/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/gru_test.py (12932B)
from caffe2.python import workspace, core, scope, gru_cell
from caffe2.python.model_helper import ModelHelper
from caffe2.python.rnn.rnn_cell_test_util import sigmoid, tanh, _prepare_rnn
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from caffe2.proto import caffe2_pb2
from functools import partial
from hypothesis import given
from hypothesis import settings as ht_settings
import hypothesis.strategies as st
import numpy as np
import unittest
def gru_unit(*args, **kwargs):
'''
Implements one GRU unit, for one time step
Shapes:
hidden_t_prev.shape = (1, N, D)
gates_out_t.shape = (1, N, G)
seq_lenths.shape = (N,)
'''
drop_states = kwargs.get('drop_states', False)
sequence_lengths = kwargs.get('sequence_lengths', True)
if sequence_lengths:
hidden_t_prev, gates_out_t, seq_lengths, timestep = args
else:
hidden_t_prev, gates_out_t, timestep = args
N = hidden_t_prev.shape[1]
D = hidden_t_prev.shape[2]
G = gates_out_t.shape[2]
t = (timestep * np.ones(shape=(N, D))).astype(np.int32)
assert t.shape == (N, D)
assert G == 3 * D
# Calculate reset, update, and output gates separately
# because output gate depends on reset gate.
gates_out_t = gates_out_t.reshape(N, 3, D)
reset_gate_t = gates_out_t[:, 0, :].reshape(N, D)
update_gate_t = gates_out_t[:, 1, :].reshape(N, D)
output_gate_t = gates_out_t[:, 2, :].reshape(N, D)
# Calculate gate outputs.
reset_gate_t = sigmoid(reset_gate_t)
update_gate_t = sigmoid(update_gate_t)
output_gate_t = tanh(output_gate_t)
if sequence_lengths:
seq_lengths = (np.ones(shape=(N, D)) *
seq_lengths.reshape(N, 1)).astype(np.int32)
assert seq_lengths.shape == (N, D)
valid = (t < seq_lengths).astype(np.int32)
else:
valid = np.ones(shape=(N, D))
assert valid.shape == (N, D)
hidden_t = update_gate_t * hidden_t_prev + \
(1 - update_gate_t) * output_gate_t
hidden_t = hidden_t * valid + hidden_t_prev * \
(1 - valid) * (1 - drop_states)
hidden_t = hidden_t.reshape(1, N, D)
return (hidden_t, )
def gru_reference(input, hidden_input,
reset_gate_w, reset_gate_b,
update_gate_w, update_gate_b,
output_gate_w, output_gate_b,
seq_lengths, drop_states=False,
linear_before_reset=False):
D = hidden_input.shape[hidden_input.ndim - 1]
T = input.shape[0]
N = input.shape[1]
G = input.shape[2]
print("Dimensions: T= ", T, " N= ", N, " G= ", G, " D= ", D)
hidden = np.zeros(shape=(T + 1, N, D))
hidden[0, :, :] = hidden_input
for t in range(T):
input_t = input[t].reshape(1, N, G)
hidden_t_prev = hidden[t].reshape(1, N, D)
# Split input contributions for three gates.
input_t = input_t.reshape(N, 3, D)
input_reset = input_t[:, 0, :].reshape(N, D)
input_update = input_t[:, 1, :].reshape(N, D)
input_output = input_t[:, 2, :].reshape(N, D)
reset_gate = np.dot(hidden_t_prev, reset_gate_w.T) + reset_gate_b
reset_gate = reset_gate + input_reset
update_gate = np.dot(hidden_t_prev, update_gate_w.T) + update_gate_b
update_gate = update_gate + input_update
if linear_before_reset:
with_linear = np.dot(
hidden_t_prev, output_gate_w.T) + output_gate_b
output_gate = sigmoid(reset_gate) * with_linear
else:
with_reset = hidden_t_prev * sigmoid(reset_gate)
output_gate = np.dot(with_reset, output_gate_w.T) + output_gate_b
output_gate = output_gate + input_output
gates_out_t = np.concatenate(
(reset_gate, update_gate, output_gate),
axis=2,
)
print(reset_gate, update_gate, output_gate, gates_out_t, sep="\n")
(hidden_t, ) = gru_unit(
hidden_t_prev,
gates_out_t,
seq_lengths,
t,
drop_states=drop_states
)
hidden[t + 1] = hidden_t
return (
hidden[1:],
hidden[-1].reshape(1, N, D),
)
def gru_unit_op_input():
'''
Create input tensor where each dimension is from 1 to 4, ndim=3 and
last dimension size is a factor of 3
hidden_t_prev.shape = (1, N, D)
'''
dims_ = st.tuples(
st.integers(min_value=1, max_value=1), # 1, one timestep
st.integers(min_value=1, max_value=4), # n
st.integers(min_value=1, max_value=4), # d
)
def create_input(dims):
dims = list(dims)
dims[2] *= 3
return hu.arrays(dims)
return dims_.flatmap(create_input)
def gru_input():
'''
Create input tensor where each dimension is from 1 to 4, ndim=3 and
last dimension size is a factor of 3
'''
dims_ = st.tuples(
st.integers(min_value=1, max_value=4), # t
st.integers(min_value=1, max_value=4), # n
st.integers(min_value=1, max_value=4), # d
)
def create_input(dims):
dims = list(dims)
dims[2] *= 3
return hu.arrays(dims)
return dims_.flatmap(create_input)
def _prepare_gru_unit_op(gc, n, d, outputs_with_grads,
forward_only=False, drop_states=False,
sequence_lengths=False,
two_d_initial_states=None):
print("Dims: (n,d) = ({},{})".format(n, d))
def generate_input_state(n, d):
if two_d_initial_states:
return np.random.randn(n, d).astype(np.float32)
else:
return np.random.randn(1, n, d).astype(np.float32)
model = ModelHelper(name='external')
with scope.NameScope("test_name_scope"):
if sequence_lengths:
hidden_t_prev, gates_t, seq_lengths, timestep = \
model.net.AddScopedExternalInputs(
"hidden_t_prev",
"gates_t",
'seq_lengths',
"timestep",
)
else:
hidden_t_prev, gates_t, timestep = \
model.net.AddScopedExternalInputs(
"hidden_t_prev",
"gates_t",
"timestep",
)
workspace.FeedBlob(
hidden_t_prev,
generate_input_state(n, d).astype(np.float32),
device_option=gc
)
workspace.FeedBlob(
gates_t,
generate_input_state(n, 3 * d).astype(np.float32),
device_option=gc
)
if sequence_lengths:
inputs = [hidden_t_prev, gates_t, seq_lengths, timestep]
else:
inputs = [hidden_t_prev, gates_t, timestep]
hidden_t = model.net.GRUUnit(
inputs,
['hidden_t'],
forget_bias=0.0,
drop_states=drop_states,
sequence_lengths=sequence_lengths,
)
model.net.AddExternalOutputs(hidden_t)
workspace.RunNetOnce(model.param_init_net)
if sequence_lengths:
# 10 is used as a magic number to simulate some reasonable timestep
# and generate some reasonable seq. lengths
workspace.FeedBlob(
seq_lengths,
np.random.randint(1, 10, size=(n,)).astype(np.int32),
device_option=gc
)
workspace.FeedBlob(
timestep,
np.random.randint(1, 10, size=(1,)).astype(np.int32),
device_option=core.DeviceOption(caffe2_pb2.CPU),
)
print("Feed {}".format(timestep))
return hidden_t, model.net
class GRUCellTest(serial.SerializedTestCase):
# Test just for GRUUnitOp
@serial.given(
seed=st.integers(0, 2**32 - 1),
input_tensor=gru_unit_op_input(),
fwd_only=st.booleans(),
drop_states=st.booleans(),
sequence_lengths=st.booleans(),
**hu.gcs
)
def test_gru_unit_op(self, seed, input_tensor, fwd_only,
drop_states, sequence_lengths, gc, dc):
np.random.seed(seed)
outputs_with_grads = [0]
ref = gru_unit
ref = partial(ref)
t, n, d = input_tensor.shape
assert d % 3 == 0
d = d // 3
ref = partial(ref, drop_states=drop_states,
sequence_lengths=sequence_lengths)
with core.DeviceScope(gc):
net = _prepare_gru_unit_op(gc, n, d,
outputs_with_grads=outputs_with_grads,
forward_only=fwd_only,
drop_states=drop_states,
sequence_lengths=sequence_lengths)[1]
# here we don't provide a real input for the net but just for one of
# its ops (RecurrentNetworkOp). So have to hardcode this name
workspace.FeedBlob("test_name_scope/external/recurrent/i2h",
input_tensor,
device_option=gc)
print(str(net.Proto()))
op = net._net.op[-1]
inputs = [workspace.FetchBlob(name) for name in op.input]
self.assertReferenceChecks(
gc,
op,
inputs,
ref,
input_device_options={"test_name_scope/timestep": hu.cpu_do},
outputs_to_check=[0],
)
# Checking for hidden_prev and gates gradients
if not fwd_only:
for param in range(2):
print("Check param {}".format(param))
self.assertGradientChecks(
device_option=gc,
op=op,
inputs=inputs,
outputs_to_check=param,
outputs_with_grads=outputs_with_grads,
threshold=0.0001,
stepsize=0.005,
input_device_options={
"test_name_scope/timestep": hu.cpu_do},
)
@given(
seed=st.integers(0, 2**32 - 1),
input_tensor=gru_input(),
fwd_only=st.booleans(),
drop_states=st.booleans(),
linear_before_reset=st.booleans(),
**hu.gcs
)
@ht_settings(max_examples=20, deadline=None)
def test_gru_main(self, seed, **kwargs):
np.random.seed(seed)
for outputs_with_grads in [[0], [1], [0, 1]]:
self.gru_base(gru_cell.GRU, gru_reference,
outputs_with_grads=outputs_with_grads,
**kwargs)
def gru_base(self, create_rnn, ref, outputs_with_grads,
input_tensor, fwd_only, drop_states, linear_before_reset, gc, dc):
print("GRU test parameters: ", locals())
t, n, d = input_tensor.shape
assert d % 3 == 0
d = d // 3
ref = partial(ref,
drop_states=drop_states,
linear_before_reset=linear_before_reset)
with core.DeviceScope(gc):
net = _prepare_rnn(
t, n, d, create_rnn,
outputs_with_grads=outputs_with_grads,
memory_optim=False,
forget_bias=0.0,
forward_only=fwd_only,
drop_states=drop_states,
linear_before_reset=linear_before_reset,
num_states=1,
)[1]
# here we don't provide a real input for the net but just for one of
# its ops (RecurrentNetworkOp). So have to hardcode this name
workspace.FeedBlob("test_name_scope/external/recurrent/i2h",
input_tensor,
device_option=gc)
op = net._net.op[-1]
inputs = [workspace.FetchBlob(name) for name in op.input]
self.assertReferenceChecks(
gc,
op,
inputs,
ref,
input_device_options={"test_name_scope/timestep": hu.cpu_do},
outputs_to_check=list(range(2)),
)
# Checking for input, gates_t_w and gates_t_b gradients
if not fwd_only:
for param in range(2):
print("Check param {}".format(param))
self.assertGradientChecks(
device_option=gc,
op=op,
inputs=inputs,
outputs_to_check=param,
outputs_with_grads=outputs_with_grads,
threshold=0.001,
stepsize=0.005,
input_device_options={
"test_name_scope/timestep": hu.cpu_do},
)
if __name__ == "__main__":
workspace.GlobalInit([
'caffe2',
'--caffe2_log_level=0',
])
unittest.main()