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import os
import json
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, List, Tuple
from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
def norm(x):
return F.rms_norm(x, (x.size(-1),))
class CastedLinear(nn.Linear):
def __init__(self, in_features, out_features):
super().__init__(in_features, out_features, bias=False)
@torch.inference_mode()
def forward(self, x):
return F.linear(x, self.weight.type_as(x))
class Rotary(nn.Module):
def __init__(self, dim, max_seq_len=65536):
super().__init__()
angular_freq = (1 / 1024) ** torch.linspace(0, 1, steps=dim//4, dtype=torch.float32)
angular_freq = torch.cat([angular_freq, angular_freq.new_zeros(dim//4)])
t = torch.arange(max_seq_len, dtype=torch.float32)
theta = torch.einsum('i,j -> ij', t, angular_freq)
self.register_buffer('cos', theta.cos(), persistent=False)
self.register_buffer('sin', theta.sin(), persistent=False)
@torch.inference_mode()
def forward(self, x):
cos, sin = self.cos[None, :x.size(-3), None, :], self.sin[None, :x.size(-3), None, :]
x1, x2 = x.float().chunk(2, dim=-1)
y1 = x1 * cos + x2 * sin
y2 = x1 * (-sin) + x2 * cos
return torch.cat((y1, y2), 3).type_as(x)
class CausalSelfAttention(nn.Module):
def __init__(self, dim, num_heads):
super().__init__()
assert dim % num_heads == 0
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.c_q = CastedLinear(dim, dim)
self.c_k = CastedLinear(dim, dim)
self.c_v = CastedLinear(dim, dim)
self.lambdas = nn.Parameter(torch.tensor([0.5, 0.5]))
self.rotary = Rotary(self.head_dim)
self.c_proj = CastedLinear(dim, dim)
self.register_buffer('kv_cache', None, persistent=False)
@torch.inference_mode()
def forward(self, x, ve):
B, T = x.size(0), x.size(1)
q = self.c_q(x).view(B, T, self.num_heads, self.head_dim)
k = self.c_k(x).view(B, T, self.num_heads, self.head_dim)
v = self.c_v(x).view(B, T, self.num_heads, self.head_dim)
if ve is not None:
v = self.lambdas[0] * v + self.lambdas[1] * ve.view_as(v)
else:
v = self.lambdas[0] * v
q, k = norm(q), norm(k)
q, k = self.rotary(q), self.rotary(k)
if self.kv_cache is not None:
k = torch.cat([self.kv_cache[0], k], dim=1)
v = torch.cat([self.kv_cache[1], v], dim=1)
self.kv_cache = torch.stack([k, v])
if hasattr(F, 'scaled_dot_product_attention'):
y = F.scaled_dot_product_attention(
q.transpose(1, 2),
k.transpose(1, 2),
v.transpose(1, 2),
is_causal=True
)
else:
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(self.head_dim))
att = att.masked_fill(
torch.triu(torch.ones(T, T, device=x.device), diagonal=1).bool(),
float('-inf')
)
att = F.softmax(att, dim=-1)
y = att @ v
y = y.transpose(1, 2).contiguous().view(B, T, -1)
y = self.c_proj(y)
return y
class MLP(nn.Module):
def __init__(self, dim):
super().__init__()
self.c_fc = CastedLinear(dim, 4 * dim)
self.c_proj = CastedLinear(4 * dim, dim)
self.c_proj.weight.data.zero_()
@torch.inference_mode()
def forward(self, x):
x = self.c_fc(x)
x = F.relu(x).square()
x = self.c_proj(x)
return x
class Block(nn.Module):
def __init__(self, model_dim, num_heads, use_attn=True):
super().__init__()
self.attn = CausalSelfAttention(model_dim, num_heads) if use_attn else None
self.mlp = MLP(model_dim)
self.lambdas = nn.Parameter(torch.tensor([1., 0.]))
@torch.inference_mode()
def forward(self, x, ve, x0):
x = self.lambdas[0] * x + self.lambdas[1] * x0
if self.attn is not None:
x = x + self.attn(norm(x), ve)
x = x + self.mlp(norm(x))
return x
class ValueEmbedding(nn.Module):
def __init__(self, vocab_size, model_dim):
super().__init__()
self.embed = nn.ModuleList([nn.Embedding(vocab_size, model_dim) for _ in range(3)])
@torch.inference_mode()
def forward(self, inputs):
ve = [emb(inputs).bfloat16() for emb in self.embed]
ve = [ve[0], ve[1], ve[2], None, None, None, None, None, None, ve[0], ve[1], ve[2]]
return ve
class ChronoGPT(nn.Module, PyTorchModelHubMixin):
def __init__(self, vocab_size, num_layers, num_heads, model_dim, **kwargs):
super().__init__()
# Removed undefined "device" reference
self.num_heads = num_heads
self.vocab_size = vocab_size
self.embed = nn.Embedding(vocab_size, model_dim)
self.blocks = nn.ModuleList([Block(model_dim, num_heads, use_attn=(i != 7))
for i in range(num_layers)])
self.value_embeds = ValueEmbedding(vocab_size, model_dim)
self.lm_head = CastedLinear(model_dim, vocab_size)
self.lm_head.weight.data.zero_()
self.num_encoder_layers = num_layers // 2
self.num_decoder_layers = num_layers - self.num_encoder_layers
self.skip_weights = nn.Parameter(torch.ones(self.num_decoder_layers))
@torch.inference_mode()
def forward(self, inputs, past_key_values=None):
B = inputs.size(0)
if inputs.dim() == 1:
inputs = inputs.unsqueeze(0)
layer_outputs = []
x0 = norm(self.embed(inputs).bfloat16())
x = x0
layer_outputs.append(norm(x))
ve = [self.value_embeds(inputs[i].view(-1)) for i in range(B)]
ve = [torch.stack([ve[b][i] for b in range(B)]) if ve[0][i] is not None else None
for i in range(len(ve[0]))]
ve_enc, ve_dec = ve[:self.num_encoder_layers], ve[self.num_encoder_layers:]
if past_key_values is not None:
for i, block in enumerate(self.blocks):
if block.attn is not None:
block.attn.kv_cache = past_key_values[i]
present = []
skip_connections = []
for i in range(self.num_encoder_layers):
block = self.blocks[i]
x = block(x, ve_enc[i], x0)
if block.attn is not None:
present.append(block.attn.kv_cache)
block.attn.kv_cache = None
skip_connections.append(x)
layer_outputs.append(norm(x))
for i in range(self.num_decoder_layers):
x = x + self.skip_weights[i] * skip_connections.pop()
block = self.blocks[self.num_encoder_layers + i]
x = block(x, ve_dec[i], x0)
layer_outputs.append(norm(x))
if block.attn is not None:
present.append(block.attn.kv_cache)
block.attn.kv_cache = None
x = norm(x)
logits = self.lm_head(x)
logits = 15 * torch.tanh(logits / 15)
return logits.float(), layer_outputs
def save_pretrained(self, save_directory, **kwargs):
os.makedirs(save_directory, exist_ok=True)
torch.save(self.state_dict(), os.path.join(save_directory, "pytorch_model.bin"))
config = {
"model_type": "ChronoGPT",
"vocab_size": self.embed.num_embeddings,
"num_layers": len(self.blocks),
"num_heads": self.num_heads,
"model_dim": self.embed.embedding_dim
}
torch.save(config, os.path.join(save_directory, "config.pt"))
with open(os.path.join(save_directory, "config.json"), "w") as f:
json.dump(config, f)
@classmethod
def from_pretrained(cls, repo_id, cache_dir=None, **kwargs):
config_path = hf_hub_download(repo_id=repo_id, filename="config.pt", cache_dir=cache_dir)
bin_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin", cache_dir=cache_dir)
config = torch.load(config_path)
model = cls(**config)
model.load_state_dict(torch.load(bin_path))
return model |