diff options
author | Jon Bratseth <bratseth@oath.com> | 2018-09-05 20:18:21 +0200 |
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committer | Jon Bratseth <bratseth@oath.com> | 2018-09-05 20:18:21 +0200 |
commit | 8e7d0ec9dd47142e2e014096edd16befb95cbc00 (patch) | |
tree | 1628ca600e4acecce7847c44e813fa5d6755c10d /model-evaluation/src/test | |
parent | be4c7dafa47211904cda7e565161566815cd89a8 (diff) |
Revert "Merge pull request #6818 from vespa-engine/revert-6813-bratseth/handle-large-constants-take-4"
This reverts commit 9394b251a6353151e01121d019052a2e0860f117, reversing
changes made to bd6bfca0bc973eb2edd4fc2ab793f2e2ca3fc248.
Diffstat (limited to 'model-evaluation/src/test')
5 files changed, 102 insertions, 15 deletions
diff --git a/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java b/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java index 60cf0d25ded..d94e5b2af1b 100644 --- a/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java +++ b/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java @@ -3,8 +3,10 @@ package ai.vespa.models.evaluation; import com.yahoo.config.subscription.ConfigGetter; import com.yahoo.config.subscription.FileSource; +import com.yahoo.path.Path; import com.yahoo.tensor.Tensor; import com.yahoo.vespa.config.search.RankProfilesConfig; +import com.yahoo.vespa.config.search.core.RankingConstantsConfig; import org.junit.Test; import java.io.File; @@ -18,15 +20,9 @@ public class ModelsEvaluatorTest { private static final double delta = 0.00000000001; - private ModelsEvaluator createModels() { - String configPath = "src/test/resources/config/rankexpression/rank-profiles.cfg"; - RankProfilesConfig config = new ConfigGetter<>(new FileSource(new File(configPath)), RankProfilesConfig.class).getConfig(""); - return new ModelsEvaluator(config); - } - @Test public void testTensorEvaluation() { - ModelsEvaluator models = createModels(); + ModelsEvaluator models = createModels("src/test/resources/config/rankexpression/"); FunctionEvaluator function = models.evaluatorOf("macros", "fourtimessum"); function.bind("var1", Tensor.from("{{x:0}:3,{x:1}:5}")); function.bind("var2", Tensor.from("{{x:0}:7,{x:1}:11}")); @@ -35,7 +31,7 @@ public class ModelsEvaluatorTest { @Test public void testEvaluationDependingOnMacroTakingArguments() { - ModelsEvaluator models = createModels(); + ModelsEvaluator models = createModels("src/test/resources/config/rankexpression/"); FunctionEvaluator function = models.evaluatorOf("macros", "secondphase"); function.bind("match", 3); function.bind("rankBoost", 5); @@ -46,6 +42,14 @@ public class ModelsEvaluatorTest { // TODO: Test that binding nonexisting variable doesn't work // TODO: Test that rebinding doesn't work // TODO: Test with nested macros - // TODO: Test TF/ONNX model + + private ModelsEvaluator createModels(String path) { + Path configDir = Path.fromString(path); + RankProfilesConfig config = new ConfigGetter<>(new FileSource(configDir.append("rank-profiles.cfg").toFile()), + RankProfilesConfig.class).getConfig(""); + RankingConstantsConfig constantsConfig = new ConfigGetter<>(new FileSource(configDir.append("ranking-constants.cfg").toFile()), + RankingConstantsConfig.class).getConfig(""); + return new ModelsEvaluator(config, constantsConfig); + } } diff --git a/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java b/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java index d45372fc7da..84e01e58280 100644 --- a/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java +++ b/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java @@ -3,8 +3,10 @@ package ai.vespa.models.evaluation; import com.yahoo.config.subscription.ConfigGetter; import com.yahoo.config.subscription.FileSource; +import com.yahoo.path.Path; import com.yahoo.searchlib.rankingexpression.ExpressionFunction; import com.yahoo.vespa.config.search.RankProfilesConfig; +import com.yahoo.vespa.config.search.core.RankingConstantsConfig; import org.junit.Test; import java.io.File; @@ -21,14 +23,41 @@ import static org.junit.Assert.assertNotNull; public class RankProfilesImporterTest { @Test - public void testImporting() { - String configPath = "src/test/resources/config/rankexpression/rank-profiles.cfg"; - RankProfilesConfig config = new ConfigGetter<>(new FileSource(new File(configPath)), RankProfilesConfig.class).getConfig(""); - Map<String, Model> models = new RankProfilesConfigImporter().importFrom(config); + public void testImportingModels() { + Map<String, Model> models = createModels("src/test/resources/config/models/"); + + assertEquals(4, models.size()); + + Model xgboost = models.get("xgboost_2_2"); + assertFunction("xgboost_2_2", + "(optimized sum of condition trees of size 192 bytes)", + xgboost); + + Model onnxMnistSoftmax = models.get("mnist_softmax"); + assertFunction("default.add", + "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b))", + onnxMnistSoftmax); + assertEquals("tensor(d1[10],d2[784])", + onnxMnistSoftmax.evaluatorOf("default.add").context().get("constant(mnist_softmax_Variable)").type().toString()); + + Model tfMnistSoftmax = models.get("mnist_softmax_saved"); + assertFunction("serving_default.y", + "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(mnist_softmax_saved_layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_saved_layer_Variable_1_read), f(a,b)(a + b))", + tfMnistSoftmax); + + Model tfMnist = models.get("mnist_saved"); + assertFunction("serving_default.y", + "join(reduce(join(map(join(reduce(join(join(join(rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add), 0.009999999776482582, f(a,b)(a * b)), rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add), f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b)), f(a)(1.050701 * if (a >= 0, a, 1.673263 * (exp(a) - 1)))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b))", + tfMnist); + } + + @Test + public void testImportingRankExpressions() { + Map<String, Model> models = createModels("src/test/resources/config/rankexpression/"); + assertEquals(18, models.size()); Model macros = models.get("macros"); - assertNotNull(macros); assertEquals("macros", macros.name()); assertEquals(4, macros.functions().size()); assertFunction("fourtimessum", "4 * (var1 + var2)", macros); @@ -44,8 +73,9 @@ public class RankProfilesImporterTest { } private void assertFunction(String name, String expression, Model model) { + assertNotNull("Model is present in config", model); ExpressionFunction function = model.function(name); - assertNotNull(function); + assertNotNull("Function '" + name + "' is in " + model, function); assertEquals(name, function.getName()); assertEquals(expression, function.getBody().getRoot().toString()); } @@ -57,4 +87,13 @@ public class RankProfilesImporterTest { assertEquals(expression, function.getBody().getRoot().toString()); } + private Map<String, Model> createModels(String path) { + Path configDir = Path.fromString(path); + RankProfilesConfig config = new ConfigGetter<>(new FileSource(configDir.append("rank-profiles.cfg").toFile()), + RankProfilesConfig.class).getConfig(""); + RankingConstantsConfig constantsConfig = new ConfigGetter<>(new FileSource(configDir.append("ranking-constants.cfg").toFile()), + RankingConstantsConfig.class).getConfig(""); + return new RankProfilesConfigImporter().importFrom(config, constantsConfig); + } + } diff --git a/model-evaluation/src/test/resources/config/models/rank-profiles.cfg b/model-evaluation/src/test/resources/config/models/rank-profiles.cfg new file mode 100644 index 00000000000..1cc36f75158 --- /dev/null +++ b/model-evaluation/src/test/resources/config/models/rank-profiles.cfg @@ -0,0 +1,14 @@ +rankprofile[0].name "mnist_saved" +rankprofile[0].fef.property[0].name "rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add).rankingScript" +rankprofile[0].fef.property[0].value "join(reduce(join(rename(input, (d0, d1), (d0, d4)), constant(mnist_saved_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(mnist_saved_dnn_hidden1_bias_read), f(a,b)(a + b))" +rankprofile[0].fef.property[1].name "rankingExpression(serving_default.y).rankingScript" +rankprofile[0].fef.property[1].value "join(reduce(join(map(join(reduce(join(join(join(rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add), 0.009999999776482582, f(a,b)(a * b)), rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add), f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b)), f(a)(1.050701 * if (a >= 0, a, 1.673263 * (exp(a) - 1)))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b))" +rankprofile[1].name "xgboost_2_2" +rankprofile[1].fef.property[0].name "rankingExpression(xgboost_2_2).rankingScript" +rankprofile[1].fef.property[0].value "if (f29 < -0.1234567, if (f56 < -0.242398, 1.71218, -1.70044), if (f109 < 0.8723473, -1.94071, 1.85965)) + if (f60 < -0.482947, if (f29 < -4.2387498, 0.784718, -0.96853), -6.23624)" +rankprofile[2].name "mnist_softmax_saved" +rankprofile[2].fef.property[0].name "rankingExpression(serving_default.y).rankingScript" +rankprofile[2].fef.property[0].value "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(mnist_softmax_saved_layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_saved_layer_Variable_1_read), f(a,b)(a + b))" +rankprofile[3].name "mnist_softmax" +rankprofile[3].fef.property[0].name "rankingExpression(default.add).rankingScript" +rankprofile[3].fef.property[0].value "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b))" diff --git a/model-evaluation/src/test/resources/config/models/ranking-constants.cfg b/model-evaluation/src/test/resources/config/models/ranking-constants.cfg new file mode 100644 index 00000000000..2b7495ace5e --- /dev/null +++ b/model-evaluation/src/test/resources/config/models/ranking-constants.cfg @@ -0,0 +1,30 @@ +constant[0].name "mnist_saved_dnn_hidden1_weights_read" +constant[0].fileref "" +constant[0].type "tensor(d3[300],d4[784])" +constant[1].name "mnist_saved_dnn_hidden2_weights_read" +constant[1].fileref "" +constant[1].type "tensor(d2[100],d3[300])" +constant[2].name "mnist_softmax_saved_layer_Variable_1_read" +constant[2].fileref "" +constant[2].type "tensor(d1[10])" +constant[3].name "mnist_saved_dnn_hidden1_bias_read" +constant[3].fileref "" +constant[3].type "tensor(d3[300])" +constant[4].name "mnist_saved_dnn_hidden2_bias_read" +constant[4].fileref "" +constant[4].type "tensor(d2[100])" +constant[5].name "mnist_softmax_Variable" +constant[5].fileref "" +constant[5].type "tensor(d1[10],d2[784])" +constant[6].name "mnist_saved_dnn_outputs_weights_read" +constant[6].fileref "" +constant[6].type "tensor(d1[10],d2[100])" +constant[7].name "mnist_softmax_saved_layer_Variable_read" +constant[7].fileref "" +constant[7].type "tensor(d1[10],d2[784])" +constant[8].name "mnist_softmax_Variable_1" +constant[8].fileref "" +constant[8].type "tensor(d1[10])" +constant[9].name "mnist_saved_dnn_outputs_bias_read" +constant[9].fileref "" +constant[9].type "tensor(d1[10])"
\ No newline at end of file diff --git a/model-evaluation/src/test/resources/config/rankexpression/ranking-constants.cfg b/model-evaluation/src/test/resources/config/rankexpression/ranking-constants.cfg new file mode 100644 index 00000000000..e69de29bb2d --- /dev/null +++ b/model-evaluation/src/test/resources/config/rankexpression/ranking-constants.cfg |