diff options
author | Jon Bratseth <bratseth@oath.com> | 2018-09-21 10:25:49 -0700 |
---|---|---|
committer | Jon Bratseth <bratseth@oath.com> | 2018-09-21 10:25:49 -0700 |
commit | 569c2f0d781b33c17aadf6929fef2388643e1d64 (patch) | |
tree | dc030bcca551415b44218701f762fac9e21e6a3a /searchlib/src | |
parent | 772b67da6040957bd975b2418f98d2f18ee69fc4 (diff) |
Propagate input type information
Diffstat (limited to 'searchlib/src')
8 files changed, 94 insertions, 57 deletions
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModel.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModel.java index 282a4c5e0a9..88b5645e2e5 100644 --- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModel.java +++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModel.java @@ -1,15 +1,19 @@ package com.yahoo.searchlib.rankingexpression.integration.ml; +import com.google.common.collect.ImmutableList; +import com.google.common.collect.ImmutableMap; import com.yahoo.collections.Pair; import com.yahoo.searchlib.rankingexpression.RankingExpression; import com.yahoo.tensor.Tensor; import com.yahoo.tensor.TensorType; import java.util.ArrayList; +import java.util.Collection; import java.util.Collections; import java.util.HashMap; import java.util.List; import java.util.Map; +import java.util.Objects; import java.util.regex.Pattern; /** @@ -26,12 +30,11 @@ public class ImportedModel { private final String source; private final Map<String, Signature> signatures = new HashMap<>(); - private final Map<String, TensorType> arguments = new HashMap<>(); + private final Map<String, TensorType> inputs = new HashMap<>(); private final Map<String, Tensor> smallConstants = new HashMap<>(); private final Map<String, Tensor> largeConstants = new HashMap<>(); private final Map<String, RankingExpression> expressions = new HashMap<>(); private final Map<String, RankingExpression> functions = new HashMap<>(); - private final Map<String, TensorType> requiredFunctions = new HashMap<>(); /** * Creates a new imported model. @@ -49,11 +52,11 @@ public class ImportedModel { /** Returns the name of this model, which can only contain the characters in [A-Za-z0-9_] */ public String name() { return name; } - /** Returns the source path (directiry or file) of this model */ + /** Returns the source path (directory or file) of this model */ public String source() { return source; } - /** Returns an immutable map of the arguments ("Placeholders") of this */ - public Map<String, TensorType> arguments() { return Collections.unmodifiableMap(arguments); } + /** Returns an immutable map of the inputs of this */ + public Map<String, TensorType> inputs() { return Collections.unmodifiableMap(inputs); } /** * Returns an immutable map of the small constants of this. @@ -71,7 +74,7 @@ public class ImportedModel { /** * Returns an immutable map of the expressions of this - corresponding to graph nodes - * which are not Inputs/Placeholders or Variables (which instead become respectively arguments and constants). + * which are not Inputs/Placeholders or Variables (which instead become respectively inputs and constants). * Note that only nodes recursively referenced by a placeholder/input are added. */ public Map<String, RankingExpression> expressions() { return Collections.unmodifiableMap(expressions); } @@ -82,9 +85,6 @@ public class ImportedModel { */ public Map<String, RankingExpression> functions() { return Collections.unmodifiableMap(functions); } - /** Returns an immutable map of the functions that must be provided by the environment running this model */ - public Map<String, TensorType> requiredFunctions() { return Collections.unmodifiableMap(requiredFunctions); } - /** Returns an immutable map of the signatures of this */ public Map<String, Signature> signatures() { return Collections.unmodifiableMap(signatures); } @@ -96,12 +96,11 @@ public class ImportedModel { /** Convenience method for returning a default signature */ Signature defaultSignature() { return signature(defaultSignatureName); } - void argument(String name, TensorType argumentType) { arguments.put(name, argumentType); } + void input(String name, TensorType argumentType) { inputs.put(name, argumentType); } void smallConstant(String name, Tensor constant) { smallConstants.put(name, constant); } void largeConstant(String name, Tensor constant) { largeConstants.put(name, constant); } void expression(String name, RankingExpression expression) { expressions.put(name, expression); } void function(String name, RankingExpression expression) { functions.put(name, expression); } - void requiredFunction(String name, TensorType type) { requiredFunctions.put(name, type); } /** * Returns all the output expressions of this indexed by name. The names consist of one or two parts @@ -109,24 +108,27 @@ public class ImportedModel { * if signatures are used, or the expression name if signatures are not used and there are multiple * expressions, and the second is the output name if signature names are used. */ - public List<Pair<String, RankingExpression>> outputExpressions() { - List<Pair<String, RankingExpression>> expressions = new ArrayList<>(); + public List<Pair<String, ExpressionWithInputs>> outputExpressions() { + List<Pair<String, ExpressionWithInputs>> expressions = new ArrayList<>(); for (Map.Entry<String, Signature> signatureEntry : signatures().entrySet()) { for (Map.Entry<String, String> outputEntry : signatureEntry.getValue().outputs().entrySet()) expressions.add(new Pair<>(signatureEntry.getKey() + "." + outputEntry.getKey(), - expressions().get(outputEntry.getValue()))); + signatureEntry.getValue().outputExpression(outputEntry.getKey()))); if (signatureEntry.getValue().outputs().isEmpty()) // fallback: Signature without outputs expressions.add(new Pair<>(signatureEntry.getKey(), - expressions().get(signatureEntry.getKey()))); + new ExpressionWithInputs(expressions().get(signatureEntry.getKey()), + signatureEntry.getValue().inputMap()))); } if (signatures().isEmpty()) { // fallback for models without signatures if (expressions().size() == 1) { Map.Entry<String, RankingExpression> singleEntry = this.expressions.entrySet().iterator().next(); - expressions.add(new Pair<>(singleEntry.getKey(), singleEntry.getValue())); + expressions.add(new Pair<>(singleEntry.getKey(), + new ExpressionWithInputs(singleEntry.getValue(), inputs))); } else { for (Map.Entry<String, RankingExpression> expressionEntry : expressions().entrySet()) { - expressions.add(new Pair<>(expressionEntry.getKey(), expressionEntry.getValue())); + expressions.add(new Pair<>(expressionEntry.getKey(), + new ExpressionWithInputs(expressionEntry.getValue(), inputs))); } } } @@ -134,7 +136,7 @@ public class ImportedModel { } /** - * A signature is a set of named inputs and outputs, where the inputs maps to argument + * A signature is a set of named inputs and outputs, where the inputs maps to input * ("placeholder") names+types, and outputs maps to expressions nodes. * Note that TensorFlow supports multiple signatures in their format, but ONNX has no explicit * concept of signatures. For now, we handle ONNX models as having a single signature. @@ -158,12 +160,20 @@ public class ImportedModel { /** * Returns an immutable map of the inputs (evaluation context) of this. This is a map from input name - * to argument (Placeholder) name in the owner of this + * in this signature to input name in the owning model */ public Map<String, String> inputs() { return Collections.unmodifiableMap(inputs); } - /** Returns the type of the argument this input references */ - public TensorType inputArgument(String inputName) { return owner().arguments().get(inputs.get(inputName)); } + /** Returns the name and type of all inputs in this signature as an immutable map */ + public Map<String, TensorType> inputMap() { + ImmutableMap.Builder<String, TensorType> inputs = new ImmutableMap.Builder<>(); + for (Map.Entry<String, String> inputEntry : inputs().entrySet()) + inputs.put(inputEntry.getKey(), owner().inputs().get(inputEntry.getValue())); + return inputs.build(); + } + + /** Returns the type of the input this input references */ + public TensorType inputArgument(String inputName) { return owner().inputs().get(inputs.get(inputName)); } /** Returns an immutable list of the expression names of this */ public Map<String, String> outputs() { return Collections.unmodifiableMap(outputs); } @@ -180,7 +190,9 @@ public class ImportedModel { public List<String> importWarnings() { return Collections.unmodifiableList(importWarnings); } /** Returns the expression this output references */ - public RankingExpression outputExpression(String outputName) { return owner().expressions().get(outputs.get(outputName)); } + public ExpressionWithInputs outputExpression(String outputName) { + return new ExpressionWithInputs(owner().expressions().get(outputs.get(outputName)), inputMap()); + } @Override public String toString() { return "signature '" + name + "'"; } @@ -192,4 +204,28 @@ public class ImportedModel { } + /** + * An expression, with the inputs (bindings) which must be supplied to evaluate it. + * All non-scalar (non-empty tensor type) inputs are always present here. Inputs not + * given explicitly here (but present in the expression) are always scalar. + */ + public static class ExpressionWithInputs { + + private final RankingExpression expression; + private final ImmutableMap<String, TensorType> inputs; + + public ExpressionWithInputs(RankingExpression expression, Map<String, TensorType> inputs) { + this.expression = Objects.requireNonNull(expression, "expression cannot be null"); + this.inputs = ImmutableMap.copyOf(inputs); + } + + public RankingExpression expression() { return expression; } + public ImmutableMap<String, TensorType> inputs() { return inputs; } + + public ExpressionWithInputs with(RankingExpression newExpression) { + return new ExpressionWithInputs(newExpression, inputs); + } + + } + } diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ModelImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ModelImporter.java index d25502fd149..b7138ad87e3 100644 --- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ModelImporter.java +++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ModelImporter.java @@ -187,8 +187,7 @@ public abstract class ModelImporter { if (operation.isInput()) { // All inputs must have dimensions with standard naming convention: d0, d1, ... OrderedTensorType standardNamingConvention = OrderedTensorType.standardType(operation.type().get()); - model.argument(operation.vespaName(), standardNamingConvention.type()); - model.requiredFunction(operation.vespaName(), standardNamingConvention.type()); + model.input(operation.vespaName(), standardNamingConvention.type()); } } diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxImporter.java index 917b0d6a389..e6bb5f40b3f 100644 --- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxImporter.java +++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxImporter.java @@ -2,7 +2,6 @@ package com.yahoo.searchlib.rankingexpression.integration.ml; -import com.yahoo.io.IOUtils; import com.yahoo.searchlib.rankingexpression.integration.ml.importer.IntermediateGraph; import com.yahoo.searchlib.rankingexpression.integration.ml.importer.onnx.GraphImporter; import onnx.Onnx; diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/BatchNormImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/BatchNormImportTestCase.java index bf9684082f4..3a1c9ec9551 100644 --- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/BatchNormImportTestCase.java +++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/BatchNormImportTestCase.java @@ -20,10 +20,11 @@ public class BatchNormImportTestCase { assertEquals("Has skipped outputs", 0, model.get().signature("serving_default").skippedOutputs().size()); - RankingExpression output = signature.outputExpression("y"); + ImportedModel.ExpressionWithInputs output = signature.outputExpression("y"); assertNotNull(output); - assertEquals("dnn/batch_normalization_3/batchnorm/add_1", output.getName()); - model.assertEqualResult("X", output.getName()); + assertEquals("dnn/batch_normalization_3/batchnorm/add_1", output.expression().getName()); + model.assertEqualResult("X", output.expression().getName()); + assertEquals("{x=tensor(d0[],d1[784])}", output.inputs().toString()); } } diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/DropoutImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/DropoutImportTestCase.java index a8f7542f3a4..4c35d843f5d 100644 --- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/DropoutImportTestCase.java +++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/DropoutImportTestCase.java @@ -19,22 +19,23 @@ public class DropoutImportTestCase { TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/files/integration/tensorflow/dropout/saved"); // Check required functions - assertEquals(1, model.get().requiredFunctions().size()); - assertTrue(model.get().requiredFunctions().containsKey("X")); + assertEquals(1, model.get().inputs().size()); + assertTrue(model.get().inputs().containsKey("X")); assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), - model.get().requiredFunctions().get("X")); + model.get().inputs().get("X")); ImportedModel.Signature signature = model.get().signature("serving_default"); assertEquals("Has skipped outputs", 0, model.get().signature("serving_default").skippedOutputs().size()); - RankingExpression output = signature.outputExpression("y"); + ImportedModel.ExpressionWithInputs output = signature.outputExpression("y"); assertNotNull(output); - assertEquals("outputs/Maximum", output.getName()); + assertEquals("outputs/Maximum", output.expression().getName()); assertEquals("join(join(imported_ml_function_test_outputs_BiasAdd, reduce(constant(test_outputs_Const), sum, d1), f(a,b)(a * b)), imported_ml_function_test_outputs_BiasAdd, f(a,b)(max(a,b)))", - output.getRoot().toString()); - model.assertEqualResult("X", output.getName()); + output.expression().getRoot().toString()); + model.assertEqualResult("X", output.expression().getName()); + assertEquals("{x=tensor(d0[],d1[784])}", output.inputs().toString()); } } diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/MnistImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/MnistImportTestCase.java index add66eece1a..b3e281ad25d 100644 --- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/MnistImportTestCase.java +++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/MnistImportTestCase.java @@ -20,11 +20,10 @@ public class MnistImportTestCase { assertEquals("Has skipped outputs", 0, model.get().signature("serving_default").skippedOutputs().size()); - RankingExpression output = signature.outputExpression("y"); + ImportedModel.ExpressionWithInputs output = signature.outputExpression("y"); assertNotNull(output); - assertEquals("dnn/outputs/add", output.getName()); - model.assertEqualResultSum("input", output.getName(), 0.00001); + assertEquals("dnn/outputs/add", output.expression().getName()); + model.assertEqualResultSum("input", output.expression().getName(), 0.00001); } - } diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxMnistSoftmaxImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxMnistSoftmaxImportTestCase.java index e20ac16a691..b5655cfbfa5 100644 --- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxMnistSoftmaxImportTestCase.java +++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxMnistSoftmaxImportTestCase.java @@ -27,27 +27,28 @@ public class OnnxMnistSoftmaxImportTestCase { Tensor constant0 = model.largeConstants().get("test_Variable"); assertNotNull(constant0); assertEquals(new TensorType.Builder().indexed("d2", 784).indexed("d1", 10).build(), - constant0.type()); + constant0.type()); assertEquals(7840, constant0.size()); Tensor constant1 = model.largeConstants().get("test_Variable_1"); assertNotNull(constant1); - assertEquals(new TensorType.Builder().indexed("d1", 10).build(), - constant1.type()); + assertEquals(new TensorType.Builder().indexed("d1", 10).build(), constant1.type()); assertEquals(10, constant1.size()); - // Check required functions (inputs) - assertEquals(1, model.requiredFunctions().size()); - assertTrue(model.requiredFunctions().containsKey("Placeholder")); - assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), - model.requiredFunctions().get("Placeholder")); + // Check inputs + assertEquals(1, model.inputs().size()); + assertTrue(model.inputs().containsKey("Placeholder")); + assertEquals(TensorType.fromSpec("tensor(d0[],d1[784])"), model.inputs().get("Placeholder")); - // Check outputs - RankingExpression output = model.defaultSignature().outputExpression("add"); + // Check signature + ImportedModel.ExpressionWithInputs output = model.defaultSignature().outputExpression("add"); assertNotNull(output); - assertEquals("add", output.getName()); + assertEquals("add", output.expression().getName()); assertEquals("join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(test_Variable), f(a,b)(a * b)), sum, d2), constant(test_Variable_1), f(a,b)(a + b))", - output.getRoot().toString()); + output.expression().getRoot().toString()); + assertEquals(TensorType.fromSpec("tensor(d0[],d1[784])"), + model.inputs().get(model.defaultSignature().inputs().get("Placeholder"))); + assertEquals("{Placeholder=tensor(d0[],d1[784])}", output.inputs().toString()); } @Test diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TensorFlowMnistSoftmaxImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TensorFlowMnistSoftmaxImportTestCase.java index ef28eb4678f..4a0362c0229 100644 --- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TensorFlowMnistSoftmaxImportTestCase.java +++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TensorFlowMnistSoftmaxImportTestCase.java @@ -38,10 +38,10 @@ public class TensorFlowMnistSoftmaxImportTestCase { assertEquals(0, model.get().functions().size()); // Check required functions - assertEquals(1, model.get().requiredFunctions().size()); - assertTrue(model.get().requiredFunctions().containsKey("Placeholder")); + assertEquals(1, model.get().inputs().size()); + assertTrue(model.get().inputs().containsKey("Placeholder")); assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), - model.get().requiredFunctions().get("Placeholder")); + model.get().inputs().get("Placeholder")); // Check signatures assertEquals(1, model.get().signatures().size()); @@ -56,11 +56,12 @@ public class TensorFlowMnistSoftmaxImportTestCase { // ... signature outputs assertEquals(1, signature.outputs().size()); - RankingExpression output = signature.outputExpression("y"); + ImportedModel.ExpressionWithInputs output = signature.outputExpression("y"); assertNotNull(output); - assertEquals("add", output.getName()); + assertEquals("add", output.expression().getName()); assertEquals("join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(test_Variable_read), f(a,b)(a * b)), sum, d2), constant(test_Variable_1_read), f(a,b)(a + b))", - output.getRoot().toString()); + output.expression().getRoot().toString()); + assertEquals("{x=tensor(d0[],d1[784])}", output.inputs().toString()); // Test execution model.assertEqualResult("Placeholder", "MatMul"); |