def npu_fused_infer_attention_score_forward(query, key, value, *, pse_shift = None, atten_mask = None, actual_seq_lengths = None, actual_seq_lengths_kv = None, dequant_scale1 = None, quant_scale1 = None, dequant_scale2 = None, quant_scale2 = None, ...
"torch_npu.dynamo.torchair.ge_concrete_graph.ge_converter.custom.fused_infer_attention_score", "torch_npu.dynamo.torchair.ge_concrete_graph.ge_converter.experimental.patch_for_hcom_allreduce", "torch_npu.utils.collect_hccl_info", } # No new entries should be added to this ...
根据官网文档,有四个flash attention计算的方法:npu_fusion_attention/npu_fused_infer_attention_score/npu_incre_flash_attention/npu_prompt_flash_attention,请问其中增量计算方法npu_incre_flash_attention是flash decoding的实现吗 KonnoYuuki0429 创建了需求 3个月前 huangyunlong 3个月前 flash decoding功能在I...
class _NPUFusedInferAttentionScoreOP(torch.autograd.Function): @staticmethod def forward(ctx, *args, **kwargs): return torch.ops.npu.fused_infer_attention_score(*args, **kwargs) @staticmethod def symbolic(g, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,...
def npu_fused_infer_attention_score_forward(query, key, value, *, pse_shift=None, atten_mask=None, actual_seq_lengths=None, actual_seq_lengths_kv=None, dequant_scale1=None, quant_scale1=None, dequant_scale2=None, quant_scale2=None, ...
def npu_fused_infer_attention_score_forward(query, key, value, *, pse_shift=None, atten_mask=None, actual_seq_lengths=None, actual_seq_lengths_kv=None, dequant_scale1=None, quant_scale1=None, dequant_scale2=None, quant_scale2=None, ...
def npu_fused_infer_attention_score_forward(query, key, value, *, pse_shift = None, atten_mask = None, actual_seq_lengths = None, actual_seq_lengths_kv = None, dequant_scale1 = None, quant_scale1 = None, dequant_scale2 = None, quant_scale2 = None, ...
def npu_fused_infer_attention_score_forward(query, key, value, *, pse_shift = None, atten_mask = None, actual_seq_lengths = None, actual_seq_lengths_kv = None, dequant_scale1 = None, quant_scale1 = None, dequant_scale2 = None, quant_scale2 = None, ...
def npu_fused_infer_attention_score_forward(query, key, value, *, pse_shift=None, atten_mask=None, actual_seq_lengths=None, actual_seq_lengths_kv=None, dequant_scale1=None, quant_scale1=None, dequant_scale2=None, quant_scale2=None, ...
def npu_fused_infer_attention_score_forward(query, key, value, *, pse_shift=None, atten_mask=None, actual_seq_lengths=None, actual_seq_lengths_kv=None, dequant_scale1=None, quant_scale1=None, dequant_scale2=None, quant_scale2=None, ...