def init_theano_devices():
"""
Only for Theano.
:rtype: list[Device.Device]|None
"""
if not BackendEngine.is_theano_selected():
return None
from returnn.util.basic import TheanoFlags
from returnn.config import get_devices_init_args
from returnn.theano.device import Device
old_device_config = ",".join(config.list('device', ['default']))
if config.value("task", "train") == "nop":
return []
if "device" in TheanoFlags:
# This is important because Theano likely already has initialized that device.
config.set("device", TheanoFlags["device"])
print("Devices: Use %s via THEANO_FLAGS instead of %s." %
(TheanoFlags["device"], old_device_config),
file=log.v4)
dev_args = get_devices_init_args(config)
assert len(dev_args) > 0
devices = [Device(**kwargs) for kwargs in dev_args]
for device in devices:
while not device.initialized:
time.sleep(0.25)
if devices[0].blocking:
print("Devices: Used in blocking / single proc mode.", file=log.v4)
else:
print("Devices: Used in multiprocessing mode.", file=log.v4)
return devices
def _init_devices(self):
"""
Initiates the required devices for a config. Same as the funtion initDevices in
rnn.py.
:param config:
:return: A list with the devices used.
"""
oldDeviceConfig = ",".join(self.config.list('device', ['default']))
if "device" in TheanoFlags:
# This is important because Theano likely already has initialized that device.
config.set("device", TheanoFlags["device"])
print("Devices: Use %s via THEANO_FLAGS instead of %s." %
(TheanoFlags["device"], oldDeviceConfig),
file=log.v4)
devArgs = get_devices_init_args(self.config)
assert len(devArgs) > 0
devices = [Device(**kwargs) for kwargs in devArgs]
for device in devices:
while not device.initialized:
time.sleep(0.25)
if devices[0].blocking:
print("Devices: Used in blocking / single proc mode.", file=log.v4)
else:
print("Devices: Used in multiprocessing mode.", file=log.v4)
return devices
def finish(self):
"""
:returns whether everything is fine.
"""
device_results, outputs_format = self.device_collect_results()
if device_results is None:
if not getattr(sys, "exited", False):
print("device crashed on batch",
self.run_start_batch_idx,
file=log.v3)
self.parent.device_crash_batch = self.run_start_batch_idx
self.crashed = True
return False
assert len(device_results) == len(self.alloc_devices) == len(
self.running_devices_batches)
if outputs_format and any(
[k.startswith("gparam:") for k in outputs_format]):
# WARNING: this code is untested and likely broken!
for i in range(len(self.alloc_devices)):
res = Device.make_result_dict(device_results[i],
outputs_format)
self.alloc_devices[i].sync_net_train_params()
devnet = self.alloc_devices[i].get_net_train_params(
self.parent.network)
vars = self.parent.network.get_all_params_vars()
for p, q in zip(vars, devnet):
p.set_value(q)
gparams = {}
for p in vars:
gparams[p] = numpy.zeros(p.get_value(
borrow=True, return_internal_type=True).shape,
dtype=theano.config.floatX)
for p in vars:
q = res["gparam:%s" % p.name]
if q.shape == p.get_value().shape:
gparams[p] = q
elif q.shape:
print(
"warning: shape for gradient does not match:",
p.get_value().shape,
q.shape,
file=log.v2)
self.parent.updater.setNetParamDeltas(gparams)
self.parent.updater.update()
self.alloc_devices[i].set_net_params(self.parent.network)
self.result = {
'batchess': self.running_devices_batches,
'results': device_results,
'result_format': outputs_format,
'num_frames': self.num_frames
}
self.eval_info = self.parent.evaluate(**self.result)
self.parent.lock.acquire()
self.print_process()
self.parent.lock.release()
return True
def evaluate(self, batchess, results, result_format, num_frames):
"""
:param list[list[EngineBatch.Batch]] batchess: batches per device
:param list[list[numpy.ndarray]] results: results per device
:param list[str]|None result_format: describes what we have in a result list
:type num_frames: NumbersDict
:returns some score or None
:rtype: dict[str] | None
"""
assert results
assert result_format # train should always have the format
assert num_frames["data"] > 0
# We can get info such as "cost:..." and more info such as gradient_norm.
# See Device.initialize().
# We might also get gparams or ctc_priors or so. We will filter them out below when not needed.
results = [
Device.make_result_dict(res, result_format) for res in results
]
if 'weights' in results[0]:
for batch, result in zip(batchess, results):
self.batches.dataset.update_weights(batch[0].seqs,
result['weights'])
del result['weights']
batch_norm_fact = 1 if not self.share_batches else 1.0 / len(
self.devices)
summed_results = {}
for key in results[0].keys():
summed_results[key] = sum([res[key]
for res in results]) * batch_norm_fact
# Accumulate for epoch stats.
for key, value in summed_results.items():
if key.startswith("gparam:"): continue
if key not in self.results:
self.results[key] = value # / float(num_frames[target])
else:
self.results[key] += value # / float(num_frames[target])
# Prepare eval info stats for this (multiple-)batch run.
eval_info = {}
for key, value in summed_results.items():
if key.startswith("gparam:"): continue
if key == "ctc_priors": continue
target = self._get_target_for_key(key)
eval_info[key] = value / float(num_frames[target])
return eval_info
def test_Device_blocking_init():
config = Config()
config.update({
"multiprocessing": False,
"blocking": True,
"device": "cpu",
"num_epochs": 1,
"num_inputs": 3,
"num_outputs": 2,
})
config.network_topology_json = """
{
"output": {"class": "softmax", "loss": "ce"}
}
"""
Device("cpu", config=config, blocking=True)
def test_single_default_target_init():
config_single_default = Config()
config_single_default.update({
"multiprocessing": False,
"blocking": True,
"device": "cpu",
"num_epochs": 1,
"num_inputs": 3,
"num_outputs": 2,
})
config_single_default.network_topology_json = """
{
"output": {"class": "softmax", "loss": "ce"}
}
"""
dev = Device("cpu", config=config_single_default, blocking=True)
num_params = get_num_params(dev.trainnet.get_all_params_vars())
assert_equal(num_params, 3 * 2 + 2, "W, b")
def test_multi_target_init():
config = Config()
config.update({
"multiprocessing": False,
"blocking": True,
"device": "cpu",
"num_epochs": 1,
"num_inputs": 3,
"num_outputs": {
"t1": 4,
"t2": 5
},
"learning_rate": 1.0,
})
config.network_topology_json = """
{
"fw0": {"class": "hidden", "activation": "identity", "n_out": 3},
"out1": {"class": "softmax", "loss": "ce", "target": "t1", "from": ["fw0"]},
"out2": {"class": "softmax", "loss": "ce", "target": "t2", "from": ["fw0"]}
}
"""
device = Device("cpu", config=config, blocking=True)
assert_true(device.trainnet, "train network initialized")
assert_true(device.testnet, "test network initialized")
param_vars = device.trainnet.get_all_params_vars()
print("params:", param_vars)
assert_equal(len(param_vars), 6, "W, b vars for each out, and fw")
num_params = get_num_params(param_vars)
assert_equal(num_params, (3 * 3 + 3) + (3 * 4 + 4) + (3 * 5 + 5),
"W, b for each out, and fw")
assert_in("fw0", device.testnet.hidden)
assert_in("out1", device.testnet.output)
assert_in("out2", device.testnet.output)
assert_is(device.testnet.j["t1"], device.testnet.output["out1"].index)
assert_true(device.updater)
update_list = device.updater.getUpdateList()
print("update list:")
pprint(update_list)
update_dict = dict(update_list)
assert_equal(len(update_dict), len(update_list),
"all params in update list only once")
assert_in("fw0", device.trainnet.hidden)
assert_equal(len(device.trainnet.hidden), 1)
assert_in("W_in_data_fw0", device.trainnet.hidden["fw0"].params)
assert_in("b_fw0", device.trainnet.hidden["fw0"].params)
assert_equal(len(device.trainnet.hidden["fw0"].params), 2)
assert_in("out1", device.trainnet.output)
assert_equal(len(device.trainnet.output), 2)
assert_in("W_in_fw0_out1", device.trainnet.output["out1"].params)
assert_in("b_out1", device.trainnet.output["out1"].params)
assert_equal(len(device.trainnet.output["out1"].params), 2)
assert_in(device.trainnet.hidden["fw0"].params["W_in_data_fw0"],
update_dict)
assert_in(device.trainnet.hidden["fw0"].params["b_fw0"], update_dict)
assert_in(device.trainnet.output["out1"].params["W_in_fw0_out1"],
update_dict)
assert_in(device.trainnet.output["out1"].params["b_out1"], update_dict)
assert_in(device.trainnet.output["out2"].params["W_in_fw0_out2"],
update_dict)
assert_in(device.trainnet.output["out2"].params["b_out2"], update_dict)
# assert_equal(len(update_dict), 6) # updater adds other stuff...
# Set net params.
net_params = {
"fw0": {
"W_in_data_fw0": numpy.identity(3, dtype="float32"),
"b_fw0": numpy.zeros((3, ), dtype="float32")
},
"out1": {
"W_in_fw0_out1":
numpy.arange(0.0, 1.2, 0.1, dtype="float32").reshape((3, 4)),
"b_out1":
numpy.arange(0.0, 4, dtype="float32")
},
"out2": {
"W_in_fw0_out2":
numpy.arange(0.0, 1.5, 0.1, dtype="float32").reshape((3, 5)),
"b_out2":
numpy.arange(0.0, 5, dtype="float32")
}
}
device.trainnet.set_params_by_dict(net_params)
device.testnet.set_params_by_dict(net_params)
# Show params.
for p in param_vars:
print("init %s:" % p)
pprint(p.get_value())
# Init dataset.
dataset = StaticDataset(data=[{
"data":
numpy.array([[0.1, 0.2, -0.3]], dtype="float32"),
"t1":
numpy.array([2]),
"t2":
numpy.array([4])
}],
output_dim=config.typed_value("num_outputs"))
dataset.init_seq_order()
assert_equal(dataset.is_data_sparse("data"), False)
assert_equal(dataset.is_data_sparse("t1"), True)
assert_equal(dataset.is_data_sparse("t2"), True)
# Copy to device allocation.
success = assign_dev_data_single_seq(device, dataset, 0)
assert_true(success, "failed to allocate & assign data")
# Check allocated data.
assert_equal(device.targets["data"].shape,
(1, 1, 3)) # input shape. (time,batch,dim)
assert_in("t1", device.targets)
assert_in("t2", device.targets)
assert_equal(device.targets["t1"].shape, (1, 1))
assert_equal(device.targets["t2"].shape, (1, 1))
assert_equal(device.output_index["data"].shape, (1, 1))
numpy.testing.assert_equal(device.output_index["data"], numpy.array([[1]]))
assert_equal(device.output_index["t1"].shape, (1, 1))
numpy.testing.assert_equal(device.output_index["t1"], numpy.array([[1]]))
# Forward test.
device.update_data()
device.testnet.costs["out1"].name = "out1_cost" # nice in the func graph
out_i1 = device.testnet.output["out1"].index
out_i1_nonzero = device.testnet.output["out1"].i
nll1, pcx1 = T.nnet.crossentropy_softmax_1hot(
x=device.testnet.output["out1"].y_m[out_i1_nonzero],
y_idx=device.testnet.output["out1"].y_data_flat[out_i1_nonzero])
forward_func = theano.function(
inputs=[device.block_start, device.block_end],
outputs=[
device.testnet.j["t1"], out_i1, out_i1_nonzero[0], nll1, pcx1,
device.testnet.costs["out1"],
device.testnet.output["out1"].p_y_given_x,
device.testnet.costs["out2"],
device.testnet.output["out2"].p_y_given_x
],
givens=device.make_givens(device.testnet),
no_default_updates=True,
on_unused_input='warn',
name="forward")
#print "forward func:"
#theano.printing.debugprint(forward_func)
net_j1, out_i1_val, out_i1_nz_val, nll1_val, pcx1_val, t1_cost, t1_y, t2_cost, t2_y = forward_func(
0, 1)
print("forward results:")
pprint(net_j1)
pprint(out_i1_val)
pprint(out_i1_nz_val)
pprint(nll1_val)
pprint(pcx1_val)
pprint(t1_cost)
pprint(t1_y)
pprint(t2_cost)
pprint(t2_y)
assert_equal(net_j1, numpy.array([[1]]))
assert_equal(out_i1_val, numpy.array([[1]]))
assert_equal(out_i1_nz_val, numpy.array([0]))
assert_almost_equal(nll1_val, numpy.array([t1_cost]))
numpy.testing.assert_almost_equal(t1_y, pcx1_val[None, ...])
assert_almost_equal(t1_cost, 1.440189698561195, places=6)
assert_almost_equal(t2_cost, 0.45191439593759336, places=6)
numpy.testing.assert_almost_equal(
t1_y,
numpy.array([[[0.0320586, 0.08714432, 0.23688282, 0.64391426]]]),
decimal=6)
numpy.testing.assert_almost_equal(t2_y,
numpy.array([[[
0.01165623, 0.03168492, 0.08612854,
0.23412166, 0.63640865
]]]),
decimal=6)
# One train step.
device.set_learning_rate(config.typed_value("learning_rate"))
device.run("train")
output_list, outputs_format = device.result()
assert_is_instance(output_list, list)
assert_true(outputs_format, "for train, we should always get the format")
outputs = Device.make_result_dict(output_list, outputs_format)
pprint(outputs)
assert_in("cost:out1", outputs)
assert_greater(outputs["cost:out1"], 0)
assert_almost_equal(outputs["cost:out1"], t1_cost)
# Get net params.
params = device.get_net_train_params(device.trainnet)
references_params = {
"W_in_data_fw0":
numpy.array([[1.00055406e+00, 5.54056978e-04, 5.54056978e-04],
[1.10811396e-03, 1.00110811e+00, 1.10811396e-03],
[-1.66217093e-03, -1.66217093e-03, 9.98337829e-01]]),
"b_fw0":
numpy.array([0.00554057, 0.00554057, 0.00554057]),
"W_in_fw0_out1":
numpy.array([[-0.00320586, 0.09128557, 0.27631172, 0.23560857],
[0.39358828, 0.48257114, 0.75262344, 0.57121715],
[0.80961758, 0.9261433, 0.77106485, 1.29317428]]),
"b_out1":
numpy.array([-0.0320586, 0.91285568, 2.76311718, 2.35608574]),
"W_in_fw0_out2":
numpy.array([[
-1.16562310e-03, 9.68315079e-02, 1.91387146e-01, 2.76587834e-01,
4.36359135e-01
],
[
4.97668754e-01, 5.93663016e-01, 6.82774291e-01,
7.53175669e-01, 9.72718271e-01
],
[
1.00349687e+00, 1.10950548e+00, 1.22583856e+00,
1.37023650e+00, 1.29092259e+00
]]),
"b_out2":
numpy.array(
[-0.01165623, 0.96831508, 1.91387146, 2.76587834, 4.36359135])
}
assert_equal(len(param_vars), len(params))
for p, v in zip(param_vars, params):
print("%s:" % p)
pprint(v)
assert_true(p.name)
numpy.testing.assert_almost_equal(references_params[p.name],
v,
decimal=6)
def test_combi_auto_enc_longer():
config = Config()
config.update({
"multiprocessing": False,
"blocking": True,
"device": "cpu",
"num_epochs": 1,
"num_inputs": 3,
"num_outputs": {
"classes": 2
},
"learning_rate": 1.0,
"adadelta": True,
"network": {
"output": {
"class": "softmax",
"loss": "ce",
"target": "classes"
},
"auto-enc": {
"class": "softmax",
"loss": "sse",
"dtype": "float32",
"target": "data"
}
}
})
device = Device("cpu", config=config, blocking=True)
# Set net params.
def get_net_params(with_auto_enc=True):
d = {
"output": {
"W_in_data_output":
numpy.arange(0.1, 0.7, 0.1, dtype="float32").reshape((3, 2)),
"b_output":
numpy.arange(0.0, 2, dtype="float32")
}
}
if with_auto_enc:
d["auto-enc"] = {
"W_in_data_auto-enc":
numpy.arange(0.1, 1.0, 0.1, dtype="float32").reshape((3, 3)),
"b_auto-enc":
numpy.arange(0.0, 3, dtype="float32")
}
return d
device.trainnet.set_params_by_dict(get_net_params())
device.testnet.set_params_by_dict(get_net_params())
# Show params.
for p in device.trainnet.get_all_params_vars():
print("init %s:" % p)
pprint(p.get_value())
# Init dataset.
dataset = DummyDataset(input_dim=config.typed_value("num_inputs"),
output_dim=config.typed_value("num_outputs"),
num_seqs=10)
dataset.init_seq_order()
cost_output_sum = 0.0
for seq_idx in range(dataset.num_seqs):
# Copy to device allocation.
success = assign_dev_data_single_seq(device, dataset, seq_idx)
assert_true(success, "failed to allocate & assign data")
# One train step.
device.set_learning_rate(config.typed_value("learning_rate"))
device.run("train")
output_list, outputs_format = device.result()
assert_is_instance(output_list, list)
assert_true(outputs_format,
"for train, we should always get the format")
outputs = Device.make_result_dict(output_list, outputs_format)
print(("seq %i" % seq_idx))
pprint(outputs)
assert_in("cost:output", outputs)
assert_in("cost:auto-enc", outputs)
cost_output_sum += outputs["cost:output"]
# Now, drop the auto-enc from the network, and redo the same thing.
del config.typed_value("network")["auto-enc"]
device = Device("cpu", config=config, blocking=True)
device.trainnet.set_params_by_dict(get_net_params(with_auto_enc=False))
device.testnet.set_params_by_dict(get_net_params(with_auto_enc=False))
for p in device.trainnet.get_all_params_vars():
print("second run, init %s:" % p)
pprint(p.get_value())
dataset.init_seq_order() # reset
cost2_output_sum = 0.0
for seq_idx in range(dataset.num_seqs):
# Copy to device allocation.
success = assign_dev_data_single_seq(device, dataset, seq_idx)
assert_true(success, "failed to allocate & assign data")
# One train step.
device.set_learning_rate(config.typed_value("learning_rate"))
device.run("train")
output_list, outputs_format = device.result()
assert_is_instance(output_list, list)
assert_true(outputs_format,
"for train, we should always get the format")
outputs = Device.make_result_dict(output_list, outputs_format)
print(("seq %i" % seq_idx))
pprint(outputs)
assert_in("cost:output", outputs)
assert_not_in("cost:auto-enc", outputs)
cost2_output_sum += outputs["cost:output"]
assert_equal(cost_output_sum, cost2_output_sum)
assert_almost_equal(cost_output_sum, 16.028842568397522, places=6)
def test_combi_auto_enc():
config = Config()
config.update({
"multiprocessing": False,
"blocking": True,
"device": "cpu",
"num_epochs": 1,
"num_inputs": 3,
"num_outputs": {
"classes": 2
},
"learning_rate": 1.0,
"network": {
"output": {
"class": "softmax",
"loss": "ce",
"target": "classes"
},
"auto-enc": {
"class": "softmax",
"loss": "sse",
"dtype": "float32",
"target": "data"
}
}
})
device = Device("cpu", config=config, blocking=True)
# Set net params.
def get_net_params(with_auto_enc=True):
d = {
"output": {
"W_in_data_output":
numpy.arange(0.1, 0.7, 0.1, dtype="float32").reshape((3, 2)),
"b_output":
numpy.arange(0.0, 2, dtype="float32")
}
}
if with_auto_enc:
d["auto-enc"] = {
"W_in_data_auto-enc":
numpy.arange(0.1, 1.0, 0.1, dtype="float32").reshape((3, 3)),
"b_auto-enc":
numpy.arange(0.0, 3, dtype="float32")
}
return d
device.trainnet.set_params_by_dict(get_net_params())
device.testnet.set_params_by_dict(get_net_params())
# Show params.
for p in device.trainnet.get_all_params_vars():
print("init %s:" % p)
pprint(p.get_value())
# Init dataset.
dataset = StaticDataset(data=[{
"data":
numpy.array([[0.1, 0.2, -0.3]], dtype="float32"),
"classes":
numpy.array([1]),
}],
output_dim=config.typed_value("num_outputs"))
dataset.init_seq_order()
# Copy to device allocation.
success = assign_dev_data_single_seq(device, dataset, 0)
assert_true(success, "failed to allocate & assign data")
# One train step.
device.set_learning_rate(config.typed_value("learning_rate"))
device.run("train")
output_list, outputs_format = device.result()
assert_is_instance(output_list, list)
assert_true(outputs_format, "for train, we should always get the format")
outputs = Device.make_result_dict(output_list, outputs_format)
pprint(outputs)
assert_in("cost:output", outputs)
assert_in("cost:auto-enc", outputs)
expected_cost_output = 0.3132616877555847
assert_almost_equal(outputs["cost:output"], expected_cost_output, places=6)
exact_cost_output = outputs["cost:output"]
assert_almost_equal(outputs["cost:auto-enc"], 1.7544001340866089, places=6)
# Now, drop the auto-enc from the network, and redo the same thing.
del config.typed_value("network")["auto-enc"]
device = Device("cpu", config=config, blocking=True)
device.trainnet.set_params_by_dict(get_net_params(with_auto_enc=False))
device.testnet.set_params_by_dict(get_net_params(with_auto_enc=False))
for p in device.trainnet.get_all_params_vars():
print("second run, init %s:" % p)
pprint(p.get_value())
dataset.init_seq_order() # reset. probably not needed
success = assign_dev_data_single_seq(device, dataset, 0)
assert_true(success, "failed to allocate & assign data")
device.set_learning_rate(config.typed_value("learning_rate"))
device.run("train")
output_list, outputs_format = device.result()
assert_is_instance(output_list, list)
assert_true(outputs_format, "for train, we should always get the format")
outputs = Device.make_result_dict(output_list, outputs_format)
pprint(outputs)
assert_in("cost:output", outputs)
assert_not_in("cost:auto-enc", outputs)
assert_almost_equal(outputs["cost:output"], expected_cost_output, places=6)
assert_equal(outputs["cost:output"], exact_cost_output)
def test_DeviceBatchRun_outputs_format():
# TODO: This is broken...
return
dev_run = DummyDeviceBatchRun(task="train")
assert len(dev_run.alloc_devices) == 1
# Simulate epoch start.
trainer = dev_run.parent
dev_run.alloc_devices[0].start_epoch_stats()
trainer.initialize()
# Simulate one batch.
dev_run.allocate()
dev_run.device_run()
dev_run.set_dummy_dev_output(outputs_format=["cost:foo"], output=[1.42])
dev_run.finish()
assert_is_instance(dev_run.result, dict)
assert_in("results", dev_run.result)
res_outputss = dev_run.result["results"]
assert_is_instance(res_outputss, list)
assert_equal(len(res_outputss), len(dev_run.alloc_devices))
res_outputs = res_outputss[0]
assert_is_instance(res_outputs, list)
res_outputs_format = dev_run.result["result_format"]
assert_is_instance(res_outputs_format, list)
res = Device.make_result_dict(res_outputs, res_outputs_format)
assert_is_instance(res, dict)
pprint(res)
# Simulate epoch end.
print("train epoch score:", trainer.score, "elapsed:",
hms(trainer.elapsed))
trainer.finalize()
dev_run.alloc_devices[0].finish_epoch_stats()
# Now simulate the eval.
dev_run = DummyDeviceBatchRun(task="eval")
assert len(dev_run.alloc_devices) == 1
# Simulate epoch start.
tester = dev_run.parent
dev_run.alloc_devices[0].start_epoch_stats()
tester.initialize()
# Simulate one batch.
dev_run.allocate()
dev_run.device_run()
dev_run.set_dummy_dev_output(outputs_format=["cost:foo", "error:foo"],
output=[1.42, 2.34])
dev_run.finish()
# Simulate epoch end.
print("eval epoch elapsed:", hms(tester.elapsed))
tester.finalize()
dev_run.alloc_devices[0].finish_epoch_stats()
print("eval results:", tester.score, tester.error)
assert_is_instance(dev_run.result, dict)
assert_in("results", dev_run.result)
res_outputss = dev_run.result["results"]
assert_is_instance(res_outputss, list)
assert_equal(len(res_outputss), len(dev_run.alloc_devices))
res_outputs = res_outputss[0]
assert_is_instance(res_outputs, list)
res_outputs_format = dev_run.result["result_format"]
assert_is_instance(res_outputs_format, list)
res = Device.make_result_dict(res_outputs, res_outputs_format)
assert_is_instance(res, dict)
pprint(res)
assert_greater(tester.score, 0)
assert_greater(tester.error, 0)