# # 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, Cifg, Peephole, No Projection, and No Clipping. n_batch = 1 n_input = 2 n_cell = 4 n_output = 4 max_time = 3 input = Input("input", "TENSOR_FLOAT32", "{{{}, {}, {}}}".format(max_time, n_batch, n_input)) fw_input_to_input_weights = Input( "fw_input_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) fw_input_to_forget_weights = Input( "fw_input_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) fw_input_to_cell_weights = Input( "fw_input_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) fw_input_to_output_weights = Input( "fw_input_to_output_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_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", "{{{}}}".format(n_cell)) fw_cell_to_forget_weights = Input( "fw_cell_to_forget_weights", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) fw_cell_to_output_weights = Input( "fw_cell_to_output_weights", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) 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", "{{{}, {}}}".format(n_output, n_cell)) fw_projection_bias = Input( "fw_projection_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_output)) bw_input_to_input_weights = Input( "bw_input_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) bw_input_to_forget_weights = Input( "bw_input_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) bw_input_to_cell_weights = Input( "bw_input_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) bw_input_to_output_weights = Input( "bw_input_to_output_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_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", "{{{}}}".format(n_cell)) bw_cell_to_forget_weights = Input( "bw_cell_to_forget_weights", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) bw_cell_to_output_weights = Input( "bw_cell_to_output_weights", "TENSOR_FLOAT32", "{{{}}}".format(n_cell)) 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", "{{{}, {}}}".format(n_output, n_cell)) bw_projection_bias = Input( "bw_projection_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_output)) 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_input)) fw_aux_input_to_input_weights = Input( "fw_aux_input_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) fw_aux_input_to_forget_weights = Input( "fw_input_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) fw_aux_input_to_cell_weights = Input( "fw_aux_input_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) fw_aux_input_to_output_weights = Input( "fw_aux_input_to_output_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) bw_aux_input_to_input_weights = Input( "bw_aux_input_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) bw_aux_input_to_forget_weights = Input( "bw_input_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) bw_aux_input_to_cell_weights = Input( "bw_aux_input_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) bw_aux_input_to_output_weights = Input( "bw_aux_input_to_output_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_input)) fw_input_layer_norm_weights = Input("input_layer_norm_weights", "TENSOR_FLOAT32", "{%d}" % n_cell) fw_forget_layer_norm_weights = Input("forget_layer_norm_weights", "TENSOR_FLOAT32", "{%d}" % n_cell) fw_cell_layer_norm_weights = Input("cell_layer_norm_weights", "TENSOR_FLOAT32", "{%d}" % n_cell) fw_output_layer_norm_weights = Input("output_layer_norm_weights", "TENSOR_FLOAT32", "{%d}" % n_cell) bw_input_layer_norm_weights = Input("input_layer_norm_weights", "TENSOR_FLOAT32", "{%d}" % n_cell) bw_forget_layer_norm_weights = Input("forget_layer_norm_weights", "TENSOR_FLOAT32", "{%d}" % n_cell) bw_cell_layer_norm_weights = Input("cell_layer_norm_weights", "TENSOR_FLOAT32", "{%d}" % n_cell) bw_output_layer_norm_weights = Input("output_layer_norm_weights", "TENSOR_FLOAT32", "{%d}" % n_cell) 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)) def test( name, 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=[],): 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) 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_forget_weights_data = [ -0.55291498, -0.42866567, 0.13056988, -0.3633365, -0.22755712, 0.28253698, 0.24407166, 0.33826375 ] bw_input_to_forget_weights_data = fw_input_to_forget_weights_data fw_input_to_cell_weights_data = [ -0.49770179, -0.27711356, -0.09624726, 0.05100781, 0.04717243, 0.48944736, -0.38535351, -0.17212132 ] bw_input_to_cell_weights_data = fw_input_to_cell_weights_data fw_input_to_output_weights_data = [ 0.10725588, -0.02335852, -0.55932593, -0.09426838, -0.44257352, 0.54939759, 0.01533556, 0.42751634 ] bw_input_to_output_weights_data = fw_input_to_output_weights_data fw_recurrent_to_forget_weights_data = [ -0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827, 0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795, -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349 ] bw_recurrent_to_forget_weights_data = fw_recurrent_to_forget_weights_data fw_recurrent_to_cell_weights_data = [ 0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711, 0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004, 0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288, 0.21193194 ] bw_recurrent_to_cell_weights_data = fw_recurrent_to_cell_weights_data fw_recurrent_to_output_weights_data = [ 0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908, -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835, 0.50248802, 0.26114327, -0.43736315, 0.33149987 ] bw_recurrent_to_output_weights_data = fw_recurrent_to_output_weights_data 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] fw_cell_to_forget_weights_data = [ 0.47485286, -0.51955009, -0.24458408, 0.31544167 ] bw_cell_to_forget_weights_data = fw_cell_to_forget_weights_data fw_cell_to_output_weights_data = [ -0.17135078, 0.82760304, 0.85573703, -0.77109635 ] bw_cell_to_output_weights_data = fw_cell_to_output_weights_data input_data = [2.0, 3.0, 3.0, 4.0, 1.0, 1.0] fw_activation_state_data = [0 for _ in range(n_batch * n_output)] bw_activation_state_data = [0 for _ in range(n_batch * n_output)] fw_cell_state_data = [0 for _ in range(n_batch * n_cell)] bw_cell_state_data = [0 for _ in range(n_batch * n_cell)] fw_golden_output_data = [ -0.36444446, -0.00352185, 0.12886585, -0.05163646, -0.42312205, -0.01218222, 0.24201041, -0.08124574, -0.358325, -0.04621704, 0.21641694, -0.06471302 ] bw_golden_output_data = [ -0.401685, -0.0232794, 0.288642, -0.123074, -0.42915, -0.00871577, 0.20912, -0.103567, -0.166398, -0.00486649, 0.0697471, -0.0537578 ] test( name="blackbox", input_data=input_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_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_cell_to_forget_weights_data = fw_cell_to_forget_weights_data, fw_cell_to_output_weights_data = fw_cell_to_output_weights_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_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_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_cell_to_forget_weights_data = bw_cell_to_forget_weights_data, bw_cell_to_output_weights_data = bw_cell_to_output_weights_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 = fw_activation_state_data, bw_activation_state_data = bw_activation_state_data, fw_cell_state_data = fw_cell_state_data, bw_cell_state_data = bw_cell_state_data, fw_output_data=fw_golden_output_data, bw_output_data=bw_golden_output_data )