| import math |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from transformers import PreTrainedModel |
| from transformers.modeling_outputs import CausalLMOutputWithPast |
| from .configuration_sovythos import SovythosConfig |
|
|
|
|
| class RMSNorm(nn.Module): |
| def __init__(self, dim, eps=1e-06): |
| super().__init__() |
| self.eps = eps |
| self.weight = nn.Parameter(torch.ones(dim)) |
|
|
| def forward(self, x): |
| dtype = x.dtype |
| x = x.float() |
| rms = x.pow(2).mean(-1, keepdim=True) |
| x = x * torch.rsqrt(rms + self.eps) |
| return self.weight * x.to(dtype) |
|
|
|
|
| def precompute_rope(head_dim, max_seq_len, theta, device, dtype=torch.float32): |
| inv_freq = 1.0 / theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim) |
| t = torch.arange(max_seq_len, device=device).float() |
| freqs = torch.outer(t, inv_freq) |
| emb = torch.cat((freqs, freqs), dim=-1) |
| return emb.cos().to(dtype), emb.sin().to(dtype) |
|
|
|
|
| def rotate_half(x): |
| x1, x2 = x.chunk(2, dim=-1) |
| return torch.cat((-x2, x1), dim=-1) |
|
|
|
|
| def apply_rope(x, cos, sin, offset=0): |
| T = x.shape[-2] |
| cos = cos[offset:offset + T][None, None, :, :] |
| sin = sin[offset:offset + T][None, None, :, :] |
| return x * cos + rotate_half(x) * sin |
|
|
|
|
| def repeat_kv(x, n_rep): |
| if n_rep == 1: |
| return x |
| B, Hkv, T, D = x.shape |
| return x[:, :, None, :, :].expand(B, Hkv, n_rep, T, D).reshape(B, Hkv * n_rep, T, D) |
|
|
|
|
| class Attention(nn.Module): |
| def __init__(self, cfg): |
| super().__init__() |
| self.n_heads = cfg.n_heads |
| self.n_kv_heads = cfg.n_kv_heads |
| self.n_rep = self.n_heads // self.n_kv_heads |
| self.head_dim = cfg.dim // cfg.n_heads |
| self.dropout = cfg.dropout |
| self.q_proj = nn.Linear(cfg.dim, cfg.n_heads * self.head_dim, bias=False) |
| self.k_proj = nn.Linear(cfg.dim, cfg.n_kv_heads * self.head_dim, bias=False) |
| self.v_proj = nn.Linear(cfg.dim, cfg.n_kv_heads * self.head_dim, bias=False) |
| self.o_proj = nn.Linear(cfg.n_heads * self.head_dim, cfg.dim, bias=False) |
| self.q_norm = RMSNorm(self.head_dim, cfg.norm_eps) |
| self.k_norm = RMSNorm(self.head_dim, cfg.norm_eps) |
|
|
| def forward(self, x, cos, sin): |
| B, T, C = x.shape |
| q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2) |
| k = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2) |
| v = self.v_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2) |
| q, k = self.q_norm(q), self.k_norm(k) |
| q = apply_rope(q, cos, sin) |
| k = apply_rope(k, cos, sin) |
| k = repeat_kv(k, self.n_rep) |
| v = repeat_kv(v, self.n_rep) |
| out = F.scaled_dot_product_attention(q, k, v, is_causal=True, dropout_p=self.dropout if self.training else 0.0) |
| out = out.transpose(1, 2).contiguous().view(B, T, -1) |
| return self.o_proj(out) |
|
|
|
|
| class FeedForward(nn.Module): |
| def __init__(self, cfg): |
| super().__init__() |
| hidden = int(2 * (4 * cfg.dim) / 3) |
| if cfg.ffn_dim_multiplier is not None: |
| hidden = int(cfg.ffn_dim_multiplier * hidden) |
| hidden = cfg.ffn_multiple_of * ((hidden + cfg.ffn_multiple_of - 1) // cfg.ffn_multiple_of) |
| self.gate_proj = nn.Linear(cfg.dim, hidden, bias=False) |
| self.up_proj = nn.Linear(cfg.dim, hidden, bias=False) |
| self.down_proj = nn.Linear(hidden, cfg.dim, bias=False) |
|
|
| def forward(self, x): |
| return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) |
|
|
|
|
| class DecoderBlock(nn.Module): |
| def __init__(self, cfg): |
| super().__init__() |
| self.input_layernorm = RMSNorm(cfg.dim, cfg.norm_eps) |
| self.self_attn = Attention(cfg) |
| self.post_attention_layernorm = RMSNorm(cfg.dim, cfg.norm_eps) |
| self.mlp = FeedForward(cfg) |
|
|
| def forward(self, x, cos, sin): |
| x = x + self.self_attn(self.input_layernorm(x), cos, sin) |
| x = x + self.mlp(self.post_attention_layernorm(x)) |
| return x |
|
|
|
|
| class SovythosPreTrainedModel(PreTrainedModel): |
| config_class = SovythosConfig |
| base_model_prefix = "model" |
| supports_gradient_checkpointing = True |
|
|
| def _init_weights(self, module): |
| if isinstance(module, nn.Linear): |
| nn.init.normal_(module.weight, mean=0.0, std=0.02) |
| if module.bias is not None: |
| nn.init.zeros_(module.bias) |
| elif isinstance(module, nn.Embedding): |
| nn.init.normal_(module.weight, mean=0.0, std=0.02) |
|
|
|
|
| class SovythosForCausalLM(SovythosPreTrainedModel): |
| def __init__(self, config): |
| super().__init__(config) |
| self.embed_tokens = nn.Embedding(config.vocab_size, config.dim) |
| self.layers = nn.ModuleList([DecoderBlock(config) for _ in range(config.n_layers)]) |
| self.norm = RMSNorm(config.dim, config.norm_eps) |
| self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=False) |
| if config.tie_embeddings: |
| self.lm_head.weight = self.embed_tokens.weight |
| self.head_dim = config.dim // config.n_heads |
| self._rope_cache = {} |
| self.post_init() |
|
|
| def get_input_embeddings(self): |
| return self.embed_tokens |
|
|
| def set_input_embeddings(self, value): |
| self.embed_tokens = value |
|
|
| def get_output_embeddings(self): |
| return self.lm_head |
|
|
| def _rope_for(self, device, dtype): |
| |
| |
| |
| |
| key = str(device) |
| if key not in self._rope_cache: |
| cos, sin = precompute_rope(self.head_dim, self.config.max_seq_len, self.config.rope_theta, device=device) |
| self._rope_cache[key] = (cos, sin) |
| return self._rope_cache[key] |
|
|
| def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs): |
| cos, sin = self._rope_for(input_ids.device, torch.float32) |
| h = self.embed_tokens(input_ids) |
| for layer in self.layers: |
| h = layer(h, cos, sin) |
| h = self.norm(h) |
| logits = self.lm_head(h) |
| loss = None |
| if labels is not None: |
| shift_logits = logits[:, :-1, :].contiguous() |
| shift_labels = labels[:, 1:].contiguous() |
| loss = F.cross_entropy( |
| shift_logits.view(-1, shift_logits.size(-1)), |
| shift_labels.view(-1), |
| ignore_index=-100, |
| ) |
| return CausalLMOutputWithPast(loss=loss, logits=logits) |
|
|
| def prepare_inputs_for_generation(self, input_ids, **kwargs): |
| return {"input_ids": input_ids} |
