| 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 BVVConfig(PretrainedConfig):
|
| model_type = "model_256_bit"
|
|
|
| def __init__(
|
| self,
|
| vocab_size = 65536,
|
| n_embed = 256,
|
| d_model = 1024,
|
| n_head = 32,
|
| n_layer = 16,
|
| block_size = 1024,
|
| pad_id = 57344,
|
| **kwargs
|
| ):
|
| super().__init__(**kwargs)
|
| self.vocab_size = vocab_size
|
| self.block_size = block_size
|
| self.n_embed = n_embed
|
| self.d_model = d_model
|
| self.n_layer = n_layer
|
| self.n_head = n_head
|
| self.pad_id = pad_id
|
| self.scale = d_model // n_embed
|
|
|
|
|
| class RotaryEmbedding(nn.Module):
|
| def __init__(self, dim):
|
| 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)
|
| return emb
|
|
|
| def apply_rotary_emb(x, rot_emb):
|
|
|
|
|
| seq_len = x.shape[-2]
|
| rot_emb = rot_emb[:seq_len]
|
|
|
| cos = torch.cos(rot_emb).unsqueeze(0).unsqueeze(0)
|
| sin = torch.sin(rot_emb).unsqueeze(0).unsqueeze(0)
|
|
|
| x_shape = x.shape
|
| x = x.reshape(*x_shape[:-1], -1, 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, d_model, n_head, block_size):
|
| super().__init__()
|
| assert d_model % n_head == 0
|
| self.d_model = d_model
|
| self.n_head = n_head
|
| self.head_dim = d_model // n_head
|
|
|
| self.q_proj = nn.Linear(d_model, d_model, bias=False)
|
| self.k_proj = nn.Linear(d_model, d_model, bias=False)
|
| self.v_proj = nn.Linear(d_model, d_model, bias=False)
|
| self.o_proj = nn.Linear(d_model, d_model, 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):
|
|
|
| B, T, C = x.shape
|
|
|
| q = self.q_proj(x)
|
| k = self.k_proj(x)
|
| v = self.v_proj(x)
|
|
|
| q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
|
| 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)
|
|
|
|
|
| rot_emb = self.rotary_emb(seq_len=T, device=x.device)
|
| q = apply_rotary_emb(q, rot_emb)
|
| k = apply_rotary_emb(k, rot_emb)
|
|
|
|
|
| attn_scores = torch.matmul(q, k.transpose(-2, -1)) * (self.head_dim ** -0.5)
|
| 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)
|
| out = out.transpose(1, 2).contiguous().view(B, T, C)
|
|
|
| return self.o_proj(out)
|
|
|
|
|
| class TransformerMLP(nn.Module):
|
| def __init__(self, d_model):
|
| super().__init__()
|
| self.net = nn.Sequential(
|
| nn.Linear(d_model, 4 * d_model),
|
| nn.GELU(),
|
| nn.Linear(4 * d_model, d_model),
|
| nn.Dropout(0.0),
|
| )
|
|
|
| def forward(self, x):
|
| return self.net(x)
|
|
|
| class TransformerBlock(nn.Module):
|
| def __init__(self, d_model, n_head, block_size):
|
| super().__init__()
|
| self.self_attn = MultiHeadSelfAttention(d_model, n_head, block_size)
|
| self.mlp = TransformerMLP(d_model)
|
| self.input_layernorm = nn.LayerNorm(d_model)
|
| self.post_attention_layernorm = nn.LayerNorm(d_model)
|
|
|
| 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 BVVForCausalLM(PreTrainedModel):
|
| config_class = BVVConfig
|
|
|
| def __init__(self, config):
|
| super().__init__(config)
|
| self.token_embeddings = nn.Embedding(config.vocab_size, config.n_embed)
|
| self.scale = config.scale
|
| self.transformer_layers = nn.Sequential(*[
|
| TransformerBlock(config.d_model, n_head=config.n_head, block_size=config.block_size) for _ in range(config.n_layer)
|
| ])
|
| self.final_layernorm = nn.LayerNorm(config.d_model)
|
| self.lm_head = nn.Linear(config.d_model, 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
|
|
|
| token_emb = self.token_embeddings(idx)
|
|
|
| token_emb = token_emb.repeat_interleave(self.scale, dim=-1)
|
|
|
| x = token_emb
|
|
|
| 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.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 |
