query:这是查询张量(query),通常形状为(batch_size, num_heads, seq_len, head_dim),表示不同批次中,每个注意力头的查询向量。 key.transpose(-1, -2):key是键张量(key),其形状通常为(batch_size, num_heads, seq_len, head_dim),通过.transpose(-1, -2)将最后两个维度转置,得到形状为(batch_size,...
bmm(query, key.transpose(1, 2)) if key_mask is not None: attn_weights = attn_weights.masked_fill(key_mask.unsqueeze(1), float('-inf')) attn_weights = F.softmax(attn_weights.float(), dim=-1, dtype=torch.float32 if attn_weights.dtype == torch.float16 else attn_weights.dtype) ...
proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N out = torch.bmm...-1).permute(0, 2, 1) key = key.view(b, c, -1) value = v...
(query_states_1, key_states, value_states, attn_mask = None, dropout_p = 0.0, is_causal = True); query_states_1 = key_states = value_states = None attn_output_1 = attn_output.transpose(1, 2); attn_output = None attn_output_2 = attn_output_1.reshape(1, 1024, 1024); attn_...
transpose=False: -1, torch.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1, torch.permute: lambda self, dim: -1, torch.pca_lowrank: lambda input, q=None, center=True, niter=2: -1, torch.pdist: lambda input, p=2: -1, torch.pinverse: lambda input, ...
bool = False, dx_transpose: bool = False): return g.op("npu::NPUFusedAttentionScore", query_layer, key_layer, value_layer, attention_mask, keep_prob_f=keep_prob, scale_f=scale, query_transpose_i=query_transpose, key_transpose_i=key_transpose, bmm_score_transpose_a_...
matmul(query, key.transpose(-2, -1)) / math.sqrt(self.d_k) # (N, nh, L, L) if mask is not None: scores = scores.masked_fill(mask == 0, -1e9) p_attn = torch.softmax(scores, dim=-1) if dropout is not None: p_attn = dropout(p_attn) return torch.matmul(p_attn, ...
def npu_bmmV2_meta(self, mat2, output_sizes): dim1 = self.size(0) dim2 = self.size(1) dim3 = mat2.size(2) return self.new_empty((dim1, dim2, dim3)) @impl(m, "npu_transpose") def npu_transpose_meta(self, perm, require_contiguous=True): output = self.p...
# 需要导入模块: import torch [as 别名]# 或者: from torch importtriu[as 别名]defp_choose(self, query, key, key_padding_mask=None, attn_mask=None, incremental_state=None):""" query: bsz, tgt_len key: bsz, src_len key_padding_mask: bsz, src_len ...
class TransformerEncoder(Encoder): """Transformer编码器""" def __init__(self, vocab_size, key_size, query_size, value_size, num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens, num_heads, num_layers, dropout, use_bias=False, **kwargs): super(TransformerEncoder, self).__init__...