Update svd_op_test to run (non-gradient) tests in eager as well as graph mode.
PiperOrigin-RevId: 311640894 Change-Id: I39b4666c461c64ffe3f33992bb536961a266abd7
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
d5e0f468cd
commit
a2ef8b5a06
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@ -3468,7 +3468,7 @@ cuda_py_test(
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name = "svd_op_test",
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size = "medium",
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srcs = ["svd_op_test.py"],
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shard_count = 20,
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shard_count = 30,
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tags = [
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"no_oss", # b/117185141.
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"nomsan", # TODO(b/117236102): Re-enable in msan build.
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@ -20,8 +20,8 @@ from __future__ import print_function
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import numpy as np
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from tensorflow.python import tf2
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from tensorflow.python.client import session
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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 ops
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from tensorflow.python.framework import test_util
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@ -31,7 +31,7 @@ from tensorflow.python.ops import gradient_checker
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from tensorflow.python.ops import gradients_impl
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from tensorflow.python.ops import linalg_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 stateless_random_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import benchmark
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from tensorflow.python.platform import test
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@ -58,35 +58,31 @@ class SvdOpTest(test.TestCase):
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"Shape must be at least rank 2 but is rank 1"):
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linalg_ops.svd(vector)
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@test_util.run_v1_only("b/120545219")
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def testConcurrentExecutesWithoutError(self):
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with self.session(use_gpu=True) as sess:
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all_ops = []
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for compute_uv_ in True, False:
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for full_matrices_ in True, False:
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matrix1 = random_ops.random_normal([5, 5], seed=42)
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matrix2 = random_ops.random_normal([5, 5], seed=42)
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if compute_uv_:
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s1, u1, v1 = linalg_ops.svd(
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matrix1, compute_uv=compute_uv_, full_matrices=full_matrices_)
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s2, u2, v2 = linalg_ops.svd(
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matrix2, compute_uv=compute_uv_, full_matrices=full_matrices_)
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all_ops += [s1, u1, v1, s2, u2, v2]
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else:
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s1 = linalg_ops.svd(
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matrix1, compute_uv=compute_uv_, full_matrices=full_matrices_)
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s2 = linalg_ops.svd(
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matrix2, compute_uv=compute_uv_, full_matrices=full_matrices_)
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all_ops += [s1, s2]
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val = self.evaluate(all_ops)
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for i in range(2):
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s = 6 * i
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self.assertAllEqual(val[s], val[s + 3]) # s1 == s2
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self.assertAllEqual(val[s + 1], val[s + 4]) # u1 == u2
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self.assertAllEqual(val[s + 2], val[s + 5]) # v1 == v2
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for i in range(2):
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s = 12 + 2 * i
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self.assertAllEqual(val[s], val[s + 1]) # s1 == s2
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@test_util.run_in_graph_and_eager_modes(use_gpu=True)
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def testExecuteMultipleWithoutError(self):
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all_ops = []
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shape = [6, 5]
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seed = [42, 24]
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for compute_uv_ in True, False:
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for full_matrices_ in True, False:
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matrix1 = stateless_random_ops.stateless_random_normal(shape, seed)
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matrix2 = stateless_random_ops.stateless_random_normal(shape, seed)
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self.assertAllEqual(matrix1, matrix2)
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if compute_uv_:
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s1, u1, v1 = linalg_ops.svd(
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matrix1, compute_uv=compute_uv_, full_matrices=full_matrices_)
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s2, u2, v2 = linalg_ops.svd(
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matrix2, compute_uv=compute_uv_, full_matrices=full_matrices_)
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all_ops += [s1, s2, u1, u2, v1, v2]
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else:
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s1 = linalg_ops.svd(
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matrix1, compute_uv=compute_uv_, full_matrices=full_matrices_)
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s2 = linalg_ops.svd(
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matrix2, compute_uv=compute_uv_, full_matrices=full_matrices_)
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all_ops += [s1, s2]
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val = self.evaluate(all_ops)
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for i in range(0, len(val), 2):
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self.assertAllEqual(val[i], val[i + 1])
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def _GetSvdOpTest(dtype_, shape_, use_static_shape_, compute_uv_,
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@ -136,8 +132,10 @@ def _GetSvdOpTest(dtype_, shape_, use_static_shape_, compute_uv_,
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identity = array_ops.matrix_band_part(array_ops.ones_like(xx), 0, 0)
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self.assertAllClose(identity, xx, atol=tol)
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@test_util.run_v1_only("b/120545219")
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@test_util.run_in_graph_and_eager_modes(use_gpu=True)
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def Test(self):
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if not use_static_shape_ and context.executing_eagerly():
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return
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is_complex = dtype_ in (np.complex64, np.complex128)
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is_single = dtype_ in (np.float32, np.complex64)
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tol = 3e-4 if is_single else 1e-12
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@ -152,48 +150,48 @@ def _GetSvdOpTest(dtype_, shape_, use_static_shape_, compute_uv_,
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low=-1.0, high=1.0,
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size=np.prod(shape_)).reshape(shape_).astype(dtype_)
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with self.session(use_gpu=True) as sess:
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if use_static_shape_:
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x_tf = constant_op.constant(x_np)
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else:
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x_tf = array_ops.placeholder(dtype_)
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if use_static_shape_:
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x_tf = constant_op.constant(x_np)
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else:
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x_tf = array_ops.placeholder(dtype_)
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if compute_uv_:
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s_tf, u_tf, v_tf = linalg_ops.svd(
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x_tf, compute_uv=compute_uv_, full_matrices=full_matrices_)
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if use_static_shape_:
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s_tf_val, u_tf_val, v_tf_val = self.evaluate([s_tf, u_tf, v_tf])
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else:
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if compute_uv_:
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s_tf, u_tf, v_tf = linalg_ops.svd(
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x_tf, compute_uv=compute_uv_, full_matrices=full_matrices_)
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if use_static_shape_:
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s_tf_val, u_tf_val, v_tf_val = self.evaluate([s_tf, u_tf, v_tf])
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else:
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with self.session(use_gpu=True) as sess:
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s_tf_val, u_tf_val, v_tf_val = sess.run(
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[s_tf, u_tf, v_tf], feed_dict={x_tf: x_np})
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else:
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s_tf = linalg_ops.svd(
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x_tf, compute_uv=compute_uv_, full_matrices=full_matrices_)
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if use_static_shape_:
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s_tf_val = self.evaluate(s_tf)
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else:
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s_tf = linalg_ops.svd(
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x_tf, compute_uv=compute_uv_, full_matrices=full_matrices_)
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if use_static_shape_:
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s_tf_val = self.evaluate(s_tf)
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else:
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with self.session(use_gpu=True) as sess:
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s_tf_val = sess.run(s_tf, feed_dict={x_tf: x_np})
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if compute_uv_:
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u_np, s_np, v_np = np.linalg.svd(
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x_np, compute_uv=compute_uv_, full_matrices=full_matrices_)
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else:
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s_np = np.linalg.svd(
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x_np, compute_uv=compute_uv_, full_matrices=full_matrices_)
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# We explicitly avoid the situation where numpy eliminates a first
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# dimension that is equal to one.
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s_np = np.reshape(s_np, s_tf_val.shape)
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if compute_uv_:
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u_np, s_np, v_np = np.linalg.svd(
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x_np, compute_uv=compute_uv_, full_matrices=full_matrices_)
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else:
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s_np = np.linalg.svd(
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x_np, compute_uv=compute_uv_, full_matrices=full_matrices_)
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# We explicitly avoid the situation where numpy eliminates a first
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# dimension that is equal to one.
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s_np = np.reshape(s_np, s_tf_val.shape)
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CompareSingularValues(self, s_np, s_tf_val, tol)
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if compute_uv_:
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CompareSingularVectors(self, u_np, u_tf_val, min(shape_[-2:]), tol)
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CompareSingularVectors(self,
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np.conj(np.swapaxes(v_np, -2, -1)), v_tf_val,
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min(shape_[-2:]), tol)
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CheckApproximation(self, x_np, u_tf_val, s_tf_val, v_tf_val,
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full_matrices_, tol)
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CheckUnitary(self, u_tf_val, tol)
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CheckUnitary(self, v_tf_val, tol)
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CompareSingularValues(self, s_np, s_tf_val, tol)
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if compute_uv_:
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CompareSingularVectors(self, u_np, u_tf_val, min(shape_[-2:]), tol)
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CompareSingularVectors(self, np.conj(np.swapaxes(v_np, -2, -1)), v_tf_val,
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min(shape_[-2:]), tol)
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CheckApproximation(self, x_np, u_tf_val, s_tf_val, v_tf_val,
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full_matrices_, tol)
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CheckUnitary(self, u_tf_val, tol)
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CheckUnitary(self, v_tf_val, tol)
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return Test
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@ -378,15 +376,15 @@ if __name__ == "__main__":
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for rows in 0, 1, 2, 5, 10, 32, 100:
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for cols in 0, 1, 2, 5, 10, 32, 100:
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for batch_dims in [(), (3,)] + [(3, 2)] * (max(rows, cols) < 10):
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shape = batch_dims + (rows, cols)
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# TF2 does not support placeholders under eager so we skip it
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for use_static_shape in set([True, tf2.enabled()]):
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full_shape = batch_dims + (rows, cols)
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for use_static_shape in set([True, False]):
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name = "%s_%s_static_shape_%s__compute_uv_%s_full_%s" % (
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dtype.__name__, "_".join(map(str, shape)), use_static_shape,
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compute_uv, full_matrices)
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_AddTest(SvdOpTest, "Svd", name,
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_GetSvdOpTest(dtype, shape, use_static_shape,
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compute_uv, full_matrices))
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dtype.__name__, "_".join(map(str, full_shape)),
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use_static_shape, compute_uv, full_matrices)
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_AddTest(
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SvdOpTest, "Svd", name,
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_GetSvdOpTest(dtype, full_shape, use_static_shape,
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compute_uv, full_matrices))
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for compute_uv in False, True:
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for full_matrices in False, True:
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dtypes = ([np.float32, np.float64] + [np.complex64, np.complex128] *
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@ -397,16 +395,16 @@ if __name__ == "__main__":
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mat_shapes += [(5, 11), (11, 5)]
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for mat_shape in mat_shapes:
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for batch_dims in [(), (3,)]:
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shape = batch_dims + mat_shape
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name = "%s_%s_compute_uv_%s_full_%s" % (
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dtype.__name__, "_".join(map(str, shape)), compute_uv,
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full_matrices)
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_AddTest(SvdGradOpTest, "SvdGrad", name,
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_GetSvdGradOpTest(dtype, shape, compute_uv, full_matrices))
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full_shape = batch_dims + mat_shape
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name = "%s_%s_compute_uv_%s_full_%s" % (dtype.__name__, "_".join(
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map(str, full_shape)), compute_uv, full_matrices)
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_AddTest(
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SvdGradOpTest, "SvdGrad", name,
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_GetSvdGradOpTest(dtype, full_shape, compute_uv, full_matrices))
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# The results are too inaccurate for float32.
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if dtype in (np.float64, np.complex128):
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_AddTest(
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SvdGradGradOpTest, "SvdGradGrad", name,
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_GetSvdGradGradOpTest(dtype, shape, compute_uv,
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_GetSvdGradGradOpTest(dtype, full_shape, compute_uv,
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full_matrices))
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test.main()
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