# # Copyright (C) 2018 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. # # LSTM Test, With Peephole, With Projection, No Clipping model = Model() n_batch = 2 n_input = 5 # n_cell and n_output have the same size when there is no projection. n_cell = 20 n_output = 16 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input)) input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell)) cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" %(n_cell)) cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell)) input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell)) projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}") output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) activation_param = Int32Scalar("activation_param", 4) # Tanh cell_clip_param = Float32Scalar("cell_clip_param", 0.) proj_clip_param = Float32Scalar("proj_clip_param", 0.) scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4))) output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) # TODO: need support for more than one output model = model.Operation("LSTM", input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param ).To([scratch_buffer, output_state_out, cell_state_out, output]) model = model.RelaxedExecution(True) input0 = {input_to_input_weights: [ 0.021393683, 0.06124551, 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-0.057112187, -0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388, -0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709, 0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263, 0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777, 0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935, -0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641, -0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996, -0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318, 0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437, -0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079, 0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237, 0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415, -0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124, -0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943, -0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311, 0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013, -0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364, -0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543, -0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102, 0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906, 0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955, 0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656], projection_bias: [], } # Batch0: 4 (input_sequence_size) * 5 (n_input) input0[input] = [0.787926, 0.151646, 0.071352, 0.118426, 0.458058] # Batch1: 4 (input_sequence_size) * 5 (n_input) input0[input].extend( [0.295743, 0.544053, 0.690064, 0.858138, 0.497181], ) input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ] input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ] output0 = { scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], cell_state_out: [ -0.0531632, -0.0118138, 0.0870833, 0.0347929, -0.076144, -0.0659219, -0.0463811, 0.0141307, -0.0127706, -0.03782, -0.00402401, -0.00571876, -0.187957, -0.0247127, 0.0711425, 0.008244, 0.0492649, 0.126972, 0.0933097, 0.29848, -0.0966178, -0.114417, 0.0387229, 0.0453255, -0.181286, -0.0651251, -0.0996879, -0.00276995, 0.0617558, -0.0100728, 0.056304, -0.077416, -0.162858, -0.0541251, 0.0571202, -0.0525331, 0.0724297, 0.171029, 0.141738, 0.295483, ], output_state_out: [ -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835577, -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, -0.0152191, -0.0259063, 0.00914318, 0.00415119, 0.017147, 0.0134203, -0.013869, 0.0287268, -0.00334694, 0.00733397, -0.0287926, -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, 0.0216801 ], } # Batch0: 4 (input_sequence_size) * 16 (n_output) output0[output] = [ -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147, 0.0134203] # Batch1: 4 (input_sequence_size) * 16 (n_output) output0[output].extend( [-0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, 0.02168], ) Example((input0, output0))