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import math
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from dataclasses import dataclass
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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class LlamaRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.eps = eps
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def forward(self, x):
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rms = torch.sqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
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x_norm = x / rms
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return self.weight * x_norm
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class LlamaRotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000):
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super().__init__()
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inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
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self.register_buffer("inv_freq", inv_freq)
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self.max_position_embeddings = max_position_embeddings
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self.dim = dim
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def forward(self, x, seq_len):
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t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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emb = torch.cat((freqs, freqs), dim=-1)
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return emb
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def rotate_half(x):
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x1, x2 = x[..., :x.shape[-1]//2], x[..., x.shape[-1]//2:]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
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cos = cos.unsqueeze(0).unsqueeze(0)
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sin = sin.unsqueeze(0).unsqueeze(0)
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cos = cos.expand(q.shape[0], q.shape[1], -1, -1)
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sin = sin.expand(k.shape[0], k.shape[1], -1, -1)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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class LlamaSdpaAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.hidden_size = config.n_embd
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self.num_heads = config.n_head
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self.head_dim = config.n_embd // config.n_head
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self.num_key_value_heads = config.n_head // 3
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
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self.rotary_emb = LlamaRotaryEmbedding(self.head_dim)
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def forward(self, x, attention_mask=None):
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B, T, C = x.size()
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q = self.q_proj(x).view(B, T, self.num_heads, self.head_dim)
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k = self.k_proj(x).view(B, T, self.num_key_value_heads, self.head_dim)
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v = self.v_proj(x).view(B, T, self.num_key_value_heads, self.head_dim)
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k = k.repeat_interleave(self.num_key_value_groups, dim=2)
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v = v.repeat_interleave(self.num_key_value_groups, dim=2)
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q = q.transpose(1, 2)
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k = k.transpose(1, 2)
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v = v.transpose(1, 2)
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rotary_emb = self.rotary_emb(x, T)
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cos, sin = rotary_emb.cos(), rotary_emb.sin()
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q, k = apply_rotary_pos_emb(q, k, cos, sin, None)
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out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
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out = out.transpose(1, 2).contiguous().view(B, T, C)
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return self.o_proj(out)
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class LlamaMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.gate_proj = nn.Linear(config.n_embd, config.intermediate_size, bias=False)
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self.up_proj = nn.Linear(config.n_embd, config.intermediate_size, bias=False)
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self.down_proj = nn.Linear(config.intermediate_size, config.n_embd, bias=False)
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self.act_fn = nn.SiLU()
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def forward(self, x):
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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class LlamaDecoderLayer(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.input_layernorm = LlamaRMSNorm(config.n_embd)
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self.self_attn = LlamaSdpaAttention(config)
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self.post_attention_layernorm = LlamaRMSNorm(config.n_embd)
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self.mlp = LlamaMLP(config)
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def forward(self, x):
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residual = x
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x = self.input_layernorm(x)
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x = self.self_attn(x)
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x = residual + x
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residual = x
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x = self.post_attention_layernorm(x)
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x = self.mlp(x)
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x = residual + x
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return x
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@dataclass
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class SmolLM2Config:
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block_size: int = 2048
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vocab_size: int = 49152
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n_layer: int = 30
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n_head: int = 9
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n_embd: int = 576
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intermediate_size: int = 1536
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num_key_value_heads: int = 3
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rms_norm_eps: float = 1e-5
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rope_theta: float = 10000.0
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initializer_range: float = 0.041666666666666664
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use_cache: bool = True
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class SmolLM2(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.embed_tokens = nn.Embedding(config.vocab_size, config.n_embd)
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self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.n_layer)])
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self.norm = LlamaRMSNorm(config.n_embd, eps=config.rms_norm_eps)
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self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
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self.embed_tokens.weight = self.lm_head.weight
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self.apply(self._init_weights)
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def _init_weights(self, module):
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if isinstance(module, nn.Linear):
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torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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torch.nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Embedding):
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torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
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def forward(self, idx, targets=None):
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B, T = idx.size()
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x = self.embed_tokens(idx)
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for layer in self.layers:
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x = layer(x)
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x = self.norm(x)
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logits = self.lm_head(x)
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loss = None
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if targets is not None:
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loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
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return logits, loss
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def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
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for _ in range(max_new_tokens):
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idx_cond = idx[:, -self.config.block_size:]
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logits, _ = self(idx_cond)
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logits = logits[:, -1, :] / temperature
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if top_k is not None:
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v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
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logits[logits < v[:, [-1]]] = float('-inf')
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probs = F.softmax(logits, dim=-1)
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idx_next = torch.multinomial(probs, num_samples=1)
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idx = torch.cat((idx, idx_next), dim=1)
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return idx |
