text stringlengths 1 93.6k |
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graph.output.remove(output_map[name])
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for name in new_names:
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if(name in output_shape_map.keys()):
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new_nv = helper.make_tensor_value_info(name, TensorProto.FLOAT, output_shape_map[name])
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else:
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new_nv = helper.make_tensor_value_info(name, TensorProto.FLOAT, None)
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graph.output.extend([new_nv])
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output_map = createGraphMemberMap(graph.output)
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# CLEANUP NODES
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# Trace all dependent nodes for the current set of output nodes defined & prepare a list of invalid nodes
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valid_node_names=[]
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for new_output_node_name in new_output_node_names:
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valid_node_names=traceDependentNodes(graph,new_output_node_name,valid_node_names,node_map, initializer_map)
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valid_node_names=list(set(valid_node_names))
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invalid_node_names = list( (set(node_map.keys()) | set(initializer_map.keys())) - set(valid_node_names))
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# Remove all the invalid nodes from the graph
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for name in invalid_node_names:
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if name in node_map.keys():
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graph.node.remove(node_map[name])
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if name in initializer_map.keys():
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graph.initializer.remove(initializer_map[name])
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if name in input_map.keys():
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graph.input.remove(input_map[name])
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# SAVE MODEL
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if(verify):
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print("output model Errors: ", onnx.checker.check_model(model))
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onnx.save(model, output_model)
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def parse_nodename_and_shape(name):
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# parses node names and shapes from input argument string
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inputs = []
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shapes = {}
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# input takes in most cases the format name:0, where 0 is the output number, and shapes
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# are appended to the same e.g. name:0[1,28,28,3]
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name_pattern = r"(?:([\w\d/\-\._:]+)(\[[\-\d,]+\])?),?"
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splits = re.split(name_pattern, name)
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for i in range(1, len(splits), 3):
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inputs.append(splits[i])
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if splits[i + 1] is not None:
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shapes[splits[i]] = [int(n) for n in splits[i + 1][1:-1].split(",")]
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if not shapes:
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shapes = None
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return inputs, shapes
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("input", help="input onnx model")
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parser.add_argument("output", help="output onnx model")
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parser.add_argument("--inputs", help="comma separated model input names appended with shapes, e.g. --inputs <nodename>[1,2,3],<nodename1>[1,2,3] ")
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parser.add_argument("--outputs", help="comma separated model output names appended with shapes, e.g. --outputs <nodename>[1,2,3],<nodename1>[1,2,3] ")
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parser.add_argument('--skipverify', dest='skipverify', action='store_true',
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help='skip verification of model. Useful if shapes are not known')
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args = parser.parse_args()
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if args.inputs:
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new_input_node_names, input_shape_map = parse_nodename_and_shape(args.inputs)
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#print(new_input_node_names)
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#print(input_shape_map)
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else:
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new_input_node_names = []
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input_shape_map = {}
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if args.outputs:
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new_output_node_names, output_shape_map = parse_nodename_and_shape(args.outputs)
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#print(new_output_node_names)
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#print(output_shape_map)
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else:
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new_output_node_names = []
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output_shape_map = {}
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onnx_edit(args.input,args.output,new_input_node_names, input_shape_map, new_output_node_names, output_shape_map, not args.skipverify)
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# <FILESEP>
|
from ast import List
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import shutil
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from pyparsing import Any
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import torch
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from transformers import (AutoTokenizer,
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AutoModelForCausalLM,
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get_linear_schedule_with_warmup,
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PreTrainedTokenizer)
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from grpo_trainer import GRPOTrainer, GRPOConfig
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from reward_funcs import (reward_punish_too_long,
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reward_unbias,
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llm_rater_reward,
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perplexity_reward,
|
repetition_reward,
|
length_reward,
|
chinese_char_ratio_reward)
|
import os
|
from loguru import logger
|
from accelerate import Accelerator
|
from transformers.data.data_collator import DataCollatorForSeq2Seq
|
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