scripts version

src/char_gpt2_scaledmatmul.py

@@ -1,117 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from einops import rearrange
-import numpy as np
-from torch.utils.tensorboard import SummaryWriter
-from datetime import datetime
-import sys
-from pathlib import Path
-torch.manual_seed(1337)
-
-#################################### Model #########################################
-
-
-# RoFormer: Enhanced Transformer with Rotary Position Embedding https://arxiv.org/abs/2104.09864
-class RoPE(nn.Module):
-    def __init__(self, dim, max_seq_len=512):
-        super().__init__()
-        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        t = torch.arange(max_seq_len).type_as(inv_freq)
-        freqs = torch.einsum('i,j->ij', t, inv_freq)
-        self.register_buffer('emb', torch.cat([freqs, freqs], dim=-1))
-
-    def rotate_half(self, x):
-        x1, x2 = x.chunk(2, dim=-1)
-        return torch.cat((-x2, x1), dim=-1)
-
-    def forward(self, x, offset=0):
-        seq_len = x.size(1)
-        emb = self.emb[offset:offset+seq_len].view(1, seq_len, -1)
-        cos = emb.cos()
-        sin = emb.sin()
-        return (x * cos) + (self.rotate_half(x) * sin)
-
-# Transformers without Normalization https://jiachenzhu.github.io/DyT/
-class DyT(nn.Module):
-    def __init__(self, num_features, alpha_init_value=0.5):
-        super().__init__()
-        self.alpha = nn.Parameter(torch.ones(1) * alpha_init_value)
-        self.weight = nn.Parameter(torch.ones(num_features))
-        self.bias = nn.Parameter(torch.zeros(num_features))
-
-    def forward(self, x):
-        x = torch.tanh(self.alpha * x)
-        return x * self.weight + self.bias
-
-# Attention Is All You Need https://arxiv.org/pdf/1706.03762v7
-# NeoBERT: A Next-Generation BERT https://arxiv.org/html/2502.19587v1
-class TransformerLayer(nn.Module):
-    def __init__(self, h_dim, num_heads=4, dropout_rate = 0.1, max_seq_len = 128):
-        super().__init__()
-        self.__dict__.update({k:v for k,v in locals().items() if k != 'self'})
-        self.q_proj = nn.Linear(h_dim, h_dim)
-        self.k_proj = nn.Linear(h_dim, h_dim)
-        self.v_proj = nn.Linear(h_dim, h_dim)
-        self.o_proj = nn.Linear(h_dim, h_dim)
-        self.ff1 = nn.Linear(h_dim, 4*h_dim)
-        self.ff2 = nn.Linear(4*h_dim, h_dim)
-        self.ln1 = DyT(h_dim)
-        self.ln2 = DyT(h_dim)
-        self.rope = RoPE(dim=h_dim//self.num_heads, max_seq_len=max_seq_len)
-        self.k1 = nn.Parameter(torch.randn(1))
-        self.k2 = nn.Parameter(torch.randn(1))
-
-    def split_to_heads(self, x, B, T, H):
-        if self.num_heads <= 1: return x
-        return rearrange(x, 'b t (n h) -> (b n) t h', b=B, t=T, n=self.num_heads)
-
-    def gather_heads(self, x, B, T, H):
-        if self.num_heads <= 1: return x
-        return rearrange(x, '(b n) t h -> b t (n h)', b=B, t=T, n=self.num_heads)
-
-    def attention(self, x):
-        q = self.rope(self.split_to_heads(self.q_proj(x), *x.shape))
-        k = self.rope(self.split_to_heads(self.k_proj(x), *x.shape))
-        v = self.split_to_heads(self.v_proj(x), *x.shape)
-        scores = self.k1 * (q @ k.transpose(1, 2)) * (self.h_dim ** -0.5)
-        tril = torch.tril(torch.ones(x.shape[1],x.shape[1])).to(self.q_proj.bias.device)
-        scores = scores.masked_fill(tril == 0, float('-inf')) # encoder does not need this line
-        attention = nn.functional.softmax(scores, dim=2)
-        return self.o_proj(self.gather_heads(self.k2 * (attention @ v), *x.shape))
-
-    def forward(self, x):
-        x = x + F.dropout1d(self.attention(self.ln1(x)), p=self.dropout_rate)
-        x = x + F.dropout1d(self.ff2(F.gelu(self.ff1(self.ln2(x)))), p=self.dropout_rate)
-        return x
-
-class GPT2ScaledMM(nn.Module):
-    def __init__(self, vocab_size, layers_num=1, h_dim=64, max_seq_len=64, num_heads=1, dropout_rate = 0.1, pixel_size=None):
-        super().__init__()
-        self.__dict__.update({k:v for k,v in locals().items() if k != 'self'})
-        self.layers = nn.ModuleList([
-            TransformerLayer(h_dim=self.h_dim, num_heads=self.num_heads, dropout_rate=self.dropout_rate, max_seq_len=self.max_seq_len) 
-            for _ in range(layers_num)])
-        self.tok_embeds = nn.Embedding(vocab_size, h_dim)
-        self.pos_embeds = nn.Parameter(torch.randn(1, self.max_seq_len, h_dim))
-        self.lm_head = nn.Linear(h_dim, vocab_size)
-
-    def forward(self, x, targets=None):
-        x = self.tok_embeds(x) + self.pos_embeds[:, :x.shape[1], :]
-        for l in self.layers:
-            x = l(x)
-        logits = self.lm_head(x) # B,T,C
-        loss = F.cross_entropy(rearrange(logits, "b t c -> b c t"), targets) if not targets is None else None
-        return logits, loss
-
-    # what is the purpose? autoregressive inference!
-    def generate(self, start_idx, max_new_tokens):
-        idx = start_idx
-        for i in range(max_new_tokens):
-            idx_cond = idx[:,-self.max_seq_len:]
-            logits, loss = self(idx_cond)
-            logits = logits[:,-1,:] # B, C
-            probs = F.softmax(logits, dim=-1)
-            idx_next = torch.multinomial(probs, num_samples=1).to(self.lm_head.bias.device)
-            idx = torch.cat([idx, idx_next], dim=1)
-        return idx