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from __future__ import annotations
import math
from typing import Any
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
try:
from .configuration_neurocoder import NeuroCoderConfig
except Exception:
from configuration_neurocoder import NeuroCoderConfig
class RMSNorm(nn.Module):
def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(hidden_size))
def forward(self, x: Tensor) -> Tensor:
rms = x.pow(2).mean(-1, keepdim=True)
return x * torch.rsqrt(rms + self.eps) * self.weight
class SelfAttention(nn.Module):
def __init__(self, config: NeuroCoderConfig) -> None:
super().__init__()
self.num_heads = config.num_heads
self.head_dim = config.head_dim
self.scale = self.head_dim ** -0.5
self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3)
self.out = nn.Linear(config.hidden_size, config.hidden_size)
def forward(
self,
x: Tensor,
past_key_value: tuple[Tensor, Tensor] | None = None,
attention_mask: Tensor | None = None,
use_cache: bool = False,
) -> tuple[Tensor, tuple[Tensor, Tensor] | None]:
bsz, seq_len, hidden = x.shape
qkv = self.qkv(x)
q, k, v = qkv.chunk(3, dim=-1)
def shape_heads(t: Tensor) -> Tensor:
return t.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
q = shape_heads(q)
k = shape_heads(k)
v = shape_heads(v)
if past_key_value is not None:
past_k, past_v = past_key_value
if past_k is not None and past_v is not None:
k = torch.cat([past_k, k], dim=2)
v = torch.cat([past_v, v], dim=2)
present = (k, v) if use_cache else None
key_len = k.shape[-2]
past_len = key_len - seq_len
attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
if seq_len > 1 or past_len > 0:
q_positions = torch.arange(
past_len,
past_len + seq_len,
device=x.device,
).unsqueeze(-1)
k_positions = torch.arange(key_len, device=x.device).unsqueeze(0)
causal_mask = (k_positions <= q_positions).unsqueeze(0).unsqueeze(0)
attn = attn.masked_fill(~causal_mask, float("-inf"))
if attention_mask is not None:
# Expect [batch, key_len] style attention mask. Keep only the last key_len
# columns so generation with cache remains aligned.
key_mask = attention_mask[:, -key_len:].to(dtype=torch.bool).unsqueeze(1).unsqueeze(1)
attn = attn.masked_fill(~key_mask, float("-inf"))
probs = F.softmax(attn, dim=-1)
out = torch.matmul(probs, v)
out = out.transpose(1, 2).contiguous().view(bsz, seq_len, hidden)
return self.out(out), present
class DenseFFN(nn.Module):
def __init__(self, config: NeuroCoderConfig) -> None:
super().__init__()
inner = config.hidden_size * config.ffn_multiplier
self.gate = nn.Linear(config.hidden_size, inner)
self.up = nn.Linear(config.hidden_size, inner)
self.down = nn.Linear(inner, config.hidden_size)
def forward(self, x: Tensor) -> Tensor:
return self.down(F.silu(self.gate(x)) * self.up(x))
class MoEFeedForward(nn.Module):
def __init__(self, config: NeuroCoderConfig) -> None:
super().__init__()
self.num_experts = config.num_experts
self.top_k = config.router_top_k
self.capacity_factor_train = config.capacity_factor_train
self.capacity_factor_infer = config.capacity_factor_infer
self.router = nn.Linear(config.hidden_size, config.num_experts, bias=False)
self.experts = nn.ModuleList([DenseFFN(config) for _ in range(config.num_experts)])
def forward(self, x: Tensor) -> tuple[Tensor, Tensor, Tensor]:
bsz, seq_len, hidden = x.shape
x_flat = x.reshape(-1, hidden)
tokens = x_flat.shape[0]
logits = self.router(x_flat)
probs = F.softmax(logits, dim=-1)
top_vals, top_idx = torch.topk(probs, k=self.top_k, dim=-1)
capacity_factor = self.capacity_factor_train if self.training else self.capacity_factor_infer
capacity = max(1, math.ceil(capacity_factor * tokens / self.num_experts))
output = torch.zeros_like(x_flat)
expert_load = []
for expert_id in range(self.num_experts):
expert = self.experts[expert_id]
assigned_indices = []
assigned_weights = []
for rank in range(self.top_k):
mask = top_idx[:, rank] == expert_id
idx = torch.nonzero(mask, as_tuple=False).squeeze(-1)
if idx.numel() == 0:
continue
weights = top_vals[idx, rank]
assigned_indices.append(idx)
assigned_weights.append(weights)
if not assigned_indices:
expert_load.append(0.0)
continue
token_indices = torch.cat(assigned_indices, dim=0)
token_weights = torch.cat(assigned_weights, dim=0)
if token_indices.numel() > capacity:
token_indices = token_indices[:capacity]
token_weights = token_weights[:capacity]
expert_in = x_flat[token_indices]
expert_out = expert(expert_in)
output[token_indices] += expert_out * token_weights.unsqueeze(-1)
expert_load.append(float(token_indices.numel() / max(tokens, 1)))
load_tensor = torch.tensor(expert_load, device=x.device)
mean_prob = probs.mean(dim=0)
aux_loss = self.num_experts * torch.sum(mean_prob * load_tensor)
z_loss = torch.mean(torch.logsumexp(logits, dim=-1) ** 2)
return output.reshape(bsz, seq_len, hidden), aux_loss, z_loss
class TransformerBlock(nn.Module):
def __init__(self, config: NeuroCoderConfig, use_moe: bool) -> None:
super().__init__()
self.norm1 = RMSNorm(config.hidden_size)
self.norm2 = RMSNorm(config.hidden_size)
self.attn = SelfAttention(config)
self.ffn = MoEFeedForward(config) if use_moe else DenseFFN(config)
self.use_moe = use_moe
def forward(
self,
x: Tensor,
past_key_value: tuple[Tensor, Tensor] | None = None,
attention_mask: Tensor | None = None,
use_cache: bool = False,
) -> tuple[Tensor, Tensor, Tensor, tuple[Tensor, Tensor] | None]:
attn_out, present = self.attn(
self.norm1(x),
past_key_value=past_key_value,
attention_mask=attention_mask,
use_cache=use_cache,
)
x = x + attn_out
aux_loss = torch.tensor(0.0, device=x.device)
z_loss = torch.tensor(0.0, device=x.device)
ffn_input = self.norm2(x)
if self.use_moe:
ffn_out, aux_loss, z_loss = self.ffn(ffn_input)
else:
ffn_out = self.ffn(ffn_input)
x = x + ffn_out
return x, aux_loss, z_loss, present
class NeuroCoderForCausalLM(PreTrainedModel):
config_class = NeuroCoderConfig
base_model_prefix = "neurocoder"
_no_split_modules = ["TransformerBlock", "MoEFeedForward"]
_supports_cache_class = False
def __init__(self, config: NeuroCoderConfig) -> None:
super().__init__(config)
self.token_embed = nn.Embedding(config.vocab_size, config.hidden_size)
self.pos_embed = nn.Embedding(config.context_length, config.hidden_size)
self.layers = nn.ModuleList(
[
TransformerBlock(config, use_moe=((idx + 1) % config.moe_every_n_layers == 0))
for idx in range(config.num_layers)
]
)
self.norm = RMSNorm(config.hidden_size)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.lm_head.weight = self.token_embed.weight
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.token_embed
def set_input_embeddings(self, value: nn.Embedding) -> None:
self.token_embed = value
def get_output_embeddings(self) -> nn.Linear:
return self.lm_head
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
self.lm_head = new_embeddings
def prepare_inputs_for_generation(
self,
input_ids: Tensor,
**kwargs: Any,
) -> dict[str, Any]:
past_key_values = kwargs.get("past_key_values")
has_past = False
if past_key_values is not None and hasattr(past_key_values, "get_seq_length"):
has_past = bool(past_key_values.get_seq_length() > 0)
elif isinstance(past_key_values, tuple) and past_key_values:
first = past_key_values[0]
has_past = bool(first and first[0] is not None and first[1] is not None)
if has_past:
input_ids = input_ids[:, -1:]
return {
"input_ids": input_ids,
"attention_mask": kwargs.get("attention_mask"),
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache", True),
}
@staticmethod
def _as_legacy_past_key_values(
past_key_values: Any,
num_layers: int,
) -> tuple[tuple[Tensor, Tensor] | None, ...]:
if past_key_values is None:
return tuple([None] * num_layers)
if hasattr(past_key_values, "to_legacy_cache"):
past_key_values = past_key_values.to_legacy_cache()
if isinstance(past_key_values, list):
past_key_values = tuple(past_key_values)
if isinstance(past_key_values, tuple):
return past_key_values
key_cache = getattr(past_key_values, "key_cache", None)
value_cache = getattr(past_key_values, "value_cache", None)
if isinstance(key_cache, list) and isinstance(value_cache, list):
pairs: list[tuple[Tensor, Tensor] | None] = []
for idx in range(num_layers):
if idx < len(key_cache) and idx < len(value_cache):
key = key_cache[idx]
value = value_cache[idx]
if key is not None and value is not None:
pairs.append((key, value))
continue
pairs.append(None)
return tuple(pairs)
return tuple([None] * num_layers)
def _reorder_cache(
self,
past_key_values: tuple[tuple[Tensor, Tensor], ...] | list[tuple[Tensor, Tensor]],
beam_idx: Tensor,
) -> tuple[tuple[Tensor, Tensor], ...]:
reordered: list[tuple[Tensor, Tensor]] = []
for key, value in past_key_values:
reordered.append((key.index_select(0, beam_idx), value.index_select(0, beam_idx)))
return tuple(reordered)
def forward(
self,
input_ids: Tensor | None = None,
attention_mask: Tensor | None = None,
labels: Tensor | None = None,
past_key_values: Any = None,
use_cache: bool | None = None,
**kwargs: Any,
) -> CausalLMOutputWithPast:
if input_ids is None:
raise ValueError("input_ids is required")
cache_enabled = bool(self.config.use_cache if use_cache is None else use_cache)
past = self._as_legacy_past_key_values(past_key_values, len(self.layers))
bsz, seq_len = input_ids.shape
past_len = 0
for entry in past:
if (
entry is not None
and isinstance(entry, tuple)
and len(entry) == 2
and entry[0] is not None
and entry[1] is not None
):
past_len = int(entry[0].shape[2])
break
pos = torch.arange(
past_len,
past_len + seq_len,
device=input_ids.device,
).unsqueeze(0).expand(bsz, seq_len)
pos = pos.clamp_max(self.config.context_length - 1)
x = self.token_embed(input_ids) + self.pos_embed(pos)
aux_loss = torch.tensor(0.0, device=input_ids.device)
z_loss = torch.tensor(0.0, device=input_ids.device)
present_key_values: list[tuple[Tensor, Tensor]] = []
for layer_idx, layer in enumerate(self.layers):
layer_past = past[layer_idx] if layer_idx < len(past) else None
x, layer_aux, layer_z, layer_present = layer(
x,
past_key_value=layer_past, # type: ignore[arg-type]
attention_mask=attention_mask,
use_cache=cache_enabled,
)
aux_loss = aux_loss + layer_aux
z_loss = z_loss + layer_z
if cache_enabled and layer_present is not None:
present_key_values.append(layer_present)
x = self.norm(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)),
labels.view(-1),
ignore_index=-100,
)
loss = loss + 0.01 * aux_loss + 0.001 * z_loss
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=tuple(present_key_values) if cache_enabled else None,
)
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