443caf8a5b
The name TensorFloat-32 is clearer than TF32, as the "TF" in TF32 may be confused for TensorFlow. Also replace word "allow" with "enable" in internal functions like "allow_tf32_execution" to match the Python API PiperOrigin-RevId: 330621742 Change-Id: I3f5518aecf4f946d241b422e6a4d566fa199185f
128 lines
4.4 KiB
C++
128 lines
4.4 KiB
C++
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "tensorflow/compiler/xla/client/lib/qr.h"
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#include "tensorflow/compiler/xla/array2d.h"
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#include "tensorflow/compiler/xla/array3d.h"
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#include "tensorflow/compiler/xla/client/lib/matrix.h"
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#include "tensorflow/compiler/xla/client/xla_builder.h"
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#include "tensorflow/compiler/xla/literal.h"
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#include "tensorflow/compiler/xla/statusor.h"
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#include "tensorflow/compiler/xla/test.h"
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#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
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#include "tensorflow/compiler/xla/tests/literal_test_util.h"
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#include "tensorflow/compiler/xla/tests/test_macros.h"
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#include "tensorflow/compiler/xla/xla_data.pb.h"
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#include "tensorflow/core/lib/core/status_test_util.h"
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#include "tensorflow/core/platform/tensor_float_32_utils.h"
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namespace {
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using QrTest = xla::ClientLibraryTestBase;
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XLA_TEST_F(QrTest, Simple) {
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// Test fails with TensorFloat-32 enabled
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tensorflow::enable_tensor_float_32_execution(false);
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xla::XlaBuilder builder(TestName());
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xla::Array2D<float> a_vals({
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{4, 6, 8, 10},
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{6, 45, 54, 63},
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{8, 54, 146, 166},
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{10, 63, 166, 310},
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});
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xla::XlaOp a;
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auto a_data = CreateR2Parameter<float>(a_vals, 0, "a", &builder, &a);
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TF_ASSERT_OK_AND_ASSIGN(
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auto result,
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xla::QRDecomposition(a, /*full_matrices=*/true, /*block_size=*/2));
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// Verifies that the decomposition composes back to the original matrix.
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//
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// This isn't a terribly demanding test, (e.g., we should verify that Q is
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// orthonormal and R is upper-triangular) but it's awkward to write such tests
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// without more linear algebra libraries. It's easier to test the numerics
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// from Python, anyway, where we have access to numpy and scipy.
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xla::BatchDot(result.q, result.r, xla::PrecisionConfig::HIGHEST);
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ComputeAndCompareR2<float>(&builder, a_vals, {a_data.get()},
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xla::ErrorSpec(1e-4, 1e-4));
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}
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XLA_TEST_F(QrTest, ZeroDiagonal) {
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// Test fails with TensorFloat-32 enabled
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tensorflow::enable_tensor_float_32_execution(false);
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xla::XlaBuilder builder(TestName());
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xla::Array2D<float> a_vals({
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{0, 1, 1},
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{1, 0, 1},
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{1, 1, 0},
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});
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xla::XlaOp a;
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auto a_data = CreateR2Parameter<float>(a_vals, 0, "a", &builder, &a);
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TF_ASSERT_OK_AND_ASSIGN(
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auto result,
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xla::QRDecomposition(a, /*full_matrices=*/true, /*block_size=*/8));
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// Verifies that the decomposition composes back to the original matrix.
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//
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// This isn't a terribly demanding test, (e.g., we should verify that Q is
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// orthonormal and R is upper-triangular) but it's awkward to write such tests
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// without more linear algebra libraries. It's easier to test the numerics
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// from Python, anyway, where we have access to numpy and scipy.
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xla::BatchDot(result.q, result.r, xla::PrecisionConfig::HIGHEST);
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ComputeAndCompareR2<float>(&builder, a_vals, {a_data.get()},
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xla::ErrorSpec(1e-4, 1e-4));
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}
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XLA_TEST_F(QrTest, SimpleBatched) {
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// Test fails with TensorFloat-32 enabled
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tensorflow::enable_tensor_float_32_execution(false);
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xla::XlaBuilder builder(TestName());
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xla::Array3D<float> a_vals({
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{
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{4, 6, 8, 10},
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{6, 45, 54, 63},
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{8, 54, 146, 166},
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{10, 63, 166, 310},
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},
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{
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{16, 24, 8, 12},
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{24, 61, 82, 48},
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{8, 82, 456, 106},
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{12, 48, 106, 62},
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},
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});
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xla::XlaOp a;
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auto a_data = CreateR3Parameter<float>(a_vals, 0, "a", &builder, &a);
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TF_ASSERT_OK_AND_ASSIGN(
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auto result,
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xla::QRDecomposition(a, /*full_matrices=*/true, /*block_size=*/2));
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xla::BatchDot(result.q, result.r, xla::PrecisionConfig::HIGHEST);
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ComputeAndCompareR3<float>(&builder, a_vals, {a_data.get()},
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xla::ErrorSpec(1e-4, 1e-4));
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}
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} // namespace
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