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Diffstat (limited to 'tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/lstm.mod.py')
| -rw-r--r-- | tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/lstm.mod.py | 161 |
1 files changed, 161 insertions, 0 deletions
diff --git a/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/lstm.mod.py b/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/lstm.mod.py new file mode 100644 index 000000000..cb1bf6010 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-p/tests/P_lstm/lstm.mod.py @@ -0,0 +1,161 @@ +# +# Copyright (C) 2017 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: No Cifg, No Peephole, No Projection, and No Clipping. + +model = Model() + +n_batch = 1 +n_input = 2 +# n_cell and n_output have the same size when there is no projection. +n_cell = 4 +n_output = 4 + +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", "{0}") +cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{0}") +cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{0}") + +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", "{0,0}") +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 = Input("activation_param", "TENSOR_INT32", "{1}") +cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}") +proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}") + +scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4))) +output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) +cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_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]) + +# Example 1. Input in operand 0, +input0 = {input_to_input_weights: [-0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524], + input_to_forget_weights: [0.09701663, 0.20334584, -0.50592935, -0.31343272, -0.40032279, 0.44781327, 0.01387155, -0.35593212], + input_to_cell_weights: [-0.50013041, 0.1370284, 0.11810488, 0.2013163, -0.20583314, 0.44344562, 0.22077113, -0.29909778], + input_to_output_weights: [-0.25065863, -0.28290087, 0.04613829, 0.40525138, 0.44272184, 0.03897077, -0.1556896, 0.19487578], + + input_gate_bias: [0.,0.,0.,0.], + forget_gate_bias: [1.,1.,1.,1.], + cell_gate_bias: [0.,0.,0.,0.], + output_gate_bias: [0.,0.,0.,0.], + + recurrent_to_input_weights: [ + -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], + + recurrent_to_cell_weights: [ + -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], + + recurrent_to_forget_weights: [ + -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], + + recurrent_to_output_weights: [ + 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], + + cell_to_input_weights: [], + cell_to_forget_weights: [], + cell_to_output_weights: [], + + projection_weights: [], + projection_bias: [], + + activation_param: [4], # Tanh + cell_clip_param: [0.], + proj_clip_param: [0.], +} + +# Instantiate examples +# TODO: Add more examples after fixing the reference issue +test_inputs = [ + [2., 3.], +# [3., 4.],[1., 1.] +] +golden_outputs = [ + [-0.02973187, 0.1229473, 0.20885126, -0.15358765,], +# [-0.03716109, 0.12507336, 0.41193449, -0.20860538], +# [-0.15053082, 0.09120187, 0.24278517, -0.12222792] +] + +for (input_tensor, output_tensor) in zip(test_inputs, golden_outputs): + output0 = { + scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], + cell_state_out: [ 0 for x in range(n_batch * n_cell) ], + output_state_out: [ 0 for x in range(n_batch * n_output) ], + output: output_tensor + } + input0[input] = input_tensor + input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ] + input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ] + Example((input0, output0)) |