mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 07:57:44 +00:00
Xinjie Shen
f20dc4482b
* upload example code for model implementation * Refactor Model Implement * add export --------- Co-authored-by: Young <afe.young@gmail.com>
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
# TODO: inherent from the benchmark base class
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import torch
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from rdagent.model_implementation.task import ModelTaskImpl
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def get_data_conf(init_val):
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# TODO: design this step in the workflow
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in_dim = 1000
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in_channels = 128
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exec_config = {"model_eval_param_init": init_val}
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node_feature = torch.randn(in_dim, in_channels)
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edge_index = torch.randint(0, in_dim, (2, 2000))
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return (node_feature, edge_index), exec_config
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class ModelImpValEval:
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"""
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Evaluate the similarity of the model structure by changing the input and observate the output.
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Assumption:
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- If the model structure is similar, the output will change in similar way when we change the input.
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- we try to initialize the model param in similar value. So only the model structure is different.
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"""
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def evaluate(self, gt: ModelTaskImpl, gen: ModelTaskImpl):
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round_n = 10
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eval_pairs: list[tuple] = []
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# run different input value
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for _ in range(round_n):
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# run different model initial parameters.
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for init_val in [-0.2, -0.1, 0.1, 0.2]:
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data, exec_config = get_data_conf(init_val)
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gt_res = gt.execute(data=data, config=exec_config)
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res = gen.execute(data=data, config=exec_config)
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eval_pairs.append((res, gt_res))
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# flat and concat the output
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res_batch, gt_res_batch = [], []
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for res, gt_res in eval_pairs:
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res_batch.append(res.reshape(-1))
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gt_res_batch.append(gt_res.reshape(-1))
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res_batch = torch.stack(res_batch)
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gt_res_batch = torch.stack(gt_res_batch)
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res_batch = res_batch.detach().numpy()
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gt_res_batch = gt_res_batch.detach().numpy()
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# pearson correlation of each hidden output
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def norm(x):
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return (x - x.mean(axis=0)) / x.std(axis=0)
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dim_corr = (norm(res_batch) * norm(gt_res_batch)).mean(axis=0) # the correlation of each hidden output
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# aggregate all the correlation
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avr_corr = dim_corr.mean()
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# FIXME:
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# It is too high(e.g. 0.944) .
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# Check if it is not a good evaluation!!
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# Maybe all the same initial params will results in extreamly high correlation without regard to the model structure.
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return avr_corr
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