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Ivme-Coder-v1 (codename Otter 1) modeling file.
RoPE + SwiGLU + RMSNorm (pre-norm) decoder-only transformer, tied embeddings.
Load with: AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutput
from transformers.generation import GenerationMixin
class IvmeCoderConfig(PretrainedConfig):
model_type = "ivme_coder"
def __init__(
self,
vocab_size=16000,
d_model=512,
n_layer=12,
n_head=8,
context_len=1024,
ffn_mult=8/3,
rope_theta=10000.0,
tie_word_embeddings=True,
**kwargs,
):
self.vocab_size = vocab_size
self.d_model = d_model
self.n_layer = n_layer
self.n_head = n_head
self.context_len = context_len
self.ffn_mult = ffn_mult
self.rope_theta = rope_theta
# Standard HF attribute names, aliased to our own names. Recent `transformers`
# generation/caching code (DynamicCache etc.) reads these directly off the
# config regardless of architecture, even for trust_remote_code models.
self.num_hidden_layers = n_layer
self.num_attention_heads = n_head
self.hidden_size = d_model
self.use_cache = False # this architecture does not implement KV caching -
# forward() always recomputes the full sequence, see note below
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x):
norm = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
return norm * self.weight
def precompute_rope(dim, max_len, theta=10000.0, device="cpu"):
inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, device=device).float() / dim))
t = torch.arange(max_len, device=device).float()
freqs = torch.outer(t, inv_freq)
return torch.cos(freqs), torch.sin(freqs)
def apply_rope(x, cos, sin):
T = x.size(2)
# Cast cos/sin to match x's dtype. precompute_rope always builds these tables in
# fp32 for precision, but if q/k arrive in bf16 (as they do when the model is
# loaded with dtype=torch.bfloat16), multiplying against fp32 cos/sin silently
# promotes the result back to fp32 - which then doesn't match `v` (which never
# passes through this function and stays in bf16), and scaled_dot_product_attention
# requires q, k, v to share one dtype.
cos = cos[:T].unsqueeze(0).unsqueeze(0).to(x.dtype)
sin = sin[:T].unsqueeze(0).unsqueeze(0).to(x.dtype)
x1, x2 = x[..., 0::2], x[..., 1::2]
rot1 = x1 * cos - x2 * sin
rot2 = x1 * sin + x2 * cos
return torch.stack([rot1, rot2], dim=-1).flatten(-2)
class CausalSelfAttention(nn.Module):
def __init__(self, d_model, n_head):
super().__init__()
self.n_head = n_head
self.d_head = d_model // n_head
self.qkv = nn.Linear(d_model, 3 * d_model, bias=False)
self.proj = nn.Linear(d_model, d_model, bias=False)
def forward(self, x, cos, sin):
B, T, C = x.shape
qkv = self.qkv(x)
q, k, v = qkv.split(C, dim=2)
q = q.view(B, T, self.n_head, self.d_head).transpose(1, 2)
k = k.view(B, T, self.n_head, self.d_head).transpose(1, 2)
v = v.view(B, T, self.n_head, self.d_head).transpose(1, 2)
q = apply_rope(q, cos, sin)
k = apply_rope(k, cos, sin)
out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
out = out.transpose(1, 2).contiguous().view(B, T, C)
return self.proj(out)
class SwiGLU(nn.Module):
def __init__(self, d_model, mult=8/3):
super().__init__()
hidden = int(d_model * mult)
hidden = (hidden + 7) // 8 * 8
self.w1 = nn.Linear(d_model, hidden, bias=False)
self.w2 = nn.Linear(d_model, hidden, bias=False)
self.w3 = nn.Linear(hidden, d_model, bias=False)
def forward(self, x):
return self.w3(F.silu(self.w1(x)) * self.w2(x))
class Block(nn.Module):
def __init__(self, d_model, n_head, ffn_mult):
super().__init__()
self.ln1 = RMSNorm(d_model)
self.attn = CausalSelfAttention(d_model, n_head)
self.ln2 = RMSNorm(d_model)
self.mlp = SwiGLU(d_model, ffn_mult)
def forward(self, x, cos, sin):
x = x + self.attn(self.ln1(x), cos, sin)
x = x + self.mlp(self.ln2(x))
return x
class IvmeCoderV1ForCausalLM(PreTrainedModel, GenerationMixin):
config_class = IvmeCoderConfig
# Required so from_pretrained's tie-recovery logic knows how to reconnect
# head.weight to tok_emb.weight when head.weight is absent from the checkpoint
# (it's deliberately excluded from the safetensors file, since it's tied storage,
# not distinct data). Without this, HF's loader treats the missing key as needing
# fresh random initialization instead of re-tying it - which silently produces a
# working-looking model with a completely untrained output head.
_tied_weights_keys = {"head.weight": "tok_emb.weight"}
def __init__(self, config):
super().__init__(config)
self.context_len = config.context_len
self.tok_emb = nn.Embedding(config.vocab_size, config.d_model)
self.blocks = nn.ModuleList(
[Block(config.d_model, config.n_head, config.ffn_mult) for _ in range(config.n_layer)]
)
self.ln_f = RMSNorm(config.d_model)
self.head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.head.weight = self.tok_emb.weight # tied embeddings
# NOTE: RoPE cos/sin tables are intentionally NOT stored as a persistent=False
# buffer here. transformers v5's from_pretrained() has a known bug where
# persistent=False buffers get overwritten with uninitialized/garbage memory
# after loading (https://github.com/huggingface/transformers/issues/44534),
# even though they're computed correctly at __init__ time. That garbage then
# silently produces huge (but finite) values through the RoPE rotation, which
# overflow to NaN inside scaled_dot_product_attention. Recomputing the tables
# fresh on every forward() call sidesteps this entirely - it's cheap (a cos/sin
# over d_head/2 * context_len elements) relative to the rest of the forward pass.
self.d_head = config.d_model // config.n_head
self.rope_theta = config.rope_theta
self.post_init()
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
def get_input_embeddings(self):
return self.tok_emb
def set_input_embeddings(self, value):
self.tok_emb = value
def get_output_embeddings(self):
return self.head
def set_output_embeddings(self, value):
self.head = value
def forward(self, input_ids, labels=None, use_cache=False, past_key_values=None,
attention_mask=None, **kwargs):
# attention_mask is accepted for API compatibility with tokenizer output /
# generate()'s kwarg validation, but intentionally unused: this model only
# supports left-padding for batched generation (see model card), and single-
# sequence causal generation (the common case) needs no mask at all.
# Recompute RoPE tables fresh each call - see note in __init__ for why this
# isn't cached in a buffer.
rope_cos, rope_sin = precompute_rope(
self.d_head, self.context_len, theta=self.rope_theta, device=input_ids.device
)
x = self.tok_emb(input_ids)
for block in self.blocks:
x = block(x, rope_cos, rope_sin)
x = self.ln_f(x)
logits = self.head(x)
loss = None
if labels is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1))
return CausalLMOutput(loss=loss, logits=logits)
def prepare_inputs_for_generation(self, input_ids, **kwargs):
# This architecture has no KV cache implementation - every generation step
# recomputes attention over the full (truncated) sequence from scratch.
# This is correct but slower than a cached model; fine at 50M params / 1024 ctx.
if input_ids.size(1) > self.context_len:
input_ids = input_ids[:, -self.context_len:]
return {"input_ids": input_ids, "use_cache": False}
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