如果发现输入已经是QPartitionExpr包装之后的算子,则调用realize进行quantize算子相关的实现 @ 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...
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...
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torch::Tensor input_temp =torch::randn({1,1,1,1,1}, toptions.dtype(torch::kFloat32)); torch::Tensor scales =torch::randn({1}, toptions.dtype(torch::kFloat64)); torch::Tensor zero_points =torch::randn({1}, toptions.dtype(torch::kFloat16));at::quantize_per_channel(input_temp...
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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}}l...
keras/engine/training.py", line 2641, in from_config functional.reconstruct_from_config(config, custom_objects)) File "/opt/vitis_ai/conda/envs/vitis-ai-tensorflow2/lib/python3.7/site-packages/keras/engine/functional.py", line 1338, in reconstruct_from_config if process_node(layer, node_dat...
python $groot/graffitize.pyc \ --in_graph $in_metagraph \ --out_graph $trainquant_graph \ --inputs $input_node \ --outputs $output_node \ --input_shape $input_shape \ --transforms 'fix_input_shape' \ 'fold_batch_norms(is_training=True)' \ 'preprocess_layers' \ 'quantize(is_...
node.extend([mat_mul_node]) test_graph(float_graph_def, {}, [mat_mul_name]) def test_conv(depth, image_width, image_height, image_batch_count, filter_size, filter_count, stride, padding, input_values, filter_values): """Tests a Conv replacement.""" input_constant_name ...
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