def train(self, output_dir):
with tempfile.TemporaryDirectory() as tmpdir:
loss_fn = nn.Sequential(
OrderedDict([
("loss",
Apply(
module=Loss(),
in_keys=[
"rule_probs",
"token_probs",
"reference_probs",
"ground_truth_actions",
],
out_key="action_sequence_loss",
)),
("pick",
mlprogram.nn.Function(Pick("action_sequence_loss")))
]))
collate = Collate(word_nl_query=CollateOptions(True, 0, -1),
nl_query_features=CollateOptions(True, 0, -1),
reference_features=CollateOptions(True, 0, -1),
actions=CollateOptions(True, 0, -1),
previous_actions=CollateOptions(True, 0, -1),
previous_action_rules=CollateOptions(
True, 0, -1),
history=CollateOptions(False, 1, 0),
hidden_state=CollateOptions(False, 0, 0),
state=CollateOptions(False, 0, 0),
ground_truth_actions=CollateOptions(True, 0,
-1)).collate
qencoder, aencoder = \
self.prepare_encoder(train_dataset, Parser())
transform = Map(self.transform_cls(qencoder, aencoder, Parser()))
model = self.prepare_model(qencoder, aencoder)
optimizer = self.prepare_optimizer(model)
train_supervised(tmpdir,
output_dir,
train_dataset,
model,
optimizer,
loss_fn,
EvaluateSynthesizer(test_dataset,
self.prepare_synthesizer(
model, qencoder,
aencoder),
{"accuracy": Accuracy()},
top_n=[5]),
"accuracy@5",
lambda x: collate(transform(x)),
1,
Epoch(100),
evaluation_interval=Epoch(100),
snapshot_interval=Epoch(100),
threshold=1.0)
return qencoder, aencoder
def test_pretrained_optimizer(self, dataset, model, loss_fn, optimizer,
synthesizer):
with tempfile.TemporaryDirectory() as tmpdir:
input = os.path.join(tmpdir, "in")
output = os.path.join(tmpdir, "out")
optimizer = torch.optim.SGD(model.parameters(), lr=np.nan)
os.makedirs(input)
torch.save(optimizer.state_dict(),
os.path.join(input, "optimizer.pt"))
train_REINFORCE(input,
output,
dataset,
synthesizer,
model,
optimizer,
lambda x: loss_fn(x) * x["reward"],
MockEvaluate("key"),
"key",
reward,
collate.collate,
1,
1,
Epoch(2),
use_pretrained_optimizer=True)
assert not os.path.exists(os.path.join(output, "log.json"))
def test_pretrained_model(self, dataset, model, loss_fn, optimizer,
synthesizer):
with tempfile.TemporaryDirectory() as tmpdir:
input = os.path.join(tmpdir, "in")
output = os.path.join(tmpdir, "out")
with torch.no_grad():
model2 = DummyModel()
model2.m.bias[:] = np.nan
os.makedirs(input)
torch.save(model2.state_dict(), os.path.join(input, "model.pt"))
train_REINFORCE(input,
output,
dataset,
synthesizer,
model,
optimizer,
lambda x: loss_fn(x) * x["reward"],
MockEvaluate("key"),
"key",
reward,
collate.collate,
1,
1,
Epoch(2),
use_pretrained_model=True)
assert not os.path.exists(os.path.join(output, "log.json"))
def test_remove_old_snapshots(self, dataset, model, loss_fn, optimizer):
with tempfile.TemporaryDirectory() as tmpdir:
output = os.path.join(tmpdir, "out")
train_supervised(output, dataset, model, optimizer, loss_fn,
MockEvaluate("key"), "key", collate.collate, 1,
Epoch(2))
assert os.path.exists(os.path.join(output, "snapshot_iter_6"))
def _run(init_dir, dataset, model, loss_fn, optimizer, rank):
with tempfile.TemporaryDirectory() as tmpdir:
distributed.initialize(init_dir, rank, 2)
output = os.path.join(tmpdir, "out")
train_supervised(output,
dataset,
model,
optimizer,
loss_fn,
MockEvaluate("key"),
"key",
collate.collate,
1,
Epoch(2),
n_dataloader_worker=0)
if rank == 0:
assert os.path.exists(os.path.join(output, "snapshot_iter_2"))
assert os.path.exists(os.path.join(output, "log.json"))
with open(os.path.join(output, "log.json")) as file:
log = json.load(file)
assert isinstance(log, list)
assert 1 == len(log)
assert 1 == len(os.listdir(os.path.join(output, "model")))
assert os.path.exists(os.path.join(output, "model.pt"))
assert os.path.exists(os.path.join(output, "optimizer.pt"))
else:
assert not os.path.exists(os.path.join(output, "snapshot_iter_2"))
assert not os.path.exists(os.path.join(output, "log.json"))
assert not os.path.exists(os.path.join(output, "model"))
return model.state_dict(), optimizer.state_dict()
def test_resume_from_checkpoint(self, dataset, model, loss_fn, optimizer):
with tempfile.TemporaryDirectory() as tmpdir:
output = os.path.join(tmpdir, "out")
train_supervised(output, dataset, model, optimizer, loss_fn,
MockEvaluate("key"), "key", collate.collate, 1,
Epoch(1))
with open(os.path.join(output, "log.json")) as file:
log = json.load(file)
train_supervised(output, dataset, model, optimizer, loss_fn,
MockEvaluate("key"), "key", collate.collate, 1,
Epoch(2))
assert os.path.exists(os.path.join(output, "snapshot_iter_6"))
with open(os.path.join(output, "log.json")) as file:
log2 = json.load(file)
assert log[0] == log2[0]
assert 2 == len(log2)
def test_skip_evaluation(self, dataset, model, loss_fn, optimizer):
with tempfile.TemporaryDirectory() as tmpdir:
output = os.path.join(tmpdir, "out")
train_supervised(output, dataset, model, optimizer, loss_fn, None,
"key", collate.collate, 1, Epoch(2))
assert os.path.exists(os.path.join(output, "snapshot_iter_6"))
assert os.path.exists(os.path.join(output, "log.json"))
with open(os.path.join(output, "log.json")) as file:
log = json.load(file)
assert isinstance(log, list)
assert 1 == len(log)
assert 0 == len(os.listdir(os.path.join(output, "model")))
assert os.path.exists(os.path.join(output, "model.pt"))
assert os.path.exists(os.path.join(output, "optimizer.pt"))
def test_happy_path(self, dataset, model, loss_fn, optimizer, synthesizer):
with tempfile.TemporaryDirectory() as tmpdir:
output = os.path.join(tmpdir, "out")
train_REINFORCE(output, output, dataset, synthesizer, model,
optimizer, lambda x: loss_fn(x) * x["reward"],
MockEvaluate("key"), "key", reward,
collate.collate, 1, 1, Epoch(2))
assert os.path.exists(os.path.join(output, "snapshot_iter_6"))
assert os.path.exists(os.path.join(output, "log.json"))
with open(os.path.join(output, "log.json")) as file:
log = json.load(file)
assert isinstance(log, list)
assert 1 == len(log)
assert 1 == len(os.listdir(os.path.join(output, "model")))
assert os.path.exists(os.path.join(output, "model.pt"))
assert os.path.exists(os.path.join(output, "optimizer.pt"))
def test_threshold(self, dataset, model, loss_fn, optimizer):
with tempfile.TemporaryDirectory() as tmpdir:
output = os.path.join(tmpdir, "out")
train_supervised(output,
dataset,
model,
optimizer,
loss_fn,
MockEvaluate("key"),
"key",
collate.collate,
1,
Epoch(2),
threshold=0.0)
assert 1 == len(os.listdir(os.path.join(output, "model")))
assert os.path.exists(os.path.join(output, "model.pt"))
assert os.path.exists(os.path.join(output, "optimizer.pt"))
def test_resume_from_eval_mode(self, dataset, loss_fn):
class DummyModel(nn.Module):
def __init__(self):
super().__init__()
self.m = nn.Linear(1, 1)
def forward(self, kwargs):
assert self.training
kwargs["value"] = self.m(kwargs["value"].float())
return kwargs
class MockEvaluate(object):
def __init__(self, key, model):
self.key = key
self.model = model
def __call__(self):
self.model.eval()
report({self.key: 0.0})
with tempfile.TemporaryDirectory() as tmpdir:
ws = os.path.join(tmpdir, "ws")
output = os.path.join(tmpdir, "out")
model = DummyModel()
train_supervised(ws, output, dataset, model,
torch.optim.SGD(model.parameters(),
lr=0.1), loss_fn,
MockEvaluate("key", model), "key",
collate.collate, 1, Epoch(2))
assert os.path.exists(os.path.join(ws, "snapshot_iter_6"))
assert os.path.exists(os.path.join(ws, "log"))
with open(os.path.join(ws, "log")) as file:
log = json.load(file)
assert isinstance(log, list)
assert 1 == len(log)
assert 1 == len(os.listdir(os.path.join(ws, "model")))
assert os.path.exists(os.path.join(output, "log.json"))
with open(os.path.join(output, "log.json")) as file:
log = json.load(file)
assert isinstance(log, list)
assert 1 == len(log)
assert 1 == len(os.listdir(os.path.join(output, "model")))
assert os.path.exists(os.path.join(output, "model.pt"))
assert os.path.exists(os.path.join(output, "optimizer.pt"))
def reinforce(self, train_dataset, encoder, output_dir):
with tempfile.TemporaryDirectory() as tmpdir:
interpreter = self.interpreter()
collate = Collate(
test_case_tensor=CollateOptions(False, 0, 0),
variables_tensor=CollateOptions(True, 0, 0),
previous_actions=CollateOptions(True, 0, -1),
hidden_state=CollateOptions(False, 0, 0),
state=CollateOptions(False, 0, 0),
ground_truth_actions=CollateOptions(True, 0, -1),
reward=CollateOptions(False, 0, 0)
)
collate_fn = Sequence(OrderedDict([
("to_episode", Map(self.to_episode(encoder,
interpreter))),
("flatten", Flatten()),
("transform", Map(self.transform(
encoder, interpreter, Parser()))),
("collate", collate.collate)
]))
model = self.prepare_model(encoder)
optimizer = self.prepare_optimizer(model)
train_REINFORCE(
output_dir, tmpdir, output_dir,
train_dataset,
self.prepare_synthesizer(model, encoder, interpreter),
model, optimizer,
torch.nn.Sequential(OrderedDict([
("policy",
torch.nn.Sequential(OrderedDict([
("loss",
Apply(
module=mlprogram.nn.action_sequence.Loss(
reduction="none"
),
in_keys=[
"rule_probs",
"token_probs",
"reference_probs",
"ground_truth_actions",
],
out_key="action_sequence_loss",
)),
("weight_by_reward",
Apply(
[("reward", "lhs"),
("action_sequence_loss", "rhs")],
"action_sequence_loss",
mlprogram.nn.Function(Mul())))
]))),
("value",
torch.nn.Sequential(OrderedDict([
("reshape_reward",
Apply(
[("reward", "x")],
"value_loss_target",
Reshape([-1, 1]))),
("BCE",
Apply(
[("value", "input"),
("value_loss_target", "target")],
"value_loss",
torch.nn.BCELoss(reduction='sum'))),
("reweight",
Apply(
[("value_loss", "lhs")],
"value_loss",
mlprogram.nn.Function(Mul()),
constants={"rhs": 1e-2})),
]))),
("aggregate",
Apply(
["action_sequence_loss", "value_loss"],
"loss",
AggregatedLoss())),
("normalize",
Apply(
[("loss", "lhs")],
"loss",
mlprogram.nn.Function(Div()),
constants={"rhs": 1})),
("pick",
mlprogram.nn.Function(
Pick("loss")))
])),
EvaluateSynthesizer(
train_dataset,
self.prepare_synthesizer(model, encoder, interpreter,
rollout=False),
{}, top_n=[]),
"generation_rate",
metrics.use_environment(
metric=metrics.TestCaseResult(
interpreter=interpreter,
metric=metrics.use_environment(
metric=metrics.Iou(),
in_keys=["actual", "expected"],
value_key="actual",
)
),
in_keys=["test_cases", "actual"],
value_key="actual",
transform=Threshold(threshold=0.9, dtype="float"),
),
collate_fn,
1, 1,
Epoch(10), evaluation_interval=Epoch(10),
snapshot_interval=Epoch(10),
use_pretrained_model=True,
use_pretrained_optimizer=True,
threshold=1.0)
def pretrain(self, output_dir):
dataset = Dataset(4, 1, 2, 1, 45, seed=0)
"""
"""
train_dataset = ListDataset([
Environment(
{"ground_truth": Circle(1)},
set(["ground_truth"]),
),
Environment(
{"ground_truth": Rectangle(1, 2)},
set(["ground_truth"]),
),
Environment(
{"ground_truth": Rectangle(1, 1)},
set(["ground_truth"]),
),
Environment(
{"ground_truth": Rotation(45, Rectangle(1, 1))},
set(["ground_truth"]),
),
Environment(
{"ground_truth": Translation(1, 1, Rectangle(1, 1))},
set(["ground_truth"]),
),
Environment(
{"ground_truth": Difference(Circle(1), Circle(1))},
set(["ground_truth"]),
),
Environment(
{"ground_truth": Union(Rectangle(1, 2), Circle(1))},
set(["ground_truth"]),
),
Environment(
{"ground_truth": Difference(Rectangle(1, 1), Circle(1))},
set(["ground_truth"]),
),
])
with tempfile.TemporaryDirectory() as tmpdir:
interpreter = self.interpreter()
train_dataset = data_transform(
train_dataset,
Apply(
module=AddTestCases(interpreter),
in_keys=["ground_truth"],
out_key="test_cases",
is_out_supervision=False,
))
encoder = self.prepare_encoder(dataset, Parser())
collate = Collate(
test_case_tensor=CollateOptions(False, 0, 0),
variables_tensor=CollateOptions(True, 0, 0),
previous_actions=CollateOptions(True, 0, -1),
hidden_state=CollateOptions(False, 0, 0),
state=CollateOptions(False, 0, 0),
ground_truth_actions=CollateOptions(True, 0, -1)
)
collate_fn = Sequence(OrderedDict([
("to_episode", Map(self.to_episode(encoder,
interpreter))),
("flatten", Flatten()),
("transform", Map(self.transform(
encoder, interpreter, Parser()))),
("collate", collate.collate)
]))
model = self.prepare_model(encoder)
optimizer = self.prepare_optimizer(model)
train_supervised(
tmpdir, output_dir,
train_dataset, model, optimizer,
torch.nn.Sequential(OrderedDict([
("loss",
Apply(
module=Loss(
reduction="sum",
),
in_keys=[
"rule_probs",
"token_probs",
"reference_probs",
"ground_truth_actions",
],
out_key="action_sequence_loss",
)),
("normalize", # divided by batch_size
Apply(
[("action_sequence_loss", "lhs")],
"loss",
mlprogram.nn.Function(Div()),
constants={"rhs": 1})),
("pick",
mlprogram.nn.Function(
Pick("loss")))
])),
None, "score",
collate_fn,
1, Epoch(100), evaluation_interval=Epoch(10),
snapshot_interval=Epoch(100)
)
return encoder, train_dataset
