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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/compiler/xla/service/gpu/backend_configs.proto
package xla.gpu;
public final class BackendConfigs {
private BackendConfigs() {}
public static void registerAllExtensions(
com.google.protobuf.ExtensionRegistryLite registry) {
}
public static void registerAllExtensions(
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registerAllExtensions(
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}
public interface CudnnConvBackendConfigOrBuilder extends
// @@protoc_insertion_point(interface_extends:xla.gpu.CudnnConvBackendConfig)
com.google.protobuf.MessageOrBuilder {
/**
*
* Opaque algorithm number of cudnn algorithm chosen for this conv.
*
*
* int64 algorithm = 1;
*/
long getAlgorithm();
/**
*
* Whether we may use tensor cores when running this conv. Even if this is
* true, cudnn may choose not to use tensor cores, e.g. because the GPU or
* selected algorithm doesn't support it.
*
*
* bool tensor_ops_enabled = 2;
*/
boolean getTensorOpsEnabled();
/**
*
* The scaling factor multiplied with the convolution result.
*
*
* double conv_result_scale = 4;
*/
double getConvResultScale();
/**
*
* The requested activation (e.g. relu) after the convolution. It is with type
* stream_executor::dnn::ActivationMode.
*
*
* int64 activation_mode = 3;
*/
long getActivationMode();
/**
*
* The scaling factor multiplied with the side input. If no side input buffer
* is provided, this field must be 0.
*
*
* double side_input_scale = 5;
*/
double getSideInputScale();
}
/**
*
* Backend config for a convolution that runs through cudnn.
*
*
* Protobuf type {@code xla.gpu.CudnnConvBackendConfig}
*/
public static final class CudnnConvBackendConfig extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:xla.gpu.CudnnConvBackendConfig)
CudnnConvBackendConfigOrBuilder {
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private CudnnConvBackendConfig() {
algorithm_ = 0L;
tensorOpsEnabled_ = false;
convResultScale_ = 0D;
activationMode_ = 0L;
sideInputScale_ = 0D;
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private long algorithm_;
/**
*
* Opaque algorithm number of cudnn algorithm chosen for this conv.
*
*
* int64 algorithm = 1;
*/
public long getAlgorithm() {
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}
public static final int TENSOR_OPS_ENABLED_FIELD_NUMBER = 2;
private boolean tensorOpsEnabled_;
/**
*
* Whether we may use tensor cores when running this conv. Even if this is
* true, cudnn may choose not to use tensor cores, e.g. because the GPU or
* selected algorithm doesn't support it.
*
*
* bool tensor_ops_enabled = 2;
*/
public boolean getTensorOpsEnabled() {
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}
public static final int CONV_RESULT_SCALE_FIELD_NUMBER = 4;
private double convResultScale_;
/**
*
* The scaling factor multiplied with the convolution result.
*
*
* double conv_result_scale = 4;
*/
public double getConvResultScale() {
return convResultScale_;
}
public static final int ACTIVATION_MODE_FIELD_NUMBER = 3;
private long activationMode_;
/**
*
* The requested activation (e.g. relu) after the convolution. It is with type
* stream_executor::dnn::ActivationMode.
*
*
* int64 activation_mode = 3;
*/
public long getActivationMode() {
return activationMode_;
}
public static final int SIDE_INPUT_SCALE_FIELD_NUMBER = 5;
private double sideInputScale_;
/**
*
* The scaling factor multiplied with the side input. If no side input buffer
* is provided, this field must be 0.
*
*
* double side_input_scale = 5;
*/
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/**
*
* Backend config for a convolution that runs through cudnn.
*
*
* Protobuf type {@code xla.gpu.CudnnConvBackendConfig}
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:xla.gpu.CudnnConvBackendConfig)
xla.gpu.BackendConfigs.CudnnConvBackendConfigOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return xla.gpu.BackendConfigs.internal_static_xla_gpu_CudnnConvBackendConfig_descriptor;
}
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super.clear();
algorithm_ = 0L;
tensorOpsEnabled_ = false;
convResultScale_ = 0D;
activationMode_ = 0L;
sideInputScale_ = 0D;
return this;
}
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throw newUninitializedMessageException(result);
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public xla.gpu.BackendConfigs.CudnnConvBackendConfig buildPartial() {
xla.gpu.BackendConfigs.CudnnConvBackendConfig result = new xla.gpu.BackendConfigs.CudnnConvBackendConfig(this);
result.algorithm_ = algorithm_;
result.tensorOpsEnabled_ = tensorOpsEnabled_;
result.convResultScale_ = convResultScale_;
result.activationMode_ = activationMode_;
result.sideInputScale_ = sideInputScale_;
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private long algorithm_ ;
/**
*
* Opaque algorithm number of cudnn algorithm chosen for this conv.
*
*
* int64 algorithm = 1;
*/
public long getAlgorithm() {
return algorithm_;
}
/**
*
* Opaque algorithm number of cudnn algorithm chosen for this conv.
*
*
* int64 algorithm = 1;
*/
public Builder setAlgorithm(long value) {
algorithm_ = value;
onChanged();
return this;
}
/**
*
* Opaque algorithm number of cudnn algorithm chosen for this conv.
*
*
* int64 algorithm = 1;
*/
public Builder clearAlgorithm() {
algorithm_ = 0L;
onChanged();
return this;
}
private boolean tensorOpsEnabled_ ;
/**
*
* Whether we may use tensor cores when running this conv. Even if this is
* true, cudnn may choose not to use tensor cores, e.g. because the GPU or
* selected algorithm doesn't support it.
*
*
* bool tensor_ops_enabled = 2;
*/
public boolean getTensorOpsEnabled() {
return tensorOpsEnabled_;
}
/**
*
* Whether we may use tensor cores when running this conv. Even if this is
* true, cudnn may choose not to use tensor cores, e.g. because the GPU or
* selected algorithm doesn't support it.
*
*
* bool tensor_ops_enabled = 2;
*/
public Builder setTensorOpsEnabled(boolean value) {
tensorOpsEnabled_ = value;
onChanged();
return this;
}
/**
*
* Whether we may use tensor cores when running this conv. Even if this is
* true, cudnn may choose not to use tensor cores, e.g. because the GPU or
* selected algorithm doesn't support it.
*
*
* bool tensor_ops_enabled = 2;
*/
public Builder clearTensorOpsEnabled() {
tensorOpsEnabled_ = false;
onChanged();
return this;
}
private double convResultScale_ ;
/**
*
* The scaling factor multiplied with the convolution result.
*
*
* double conv_result_scale = 4;
*/
public double getConvResultScale() {
return convResultScale_;
}
/**
*
* The scaling factor multiplied with the convolution result.
*
*
* double conv_result_scale = 4;
*/
public Builder setConvResultScale(double value) {
convResultScale_ = value;
onChanged();
return this;
}
/**
*
* The scaling factor multiplied with the convolution result.
*
*
* double conv_result_scale = 4;
*/
public Builder clearConvResultScale() {
convResultScale_ = 0D;
onChanged();
return this;
}
private long activationMode_ ;
/**
*
* The requested activation (e.g. relu) after the convolution. It is with type
* stream_executor::dnn::ActivationMode.
*
*
* int64 activation_mode = 3;
*/
public long getActivationMode() {
return activationMode_;
}
/**
*
* The requested activation (e.g. relu) after the convolution. It is with type
* stream_executor::dnn::ActivationMode.
*
*
* int64 activation_mode = 3;
*/
public Builder setActivationMode(long value) {
activationMode_ = value;
onChanged();
return this;
}
/**
*
* The requested activation (e.g. relu) after the convolution. It is with type
* stream_executor::dnn::ActivationMode.
*
*
* int64 activation_mode = 3;
*/
public Builder clearActivationMode() {
activationMode_ = 0L;
onChanged();
return this;
}
private double sideInputScale_ ;
/**
*
* The scaling factor multiplied with the side input. If no side input buffer
* is provided, this field must be 0.
*
*
* double side_input_scale = 5;
*/
public double getSideInputScale() {
return sideInputScale_;
}
/**
*
* The scaling factor multiplied with the side input. If no side input buffer
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*
*
* double side_input_scale = 5;
*/
public Builder setSideInputScale(double value) {
sideInputScale_ = value;
onChanged();
return this;
}
/**
*
* The scaling factor multiplied with the side input. If no side input buffer
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*
*
* double side_input_scale = 5;
*/
public Builder clearSideInputScale() {
sideInputScale_ = 0D;
onChanged();
return this;
}
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// @@protoc_insertion_point(builder_scope:xla.gpu.CudnnConvBackendConfig)
}
// @@protoc_insertion_point(class_scope:xla.gpu.CudnnConvBackendConfig)
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