Depthwise convolution tests changed.
PiperOrigin-RevId: 261154825
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
219e8d8d6f
commit
623abf22f8
@ -296,9 +296,12 @@ xla_test(
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xla_test(
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name = "conv_depthwise_test",
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timeout = "long",
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srcs = ["conv_depthwise_test.cc"],
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srcs = [
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"conv_depthwise_test.cc",
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],
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shard_count = 50,
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deps = [
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":conv_depthwise_common",
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":test_macros_header",
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"//tensorflow/compiler/xla:execution_options_util",
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"//tensorflow/compiler/xla:status_macros",
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@ -709,6 +712,27 @@ cc_library(
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],
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)
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cc_library(
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name = "conv_depthwise_common",
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testonly = True,
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srcs = ["conv_depthwise_common.cc"],
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hdrs = ["conv_depthwise_common.h"],
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deps = [
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":test_macros_header",
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"//tensorflow/compiler/xla:execution_options_util",
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"//tensorflow/compiler/xla:status_macros",
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"//tensorflow/compiler/xla:test",
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"//tensorflow/compiler/xla/client:xla_computation",
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"//tensorflow/compiler/xla/service:bfloat16_normalization",
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"//tensorflow/compiler/xla/service:despecializer",
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"//tensorflow/compiler/xla/service:hlo_parser",
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"//tensorflow/compiler/xla/tests:client_library_test_base",
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"//tensorflow/compiler/xla/tests:hlo_test_base",
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"//tensorflow/compiler/xla/tests:xla_internal_test_main",
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"@com_google_absl//absl/types:optional",
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],
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)
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xla_test(
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name = "exhaustive_unary_test_f32_or_smaller",
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srcs = ["exhaustive_unary_test.cc"],
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135
tensorflow/compiler/xla/tests/conv_depthwise_common.cc
Normal file
135
tensorflow/compiler/xla/tests/conv_depthwise_common.cc
Normal file
@ -0,0 +1,135 @@
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/* Copyright 2017 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/tests/conv_depthwise_common.h"
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#include "absl/types/optional.h"
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#include "tensorflow/compiler/xla/client/xla_computation.h"
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#include "tensorflow/compiler/xla/execution_options_util.h"
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#include "tensorflow/compiler/xla/service/bfloat16_normalization.h"
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#include "tensorflow/compiler/xla/service/despecializer.h"
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#include "tensorflow/compiler/xla/service/hlo_parser.h"
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#include "tensorflow/compiler/xla/status_macros.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/hlo_test_base.h"
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#include "tensorflow/compiler/xla/tests/test_macros.h"
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namespace xla {
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string GetFloatDataType(bool use_bfloat16) {
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return use_bfloat16 ? "bf16" : "f32";
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}
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string DepthwiseConvolution2DTestDataToString(
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const ::testing::TestParamInfo<
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::testing::tuple<DepthwiseConvolution2DSpec, bool>>& data) {
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const auto& spec = ::testing::get<0>(data.param);
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const string data_type = GetFloatDataType(::testing::get<1>(data.param));
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string str = absl::StrCat(
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"activation_dims_", absl::StrJoin(spec.activation_dims, "x"),
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"_activation_layout_", absl::StrJoin(spec.activation_layout, "_"),
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"_kernel_dims_", absl::StrJoin(spec.kernel_dims, "x"), "_kernel_layout_",
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absl::StrJoin(spec.kernel_layout, "_"), "_output_dims_",
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absl::StrJoin(spec.output_dims, "x"), "_output_layout_",
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absl::StrJoin(spec.output_layout, "_"), data_type);
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// -1 indicates non-existence.
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if (spec.stride != -1) {
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absl::StrAppend(&str, "_lhs_dilation_", spec.lhs_dilate, "x1");
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}
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// Test names are not allowed to contain the '-' character.
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absl::c_replace(str, '-', 'n');
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return str;
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}
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string BuildHloTextDepthwiseConvolution2D(
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const DepthwiseConvolution2DSpec& spec, bool use_bfloat16,
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bool is_scheduled) {
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const string data_type = GetFloatDataType(use_bfloat16);
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const string sched_tag = is_scheduled ? ", is_scheduled=true " : "";
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if (spec.activation_dims[1] == 1 && spec.kernel_dims[1] == 2) {
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return absl::StrFormat(
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R"(
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HloModule TensorFlowDepthwiseConv %s
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ENTRY main {
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activation = %s[%s]{%s} parameter(0)
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kernel = %s[%s]{%s} parameter(1)
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ROOT conv = %s[%s]{%s} convolution(%s[%s]{%s} activation, %s[%s]{%s} kernel),
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window={size=%dx%d pad=1_1x%d_%d rhs_dilate=1x%d}, dim_labels=b01f_01io->b01f,
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feature_group_count=%d
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}
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)",
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sched_tag, data_type, absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), data_type,
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absl::StrJoin(spec.output_dims, ","),
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absl::StrJoin(spec.output_layout, ","), data_type,
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absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), spec.window, spec.window,
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spec.window, spec.window, spec.window, spec.output_feature);
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} else if (spec.stride == -1) {
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return absl::StrFormat(
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R"(
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HloModule TensorFlowDepthwiseConv %s
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ENTRY main {
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activation = %s[%s]{%s} parameter(0)
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kernel = %s[%s]{%s} parameter(1)
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ROOT conv = %s[%s]{%s} convolution(%s[%s]{%s} activation, %s[%s]{%s} kernel),
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window={size=%dx%d}, dim_labels=b01f_01io->b01f,
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feature_group_count=%d
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}
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)",
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sched_tag, data_type, absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), data_type,
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absl::StrJoin(spec.output_dims, ","),
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absl::StrJoin(spec.output_layout, ","), data_type,
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absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), spec.window, spec.window,
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spec.output_feature);
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} else {
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return absl::StrFormat(
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R"(
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HloModule TensorFlowDepthwiseConv %s
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ENTRY main {
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activation = %s[%s]{%s} parameter(0)
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kernel = %s[%s]{%s} parameter(1)
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ROOT conv = %s[%s]{%s} convolution(%s[%s]{%s} activation, %s[%s]{%s} kernel),
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window={size=%dx%d stride=%dx1 pad=%d_%dx0_0 lhs_dilate=%dx1},
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dim_labels=b01f_01io->b01f, feature_group_count=%d
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}
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)",
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sched_tag, data_type, absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), data_type,
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absl::StrJoin(spec.output_dims, ","),
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absl::StrJoin(spec.output_layout, ","), data_type,
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absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), spec.window, spec.window,
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spec.stride, 0, 0, spec.lhs_dilate, spec.output_feature);
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}
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}
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} // namespace xla
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53
tensorflow/compiler/xla/tests/conv_depthwise_common.h
Normal file
53
tensorflow/compiler/xla/tests/conv_depthwise_common.h
Normal file
@ -0,0 +1,53 @@
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/* Copyright 2017 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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#ifndef TENSORFLOW_COMPILER_XLA_TESTS_CONV_DEPTHWISE_COMMON_H_
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#define TENSORFLOW_COMPILER_XLA_TESTS_CONV_DEPTHWISE_COMMON_H_
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#include "absl/types/optional.h"
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#include "tensorflow/compiler/xla/client/xla_computation.h"
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#include "tensorflow/compiler/xla/execution_options_util.h"
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#include "tensorflow/compiler/xla/service/bfloat16_normalization.h"
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#include "tensorflow/compiler/xla/service/despecializer.h"
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#include "tensorflow/compiler/xla/service/hlo_parser.h"
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#include "tensorflow/compiler/xla/status_macros.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/hlo_test_base.h"
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#include "tensorflow/compiler/xla/tests/test_macros.h"
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namespace xla {
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string GetFloatDataType(bool use_bfloat16);
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struct DepthwiseConvolution2DSpec {
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int64 output_feature, window, stride, pad, lhs_dilate;
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std::vector<int64> activation_dims;
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std::vector<int64> activation_layout;
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std::vector<int64> kernel_dims;
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std::vector<int64> kernel_layout;
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std::vector<int64> output_dims;
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std::vector<int64> output_layout;
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};
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string DepthwiseConvolution2DTestDataToString(
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const ::testing::TestParamInfo<
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::testing::tuple<DepthwiseConvolution2DSpec, bool>>& data);
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string BuildHloTextDepthwiseConvolution2D(
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const DepthwiseConvolution2DSpec& spec, bool use_bfloat16,
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bool is_scheduled = false);
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} // namespace xla
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#endif // TENSORFLOW_COMPILER_XLA_TESTS_CONV_DEPTHWISE_COMMON_H_
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@ -22,26 +22,13 @@ limitations under the License.
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#include "tensorflow/compiler/xla/status_macros.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/conv_depthwise_common.h"
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#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
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#include "tensorflow/compiler/xla/tests/test_macros.h"
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namespace xla {
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namespace {
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string GetFloatDataType(bool use_bfloat16) {
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return use_bfloat16 ? "bf16" : "f32";
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}
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struct DepthwiseConvolution2DSpec {
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int64 output_feature, window, stride, pad, lhs_dilate;
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std::vector<int64> activation_dims;
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std::vector<int64> activation_layout;
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std::vector<int64> kernel_dims;
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std::vector<int64> kernel_layout;
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std::vector<int64> output_dims;
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std::vector<int64> output_layout;
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};
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class DepthwiseConvolution2DTest
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: public HloTestBase,
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public ::testing::WithParamInterface<
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@ -70,6 +57,7 @@ static std::vector<DepthwiseConvolution2DSpec> GetConv2DTestCases() {
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config.kernel_dims = {kernel_size, kernel_size, 1, feature};
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config.kernel_layout = {3, 2, 1, 0};
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config.output_layout = {3, 0, 2, 1};
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if (activation_size == 1 && kernel_size == 2) {
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// Test for outer dim.
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@ -87,127 +75,12 @@ static std::vector<DepthwiseConvolution2DSpec> GetConv2DTestCases() {
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config.output_dims = {batch, activation_size - kernel_size + 1,
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activation_size - kernel_size + 1, feature};
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}
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// Try this layout for all kernel shapes.
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config.output_layout = {3, 0, 2, 1};
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config_set.push_back(config);
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// Try other layouts only for certain kernel shapes.
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if (kernel_size % 2 == 0) {
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config.activation_layout = {0, 3, 2, 1};
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config_set.push_back(config);
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config.output_layout = {0, 3, 2, 1};
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config_set.push_back(config);
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config.activation_layout = {3, 0, 2, 1};
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config_set.push_back(config);
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}
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}
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return config_set;
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}
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string DepthwiseConvolution2DTestDataToString(
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const ::testing::TestParamInfo<
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::testing::tuple<DepthwiseConvolution2DSpec, bool>>& data) {
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const auto& spec = ::testing::get<0>(data.param);
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const string data_type = GetFloatDataType(::testing::get<1>(data.param));
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string str = absl::StrCat(
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"activation_dims_", absl::StrJoin(spec.activation_dims, "x"),
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"_activation_layout_", absl::StrJoin(spec.activation_layout, "_"),
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"_kernel_dims_", absl::StrJoin(spec.kernel_dims, "x"), "_kernel_layout_",
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absl::StrJoin(spec.kernel_layout, "_"), "_output_dims_",
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absl::StrJoin(spec.output_dims, "x"), "_output_layout_",
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absl::StrJoin(spec.output_layout, "_"), data_type);
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// -1 indicates non-existence.
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if (spec.stride != -1) {
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absl::StrAppend(&str, "_lhs_dilation_", spec.lhs_dilate, "x1");
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}
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// Test names are not allowed to contain the '-' character.
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absl::c_replace(str, '-', 'n');
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return str;
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}
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string BuildHloTextDepthwiseConvolution2D(
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const DepthwiseConvolution2DSpec& spec, bool use_bfloat16) {
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const string data_type = GetFloatDataType(use_bfloat16);
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if (spec.activation_dims[1] == 1 && spec.kernel_dims[1] == 2) {
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return absl::StrFormat(
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R"(
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HloModule TensorFlowDepthwiseConv
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ENTRY main {
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activation = %s[%s]{%s} parameter(0)
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kernel = %s[%s]{%s} parameter(1)
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ROOT conv = %s[%s]{%s} convolution(%s[%s]{%s} activation, %s[%s]{%s} kernel),
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window={size=%dx%d pad=1_1x%d_%d rhs_dilate=1x%d}, dim_labels=b01f_01io->b01f,
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feature_group_count=%d
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}
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)",
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data_type, absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), data_type,
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absl::StrJoin(spec.output_dims, ","),
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absl::StrJoin(spec.output_layout, ","), data_type,
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absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), spec.window, spec.window,
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spec.window, spec.window, spec.window, spec.output_feature);
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} else if (spec.stride == -1) {
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return absl::StrFormat(
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R"(
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HloModule TensorFlowDepthwiseConv
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ENTRY main {
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activation = %s[%s]{%s} parameter(0)
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kernel = %s[%s]{%s} parameter(1)
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ROOT conv = %s[%s]{%s} convolution(%s[%s]{%s} activation, %s[%s]{%s} kernel),
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window={size=%dx%d}, dim_labels=b01f_01io->b01f,
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feature_group_count=%d
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}
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)",
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data_type, absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), data_type,
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absl::StrJoin(spec.output_dims, ","),
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absl::StrJoin(spec.output_layout, ","), data_type,
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absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
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absl::StrJoin(spec.kernel_dims, ","),
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absl::StrJoin(spec.kernel_layout, ","), spec.window, spec.window,
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spec.output_feature);
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} else {
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return absl::StrFormat(
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R"(
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HloModule TensorFlowDepthwiseConv
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ENTRY main {
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activation = %s[%s]{%s} parameter(0)
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kernel = %s[%s]{%s} parameter(1)
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ROOT conv = %s[%s]{%s} convolution(%s[%s]{%s} activation, %s[%s]{%s} kernel),
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window={size=%dx%d stride=%dx1 pad=%d_%dx0_0 lhs_dilate=%dx1},
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dim_labels=b01f_01io->b01f, feature_group_count=%d
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}
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)",
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data_type, absl::StrJoin(spec.activation_dims, ","),
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absl::StrJoin(spec.activation_layout, ","), data_type,
|
||||
absl::StrJoin(spec.kernel_dims, ","),
|
||||
absl::StrJoin(spec.kernel_layout, ","), data_type,
|
||||
absl::StrJoin(spec.output_dims, ","),
|
||||
absl::StrJoin(spec.output_layout, ","), data_type,
|
||||
absl::StrJoin(spec.activation_dims, ","),
|
||||
absl::StrJoin(spec.activation_layout, ","), data_type,
|
||||
absl::StrJoin(spec.kernel_dims, ","),
|
||||
absl::StrJoin(spec.kernel_layout, ","), spec.window, spec.window,
|
||||
spec.stride, 0, 0, spec.lhs_dilate, spec.output_feature);
|
||||
}
|
||||
}
|
||||
|
||||
XLA_TEST_P(DepthwiseConvolution2DTest, DoIt) {
|
||||
const DepthwiseConvolution2DSpec& spec = ::testing::get<0>(GetParam());
|
||||
|
||||
Loading…
Reference in New Issue
Block a user