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from caffe2.python import workspace, core, rnn_cell
from caffe2.python.model_helper import ModelHelper
from caffe2.python.rnn.rnn_cell_test_util import tanh
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given
from hypothesis import settings as ht_settings
import hypothesis.strategies as st
import numpy as np
import unittest
def basic_rnn_reference(input, hidden_initial,
i2h_w, i2h_b,
gate_w, gate_b,
seq_lengths,
drop_states,
use_sequence_lengths):
D = hidden_initial.shape[-1]
T = input.shape[0]
N = input.shape[1]
if seq_lengths is not None:
seq_lengths = (np.ones(shape=(N, D)) *
seq_lengths.reshape(N, 1)).astype(np.int32)
ret = []
hidden_prev = hidden_initial
for t in range(T):
input_fc = np.dot(input[t], i2h_w.T) + i2h_b
recur_fc = np.dot(hidden_prev, gate_w.T) + gate_b
hidden_t = tanh(input_fc + recur_fc)
if seq_lengths is not None:
valid = (t < seq_lengths).astype(np.int32)
assert valid.shape == (N, D), (valid.shape, (N, D))
hidden_t = hidden_t * valid + \
hidden_prev * (1 - valid) * (1 - drop_states)
ret.append(hidden_t)
hidden_prev = hidden_t
return ret
class BasicRNNCellTest(hu.HypothesisTestCase):
@given(
seed=st.integers(0, 2**32 - 1),
seq_length=st.integers(min_value=1, max_value=5),
batch_size=st.integers(min_value=1, max_value=5),
input_size=st.integers(min_value=1, max_value=5),
hidden_size=st.integers(min_value=1, max_value=5),
drop_states=st.booleans(),
sequence_lengths=st.booleans(),
**hu.gcs
)
@ht_settings(max_examples=15)
def test_basic_rnn(self, seed, seq_length, batch_size, input_size, hidden_size,
drop_states, sequence_lengths, gc, dc):
np.random.seed(seed)
seq_lengths_data = np.random.randint(
1, seq_length + 1, size=(batch_size,)).astype(np.int32)
input_blob_data = np.random.randn(
seq_length, batch_size, input_size).astype(np.float32)
initial_h_data = np.random.randn(
batch_size, hidden_size).astype(np.float32)
gates_t_w_data = np.random.randn(
hidden_size, hidden_size).astype(np.float32)
gates_t_b_data = np.random.randn(
hidden_size).astype(np.float32)
i2h_w_data = np.random.randn(
hidden_size, input_size).astype(np.float32)
i2h_b_data = np.random.randn(
hidden_size).astype(np.float32)
with core.DeviceScope(gc):
with hu.temp_workspace():
workspace.FeedBlob(
'input_blob', input_blob_data, device_option=gc)
workspace.FeedBlob(
'seq_lengths', seq_lengths_data, device_option=gc)
workspace.FeedBlob(
'initial_h', initial_h_data, device_option=gc)
workspace.FeedBlob(
'basic_rnn/gates_t_w', gates_t_w_data, device_option=gc)
workspace.FeedBlob(
'basic_rnn/gates_t_b', gates_t_b_data, device_option=gc)
workspace.FeedBlob(
'basic_rnn/i2h_w', i2h_w_data, device_option=gc)
workspace.FeedBlob(
'basic_rnn/i2h_b', i2h_b_data, device_option=gc)
model = ModelHelper(name='model')
hidden_t_all, _ = rnn_cell.BasicRNN(
model,
'input_blob',
'seq_lengths' if sequence_lengths else None,
['initial_h'],
input_size,
hidden_size,
"basic_rnn",
activation='tanh',
forward_only=True,
drop_states=drop_states)
workspace.RunNetOnce(model.net)
result = workspace.FetchBlob(hidden_t_all)
reference = basic_rnn_reference(
input_blob_data,
initial_h_data,
i2h_w_data,
i2h_b_data,
gates_t_w_data,
gates_t_b_data,
seq_lengths_data if sequence_lengths else None,
drop_states=drop_states,
use_sequence_lengths=sequence_lengths
)
np.testing.assert_allclose(result, reference, atol=1e-4, rtol=1e-4)
if __name__ == "__main__":
workspace.GlobalInit([
'caffe2',
'--caffe2_log_level=0',
])
unittest.main()
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