chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2025 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 unittest
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import collective.test_communication_api_base as test_base
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import paddle
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from paddle import nn
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TEST_CONFIGS = {
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"2_card_tests": [
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{
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"world_size": 2,
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"tp": 2,
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"dp": 1,
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"sharding_degree": 1,
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"has_bias": "True",
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},
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{
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"world_size": 2,
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"tp": 2,
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"dp": 1,
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"sharding_degree": 1,
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"has_bias": "True",
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},
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{
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"world_size": 2,
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"tp": 2,
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"dp": 1,
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"sharding_degree": 1,
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"has_bias": "False",
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},
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{
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"world_size": 2,
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"tp": 2,
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"dp": 1,
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"sharding_degree": 1,
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"has_bias": "False",
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},
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{
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"world_size": 2,
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"tp": 2,
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"dp": 1,
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"sharding_degree": 1,
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"has_bias": "False",
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},
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{
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"test_type": "layer",
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"layer_type": "ColumnSequenceParallelLinear",
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"world_size": 2,
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"tp": 2,
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"dp": 1,
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"sharding_degree": 1,
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"has_bias": "True",
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},
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{
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"world_size": 2,
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"tp": 2,
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"dp": 1,
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"sharding_degree": 1,
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"has_bias": "True",
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},
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{
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"world_size": 2,
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"tp": 2,
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"sharding_degree": 1,
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"has_bias": "False",
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},
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{
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"world_size": 2,
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"tp": 1,
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"sharding_degree": 2,
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"has_bias": "False",
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},
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{
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"world_size": 2,
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"tp": 1,
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"sharding_degree": 2,
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"has_bias": "False",
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},
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{
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"world_size": 2,
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"tp": 2,
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"sharding_degree": 1,
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"has_bias": "True",
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"master_weight": "True",
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},
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{
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"world_size": 2,
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"tp": 1,
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"sharding_degree": 2,
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"has_bias": "True",
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"master_weight": "True",
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},
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{
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"world_size": 2,
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"tp": 1,
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"sharding_degree": 2,
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"has_bias": "True",
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"master_weight": "True",
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},
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],
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"4_card_tests": [
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{
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"world_size": 4,
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"tp": 4,
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"dp": 1,
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"sharding_degree": 1,
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"has_bias": "True",
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},
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{
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"world_size": 4,
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"tp": 4,
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"dp": 1,
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"sharding_degree": 1,
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"has_bias": "True",
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},
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{
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"world_size": 4,
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"tp": 2,
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"dp": 2,
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"sharding_degree": 1,
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"has_bias": "True",
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},
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{
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"world_size": 4,
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"tp": 2,
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"dp": 2,
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"sharding_degree": 1,
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"has_bias": "True",
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},
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],
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"4_card_hv_group_tests": [
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{
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"world_size": 4,
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"tp": 2,
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"pp": 2,
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"sharding_degree": 1,
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"has_bias": "True",
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"test_using_hv_group": 1,
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},
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],
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"2_card_hv_group_tests": [
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{
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"world_size": 2,
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"tp": 2,
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"pp": 1,
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"sharding_degree": 1,
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"has_bias": "True",
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"test_using_hv_group": 1,
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},
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],
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"sharding3_with_convert2cpu_tests": [
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{
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"world_size": 2,
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"tp": 1,
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"pp": 1,
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"sharding_degree": 2,
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"has_bias": "True",
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},
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],
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}
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class TestFullParamWith2Devices(test_base.CommunicationTestDistBase):
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def setUp(self):
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super().setUp(num_of_devices=2, timeout=240)
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def test_full_param(self):
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for config in TEST_CONFIGS["2_card_tests"]:
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envs = {k: str(v) for k, v in config.items()}
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envs["test_using_hv_group"] = "0"
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self.run_test_case(
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"model_full_param_logic.py",
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user_defined_envs=envs,
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)
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class TestFullParamWith4Devices(test_base.CommunicationTestDistBase):
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def setUp(self):
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super().setUp(num_of_devices=4, timeout=240)
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def test_full_param(self):
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for config in TEST_CONFIGS["4_card_tests"]:
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envs = {k: str(v) for k, v in config.items()}
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envs["test_using_hv_group"] = "0"
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self.run_test_case(
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"model_full_param_logic.py",
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user_defined_envs=envs,
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)
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class TestFullParamWithSingleDevices(unittest.TestCase):
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class SimpleMLP(nn.Layer):
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def __init__(self, hidden_size=100, has_bias=False):
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super().__init__()
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self.embedding = nn.Embedding(24, hidden_size)
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self.linear1 = nn.Linear(
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hidden_size, hidden_size, bias_attr=has_bias
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)
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self.linear2 = nn.Linear(
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hidden_size, hidden_size, bias_attr=has_bias
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)
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self.llm_head = nn.Linear(hidden_size, 24, bias_attr=False)
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def forward(self, x):
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x = self.embedding(x)
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x = self.linear1(x)
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x = self.linear2(x)
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x = self.llm_head(x)
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return x
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def test_full_param(self):
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self.batch_size = 2
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self.hidden_size = 32
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self.has_bias = True
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model = self.SimpleMLP(
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hidden_size=self.hidden_size, has_bias=self.has_bias
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)
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model = paddle.amp.decorate(
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models=model, optimizers=None, level="O2", dtype="float16"
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)
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model.train()
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model_state_dict = model.state_dict()
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for k, v in model_state_dict.items():
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ones = paddle.ones_like(v)
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paddle.assign(ones, v)
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if k == "linear1.weight":
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zeros = paddle.zeros_like(v)
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paddle.assign(zeros, v)
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aoa_config = {
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"aoa_statements": [
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"linear1.weight, linear2.weight -> fused_weight, axis=1"
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"embedding.weight -> embedding.weight, dtype = 'float32'"
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]
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}
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full_param_iter = model.full(aoa_config)
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full_param = dict(full_param_iter)
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param_shape = {
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# "linear1.weight" : [32,32],
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# "linear2.weight" : [32, 32],
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"embedding.weight": [24, 32],
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"linear1.bias": [32],
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"linear2.bias": [32],
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"llm_head.weight": [24, 32],
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"fused_weight": [32, 64],
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}
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for name, shape in param_shape.items():
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if name == "fused_weight":
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continue
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if not self.has_bias:
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if ".bias" in name:
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continue
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assert name in full_param.keys()
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tensor = full_param[name]
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answer = paddle.ones_like(tensor)
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assert tensor._md5sum() == answer._md5sum()
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if name == "embedding.weight":
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assert tensor.dtype == paddle.float32
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assert "fused_weight" in full_param.keys()
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ones = paddle.ones([32, 32], 'float16')
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zeros = paddle.zeros([32, 32], 'float16')
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answer = paddle.concat([zeros, ones], axis=1)
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assert full_param["fused_weight"]._md5sum() == answer._md5sum()
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class TestFullParamHVGroupWith2Devices(test_base.CommunicationTestDistBase):
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def setUp(self):
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super().setUp(num_of_devices=2, timeout=240)
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def test_full_param(self):
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for config in TEST_CONFIGS["2_card_hv_group_tests"]:
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envs = {k: str(v) for k, v in config.items()}
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envs["test_using_hv_group"] = "1"
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self.run_test_case(
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"model_full_param_logic.py",
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user_defined_envs=envs,
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)
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class TestFullParamHVGroupWith4Devices(test_base.CommunicationTestDistBase):
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def setUp(self):
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super().setUp(num_of_devices=4, timeout=240)
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def test_full_param(self):
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for config in TEST_CONFIGS["4_card_hv_group_tests"]:
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envs = {k: str(v) for k, v in config.items()}
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envs["test_using_hv_group"] = "1"
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self.run_test_case(
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"model_full_param_logic.py",
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user_defined_envs=envs,
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)
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class TestFullParamWithSharding3(test_base.CommunicationTestDistBase):
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def setUp(self):
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super().setUp(num_of_devices=2, timeout=240)
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def test_full_param(self):
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for config in TEST_CONFIGS["sharding3_with_convert2cpu_tests"]:
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envs = {k: str(v) for k, v in config.items()}
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envs["test_using_hv_group"] = "0"
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envs["test_with_sharding3"] = "1"
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self.run_test_case(
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"model_full_param_logic.py",
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user_defined_envs=envs,
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)
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if __name__ == "__main__":
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unittest.main()
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