bias = bias if bias is None else convert_to_tensor(bias) mask = mask if mask is None else convert_to_tensor(mask, dtype="bool") if mask is not None: # Explicit set `is_causal` to `False` when `mask` is not `None`. is_causal = False mask = torch.where(mask, 0.0, _get_la...
src/tgt/memory_key_padding_mask(可选): 键填充掩码。 src/tgt/memory_is_causal(可选): 指定是否应用因果掩码。 输出 输出Tensor 的形状为(T, N, E)或(N, T, E)(如果batch_first=True),其中T是目标序列长度,N是批次大小,E是特征数。 示例代码 import torch import torch.nn as nn # 创建 Transfo...
使用pytorch的基本张量操作和基础函数实现transformersCausalDecoder的前向计算过程 """实现transformer CausalDecoder 和一个简易的GPT参考:1. pytorch官方文档 https://pytorch.org/docs/stable/index.html2. nanoGPT https://github.com/karpathy/nanoGPT"""importmathimporttorchimporttorch.nnasnnimporttorch.nn.fun...
is_causal=None,attn_mask=None,key_padding_mask=torch.tensor([[0.,1]],dtype=torch.float))#issue is over here "key_padding_mask=torch.tensor([[0.,1]],dtype=torch.float)" which is wrong output,however key_padding_mask=torch.tensor([[1.,0]],dtype=torch.float) is normal output as ...
`import torch from transformers import AutoModelForCausalLM, AutoTokenizer torch.set_default_dtype(torch.bfloat16) tokenizer = AutoTokenizer.from_pretrained("hpcai-tech/grok-1", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( "hpcai-tech/grok-1", trust_remote_code=True,...
(self, config, device="cpu", dtype=torch.float32): super().__init__() assert config.n_embd % config.n_head == 0 self.resid_drop = nn.Dropout(config.resid_pdrop) # output projection self.c_proj = nn.Linear(config.n_embd, config.n_embd, device=device, dtype=dtype) # Causal ...
调用train_val_test_data_provider以获取train/val/测试数据集 ifargs.virtual_pipeline_model_parallel_sizeisnotNone: all_data_iterators=[ build_train_valid_test_data_iterators(train_valid_test_dataset_provider) for_inrange(len(model)) ] train_data_iterator=[data_iterators[0]fordata_iteratorsinall_...
self.causal_conv=CausalConv1D(1, 1, 4) self.Wz=BlockDiagonal(input_size, hidden_size, num_heads) self.Wi=BlockDiagonal(input_size, hidden_size, num_heads) self.Wf=BlockDiagonal(input_size, hidden_size, num_heads) self.Wo=BlockDiagonal(input_size, hidden_size, num_heads) ...
self.causal_conv=CausalConv1D(1, 1, 4) self.Wz=BlockDiagonal(input_size, hidden_size, num_heads) self.Wi=BlockDiagonal(input_size, hidden_size, num_heads) self.Wf=BlockDiagonal(input_size, hidden_size, num_heads) self.Wo=BlockDiagonal(input_size, hidden_size, num_heads) ...
class CausalSelfAttention(nn.Module): def __init__(self, num_heads: int, embed_dimension: int, bias: bool=False, is_causal: bool=False, dropout:float=0.0): super().__init__() assert embed_dimension % num_heads == 0 # key, query, value projections for all heads, but in a batch...