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from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
import torch
import torch.nn as nn
from torch.nn import functional as F
from transformers.modeling_outputs import CausalLMOutput
class BVVAbsConfig(PretrainedConfig):
model_type = "bvv_abs"
def __init__(
self,
vocab_size = 131072,
n_embd = 4096,
n_head = 32,
n_layer = 4,
block_size = 1024,
pad_id = 57344,
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.block_size = block_size
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.pad_id = pad_id
class RotaryEmbedding(nn.Module):
def __init__(self, dim): # dim = head_dim (?? n_embd!)
super().__init__()
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
def forward(self, seq_len, device):
t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
freqs = torch.einsum('i,j->ij', t, self.inv_freq)
emb = torch.cat([freqs, freqs], dim=-1) # (seq_len, dim)
return emb
def apply_rotary_emb(x, rot_emb):
# x: (B, n_head, seq_len, head_dim)
# rot_emb: (seq_len, head_dim)
seq_len = x.shape[-2]
rot_emb = rot_emb[:seq_len]
cos = torch.cos(rot_emb).unsqueeze(0).unsqueeze(0) # (1, 1, seq_len, head_dim)
sin = torch.sin(rot_emb).unsqueeze(0).unsqueeze(0)
x_shape = x.shape
x = x.reshape(*x_shape[:-1], -1, 2) # (..., head_dim/2, 2)
x1 = x[..., 0]
x2 = x[..., 1]
cos = cos.reshape(*cos.shape[:-1], -1, 2)[..., 0]
sin = sin.reshape(*sin.shape[:-1], -1, 2)[..., 0]
x1_rot = x1 * cos - x2 * sin
x2_rot = x1 * sin + x2 * cos
x_rot = torch.stack([x1_rot, x2_rot], dim=-1)
return x_rot.reshape(x_shape)
class MultiHeadSelfAttention(nn.Module):
def __init__(self, n_embd, n_head, block_size):
super().__init__()
assert n_embd % n_head == 0
self.n_embd = n_embd
self.n_head = n_head
self.head_dim = n_embd // n_head
self.q_proj = nn.Linear(n_embd, n_embd, bias=False)
self.k_proj = nn.Linear(n_embd, n_embd, bias=False)
self.v_proj = nn.Linear(n_embd, n_embd, bias=False)
self.o_proj = nn.Linear(n_embd, n_embd, bias=False)
self.rotary_emb = RotaryEmbedding(self.head_dim)
self.dropout = nn.Dropout(0.0)
self.register_buffer(
"tril", torch.tril(torch.ones(block_size, block_size)), persistent=False
)
def forward(self, x):
# x: (B, T, n_embd)
B, T, C = x.shape
q = self.q_proj(x) # (B, T, n_embd)
k = self.k_proj(x)
v = self.v_proj(x)
q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2) # (B, n_head, T, head_dim)
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)
# Rotary embeddings
rot_emb = self.rotary_emb(seq_len=T, device=x.device) # (T, head_dim)
q = apply_rotary_emb(q, rot_emb)
k = apply_rotary_emb(k, rot_emb)
# Attention
attn_scores = torch.matmul(q, k.transpose(-2, -1)) * (self.head_dim ** -0.5) # (B, n_head, T, T)
attn_scores = attn_scores.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
attn_probs = F.softmax(attn_scores, dim=-1)
attn_probs = self.dropout(attn_probs)
out = torch.matmul(attn_probs, v) # (B, n_head, T, head_dim)
out = out.transpose(1, 2).contiguous().view(B, T, C) # (B, T, n_embd)
return self.o_proj(out)
class TransformerMLP(nn.Module):
def __init__(self, n_embd):
super().__init__()
self.net = nn.Sequential(
nn.Linear(n_embd, 4 * n_embd),
nn.GELU(),
nn.Linear(4 * n_embd, n_embd),
nn.Dropout(0.0),
)
def forward(self, x):
return self.net(x)
class TransformerBlock(nn.Module):
def __init__(self, n_embd, n_head, block_size):
super().__init__()
self.self_attn = MultiHeadSelfAttention(n_embd, n_head, block_size)
self.mlp = TransformerMLP(n_embd)
self.input_layernorm = nn.LayerNorm(n_embd)
self.post_attention_layernorm = nn.LayerNorm(n_embd)
def forward(self, x):
x = x + self.self_attn(self.input_layernorm(x))
x = x + self.mlp(self.post_attention_layernorm(x))
return x
class BVVAbsForCausalLM(PreTrainedModel):
config_class = BVVAbsConfig
def __init__(self, config):
super().__init__(config)
self.token_embeddings = nn.Embedding(config.vocab_size, config.n_embd)
self.transformer_layers = nn.Sequential(*[
TransformerBlock(config.n_embd, n_head=config.n_head, block_size=config.block_size) for _ in range(config.n_layer)
])
self.final_layernorm = nn.LayerNorm(config.n_embd)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
self.apply(self._init_weights)
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):
B, T = idx.shape
x = self.token_embeddings(idx)
x = self.transformer_layers(x)
x = self.final_layernorm(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
#logits_flat = logits.view(-1, logits.size(-1))
#targets_flat = targets.view(-1)
logits_flat = logits.reshape(-1, logits.size(-1))
targets_flat = targets.reshape(-1)
loss = F.cross_entropy(logits_flat, targets_flat, ignore_index = 57344)
return CausalLMOutput(
logits=logits,
loss=loss,
)
def generate(self,
input_ids=None,
max_new_tokens=None,
max_length=None,
temperature=1.0,
top_k=None,
top_p=None,
do_sample=True,
pad_token_id=None,
eos_token_id=None,
**kwargs):
if input_ids is None:
raise ValueError("Input_ids must be provided")
idx = input_ids
if max_new_tokens is None:
if max_length is not None:
max_new_tokens = max_length - idx.shape[1]
else:
max_new_tokens = 50
with torch.no_grad():
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.config.block_size:]
outputs = self(idx_cond)
logits = outputs.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')
if top_p is not None:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
logits[indices_to_remove] = float('-inf')
probs = F.softmax(logits, dim=-1)
if do_sample:
idx_next = torch.multinomial(probs, num_samples=1)
else:
idx_next = torch.argmax(logits, dim=-1, keepdim=True)
idx = torch.cat((idx, idx_next), dim=1)
if eos_token_id is not None and (idx_next == eos_token_id).any():
break
return idx |