"sum(param.numel() for param in model.parameters())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## P-tuning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PEFT Step1 配置文件" ] }, { "cell_type": "code", "execution_cou...
print(f"Number of parameters:{sum(p.numel()forpinmodel.parameters()ifp.requires_grad)}") torch.random.manual_seed(0) ifargs.promptisNone: input_ids=torch.randint(1,1000, (args.batch,args.promptlen),dtype=torch.long,device="cuda") ...
print_trainable_parameters() else: def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): num_params = param.numel() # if using DS Zero 3 and the weights ...
FUNCTION testTableFunction(p_name XSQL_TABLE_OF_VARCHAR, p_value XSQL_TABLE_OF_VARCHAR) RETURN VARCHAR2 IS lv_ret VARCHAR2(4000); lv_numElts INTEGER; BEGIN IF p_name IS NOT NULL THEN lv_numElts := p_name.COUNT; FOR j IN 1..lv_numElts LOOP IF (j > 1) THEN lv_ret := lv_...
print_trainable_parameters() else: def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): num_params = param.numel() # if using DS Zero 3 and the weights ...
[]) else: model = AutoModelForCausalLM.from_config(config) n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values()) logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params") # We resize the embeddings only when ...
(self.pre_seq_len,PRE_TRAIN_CONFIG.num_hidden_layers,PRE_TRAIN_CONFIG.hidden_size)requires_grad_param=0total_param=0forname,paraminself.named_parameters():total_param+=param.numel()ifparam.requires_grad:requires_grad_param+=param.numel()print('totalparam:{},trainable param:{},trainable/total...
# as different ranks might have different number of parameters (e.g., only rank 0 has bias). params_seqparallel = { name: p for name, p in model.named_parameters() if getattr(p, "_sequence_parallel", False) } grads = [p.grad for _, p in sorted(params_seqparallel.items())] ...
for name, param in self.deberta.named_parameters(): deberta_param += param.numel() all_param = 0 for name, param in self.named_parameters(): all_param += param.numel() total_param = all_param - deberta_param print('total param is {}'.format(total_param)) # 9860105 de...