355 lines
14 KiB
Python
355 lines
14 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 low-level eager execution primitives."""
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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 sys
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import traceback
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import numpy as np
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from tensorflow.python import pywrap_tfe
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from tensorflow.python.eager import backprop
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from tensorflow.python.eager import context
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from tensorflow.python.eager import core
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from tensorflow.python.eager import def_function
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from tensorflow.python.eager import test
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from tensorflow.python.framework import constant_op
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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 ops
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from tensorflow.python.framework import test_ops
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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.ops import math_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import resource_variable_ops
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class Tests(test.TestCase):
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastpathExecute_MatMulCorrectResponse(self):
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a_2_by_2 = random_ops.random_uniform((2, 2))
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b_2_by_2 = random_ops.random_uniform((2, 2))
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a_100_by_784 = random_ops.random_uniform((100, 784))
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b_100_by_784 = random_ops.random_uniform((100, 784))
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ctx = context.context()
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ctx.ensure_initialized()
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self.assertAllClose(
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math_ops.matmul(a_2_by_2, b_2_by_2),
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pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
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"MatMul", None, None, a_2_by_2,
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b_2_by_2, "transpose_a", False,
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"transpose_b", False))
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self.assertAllClose(
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math_ops.matmul(a_100_by_784, b_100_by_784, transpose_b=True),
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pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
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"MatMul", None, None, a_100_by_784,
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b_100_by_784, "transpose_a", False,
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"transpose_b", True))
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastpathExecute_ResourceVariableMatMulCorrectResponse(self):
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ctx = context.context()
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ctx.ensure_initialized()
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a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
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m = resource_variable_ops.ResourceVariable(a_2_by_2)
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x = pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
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"MatMul", None, None, m, m,
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"transpose_a", False, "transpose_b",
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False)
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y = pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
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"MatMul", None, None, a_2_by_2,
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a_2_by_2, "transpose_a", False,
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"transpose_b", False)
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self.assertAllEqual(x, y)
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastpathExecute_TapeWrite(self):
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ctx = context.context()
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ctx.ensure_initialized()
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with backprop.GradientTape(persistent=True) as tape:
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a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
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tape.watch(a_2_by_2)
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z = pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
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"MatMul", None, None, a_2_by_2,
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a_2_by_2, "transpose_a", False,
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"transpose_b", False)
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dz_dy = tape.gradient(z, [a_2_by_2])[0]
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self.assertAllEqual(dz_dy.numpy(),
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constant_op.constant(4.0, shape=[2, 2]).numpy())
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastpathExecute_ResourceVariableTapeWrite(self):
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ctx = context.context()
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ctx.ensure_initialized()
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with backprop.GradientTape(persistent=True) as tape:
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a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
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m = resource_variable_ops.ResourceVariable(a_2_by_2)
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tape.watch(m)
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z = pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
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"MatMul", None, None, m, m,
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"transpose_a", False, "transpose_b",
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False)
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dz_dy = tape.gradient(z, [m])[0]
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self.assertAllEqual(dz_dy.numpy(),
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constant_op.constant(4.0, shape=[2, 2]).numpy())
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# Tests homogeneous list op
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastpathExecute_AddNCorrectResponse(self):
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ctx = context.context()
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ctx.ensure_initialized()
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a_2_by_2 = random_ops.random_uniform((2, 2))
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b_2_by_2 = random_ops.random_uniform((2, 2))
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self.assertAllClose(
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math_ops.add_n([a_2_by_2, b_2_by_2]),
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pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name, "AddN",
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None, None, [a_2_by_2, b_2_by_2]))
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# Tests homogeneous list op
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastpathExecute_AddNTapeWrite(self):
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ctx = context.context()
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ctx.ensure_initialized()
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a_2_by_2 = random_ops.random_uniform((2, 2))
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b_2_by_2 = random_ops.random_uniform((2, 2))
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with backprop.GradientTape(persistent=True) as tape:
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tape.watch(a_2_by_2)
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tape.watch(b_2_by_2)
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z1 = pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
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"AddN", None, None,
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[a_2_by_2, b_2_by_2])
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z2 = math_ops.add_n([a_2_by_2, b_2_by_2])
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dz1_dy = tape.gradient(z1, [a_2_by_2])[0]
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dz2_dy = tape.gradient(z2, [a_2_by_2])[0]
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self.assertAllEqual(dz1_dy.numpy(), dz2_dy.numpy())
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# Tests heterogeneous list op
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastpathExecute_IdentityNCorrectResponse(self):
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ctx = context.context()
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ctx.ensure_initialized()
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a_2_by_2 = random_ops.random_uniform((2, 2))
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b_2_by_2 = random_ops.random_uniform((2, 2))
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self.assertAllClose(
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array_ops.identity_n([a_2_by_2, b_2_by_2]),
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pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
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"IdentityN", None, None,
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[a_2_by_2, b_2_by_2]))
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# Tests heterogeneous list op
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastpathExecute_IdentityNTapeWrite(self):
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ctx = context.context()
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ctx.ensure_initialized()
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a_2_by_2 = random_ops.random_uniform((2, 2))
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b_2_by_2 = random_ops.random_uniform((2, 2))
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with backprop.GradientTape(persistent=True) as tape:
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tape.watch(a_2_by_2)
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tape.watch(b_2_by_2)
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z1 = pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
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"IdentityN", None, None,
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[a_2_by_2, b_2_by_2])
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z2 = array_ops.identity_n([a_2_by_2, b_2_by_2])
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dz1_dy = tape.gradient(z1[0], [a_2_by_2])[0]
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dz2_dy = tape.gradient(z2[0], [a_2_by_2])[0]
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self.assertAllEqual(dz1_dy.numpy(), dz2_dy.numpy())
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastpathExecute_InvalidInputs(self):
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a_2_by_2 = random_ops.random_uniform((2, 2))
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ctx = context.context()
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ctx.ensure_initialized()
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assert ctx.executing_eagerly(
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), "The prototype doesn't contain C code for graph construction"
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ctx_handle = ctx._handle # pylint: disable=protected-access
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# Not enough base params
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with self.assertRaisesRegexp(ValueError,
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"at least 5 items in the input tuple"):
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pywrap_tfe.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name, "Identity")
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# Not enough inputs
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with self.assertRaisesRegexp(ValueError,
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"Expected to be at least 6, was 5"):
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pywrap_tfe.TFE_Py_FastPathExecute(ctx_handle, ctx_handle, "Identity",
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None, [])
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# Bad type
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with self.assertRaisesRegexp(TypeError, "expected a string for op_name"):
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pywrap_tfe.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name, ctx_handle,
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None, [], a_2_by_2)
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testFastPathExecute_InvalidAttributes(self):
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split_dim = constant_op.constant(0, dtype=dtypes.int32)
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value = constant_op.constant([0, 1, 2, 3], dtype=dtypes.float32)
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ctx = context.context()
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ctx.ensure_initialized()
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ctx_handle = ctx._handle
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with self.assertRaises(core._FallbackException):
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pywrap_tfe.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name, "Split",
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None, None, split_dim, value,
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"num_split", -1)
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@test_util.assert_no_new_tensors
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@test_util.assert_no_garbage_created
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def testInvalidNumOutputs(self):
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with self.assertRaisesRegexp(
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Exception, r"Value for number_attr\(\) -1 < 0 \[Op:Split\]"):
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array_ops.split(value=[1, 2, 3], num_or_size_splits=-1)
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with self.assertRaisesRegexp(
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Exception,
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"Value for attr 'num_split' of 0 must be at least minimum 1"):
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array_ops.split(value=[1, 2, 3], num_or_size_splits=0)
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def testIsFunction(self):
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ctx = context.context()
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self.assertFalse(ctx.has_function("not_a_function"))
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@def_function.function
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def f():
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return 1.
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self.assertTrue(ctx.has_function(f.get_concrete_function().name))
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def testEagerExecute_InvalidType(self):
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# Test case for GitHub issue 26879.
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with ops.Graph().as_default():
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a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
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m = resource_variable_ops.ResourceVariable(a_2_by_2)
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with self.assertRaisesRegexp(TypeError,
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"Expected list for 'values' argument"):
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_ = array_ops.stack(m, axis=1)
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def testGraphResourceVariableRaisesFallback(self):
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with ops.Graph().as_default():
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a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
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m = resource_variable_ops.ResourceVariable(a_2_by_2)
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ctx = context.context()
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ctx.ensure_initialized()
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with self.assertRaises(core._FallbackException):
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pywrap_tfe.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name, "MatMul",
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None, None, m, m, "transpose_a", False,
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"transpose_b", False)
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def testOpDefDefaultType(self):
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im = np.random.randint(
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low=0, high=65535, size=100, dtype=np.uint16).reshape(10, 10, 1)
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context.ensure_initialized()
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fastpath_dtype = test_ops.dtype_with_default_op(im).numpy()
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slowpath_dtype = test_ops.dtype_with_default_op_eager_fallback(
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im, None, context.context()).numpy()
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# Ensure the fastpath and slowpath eager paths work.
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self.assertEqual(fastpath_dtype, slowpath_dtype)
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with ops.Graph().as_default(), self.cached_session():
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graph_dtype_symbolic = test_ops.dtype_with_default_op(im)
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graph_dtype = self.evaluate(graph_dtype_symbolic)
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# Ensure the eager path matches the graph path.
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self.assertEqual(fastpath_dtype, graph_dtype)
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# Unfortunately, as of now, this doesn't work as expected on def_functions,
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# since we convert the numpy arrays to tensors pre-tracing (which won't get
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# overriddent by the default type).
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@def_function.function
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def func(im):
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return test_ops.dtype_with_default_op(im)
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function_dtype = func(im).numpy()
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self.assertNotEqual(fastpath_dtype, function_dtype)
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# Captures are OK, since they don't go through the conversion path.
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@def_function.function
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def func_captured():
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return test_ops.dtype_with_default_op(im)
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function_dtype = func_captured().numpy()
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self.assertEqual(fastpath_dtype, function_dtype)
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def testConvertFromArrayInterface(self):
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context.ensure_initialized()
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ctx = context.context()
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class MyArrayClass(object):
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def __init__(self):
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self.array = np.random.random(16)
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def __array__(self):
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return self.array
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a = MyArrayClass()
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t = ops.EagerTensor(a, device=ctx.device_name, dtype=None)
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self.assertAllEqual(t, a)
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# TODO(b/147830189): Converting from EagerTensor should work.
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# _ = ops.EagerTensor(t, device=ctx.device_name, dtype=None)
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# TODO(b/147828820): Converting with tensors should work.
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# _ = ops.EagerTensor([[t]], device=ctx.device_name, dtype=None)
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def testFallbackErrorNotVisibleWhenFallbackMethodRaises(self):
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ctx = context.context()
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ctx.ensure_initialized()
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try:
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math_ops.mat_mul([[1., 1.] * 2], [[1., 1.] * 3])
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except errors.InvalidArgumentError:
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etype, value, tb = sys.exc_info()
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full_exception_text = " ".join(
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traceback.format_exception(etype, value, tb))
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self.assertNotRegex(full_exception_text, "_FallbackException")
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if __name__ == "__main__":
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test.main()
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