modeling_gator.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from transformers import PreTrainedModel
from .configuration_gator import GatorConfig

class RMSNorm(nn.Module):
    def __init__(self, dim, eps=1e-5):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
    def forward(self, x):
        norm = x.norm(2, dim=-1, keepdim=True) / math.sqrt(x.shape[-1])
        return self.weight * (x / (norm + self.eps))

class Rope(nn.Module):
    def __init__(self, d_model, max_len=1024):
        super().__init__()
        assert d_model % 2 == 0
        self.register_buffer("pos", torch.arange(max_len).unsqueeze(1))
        self.register_buffer("inv_freq", torch.exp(
            torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model)))
    def forward(self, x):
        t = x.size(1)
        freqs = self.pos[:t] * self.inv_freq
        cos, sin = torch.cos(freqs), torch.sin(freqs)
        x = x.view(*x.shape[:-1], -1, 2)
        x1, x2 = x[...,0], x[...,1]
        x_rot = torch.stack([x1*cos - x2*sin, x1*sin + x2*cos], dim=-1)
        return x_rot.view(*x.shape[:-2], -1)

class GQA(nn.Module):
    def __init__(self, d_model, n_heads, gqa_groups, max_len):
        super().__init__()
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.n_kv = n_heads // gqa_groups
        self.q_proj = nn.Linear(d_model, n_heads*self.head_dim, bias=False)
        self.k_proj = nn.Linear(d_model, self.n_kv*self.head_dim, bias=False)
        self.v_proj = nn.Linear(d_model, self.n_kv*self.head_dim, bias=False)
        self.o_proj = nn.Linear(d_model, d_model, bias=False)
        self.rope_q = Rope(n_heads*self.head_dim, max_len)
        self.rope_k = Rope(self.n_kv*self.head_dim, max_len)
    def forward(self, x):
        B,T,C = x.shape
        q = self.rope_q(self.q_proj(x)).view(B,T,self.n_heads,self.head_dim).transpose(1,2)
        k = self.rope_k(self.k_proj(x)).view(B,T,self.n_kv,self.head_dim).transpose(1,2)
        v = self.v_proj(x).view(B,T,self.n_kv,self.head_dim).transpose(1,2)
        expand = self.n_heads // self.n_kv
        k = k.repeat_interleave(expand, dim=1)
        v = v.repeat_interleave(expand, dim=1)
        attn = torch.softmax((q @ k.transpose(-2,-1))/math.sqrt(self.head_dim), dim=-1)
        out = attn @ v
        out = out.transpose(1,2).contiguous().view(B,T,C)
        return self.o_proj(out)

class MLP(nn.Module):
    def __init__(self, d_model, d_ff):
        super().__init__()
        self.fc1 = nn.Linear(d_model, 2*d_ff, bias=False)
        self.fc2 = nn.Linear(d_ff, d_model, bias=False)
    def forward(self,x):
        up, gate = self.fc1(x).chunk(2, dim=-1)
        return self.fc2(up * F.silu(gate))

class Block(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.rms1 = RMSNorm(cfg.hidden_size)
        self.rms2 = RMSNorm(cfg.hidden_size)
        self.attn = GQA(cfg.hidden_size, cfg.num_attention_heads, 2, cfg.max_position_embeddings)
        self.mlp = MLP(cfg.hidden_size, 2*cfg.hidden_size)
    def forward(self,x):
        x = x + self.attn(self.rms1(x))
        x = x + self.mlp(self.rms2(x))
        return x

class GatorModel(PreTrainedModel):
    config_class = GatorConfig
    def __init__(self, config):
        super().__init__(config)
        self.embed = nn.Embedding(config.vocab_size, config.hidden_size)
        self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_hidden_layers)])
        self.norm = RMSNorm(config.hidden_size)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.lm_head.weight = self.embed.weight
    def forward(self, input_ids):
        h = self.embed(input_ids)
        for blk in self.blocks: h = blk(h)
        h = self.norm(h)
        return {"logits": self.lm_head(h)}

class GatorForCausalLM(PreTrainedModel):
    config_class = GatorConfig
    base_model_prefix = "gator"

    def __init__(self, config):
        super().__init__(config)
        self.gator = GatorModel(config)
        self.post_init()

    @torch.no_grad()
    def forward(self, input_ids, temperature=0.8, top_k=5):
        logits = self.gator(input_ids)["logits"][:, -1, :] / temperature
        topk = torch.topk(logits, k=top_k, dim=-1)
        probs = torch.softmax(topk.values, dim=-1)
        next_token = topk.indices.gather(-1, torch.multinomial(probs, 1))
        return next_token.squeeze().item()