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5eb1cd7 6508755 5eb1cd7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | from typing import Optional
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, eps=1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
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, max_position_embeddings=2048, base=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, seq_len):
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):
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)
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
class SwiGLU(nn.Module):
def __init__(self, config):
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):
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class GroupedQueryAttention(nn.Module):
def __init__(self, config):
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, rot_cos, rot_sin):
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):
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, rot_cos, rot_sin):
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):
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 _init_weights(self, module):
std = 0.02
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
def forward(self, input_ids, labels=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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