wehub-resource-sync
87 lines
2.3 KiB
Python
Executable File
87 lines
2.3 KiB
Python
Executable File
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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import numpy as np
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import itertools
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from ..utils import *
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import collections.abc
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def index_to_feature(p, dims):
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"""convert index form (single integer) to feature form (vector)"""
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feature = []
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for dim in dims:
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feature.append(p % dim)
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p //= dim
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return feature
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def feature_to_index(feature, dims):
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"""convert feature form (vector) to index form (single integer)"""
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p = 0
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for j, k in enumerate(feature):
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print("j:", "k:", k, "dims", dims[:j])
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p += int(np.prod(dims[:j])) * k
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return p
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def dict_to_dims(tuning_space):
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dims = []
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for key, val in tuning_space.items():
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if isinstance(val, dict):
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dims.extend(dict_to_dims(val))
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elif isinstance(val, list):
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dims.append(len(val))
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else:
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dims.append(1)
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return dims
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def gen_combinations(d: dict):
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keys, values = d.keys(), d.values()
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for v in values:
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if not isinstance(v, list):
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v = [v]
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values_choices = (gen_combinations(v) if isinstance(v, dict) else get_list(v) for v in values)
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for comb in itertools.product(*values_choices):
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yield dict(zip(keys, comb))
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def flatten(d, parent_key='', sep='_'):
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items = []
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for k, v in d.items():
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new_key = parent_key + sep + k if parent_key else k
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if isinstance(v, collections.abc.MutableMapping):
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items.extend(flatten(v, new_key, sep=sep).items())
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else:
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items.append((new_key, v))
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return dict(items)
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def dict_to_feature(feature_dict, keys, max_value=None):
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"""Extract values from dict"""
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feature = []
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for key, val in feature_dict.items(): # First level
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if key not in keys:
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continue
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if val is None or val == "auto" or key == "autotuning" or val == "":
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continue
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if isinstance(val, dict):
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feature.append(dict_to_feature(val, max_value))
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else:
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feature.append(float(val))
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# normalization, should not matter in tree models
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if max_value is not None:
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norm_feature = []
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for f, mv in zip(feature, max_value):
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norm_feature.append(f / mv)
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feature = norm_feature
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return feature
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