@ tvm/src/relay/quantize/http://partition.cc ExprQPartitionExprNode::Realize()const{constQConfig&cfg=QConfig::Current();Exprret=CastHint(this->expr,cfg->dtype_input);returnStopFusion(ret);} 即添加了图中的cast_hint节点和stop_fusion节点。QPartitionExpr使用了TempExpr节点类型,个人理解就是lazy实现...
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...
InputTensor 类型:constDML_TENSOR_DESC* 包含输入的张量。 OutputTensor 类型:constDML_TENSOR_DESC* 要写入结果的输出张量。 OutputScaleTensor 类型:constDML_TENSOR_DESC* 要写入OutputTensor 的输出比例系数的输出张量。OutputScaleTensor中预期的元素数为 1。
torch.quantize_per_tensor(input,scale, zero_point, dtype)实现8位量化: 摘要:对该函数各个参数的分析 量化: 计算机运算时,默认32位浮点数,若将32位浮点数,变成8位定点数,会快很多。 目前pytorch中的反向传播不支持量化,所以该量化只用于评估训练好的模型,或者将32位浮点数模型存储为8位定点数模型,读取8位...
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MASTER_ADDR=os.environ.get('MASTER_ADDR','127.0.0.1')MASTER_PORT=os.environ.get('MASTER_PORT','7777')WORLD_SIZE=int(os.environ.get("WORLD_SIZE",1))GLOBAL_RANK=int(os.environ.get('RANK',-1))LOCAL_RANK=int(os.environ.get('LOCAL_RANK',-1))NUM_GPUS_PER_NO...
structDML_ELEMENT_WISE_QUANTIZE_LINEAR_OPERATOR_DESC{constDML_TENSOR_DESC *InputTensor;constDML_TENSOR_DESC *ScaleTensor;constDML_TENSOR_DESC *ZeroPointTensor;constDML_TENSOR_DESC *OutputTensor; }; Members InputTensor Type:constDML_TENSOR_DESC* ...
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...
DML_GRAPH_NODE_TYPE enumeration DML_GRU_OPERATOR_DESC structure DML_INPUT_GRAPH_EDGE_DESC structure DML_INTERMEDIATE_GRAPH_EDGE_DESC structure DML_INTERPOLATION_MODE enumeration DML_IS_INFINITY_MODE enumeration DML_JOIN_OPERATOR_DESC structure DML_LOCAL_RESPONSE_NORMALIZATION_GRAD_OPERATO...
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): ...