diff options
Diffstat (limited to 'nn/runtime/test/specs/V1_3')
-rw-r--r-- | nn/runtime/test/specs/V1_3/bidirectional_sequence_lstm.mod.py | 550 |
1 files changed, 550 insertions, 0 deletions
diff --git a/nn/runtime/test/specs/V1_3/bidirectional_sequence_lstm.mod.py b/nn/runtime/test/specs/V1_3/bidirectional_sequence_lstm.mod.py new file mode 100644 index 000000000..204b7f125 --- /dev/null +++ b/nn/runtime/test/specs/V1_3/bidirectional_sequence_lstm.mod.py @@ -0,0 +1,550 @@ +# +# Copyright (C) 2019 The Android Open Source Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Bidirectional Sequence LSTM Test: +# FLOAT32, No Layer Normalization, No Cifg, No Peephole, No Projection, and No Clipping. + + +def test(name, + n_batch, + n_fw_input, + n_bw_input, + n_cell, + n_output, + max_time, + input_data=[], + fw_input_to_input_weights_data=[], + fw_input_to_forget_weights_data=[], + fw_input_to_cell_weights_data=[], + fw_input_to_output_weights_data=[], + fw_recurrent_to_input_weights_data=[], + fw_recurrent_to_forget_weights_data=[], + fw_recurrent_to_cell_weights_data=[], + fw_recurrent_to_output_weights_data=[], + fw_cell_to_input_weights_data=[], + fw_cell_to_forget_weights_data=[], + fw_cell_to_output_weights_data=[], + fw_input_gate_bias_data=[], + fw_forget_gate_bias_data=[], + fw_cell_bias_data=[], + fw_output_gate_bias_data=[], + fw_projection_weights_data=[], + fw_projection_bias_data=[], + bw_input_to_input_weights_data=[], + bw_input_to_forget_weights_data=[], + bw_input_to_cell_weights_data=[], + bw_input_to_output_weights_data=[], + bw_recurrent_to_input_weights_data=[], + bw_recurrent_to_forget_weights_data=[], + bw_recurrent_to_cell_weights_data=[], + bw_recurrent_to_output_weights_data=[], + bw_cell_to_input_weights_data=[], + bw_cell_to_forget_weights_data=[], + bw_cell_to_output_weights_data=[], + bw_input_gate_bias_data=[], + bw_forget_gate_bias_data=[], + bw_cell_bias_data=[], + bw_output_gate_bias_data=[], + bw_projection_weights_data=[], + bw_projection_bias_data=[], + fw_activation_state_data=[], + fw_cell_state_data=[], + bw_activation_state_data=[], + bw_cell_state_data=[], + aux_input_data=[], + fw_aux_input_to_input_weights_data=[], + fw_aux_input_to_forget_weights_data=[], + fw_aux_input_to_cell_weights_data=[], + fw_aux_input_to_output_weights_data=[], + bw_aux_input_to_input_weights_data=[], + bw_aux_input_to_forget_weights_data=[], + bw_aux_input_to_cell_weights_data=[], + bw_aux_input_to_output_weights_data=[], + fw_input_layer_norm_weights_data=[], + fw_forget_layer_norm_weights_data=[], + fw_cell_layer_norm_weights_data=[], + fw_output_layer_norm_weights_data=[], + bw_input_layer_norm_weights_data=[], + bw_forget_layer_norm_weights_data=[], + bw_cell_layer_norm_weights_data=[], + bw_output_layer_norm_weights_data=[], + fw_output_data=[], + bw_output_data=[], + ignore_fw_output=False): + input = Input("input", "TENSOR_FLOAT32", + "{{{}, {}, {}}}".format(max_time, n_batch, n_fw_input)) + + fw_input_to_input_weights = Input("fw_input_to_input_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_fw_input)) + fw_input_to_forget_weights = Input("fw_input_to_forget_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_fw_input)) + fw_input_to_cell_weights = Input("fw_input_to_cell_weights", "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_fw_input)) + fw_input_to_output_weights = Input("fw_input_to_output_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_fw_input)) + + fw_recurrent_to_input_weights = Input("fw_recurrent_to_input_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_output)) + fw_recurrent_to_forget_weights = Input("fw_recurrent_to_forget_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_output)) + fw_recurrent_to_cell_weights = Input("fw_recurrent_to_cell_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_output)) + fw_recurrent_to_output_weights = Input("fw_recurrent_to_output_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_output)) + + fw_cell_to_input_weights = Input("fw_cell_to_input_weights", "TENSOR_FLOAT32", + "{0}") + fw_cell_to_forget_weights = Input("fw_cell_to_forget_weights", + "TENSOR_FLOAT32", "{0}") + fw_cell_to_output_weights = Input("fw_cell_to_output_weights", + "TENSOR_FLOAT32", "{0}") + + fw_input_gate_bias = Input("fw_input_gate_bias", "TENSOR_FLOAT32", + "{{{}}}".format(n_cell)) + fw_forget_gate_bias = Input("fw_forget_gate_bias", "TENSOR_FLOAT32", + "{{{}}}".format(n_cell)) + fw_cell_bias = Input("fw_cell_bias", "TENSOR_FLOAT32", + "{{{}}}".format(n_cell)) + fw_output_gate_bias = Input("fw_output_gate_bias", "TENSOR_FLOAT32", + "{{{}}}".format(n_cell)) + + fw_projection_weights = Input("fw_projection_weights", "TENSOR_FLOAT32", + "{0}") + fw_projection_bias = Input("fw_projection_bias", "TENSOR_FLOAT32", "{0}") + + bw_input_to_input_weights = Input("bw_input_to_input_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_bw_input)) + bw_input_to_forget_weights = Input("bw_input_to_forget_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_bw_input)) + bw_input_to_cell_weights = Input("bw_input_to_cell_weights", "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_bw_input)) + bw_input_to_output_weights = Input("bw_input_to_output_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_bw_input)) + + bw_recurrent_to_input_weights = Input("bw_recurrent_to_input_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_output)) + bw_recurrent_to_forget_weights = Input("bw_recurrent_to_forget_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_output)) + bw_recurrent_to_cell_weights = Input("bw_recurrent_to_cell_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_output)) + bw_recurrent_to_output_weights = Input("bw_recurrent_to_output_weights", + "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_cell, n_output)) + + bw_cell_to_input_weights = Input("bw_cell_to_input_weights", "TENSOR_FLOAT32", + "{0}") + bw_cell_to_forget_weights = Input("bw_cell_to_forget_weights", + "TENSOR_FLOAT32", "{0}") + bw_cell_to_output_weights = Input("bw_cell_to_output_weights", + "TENSOR_FLOAT32", "{0}") + + bw_input_gate_bias = Input("bw_input_gate_bias", "TENSOR_FLOAT32", + "{{{}}}".format(n_cell)) + bw_forget_gate_bias = Input("bw_forget_gate_bias", "TENSOR_FLOAT32", + "{{{}}}".format(n_cell)) + bw_cell_bias = Input("bw_cell_bias", "TENSOR_FLOAT32", + "{{{}}}".format(n_cell)) + bw_output_gate_bias = Input("bw_output_gate_bias", "TENSOR_FLOAT32", + "{{{}}}".format(n_cell)) + + bw_projection_weights = Input("bw_projection_weights", "TENSOR_FLOAT32", + "{0}") + bw_projection_bias = Input("bw_projection_bias", "TENSOR_FLOAT32", "{0}") + + fw_activation_state = Input("fw_activatiom_state", "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_batch, n_output)) + fw_cell_state = Input("fw_cell_state", "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_batch, n_cell)) + + bw_activation_state = Input("bw_activatiom_state", "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_batch, n_output)) + bw_cell_state = Input("bw_cell_state", "TENSOR_FLOAT32", + "{{{}, {}}}".format(n_batch, n_cell)) + + aux_input = Input("input", "TENSOR_FLOAT32", + "{{{}, {}, {}}}".format(max_time, n_batch, n_bw_input)) + + fw_aux_input_to_input_weights = Input("fw_aux_input_to_input_weights", + "TENSOR_FLOAT32", "{0}") + fw_aux_input_to_forget_weights = Input("fw_input_to_forget_weights", + "TENSOR_FLOAT32", "{0}") + fw_aux_input_to_cell_weights = Input("fw_aux_input_to_cell_weights", + "TENSOR_FLOAT32", "{0}") + fw_aux_input_to_output_weights = Input("fw_aux_input_to_output_weights", + "TENSOR_FLOAT32", "{0}") + + bw_aux_input_to_input_weights = Input("bw_aux_input_to_input_weights", + "TENSOR_FLOAT32", "{0}") + bw_aux_input_to_forget_weights = Input("bw_input_to_forget_weights", + "TENSOR_FLOAT32", "{0}") + bw_aux_input_to_cell_weights = Input("bw_aux_input_to_cell_weights", + "TENSOR_FLOAT32", "{0}") + bw_aux_input_to_output_weights = Input("bw_aux_input_to_output_weights", + "TENSOR_FLOAT32", "{0}") + + fw_input_layer_norm_weights = Input("input_layer_norm_weights", + "TENSOR_FLOAT32", "{0}") + fw_forget_layer_norm_weights = Input("forget_layer_norm_weights", + "TENSOR_FLOAT32", "{0}") + fw_cell_layer_norm_weights = Input("cell_layer_norm_weights", + "TENSOR_FLOAT32", "{0}") + fw_output_layer_norm_weights = Input("output_layer_norm_weights", + "TENSOR_FLOAT32", "{0}") + + bw_input_layer_norm_weights = Input("input_layer_norm_weights", + "TENSOR_FLOAT32", "{0}") + bw_forget_layer_norm_weights = Input("forget_layer_norm_weights", + "TENSOR_FLOAT32", "{0}") + bw_cell_layer_norm_weights = Input("cell_layer_norm_weights", + "TENSOR_FLOAT32", "{0}") + bw_output_layer_norm_weights = Input("output_layer_norm_weights", + "TENSOR_FLOAT32", "{0}") + + fw_output = Output("fw_output", "TENSOR_FLOAT32", + "{{{}, {}, {}}}".format(max_time, n_batch, n_output)) + bw_output = Output("bw_output", "TENSOR_FLOAT32", + "{{{}, {}, {}}}".format(max_time, n_batch, n_output)) + + if ignore_fw_output: + fw_output = IgnoredOutput( + "fw_output", "TENSOR_FLOAT32", + "{{{}, {}, {}}}".format(max_time, n_batch, n_output)) + activation = Int32Scalar("activation", 4) + cell_clip = Float32Scalar("cell_clip", 0.0) + proj_clip = Float32Scalar("proj_clip", 0.0) + merge_outputs = BoolScalar("merge_outputs", False) + time_major = BoolScalar("time_major", True) + + model = Model().Operation( + "BIDIRECTIONAL_SEQUENCE_LSTM", + input, + fw_input_to_input_weights, + fw_input_to_forget_weights, + fw_input_to_cell_weights, + fw_input_to_output_weights, + fw_recurrent_to_input_weights, + fw_recurrent_to_forget_weights, + fw_recurrent_to_cell_weights, + fw_recurrent_to_output_weights, + fw_cell_to_input_weights, + fw_cell_to_forget_weights, + fw_cell_to_output_weights, + fw_input_gate_bias, + fw_forget_gate_bias, + fw_cell_bias, + fw_output_gate_bias, + fw_projection_weights, + fw_projection_bias, + bw_input_to_input_weights, + bw_input_to_forget_weights, + bw_input_to_cell_weights, + bw_input_to_output_weights, + bw_recurrent_to_input_weights, + bw_recurrent_to_forget_weights, + bw_recurrent_to_cell_weights, + bw_recurrent_to_output_weights, + bw_cell_to_input_weights, + bw_cell_to_forget_weights, + bw_cell_to_output_weights, + bw_input_gate_bias, + bw_forget_gate_bias, + bw_cell_bias, + bw_output_gate_bias, + bw_projection_weights, + bw_projection_bias, + fw_activation_state, + fw_cell_state, + bw_activation_state, + bw_cell_state, + aux_input, + fw_aux_input_to_input_weights, + fw_aux_input_to_forget_weights, + fw_aux_input_to_cell_weights, + fw_aux_input_to_output_weights, + bw_aux_input_to_input_weights, + bw_aux_input_to_forget_weights, + bw_aux_input_to_cell_weights, + bw_aux_input_to_output_weights, + activation, + cell_clip, + proj_clip, + merge_outputs, + time_major, + fw_input_layer_norm_weights, + fw_forget_layer_norm_weights, + fw_cell_layer_norm_weights, + fw_output_layer_norm_weights, + bw_input_layer_norm_weights, + bw_forget_layer_norm_weights, + bw_cell_layer_norm_weights, + bw_output_layer_norm_weights, + ).To(fw_output, bw_output).IntroducedIn("V1_2") + + example = Example( + { + input: input_data, + fw_input_to_input_weights: fw_input_to_input_weights_data, + fw_input_to_forget_weights: fw_input_to_forget_weights_data, + fw_input_to_cell_weights: fw_input_to_cell_weights_data, + fw_input_to_output_weights: fw_input_to_output_weights_data, + fw_recurrent_to_input_weights: fw_recurrent_to_input_weights_data, + fw_recurrent_to_forget_weights: fw_recurrent_to_forget_weights_data, + fw_recurrent_to_cell_weights: fw_recurrent_to_cell_weights_data, + fw_recurrent_to_output_weights: fw_recurrent_to_output_weights_data, + fw_cell_to_input_weights: fw_cell_to_input_weights_data, + fw_cell_to_forget_weights: fw_cell_to_forget_weights_data, + fw_cell_to_output_weights: fw_cell_to_output_weights_data, + fw_input_gate_bias: fw_input_gate_bias_data, + fw_forget_gate_bias: fw_forget_gate_bias_data, + fw_cell_bias: fw_cell_bias_data, + fw_output_gate_bias: fw_output_gate_bias_data, + fw_projection_weights: fw_projection_weights_data, + fw_projection_bias: fw_projection_bias_data, + bw_input_to_input_weights: bw_input_to_input_weights_data, + bw_input_to_forget_weights: bw_input_to_forget_weights_data, + bw_input_to_cell_weights: bw_input_to_cell_weights_data, + bw_input_to_output_weights: bw_input_to_output_weights_data, + bw_recurrent_to_input_weights: bw_recurrent_to_input_weights_data, + bw_recurrent_to_forget_weights: bw_recurrent_to_forget_weights_data, + bw_recurrent_to_cell_weights: bw_recurrent_to_cell_weights_data, + bw_recurrent_to_output_weights: bw_recurrent_to_output_weights_data, + bw_cell_to_input_weights: bw_cell_to_input_weights_data, + bw_cell_to_forget_weights: bw_cell_to_forget_weights_data, + bw_cell_to_output_weights: bw_cell_to_output_weights_data, + bw_input_gate_bias: bw_input_gate_bias_data, + bw_forget_gate_bias: bw_forget_gate_bias_data, + bw_cell_bias: bw_cell_bias_data, + bw_output_gate_bias: bw_output_gate_bias_data, + bw_projection_weights: bw_projection_weights_data, + bw_projection_bias: bw_projection_bias_data, + fw_activation_state: fw_activation_state_data, + fw_cell_state: fw_cell_state_data, + bw_activation_state: bw_activation_state_data, + bw_cell_state: bw_cell_state_data, + aux_input: aux_input_data, + fw_aux_input_to_input_weights: fw_aux_input_to_input_weights_data, + fw_aux_input_to_forget_weights: fw_aux_input_to_forget_weights_data, + fw_aux_input_to_cell_weights: fw_aux_input_to_cell_weights_data, + fw_aux_input_to_output_weights: fw_aux_input_to_output_weights_data, + bw_aux_input_to_input_weights: bw_aux_input_to_input_weights_data, + bw_aux_input_to_forget_weights: bw_aux_input_to_forget_weights_data, + bw_aux_input_to_cell_weights: bw_aux_input_to_cell_weights_data, + bw_aux_input_to_output_weights: bw_aux_input_to_output_weights_data, + fw_input_layer_norm_weights: fw_input_layer_norm_weights_data, + fw_forget_layer_norm_weights: fw_forget_layer_norm_weights_data, + fw_cell_layer_norm_weights: fw_cell_layer_norm_weights_data, + fw_output_layer_norm_weights: fw_output_layer_norm_weights_data, + bw_input_layer_norm_weights: bw_input_layer_norm_weights_data, + bw_forget_layer_norm_weights: bw_forget_layer_norm_weights_data, + bw_cell_layer_norm_weights: bw_cell_layer_norm_weights_data, + bw_output_layer_norm_weights: bw_output_layer_norm_weights_data, + fw_output: fw_output_data, + bw_output: bw_output_data, + }, + model=model, + name=name) + + +fw_input_to_input_weights_data = [ + -0.45018822, -0.02338299, -0.0870589, + -0.34550029, 0.04266912, -0.15680569, + -0.34856534, 0.43890524 +] +bw_input_to_input_weights_data = fw_input_to_input_weights_data + +fw_input_to_forget_weights_data = [ + 0.09701663, 0.20334584, -0.50592935, + -0.31343272, -0.40032279, 0.44781327, + 0.01387155, -0.35593212 +] +bw_input_to_forget_weights_data = fw_input_to_forget_weights_data + +fw_input_to_cell_weights_data = [ + -0.50013041, 0.1370284, 0.11810488, 0.2013163, + -0.20583314, 0.44344562, 0.22077113, + -0.29909778 +] +bw_input_to_cell_weights_data = fw_input_to_cell_weights_data + +fw_input_to_output_weights_data = [ + -0.25065863, -0.28290087, 0.04613829, + 0.40525138, 0.44272184, 0.03897077, -0.1556896, + 0.19487578 +] +bw_input_to_output_weights_data = fw_input_to_output_weights_data + +fw_recurrent_to_input_weights_data = [ + -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324, + -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, + -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296 +] +bw_recurrent_to_input_weights_data = fw_recurrent_to_input_weights_data + +fw_recurrent_to_forget_weights_data = [ + -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892, + -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436, + 0.28053468, 0.01560611, -0.20127171, -0.01140004 +] +bw_recurrent_to_forget_weights_data = fw_recurrent_to_forget_weights_data + +fw_recurrent_to_cell_weights_data = [ + -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841, + -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659, + -0.46367589, 0.26016325, -0.03894562, -0.16368064 +] +bw_recurrent_to_cell_weights_data = fw_recurrent_to_cell_weights_data + +fw_recurrent_to_output_weights_data = [ + 0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793, + 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421, + -0.51818722, -0.15390486, 0.0468148, 0.39922136 +] +bw_recurrent_to_output_weights_data = fw_recurrent_to_output_weights_data + +fw_input_gate_bias_data = [0.0, 0.0, 0.0, 0.0] +bw_input_gate_bias_data = [0.0, 0.0, 0.0, 0.0] + +fw_forget_gate_bias_data = [1.0, 1.0, 1.0, 1.0] +bw_forget_gate_bias_data = [1.0, 1.0, 1.0, 1.0] + +fw_cell_bias_data = [0.0, 0.0, 0.0, 0.0] +bw_cell_bias_data = [0.0, 0.0, 0.0, 0.0] + +fw_output_gate_bias_data = [0.0, 0.0, 0.0, 0.0] +bw_output_gate_bias_data = [0.0, 0.0, 0.0, 0.0] + +input_data = [2.0, 3.0, 3.0, 4.0, 1.0, 1.0] + +fw_golden_output_data = [ + -0.02973187, 0.1229473, 0.20885126, -0.15358765, + -0.03716109, 0.12507336, 0.41193449, -0.20860538, + -0.15053082, 0.09120187, 0.24278517, -0.12222792 +] +bw_golden_output_data = [ + -0.0806187, 0.139077, 0.400476, -0.197842, + -0.0332076, 0.123838, 0.309777, -0.17621, + -0.0490733, 0.0739237, 0.067706, -0.0208124 +] + +n_batch = 1 +n_fw_input = 2 +n_bw_input = 2 +n_cell = 4 +n_output = 4 +max_time = 3 + +test( + name="parallel_linking", + n_batch=n_batch, + n_fw_input=n_fw_input, + n_bw_input=n_bw_input, + n_cell=n_cell, + n_output=n_output, + max_time=max_time, + input_data=input_data, + fw_input_to_input_weights_data=fw_input_to_input_weights_data, + fw_input_to_forget_weights_data=fw_input_to_forget_weights_data, + fw_input_to_cell_weights_data=fw_input_to_cell_weights_data, + fw_input_to_output_weights_data=fw_input_to_output_weights_data, + fw_recurrent_to_input_weights_data=fw_recurrent_to_input_weights_data, + fw_recurrent_to_forget_weights_data=fw_recurrent_to_forget_weights_data, + fw_recurrent_to_cell_weights_data=fw_recurrent_to_cell_weights_data, + fw_recurrent_to_output_weights_data=fw_recurrent_to_output_weights_data, + fw_input_gate_bias_data=fw_input_gate_bias_data, + fw_forget_gate_bias_data=fw_forget_gate_bias_data, + fw_cell_bias_data=fw_cell_bias_data, + fw_output_gate_bias_data=fw_output_gate_bias_data, + bw_input_to_input_weights_data=bw_input_to_input_weights_data, + bw_input_to_forget_weights_data=bw_input_to_forget_weights_data, + bw_input_to_cell_weights_data=bw_input_to_cell_weights_data, + bw_input_to_output_weights_data=bw_input_to_output_weights_data, + bw_recurrent_to_input_weights_data=bw_recurrent_to_input_weights_data, + bw_recurrent_to_forget_weights_data=bw_recurrent_to_forget_weights_data, + bw_recurrent_to_cell_weights_data=bw_recurrent_to_cell_weights_data, + bw_recurrent_to_output_weights_data=bw_recurrent_to_output_weights_data, + bw_input_gate_bias_data=bw_input_gate_bias_data, + bw_forget_gate_bias_data=bw_forget_gate_bias_data, + bw_cell_bias_data=bw_cell_bias_data, + bw_output_gate_bias_data=bw_output_gate_bias_data, + fw_activation_state_data=[0] * (n_batch * n_output), + bw_activation_state_data=[0] * (n_batch * n_output), + fw_cell_state_data=[0] * (n_batch * n_cell), + bw_cell_state_data=[0] * (n_batch * n_cell), + aux_input_data=input_data, + fw_output_data=fw_golden_output_data, + bw_output_data=bw_golden_output_data) + +n_batch = 1 +n_fw_input = 3 +n_bw_input = 2 +n_cell = 4 +n_output = 4 +max_time = 3 + +test( + name="parallel_linking", + n_batch=n_batch, + n_fw_input=n_fw_input, + n_bw_input=n_bw_input, + n_cell=n_cell, + n_output=n_output, + max_time=max_time, + input_data=[0] * (max_time * n_batch * n_fw_input), + fw_input_to_input_weights_data=[0] * (n_cell * n_fw_input), + fw_input_to_forget_weights_data=[0] * (n_cell * n_fw_input), + fw_input_to_cell_weights_data=[0] * (n_cell * n_fw_input), + fw_input_to_output_weights_data=[0] * (n_cell * n_fw_input), + fw_recurrent_to_input_weights_data=fw_recurrent_to_input_weights_data, + fw_recurrent_to_forget_weights_data=fw_recurrent_to_forget_weights_data, + fw_recurrent_to_cell_weights_data=fw_recurrent_to_cell_weights_data, + fw_recurrent_to_output_weights_data=fw_recurrent_to_output_weights_data, + fw_input_gate_bias_data=fw_input_gate_bias_data, + fw_forget_gate_bias_data=fw_forget_gate_bias_data, + fw_cell_bias_data=fw_cell_bias_data, + fw_output_gate_bias_data=fw_output_gate_bias_data, + bw_input_to_input_weights_data=bw_input_to_input_weights_data, + bw_input_to_forget_weights_data=bw_input_to_forget_weights_data, + bw_input_to_cell_weights_data=bw_input_to_cell_weights_data, + bw_input_to_output_weights_data=bw_input_to_output_weights_data, + bw_recurrent_to_input_weights_data=bw_recurrent_to_input_weights_data, + bw_recurrent_to_forget_weights_data=bw_recurrent_to_forget_weights_data, + bw_recurrent_to_cell_weights_data=bw_recurrent_to_cell_weights_data, + bw_recurrent_to_output_weights_data=bw_recurrent_to_output_weights_data, + bw_input_gate_bias_data=bw_input_gate_bias_data, + bw_forget_gate_bias_data=bw_forget_gate_bias_data, + bw_cell_bias_data=bw_cell_bias_data, + bw_output_gate_bias_data=bw_output_gate_bias_data, + fw_activation_state_data=[0] * (n_batch * n_output), + bw_activation_state_data=[0] * (n_batch * n_output), + fw_cell_state_data=[0] * (n_batch * n_cell), + bw_cell_state_data=[0] * (n_batch * n_cell), + aux_input_data=input_data, + fw_output_data=fw_golden_output_data, + bw_output_data=bw_golden_output_data, + ignore_fw_output=True) |