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import torch
import torch.nn as nn
import math
from transformers import PreTrainedModel, PreTrainedConfig
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
class NanoGPTCompressedConfig(PreTrainedConfig):
model_type = "nanogpt_compressed"
def __init__(
self,
vocab_size=6060,
block_size=1024,
n_layer=8,
n_head=8,
n_embd=512,
dropout=0.0,
bias=True,
compression_method="fixed_low_rank_mlp",
compression_rank=128,
compressed_layers=[1],
**kwargs
):
self.vocab_size = vocab_size
self.block_size = block_size
self.n_layer = n_layer
self.n_head = n_head
self.n_embd = n_embd
self.dropout = dropout
self.bias = bias
self.compression_method = compression_method
self.compression_rank = compression_rank
self.compressed_layers = compressed_layers
super().__init__(**kwargs)
class LowRankLinear(nn.Module):
def __init__(self, input_dim, output_dim, rank=16, bias=True):
super().__init__()
self.rank = rank
self.input_dim = input_dim
self.output_dim = output_dim
self.U = nn.Parameter(torch.randn(input_dim, rank) * 0.02)
self.V = nn.Parameter(torch.randn(rank, output_dim) * 0.02)
if bias:
self.bias = nn.Parameter(torch.zeros(output_dim))
else:
self.register_parameter('bias', None)
def forward(self, x):
result = (x @ self.U) @ self.V
if self.bias is not None:
result = result + self.bias
return result
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.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.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):
B, T, C = x.size()
q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
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
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, layer_idx=None):
super().__init__()
self.layer_idx = layer_idx
# Check if this layer should be compressed
if (hasattr(config, 'compressed_layers') and
layer_idx is not None and
layer_idx in config.compressed_layers):
print(f"Creating compressed MLP for layer {layer_idx}")
rank = getattr(config, 'compression_rank', 128)
self.c_fc = LowRankLinear(config.n_embd, 4 * config.n_embd, rank, bias=config.bias)
self.c_proj = LowRankLinear(4 * config.n_embd, config.n_embd, rank, bias=config.bias)
else:
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
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 = F.gelu(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
class Block(nn.Module):
def __init__(self, config, layer_idx=None):
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, layer_idx=layer_idx)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class NanoGPTCompressedModel(PreTrainedModel):
config_class = NanoGPTCompressedConfig
def __init__(self, config):
super().__init__(config)
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, layer_idx=i) for i 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)
# Share weights
self.transformer.wte.weight = self.lm_head.weight
# Initialize weights
self.apply(self._init_weights)
for pn, p in self.named_parameters():
if pn.endswith('c_proj.weight') or pn.endswith('c_proj.V'):
torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))
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, f"Cannot forward sequence of length {t}, block size is only {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 CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=None,
hidden_states=None,
attentions=None,
cross_attentions=None,
)
@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 = 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
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