From fe939900b540e95cd583950f42ae251c781bd14f Mon Sep 17 00:00:00 2001 From: mattsoh Date: Fri, 20 Sep 2024 20:09:25 +0100 Subject: [PATCH] changes --- helper.py | 3 +- helper_simple.py | 155 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 157 insertions(+), 1 deletion(-) create mode 100644 helper_simple.py diff --git a/helper.py b/helper.py index 2449082..f17bf71 100644 --- a/helper.py +++ b/helper.py @@ -212,8 +212,9 @@ def generate_and_print_sample(model, tokenizer, device, start_context): with torch.no_grad(): token_ids = generate(model=model, idx=encoded, max_new_tokens=100, context_size=context_size) decoded_text = token_ids_to_text(token_ids, tokenizer) - print(decoded_text.replace("\n", " ")) + # print(decoded_text.replace("\n", " ")) model.train() + return decoded_text def assign(left, right): if left.shape != right.shape: diff --git a/helper_simple.py b/helper_simple.py new file mode 100644 index 0000000..2e10fc4 --- /dev/null +++ b/helper_simple.py @@ -0,0 +1,155 @@ +import torch +import torch.nn as nn + +class MultiHeadAttn(nn.Module): + def __init__(self, d_in, d_out, ctx_len, dropout, num_heads, qkv_bias=False): + super().__init__() + assert d_out % num_heads == 0, "d_out must be divisible by num_heads" + self.d_out = d_out + self.num_heads = num_heads + self.head_dim = d_out // num_heads + self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias) + self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias) + self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias) + self.out_proj = nn.Linear(d_out, d_out) + self.dropout = nn.Dropout(dropout) + self.register_buffer('mask', torch.triu(torch.ones(ctx_len, ctx_len), diagonal=1)) + + def forward(self, x): + b, num_tokens, d_in = x.shape + keys = self.W_key(x).view(b, num_tokens, self.num_heads, self.head_dim).transpose(1, 2) + queries = self.W_query(x).view(b, num_tokens, self.num_heads, self.head_dim).transpose(1, 2) + values = self.W_value(x).view(b, num_tokens, self.num_heads, self.head_dim).transpose(1, 2) + attn_scores = queries @ keys.transpose(2, 3) + mask_bool = self.mask.bool()[:num_tokens, :num_tokens] + attn_scores.masked_fill_(mask_bool, -torch.inf) + attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1) + attn_weights = self.dropout(attn_weights) + context_vec = (attn_weights @ values).transpose(1, 2).reshape(b, num_tokens, self.d_out) + return self.out_proj(context_vec) + +class FeedForward(nn.Module): + def __init__(self, cfg): + super().__init__() + self.layers = nn.Sequential( + nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]), + GELU(), + nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]), + ) + + def forward(self, x): + return self.layers(x) + +class GELU(nn.Module): + def forward(self, x): + return 0.5 * x * (1 + torch.tanh( + torch.sqrt(torch.tensor(2.0 / torch.pi)) * (x + 0.044715 * x**3) + )) + +class LayerNorm(nn.Module): + def __init__(self, emb_dim): + super().__init__() + self.eps = 1e-5 + self.scale = nn.Parameter(torch.ones(emb_dim)) + self.shift = nn.Parameter(torch.zeros(emb_dim)) + + def forward(self, x): + mean = x.mean(dim=-1, keepdim=True) + var = x.var(dim=-1, keepdim=True, unbiased=False) + norm_x = (x - mean) / torch.sqrt(var + self.eps) + return self.scale * norm_x + self.shift + +class TransformerBlock(nn.Module): + def __init__(self, cfg): + super().__init__() + self.att = MultiHeadAttn( + d_in=cfg["emb_dim"], + d_out=cfg["emb_dim"], + ctx_len=cfg["context_length"], + num_heads=cfg["n_heads"], + dropout=cfg["drop_rate"], + qkv_bias=cfg["qkv_bias"]) + self.ff = FeedForward(cfg) + self.norm1 = LayerNorm(cfg["emb_dim"]) + self.norm2 = LayerNorm(cfg["emb_dim"]) + self.drop_resid = nn.Dropout(cfg["drop_rate"]) + + def forward(self, x): + save = x + x = self.norm1(x) + x = self.att(x) + x = self.drop_resid(x) + x += save + save = x + x = self.norm2(x) + x = self.ff(x) + x = self.drop_resid(x) + x += save + return x + +class GPT(nn.Module): + def __init__(self, cfg): + super().__init__() + self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"]) + self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"]) + self.drop_emb = nn.Dropout(cfg["drop_rate"]) + self.trf_blocks = nn.Sequential( + *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])] + ) + self.final_norm = LayerNorm(cfg["emb_dim"]) + self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False) + + def forward(self, in_idx): + batch_size, seq_len = in_idx.shape + tok_embeds = self.tok_emb(in_idx) + pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device)) + x = tok_embeds + pos_embeds + x = self.drop_emb(x) + x = self.trf_blocks(x) + x = self.final_norm(x) + return self.out_head(x) + +def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None): + for _ in range(max_new_tokens): + idx_cond = idx[:, -context_size:] + with torch.no_grad(): + logits = model(idx_cond) + logits = logits[:, -1, :] + + if top_k is not None: + top_logits, _ = torch.topk(logits, top_k) + min_val = top_logits[:, -1] + logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits) + + if temperature > 0.0: + logits = logits / temperature + probs = torch.softmax(logits, dim=-1) + idx_next = torch.multinomial(probs, num_samples=1) + else: + idx_next = torch.argmax(logits, dim=-1, keepdim=True) + + if eos_id is not None and idx_next == eos_id: + break + + idx = torch.cat((idx, idx_next), dim=1) + + return idx + +def generate_and_print_sample(model, tokenizer, device, start_context): + model.eval() + context_size = model.pos_emb.weight.shape[0] + encoded = text_to_token_ids(start_context, tokenizer).to(device) + with torch.no_grad(): + token_ids = generate(model=model, idx=encoded, max_new_tokens=100, context_size=context_size) + decoded_text = token_ids_to_text(token_ids, tokenizer) + model.train() + return decoded_text + +def text_to_token_ids(text, tokenizer): + encoded = tokenizer.encode(text, allowed_special={"<|endoftext|>"}) + encoded_tensor = torch.tensor(encoded).unsqueeze(0) + return encoded_tensor + +def token_ids_to_text(token_ids, tokenizer): + flat = token_ids.squeeze(0) + return tokenizer.decode(flat.tolist()) \ No newline at end of file