mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
152d2ae3a8
Summary: Add CUDA version of GRU operator Reviewed By: jamesr66a Differential Revision: D5571043 fbshipit-source-id: 332aa64fc8a9116cc33382f2b2907080e58c13b3
332 lines
11 KiB
Python
332 lines
11 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
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
|
|
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
|
|
|
|
|
|
def gru_unit(hidden_t_prev, gates_out_t,
|
|
seq_lengths, timestep, drop_states=False):
|
|
'''
|
|
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,)
|
|
'''
|
|
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)
|
|
seq_lengths = (np.ones(shape=(N, D)) *
|
|
seq_lengths.reshape(N, 1)).astype(np.int32)
|
|
assert seq_lengths.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)
|
|
|
|
valid = (t < seq_lengths).astype(np.int32)
|
|
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):
|
|
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
|
|
|
|
output_gate = np.dot(hidden_t_prev * sigmoid(reset_gate), 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=hidden_t_prev,
|
|
gates_out_t=gates_out_t,
|
|
seq_lengths=seq_lengths,
|
|
timestep=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,
|
|
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"):
|
|
hidden_t_prev, gates_t, seq_lengths, timestep = \
|
|
model.net.AddScopedExternalInputs(
|
|
"hidden_t_prev",
|
|
"gates_t",
|
|
'seq_lengths',
|
|
"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
|
|
)
|
|
|
|
hidden_t = model.net.GRUUnit(
|
|
[
|
|
hidden_t_prev,
|
|
gates_t,
|
|
seq_lengths,
|
|
timestep,
|
|
],
|
|
['hidden_t'],
|
|
forget_bias=0.0,
|
|
drop_states=drop_states,
|
|
)
|
|
model.net.AddExternalOutputs(hidden_t)
|
|
workspace.RunNetOnce(model.param_init_net)
|
|
|
|
# 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(hu.HypothesisTestCase):
|
|
|
|
# Test just for GRUUnitOp
|
|
@given(
|
|
input_tensor=gru_unit_op_input(),
|
|
fwd_only=st.booleans(),
|
|
drop_states=st.booleans(),
|
|
**hu.gcs
|
|
)
|
|
@ht_settings(max_examples=15)
|
|
def test_gru_unit_op(self, input_tensor, fwd_only, drop_states, gc, dc):
|
|
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)
|
|
|
|
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)[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={op.input[3]: 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={op.input[3]: hu.cpu_do},
|
|
)
|
|
|
|
@given(
|
|
input_tensor=gru_input(),
|
|
fwd_only=st.booleans(),
|
|
drop_states=st.booleans(),
|
|
**hu.gcs
|
|
)
|
|
@ht_settings(max_examples=15)
|
|
def test_gru_main(self, **kwargs):
|
|
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, 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)
|
|
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)[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={"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={"timestep": hu.cpu_do},
|
|
)
|