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# Copyright (c) OpenMMLab. All rights reserved.
import logging
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.runner.fp16_utils import auto_fp16
from mmcv.utils import IS_IPU_AVAILABLE
if IS_IPU_AVAILABLE:
from mmcv.device.ipu import cfg2options, ipu_model_wrapper
from mmcv.device.ipu.utils import compare_ndarray
skip_no_ipu = pytest.mark.skipif(
not IS_IPU_AVAILABLE, reason='test case under ipu environment')
class MyBN(nn.BatchNorm2d):
def forward(self, *args, **kwargs):
result = super().forward(*args, **kwargs)
return result, self.running_mean
# TODO Once the model training and inference interfaces
# of MMCLS and MMDET are unified,
# construct the model according to the unified standards
class ToyModel(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 3, 1)
self.bn = MyBN(3)
self.relu = nn.ReLU6()
self.fp16_enabled = False
@auto_fp16(apply_to=('img', ))
def forward(self, img, return_loss=True, **kwargs):
x = self.conv(img)
x, running_mean = self.bn(x)
x = self.relu(x)
if return_loss:
loss = ((x - kwargs['gt_label'])**2).sum()
return {
'loss': loss,
'loss_list': [loss, loss],
'loss_dict': {
'loss1': loss
}
}
return x
def _parse_losses(self, losses):
return losses['loss'], losses['loss']
def train_step(self, data, optimizer=None, **kwargs):
losses = self(**data)
loss, log_vars = self._parse_losses(losses)
outputs = dict(
loss=loss, log_vars=log_vars, num_samples=len(data['img'].data))
return outputs
@skip_no_ipu
def test_build_model():
for execution_strategy in \
['SameAsIpu', 'ShardedExecution', 'error_strategy']:
if execution_strategy == 'error_strategy':
def maybe_catch_error(_error):
return pytest.raises(_error)
else:
class NullContextManager:
def __enter__(self, ):
pass
def __exit__(self, exc_type, exc_value, exc_traceback):
pass
def maybe_catch_error(_error):
return NullContextManager()
with maybe_catch_error(NotImplementedError):
options_cfg = dict(
randomSeed=888,
enableExecutableCaching='cache_engine',
train_cfg=dict(
executionStrategy=execution_strategy,
Training=dict(gradientAccumulation=8),
availableMemoryProportion=[0.3, 0.3, 0.3, 0.3]),
eval_cfg=dict(deviceIterations=1, ),
partialsType='half')
ipu_options = cfg2options(options_cfg)
model = ToyModel()
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
logger = logging.getLogger()
modules_to_record = None
ipu_model_cfg = dict(
train_split_edges=[dict(layer_to_call='conv', ipu_id=0)],
train_ckpt_nodes=['bn', 'conv'])
fp16_cfg = {'loss_scale': 0.5}
ipu_model = ipu_model_wrapper(
model,
ipu_options,
optimizer,
logger,
modules_to_record=modules_to_record,
ipu_model_cfg=ipu_model_cfg,
fp16_cfg=fp16_cfg)
ipu_model.train()
ipu_model.eval()
ipu_model.train()
def run_model(ipu_options,
fp16_cfg,
modules_to_record,
ipu_model_wrapper_func,
only_eval=False):
model = ToyModel()
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\
if not only_eval else None
logger = logging.getLogger()
ipu_model_cfg = dict(
train_split_edges=[dict(layer_to_call='conv', ipu_id=0)],
train_ckpt_nodes=['bn', 'conv'])
ipu_model = ipu_model_wrapper_func(
model,
ipu_options,
optimizer,
logger,
modules_to_record=modules_to_record,
ipu_model_cfg=ipu_model_cfg,
fp16_cfg=fp16_cfg)
def get_dummy_input(training):
if training:
return {
'data': {
'img': torch.rand((16, 3, 10, 10)),
'gt_label': torch.rand((16, 3, 10, 10))
}
}
else:
return {
'img': torch.rand((16, 3, 10, 10)),
'img_metas': {
'img': torch.rand((16, 3, 10, 10))
},
'return_loss': False
}
if not only_eval:
training = True
ipu_model.train()
for _ in range(3):
dummy_input = get_dummy_input(training)
output = ipu_model.train_step(**dummy_input)
training = False
ipu_model.eval()
for _ in range(3):
dummy_input = get_dummy_input(training)
output = ipu_model(**dummy_input)
return output, ipu_model
@skip_no_ipu
def test_run_model():
# test feature alignment not support gradientAccumulation mode
options_cfg = dict(
randomSeed=888,
enableExecutableCaching='cache_engine',
train_cfg=dict(
executionStrategy='SameAsIpu',
Training=dict(gradientAccumulation=8),
availableMemoryProportion=[0.3, 0.3, 0.3, 0.3],
),
eval_cfg=dict(deviceIterations=1, ),
partialsType='half')
ipu_options = cfg2options(options_cfg)
modules_to_record = ['bn']
with pytest.raises(AssertionError, match='Feature alignment'):
run_model(ipu_options, None, modules_to_record, ipu_model_wrapper)
# test feature alignment not support multi-replica mode
options_cfg = dict(
randomSeed=888,
replicationFactor=2,
enableExecutableCaching='cache_engine',
train_cfg=dict(
executionStrategy='SameAsIpu',
availableMemoryProportion=[0.3, 0.3, 0.3, 0.3],
),
eval_cfg=dict(deviceIterations=1, ),
partialsType='half')
ipu_options = cfg2options(options_cfg)
modules_to_record = ['bn']
with pytest.raises(AssertionError, match='Feature alignment'):
run_model(ipu_options, None, modules_to_record, ipu_model_wrapper)
# test feature alignment not support fp16 mode
options_cfg = dict(
randomSeed=888,
enableExecutableCaching='cache_engine',
train_cfg=dict(
executionStrategy='SameAsIpu',
availableMemoryProportion=[0.3, 0.3, 0.3, 0.3],
),
eval_cfg=dict(deviceIterations=1, ),
partialsType='half')
ipu_options = cfg2options(options_cfg)
fp16_cfg = {
'loss_scale': 0.5,
'velocity_accum_type': 'half',
'accum_type': 'half'
}
modules_to_record = ['bn']
with pytest.raises(NotImplementedError):
run_model(ipu_options, fp16_cfg, modules_to_record, ipu_model_wrapper)
# test velocity_accum_type and accum_type
fp16_cfg = {
'loss_scale': 0.5,
'velocity_accum_type': 'float',
'accum_type': 'float'
}
run_model(ipu_options, fp16_cfg, None, ipu_model_wrapper)
# test compile and run
options_cfg = dict(
randomSeed=888,
enableExecutableCaching='cache_engine',
train_cfg=dict(
executionStrategy='SameAsIpu',
availableMemoryProportion=[0.3, 0.3, 0.3, 0.3],
),
eval_cfg=dict(deviceIterations=1, ),
partialsType='half')
ipu_options = cfg2options(options_cfg)
modules_to_record = ['bn']
run_model(ipu_options, None, modules_to_record, ipu_model_wrapper)
# test feature alignment
options_cfg = dict(
randomSeed=888,
enableExecutableCaching='cache_engine',
train_cfg=dict(
executionStrategy='SameAsIpu',
availableMemoryProportion=[0.3, 0.3, 0.3, 0.3],
),
eval_cfg=dict(deviceIterations=1, ))
ipu_options = cfg2options(options_cfg)
modules_to_record = None
run_model(ipu_options, None, modules_to_record, ipu_model_wrapper)
# test inference mode
options_cfg = dict(
randomSeed=888,
enableExecutableCaching='cache_engine',
train_cfg=dict(
executionStrategy='SameAsIpu',
availableMemoryProportion=[0.3, 0.3, 0.3, 0.3],
),
eval_cfg=dict(deviceIterations=1, ),
partialsType='half')
ipu_options = cfg2options(options_cfg)
fp16_cfg = {'loss_scale': 0.5}
modules_to_record = None
_, ipu_model = run_model(
ipu_options,
fp16_cfg,
modules_to_record,
ipu_model_wrapper,
only_eval=True)
with pytest.raises(RuntimeError):
ipu_model.train()
with pytest.raises(ValueError):
ipu_model.train(123)
_, ipu_model = run_model(ipu_options, None, modules_to_record,
ipu_model_wrapper)
# test NotImplementedError in __call__
ipu_model.train()
with pytest.raises(NotImplementedError):
ipu_model()
# test parse_losses
with pytest.raises(TypeError):
ipu_model._model.model._parse_losses({'loss': None})
@skip_no_ipu
def test_compare_tensor():
compare_ndarray(np.random.rand(3, 4), np.random.rand(3, 4))
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