def _create_gru(cls, init_model, pred_model, n, opset_version):
assert init_model is not None, "cannot convert GRUs without access to the full model"
assert pred_model is not None, "cannot convert GRUs without access to the full model"
attrs = dict(n.attrs) # make a copy, which is safe to mutate
hidden_size = attrs.pop('hidden_size')
linear_before_reset = attrs.pop('linear_before_reset')
assert not attrs, "unsupported GRU attributes: " + str(attrs.keys())
input_blob, W, R, B, sequence_lens, initial_h = n.inputs
if sequence_lens == "":
sequence_lens = None
input_size = cls._rnn_shape_inference(init_model, pred_model, n,
input_blob, W)
if input_size is None:
raise RuntimeError(
"best-effort shape inference for GRU input failed")
name = dummy_name()
init_net = core.Net("init-net")
pred_mh = ModelHelper()
hidden_t_all, hidden_t_last = gru_cell.GRU(
pred_mh,
input_blob,
sequence_lens, [initial_h],
input_size,
hidden_size,
name,
drop_states=True,
forward_only=True,
linear_before_reset=linear_before_reset)
# input and recurrence biases are squashed together in onnx but not in caffe2
Bi = name + "_bias_i2h"
Br = name + "_bias_gates"
init_net.Slice(B, Bi, starts=[0 * hidden_size], ends=[3 * hidden_size])
init_net.Slice(B, Br, starts=[3 * hidden_size], ends=[6 * hidden_size])
# caffe2 has a different order from onnx. We need to rearrange
# z r h -> r z h
#
# TODO implement support for return_params in gru_cell.GRU.
# Until then, hardcode blob names.
reforms = ((W, 'i2h_w', True, [
(0, input_size)
]), (R, 'gate_t_w', False, [(0, hidden_size)]),
(Bi, 'i2h_b', True, []), (Br, 'gate_t_b', False, []))
for name_from, name_to, do_concat, extra_dims in reforms:
xz, xr, xh = [
'%s/%s_%s' % (name, prefix, name_to)
for prefix in ('update', 'reset', 'output')
]
for i, x in enumerate([xz, xr, xh]):
dim0 = i * hidden_size, (i + 1) * hidden_size
starts, ends = zip(dim0, *extra_dims)
init_net.Slice(name_from, x, starts=starts, ends=ends)
if do_concat:
init_net.Concat([xr, xz, xh],
['%s/%s' % (name, name_to),
dummy_name()],
axis=0)
pred_mh.net = pred_mh.net.Clone("dummy-clone-net",
blob_remap={
hidden_t_all: n.outputs[0],
hidden_t_last: n.outputs[1]
})
return Caffe2Ops(list(pred_mh.Proto().op), list(init_net.Proto().op),
list(pred_mh.Proto().external_input))
def make_cell(*args, **kwargs):
return gru_cell.GRU(*args,
linear_before_reset=linear_before_reset,
**kwargs)
def make_gru(direction_offset):
name = dummy_name()
# input and recurrence biases are squashed together in
# onnx but not in caffe2
bias_offset = 6 * direction_offset * hidden_size
Bi = init_net.Slice(B, name + "_bias_i2h",
starts=[bias_offset + 0 * hidden_size],
ends =[bias_offset + 3 * hidden_size])
Br = init_net.Slice(B, name + "_bias_gates",
starts=[bias_offset + 3 * hidden_size],
ends =[bias_offset + 6 * hidden_size])
weight_offset = 3 * direction_offset * hidden_size
W_ = init_net.Slice(W, name + '/i2h_w_pre',
starts=[weight_offset + 0 * hidden_size, 0],
ends =[weight_offset + 3 * hidden_size,-1])
R_ = init_net.Slice(R, name + '/gates_t_w_pre',
starts=[weight_offset + 0 * hidden_size, 0],
ends =[weight_offset + 3 * hidden_size,-1])
# caffe2 has a different order from onnx. We need to rearrange
# z r h -> r z h
reforms = ((W_, 'i2h_w', True, [(0,-1)]),
(R_, 'gate_t_w', False, [(0,-1)]),
(Bi, 'i2h_b', True, []),
(Br, 'gate_t_b', False, []))
for name_from, name_to, do_concat, extra_dims in reforms:
xz, xr, xh = ['%s/%s_%s' % (name, prefix, name_to) for prefix in ('update', 'reset', 'output')]
for i, x in enumerate([xz, xr, xh]):
dim0 = i * hidden_size, (i+1) * hidden_size
starts, ends = zip(dim0, *extra_dims)
init_net.Slice(name_from, x, starts=starts, ends=ends)
if do_concat:
init_net.Concat([xr, xz, xh], ['%s/%s' % (name, name_to), dummy_name()], axis=0)
initial_h_sliced = name + '/initial_h'
init_net.Slice(initial_h, initial_h_sliced,
starts=[direction_offset + 0, 0, 0],
ends =[direction_offset + 1,-1,-1])
if direction_offset == 1:
input = pred_mh.net.ReversePackedSegs(
[input_blob, sequence_lens], name + "/input-reversed")
else:
input = input_blob
hidden_t_all, hidden_t_last = gru_cell.GRU(
pred_mh,
input,
sequence_lens,
[initial_h_sliced],
input_size,
hidden_size,
name,
drop_states=True,
forward_only=True,
linear_before_reset=linear_before_reset
)
if direction_offset == 1:
hidden_t_all = pred_mh.net.ReversePackedSegs(
[hidden_t_all, sequence_lens], name + "/output-reversed")
return hidden_t_all, hidden_t_last