# Copyright 2017 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. # ============================================================================== """Functional tests for XLA TensorArray Ops.""" import numpy as np from tensorflow.compiler.tests import xla_test from tensorflow.python.compiler.xla import xla from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import gen_data_flow_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import tensor_array_grad # pylint: disable=unused-import from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test def _make_converter(dtype): def _converter(x): return np.asarray(x).astype(dtype.as_numpy_dtype) return _converter # This lets me define `fn` repeatedly to pass to xla.compile. # # pylint: disable=function-redefined @test_util.run_v1_only("b/") # Support TF2 list operations @test_util.with_control_flow_v2 class TensorArrayTest(xla_test.XLATestCase): @test_util.disable_control_flow_v2("Tries to evaluate flow") def testTensorArrayWriteRead(self): with self.session() as session, self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) w0 = ta.write(0, [[4.0, 5.0]]) w1 = w0.write(1, [[1.0, 3.0]]) w2 = w1.write(2, [[7.0, -8.5]]) r0 = w2.read(0) r1 = w2.read(1) r2 = w2.read(2) flow = w2.flow return [r0, r1, r2, flow] d0, d1, d2, flow_val = self.evaluate(xla.compile(fn)) self.assertAllEqual([[4.0, 5.0]], d0) self.assertAllEqual([[1.0, 3.0]], d1) self.assertAllEqual([[7.0, -8.5]], d2) self.assertAllEqual([], flow_val.shape) def _testTensorArrayWritePack(self, tf_dtype): with self.session(), self.test_scope(): convert = _make_converter(tf_dtype) def fn(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) w0 = ta.write(0, convert([[4.0, 5.0]])) w1 = w0.write(1, convert([[6.0, 7.0]])) w2 = w1.write(2, convert([[8.0, 9.0]])) return w2.stack() self.assertAllEqual( convert([[[4.0, 5.0]], [[6.0, 7.0]], [[8.0, 9.0]]]), self.evaluate(xla.compile(fn)[0])) def testTensorArrayWritePack(self): for dtype in self.numeric_tf_types: self._testTensorArrayWritePack(dtype) def testEmptyTensorArrayPack(self): with self.session(), self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) empty_element = np.zeros((0, 1), dtype=np.float32) w0 = ta.write(0, empty_element) w1 = w0.write(1, empty_element) w2 = w1.write(2, empty_element) return w2.stack() self.assertAllEqual([3, 0, 1], self.evaluate(xla.compile(fn)[0]).shape) def _testTensorArrayWriteConcat(self, tf_dtype): with self.session(), self.test_scope(): convert = _make_converter(tf_dtype) def fn(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) w0 = ta.write(0, convert([[4.0, 5.0], [104.0, 105.0]])) w1 = w0.write(1, convert([[6.0, 7.0], [106.0, 107.0]])) w2 = w1.write(2, convert([[8.0, 9.0], [124.0, 125.0]])) return w2.concat() self.assertAllEqual( convert([[4.0, 5.0], [104.0, 105.0], [6.0, 7.0], [106.0, 107.0], [8.0, 9.0], [124.0, 125.0]]), self.evaluate(xla.compile(fn)[0])) @test_util.disable_control_flow_v2("b/122315751 (concat)") def testTensorArrayWriteConcat(self): for dtype in self.numeric_tf_types: self._testTensorArrayWriteConcat(dtype) def _testTensorArrayUnpackRead(self, tf_dtype): with self.session() as session, self.test_scope(): convert = _make_converter(tf_dtype) def fn(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) # Unpack a vector into scalars w0 = ta.unstack(convert([1.0, 2.0, 3.0])) r0 = w0.read(0) r1 = w0.read(1) r2 = w0.read(2) return [r0, r1, r2] d0, d1, d2 = self.evaluate(xla.compile(fn)) self.assertAllEqual(convert(1.0), d0) self.assertAllEqual(convert(2.0), d1) self.assertAllEqual(convert(3.0), d2) def fn(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) # Unpack a matrix into vectors. w1 = ta.unstack( convert([[1.0, 1.03125], [2.0, 2.03125], [3.0, 3.03125]])) r0 = w1.read(0) r1 = w1.read(1) r2 = w1.read(2) return [r0, r1, r2] d0, d1, d2 = self.evaluate(xla.compile(fn)) self.assertAllEqual(convert([1.0, 1.03125]), d0) self.assertAllEqual(convert([2.0, 2.03125]), d1) self.assertAllEqual(convert([3.0, 3.03125]), d2) def fn(): # Reset ta because we're going to change the shape, else shape # inference will throw an error. ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) # Try unpacking an empty matrix, which should not cause an error. w2 = ta.unstack(convert([[], [], []])) r0 = w2.read(0) r1 = w2.read(1) r2 = w2.read(2) return [r0, r1, r2] d0, d1, d2 = self.evaluate(xla.compile(fn)) self.assertAllEqual(convert([]), d0) self.assertAllEqual(convert([]), d1) self.assertAllEqual(convert([]), d2) def _testTensorArrayUnpackReadMaybeLegacy(self): for dtype in self.numeric_tf_types: self._testTensorArrayUnpackRead(dtype) def testTensorArrayUnpackRead(self): self._testTensorArrayUnpackReadMaybeLegacy() def _testTensorArraySplitRead(self, tf_dtype): with self.session() as session, self.test_scope(): convert = _make_converter(tf_dtype) def fn(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) # Split an empty vector. lengths = constant_op.constant([0, 0, 0]) w0 = ta.split(convert([]), lengths=lengths) r0 = w0.read(0) r1 = w0.read(1) r2 = w0.read(2) return [r0, r1, r2] d0, d1, d2 = self.evaluate(xla.compile(fn)) self.assertAllEqual(convert([]), d0) self.assertAllEqual(convert([]), d1) self.assertAllEqual(convert([]), d2) def fn(): # Split a vector. ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) lengths = constant_op.constant([1, 1, 1]) w0 = ta.split(convert([1.0, 2.0, 3.0]), lengths=lengths) r0 = w0.read(0) r1 = w0.read(1) r2 = w0.read(2) return [r0, r1, r2] d0, d1, d2 = self.evaluate(xla.compile(fn)) self.assertAllEqual(convert([1.0]), d0) self.assertAllEqual(convert([2.0]), d1) self.assertAllEqual(convert([3.0]), d2) def fn(): # Split a matrix. ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) lengths = constant_op.constant([1, 1, 1]) w0 = ta.split( convert([[1.0, 101.0], [2.0, 121.0], [3.0, 127.0]]), lengths=lengths) r0 = w0.read(0) r1 = w0.read(1) r2 = w0.read(2) return [r0, r1, r2] d0, d1, d2 = self.evaluate(xla.compile(fn)) self.assertAllEqual(convert([[1.0, 101.0]]), d0) self.assertAllEqual(convert([[2.0, 121.0]]), d1) self.assertAllEqual(convert([[3.0, 127.0]]), d2) @test_util.disable_control_flow_v2("b/122315872 (split)") def testTensorArraySplitRead(self): for dtype in self.numeric_tf_types: self._testTensorArraySplitRead(dtype) @test_util.disable_control_flow_v2("TensorArray.grad is not supported in v2") def testTensorGradArrayWriteRead(self): with self.session() as session, self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) w0 = ta.write(0, [[4.0]]) w1 = w0.write(1, [[1.0]]) w2 = w1.write(2, [[-3.0]]) g_ta = w2.grad("grad") g_w0 = g_ta.write(0, [[5.0]]) g_w1 = g_w0.write(1, [[2.0]]) g_w2 = g_w1.write(2, [[-2.0]]) r0 = w2.read(0) r1 = w2.read(1) r2 = w2.read(2) g_r0 = g_w2.read(0) g_r1 = g_w2.read(1) g_r2 = g_w2.read(2) return [r0, r1, r2, g_r0, g_r1, g_r2] d0, d1, d2, g_d0, g_d1, g_d2 = self.evaluate(xla.compile(fn)) self.assertAllEqual([[4.0]], d0) self.assertAllEqual([[1.0]], d1) self.assertAllEqual([[-3.0]], d2) self.assertAllEqual([[5.0]], g_d0) self.assertAllEqual([[2.0]], g_d1) self.assertAllEqual([[-2.0]], g_d2) @test_util.disable_control_flow_v2("TensorArray.grad is not supported in v2") def testTensorGradArrayDynamicWriteRead(self): with self.session() as session, self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) w0 = ta.write(0, [[4.0]]) w1 = w0.write(1, [[1.0]]) w2 = w1.write(2, [[-3.0]]) g_ta = w2.grad("grad") # Get gradient array here so we know the shape s = w2.size() g_s = g_ta.size() g_w0 = g_ta.write(0, [[5.0]]) g_w1 = g_w0.write(1, [[2.0]]) g_w2 = g_w1.write(2, [[-2.0]]) r0 = w2.read(0) r1 = w2.read(1) r2 = w2.read(2) g_r0 = g_w2.read(0) g_r1 = g_w2.read(1) g_r2 = g_w2.read(2) return [r0, r1, r2, g_r0, g_r1, g_r2, s, g_s] d0, d1, d2, g_d0, g_d1, g_d2, vs, g_vs = self.evaluate(xla.compile(fn)) self.assertAllEqual([[4.0]], d0) self.assertAllEqual([[1.0]], d1) self.assertAllEqual([[-3.0]], d2) self.assertAllEqual([[5.0]], g_d0) self.assertAllEqual([[2.0]], g_d1) self.assertAllEqual([[-2.0]], g_d2) self.assertAllEqual(3, vs) self.assertAllEqual(3, g_vs) @test_util.disable_control_flow_v2("TensorArray.grad is not supported in v2") def testTensorGradAccessTwiceReceiveSameObject(self): with self.session() as session, self.test_scope(): ta_out = {} def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3, element_shape=[1, 2]) g_ta_0 = ta.grad("grad") g_ta_1 = ta.grad("grad") ta_out[0] = g_ta_0.handle ta_out[1] = g_ta_1.handle with ops.control_dependencies([g_ta_0.write(0, [[4.0, 5.0]]).flow]): # Write with one gradient handle, read with another copy of it r1_0 = g_ta_1.read(0) with ops.control_dependencies([g_ta_0.handle.op, g_ta_1.handle.op]): return [r1_0] [d_r1_0] = self.evaluate(xla.compile(fn)) self.assertAllEqual([[4.0, 5.0]], d_r1_0) # Can't assert this because adding a side output like we have here fails # as follows: # # ValueError: Operation u'TensorArrayGrad/TensorArrayGradV3' has been # marked as not fetchable. # # On the other hand, legitimately returning the handle from the # xla.compile function fails because we don't support DT_RESOURCE outputs # from XLA clusters. # # self.assertAllEqual(ta_out[0], ta_out[1]) @test_util.disable_control_flow_v2("b/124334470") def testTensorArrayWriteWrongIndexOrDataTypeFails(self): with self.session(), self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) return ta.write(-1, constant_op.constant(7)).flow # Test writing the wrong datatype. # TODO(b/129870929): Remove InvalidArgumentError/second regexp after all # callers provide proper init dtype. with self.assertRaisesRegex( (ValueError, errors.InvalidArgumentError), r"(" r"conversion requested dtype float32 for Tensor with dtype int32" r"|" r"TensorArray dtype is float but op has dtype int32" r")"): xla.compile(fn)[0].eval() @test_util.disable_control_flow_v2("b/124334096 verify dtype") def testTensorArrayReadWrongIndexOrDataTypeFails(self): # Find two different floating point types, create an array of # the first type, but try to read the other type. if len(self.float_types) > 1: dtype1, dtype2 = list(self.float_types)[:2] with self.session(), self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtype1, tensor_array_name="foo", size=3) w0 = ta.write(0, math_ops.cast([[4.0, 5.0]], dtype1)) # Test reading wrong datatype. return gen_data_flow_ops.tensor_array_read_v3( handle=w0.handle, index=0, dtype=dtype2, flow_in=w0.flow) with self.assertRaisesOpError("TensorArray dtype is "): self.evaluate(xla.compile(fn)) def fn(): ta = tensor_array_ops.TensorArray( dtype=dtype1, tensor_array_name="foo", size=3) w0 = ta.write(0, math_ops.cast([[4.0, 5.0]], dtype1)) # Test reading from a different index than the one we wrote to with ops.control_dependencies([w0.read(1)]): return 1.0 xla.compile(fn)[0].eval() @test_util.disable_control_flow_v2("b/122315872 (split)") def testTensorArraySplitIncompatibleShapesFails(self): with self.session(), self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3, infer_shape=False) return ta.split([1.0, 2.0, 3.0], 1).flow with self.assertRaisesWithPredicateMatch( ValueError, r"Shape must be rank 1 but is rank 0"): xla.compile(fn)[0].eval() def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3, infer_shape=False) return ta.split([1.0, 2.0, 3.0], [1, 2, 3]).flow with self.assertRaisesOpError( r"lengths must be equal: 1 vs. 2"): xla.compile(fn)[0].eval() def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3, infer_shape=False) return ta.split(1.0, [1]).flow with self.assertRaisesOpError( r"value must have rank >= 1"): xla.compile(fn)[0].eval() def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=2, infer_shape=False) return ta.split([1.0], [1]).flow with self.assertRaisesOpError( r"TensorArray's size is not equal to the size of lengths " r"\(1 vs. 2\)"): xla.compile(fn)[0].eval() def _testTensorArrayWriteGradientAddMultipleAdds(self, dtype): with self.session(), self.test_scope(): c = lambda x: np.asarray(x, dtype=dtype.as_numpy_dtype) def fn(): ta = tensor_array_ops.TensorArray( dtype=dtype, tensor_array_name="foo", size=3, infer_shape=False) w0 = ta.write(2, c(3.0)) w1 = w0.write(2, c(4.0)) ta_grad = w1.grad("grad") w0_grad = ta_grad.write(2, c(3.0)) w1_grad = w0_grad.write(2, c(4.0)) w2_grad = w1_grad.write(2, c(5.0)) return w2_grad.read(2) # Assert that aggregation works correctly self.assertAllEqual(c(12.00), xla.compile(fn)[0]) def fn(): ta = tensor_array_ops.TensorArray( dtype=dtype, tensor_array_name="foo", size=3, infer_shape=False) w0 = ta.write(2, c(3.0)) w1 = w0.write(2, c(4.0)) ta_grad = w1.grad("grad") # Using differing shapes causes an exception wb0_grad = ta_grad.write(1, c(1.0)) wb1_grad = wb0_grad.write(1, c([1.0])) return wb1_grad.flow with self.assertRaisesOpError( r"Mismatched TensorArray sizes"): xla.compile(fn)[0].eval() @test_util.disable_control_flow_v2("TensorArray.grad is not supported in v2") def testTensorArrayWriteGradientAddMultipleAdds(self): for dtype in self.numeric_tf_types: self._testTensorArrayWriteGradientAddMultipleAdds(dtype) def testMultiTensorArray(self): with self.session(), self.test_scope(): def fn(): h1 = tensor_array_ops.TensorArray( size=1, dtype=dtypes.float32, tensor_array_name="foo") w1 = h1.write(0, 4.0) r1 = w1.read(0) h2 = tensor_array_ops.TensorArray( size=1, dtype=dtypes.float32, tensor_array_name="bar") w2 = h2.write(0, 5.0) r2 = w2.read(0) return r1 + r2 self.assertAllClose(9.0, self.evaluate(xla.compile(fn)[0])) def _testTensorArrayGradientWriteReadType(self, dtype): with self.session() as session, self.test_scope(): c = lambda x: np.array(x, dtype=dtype) def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.as_dtype(dtype), tensor_array_name="foo", size=3, infer_shape=False) value_0 = constant_op.constant(c([[4.0, 5.0]])) value_1 = constant_op.constant(c([[3.0, 3.5]])) w0 = ta.write(0, value_0) w1 = w0.write(1, value_1) r0 = w1.read(0) r1 = w1.read(1) r0_2 = w1.read(0) # Test individual components' gradients grad_just_r0 = gradients_impl.gradients( ys=[r0], xs=[value_0], grad_ys=[c([[2.0, 3.0]])]) grad_r0_r0_2 = gradients_impl.gradients( ys=[r0, r0_2], xs=[value_0], grad_ys=[c([[2.0, 3.0]]), c([[1.0, -1.0]])]) grad_just_r1 = gradients_impl.gradients( ys=[r1], xs=[value_1], grad_ys=[c([[-2.0, -4.0]])]) # Test combined gradients grad = gradients_impl.gradients( ys=[r0, r0_2, r1], xs=[value_0, value_1], grad_ys=[c([[2.0, 3.0]]), c([[1.0, -1.0]]), c([[-2.0, -10.0]])]) return [grad_just_r0, grad_r0_r0_2, grad_just_r1, grad] [grad_just_r0_vals, grad_r0_r0_2_vals, grad_just_r1_vals, grad_vals] = self.evaluate(xla.compile(fn)) self.assertAllEqual(c([[2.0, 3.0]]), grad_just_r0_vals[0]) self.assertAllEqual(c([[3.0, 2.0]]), grad_r0_r0_2_vals[0]) self.assertAllEqual(c([[-2.0, -4.0]]), grad_just_r1_vals[0]) self.assertEqual(len(grad_vals), 2) self.assertAllEqual(c([[3.0, 2.0]]), grad_vals[0]) self.assertAllEqual(c([[-2.0, -10.0]]), grad_vals[1]) def testTensorArrayGradientWriteRead(self): for dtype in self.float_types: self._testTensorArrayGradientWriteReadType(dtype) for dtype in self.complex_types: self._testTensorArrayGradientWriteReadType(dtype) def _testTensorArrayGradientWritePackConcatAndRead(self): with self.session() as sess, self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=2, clear_after_read=False) value_0 = constant_op.constant([-1.0, 1.0]) value_1 = constant_op.constant([-10.0, 10.0]) w0 = ta.write(0, value_0) w1 = w0.write(1, value_1) p0 = w1.stack() r0 = w1.read(0) s0 = w1.concat() # Test gradient accumulation between read(0), pack(), and concat(). with ops.control_dependencies([p0, r0, s0]): return gradients_impl.gradients( ys=[p0, r0, s0], xs=[value_0, value_1], grad_ys=[ [[2.0, 3.0], [4.0, 5.0]], # stack gradient [-0.5, 1.5], # read(0) gradient [20.0, 30.0, 40.0, 50.0], # concat gradient ]) grad_vals = self.evaluate(xla.compile(fn)) # 2 + 2 entries self.assertAllClose([2.0 - 0.5 + 20.0, 3.0 + 1.5 + 30.0], grad_vals[0]) self.assertAllEqual([4.0 + 40.0, 5.0 + 50.0], grad_vals[1]) @test_util.disable_control_flow_v2("b/122315751 (concat)") def testTensorArrayGradientWritePackConcatAndRead(self): self._testTensorArrayGradientWritePackConcatAndRead() def testTensorArrayReadTwice(self): with self.session(), self.test_scope(): def fn(): value = constant_op.constant([[1.0, -1.0], [10.0, -10.0]]) ta_readtwice = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=2, clear_after_read=False) w_readtwice = ta_readtwice.unstack(value) r0_readtwice = w_readtwice.read(0) with ops.control_dependencies([r0_readtwice]): r1_readtwice = w_readtwice.read(0) return [r0_readtwice, r1_readtwice] self.assertAllEqual([1.0, -1.0], self.evaluate(xla.compile(fn))[0]) def _testTensorArrayGradientUnpackRead(self): with self.session() as session, self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=2, clear_after_read=False) value = constant_op.constant([[1.0, -1.0], [10.0, -10.0]]) w = ta.unstack(value) r0 = w.read(0) r0_1 = w.read(0) r1 = w.read(1) # Test combined gradients + aggregation of read(0). return gradients_impl.gradients( ys=[r0, r0_1, r1], xs=[value], grad_ys=[[2.0, 3.0], [-1.5, 1.5], [4.0, 5.0]]) grad_vals = self.evaluate(xla.compile(fn)) self.assertEqual(len(grad_vals), 1) self.assertAllEqual([[2.0 - 1.5, 3.0 + 1.5], [4.0, 5.0]], grad_vals[0]) def testTensorArrayGradientUnpackRead(self): self._testTensorArrayGradientUnpackRead() @test_util.disable_control_flow_v2("b/122315751(concat), b/122315872(split)") def testTensorArrayGradientSplitConcat(self): with self.session() as session, self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=2) value = constant_op.constant([[1.0, -1.0], [10.0, -10.0], [100.0, -100.0], [1000.0, -1000.0]]) w = ta.split(value, [2, 2]) r = w.concat() # Test combined gradients return gradients_impl.gradients( ys=[r], xs=[value], grad_ys=[[[2.0, -2.0], [20.0, -20.0], [200.0, -200.0], [2000.0, -2000.0]]]) grad_vals = self.evaluate(xla.compile(fn)) self.assertEqual(len(grad_vals), 1) self.assertAllEqual([[2.0, -2.0], [20.0, -20.0], [200.0, -200.0], [2000.0, -2000.0]], grad_vals[0]) def testCloseTensorArray(self): with self.session() as session, self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) with ops.control_dependencies([ta.close()]): return 1.0 self.evaluate(xla.compile(fn)[0]) def testSizeTensorArray(self): with self.session(), self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) return ta.size() self.assertAllEqual(3, self.evaluate(xla.compile(fn))[0]) def testWriteCloseTensorArray(self): with self.session(), self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3, infer_shape=False) w0 = ta.write(0, [[4.0, 5.0]]) w1 = w0.write(1, [[3.0, 1.0]]) with ops.control_dependencies([w1.close()]): return 1.0 self.evaluate(xla.compile(fn)) # TODO(phawkins): implement while loops. # def _testWhileLoopWritePackGradients(self, dynamic_size, dtype): # np_dtype = dtype.as_numpy_dtype # with self.session() as session, self.test_scope(): # v0 = array_ops.identity(np.arange(3 * 5, dtype=np_dtype).reshape(3, 5)) # var = variables.Variable(np.arange(100, 105, dtype=np_dtype)) # state0 = array_ops.identity(np.array([1] * 5, dtype=np_dtype)) # ta = tensor_array_ops.TensorArray( # dtype=dtype, # tensor_array_name="foo", # size=0 if dynamic_size else 3, # dynamic_size=dynamic_size) # time_0 = array_ops.identity(0) # def body(time, ta_t, state): # sliced = array_ops.slice( # v0, begin=array_ops_stack.stack([time, 0]), size=[1, -1]) # sliced = array_ops.squeeze(sliced) # out = sliced + var + state # state += sliced # ta_t = ta_t.write(time, out) # return (time + 1, ta_t, state) # (unused_0, h_final, unused_2) = control_flow_ops.while_loop( # cond=lambda time, unused_1, unused_2: time < 3, # body=body, # loop_vars=(time_0, ta, state0), # shape_invariants=(time_0.get_shape(), tensor_shape.unknown_shape(), # tensor_shape.unknown_shape()), # parallel_iterations=3) # vout = h_final.stack() # grad_val = -np.arange(3 * 5, dtype=np_dtype).reshape(3, 5) # v0_grad = gradients_impl.gradients([vout], [v0], [grad_val])[0] # state0_grad = gradients_impl.gradients([vout], [state0], [grad_val])[0] # var_grad = gradients_impl.gradients([vout], [var], [grad_val])[0] # self.evaluate(variables.global_variables_initializer()) # state0_t, var_t, v0_t, vout_t, v0_grad_t, var_grad_t, state0_grad_t = ( # self.evaluate([state0, var, v0, vout, v0_grad, var_grad, state0_grad]) # ) # just_v0_grad_t, = self.evaluate([v0_grad]) # # state = [ state0 | state0 + v0[0] | state0 + v0[0] + v0[1] ] # # vout = [ v0[0] + var + state[0] | # # v0[1] + var + state[1] | # # v0[2] + var + state[2] ] # # = [ v0[0] + var + state0 | # # v0[1] + var + state0 + v0[0] | # # v0[2] + var + state0 + v0[0] + v0[1] ] # # # # d(vout[0])/d(v0) = [1 | 0 | 0 ] # # d(vout[1])/d(v0) = [1 | 1 | 0 ] # # d(vout[2])/d(v0) = [1 | 1 | 1 ] # # d(vout)/d(var) = [1 | 1 | 1] # # d(vout)/d(state0) = [ 1 | 1 | 1 ] # state_per_time = np.array( # [state0_t, state0_t + v0_t[0, :], # state0_t + v0_t[0, :] + v0_t[1, :]]) # # Compare forward prop # self.assertAllClose(v0_t + var_t + state_per_time, vout_t) # # Compare backward prop # expected_v0_grad_t = np.array([ # grad_val[0, :] + grad_val[1, :] + grad_val[2, :], # grad_val[1, :] + grad_val[2, :], grad_val[2, :] # ]) # self.assertAllEqual(expected_v0_grad_t, v0_grad_t) # self.assertAllEqual(expected_v0_grad_t, just_v0_grad_t) # self.assertAllClose(grad_val.sum(axis=0), var_grad_t) # self.assertAllClose(grad_val.sum(axis=0), state0_grad_t) # def testWhileLoopWritePackGradients(self): # self._testWhileLoopWritePackGradients( # dynamic_size=False, dtype=dtypes.float32) # # TODO(ebrevdo): re-enable when While supports non-float32 gradients. # # self._testWhileLoopWritePackGradients( # # dynamic_size=False, dtype=tf.int64) # def testWhileLoopDynamicWritePackGradients(self): # self._testWhileLoopWritePackGradients( # dynamic_size=True, dtype=dtypes.float32) # def testGradSerialTwoLoops(self): # with self.session(), self.test_scope(): # num_steps = 100 # acc = tensor_array_ops.TensorArray( # dtype=dtypes.float32, # size=num_steps, # clear_after_read=False, # element_shape=tensor_shape.scalar()) # i = constant_op.constant(0, name="i") # x = constant_op.constant(2.0, name="x") # c = lambda i, acc: i < 5 # def b(i, acc): # x1 = cond.cond( # math_ops.equal(i, 0), lambda: x, # lambda: math_ops.multiply(acc.read(i - 1), 2.0)) # return i + 1, acc.write(i, x1) # i1, acc1 = control_flow_ops.while_loop(c, b, [i, acc]) # z = constant_op.constant(0.0) # def fn(i, acc): # return i + 1, acc.write(i, z) # _, acc2 = control_flow_ops.while_loop(lambda i, acc: i < num_steps, fn, # [i1, acc1]) # r = acc2.stack() # grad = gradients_impl.gradients(r, [x])[0] # self.assertAllClose(31.0, self.evaluate(grad)) def testSumOfTwoReadVariablesWithoutRepeatGrad(self): with self.session() as session, self.test_scope(): g0 = -(np.arange(3 * 5, dtype=np.float32).reshape(3, 5) + 1) def fn(): a = array_ops.identity( np.arange(3 * 5, dtype=np.float32).reshape(3, 5) + 1) b = array_ops.identity( np.arange(3 * 5, dtype=np.float32).reshape(3, 5) + 1 + 3 * 5) ta = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=2) ta = ta.write(0, a, name="write_a") ta = ta.write(1, b, name="write_b") c = ( ta.read(0, name="read_a_0") + # a + b ta.read(1, name="read_b_0")) grad_a = gradients_impl.gradients([c], [a], [g0])[0] # d(a+b)/da = 1 grad_b = gradients_impl.gradients([c], [b], [g0])[0] # d(a+b)/db = 1 return [grad_a, grad_b] grad_a, grad_b = xla.compile(fn) # Test gradients calculated individually grad_a_t, = self.evaluate([grad_a]) self.assertAllEqual(grad_a_t, g0) grad_b_t, = self.evaluate([grad_b]) self.assertAllEqual(grad_b_t, g0) # Test gradients calculated jointly. joint_grad_a_t, joint_grad_b_t = self.evaluate([grad_a, grad_b]) self.assertAllEqual(joint_grad_a_t, g0) self.assertAllEqual(joint_grad_b_t, g0) def testWriteShape(self): with self.session(), self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) c0 = constant_op.constant([4.0, 5.0]) w0 = ta.write(0, c0) r0 = w0.read(0) return [c0, r0] c0, r0 = xla.compile(fn) self.assertAllEqual(c0.get_shape(), r0.get_shape()) def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) c1 = constant_op.constant([6.0, 7.0]) w0 = ta.write(0, c0) w1 = w0.write(1, c1) r0 = w1.read(0) r1 = w1.read(1) return [r0, c1, r1] [r0, c1, r1] = xla.compile(fn) self.assertAllEqual(c0.get_shape(), r0.get_shape()) self.assertAllEqual(c1.get_shape(), r1.get_shape()) def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) w0 = ta.write(0, c0) c2 = constant_op.constant([4.0, 5.0, 6.0]) return w0.write(0, c2).flow with self.assertRaises(ValueError): self.evaluate(xla.compile(fn)) def _testGradientWhenNotAllComponentsRead(self): with self.session() as session, self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=2) x = constant_op.constant([2.0, 3.0]) w = ta.unstack(x) r0 = w.read(0) # Calculate (dr0/dx0, dr0/dx1). since r0 = x0, gradients are (1, 0). return gradients_impl.gradients(ys=[r0], xs=[x], grad_ys=[1.0]) grad_r0_vals = self.evaluate(xla.compile(fn))[0] self.assertAllEqual(grad_r0_vals, [1.0, 0.0]) def testGradientWhenNotAllComponentsRead(self): self._testGradientWhenNotAllComponentsRead() def _testTensorArrayEvalEmpty(self): with self.session(), self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, size=0, infer_shape=False) return ta.stack() with self.assertRaisesWithPredicateMatch( errors.InvalidArgumentError, "Uninitialized TensorArray passed to " "TensorArrayStack/TensorArrayGatherV3"): xla.compile(fn)[0].eval() @test_util.disable_control_flow_v2("b/124335246") def testTensorArrayEvalEmpty(self): self._testTensorArrayEvalEmpty() def _testTensorArrayEvalEmptyWithDefault(self): with self.session(), self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, size=0, infer_shape=True) size = ta.size() ta = ta.unstack(array_ops.zeros([0, 3, 5])) return [size, ta.stack()] [size, stack] = self.evaluate(xla.compile(fn)) self.assertEqual(0, size) self.assertAllEqual([0, 3, 5], stack.shape) # Concatenating zero tensors along their first dimension gives a # first dimension of zero if not control_flow_util.ENABLE_CONTROL_FLOW_V2: def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, size=0, infer_shape=True) ta = ta.unstack(array_ops.zeros([0, 3, 5])) return ta.concat() # TODO(b/122315751): Enable this. self.assertAllEqual([0, 5], self.evaluate(xla.compile(fn))[0].shape) def testTensorArrayEvalEmptyWithDefault(self): self._testTensorArrayEvalEmptyWithDefault() def _testTensorArrayScatterRead(self, tf_dtype): with self.session() as session, self.test_scope(): convert = _make_converter(tf_dtype) id0 = array_ops.placeholder(dtypes.int32) id1 = array_ops.placeholder(dtypes.int32) def fn(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=10) indices = constant_op.constant([1, 8]) value = constant_op.constant(convert([[1.0, 5.0], [10.0, 20.0]])) w = ta.scatter(indices, value) r0 = w.read(id0) r1 = w.read(id1) return [r0, r1] # Test aggregation of read read_vals = session.run(xla.compile(fn), feed_dict={id0: 1, id1: 8}) self.assertAllEqual(convert([1.0, 5.0]), read_vals[0]) self.assertAllEqual(convert([10.0, 20.0]), read_vals[1]) @test_util.disable_control_flow_v2("b/122315734 (scatter)") def testTensorArrayScatterRead(self): for dtype in self.numeric_tf_types: self._testTensorArrayScatterRead(dtype) self._testTensorArrayScatterRead(dtypes.bool) @test_util.disable_control_flow_v2("b/122315734 (scatter)") def testTensorArrayScatterReadAndGradients(self): with self.session() as session, self.test_scope(): id0 = array_ops.placeholder(dtypes.int32) id1 = array_ops.placeholder(dtypes.int32) def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=10) indices = constant_op.constant([1, 8]) value = constant_op.constant([[1.0, -1.0], [10.0, -10.0]]) w = ta.scatter(indices, value) r0 = w.read(id0) r1 = w.read(id1) # Test combined gradients + aggregation of read(0). grad = gradients_impl.gradients( ys=[r0, r1], xs=[value], grad_ys=[[2.0, 3.0], [4.0, 5.0]]) return [[r0, r1], grad] read_vals, grad_vals = session.run( xla.compile(fn), feed_dict={ id0: 1, id1: 8 }) self.assertEqual(len(read_vals), 2) self.assertEqual(len(grad_vals), 1) self.assertAllEqual([1.0, -1.0], read_vals[0]) self.assertAllEqual([10.0, -10.0], read_vals[1]) self.assertAllEqual([[2.0, 3.0], [4.0, 5.0]], grad_vals[0]) @test_util.disable_control_flow_v2("b/122315378 (gather)") def testTensorArrayWriteGatherAndGradients(self): with self.session() as session, self.test_scope(): def fn(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=10) values = constant_op.constant([[1.0 * x, -1.0 * x] for x in range(10)]) indices = constant_op.constant([1, 8]) w = ta.unstack(values) g = w.gather(indices) # Test combined gradients + aggregation of read(0). grad = gradients_impl.gradients( ys=[g], xs=[values], grad_ys=[[[2.0, 3.0], [4.0, 5.0]]]) return [[g], grad] g_vals, grad_vals = self.evaluate(xla.compile(fn)) # Gradients for 8 of the 10 unread components are zero. expected_grad = np.zeros((10, 2)) expected_grad[1] = [2.0, 3.0] expected_grad[8] = [4.0, 5.0] self.assertEqual(len(g_vals), 1) self.assertEqual(len(grad_vals), 1) self.assertAllEqual([[1.0, -1.0], [8.0, -8.0]], g_vals[0]) self.assertAllEqual(expected_grad, grad_vals[0]) def testTensorArrayIdentity(self): with self.session() as session, self.test_scope(): tensor_arrays = {} v0 = resource_variable_ops.ResourceVariable(0.0) v1 = resource_variable_ops.ResourceVariable(0.0) def fn(): ta0 = tensor_array_ops.TensorArray( dtype=dtypes.float32, size=2, infer_shape=False) ta1 = tensor_array_ops.TensorArray( dtype=dtypes.int32, size=4, infer_shape=True) ta0 = ta0.write(0, 0.) ta1 = ta1.write(0, 1) with ops.control_dependencies([v0.assign_add(1.0)]): ta0 = ta0.identity() with ops.control_dependencies([v1.assign_add(1.0)]): ta1 = ta1.identity() read0 = ta0.read(0) read1 = ta1.read(0) size0 = ta0.size() size1 = ta1.size() tensor_arrays[0] = ta0 tensor_arrays[1] = ta1 return [read0, read1, size0, size1, v0, v1] self.evaluate(variables.global_variables_initializer()) read0_v, read1_v, size0_v, size1_v, v0, v1 = self.evaluate( xla.compile(fn)) # Tests correct properties on new TensorArrays. self.assertEqual(dtypes.float32, tensor_arrays[0].dtype) self.assertEqual(dtypes.int32, tensor_arrays[1].dtype) # Tests that the control dependencies was added and executed. self.assertEqual(1.0, v0) self.assertEqual(1.0, v1) # Tests correct TensorArray. self.assertEqual(read0_v, 0) self.assertEqual(read1_v, 1) self.assertEqual(size0_v, 2) self.assertEqual(size1_v, 4) if __name__ == "__main__": test.main()