d16e734273
Before the change the output of XlaPad had a known rank but unknown dimensions. PiperOrigin-RevId: 349600804 Change-Id: Ie5e2177499ac4a9d4a5ca3c890adc9798a46fbf1
623 lines
23 KiB
Python
623 lines
23 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for XLA op wrappers."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import functools
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.compiler.tf2xla.python import xla
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from tensorflow.compiler.xla import xla_data_pb2
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import function
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.platform import googletest
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class XlaOpsNumericalTest(xla_test.XLATestCase, parameterized.TestCase):
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def _assertOpOutputMatchesExpected(self, op, args, expected,
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equality_fn=None):
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with self.session() as session:
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with self.test_scope():
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placeholders = [
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array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape)
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for arg in args
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]
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feeds = {placeholders[i]: args[i] for i in range(0, len(args))}
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output = op(*placeholders)
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result = session.run(output, feeds)
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if not equality_fn:
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equality_fn = self.assertAllClose
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equality_fn(result, expected, rtol=1e-3)
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def testAdd(self):
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for dtype in self.numeric_types:
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self._assertOpOutputMatchesExpected(
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xla.add,
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args=(np.array([1, 2, 3], dtype=dtype),
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np.array([4, 5, 6], dtype=dtype)),
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expected=np.array([5, 7, 9], dtype=dtype))
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self._assertOpOutputMatchesExpected(
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lambda x, y: xla.add(x, y, broadcast_dims=(0,)),
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args=(np.array([[1, 2], [3, 4]], dtype=dtype),
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np.array([7, 11], dtype=dtype)),
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expected=np.array([[8, 9], [14, 15]], dtype=dtype))
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self._assertOpOutputMatchesExpected(
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lambda x, y: xla.add(x, y, broadcast_dims=(1,)),
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args=(np.array([[1, 2], [3, 4]], dtype=dtype),
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np.array([7, 11], dtype=dtype)),
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expected=np.array([[8, 13], [10, 15]], dtype=dtype))
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def testBroadcast(self):
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for dtype in self.numeric_types:
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v = np.arange(4, dtype=np.int32).astype(dtype).reshape([2, 2])
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self._assertOpOutputMatchesExpected(
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lambda x: xla.broadcast(x, (7, 42)),
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args=(v,),
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expected=np.tile(v, (7, 42, 1, 1)))
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@test_util.disable_mlir_bridge('Not supported yet')
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def testGather(self):
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operand = np.arange(10, dtype=np.int32).reshape([2, 5])
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start_indices = np.array([2], np.int32)
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slice_sizes = np.array([1, 3], np.int32)
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def gather(operand, start_indices):
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dimension_numbers = xla_data_pb2.GatherDimensionNumbers()
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dimension_numbers.offset_dims.extend([1])
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dimension_numbers.collapsed_slice_dims.extend([0])
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dimension_numbers.start_index_map.extend([0])
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dimension_numbers.index_vector_dim = 1
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return xla.gather(operand, start_indices, dimension_numbers, slice_sizes)
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self._assertOpOutputMatchesExpected(
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gather,
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args=(operand, start_indices),
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expected=np.array([[5, 6, 7]]))
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@test_util.disable_mlir_bridge('Dynamic result types not supported')
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def testShiftRightLogical(self):
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self._assertOpOutputMatchesExpected(
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xla.shift_right_logical,
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args=(np.array([-1, 16], dtype=np.int32), np.int32(4)),
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expected=np.array([0x0FFFFFFF, 1], dtype=np.int32))
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self._assertOpOutputMatchesExpected(
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xla.shift_right_logical,
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args=(np.array([0xFFFFFFFF, 16], dtype=np.uint32), np.uint32(4)),
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expected=np.array([0x0FFFFFFF, 1], dtype=np.uint32))
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@test_util.disable_mlir_bridge('Dynamic result types not supported')
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def testShiftRightArithmetic(self):
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self._assertOpOutputMatchesExpected(
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xla.shift_right_arithmetic,
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args=(np.array([-1, 16], dtype=np.int32), np.int32(4)),
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expected=np.array([-1, 1], dtype=np.int32))
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self._assertOpOutputMatchesExpected(
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xla.shift_right_arithmetic,
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args=(np.array([0xFFFFFFFF, 16], dtype=np.uint32), np.uint32(4)),
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expected=np.array([0xFFFFFFFF, 1], dtype=np.uint32))
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PRECISION_VALUES = (None, xla_data_pb2.PrecisionConfig.DEFAULT,
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xla_data_pb2.PrecisionConfig.HIGH,
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xla_data_pb2.PrecisionConfig.HIGHEST)
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@parameterized.parameters(*PRECISION_VALUES)
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def testConv(self, precision):
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for dtype in set(self.float_types).intersection(
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set([dtypes.bfloat16.as_numpy_dtype, np.float32])):
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def conv_1d_fn(lhs, rhs):
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dnums = xla_data_pb2.ConvolutionDimensionNumbers()
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num_spatial_dims = 1
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dnums.input_batch_dimension = 0
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dnums.input_feature_dimension = 1
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dnums.output_batch_dimension = 0
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dnums.output_feature_dimension = 1
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dnums.kernel_output_feature_dimension = 0
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dnums.kernel_input_feature_dimension = 1
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dnums.input_spatial_dimensions.extend(range(2, 2 + num_spatial_dims))
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dnums.kernel_spatial_dimensions.extend(range(2, 2 + num_spatial_dims))
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dnums.output_spatial_dimensions.extend(range(2, 2 + num_spatial_dims))
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precision_config = None
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if precision:
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precision_config = xla_data_pb2.PrecisionConfig()
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precision_config.operand_precision.extend([precision, precision])
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return xla.conv(
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lhs,
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rhs,
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window_strides=(1,),
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padding=((2, 1),),
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lhs_dilation=(1,),
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rhs_dilation=(2,),
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dimension_numbers=dnums)
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self._assertOpOutputMatchesExpected(
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conv_1d_fn,
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args=(
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np.array([[[3, 4, 5, 6]]], dtype=dtype),
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np.array([[[-2, -3]]], dtype=dtype),
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),
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expected=np.array([[[-9, -12, -21, -26, -10]]], dtype=dtype))
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@parameterized.parameters(*PRECISION_VALUES)
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def testDotGeneral(self, precision):
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for dtype in self.float_types:
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def dot_fn(lhs, rhs):
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dnums = xla_data_pb2.DotDimensionNumbers()
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dnums.lhs_contracting_dimensions.append(2)
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dnums.rhs_contracting_dimensions.append(1)
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dnums.lhs_batch_dimensions.append(0)
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dnums.rhs_batch_dimensions.append(0)
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precision_config = None
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if precision:
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precision_config = xla_data_pb2.PrecisionConfig()
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precision_config.operand_precision.extend([precision, precision])
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return xla.dot_general(
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lhs,
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rhs,
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dimension_numbers=dnums,
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precision_config=precision_config)
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lhs = np.array(
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[
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[[1, 2], [3, 4]],
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[[5, 6], [7, 8]],
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], dtype=dtype)
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rhs = np.array(
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[
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[[1, 2, 3], [4, 5, 6]],
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[[7, 8, 9], [10, 11, 12]],
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], dtype=dtype)
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self._assertOpOutputMatchesExpected(
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dot_fn,
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args=(lhs, rhs),
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expected=np.array(
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[
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[[9, 12, 15], [19, 26, 33]],
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[[95, 106, 117], [129, 144, 159]],
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],
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dtype=dtype))
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def testNeg(self):
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for dtype in self.numeric_types - {np.uint8, np.int8}:
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self._assertOpOutputMatchesExpected(
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xla.neg,
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args=(np.array([1, 2, 3], dtype=dtype),),
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expected=np.array([-1, -2, -3], dtype=dtype))
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def testPad(self):
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for dtype in self.numeric_types:
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def pad_fn(x):
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return xla.pad(
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x,
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padding_value=7,
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padding_low=[2, 1],
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padding_high=[1, 2],
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padding_interior=[1, 0])
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self._assertOpOutputMatchesExpected(
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pad_fn,
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args=(np.arange(4, dtype=np.int32).astype(dtype).reshape([2, 2]),),
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expected=np.array(
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[[7, 7, 7, 7, 7], [7, 7, 7, 7, 7], [7, 0, 1, 7, 7],
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[7, 7, 7, 7, 7], [7, 2, 3, 7, 7], [7, 7, 7, 7, 7]],
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dtype=dtype))
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def testPadNegative(self):
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for dtype in self.numeric_types:
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def pad_fn(x):
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return xla.pad(
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x,
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padding_value=7,
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padding_low=[0, -1],
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padding_high=[1, -2],
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padding_interior=[1, 2])
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self._assertOpOutputMatchesExpected(
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pad_fn,
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args=(np.arange(6, dtype=np.int32).astype(dtype).reshape([2, 3]),),
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expected=np.array(
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[[7, 7, 1, 7], [7, 7, 7, 7], [7, 7, 4, 7], [7, 7, 7, 7]],
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dtype=dtype))
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def testPadShapeInference(self):
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a = array_ops.placeholder(np.float32, shape=(2, 3))
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c = xla.pad(
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a,
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padding_value=7,
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padding_low=[2, 1],
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padding_high=[1, 2],
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padding_interior=[1, 4])
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self.assertEqual(c.shape, tensor_shape.TensorShape([6, 14]))
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c = xla.pad(
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a,
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padding_value=7,
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padding_low=[2, -2],
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padding_high=[1, -2],
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padding_interior=[1, 2])
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self.assertEqual(c.shape, tensor_shape.TensorShape([6, 3]))
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# 0-sized input dimension and interior padding
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c = xla.pad(
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array_ops.placeholder(np.float32, shape=(2, 0)),
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padding_value=7,
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padding_low=[2, 1],
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padding_high=[1, 1],
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padding_interior=[1, 2])
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self.assertEqual(c.shape, tensor_shape.TensorShape([6, 2]))
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with self.assertRaisesRegex(
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ValueError, 'padding_value input must be scalar, found rank 1 '):
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xla.pad(
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a,
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padding_value=[0, 1],
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padding_low=[0, 0],
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padding_high=[0, 0],
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padding_interior=[0, 0])
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with self.assertRaisesRegex(ValueError,
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'padding_low must be a 1D tensor of size 2 '):
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xla.pad(
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a,
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padding_value=7,
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padding_low=[0, 0, 0],
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padding_high=[0, 0],
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padding_interior=[0, 0])
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with self.assertRaisesRegex(ValueError,
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'padding_high must be a 1D tensor of size 2 '):
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xla.pad(
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a,
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padding_value=7,
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padding_low=[0, 0],
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padding_high=[0, 0, 0],
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padding_interior=[0, 0])
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with self.assertRaisesRegex(
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ValueError, 'padding_interior must be a 1D tensor of size 2 '):
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xla.pad(
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a,
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padding_value=7,
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padding_low=[0, 0],
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padding_high=[0, 0],
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padding_interior=[0])
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with self.assertRaisesRegex(
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ValueError,
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'padding_interior must contain only non-negative values, found -2 '):
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xla.pad(
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a,
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padding_value=7,
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padding_low=[0, 0],
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padding_high=[0, 0],
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padding_interior=[-2, 0])
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with self.assertRaisesRegex(
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ValueError, 'resulting padded dimension has negative size -1 '):
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xla.pad(
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a,
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padding_value=7,
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padding_low=[-3, 0],
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padding_high=[0, 0],
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padding_interior=[0, 0])
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@test_util.disable_mlir_bridge('Not supported yet')
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def testReduce(self):
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for dtype in set(self.numeric_types).intersection(
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set([dtypes.bfloat16.as_numpy_dtype, np.float32])):
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@function.Defun(dtype, dtype)
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def sum_reducer(x, y):
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return x + y
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def sum_reduction(dims):
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def fn(x):
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return xla.reduce(
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x, init_value=0, dimensions_to_reduce=dims, reducer=sum_reducer)
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return fn
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self._assertOpOutputMatchesExpected(
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sum_reduction(dims=[]),
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args=(np.arange(12, dtype=np.int32).astype(dtype).reshape([3, 4]),),
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expected=np.arange(12, dtype=np.int32).astype(dtype).reshape([3, 4]))
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self._assertOpOutputMatchesExpected(
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sum_reduction(dims=[0]),
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args=(np.arange(12, dtype=np.int32).astype(dtype).reshape([3, 4]),),
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expected=np.array([12, 15, 18, 21], dtype=dtype))
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self._assertOpOutputMatchesExpected(
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sum_reduction(dims=[1]),
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args=(np.arange(12, dtype=np.int32).astype(dtype).reshape([3, 4]),),
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expected=np.array([6, 22, 38], dtype=dtype))
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self._assertOpOutputMatchesExpected(
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sum_reduction(dims=[0, 1]),
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args=(np.arange(12, dtype=np.int32).astype(dtype).reshape([3, 4]),),
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expected=dtype(66))
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@function.Defun(dtype, dtype)
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def mul_reducer(x, y):
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return x * y
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def mul_reduction(dims):
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def fn(x):
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return xla.reduce(
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x, init_value=1, dimensions_to_reduce=dims, reducer=mul_reducer)
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return fn
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self._assertOpOutputMatchesExpected(
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mul_reduction(dims=[0]),
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args=(np.arange(12, dtype=np.int32).astype(dtype).reshape([3, 4]),),
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expected=np.array([0, 45, 120, 231], dtype=dtype))
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@test_util.disable_mlir_bridge('Not supported yet')
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def testVariadicReduce(self):
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for dtype in set(self.numeric_types).intersection(
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set([np.float32, np.complex64])):
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@def_function.function
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def kahan_sum_reducer(t0, t1):
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(s0, c0), (s1, c1) = t0, t1
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s0minusc = s0 - (c0 + c1)
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t = s1 + s0minusc
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c = (t - s1) - s0minusc
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s = t
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return s, c
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def kahan_sum_reduction(dims, output_idx):
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def fn(x):
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arg = array_ops.zeros([], dtype) # pylint: disable=cell-var-from-loop
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reducer = kahan_sum_reducer.get_concrete_function(
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(arg, arg), (arg, arg))
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return xla.variadic_reduce(
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(x, array_ops.zeros_like(x)),
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init_value=(arg, arg),
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dimensions_to_reduce=dims,
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reducer=reducer)[output_idx]
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return fn
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xs = np.array([1e5, np.pi, -1e5, np.exp(1.)])
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xs = np.array([xs, xs[::-1] / 3, xs / 7], dtype)
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self._assertOpOutputMatchesExpected(
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kahan_sum_reduction(dims=[], output_idx=0),
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args=(xs,), expected=xs)
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self._assertOpOutputMatchesExpected(
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kahan_sum_reduction(dims=[], output_idx=1),
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args=(xs,), expected=np.zeros_like(xs))
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shuffle_indices = np.argsort(np.random.randn(xs.shape[0]))
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self._assertOpOutputMatchesExpected(
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kahan_sum_reduction(dims=[0], output_idx=0),
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args=(xs[shuffle_indices],),
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expected=np.array([np.exp(1) / 3 + 1e5 * 8 / 7,
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np.pi * 8 / 7 - 1e5 / 3,
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-1e5 * 8 / 7 + np.pi / 3,
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np.exp(1) * 8 / 7 + 1e5 / 3], dtype=dtype))
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error_term_equality = functools.partial(self.assertAllClose, atol=.005)
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self._assertOpOutputMatchesExpected(
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kahan_sum_reduction(dims=[0], output_idx=1),
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args=(xs[shuffle_indices],), expected=np.zeros_like(xs[0]),
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equality_fn=error_term_equality)
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shuffle_indices = np.argsort(np.random.randn(xs.shape[1]))
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self._assertOpOutputMatchesExpected(
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kahan_sum_reduction(dims=[1], output_idx=0),
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args=(xs[:, shuffle_indices],),
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expected=np.array([np.pi + np.exp(1.),
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(np.pi + np.exp(1.)) / 3,
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(np.pi + np.exp(1.)) / 7], dtype=dtype))
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self._assertOpOutputMatchesExpected(
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kahan_sum_reduction(dims=[1], output_idx=1),
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args=(xs[:, shuffle_indices],), expected=np.zeros_like(xs[:, 0]),
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equality_fn=error_term_equality)
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# Now, shuffle both dims.
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xs = xs[np.argsort(np.random.randn(xs.shape[0]))]
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xs = xs[:, np.argsort(np.random.randn(xs.shape[1]))]
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self._assertOpOutputMatchesExpected(
|
|
kahan_sum_reduction(dims=[0, 1], output_idx=0),
|
|
args=(xs,), expected=dtype((np.pi + np.exp(1.)) * 31 / 21))
|
|
self._assertOpOutputMatchesExpected(
|
|
kahan_sum_reduction(dims=[0, 1], output_idx=1),
|
|
args=(xs,), expected=dtype(0),
|
|
equality_fn=error_term_equality)
|
|
|
|
@test_util.disable_mlir_bridge('Not supported yet')
|
|
def testSelectAndScatter(self):
|
|
for dtype in set(self.numeric_types).intersection(
|
|
set([dtypes.bfloat16.as_numpy_dtype, np.float32])):
|
|
|
|
@function.Defun(dtype, dtype)
|
|
def add_scatter(x, y):
|
|
return x + y
|
|
|
|
@function.Defun(dtype, dtype)
|
|
def ge_select(x, y):
|
|
return x >= y
|
|
|
|
def test_fn(operand, source):
|
|
return xla.select_and_scatter(
|
|
operand,
|
|
window_dimensions=[2, 3, 1, 1],
|
|
window_strides=[2, 2, 1, 1],
|
|
padding=[[0, 0]] * 4,
|
|
source=source,
|
|
init_value=0,
|
|
select=ge_select,
|
|
scatter=add_scatter)
|
|
|
|
self._assertOpOutputMatchesExpected(
|
|
test_fn,
|
|
args=(np.array(
|
|
[[7, 2, 5, 3, 8], [3, 8, 9, 3, 4], [1, 5, 7, 5, 6],
|
|
[0, 6, 2, 10, 2]],
|
|
dtype=dtype).reshape((4, 5, 1, 1)),
|
|
np.array([[2, 6], [3, 1]], dtype=dtype).reshape((2, 2, 1, 1))),
|
|
expected=np.array(
|
|
[[0, 0, 0, 0, 0], [0, 0, 8, 0, 0], [0, 0, 3, 0, 0],
|
|
[0, 0, 0, 1, 0]],
|
|
dtype=dtype).reshape((4, 5, 1, 1)))
|
|
|
|
def testTranspose(self):
|
|
for dtype in self.numeric_types:
|
|
v = np.arange(4, dtype=np.int32).astype(dtype).reshape([2, 2])
|
|
self._assertOpOutputMatchesExpected(
|
|
lambda x: xla.transpose(x, [1, 0]), args=(v,), expected=v.T)
|
|
|
|
def testDynamicSlice(self):
|
|
for dtype in self.numeric_types:
|
|
self._assertOpOutputMatchesExpected(
|
|
xla.dynamic_slice,
|
|
args=(np.arange(1000,
|
|
dtype=np.int32).astype(dtype).reshape([10, 10, 10]),
|
|
np.array([5, 7, 3]), np.array([2, 3, 2])),
|
|
expected=np.array(
|
|
np.array([[[573, 574], [583, 584], [593, 594]],
|
|
[[673, 674], [683, 684], [693, 694]]]),
|
|
dtype=dtype))
|
|
|
|
@test_util.disable_mlir_bridge('Error handling')
|
|
def testDynamicSliceWithIncorrectStartIndicesShape(self):
|
|
with self.session() as session:
|
|
with self.test_scope():
|
|
output = xla.dynamic_slice(
|
|
np.arange(1000, dtype=np.int32).reshape([10, 10, 10]),
|
|
np.array([5, 7]), np.array([2, 3, 4]))
|
|
with self.assertRaises(errors.InvalidArgumentError) as invalid_arg_error:
|
|
session.run(output)
|
|
self.assertRegex(
|
|
invalid_arg_error.exception.message,
|
|
(r'start_indices must be a vector with length equal to input rank, '
|
|
r'but input rank is 3 and start_indices has shape \[2\].*'))
|
|
|
|
@test_util.disable_mlir_bridge('Error handling')
|
|
def testDynamicSliceWithIncorrectSizeIndicesShape(self):
|
|
with self.session() as session:
|
|
with self.test_scope():
|
|
output = xla.dynamic_slice(
|
|
np.arange(1000, dtype=np.int32).reshape([10, 10, 10]),
|
|
np.array([5, 7, 3]), np.array([2, 3]))
|
|
with self.assertRaises(errors.InvalidArgumentError) as invalid_arg_error:
|
|
session.run(output)
|
|
self.assertRegex(
|
|
invalid_arg_error.exception.message,
|
|
(r'size_indices must be a vector with length equal to input rank, '
|
|
r'but input rank is 3 and size_indices has shape \[2\].*'))
|
|
|
|
|
|
class XlaOpsShapeInferenceTest(xla_test.XLATestCase, parameterized.TestCase):
|
|
|
|
def testDotDifferentNumberOfContractingDimensions(self):
|
|
a = array_ops.placeholder(np.float32, shape=(4, 4, 4, 4))
|
|
b = array_ops.placeholder(np.float32, shape=(4, 4, 4, 4))
|
|
|
|
dim_nums = xla_data_pb2.DotDimensionNumbers()
|
|
dim_nums.lhs_contracting_dimensions.append(2)
|
|
dim_nums.rhs_contracting_dimensions.append(2)
|
|
dim_nums.rhs_contracting_dimensions.append(3)
|
|
|
|
with self.assertRaisesRegex(ValueError,
|
|
'Must specify the same number of contracting '
|
|
'dimensions for lhs and rhs. Got: 1 and 2'):
|
|
xla.dot_general(a, b, dim_nums)
|
|
|
|
def testDotDifferentContractingDimensionsSizes(self):
|
|
a = array_ops.placeholder(np.float32, shape=(2, 2, 2, 2))
|
|
b = array_ops.placeholder(np.float32, shape=(4, 4, 4, 4))
|
|
|
|
dim_nums = xla_data_pb2.DotDimensionNumbers()
|
|
dim_nums.lhs_contracting_dimensions.append(2)
|
|
dim_nums.rhs_contracting_dimensions.append(3)
|
|
|
|
with self.assertRaisesRegex(ValueError,
|
|
'Contracting dimension sizes do not match. '
|
|
'Got: 2 and 4'):
|
|
xla.dot_general(a, b, dim_nums)
|
|
|
|
def testDotDifferentNumberOfBatchDimensions(self):
|
|
a = array_ops.placeholder(np.float32, shape=(4, 4, 4, 4))
|
|
b = array_ops.placeholder(np.float32, shape=(4, 4, 4, 4))
|
|
|
|
dim_nums = xla_data_pb2.DotDimensionNumbers()
|
|
dim_nums.lhs_batch_dimensions.append(2)
|
|
dim_nums.rhs_batch_dimensions.append(2)
|
|
dim_nums.rhs_batch_dimensions.append(3)
|
|
|
|
with self.assertRaisesRegex(ValueError,
|
|
'Must specify the same number of batch '
|
|
'dimensions for lhs and rhs. Got: 1 and 2'):
|
|
xla.dot_general(a, b, dim_nums)
|
|
|
|
def testDotDifferentBatchDimensionsSizes(self):
|
|
a = array_ops.placeholder(np.float32, shape=(2, 2, 2, 2))
|
|
b = array_ops.placeholder(np.float32, shape=(4, 4, 4, 2))
|
|
|
|
dim_nums = xla_data_pb2.DotDimensionNumbers()
|
|
dim_nums.lhs_contracting_dimensions.append(2)
|
|
dim_nums.rhs_contracting_dimensions.append(3)
|
|
dim_nums.lhs_batch_dimensions.append(0)
|
|
dim_nums.rhs_batch_dimensions.append(0)
|
|
|
|
with self.assertRaisesRegex(ValueError,
|
|
'Batch dimension sizes do not match. '
|
|
'Got: 2 and 4'):
|
|
xla.dot_general(a, b, dim_nums)
|
|
|
|
def testDotShapeInference(self):
|
|
a = array_ops.placeholder(np.float32, shape=(1, 2, 3, 4))
|
|
b = array_ops.placeholder(np.float32, shape=(4, 3, 2, 1))
|
|
|
|
dim_nums = xla_data_pb2.DotDimensionNumbers()
|
|
dim_nums.lhs_contracting_dimensions.append(1)
|
|
dim_nums.rhs_contracting_dimensions.append(2)
|
|
dim_nums.lhs_batch_dimensions.append(3)
|
|
dim_nums.rhs_batch_dimensions.append(0)
|
|
|
|
c = xla.dot_general(a, b, dim_nums)
|
|
self.assertEqual(c.shape, tensor_shape.TensorShape([4, 1, 3, 3, 1]))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
# This test is using Tensorflow sessions which are not compatible with eager
|
|
# mode.
|
|
ops.disable_eager_execution()
|
|
googletest.main()
|