-
Notifications
You must be signed in to change notification settings - Fork 60
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
43550ba
commit db939b0
Showing
2 changed files
with
161 additions
and
1 deletion.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,161 @@ | ||
# coding=utf-8 | ||
# Copyright 2023 The HuggingFace Inc. team. 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. | ||
"""General tests related to distributed training.""" | ||
|
||
import contextlib | ||
import pytest | ||
from typing import TYPE_CHECKING, Dict | ||
from tests.distributed.utils import create_static_seed_patcher | ||
|
||
import torch | ||
import torch_xla.core.xla_model as xm | ||
from neuronx_distributed.parallel_layers.parallel_state import get_tensor_model_parallel_group | ||
from neuronx_distributed.utils.model_utils import move_model_to_device | ||
from neuronx_distributed.pipeline import NxDPPModel | ||
|
||
from transformers import AutoModelForCausalLM, LlamaForCausalLM | ||
|
||
from optimum.neuron.accelerate import NeuronAccelerator | ||
from optimum.neuron.accelerate.utils.dataclasses import ModelParallelismPlugin, NeuronDistributedType | ||
from optimum.neuron.distributed.utils import lazy_load_for_parallelism, make_optimizer_constructor_lazy | ||
|
||
from .distributed import DistributedTest | ||
|
||
|
||
if TYPE_CHECKING: | ||
from transformers import PreTrainedModel | ||
|
||
|
||
def create_accelerator_for_mp(tp_size: int, pp_size: int, zero_1: bool = False) -> NeuronAccelerator: | ||
mp_plugin = ModelParallelismPlugin( | ||
tensor_parallel_size=tp_size, | ||
parallelize_embeddings=True, | ||
sequence_parallel_enabled=True, | ||
pipeline_parallel_size=pp_size, | ||
) | ||
return NeuronAccelerator(mp_plugin=mp_plugin, zero_1=zero_1) | ||
|
||
|
||
def get_model(tp_size: int = 1, pp_size: int = 1, lazy_load: bool = False, use_static_seed_patcher: bool = False) -> "PreTrainedModel": | ||
model_name = "michaelbenayoun/llama-2-tiny-16layers-random" | ||
if lazy_load: | ||
ctx = lazy_load_for_parallelism(tensor_parallel_size=tp_size, pipeline_parallel_size=pp_size) | ||
else: | ||
ctx = contextlib.nullcontext() | ||
if use_static_seed_patcher: | ||
seed_patcher = create_static_seed_patcher(LlamaForCausalLM, 42) | ||
else: | ||
seed_patcher = contextlib.nullcontext() | ||
with ctx: | ||
with seed_patcher: | ||
return AutoModelForCausalLM.from_pretrained(model_name) | ||
|
||
def get_optimizer(model: torch.nn.Module, lazy: bool, with_groups: bool) -> torch.optim.Optimizer: | ||
adam_cls = torch.optim.AdamW | ||
if lazy: | ||
adam_cls = make_optimizer_constructor_lazy(adam_cls) | ||
|
||
if with_groups: | ||
groups = [ | ||
{"params": (p for idx, p in enumerate(model.parameters()) if idx % 2 == 0), "lr": 1e-2}, | ||
{"params": (p for idx, p in enumerate(model.parameters()) if idx % 2 == 1), "lr": 1e-6}, | ||
] | ||
else: | ||
groups = model.parameters() | ||
|
||
return adam_cls(groups) | ||
|
||
|
||
class TestCommonDistributed(DistributedTest): | ||
# TODO: add dp + tp + pp configuration. | ||
@pytest.fixture(scope="class", params=[[2, 1, 1], [2, 2, 1], [2, 1, 2]], ids=["dp=2", "tp=2", "pp=2"]) | ||
def parallel_sizes(self, request): | ||
return request.param | ||
|
||
@pytest.fixture(scope="class", params=[False, True], ids=["no_lazy_load", "lazy_load"]) | ||
def lazy_load(self, request): | ||
return request.param | ||
|
||
@pytest.fixture(scope="class", params=[False, True], ids=["no_lazy_optimizer", "lazy_optimizer"]) | ||
def lazy_optimizer(self, request): | ||
return request.param | ||
|
||
@pytest.fixture(scope="class", params=[False, True], ids=["without_groups", "with_groups"]) | ||
def with_groups(self, request): | ||
return request.param | ||
|
||
@pytest.fixture(scope="class", params=[False, True], ids=["no_zero_1", "zero_1"]) | ||
def zero_1(self, request): | ||
return request.param | ||
|
||
def test_optimizer_parameters_match_models_parameters(self, lazy_load, lazy_optimizer, with_groups, zero_1, parallel_sizes): | ||
num_workers, tp_size, pp_size = parallel_sizes | ||
dp_size = num_workers // (tp_size * pp_size) | ||
if dp_size == 1 and zero_1: | ||
pytest.skip("zero_1 needs to be tested only for dp_size > 1") | ||
|
||
model = get_model(tp_size=tp_size, pp_size=pp_size, lazy_load=lazy_load) | ||
optimizer = get_optimizer(model, lazy_optimizer, with_groups) | ||
|
||
accelerator = create_accelerator_for_mp(tp_size, pp_size, zero_1=zero_1) | ||
assert accelerator.state.distributed_type is NeuronDistributedType.MODEL_PARALLELISM | ||
|
||
model, optimizer = accelerator.prepare(model, optimizer) | ||
|
||
if isinstance(model, NxDPPModel): | ||
model_parameters = set(model.local_parameters()) | ||
else: | ||
model_parameters = set(model.parameters()) | ||
optimizer_parameters = set(p for group in optimizer.param_groups for p in group["params"]) | ||
|
||
assert model_parameters == optimizer_parameters | ||
|
||
def test_lazy_load(self, parallel_sizes): | ||
_, tp_size, pp_size = parallel_sizes | ||
|
||
model = get_model(tp_size=tp_size, pp_size=pp_size, lazy_load=False, use_static_seed_patcher=True) | ||
move_model_to_device(model, xm.xla_device()) | ||
orig_parameters: Dict[str, torch.nn.Parameter] = dict(model.named_parameters()) | ||
|
||
accelerator = create_accelerator_for_mp(tp_size, pp_size) | ||
lazy_model = get_model(tp_size=tp_size, pp_size=pp_size, lazy_load=True, use_static_seed_patcher=True) | ||
lazy_model = accelerator.prepare(lazy_model) | ||
|
||
xm.mark_step() | ||
|
||
if pp_size > 1: | ||
named_parameters = lazy_model.local_named_parameters() | ||
else: | ||
named_parameters = lazy_model.named_parameters() | ||
|
||
for name, param in named_parameters: | ||
orig = orig_parameters[name] | ||
if orig.shape != param.shape: | ||
if orig.dim() == 1: | ||
gather_dim = 0 | ||
elif orig.dim() == 2: | ||
gather_dim = 1 if orig.shape[0] == param.shape[0] else 0 | ||
else: | ||
raise ValueError(f"The case where the weight as a rank of {orig.dim()} is not supported.") | ||
gathered = [torch.empty(param.shape) for _ in range(tp_size)] | ||
torch.distributed.all_gather(gathered, param, group=get_tensor_model_parallel_group()) | ||
gathered_param = torch.cat(gathered, dim=gather_dim) | ||
orig = orig.to("cpu") | ||
xm.mark_step() | ||
else: | ||
gathered_param = param | ||
print(f"Comparing parameter named {name}") | ||
torch.testing.assert_allclose(orig, gathered_param) | ||
|