wehub-resource-sync
435 lines
18 KiB
Python
435 lines
18 KiB
Python
# Copyright 2015 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 Adam."""
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import numpy as np
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from tensorflow.python.client import session
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from tensorflow.python.compiler.xla.experimental import xla_sharding
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from tensorflow.python.eager import context
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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 indexed_slices
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from tensorflow.python.framework import 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 ref_variable
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.training import adam
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def adam_update_numpy(param,
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g_t,
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t,
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m,
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v,
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alpha=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8):
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alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
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m_t = beta1 * m + (1 - beta1) * g_t
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v_t = beta2 * v + (1 - beta2) * g_t * g_t
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param_t = param - alpha_t * m_t / (np.sqrt(v_t) + epsilon)
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return param_t, m_t, v_t
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class AdamOptimizerTest(test.TestCase):
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def doTestSparse(self, use_resource=False):
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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# Initialize variables for numpy implementation.
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m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
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var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
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grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
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grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
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if use_resource:
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var0 = resource_variable_ops.ResourceVariable(var0_np)
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var1 = resource_variable_ops.ResourceVariable(var1_np)
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else:
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var0 = ref_variable.RefVariable(var0_np)
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var1 = ref_variable.RefVariable(var1_np)
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grads0_np_indices = np.array([0, 1], dtype=np.int32)
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grads0 = indexed_slices.IndexedSlices(
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constant_op.constant(grads0_np),
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constant_op.constant(grads0_np_indices), constant_op.constant([2]))
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grads1_np_indices = np.array([0, 1], dtype=np.int32)
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grads1 = indexed_slices.IndexedSlices(
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constant_op.constant(grads1_np),
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constant_op.constant(grads1_np_indices), constant_op.constant([2]))
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opt = adam.AdamOptimizer()
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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self.assertAllClose([3.0, 4.0], self.evaluate(var1))
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beta1_power, beta2_power = opt._get_beta_accumulators()
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# Run 3 steps of Adam
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for t in range(1, 4):
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self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power))
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self.assertAllCloseAccordingToType(0.999**t,
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self.evaluate(beta2_power))
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update.run()
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var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
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var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
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# Validate updated params
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self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
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self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
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def testSparse(self):
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with ops.Graph().as_default():
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self.doTestSparse(use_resource=False)
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def testResourceSparse(self):
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with ops.Graph().as_default():
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self.doTestSparse(use_resource=True)
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def testSparseDevicePlacement(self):
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with ops.Graph().as_default():
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for index_dtype in [dtypes.int32, dtypes.int64]:
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with self.cached_session(force_gpu=test.is_gpu_available()):
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# If a GPU is available, tests that all optimizer ops can be placed on
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# it (i.e. they have GPU kernels).
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var = variables.Variable([[1.0], [2.0]])
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indices = constant_op.constant([0, 1], dtype=index_dtype)
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gathered_sum = math_ops.reduce_sum(array_ops.gather(var, indices))
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optimizer = adam.AdamOptimizer(3.0)
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minimize_op = optimizer.minimize(gathered_sum)
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self.evaluate(variables.global_variables_initializer())
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minimize_op.run()
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def testGatherGradientWithBadIndicesPolicy(self):
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with ops.Graph().as_default():
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with self.cached_session(force_gpu=test.is_gpu_available()):
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var = variables.Variable([1.0, 2.0])
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indices = constant_op.constant([[1], [-1], [0]], dtype=dtypes.int32)
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out = array_ops.gather_nd(var,
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array_ops.expand_dims(indices, axis=-1),
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batch_dims=0,
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bad_indices_policy="IGNORE")
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optimizer = adam.AdamOptimizer(2.0, 0.0, 1.0)
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minimize_op = optimizer.minimize(out)
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self.evaluate(variables.global_variables_initializer())
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minimize_op.run()
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def testSparseRepeatedIndices(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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repeated_index_update_var = variables.Variable(
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[[1.0], [2.0]], dtype=dtype)
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aggregated_update_var = variables.Variable(
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[[1.0], [2.0]], dtype=dtype)
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grad_repeated_index = indexed_slices.IndexedSlices(
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constant_op.constant(
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[0.1, 0.1], shape=[2, 1], dtype=dtype),
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constant_op.constant([1, 1]),
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constant_op.constant([2, 1]))
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grad_aggregated = indexed_slices.IndexedSlices(
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constant_op.constant(
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[0.2], shape=[1, 1], dtype=dtype),
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constant_op.constant([1]),
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constant_op.constant([2, 1]))
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repeated_update = adam.AdamOptimizer().apply_gradients(
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[(grad_repeated_index, repeated_index_update_var)])
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aggregated_update = adam.AdamOptimizer().apply_gradients(
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[(grad_aggregated, aggregated_update_var)])
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self.evaluate(variables.global_variables_initializer())
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self.assertAllClose(aggregated_update_var,
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self.evaluate(repeated_index_update_var))
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for _ in range(3):
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repeated_update.run()
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aggregated_update.run()
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self.assertAllClose(aggregated_update_var,
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self.evaluate(repeated_index_update_var))
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def doTestBasic(self, use_resource=False, use_callable_params=False):
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if context.executing_eagerly() and not use_resource:
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self.skipTest(
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"Skipping test with use_resource=False and executing eagerly.")
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for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
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with self.session(graph=ops.Graph()):
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# Initialize variables for numpy implementation.
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m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
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var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
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grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
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grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
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if use_resource:
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var0 = resource_variable_ops.ResourceVariable(
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var0_np, name="var0_%d" % i)
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var1 = resource_variable_ops.ResourceVariable(
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var1_np, name="var1_%d" % i)
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else:
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var0 = ref_variable.RefVariable(var0_np)
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var1 = ref_variable.RefVariable(var1_np)
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grads0 = constant_op.constant(grads0_np)
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grads1 = constant_op.constant(grads1_np)
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learning_rate = lambda: 0.001
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beta1 = lambda: 0.9
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beta2 = lambda: 0.999
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epsilon = lambda: 1e-8
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if not use_callable_params:
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learning_rate = learning_rate()
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beta1 = beta1()
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beta2 = beta2()
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epsilon = epsilon()
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opt = adam.AdamOptimizer(learning_rate=learning_rate)
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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opt_variables = opt.variables()
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beta1_power, beta2_power = opt._get_beta_accumulators()
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self.assertTrue(beta1_power is not None)
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self.assertTrue(beta2_power is not None)
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self.assertIn(beta1_power, opt_variables)
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self.assertIn(beta2_power, opt_variables)
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# Ensure that non-slot variables are the same type as the requested
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# variables.
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self.assertEqual(
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use_resource,
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resource_variable_ops.is_resource_variable(beta1_power))
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self.assertEqual(
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use_resource,
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resource_variable_ops.is_resource_variable(beta2_power))
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if not context.executing_eagerly():
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with ops.Graph().as_default():
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# Shouldn't return non-slot variables from other graphs.
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self.assertEqual(0, len(opt.variables()))
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self.evaluate(variables.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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self.assertAllClose([3.0, 4.0], self.evaluate(var1))
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beta1_power, beta2_power = opt._get_beta_accumulators()
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# Run 3 steps of Adam
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for t in range(1, 4):
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if not context.executing_eagerly():
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self.evaluate(update)
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elif t > 1:
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opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.assertAllCloseAccordingToType(0.9**(t + 1),
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self.evaluate(beta1_power))
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self.assertAllCloseAccordingToType(0.999**(t + 1),
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self.evaluate(beta2_power))
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var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
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var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
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# Validate updated params
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self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
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self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
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if use_resource:
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self.assertEqual("var0_%d/Adam:0" % (i,),
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opt.get_slot(var=var0, name="m").name)
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def testBasic(self):
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with self.cached_session():
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self.doTestBasic(use_resource=False)
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@test_util.run_in_graph_and_eager_modes
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@test_util.disable_tfrt("b/168527439: invalid runtime fallback "
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"resource variable reference on GPU.")
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def testResourceBasic(self):
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self.doTestBasic(use_resource=True)
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@test_util.disable_tfrt("b/153089059: cannot create half tensor on GPU.")
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def testBasicCallableParams(self):
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with context.eager_mode():
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self.doTestBasic(use_resource=True, use_callable_params=True)
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def testTensorLearningRate(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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# Initialize variables for numpy implementation.
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m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
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var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
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grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
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grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
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var0 = variables.Variable(var0_np)
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var1 = variables.Variable(var1_np)
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grads0 = constant_op.constant(grads0_np)
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grads1 = constant_op.constant(grads1_np)
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opt = adam.AdamOptimizer(constant_op.constant(0.001))
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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self.assertAllClose([3.0, 4.0], self.evaluate(var1))
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beta1_power, beta2_power = opt._get_beta_accumulators()
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# Run 3 steps of Adam
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for t in range(1, 4):
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self.assertAllCloseAccordingToType(0.9**t,
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self.evaluate(beta1_power))
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self.assertAllCloseAccordingToType(0.999**t,
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self.evaluate(beta2_power))
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update.run()
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var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
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var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
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# Validate updated params
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self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
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self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
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def testSharing(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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# Initialize variables for numpy implementation.
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m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
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var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
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grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
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grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
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var0 = variables.Variable(var0_np)
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var1 = variables.Variable(var1_np)
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grads0 = constant_op.constant(grads0_np)
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grads1 = constant_op.constant(grads1_np)
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opt = adam.AdamOptimizer()
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update1 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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update2 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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beta1_power, beta2_power = opt._get_beta_accumulators()
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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self.assertAllClose([3.0, 4.0], self.evaluate(var1))
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# Run 3 steps of intertwined Adam1 and Adam2.
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for t in range(1, 4):
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self.assertAllCloseAccordingToType(0.9**t,
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self.evaluate(beta1_power))
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self.assertAllCloseAccordingToType(0.999**t,
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self.evaluate(beta2_power))
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if t % 2 == 0:
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update1.run()
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else:
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update2.run()
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var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
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var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
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# Validate updated params
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self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
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self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
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@test_util.disable_tfrt("b/168527439: invalid runtime fallback "
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"resource variable reference on GPU.")
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def testTwoSessions(self):
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optimizer = adam.AdamOptimizer()
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with context.eager_mode():
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var0 = variables.Variable(np.array([1.0, 2.0]), name="v0")
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grads0 = constant_op.constant(np.array([0.1, 0.1]))
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optimizer.apply_gradients([(grads0, var0)])
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g = ops.Graph()
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with g.as_default():
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with session.Session():
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var0 = variables.Variable(np.array([1.0, 2.0]), name="v0")
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grads0 = constant_op.constant(np.array([0.1, 0.1]))
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optimizer.apply_gradients([(grads0, var0)])
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gg = ops.Graph()
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with gg.as_default():
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with session.Session():
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var0 = variables.Variable(np.array([1.0, 2.0]), name="v0")
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grads0 = constant_op.constant(np.array([0.1, 0.1]))
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# If the optimizer saves any state not keyed by graph the following line
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# fails.
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optimizer.apply_gradients([(grads0, var0)])
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@test_util.disable_tfrt("b/168527439: invalid runtime fallback "
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"resource variable reference on GPU.")
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def testSlotsUniqueEager(self):
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with context.eager_mode():
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v1 = resource_variable_ops.ResourceVariable(1.)
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v2 = resource_variable_ops.ResourceVariable(1.)
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opt = adam.AdamOptimizer(1.)
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opt.minimize(lambda: v1 + v2)
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# There should be two non-slot variables, and two unique slot variables
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# for v1 and v2 respectively.
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self.assertEqual(6, len({id(v) for v in opt.variables()}))
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@test_util.deprecated_graph_mode_only
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def testXlaSharding(self):
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dtype = dtypes.float32
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with self.session(graph=ops.Graph()):
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# Initialize variables for numpy implementation.
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var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
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grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
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grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
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var0 = resource_variable_ops.ResourceVariable(var0_np, name="var0")
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var1 = resource_variable_ops.ResourceVariable(var1_np, name="var1")
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var0, var1 = [
|
|
xla_sharding.mesh_split(
|
|
v, np.array([0, 1]), [0], use_sharding_op=False)
|
|
for v in (var0, var1)
|
|
]
|
|
grads0 = constant_op.constant(grads0_np)
|
|
grads1 = constant_op.constant(grads1_np)
|
|
|
|
learning_rate = lambda: 0.001
|
|
|
|
opt = adam.AdamOptimizer(learning_rate=learning_rate)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
|
|
self.evaluate(variables.global_variables_initializer())
|
|
self.evaluate(update)
|
|
# The beta accumulators are not sharded.
|
|
beta1_power, beta2_power = opt._get_beta_accumulators()
|
|
self.assertIsNone(xla_sharding.get_tensor_sharding(beta1_power))
|
|
self.assertIsNone(xla_sharding.get_tensor_sharding(beta2_power))
|
|
|
|
# Variables and slots are sharded.
|
|
for v in (var0, var1):
|
|
self.assertIsNotNone(xla_sharding.get_tensor_sharding(v))
|
|
for slot_name in ("m", "v"):
|
|
slot = opt.get_slot(v, slot_name)
|
|
self.assertIsNotNone(xla_sharding.get_tensor_sharding(slot))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|