num_parameters: 1557686400 => bytes: 3115372800 allocated 2971 MiB for model parameters batch_size B=16 * seq_len T=1024 * num_processes=8 and total_batch_size=1048576 => setting grad_accum_steps=8 created directory: log_gpt2_1558M allocating 40409 MiB for activations val loss 11.129390 ...
dim=1) return packed_tensor, True, None现在，我们可以创建训练函数，使用我们所有的歌词来微调GPT-2，这样它就可以预测未来高质量的歌词。def train(dataset, model, tokenizer, batch_size=16, epochs=5, lr=2e-5, max_seq_len=400, warmup_steps=200, gpt2_type="gpt2", output_dir="...
channels: 768 num_parameters: 124439808 train dataset num_batches: 1192 val dataset num_batches: 128 num_activations: 73323776 val loss 5.252026 step 0: train loss 5.356189 (took 1452.121000 ms)step 1: train loss 4.301069 (took 1288.673000 ms)step 2: train loss 4.623322 (took 1369.3...
parameters(), grad_clip) # 步进优化器并更新scaler scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) # 计时部分 t1 = time.time() dt = t1 - t0 t0 = t1 if iter_num % log_interval == 0 and master_process: # 获取近似的总损失之和, 由于log_interval在初始...
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4) for i in range(50): x, y = train_loader.next_batch() x, y = x.to(device), y.to(device) optimizer.zero_grad() #have to start with a zero gradient logits, loss = model(x, y) loss.backward() #adds to the gradient...
model parallel seed: 3952 and data parallel seed: 1234 configuring data > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432) > found end-of-document token: 50256 building GPT2 model ... > number of parameters on model parallel rank 0: 178100224 > number of parameters ...
[GPT-2]max_seq_len: 1024vocab_size: 50257num_layers: 12num_heads: 12channels: 768num_parameters: 124439808train dataset num_batches: 1192val dataset num_batches: 128num_activations: 73323776val loss 5.252026step 0: train loss 5.356189 (took 1452.121000 ms)step 1: train loss 4.301069 (...
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)# 定义优化器foriinrange(epoch): total_loss =0forbatch_idx, (data, target)inenumerate(train_loader): data, target = Variable(data).to(device), Variable( target).to(device) ...
lr =1e-3optim = torch.optim.AdamW(m.parameters(), lr=lr) Belowisa very simple training loop. epochs =5000eval_steps =1000# perform evaluation in every n stepsforepinrange(epochs): xb, yb = train_loader.get_batch() logits, loss = m(xb, yb) ...
optimizer = paddle.optimizer.Adam(parameters=model.parameters()) 训练循环 for epoch in range(num_epochs): for batch in dataloader: # 获取输入数据 inputs, targets = batch # 前向传播 outputs = model(inputs) # 计算损失 loss = paddle.nn.functional.cross_entropy(outputs, targets) # 反向传播和...