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#
# 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.
#
# Test for QUANTIZED_LSTM op.
import copy
model = Model()
batch_size = 2
input_size = 5
num_units = 4
output_size = 3
input = Input("input",
("TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}" % (batch_size, input_size), 0.0078125, 0))
input_to_input_weights = Input("input_to_input_weights",
("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, input_size), 0.00784314, 0))
input_to_forget_weights = Input("input_to_forget_weights",
("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, input_size), 0.00784314, 0))
input_to_cell_weights = Input("input_to_cell_weights",
("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, input_size), 0.00784314, 0))
input_to_output_weights = Input("input_to_output_weights",
("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, input_size), 0.00784314, 0))
recurrent_to_input_weights = Input("recurrent_to_intput_weights",
("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, output_size),
0.00784314, 0))
recurrent_to_forget_weights = Input("recurrent_to_forget_weights",
("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, output_size),
0.00784314, 0))
recurrent_to_cell_weights = Input("recurrent_to_cell_weights",
("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, output_size),
0.00784314, 0))
recurrent_to_output_weights = Input("recurrent_to_output_weights",
("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, output_size),
0.00784314, 0))
cell_to_input_weights = Input("cell_to_input_weights",
("TENSOR_QUANT16_SYMM", "{%d}" % (num_units), 1.0, 0))
cell_to_forget_weights = Input("cell_to_forget_weights",
("TENSOR_QUANT16_SYMM", "{%d}" % (num_units), 1.0, 0))
cell_to_output_weights = Input("cell_to_output_weights",
("TENSOR_QUANT16_SYMM", "{%d}" % (num_units), 1.0, 0))
input_gate_bias = Input("input_gate_bias",
("TENSOR_INT32", "{%d}" % (num_units), 4.65661e-08, 0))
forget_gate_bias = Input("forget_gate_bias",
("TENSOR_INT32", "{%d}" % (num_units), 4.65661e-08, 0))
cell_gate_bias = Input("cell_gate_bias",
("TENSOR_INT32", "{%d}" % (num_units), 4.65661e-08, 0))
output_gate_bias = Input("output_gate_bias",
("TENSOR_INT32", "{%d}" % (num_units), 4.65661e-08, 0))
projection_weights = Input("projection_weights",
("TENSOR_QUANT8_SYMM", "{%d,%d}" % (output_size, num_units), 0.00392157, 0))
projection_bias = Input("projection_bias", "TENSOR_INT32", "{%d}" % (output_size))
output_state_in = Input("output_state_in",
("TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}" % (batch_size, output_size),
3.05176e-05, 0))
cell_state_in = Input("cell_state_in",
("TENSOR_QUANT16_SYMM", "{%d, %d}" % (batch_size, num_units), 3.05176e-05, 0))
input_layer_norm_weights = Input("input_layer_norm_weights",
("TENSOR_QUANT16_SYMM", "{%d}" % num_units, 3.05182e-05, 0))
forget_layer_norm_weights = Input("forget_layer_norm_weights",
("TENSOR_QUANT16_SYMM", "{%d}" % num_units, 3.05182e-05, 0))
cell_layer_norm_weights = Input("cell_layer_norm_weights",
("TENSOR_QUANT16_SYMM", "{%d}" % num_units, 3.05182e-05, 0))
output_layer_norm_weights = Input("output_layer_norm_weights",
("TENSOR_QUANT16_SYMM", "{%d}" % num_units, 3.05182e-05, 0))
cell_clip = Float32Scalar("cell_clip", 0.)
projection_clip = Float32Scalar("projection_clip", 0.)
input_intermediate_scale = Float32Scalar("input_intermediate_scale", 0.007059)
forget_intermediate_scale = Float32Scalar("forget_intermediate_scale", 0.007812)
cell_intermediate_scale = Float32Scalar("cell_intermediate_scale", 0.007059)
output_intermediate_scale = Float32Scalar("output_intermediate_scale", 0.007812)
hidden_state_zero_point = Int32Scalar("hidden_state_zero_point", 0)
hidden_state_scale = Float32Scalar("hidden_state_scale", 0.007)
output_state_out = Output("output_state_out",
("TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}" % (batch_size, output_size),
3.05176e-05, 0))
cell_state_out = Output("cell_state_out",
("TENSOR_QUANT16_SYMM", "{%d, %d}" % (batch_size, num_units), 3.05176e-05, 0))
output = Output("output",
("TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}" % (batch_size, output_size),
3.05176e-05, 0))
model = model.Operation(
"QUANTIZED_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, input_layer_norm_weights, forget_layer_norm_weights,
cell_layer_norm_weights, output_layer_norm_weights, cell_clip, projection_clip,
input_intermediate_scale, forget_intermediate_scale, cell_intermediate_scale,
output_intermediate_scale, hidden_state_zero_point, hidden_state_scale).To([output_state_out,
cell_state_out, output])
# Example 1. Input in operand 0,
input0 = {
input_to_input_weights: [
64, 77, 89, -102, -115, 13, 25, 38, -51, 64, -102, 89, -77, 64, -51, -64, -51, -38, -25, -13
],
input_to_forget_weights: [
-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64
],
input_to_cell_weights: [
-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77
],
input_to_output_weights: [
-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51
],
input_gate_bias: [644245, 3221226, 4724464, 8160438],
forget_gate_bias: [2147484, -6442451, -4294968, 2147484],
cell_gate_bias: [-1073742, 15461883, 5368709, 1717987],
output_gate_bias: [1073742, -214748, 4294968, 2147484],
recurrent_to_input_weights: [
-25, -38, 51, 13, -64, 115, -25, -38, -89, 6, -25, -77
],
recurrent_to_forget_weights: [
-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25
],
recurrent_to_cell_weights: [
-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25
],
recurrent_to_output_weights: [
38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25
],
projection_weights: [
-25, 51, 3, -51, 25, 127, 77, 20, 18, 51, -102, 51
],
projection_bias: [ 0 for _ in range(output_size) ],
input_layer_norm_weights: [3277, 6553, 9830, 16384],
forget_layer_norm_weights: [6553, 6553, 13107, 9830],
cell_layer_norm_weights: [22937, 6553, 9830, 26214],
output_layer_norm_weights: [19660, 6553, 6553, 16384],
}
test_input = [90, 102, 13, 26, 38, 102, 13, 26, 51, 64]
golden_output = [
127, 127, -108, -67, 127, 127
]
output0 = {
output_state_out: golden_output,
cell_state_out: [-14650, 8939, 5771, 6715, -11843, 7847, 1508, 12939],
output: golden_output,
}
input0[input] = test_input
input0[output_state_in] = [ 0 for _ in range(batch_size * output_size) ]
input0[cell_state_in] = [ 0 for _ in range(batch_size * num_units) ]
input0[cell_to_input_weights] = [0 for _ in range(num_units) ]
input0[cell_to_forget_weights] = [0 for _ in range(num_units) ]
input0[cell_to_output_weights] = [0 for _ in range(num_units) ]
Example((input0, output0))
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