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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from .configuration_modern_llm import ModernLLMConfig
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
variance = x.pow(2).mean(-1, keepdim=True)
return x * torch.rsqrt(variance + self.eps) * self.weight
class RotaryEmbedding(nn.Module):
def __init__(self, dim: int, max_position_embeddings: int = 2048, base: float = 1000000.0):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
def forward(self, x: torch.Tensor, seq_len: int):
t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
freqs = torch.outer(t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
return emb.cos(), emb.sin()
def rotate_half(x: torch.Tensor) -> torch.Tensor:
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin):
cos = cos.unsqueeze(0).unsqueeze(2).to(q.dtype)
sin = sin.unsqueeze(0).unsqueeze(2).to(q.dtype)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class SwiGLU(nn.Module):
def __init__(self, config: ModernLLMConfig):
super().__init__()
self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class GroupedQueryAttention(nn.Module):
def __init__(self, config: ModernLLMConfig):
super().__init__()
self.num_heads = config.num_attention_heads
self.head_dim = config.hidden_size // config.num_attention_heads
self.num_kv_heads = config.num_key_value_heads
self.num_kv_groups = self.num_heads // self.num_kv_heads
self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=False)
def forward(self, x: torch.Tensor, rot_cos: torch.Tensor, rot_sin: torch.Tensor) -> torch.Tensor:
batch_size, seq_len, _ = x.shape
q = self.q_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim)
k = self.k_proj(x).view(batch_size, seq_len, self.num_kv_heads, self.head_dim)
v = self.v_proj(x).view(batch_size, seq_len, self.num_kv_heads, self.head_dim)
q, k = apply_rotary_pos_emb(q, k, rot_cos, rot_sin)
k = k.repeat_interleave(self.num_kv_groups, dim=2)
v = v.repeat_interleave(self.num_kv_groups, dim=2)
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
out = out.transpose(1, 2).contiguous().view(batch_size, seq_len, -1)
return self.o_proj(out)
class TransformerBlock(nn.Module):
def __init__(self, config: ModernLLMConfig):
super().__init__()
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.self_attn = GroupedQueryAttention(config)
self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.mlp = SwiGLU(config)
def forward(self, x: torch.Tensor, rot_cos: torch.Tensor, rot_sin: torch.Tensor) -> torch.Tensor:
x = x + self.self_attn(self.input_layernorm(x), rot_cos, rot_sin)
x = x + self.mlp(self.post_attention_layernorm(x))
return x
class ModernLLMForCausalLM(PreTrainedModel):
config_class = ModernLLMConfig
def __init__(self, config: ModernLLMConfig):
super().__init__(config)
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([TransformerBlock(config) for _ in range(config.num_hidden_layers)])
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.rotary_emb = RotaryEmbedding(
config.hidden_size // config.num_attention_heads,
config.max_position_embeddings,
config.rope_theta,
)
self.post_init()
def forward(self, input_ids: torch.LongTensor, labels: Optional[torch.LongTensor] = None, **kwargs):
_, seq_len = input_ids.shape
x = self.embed_tokens(input_ids)
cos, sin = self.rotary_emb(x, seq_len)
for layer in self.layers:
x = layer(x, cos, sin)
x = self.norm(x)
logits = self.lm_head(x)
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, self.config.vocab_size), shift_labels.view(-1))
return {"loss": loss, "logits": logits}
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