diff options
author | Viet Dang <vddang@google.com> | 2020-02-20 16:24:29 +0000 |
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committer | Slava Shklyaev <slavash@google.com> | 2020-03-13 13:42:08 +0000 |
commit | 0c17328ba1906450477da315f80b472fa47e5ccd (patch) | |
tree | 4c4efae14eccd389a7077f8ce849a8367200f2a4 /nn/runtime/test/specs/V1_3 | |
parent | 1b16bee7b521791ef398357b95d883688840938c (diff) | |
download | ml-0c17328ba1906450477da315f80b472fa47e5ccd.tar.gz |
Adds a test for quantized LSTM op for CIFG, Layer Norm.
Also fixes a minor typo.
Bug: 148938903
Test: NeuralNetworksTest_static
Change-Id: I44463611b258c6ee2345211abe11cbba5694610d
Merged-In: I44463611b258c6ee2345211abe11cbba5694610d
(cherry picked from commit a91b2175d5110656b759999209b5d7e6ec6f643b)
Diffstat (limited to 'nn/runtime/test/specs/V1_3')
-rw-r--r-- | nn/runtime/test/specs/V1_3/qlstm_noprojection.mod.py | 172 | ||||
-rw-r--r-- | nn/runtime/test/specs/V1_3/qlstm_projection.mod.py | 2 |
2 files changed, 173 insertions, 1 deletions
diff --git a/nn/runtime/test/specs/V1_3/qlstm_noprojection.mod.py b/nn/runtime/test/specs/V1_3/qlstm_noprojection.mod.py new file mode 100644 index 000000000..41704f83b --- /dev/null +++ b/nn/runtime/test/specs/V1_3/qlstm_noprojection.mod.py @@ -0,0 +1,172 @@ +# +# Copyright (C) 2020 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 = 4 + +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_input_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. CIFG, Layer Norm. +input0 = { + input_to_input_weights: [], + 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: [], + 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: [], + recurrent_to_forget_weights: [ + -64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25, 25, 38, -13, 51 + ], + recurrent_to_cell_weights: [ + -38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25, 38, -13, 25, 64 + ], + recurrent_to_output_weights: [ + 38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25, 13, 64, 25, -38 + ], + projection_weights: [], + projection_bias: [], + input_layer_norm_weights: [], + forget_layer_norm_weights: [6553, 6553, 13107, 9830], + cell_layer_norm_weights: [22937, 6553, 9830, 26214], + output_layer_norm_weights: [19660, 6553, 6553, 16384], + output_state_in: [ 0 for _ in range(batch_size * output_size) ], + cell_state_in: [ 0 for _ in range(batch_size * num_units) ], + cell_to_input_weights: [], + cell_to_forget_weights: [], + cell_to_output_weights: [], +} + +test_input = [90, 102, 13, 26, 38, 102, 13, 26, 51, 64] + +golden_output = [ + -15, 21, 14, 20, -15, 15, 5, 27 +] + +output0 = { + output_state_out: golden_output, + cell_state_out: [-11692, 9960, 5491, 8861, -9422, 7726, 2056, 13149], + output: golden_output, +} + +input0[input] = test_input + +Example((input0, output0)) diff --git a/nn/runtime/test/specs/V1_3/qlstm_projection.mod.py b/nn/runtime/test/specs/V1_3/qlstm_projection.mod.py index 8895962cc..07fa1ecec 100644 --- a/nn/runtime/test/specs/V1_3/qlstm_projection.mod.py +++ b/nn/runtime/test/specs/V1_3/qlstm_projection.mod.py @@ -36,7 +36,7 @@ input_to_cell_weights = Input("input_to_cell_weights", 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", +recurrent_to_input_weights = Input("recurrent_to_input_weights", ("TENSOR_QUANT8_SYMM", "{%d, %d}" % (num_units, output_size), 0.00784314, 0)) recurrent_to_forget_weights = Input("recurrent_to_forget_weights", |