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+#
+# 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.
+#
+
+import copy
+
+# LSTM Test: Layer Normalization, No Cifg, Peephole, Projection, and No Clipping.
+model = Model()
+
+n_batch = 2
+n_input = 5
+n_cell = 4
+n_output = 3
+
+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.)
+
+input_layer_norm_weights = Input("input_layer_norm_weights", "TENSOR_FLOAT32",
+ "{%d}" % n_cell)
+forget_layer_norm_weights = Input("forget_layer_norm_weights", "TENSOR_FLOAT32",
+ "{%d}" % n_cell)
+cell_layer_norm_weights = Input("cell_layer_norm_weights", "TENSOR_FLOAT32",
+ "{%d}" % n_cell)
+output_layer_norm_weights = Input("output_layer_norm_weights", "TENSOR_FLOAT32",
+ "{%d}" % n_cell)
+
+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))
+
+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,
+ input_layer_norm_weights, forget_layer_norm_weights,
+ cell_layer_norm_weights, output_layer_norm_weights).To(
+ [scratch_buffer, output_state_out, cell_state_out, output])
+
+# Example 1. Input in operand 0,
+input0 = {
+ input_to_input_weights: [
+ 0.5, 0.6, 0.7, -0.8, -0.9, 0.1, 0.2, 0.3, -0.4, 0.5, -0.8, 0.7, -0.6,
+ 0.5, -0.4, -0.5, -0.4, -0.3, -0.2, -0.1
+ ],
+ input_to_forget_weights: [
+ -0.6, -0.1, 0.3, 0.2, 0.9, -0.5, -0.2, -0.4, 0.3, -0.8, -0.4, 0.3, -0.5,
+ -0.4, -0.6, 0.3, -0.4, -0.6, -0.5, -0.5
+ ],
+ input_to_cell_weights: [
+ -0.4, -0.3, -0.2, -0.1, -0.5, 0.5, -0.2, -0.3, -0.2, -0.6, 0.6, -0.1,
+ -0.4, -0.3, -0.7, 0.7, -0.9, -0.5, 0.8, 0.6
+ ],
+ input_to_output_weights: [
+ -0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3, -0.3, -0.8, -0.2, 0.6, -0.2,
+ 0.4, -0.7, -0.3, -0.5, 0.1, 0.5, -0.6, -0.4
+ ],
+ input_gate_bias: [0.03, 0.15, 0.22, 0.38],
+ forget_gate_bias: [0.1, -0.3, -0.2, 0.1],
+ cell_gate_bias: [-0.05, 0.72, 0.25, 0.08],
+ output_gate_bias: [0.05, -0.01, 0.2, 0.1],
+ recurrent_to_input_weights: [
+ -0.2, -0.3, 0.4, 0.1, -0.5, 0.9, -0.2, -0.3, -0.7, 0.05, -0.2, -0.6
+ ],
+ recurrent_to_cell_weights: [
+ -0.3, 0.2, 0.1, -0.3, 0.8, -0.08, -0.2, 0.3, 0.8, -0.6, -0.1, 0.2
+ ],
+ recurrent_to_forget_weights: [
+ -0.5, -0.3, -0.5, -0.2, 0.6, 0.4, 0.9, 0.3, -0.1, 0.2, 0.5, 0.2
+ ],
+ recurrent_to_output_weights: [
+ 0.3, -0.1, 0.1, -0.2, -0.5, -0.7, -0.2, -0.6, -0.1, -0.4, -0.7, -0.2
+ ],
+ cell_to_input_weights: [0.05, 0.1, 0.25, 0.15],
+ cell_to_forget_weights: [-0.02, -0.15, -0.25, -0.03],
+ cell_to_output_weights: [0.1, -0.1, -0.5, 0.05],
+ projection_weights: [
+ -0.1, 0.2, 0.01, -0.2, 0.1, 0.5, 0.3, 0.08, 0.07, 0.2, -0.4, 0.2
+ ],
+ projection_bias: [],
+ input_layer_norm_weights: [0.1, 0.2, 0.3, 0.5],
+ forget_layer_norm_weights: [0.2, 0.2, 0.4, 0.3],
+ cell_layer_norm_weights: [0.7, 0.2, 0.3, 0.8],
+ output_layer_norm_weights: [0.6, 0.2, 0.2, 0.5]
+}
+
+test_inputs = [[0.7, 0.8, 0.1, 0.2, 0.3, 0.3, 0.2, 0.9, 0.8, 0.1],
+ [0.8, 0.1, 0.2, 0.4, 0.5, 0.1, 0.5, 0.2, 0.4, 0.2],
+ [0.2, 0.7, 0.7, 0.1, 0.7, 0.6, 0.9, 0.2, 0.5, 0.7]]
+golden_cell_states = [
+ [
+ -0.451771229505539, 0.376915663480759, 0.225425109267235, 0.232406347990036, -0.252585828304291, 0.330421179533005, 0.017305245622993, 0.366601228713989
+ ],
+ [
+ -0.645632147789001, 0.518238246440887, 0.168679088354111, 0.555787742137909, -0.493674814701080, 0.475847363471985, 0.106874041259289, 0.504309654235840
+ ],
+ [-0.742560744285583, 0.579139292240143, 0.114988230168819, 0.649957716464996, -0.686565399169922, 0.548869132995605, 0.173138767480850, 0.587379336357117],
+]
+cell_states = [[0, 0, 0, 0, 0, 0, 0, 0]] + golden_cell_states[:2]
+
+golden_outputs = [
+ [0.024407668039203, 0.128027379512787, -0.001709178090096, -0.006924282759428, 0.084874063730240, 0.063444979488850],
+ [0.013764165341854, 0.140751048922539, 0.039583537727594, -0.004039138555527, 0.139963015913963, 0.072681039571762],
+ [-0.004592306911945, 0.155278354883194, 0.083737745881081, 0.007527053356171, 0.161902531981468, 0.056137066334486],
+]
+output_states = [[0, 0, 0, 0, 0, 0]] + golden_outputs[:2]
+
+tests = zip(
+ test_inputs, output_states, cell_states, golden_cell_states, golden_outputs)
+
+for test_input, output_state, cell_state, golden_state, golden_output in tests:
+ cur_input = copy.deepcopy(input0)
+ cur_input[input] = test_input
+ cur_input[output_state_in] = output_state
+ cur_input[cell_state_in] = cell_state
+ cur_output = {
+ scratch_buffer: [0] * (n_batch * n_cell * 4),
+ cell_state_out: golden_state,
+ output_state_out: golden_output,
+ output: golden_output
+ }
+ Example((cur_input, cur_output), name="NoCifgPeepholeProjectionNoClippingLayerNormLstm")
+
+
+# LSTM Test: Layer Normalization, Cifg, Peephole, Projection, and No Clipping.
+model = Model()
+
+n_batch = 2
+n_input = 5
+n_cell = 4
+n_output = 3
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
+
+input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32",
+ "{0, 0}")
+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",
+ "{0, 0}")
+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",
+ "{%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", "{0}")
+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.)
+
+input_layer_norm_weights = Input("input_layer_norm_weights", "TENSOR_FLOAT32",
+ "{0}")
+forget_layer_norm_weights = Input("forget_layer_norm_weights", "TENSOR_FLOAT32",
+ "{%d}" % n_cell)
+cell_layer_norm_weights = Input("cell_layer_norm_weights", "TENSOR_FLOAT32",
+ "{%d}" % n_cell)
+output_layer_norm_weights = Input("output_layer_norm_weights", "TENSOR_FLOAT32",
+ "{%d}" % n_cell)
+
+scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32",
+ "{%d, %d}" % (n_batch, (n_cell * 3)))
+output_state_out = IgnoredOutput("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))
+
+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,
+ input_layer_norm_weights, forget_layer_norm_weights,
+ cell_layer_norm_weights, output_layer_norm_weights).To(
+ [scratch_buffer, output_state_out, cell_state_out, output])
+
+# Example 1. Input in operand 0,
+input0 = {
+ input_to_input_weights: [],
+ input_to_forget_weights: [
+ -0.6, -0.1, 0.3, 0.2, 0.9, -0.5, -0.2, -0.4, 0.3, -0.8, -0.4, 0.3, -0.5,
+ -0.4, -0.6, 0.3, -0.4, -0.6, -0.5, -0.5
+ ],
+ input_to_cell_weights: [
+ -0.4, -0.3, -0.2, -0.1, -0.5, 0.5, -0.2, -0.3, -0.2, -0.6, 0.6, -0.1,
+ -0.4, -0.3, -0.7, 0.7, -0.9, -0.5, 0.8, 0.6
+ ],
+ input_to_output_weights: [
+ -0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3, -0.3, -0.8, -0.2, 0.6, -0.2,
+ 0.4, -0.7, -0.3, -0.5, 0.1, 0.5, -0.6, -0.4
+ ],
+ input_gate_bias: [],
+ forget_gate_bias: [0.1, -0.3, -0.2, 0.1],
+ cell_gate_bias: [-0.05, 0.72, 0.25, 0.08],
+ output_gate_bias: [0.05, -0.01, 0.2, 0.1],
+ recurrent_to_input_weights: [],
+ recurrent_to_cell_weights: [
+ -0.3, 0.2, 0.1, -0.3, 0.8, -0.08, -0.2, 0.3, 0.8, -0.6, -0.1, 0.2
+ ],
+ recurrent_to_forget_weights: [
+ -0.5, -0.3, -0.5, -0.2, 0.6, 0.4, 0.9, 0.3, -0.1, 0.2, 0.5, 0.2
+ ],
+ recurrent_to_output_weights: [
+ 0.3, -0.1, 0.1, -0.2, -0.5, -0.7, -0.2, -0.6, -0.1, -0.4, -0.7, -0.2
+ ],
+ cell_to_input_weights: [],
+ cell_to_forget_weights: [-0.02, -0.15, -0.25, -0.03],
+ cell_to_output_weights: [0.1, -0.1, -0.5, 0.05],
+ projection_weights: [
+ -0.1, 0.2, 0.01, -0.2, 0.1, 0.5, 0.3, 0.08, 0.07, 0.2, -0.4, 0.2
+ ],
+ projection_bias: [],
+ input_layer_norm_weights: [],
+ forget_layer_norm_weights: [0.2, 0.2, 0.4, 0.3],
+ cell_layer_norm_weights: [0.7, 0.2, 0.3, 0.8],
+ output_layer_norm_weights: [0.6, 0.2, 0.2, 0.5]
+}
+
+test_inputs = [[0.7, 0.8, 0.1, 0.2, 0.3, 0.3, 0.2, 0.9, 0.8, 0.1],
+ [0.8, 0.1, 0.2, 0.4, 0.5, 0.1, 0.5, 0.2, 0.4, 0.2],
+ [0.2, 0.7, 0.7, 0.1, 0.7, 0.6, 0.9, 0.2, 0.5, 0.7]]
+golden_cell_states = [
+ [
+ -0.3510298, 0.4261035, 0.2146365, 0.2771652, -0.1885517, 0.3252200, 0.0203665, 0.4896766
+ ],
+ [
+ -0.5069088, 0.5386363, 0.1980069, 0.5355753, -0.3866257, 0.4749442, 0.1074765, 0.7124508
+ ],
+ [
+ -0.5736622, 0.5952501, 0.1292950, 0.7110270, -0.5323033, 0.5556133, 0.1800992, 0.7845056
+ ],
+]
+cell_states = [[0, 0, 0, 0, 0, 0, 0, 0]] + golden_cell_states[:2]
+
+golden_outputs = [
+ [0.02129706, 0.140816242, 0.0112733059, -0.0226350538, 0.0916948169, 0.0769175813],
+ [0.0132302344, 0.152308047, 0.0346313119, -0.0269966982, 0.149707705, 0.094149217],
+ [-0.0123688057, 0.165790111, 0.0893077999, -0.0103429332, 0.173016444, 0.0720508844],
+]
+output_states = [[0, 0, 0, 0, 0, 0]] + golden_outputs[:2]
+
+tests = zip(
+ test_inputs, output_states, cell_states, golden_cell_states, golden_outputs)
+
+for test_input, output_state, cell_state, golden_state, golden_output in tests:
+ cur_input = copy.deepcopy(input0)
+ cur_input[input] = test_input
+ cur_input[output_state_in] = output_state
+ cur_input[cell_state_in] = cell_state
+ cur_output = {
+ scratch_buffer: [0] * (n_batch * n_cell * 3),
+ cell_state_out: golden_state,
+ output_state_out: golden_output,
+ output: golden_output
+ }
+ Example((cur_input, cur_output), name="CifgPeepholeProjectionNoClippingLayerNormLstm")
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-20 12:32:41 +0000