| # Copyright 2018 The TensorFlow Authors. All Rights Reserved. |
| # |
| # 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. |
| # ============================================================================== |
| """Tests for quantized operations.""" |
| |
| import math |
| |
| import numpy as np |
| |
| from tensorflow.compiler.tests import xla_test |
| from tensorflow.compiler.tf2xla.python import xla |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import ops |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.ops import bitwise_ops |
| from tensorflow.python.ops import math_ops |
| from tensorflow.python.platform import googletest |
| |
| |
| class QuantizedOpsTest(xla_test.XLATestCase): |
| |
| # Verify that quantized types can be clustered by XLA. |
| def testQuantizedTypeRoundtrip(self): |
| with self.session() as session: |
| for dtype in self.quantized_tf_types: |
| in_values = np.array([1, 2, 3, 4, 5, 6]) |
| expected = [[1, 2], [3, 4], [5, 6]] |
| with self.test_scope(): |
| p = array_ops.placeholder(dtype=dtypes.int32) |
| x = math_ops.cast(p, dtype) |
| x = array_ops.reshape(x, [3, 2]) |
| |
| value = session.run(x, {p: in_values}) |
| self.assertAllEqual(value, expected) |
| |
| |
| class DequantizedOpsTest(xla_test.XLATestCase): |
| |
| def pack_uint8_r2_to_uint32(self, test_input): |
| num_rows, num_columns = test_input.get_shape().as_list() |
| num_output_columns = int(math.ceil(num_columns / 4.0)) |
| padding_input = array_ops.pad( |
| math_ops.cast(test_input, dtype=dtypes.uint8), |
| constant_op.constant([[ |
| 0, |
| 0, |
| ], [0, num_output_columns * 4 - num_columns]])) |
| output = array_ops.zeros([num_rows, num_output_columns], |
| dtype=dtypes.uint32) |
| num_elements_per_pack = 4 |
| shift_bits = 8 |
| |
| iota_r1 = math_ops.range(num_output_columns * num_elements_per_pack) |
| |
| for p in range(num_elements_per_pack): |
| selected_index = math_ops.equal( |
| math_ops.mod(iota_r1, num_elements_per_pack), p) |
| gather_index = array_ops.boolean_mask(iota_r1, selected_index) |
| gathered_input = array_ops.gather(padding_input, gather_index, axis=1) |
| total_shift_bits = shift_bits * (num_elements_per_pack - p - 1) |
| left_shift_input = bitwise_ops.left_shift( |
| math_ops.cast(gathered_input, dtype=dtypes.uint32), total_shift_bits) |
| output = bitwise_ops.bitwise_or(output, left_shift_input) |
| return output |
| |
| def testDequantizeQuint8(self): |
| num_rows = 100 |
| num_columns = 3547 |
| random_input = np.random.normal(128.0, 10.0, [num_rows, num_columns]) |
| with self.session() as session: |
| with ops.device("CPU"): |
| test_input = ops.convert_to_tensor(random_input, dtype=dtypes.float32) |
| transposed_input = array_ops.transpose(test_input, [1, 0]) |
| quantized_input = array_ops.quantize(transposed_input, 0.0, 255.0, |
| dtypes.quint8) |
| packed_input = self.pack_uint8_r2_to_uint32(quantized_input.output) |
| with self.test_scope(): |
| transposed_quantized_output = xla.dequantize(packed_input, 0.0, 255.0, |
| "MIN_COMBINED", True) |
| quantized_output = array_ops.slice(transposed_quantized_output, [0, 0], |
| [num_rows, num_columns]) |
| |
| value = session.run(quantized_output) |
| self.assertAllClose(value, random_input, 1.0) |
| |
| |
| if __name__ == "__main__": |
| googletest.main() |