pub fn from(
        root: Arc<Node>,
        device: wgpu::Device,
        queue: wgpu::Queue,
        onnx_opset_version: i64,
    ) -> Result<GpuModel, GpuError> {
        let mut gpu_model = GpuModel {
            device,
            queue,
            onnx_opset_version,
            steps: vec![],
            inference_outputs: HashMap::new(),
        };

        // Walk the IR DAG and encode into GPU execution steps
        let mut readable_nodes: HashSet<NodeIdentifier> = HashSet::new();
        let mut node_outputs = HashMap::<NodeIdentifier, Vec<GpuTensor>>::new();
        let mut nodes_seen = HashSet::new();
        gpu_model.sequence(
            root.clone(),
            &mut readable_nodes,
            &mut node_outputs,
            &mut nodes_seen,
        )?;

        // Find out which outputs we should return as inference outputs
        if let NodeDefinition::Outputs { names } = &root.definition {
            for (usize, output_name) in names.iter().enumerate() {
                let input = &root.inputs[usize];
                gpu_model.inference_outputs.insert(
                    output_name.to_string(),
                    match &input.source_node.definition {
                        NodeDefinition::Operator(_) | NodeDefinition::Tensor(_) => {
                            let source_identifier = input.source_node.identifier();
                            let outputs = &node_outputs[&source_identifier];
                            let tensor = outputs[input.output_index].clone();
                            InferenceOutput::Tensor(tensor)
                        }
                        NodeDefinition::Input(proto) => {
                            InferenceOutput::InferenceInput(proto.get_name().to_string())
                        }
                        NodeDefinition::Outputs { .. } => {
                            unimplemented!("output after output node")
                        }
                        NodeDefinition::Missing => {
                            unimplemented!("optional input after output node")
                        }
                    },
                );
            }
        } else {
            unimplemented!("reading from non-outputs IR node")
        }

        // Upload the data (for initializers etc.) by submitting an empty command queue
        log::info!("submit initializer buffers");
        let encoder = gpu_model
            .device
            .create_command_encoder(&wgpu::CommandEncoderDescriptor::default());
        gpu_model.queue.submit(Some(encoder.finish()));

        Ok(gpu_model)
    }
/// Create a version of the specified model for which inference can be performed using the powers of the GPU
Create a version of the specified model for which inference can be performed using the powers of the GPU
Create a version of the specified model for which inference can be performed using the powers of the GPU
pub fn from(
        root: Arc<Node>,
        device: wgpu::Device,
        queue: wgpu::Queue,
        onnx_opset_version: i64,
    ) -> Result<GpuModel, GpuError> {
        let mut gpu_model = GpuModel {
            device,
            queue,
            onnx_opset_version,
            steps: vec![],
            inference_outputs: HashMap::new(),
        };
        // Walk the IR DAG and encode into GPU execution steps
        let mut readable_nodes: HashSet<NodeIdentifier> = HashSet::new();
        let mut node_outputs = HashMap::<NodeIdentifier, Vec<GpuTensor>>::new();
        let mut nodes_seen = HashSet::new();
        gpu_model.sequence(
            root.clone(),
            &mut readable_nodes,
            &mut node_outputs,
            &mut nodes_seen,
        )?;
        // Find out which outputs we should return as inference outputs
        if let NodeDefinition::Outputs { names } = &root.definition {
            for (usize, output_name) in names.iter().enumerate() {
                let input = &root.inputs[usize];
                gpu_model.inference_outputs.insert(
                    output_name.to_string(),
                    match &input.source_node.definition {
                        NodeDefinition::Operator(_) | NodeDefinition::Tensor(_) => {
                            let source_identifier = input.source_node.identifier();
                            let outputs = &node_outputs[&source_identifier];
                            let tensor = outputs[input.output_index].clone();
                            InferenceOutput::Tensor(tensor)
                        }
                        NodeDefinition::Input(proto) => {
                            InferenceOutput::InferenceInput(proto.get_name().to_string())
                        }
                        NodeDefinition::Outputs { .. } => {
                            unimplemented!("output after output node")
                        }
                        NodeDefinition::Missing => {
                            unimplemented!("optional input after output node")
                        }
                    },
                );
            }
        } else {
            unimplemented!("reading from non-outputs IR node")
        }
        // Upload the data (for initializers etc.) by submitting an empty command queue
        log::info!("submit initializer buffers");
        let encoder = gpu_model
            .device
            .create_command_encoder(&wgpu::CommandEncoderDescriptor::default());
        gpu_model.queue.submit(Some(encoder.finish()));
        Ok(gpu_model)
    }
pub fn from ( root : Arc < Node > , device : wgpu :: Device , queue : wgpu :: Queue , onnx_opset_version : i64 , ) -> Result < GpuModel , GpuError > { let mut gpu_model = GpuModel { device , queue , onnx_opset_version , steps : vec ! [ ] , inference_outputs : HashMap :: new ( ) , } ; // Walk the IR DAG and encode into GPU execution steps let mut readable_nodes : HashSet < NodeIdentifier > = HashSet :: new ( ) ; let mut node_outputs = HashMap :: < NodeIdentifier , Vec < GpuTensor > > :: new ( ) ; let mut nodes_seen = HashSet :: new ( ) ; gpu_model . sequence ( root . clone ( ) , & mut readable_nodes , & mut node_outputs , & mut nodes_seen , ) ? ; // Find out which outputs we should return as inference outputs if let NodeDefinition :: Outputs { names } = & root . definition { for ( usize , output_name ) in names . iter ( ) . enumerate ( ) { let input = & root . inputs [ usize ] ; gpu_model . inference_outputs . insert ( output_name . to_string ( ) , match & input . source_node . definition { NodeDefinition :: Operator ( _ ) | NodeDefinition :: Tensor ( _ ) => { let source_identifier = input . source_node . identifier ( ) ; let outputs = & node_outputs [ & source_identifier ] ; let tensor = outputs [ input . output_index ] . clone ( ) ; InferenceOutput :: Tensor ( tensor ) } NodeDefinition :: Input ( proto ) => { InferenceOutput :: InferenceInput ( proto . get_name ( ) . to_string ( ) ) } NodeDefinition :: Outputs { .. } => { unimplemented ! ( " " ) } NodeDefinition :: Missing => { unimplemented ! ( " " ) } } , ) ; } } else { unimplemented ! ( " " ) } // Upload the data (for initializers etc.) by submitting an empty command queue log :: info ! ( " " ) ; let encoder = gpu_model . device . create_command_encoder ( & wgpu :: CommandEncoderDescriptor :: default ( ) ) ; gpu_model . queue . submit ( Some ( encoder . finish ( ) ) ) ; Ok ( gpu_model ) }
Create a version of the specified model for which inference can be performed using the powers of the GPU
Create a version of the specified model for which inference can be performed using the powers of the GPU
root
Arc<Node>
device
wgpu::Device
queue
wgpu::Queue
onnx_opset_version
i64
root
Arc<Node>
device
wgpu::Device
queue
wgpu::Queue
onnx_opset_version
i64