wehub-resource-sync
157 lines
5.3 KiB
Python
157 lines
5.3 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import re
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from collections import OrderedDict
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import numpy as np
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import paddle
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from paddle.distributed.fleet.utils import mix_precision_utils
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from paddle.nn import Linear, ReLU
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logging.basicConfig(level="INFO", format="%(message)s")
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class MLP(paddle.nn.Layer):
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def __init__(self, linear_size=1000):
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super().__init__()
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self._linear1 = Linear(linear_size, linear_size)
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self._linear2 = Linear(linear_size, linear_size)
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self._linear3 = Linear(linear_size, 10)
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self._relu = ReLU()
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def forward(self, inputs):
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y = self._linear1(inputs)
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y = self._linear2(y)
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y = self._linear3(y)
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y = self._relu(y)
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return y
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class RandomDataset(paddle.io.Dataset):
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def __init__(self, num_samples=200, linear_size=1000):
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self.num_samples = num_samples
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self.linear_size = linear_size
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self.samples = []
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for i in range(num_samples):
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img = np.random.rand(self.linear_size).astype('float32')
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self.samples.append(img)
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def __getitem__(self, idx):
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return self.samples[idx]
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def __len__(self):
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return self.num_samples
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def create_optimizer(model, use_pure_bf16, use_main_grad):
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if use_main_grad:
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assert use_pure_bf16
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model = mix_precision_utils.MixPrecisionLayer(model, dtype="bfloat16")
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optimizer = paddle.optimizer.AdamW(
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parameters=model.parameters(),
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learning_rate=0.00001,
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weight_decay=0.00001,
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grad_clip=paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0),
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multi_precision=use_pure_bf16,
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)
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if use_main_grad:
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optimizer = mix_precision_utils.MixPrecisionOptimizer(optimizer)
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return optimizer
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def save_model_parameters(model):
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param_dict = OrderedDict()
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for param in model.parameters():
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param_dict[param.name] = param
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return param_dict
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def _extract_linear_order(param_names):
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# for param_names from model.state_dict, they are as like: ["_linear1.weight", "_linear1.bias"]
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# for master weight names from optimizer.state_dict, they are as like: ["linear_6.w_0", "linear_6.b_0"]
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param_order = []
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for name in param_names:
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param_id = re.findall(r"\d+", name)
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assert len(param_id) >= 1
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param_order.append(int(param_id[0]))
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return list(set(param_order))
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def _extract_param_order_dict(model_param_dict_o1, model_param_dict_o2):
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param_names_o1 = list(model_param_dict_o1.keys())
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param_order_o1 = _extract_linear_order(param_names_o1)
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param_order_o1.sort()
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param_names_o2 = list(model_param_dict_o2.keys())
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param_order_o2 = _extract_linear_order(param_names_o2)
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param_order_o2.sort()
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assert len(param_order_o1) == len(param_order_o2)
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param_order_dict = {}
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for i in range(len(param_order_o1)):
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param_order_dict[param_order_o2[i]] = param_order_o1[i]
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logging.info(f"-- param_names_o1: {param_names_o1}")
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logging.info(f"-- param_names_o2: {param_names_o2}")
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logging.info(f"param_order_dict: {param_order_dict}")
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return param_order_dict
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def compare_state_dict(
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model_param_dict_o1, model_param_dict_o2, optimizer_state_dict_o2
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):
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master_weights = None
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if optimizer_state_dict_o2.get("master_weights", None) is not None:
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master_weights = optimizer_state_dict_o2["master_weights"]
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assert master_weights is not None
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master_weights_names = list(master_weights.keys())
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param_names = list(model_param_dict_o1.keys())
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param_order_dict = _extract_param_order_dict(
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model_param_dict_o1, model_param_dict_o2
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)
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param_master_pair = []
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# We assume the order of params in param_names and master_weights_names is the same.
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param_id = 0
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for master_weight_name in master_weights_names:
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master_weight_id = re.findall(r"\d+", master_weight_name)[0]
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param_id = param_order_dict[int(master_weight_id)]
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for param_name in param_names:
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if (
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master_weight_name.endswith("w_0")
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and param_name.endswith("weight")
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) or (
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master_weight_name.endswith("b_0")
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and param_name.endswith("bias")
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):
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name_prefix = "linear" + param_id
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if name_prefix in param_name:
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param_master_pair.append([param_name, master_weight_name])
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logging.info(f"-- master_weights_names: {master_weights_names}")
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for pair in param_master_pair:
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param_name = pair[0]
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master_weight_name = pair[1]
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logging.info(f"-- compare {param_name} with {master_weight_name}")
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param_o1 = model_param_dict_o1[param_name]
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master_param_o2 = master_weights[master_weight_name]
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np.testing.assert_array_equal(param_o1.numpy(), master_param_o2.numpy())
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