Run PyTorch LLMs locally on servers, desktop and mobile - torchchat/quantization/quantize.py at main · kuizhiqing/torchchat
Calculates the quantization scale and zero point values necessary to quantize the *InputTensor*, then applies that quantization, writing the result to *OutputTensor*.
Performs the following linear quantization function on every element in *InputTensor* with respect to its corresponding element in *ScaleTensor* and `ZeroPointTensor`, placing the results in the corresponding element of *OutputTensor*.
SnapshotWindowPlanNode<TInput,TState,TResult> SortingTechnique SprayPlanNode StitchPlanNode 可流式传输 StreamCache<TKey,TPayload> StreamEvent StreamEvent<TPayload> StreamEventKind StreamMessageKind StreamProcessingException StreamProperties<TKey,TPayload> ...
ERROR:root:In node 0 (parseGraph): INVALID_NODE: Invalid Node - head.cls_preds.0.bias_DequantizeLinear head.cls_preds.0.bias_DequantizeLinear_dequantize_scale_node: only activation types allowed as input to this layer. Traceback (most recent call last): ...
the output node to be quantized model_output = 'output0' # Quantize the model directly from the file path quantized_model_path = 'quantized_model.onnx' quantize_dynamic(model_input=onnx_model_path, model_output=quantized_model_path, per_channel=False, # Adjust as needed weight_type=Quant...
data_files:{{train_path}}column_map:input:instruction output:output train_on_input:Falsepacked:Falsesplit:train seed:null shuffle:True# Loggingoutput_dir:{{log_dir}}/lora_finetune_output metric_logger:_component_:torchtune.training.metric_logging.{{metric_logger}}log_di...
NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 106 Model name: Intel(R) Xeon(R) Platinum XXXX CPU @ 2.70GHz Stepping: 6 CPU MHz: 2699.998 BogoMIPS: 5399.99 Hypervisor vendor: KVM Virtualization type: full L1d cache: 48K ...
blob: 6916dea1e47a628f31a7df3daac1ff6868903b91 (plain) () 157158159256257258259260261262518
A method, network node and processor for processing uplink signals transmitted from a wireless device (WD) to provide a combination of quantize-forwarding and decode-forwarding relayed signals in massive multiple input multiple output (MIMO) heterogeneous networks (HetNets). According to one aspect,...