Run PyTorch LLMs locally on servers, desktop and mobile - torchchat/quantization/quantize.py at main · kuizhiqing/torchchat
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...
它通过按数量级对数聚合来实现这一点, 但在一个数量级内线性聚合。 Example var llquantize = require('llquantize') , llq = llquantize() // Input some data points. llq(0.54); llq(0.55) llq(2); llq(3) llq(12); llq(14) llq(24) llq(124); llq(199) // Get the accumulated data. ll...
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> ...
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_dir...
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): ...
blob: 6916dea1e47a628f31a7df3daac1ff6868903b91 (plain) () 157158159256257258259260261262518
🐛 Describe the bug torch.quantize_per_channel causes FPE with specific input. Test code: import torch input_temp = torch.randn([1,1,1,1,1], dtype=torch.float32) scales = torch.randn((1), dtype=torch.float64) zero_points = torch.randn((1)...