已合并
[fix] remove unnecessary same logs #5211
[fix] remove unnecessary same logs #5211
已合并
culechan创建于 6月18日
3 个文件变更+0-3
Mop_plugin/ops/opapi/ApplyAdamKernelNpuOpApi.cpp+0-1
@@ -65,7 +65,6 @@ std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> npu_apply_adam_out(
65 beta1_tensor, beta2_tensor, epsilon_tensor, grad, use_locking_value, use_nesterov_value);65 beta1_tensor, beta2_tensor, epsilon_tensor, grad, use_locking_value, use_nesterov_value);
66 return std::tie(var, m, v);66 return std::tie(var, m, v);
67 } else {67 } else {
68- TORCH_NPU_WARN("current soc not support aclnn");
69 return acl_op::npu_apply_adam_out(beta1_power, beta2_power, lr,68 return acl_op::npu_apply_adam_out(beta1_power, beta2_power, lr,
70 beta1, beta2, epsilon, grad, use_locking, use_nesterov, var, m, v);69 beta1, beta2, epsilon, grad, use_locking, use_nesterov, var, m, v);
71 }70 }
Mop_plugin/ops/opapi/ApplyAdamWKernelNpuOpApi.cpp+0-1
@@ -78,7 +78,6 @@ std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> npu_apply_adam_w_out(
78 max_grad_norm, amsgrad_value, maximize_value);78 max_grad_norm, amsgrad_value, maximize_value);
79 return std::tie(var, m, v);79 return std::tie(var, m, v);
80 } else {80 } else {
81- TORCH_NPU_WARN("current soc not support aclnn");
82 return acl_op::npu_apply_adam_w_out(beta1_power, beta2_power, lr,81 return acl_op::npu_apply_adam_w_out(beta1_power, beta2_power, lr,
83 weight_decay, beta1, beta2, epsilon, grad, max_grad_norm, amsgrad, maximize, var, m, v);82 weight_decay, beta1, beta2, epsilon, grad, max_grad_norm, amsgrad, maximize, var, m, v);
84 }83 }
Mop_plugin/ops/opapi/QuantConv2DKernelNpuOpApi.cpp+0-1
@@ -147,7 +147,6 @@ at::Tensor npu_quant_conv2d(const at::Tensor& input, const at::Tensor& weight, c
147 round_mode, input_dtype, weight_dtype, output_dtype, bias, offset);147 round_mode, input_dtype, weight_dtype, output_dtype, bias, offset);
148 } else {148 } else {
149 // aclnn only support 950 currently149 // aclnn only support 950 currently
150- TORCH_NPU_WARN("current soc not support aclnn");
151 return acl_op::npu_quant_conv2d(input, weight, scale, strides, pads, dilations, groups, offset_x,150 return acl_op::npu_quant_conv2d(input, weight, scale, strides, pads, dilations, groups, offset_x,
152 round_mode, output_dtype, bias, offset, input_dtype, weight_dtype);151 round_mode, output_dtype, bias, offset, input_dtype, weight_dtype);
153 }152 }