block_size, config.n_embd)
+        self.drop = nn.Dropout(config.dropout)
+        self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(config.n_embd)
+        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        self.apply(self._init_weights)
+        for pn, p in self.named_parameters():
+            if pn.endswith("proj.weight"):
+                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+            if m.bias is not None:
+                nn.init.zeros_(m.bias)
+        elif isinstance(m, nn.Embedding):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+
+    def num_params(self) -> int:
+        return sum(p.numel() for p in self.parameters())
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        assert T <= self.config.block_size, f"seq len {T} > block_size {self.config.block_size}"
+        pos = torch.arange(T, device=idx.device)
+        x = self.drop(self.tok_emb(idx) + self.pos_emb(pos))
+        for blk in self.blocks:
+            x = blk(x)
+        x = self.ln_f(x)
+        logits = self.head(x)
+        if targets is None:
+            return logits, None
+        loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens: int, temperature: float = 1.0, top_k=None):
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -self.config.block_size :]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            if top_k is not None:
+                v, _ = torch.topk(logits, top_k)
+                logits[logits < v[:, [-1]]] = -float("inf")
+            probs = F.softmax(logits, dim=-1)
+            nxt = torch.multinomial(probs, 1)
+            idx = torch.cat([idx, nxt], dim=1)
+        return idx