wehub-resource-sync
329 lines
9.3 KiB
Python
329 lines
9.3 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import collective.test_communication_api_base as test_base
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
|
|
TEST_CONFIGS = {
|
|
"2_card_tests": [
|
|
{
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"dp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"dp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"dp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "False",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"dp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "False",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"dp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "False",
|
|
},
|
|
{
|
|
"test_type": "layer",
|
|
"layer_type": "ColumnSequenceParallelLinear",
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"dp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"dp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"sharding_degree": 1,
|
|
"has_bias": "False",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 1,
|
|
"sharding_degree": 2,
|
|
"has_bias": "False",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 1,
|
|
"sharding_degree": 2,
|
|
"has_bias": "False",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
"master_weight": "True",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 1,
|
|
"sharding_degree": 2,
|
|
"has_bias": "True",
|
|
"master_weight": "True",
|
|
},
|
|
{
|
|
"world_size": 2,
|
|
"tp": 1,
|
|
"sharding_degree": 2,
|
|
"has_bias": "True",
|
|
"master_weight": "True",
|
|
},
|
|
],
|
|
"4_card_tests": [
|
|
{
|
|
"world_size": 4,
|
|
"tp": 4,
|
|
"dp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
},
|
|
{
|
|
"world_size": 4,
|
|
"tp": 4,
|
|
"dp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
},
|
|
{
|
|
"world_size": 4,
|
|
"tp": 2,
|
|
"dp": 2,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
},
|
|
{
|
|
"world_size": 4,
|
|
"tp": 2,
|
|
"dp": 2,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
},
|
|
],
|
|
"4_card_hv_group_tests": [
|
|
{
|
|
"world_size": 4,
|
|
"tp": 2,
|
|
"pp": 2,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
"test_using_hv_group": 1,
|
|
},
|
|
],
|
|
"2_card_hv_group_tests": [
|
|
{
|
|
"world_size": 2,
|
|
"tp": 2,
|
|
"pp": 1,
|
|
"sharding_degree": 1,
|
|
"has_bias": "True",
|
|
"test_using_hv_group": 1,
|
|
},
|
|
],
|
|
"sharding3_with_convert2cpu_tests": [
|
|
{
|
|
"world_size": 2,
|
|
"tp": 1,
|
|
"pp": 1,
|
|
"sharding_degree": 2,
|
|
"has_bias": "True",
|
|
},
|
|
],
|
|
}
|
|
|
|
|
|
class TestFullParamWith2Devices(test_base.CommunicationTestDistBase):
|
|
def setUp(self):
|
|
super().setUp(num_of_devices=2, timeout=240)
|
|
|
|
def test_full_param(self):
|
|
for config in TEST_CONFIGS["2_card_tests"]:
|
|
envs = {k: str(v) for k, v in config.items()}
|
|
envs["test_using_hv_group"] = "0"
|
|
self.run_test_case(
|
|
"model_full_param_logic.py",
|
|
user_defined_envs=envs,
|
|
)
|
|
|
|
|
|
class TestFullParamWith4Devices(test_base.CommunicationTestDistBase):
|
|
def setUp(self):
|
|
super().setUp(num_of_devices=4, timeout=240)
|
|
|
|
def test_full_param(self):
|
|
for config in TEST_CONFIGS["4_card_tests"]:
|
|
envs = {k: str(v) for k, v in config.items()}
|
|
envs["test_using_hv_group"] = "0"
|
|
self.run_test_case(
|
|
"model_full_param_logic.py",
|
|
user_defined_envs=envs,
|
|
)
|
|
|
|
|
|
class TestFullParamWithSingleDevices(unittest.TestCase):
|
|
class SimpleMLP(nn.Layer):
|
|
def __init__(self, hidden_size=100, has_bias=False):
|
|
super().__init__()
|
|
self.embedding = nn.Embedding(24, hidden_size)
|
|
self.linear1 = nn.Linear(
|
|
hidden_size, hidden_size, bias_attr=has_bias
|
|
)
|
|
self.linear2 = nn.Linear(
|
|
hidden_size, hidden_size, bias_attr=has_bias
|
|
)
|
|
self.llm_head = nn.Linear(hidden_size, 24, bias_attr=False)
|
|
|
|
def forward(self, x):
|
|
x = self.embedding(x)
|
|
x = self.linear1(x)
|
|
x = self.linear2(x)
|
|
x = self.llm_head(x)
|
|
return x
|
|
|
|
def test_full_param(self):
|
|
self.batch_size = 2
|
|
self.hidden_size = 32
|
|
self.has_bias = True
|
|
model = self.SimpleMLP(
|
|
hidden_size=self.hidden_size, has_bias=self.has_bias
|
|
)
|
|
model = paddle.amp.decorate(
|
|
models=model, optimizers=None, level="O2", dtype="float16"
|
|
)
|
|
model.train()
|
|
model_state_dict = model.state_dict()
|
|
|
|
for k, v in model_state_dict.items():
|
|
ones = paddle.ones_like(v)
|
|
paddle.assign(ones, v)
|
|
if k == "linear1.weight":
|
|
zeros = paddle.zeros_like(v)
|
|
paddle.assign(zeros, v)
|
|
|
|
aoa_config = {
|
|
"aoa_statements": [
|
|
"linear1.weight, linear2.weight -> fused_weight, axis=1"
|
|
"embedding.weight -> embedding.weight, dtype = 'float32'"
|
|
]
|
|
}
|
|
|
|
full_param_iter = model.full(aoa_config)
|
|
full_param = dict(full_param_iter)
|
|
|
|
param_shape = {
|
|
# "linear1.weight" : [32,32],
|
|
# "linear2.weight" : [32, 32],
|
|
"embedding.weight": [24, 32],
|
|
"linear1.bias": [32],
|
|
"linear2.bias": [32],
|
|
"llm_head.weight": [24, 32],
|
|
"fused_weight": [32, 64],
|
|
}
|
|
|
|
for name, shape in param_shape.items():
|
|
if name == "fused_weight":
|
|
continue
|
|
if not self.has_bias:
|
|
if ".bias" in name:
|
|
continue
|
|
assert name in full_param.keys()
|
|
tensor = full_param[name]
|
|
answer = paddle.ones_like(tensor)
|
|
assert tensor._md5sum() == answer._md5sum()
|
|
if name == "embedding.weight":
|
|
assert tensor.dtype == paddle.float32
|
|
assert "fused_weight" in full_param.keys()
|
|
ones = paddle.ones([32, 32], 'float16')
|
|
zeros = paddle.zeros([32, 32], 'float16')
|
|
answer = paddle.concat([zeros, ones], axis=1)
|
|
assert full_param["fused_weight"]._md5sum() == answer._md5sum()
|
|
|
|
|
|
class TestFullParamHVGroupWith2Devices(test_base.CommunicationTestDistBase):
|
|
def setUp(self):
|
|
super().setUp(num_of_devices=2, timeout=240)
|
|
|
|
def test_full_param(self):
|
|
for config in TEST_CONFIGS["2_card_hv_group_tests"]:
|
|
envs = {k: str(v) for k, v in config.items()}
|
|
envs["test_using_hv_group"] = "1"
|
|
self.run_test_case(
|
|
"model_full_param_logic.py",
|
|
user_defined_envs=envs,
|
|
)
|
|
|
|
|
|
class TestFullParamHVGroupWith4Devices(test_base.CommunicationTestDistBase):
|
|
def setUp(self):
|
|
super().setUp(num_of_devices=4, timeout=240)
|
|
|
|
def test_full_param(self):
|
|
for config in TEST_CONFIGS["4_card_hv_group_tests"]:
|
|
envs = {k: str(v) for k, v in config.items()}
|
|
envs["test_using_hv_group"] = "1"
|
|
self.run_test_case(
|
|
"model_full_param_logic.py",
|
|
user_defined_envs=envs,
|
|
)
|
|
|
|
|
|
class TestFullParamWithSharding3(test_base.CommunicationTestDistBase):
|
|
def setUp(self):
|
|
super().setUp(num_of_devices=2, timeout=240)
|
|
|
|
def test_full_param(self):
|
|
for config in TEST_CONFIGS["sharding3_with_convert2cpu_tests"]:
|
|
envs = {k: str(v) for k, v in config.items()}
|
|
envs["test_using_hv_group"] = "0"
|
|
envs["test_with_sharding3"] = "1"
|
|
self.run_test_case(
|
|
"model_full_param_logic.py",
|
|
user_defined_envs=envs,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|