inner_dim * 3, bias = False) self.to_out = nn.Sequential( nn.Linear(inner_dim, ...
(anonymous namespace)::wrapper_CPU___transform_bias_rescale_qkv>, std::tuple<at::Tensor, at::Tensor, at::Tensor>, c10::guts::typelist::typelist<at::Tensor const&, at::Tensor const&, long> >, false>::call(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, ...
bias_after_scale=True, ) qk_matmul_op = OpConfig( "matmul_v2", inputs={"X": ["scale_out"], "Y": ["transpose2_2_out"]}, outputs={"Out": ["qk_matmul_out"]}, trans_x=False, trans_y=False, ) qk_softmax_op = OpConfig( "softmax", inputs={"X": ["qk_matmul_out"]}...
qk_bias query_states, key_states = torch.unbind(query_key, dim=2) @@ -95,80 +121,71 @@ def yuan_attention_forward( if past_key_value is not None: # reuse k, v, self_attention key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past...