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import torch
from tests.test_utilities import Utils
from megatron.core import ModelParallelConfig
import megatron.core.pipeline_parallel.schedules as schedule
from pytest_mock import mocker
import pytest
rank = Utils.rank
def test_get_forward_backward_func():
Utils.initialize_model_parallel(tensor_model_parallel_size=2, pipeline_model_parallel_size=1)
assert(schedule.get_forward_backward_func() == schedule.forward_backward_no_pipelining)
Utils.destroy_model_parallel()
Utils.initialize_model_parallel(tensor_model_parallel_size=2, pipeline_model_parallel_size=4)
assert(schedule.get_forward_backward_func() == schedule.forward_backward_pipelining_without_interleaving)
Utils.destroy_model_parallel()
Utils.initialize_model_parallel(tensor_model_parallel_size=2, pipeline_model_parallel_size=4, virtual_pipeline_model_parallel_size=2)
assert(schedule.get_forward_backward_func() == schedule.forward_backward_pipelining_with_interleaving)
Utils.destroy_model_parallel()
def test_deallocate_output_tensor():
out = torch.tensor([[1, 2, 3], [4, 5, 6]])
schedule.deallocate_output_tensor(out)
assert(out.nelement() == 1)
def test_forward_backward_func_without_pipeline_parallel(mocker):
from megatron.core.pipeline_parallel import get_forward_backward_func
Utils.initialize_model_parallel(tensor_model_parallel_size=2, pipeline_model_parallel_size=1)
def forward_step_func(data_iterator, model):
import os
rank = int(os.environ['LOCAL_RANK'])
dummy_data = torch.ones(1,4)
def loss_func(output_tensor):
return rank, {'loss_reduced':rank}
return model(dummy_data), loss_func
model = torch.nn.Linear(4,1)
model.model_type = 'unit-test'
def set_input_tensor(input_tensor):
return None
model.set_input_tensor = set_input_tensor
forward_backward_func = get_forward_backward_func()
assert(schedule.get_forward_backward_func() == schedule.forward_backward_no_pipelining)
mocker.patch("megatron.core.pipeline_parallel.schedules.custom_backward", return_value=2)
config = ModelParallelConfig(
pipeline_model_parallel_size = 1
)
model.config = config
losses_reduced = forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=None,
model=[model],
num_microbatches=4,
seq_length=None,
micro_batch_size=None,
forward_only=False)
loss_reduced_expected = [{'loss_reduced': rank}, {'loss_reduced': rank}, {'loss_reduced': rank}, {'loss_reduced': rank}]
for i,j in zip(losses_reduced, loss_reduced_expected):
print(losses_reduced)
assert(i['loss_reduced'] == j['loss_reduced'])
Utils.destroy_model_parallel()
def test_forward_backward_func_with_pipeline_parallel(mocker):
from megatron.core.pipeline_parallel import get_forward_backward_func
Utils.initialize_model_parallel(tensor_model_parallel_size=1, pipeline_model_parallel_size=4)
def forward_step_func(data_iterator, model):
import os
rank = int(os.environ['LOCAL_RANK'])
def loss_func(output_tensor):
return rank, {'loss_reduced':rank}
return torch.rand(512,8,256).cuda(), loss_func
model = torch.nn.Linear(4,1)
model.model_type = 'unit-test'
def set_input_tensor(input_tensor):
return None
model.set_input_tensor = set_input_tensor
forward_backward_func = get_forward_backward_func()
assert(schedule.get_forward_backward_func() == schedule.forward_backward_pipelining_without_interleaving)
sequence_length = 512
micro_batch_size = 8
hidden_size = 256
config = ModelParallelConfig(
pipeline_model_parallel_size = 4,
sequence_parallel = False
)
model.config = config
losses_reduced = forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=None,
dtype=torch.float32,
model=[model],
num_microbatches= micro_batch_size,
seq_length=sequence_length,
micro_batch_size=micro_batch_size,
forward_only=True)
loss_reduced_expected = [{'loss_reduced': rank}, {'loss_reduced': rank}, {'loss_reduced': rank}, {'loss_reduced': rank}]
for i,j in zip(losses_reduced, loss_reduced_expected):
print(losses_reduced)
assert(i['loss_reduced'] == j['loss_reduced'])
Utils.destroy_model_parallel()
"""
def test_forward_backward_func_with_interleaving(mocker):
from megatron.core.pipeline_parallel import get_forward_backward_func
from megatron.core.enums import ModelType
Utils.initialize_model_parallel(tensor_model_parallel_size=1, pipeline_model_parallel_size=4, virtual_pipeline_model_parallel_size=2)
def forward_step_func(data_iterator, model):
import os
rank = int(os.environ['LOCAL_RANK'])
def loss_func(output_tensor):
return rank, {'loss_reduced':rank}
return torch.rand(512,8,256).cuda(), loss_func
model = torch.nn.Linear(4,1)
def set_input_tensor(input_tensor):
return None
model.set_input_tensor = set_input_tensor
forward_backward_func = get_forward_backward_func()
assert(schedule.get_forward_backward_func() == schedule.forward_backward_pipelining_with_interleaving)
sequence_length = 512
micro_batch_size = 8
hidden_size = 256
mocker.patch("megatron.core.pipeline_parallel.schedules.custom_backward", return_value=2)
with pytest.raises(RuntimeError):
model.model_type = ModelType.encoder_and_decoder
forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=range(0,100),
dtype=torch.float32,
model=[model, model],
num_microbatches= micro_batch_size,
tensor_shape=[sequence_length, micro_batch_size, hidden_size],
decoder_seq_length=sequence_length,
sequence_parallel=False,
forward_only=True)
with pytest.raises(RuntimeError):
model.model_type = ModelType.encoder_or_decoder
forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=range(0,100),
dtype=torch.float32,
model=[model, model],
num_microbatches= micro_batch_size,
tensor_shape=[sequence_length, micro_batch_size, hidden_size],
decoder_seq_length=256,
sequence_parallel=False,
forward_only=True)
with pytest.raises(RuntimeError):
model.model_type = ModelType.encoder_or_decoder
forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=range(0,100),
dtype=torch.float32,
model=[model, model],
num_microbatches= 7,
tensor_shape=[sequence_length, micro_batch_size, hidden_size],
decoder_seq_length=512,
sequence_parallel=False,
forward_only=True)
model.model_type = ModelType.encoder_or_decoder
losses_reduced = forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=range(0,100),
dtype=torch.float32,
model=[model, model],
num_microbatches= micro_batch_size,
tensor_shape=[sequence_length, micro_batch_size, hidden_size],
decoder_seq_length=sequence_length,
sequence_parallel=True,
forward_only=True)
loss_reduced_expected = [{'loss_reduced': rank}, {'loss_reduced': rank}, {'loss_reduced': rank}, {'loss_reduced': rank}]
for i,j in zip(losses_reduced, loss_reduced_expected):
print(losses_reduced)
assert(i['loss_reduced'] == j['loss_reduced'])
Utils.destroy_model_parallel()
"""
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