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Bidirectional Elman RNN export to ONNX #5120

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Merged
ezyang merged 1 commit into from
Feb 9, 2018
Merged

Bidirectional Elman RNN export to ONNX #5120

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107 changes: 77 additions & 30 deletions torch/onnx/symbolic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -573,25 +573,38 @@ def symbolic(g, input, all_weights, h0, batch_sizes):
return _unimplemented("RNN", "batch_first")
if dropout:
return _unimplemented("RNN", "dropout")
if bidirectional:
return _unimplemented("RNN", "bidirectional")

unidirectional = not bidirectional

prev_output = input
h_outs = []

sequence_lens = unused(g) if batch_sizes is None else batch_sizes

for i in range(num_layers):
weight_ih, weight_hh, bias_ih, bias_hh = all_weights[i]

bias_concat = g.op('Concat', bias_ih, bias_hh, axis_i=0)

h_in = h0 if num_layers == 1 else g.op('Slice', h0, axes_i=[0], starts_i=[i], ends_i=[i + 1])
if unidirectional:
weight_ih, weight_hh, bias_ih, bias_hh = all_weights[i]
bias_concat = g.op('Concat', bias_ih, bias_hh, axis_i=0)

h_in = h0 if num_layers == 1 else g.op('Slice', h0, axes_i=[0], starts_i=[i], ends_i=[i + 1])
else:
weight_ih = g.op('Concat', all_weights[2 * i][0], all_weights[2 * i + 1][0], axis_i=0)

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weight_hh = g.op('Concat', all_weights[2 * i][1], all_weights[2 * i + 1][1], axis_i=0)
bias_concat = g.op('Concat',
all_weights[2 * i][2],
all_weights[2 * i][3],
all_weights[2 * i + 1][2],
all_weights[2 * i + 1][3],
axis_i=0)

h_in = h0 if num_layers == 1 else g.op('Slice', h0, axes_i=[0], starts_i=[2 * i], ends_i=[2 * i + 2])

inputs = [prev_output, weight_ih, weight_hh, bias_concat, sequence_lens, h_in]
extra_kwargs = {} if unidirectional else {'direction_s': 'bidirectional'}
prev_output, h_out = g.op('RNN', *inputs, outputs=2,
hidden_size_i=hidden_size,
activations_s=[nonlinearity.lower()])
activations_s=[nonlinearity.lower()],
**extra_kwargs)
h_outs.append(h_out)
h_outs = h_out if num_layers == 1 else g.op('Concat', *h_outs, axis_i=0)
return prev_output, h_outs
Expand All @@ -605,8 +618,8 @@ def symbolic(g, input, all_weights, h0_and_c0, batch_sizes):
return _unimplemented("LSTM", "batch_first")
if dropout:
return _unimplemented("LSTM", "dropout")
if bidirectional:
return _unimplemented("LSTM", "bidirectional")

unidirectional = not bidirectional

h0, c0 = h0_and_c0

Expand All @@ -616,18 +629,36 @@ def symbolic(g, input, all_weights, h0_and_c0, batch_sizes):
sequence_lens = unused(g) if batch_sizes is None else batch_sizes

for i in range(num_layers):
# pytorch is input, forget, cell, output.
# onnx is input, output, forget, cell.
weight_ih, weight_hh, bias_ih, bias_hh = \
[reform_weights(g, w, hidden_size, [(0, 1), (3, 4), (1, 3)]) for w in all_weights[i]]

bias_concat = g.op('Concat', bias_ih, bias_hh, axis_i=0)

h_in = h0 if num_layers == 1 else g.op('Slice', h0, axes_i=[0], starts_i=[i], ends_i=[i + 1])
c_in = c0 if num_layers == 1 else g.op('Slice', c0, axes_i=[0], starts_i=[i], ends_i=[i + 1])
if unidirectional:
# pytorch is input, forget, cell, output.
# onnx is input, output, forget, cell.
weight_ih, weight_hh, bias_ih, bias_hh = \
[reform_weights(g, w, hidden_size, [(0, 1), (3, 4), (1, 3)]) for w in all_weights[i]]

bias_concat = g.op('Concat', bias_ih, bias_hh, axis_i=0)

h_in = h0 if num_layers == 1 else g.op('Slice', h0, axes_i=[0], starts_i=[i], ends_i=[i + 1])
c_in = c0 if num_layers == 1 else g.op('Slice', c0, axes_i=[0], starts_i=[i], ends_i=[i + 1])
else:
# pytorch is input, forget, cell, output.
# onnx is input, output, forget, cell.
weight_ih_f, weight_hh_f, bias_ih_f, bias_hh_f = \
[reform_weights(g, w, hidden_size, [(0, 1), (3, 4), (1, 3)]) for w in all_weights[2 * i]]
weight_ih_b, weight_hh_b, bias_ih_b, bias_hh_b = \
[reform_weights(g, w, hidden_size, [(0, 1), (3, 4), (1, 3)]) for w in all_weights[2 * i + 1]]

weight_ih = g.op('Concat', weight_ih_f, weight_ih_b, axis_i=0)
weight_hh = g.op('Concat', weight_hh_f, weight_hh_b, axis_i=0)
bias_concat = g.op('Concat', bias_ih_f, bias_hh_f, bias_ih_b, bias_hh_b, axis_i=0)

h_in = h0 if num_layers == 1 else g.op('Slice', h0, axes_i=[0], starts_i=[2 * i], ends_i=[2 * i + 2])
c_in = c0 if num_layers == 1 else g.op('Slice', c0, axes_i=[0], starts_i=[2 * i], ends_i=[2 * i + 2])

inputs = [prev_output, weight_ih, weight_hh, bias_concat, sequence_lens, h_in, c_in]
prev_output, h_out = g.op('LSTM', *inputs, outputs=2, hidden_size_i=hidden_size)
extra_kwargs = {} if unidirectional else {'direction_s': 'bidirectional'}
prev_output, h_out = g.op('LSTM', *inputs, outputs=2,
hidden_size_i=hidden_size,
**extra_kwargs)
h_outs.append(h_out)
h_outs = h_out if num_layers == 1 else g.op('Concat', *h_outs, axis_i=0)
return prev_output, h_outs, None
Expand All @@ -641,27 +672,43 @@ def symbolic(g, input, all_weights, h0, batch_sizes):
return _unimplemented("GRU", "batch_first")
if dropout:
return _unimplemented("GRU", "dropout")
if bidirectional:
return _unimplemented("GRU", "bidirectional")

unidirectional = not bidirectional

prev_output = input
h_outs = []

sequence_lens = unused(g) if batch_sizes is None else batch_sizes

for i in range(num_layers):
# pytorch is reset, input, hidden
# onnx is input, reset, hidden
weight_ih, weight_hh, bias_ih, bias_hh = \
[reform_weights(g, w, hidden_size, [(1, 2), (0, 1), (2, 3)]) for w in all_weights[i]]
if unidirectional:
# pytorch is reset, input, hidden
# onnx is input, reset, hidden
weight_ih, weight_hh, bias_ih, bias_hh = \
[reform_weights(g, w, hidden_size, [(1, 2), (0, 1), (2, 3)]) for w in all_weights[i]]

bias_concat = g.op('Concat', bias_ih, bias_hh, axis_i=0)

h_in = h0 if num_layers == 1 else g.op('Slice', h0, axes_i=[0], starts_i=[i], ends_i=[i + 1])
else:
# pytorch is reset, input, hidden
# onnx is input, reset, hidden
weight_ih_f, weight_hh_f, bias_ih_f, bias_hh_f = \
[reform_weights(g, w, hidden_size, [(1, 2), (0, 1), (2, 3)]) for w in all_weights[2 * i]]
weight_ih_b, weight_hh_b, bias_ih_b, bias_hh_b = \
[reform_weights(g, w, hidden_size, [(1, 2), (0, 1), (2, 3)]) for w in all_weights[2 * i + 1]]

bias_concat = g.op('Concat', bias_ih, bias_hh, axis_i=0)
weight_ih = g.op('Concat', weight_ih_f, weight_ih_b, axis_i=0)
weight_hh = g.op('Concat', weight_hh_f, weight_hh_b, axis_i=0)
bias_concat = g.op('Concat', bias_ih_f, bias_hh_f, bias_ih_b, bias_hh_b, axis_i=0)

h_in = h0 if num_layers == 1 else g.op('Slice', h0, axes_i=[0], starts_i=[i], ends_i=[i + 1])
h_in = h0 if num_layers == 1 else g.op('Slice', h0, axes_i=[0], starts_i=[2 * i], ends_i=[2 * i + 2])

inputs = [prev_output, weight_ih, weight_hh, bias_concat, sequence_lens, h_in]
prev_output, h_out = g.op(
'GRU', *inputs, outputs=2, hidden_size_i=hidden_size, linear_before_reset_i=1)
extra_kwargs = {} if unidirectional else {'direction_s': 'bidirectional'}
prev_output, h_out = g.op('GRU', *inputs, outputs=2,
hidden_size_i=hidden_size, linear_before_reset_i=1,
**extra_kwargs)
h_outs.append(h_out)
h_outs = h_out if num_layers == 1 else g.op('Concat', *h_outs, axis_i=0)
return prev_output, h_outs
Expand Down