diff options
Diffstat (limited to 'model-integration/src/test/java/ai/vespa/rankingexpression')
4 files changed, 592 insertions, 13 deletions
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/BertImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/BertImportTestCase.java new file mode 100644 index 00000000000..f4ed2f1b64d --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/BertImportTestCase.java @@ -0,0 +1,281 @@ +// Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. +package ai.vespa.rankingexpression.importer.onnx; + +import ai.vespa.rankingexpression.importer.ImportedModel; +import com.yahoo.io.IOUtils; +import com.yahoo.searchlib.rankingexpression.RankingExpression; +import com.yahoo.searchlib.rankingexpression.Reference; +import com.yahoo.searchlib.rankingexpression.evaluation.Context; +import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex; +import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue; +import com.yahoo.searchlib.rankingexpression.evaluation.MapContext; +import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue; +import com.yahoo.searchlib.rankingexpression.evaluation.Value; +import com.yahoo.searchlib.rankingexpression.parser.ParseException; +import com.yahoo.searchlib.rankingexpression.rule.CompositeNode; +import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode; +import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode; +import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode; +import com.yahoo.tensor.Tensor; +import com.yahoo.tensor.TensorType; +import com.yahoo.tensor.functions.Generate; +import com.yahoo.tensor.functions.ScalarFunction; +import com.yahoo.tensor.functions.Slice; +import org.junit.Ignore; +import org.junit.Test; +import org.tensorflow.op.core.Rank; + +import java.io.BufferedReader; +import java.io.IOException; +import java.sql.Ref; +import java.util.HashSet; +import java.util.List; +import java.util.Map; +import java.util.Set; + +import static org.junit.Assert.assertEquals; + +/** + * @author lesters + */ +public class BertImportTestCase extends TestableModel { + + @Test + public void test() throws Exception { + String filename = "/Users/lesters/github/onnx-models/text/machine_comprehension/bert-squad/java.txt"; + List<String> lines = IOUtils.getLines(filename); + Tensor tensor = Tensor.from(lines.get(0)); + TestableModelContext context = new TestableModelContext(); + context.put("test", new TensorValue(tensor)); + + // Tensor: tensor(d1[1],d2[256],d4[12],d5[64]) + + String expr = "tensor(d0[256],d1[768])" + + "((test{" + + "d1:(floor(0.0)), " + + "d2:(floor(((768.0 * d0 + d1) % 196608) / 768.0)), " + + "d4:(floor(((768.0 * d0 + d1) % 768.0) / 64.0)), " + + "d5:(floor((768.0 * d0 + d1) % 64.0))" + + "}))"; + Tensor result = new RankingExpression(expr).evaluate(context).asTensor(); + + assertEquals(result.sum(), -6074.247); + } + + @Ignore + @Test + public void testBertImport() { + ImportedModel model = new OnnxImporter().importModel("test", "/Users/lesters/github/onnx-models/text/machine_comprehension/bert-squad/bertsquad8_modified.onnx"); +// ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/bert/bertsquad8_modified.onnx"); +// ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/bert/bertsquad10.onnx"); +// assertEquals(0, model.signature("default").skippedOutputs().size()); +// Tensor onnxResult = evaluateVespa(model, "output", model.inputs()); +// assertEquals(Tensor.from("tensor(d0[1],d1[2]):[[0.28258783057229725, -0.0685615853647904]]"), onnxResult); + + String filename = "/Users/lesters/github/onnx-models/text/machine_comprehension/bert-squad/context.vespa"; + + // bert/encoder/layer_0/attention/self/mul_2 + assert null != model.largeConstants().get("test_bert_encoder_layer_0_attention_self_Reshape_3__294"); + + TestableModelContext context; + if (true) { + // inputs + Tensor unique_ids_raw_output__9 = Tensor.from("tensor(d0[1]):[1]"); + Tensor input_ids = Tensor.from("tensor(d0[1],d1[256]):[101,2073,2003,1996,5661,10549,2000,2175,1029,102,1999,2049,2220,2086,1010,1996,2047,4680,2415,3478,2000,3113,5270,1998,6599,10908,1012,1031,2260,1033,2011,2526,1010,2116,13773,3028,5661,2020,10549,1996,2172,3469,9587,9363,2638,2415,1999,2624,3799,2058,1996,2624,4560,4680,2415,2349,2000,1996,3732,1005,1055,3132,2686,1012,1037,10428,5468,2000,5446,2019,4935,3081,1037,3309,4171,3478,2000,3362,1996,3223,2048,1011,12263,3484,2000,3413,1012,1999,2238,2384,1010,2136,2624,4560,2328,1996,2148,2534,1010,1037,1002,1020,1012,6255,2454,1010,2630,1998,2317,9311,1010,5815,3770,1010,2199,2675,2519,1006,1021,1010,4278,25525,1007,1997,8327,2686,102,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]"); + Tensor input_mask = Tensor.from("tensor(d0[1],d1[256]):[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]"); + Tensor segment_ids = Tensor.from("tensor(d0[1],d1[256]):[0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]"); + + context = contextFrom(model); + context.put("unique_ids_raw_output___9", new TensorValue(unique_ids_raw_output__9)); + context.put("input_ids", new TensorValue(input_ids)); + context.put("input_mask", new TensorValue(input_mask)); + context.put("segment_ids", new TensorValue(segment_ids)); + + context.write(filename); + } else { + context = TestableModelContext.read(filename); + } + + // expected outputs from onnxruntime + Tensor unique_ids = Tensor.from("tensor(d0[1]):[1]"); + Tensor unstack_0 = Tensor.from("tensor(d0[1],d1[256]):[-7.169589,-8.165145,-8.795558,-8.276284,-8.408593,-8.313643,-8.421538,-8.771402,-8.71111,-8.014886,-6.4415646,-7.5764513,-7.7209125,-8.689668,-8.05441,-4.357495,-4.082243,-2.4557219,-6.421309,-7.8627315,-8.612887,-8.122109,-8.072487,-8.678347,-8.467162,-8.818881,-8.143695,-6.412044,-7.765201,-8.125683,-2.5739796,-4.2929254,-7.8812947,-2.4893682,-1.5166948,-6.2354407,-4.039099,-5.6837378,-0.41342238,3.0958412,1.5454307,-0.89450985,6.0985346,-4.108738,-4.67186,-2.6797965,-0.65007347,4.2300944,-2.9132476,-4.853151,1.1584995,4.041984,-3.5257776,-2.3050616,-4.363427,-7.1510825,-8.426602,-6.6682553,-7.027374,-8.076435,-8.3017435,-6.9958987,-7.243815,-7.1347113,-7.5506253,-7.771371,-8.606251,-7.472072,-7.902196,-7.563202,-7.330995,-7.5767503,-7.8097973,-6.645113,-8.927777,-8.438513,-8.708496,-8.474434,-8.231956,-8.635139,-7.8764973,-8.80273,-9.103729,-9.07057,-8.610826,-9.084642,-8.795743,-7.2711506,-7.733648,-8.708181,-8.020964,-7.1652384,-7.9469404,-9.461184,-8.624146,-7.2252526,-6.4015207,-9.220176,-9.195709,-8.228707,-7.9646325,-8.685807,-8.980191,-8.858017,-9.290145,-8.921865,-7.656322,-8.872562,-8.898288,-8.683226,-9.219653,-8.371141,-7.130355,-8.930712,-9.05438,-8.771264,-9.621703,-8.550959,-7.327657,-9.138217,-9.377564,-9.111144,-9.653343,-8.726485,-8.215803,-9.300696,-8.044907,-8.641199,-8.641449,-8.640882,-8.641207,-8.648375,-8.645785,-8.639973,-8.650788,-8.660226,-8.6503525,-8.6601925,-8.647732,-8.652576,-8.665123,-8.653585,-8.653888,-8.661093,-8.661934,-8.6514845,-8.662573,-8.671499,-8.661195,-8.667901,-8.666959,-8.659721,-8.673244,-8.678537,-8.66441,-8.651034,-8.660175,-8.659063,-8.657169,-8.6603565,-8.6569,-8.649067,-8.651927,-8.6421995,-8.649052,-8.6478615,-8.6426935,-8.646153,-8.646865,-8.636821,-8.643324,-8.645994,-8.639597,-8.647679,-8.655649,-8.6609745,-8.654906,-8.6613455,-8.656511,-8.663024,-8.675192,-8.663131,-8.665018,-8.652522,-8.661668,-8.66894,-8.670112,-8.67217,-8.657303,-8.651893,-8.652592,-8.650168,-8.640702,-8.636455,-8.647628,-8.638621,-8.648,-8.656844,-8.649821,-8.657603,-8.648884,-8.661986,-8.663507,-8.652322,-8.662775,-8.664504,-8.662872,-8.668943,-8.6559105,-8.655738,-8.671845,-8.6666,-8.659552,-8.679308,-8.659756,-8.664594,-8.6688175,-8.666396,-8.673796,-8.65924,-8.664916,-8.6703005,-8.6611395,-8.660061,-8.660967,-8.672797,-8.66394,-8.657039,-8.671023,-8.663469,-8.659371,-8.6713705,-8.659359,-8.649764,-8.6620035,-8.656843,-8.654225,-8.661666,-8.647326,-8.652874,-8.650523,-8.644273,-8.649993,-8.65307,-8.645219,-8.6537075,-8.655814,-8.654312,-8.658724,-8.666763,-8.654713,-8.662302,-8.672376,-8.661079,-8.659652,-8.661736]"); + Tensor unstack_1 = Tensor.from("tensor(d0[1],d1[256]):[-5.1743593,-8.167716,-8.096918,-8.610186,-8.627197,-8.518608,-8.413071,-8.04796,-8.405228,-5.775467,-8.891069,-8.499419,-8.482899,-7.4575906,-5.1060586,-9.029796,-7.9796743,-7.411322,-0.62632525,-8.209348,-8.202109,-8.436105,-8.226212,-8.245562,-7.7150273,-5.4672513,-6.134469,-8.531252,-7.390566,-6.717802,-7.9110403,-5.084878,-5.02966,-7.6901536,-7.6643076,-0.42670453,-4.2289968,-6.957412,-5.192218,-6.1616683,-6.4489427,-3.5914042,-3.7853065,-6.857571,-2.3781726,6.1620126,-3.007885,-4.688912,6.258016,-5.2202945,-6.7945094,-5.1450105,0.7468612,-4.919924,5.489712,-7.307814,-7.952,-9.152897,-6.4863043,-8.328119,-7.9448185,-8.395245,-3.6581624,-0.8252581,-8.731679,-8.624653,-7.61354,-8.755644,-8.341698,-8.758186,-5.954141,-8.560192,-8.833243,-7.6137505,-5.96118,-8.43961,-8.188338,-8.373185,-8.683964,-8.246368,-8.824446,-8.05728,-7.623751,-7.56998,-8.277908,-6.8986,-6.8709283,-9.279125,-8.84588,-7.6791453,-5.2976,-9.191589,-8.797903,-6.440836,-8.179676,-9.236156,-8.972708,-5.8724217,-6.928253,-8.685118,-8.84946,-8.293621,-7.8572874,-8.053903,-7.398021,-7.549705,-9.004784,-8.060446,-7.950672,-7.2188964,-6.497633,-8.454956,-9.045556,-7.8463507,-7.771165,-8.067679,-5.9176393,-8.09684,-8.5619955,-7.5696144,-7.100621,-7.0136676,-6.464568,-8.108538,-8.516457,-5.488856,-5.853514,-8.457255,-8.457188,-8.454844,-8.448273,-8.447864,-8.447953,-8.445874,-8.444903,-8.442595,-8.448225,-8.443758,-8.451776,-8.447646,-8.440473,-8.447313,-8.44705,-8.443977,-8.442994,-8.449743,-8.441086,-8.433916,-8.43898,-8.435363,-8.434594,-8.431777,-8.433416,-8.433545,-8.442987,-8.453411,-8.450068,-8.4503565,-8.451651,-8.450909,-8.454222,-8.456041,-8.452284,-8.449699,-8.454986,-8.455096,-8.459543,-8.458114,-8.458371,-8.4632635,-8.458183,-8.457299,-8.458008,-8.452067,-8.444335,-8.442348,-8.445211,-8.441855,-8.443939,-8.441303,-8.436119,-8.442878,-8.439337,-8.446676,-8.441184,-8.438475,-8.440033,-8.4386015,-8.447922,-8.455316,-8.452563,-8.454967,-8.459164,-8.460839,-8.453004,-8.451543,-8.446279,-8.441412,-8.448481,-8.446184,-8.448539,-8.445241,-8.444487,-8.450539,-8.446448,-8.446319,-8.447268,-8.440758,-8.448286,-8.447366,-8.437631,-8.441085,-8.444475,-8.431786,-8.441355,-8.436929,-8.432141,-8.436456,-8.435032,-8.445299,-8.442143,-8.438964,-8.445743,-8.445099,-8.444958,-8.438029,-8.439503,-8.446831,-8.43919,-8.442334,-8.446472,-8.442076,-8.449043,-8.451941,-8.449556,-8.454564,-8.455859,-8.452123,-8.461076,-8.45802,-8.456931,-8.458485,-8.45496,-8.4508295,-8.453123,-8.451649,-8.451098,-8.450148,-8.446929,-8.44253,-8.44839,-8.444667,-8.437894,-8.444409,-8.444666,-8.441956]"); + + +// model.functions().forEach((k, v) -> { +// evaluateFunction(context, model, k, ""); +// }); + +// RankingExpression e = model.expressions().get("unique_ids_graph_outputs_Identity__10"); +// evaluateFunctionDependencies(context, model, e.getRoot(), ""); +// Tensor result = e.evaluate(context).asTensor(); +// assertEquals(result, unique_ids); + + RankingExpression e = model.expressions().get("bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1"); + + evaluateFunctionDependencies(context, model, e.getRoot(), ""); + context.write(filename); + Tensor result = e.evaluate(context).asTensor(); + double sum = result.sum().asDouble(); + System.out.println(sum); + + Tensor matmul1 = model.expressions().get("bert/encoder/layer_0/attention/self/MatMul_1").evaluate(context).asTensor(); + + Tensor transpose = model.expressions().get("bert/encoder/layer_0/attention/self/transpose_3").evaluate(context).asTensor(); + String cast = model.largeConstants().get("test_bert_encoder_layer_0_attention_self_Reshape_3__294"); + Tensor reshape = model.expressions().get("bert/encoder/layer_0/attention/self/Reshape_3").evaluate(context).asTensor(); + Tensor matmul = model.expressions().get("bert/encoder/layer_0/attention/output/dense/MatMul").evaluate(context).asTensor(); + Tensor add = model.expressions().get("bert/encoder/layer_0/attention/output/dense/BiasAdd").evaluate(context).asTensor(); + + Tensor add1 = model.expressions().get("bert/encoder/layer_0/attention/output/add").evaluate(context).asTensor(); + Tensor add2 = model.expressions().get("bert/encoder/layer_0/attention/output/LayerNorm/batchnorm/add_1").evaluate(context).asTensor(); + Tensor add3 = model.expressions().get("bert/encoder/layer_0/output/add").evaluate(context).asTensor(); + Tensor add4 = model.expressions().get("bert/encoder/layer_0/output/LayerNorm/batchnorm/add_1").evaluate(context).asTensor(); + + assertEquals(result, unique_ids); + +// Tensor result = model.expressions().get("unique_ids_graph_outputs_Identity__10").evaluate(context).asTensor(); +// assertEquals(result, unique_ids); + +// result = model.expressions().get("unstack_graph_outputs_Identity__7").evaluate(context).asTensor(); // or map from signature outputs +// assertEquals(result, unstack_0); + + // en feil her i outputs: har bare en: unstack, men vi må ha to: unstack:0 og unstack:1 + + } + + + private void evaluateFunction(Context context, ImportedModel model, String functionName, String in) { + if (!context.names().contains(functionName)) { + RankingExpression e = RankingExpression.from(model.functions().get(functionName)); + System.out.println(in + "Looking for dependencies of function " + functionName + ": " + e.toString()); + evaluateFunctionDependencies(context, model, e.getRoot(), in); + System.out.println(in + "Evaluating function " + functionName + ": " + e.toString()); + long start = System.currentTimeMillis(); + Tensor result = e.evaluate(context).asTensor(); + context.put(functionName, new TensorValue(result)); + long end = System.currentTimeMillis(); + System.out.println(in + "[" + (end - start) + "] completed " + functionName + " (" + result.type() + "), context is: " + context.names().size() + " " + contextSize(context)); + } else { + System.out.println(in + "Function " + functionName + " already evaluated..."); + } + } + + private long contextSize(Context context) { + long size = 0; + for (String name : context.names()) { + Tensor val = context.getTensor(name); + if (val != null) size += val.size(); + } + return size; + } + + private void evaluateFunctionDependencies(Context context, ImportedModel model, ExpressionNode node, String in) { + if (node instanceof ReferenceNode) { + String name = node.toString(); + ReferenceNode ref = (ReferenceNode) node; + if (ref.getName().equals("constant")) { + String constant = ref.getArguments().expressions().get(0).toString(); + if (!context.names().contains(constant)) { + String value = null; + if (model.smallConstants().containsKey(constant)) { + value = model.smallConstants().get(constant); + } + if (model.largeConstants().containsKey(constant)) { + value = model.largeConstants().get(constant); + } + if (value != null) { + System.out.println(in + "Adding constant: " + name); + long start = System.currentTimeMillis(); + Tensor val = Tensor.from(value); + context.put(name, new TensorValue(val)); + long end = System.currentTimeMillis(); + System.out.println(in + "Added constant: " + name + " (" + val.type() + ") in [" + (end - start) + "]"); + } + } + } + if (model.functions().containsKey(name)) { + evaluateFunction(context, model, name, in + " "); + } + } + else if (node instanceof CompositeNode) { + if (node instanceof TensorFunctionNode && ((TensorFunctionNode)node).function() instanceof Generate) { + Generate generate = (Generate) ((TensorFunctionNode)node).function(); + TensorFunctionNode.ExpressionScalarFunction func = (TensorFunctionNode.ExpressionScalarFunction) generate.getBoundGenerator(); + if (func != null) { + ExpressionNode bound = func.getExpression(); + if (bound.toString().contains("imported_ml_")) { + System.out.println(in + "Found expression inside generator: " + bound.toString()); + evaluateFunctionDependencies(context, model, bound, in); + } + } + } + else if (node instanceof TensorFunctionNode && ((TensorFunctionNode)node).function() instanceof Slice) { + Slice<Reference> slice = (Slice<Reference>) ((TensorFunctionNode)node).function(); + for (Slice.DimensionValue<Reference> value : slice.getSubspaceAddress()) { + TensorFunctionNode.ExpressionScalarFunction func = (TensorFunctionNode.ExpressionScalarFunction) value.index().orElse(null); + if (func != null) { + ExpressionNode bound = func.getExpression(); + if (bound.toString().contains("imported_ml_")) { + System.out.println(in + "Found expression inside slice: " + bound.toString()); + evaluateFunctionDependencies(context, model, bound, in); + } + } + } + } + for (ExpressionNode child : ((CompositeNode)node).children()) { + evaluateFunctionDependencies(context, model, child, in); + } + } + } + + static TestableModelContext contextFrom(ImportedModel result) { + TestableModelContext context = new TestableModelContext(); + if (result != null) { + result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor)))); + result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor)))); + } + return context; + } + + private static class TestableModelContext extends MapContext implements ContextIndex { + @Override + public int size() { + return bindings().size(); + } + @Override + public int getIndex(String name) { + throw new UnsupportedOperationException(this + " does not support index lookup by name"); + } + + public void write(String filename) { + try { + for (Map.Entry<String, Value> entry: bindings().entrySet()) { + String line = entry.getKey() + "\t" + entry.getValue().asTensor() + "\n"; + IOUtils.writeFile(filename, line, true); + } + } catch (IOException e) { + throw new RuntimeException(e); + } + } + + public static TestableModelContext read(String filename) { + System.out.println("Reading content from " + filename); + TestableModelContext context = new TestableModelContext(); + try (BufferedReader reader = IOUtils.createReader(filename)) { + String line; + while (null != (line = reader.readLine())) { + String[] strings = line.trim().split("\t"); + String name = strings[0]; + Tensor tensor = Tensor.from(strings[1]); + context.put(name, new TensorValue(tensor)); + } + } catch (IOException e) { + throw new RuntimeException(e); + } + System.out.println("Done reading context"); + return context; + } + } + +} diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxOperationsTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxOperationsTestCase.java index 94c5577357b..0c9acc9b372 100644 --- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxOperationsTestCase.java +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxOperationsTestCase.java @@ -107,6 +107,18 @@ public class OnnxOperationsTestCase { assertEval("less", x, y, evaluate("join(x, rename(y, d0, d2), f(a,b)(a < b))", x, y)); assertEval("equal", x, y, evaluate("join(x, rename(y, d0, d2), f(a,b)(a == b))", x, y)); assertEval("pow", x, y, evaluate("join(x, rename(y, d0, d2), f(a,b)(pow(a,b)))", x, y)); + + // broadcasting - opposite order + x = evaluate("random(d0[4]) + 1"); + y = evaluate("random(d0[2],d1[3],d2[4]) + 1"); + assertEval("add", x, y, evaluate("rename(x, d0, d2) + y", x, y)); + assertEval("sub", x, y, evaluate("rename(x, d0, d2) - y", x, y)); + assertEval("mul", x, y, evaluate("rename(x, d0, d2) * y", x, y)); + assertEval("div", x, y, evaluate("rename(x, d0, d2) / y", x, y)); + assertEval("greater", x, y, evaluate("join(rename(x, d0, d2), y, f(a,b)(a > b))", x, y)); + assertEval("less", x, y, evaluate("join(rename(x, d0, d2), y, f(a,b)(a < b))", x, y)); + assertEval("equal", x, y, evaluate("join(rename(x, d0, d2), y, f(a,b)(a == b))", x, y)); + assertEval("pow", x, y, evaluate("join(rename(x, d0, d2), y, f(a,b)(pow(a,b)))", x, y)); } @Test @@ -185,9 +197,55 @@ public class OnnxOperationsTestCase { @Test public void testMatMul1() throws ParseException { - Tensor a = evaluate("tensor(d0[2],d1[3]):[1, 2, 3, 4, 5, 6]"); - Tensor b = evaluate("tensor(d0[3],d1[2]):[7, 8, 9, 10, 11, 12]"); - assertEval("matmul", a, b, evaluate("tensor(d0[2],d1[2]):[58, 64, 139, 154]")); + Tensor a = evaluate("tensor(d0[6]):[1,2,3,4,5,6]"); + Tensor b = evaluate("tensor(d0[6]):[1,2,3,4,5,6]"); + assertEval("matmul", a, b, evaluate("91")); + + a = evaluate("tensor(d0[3]):[1,2,3]"); + b = evaluate("tensor(d0[3],d1[2]):[1,2,3,4,5,6]"); + assertEval("matmul", a, b, evaluate("tensor(d0[2]):[22, 28]")); + + a = evaluate("tensor(d0[2],d1[3]):[1,2,3,4,5,6]"); + b = evaluate("tensor(d0[3]):[1,2,3]"); + assertEval("matmul", a, b, evaluate("tensor(d0[2]):[14, 32]")); + + a = evaluate("tensor(d0[2],d1[3]):[1,2,3,4,5,6]"); + b = evaluate("tensor(d0[3],d1[2]):[1,2,3,4,5,6]"); + assertEval("matmul", a, b, evaluate("tensor(d0[2],d1[2]):[22,28,49,64]")); + + a = evaluate("tensor(d0[1],d1[2],d2[3]):[1,2,3,4,5,6]"); + b = evaluate("tensor(d0[3],d1[2]):[1,2,3,4,5,6]"); +// assertEval("matmul", a, b, evaluate("tensor(d0[1],d1[2],d2[2]):[22,28,49,64]")); + + a = evaluate("tensor(d0[2],d1[3]):[1,2,3,4,5,6]"); + b = evaluate("tensor(d0[1],d1[3],d2[2]):[1,2,3,4,5,6]"); +// assertEval("matmul", a, b, evaluate("tensor(d0[1],d1[2],d2[2]):[22,28,49,64]")); + + a = evaluate("tensor(d0[1],d1[2],d2[3]):[1,2,3,4,5,6]"); + b = evaluate("tensor(d0[1],d1[3],d2[2]):[1,2,3,4,5,6]"); + assertEval("matmul", a, b, evaluate("tensor(d0[1],d1[2],d2[2]):[22,28,49,64]")); + + a = evaluate("tensor(d0[1],d1[1],d2[2],d3[3]):[1,2,3,4,5,6]"); + b = evaluate("tensor(d0[1],d1[4],d2[3],d3[2]):[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]"); +// assertEval("matmul", a, b, evaluate("tensor(d0[1],d1[4],d2[2],d3[2]):[22,28,49,64,58,64,139,154,94,100,229,244,130,136,319,334]")); + + a = evaluate("tensor(d0[1],d1[4],d2[2],d3[3]):[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]"); + b = evaluate("tensor(d0[1],d1[4],d2[3],d3[2]):[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]"); + assertEval("matmul", a, b, evaluate("tensor(d0[1],d1[4],d2[2],d3[2]):[22,28,49,64,220,244,301,334,634,676,769,820,1264,1324,1453,1522]")); + + +// a = evaluate("tensor(d0[1],d1[1],d2[2],d3[3]):[1,2,3,4,5,6]"); +// b = evaluate("tensor(d0[1],d1[4],d2[3],d3[2]):[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]"); +// assertEval("matmul", a, b, evaluate("tensor(d0[1],d1[4],d2[2],d3[2]):[22,28,49,64,58,64,139,154,94,100,229,244,130,136,319,334]")); + +// a = evaluate("tensor(d0[1],d1[2],d2[3]):[1,2,3,4,5,6]"); +// assertEval("matmul", a, b, evaluate("tensor(d0[1],d1[4],d2[2],d3[2]):[22,28,49,64,58,64,139,154,22,28,49,64,58,64,139,154]")); + +// a = evaluate("tensor(d0[2],d1[3]):[1,2,3,4,5,6]"); +// assertEval("matmul", a, b, evaluate("tensor(d0[1],d1[4],d2[2],d3[2]):[22,28,49,64,58,64,139,154,22,28,49,64,58,64,139,154]")); + +// a = evaluate("tensor(d0[3]]):[1,2,3]"); +// assertEval("matmul", a, b, evaluate("tensor(d0[1],d1[4],d2[2]):[22,28,58,64,22,28,58,64]")); } @Test @@ -217,6 +275,10 @@ public class OnnxOperationsTestCase { y = evaluate("tensor(d0[4]):[3,2,-1,1]"); assertEval("reshape", x, y, evaluate("tensor(d0[3],d1[2],d2[1],d3[1]):[1,2,3,4,5,6]")); + + x = evaluate("tensor(d0[1],d1[2],d2[2],d3[3]):[1,2,3,4,5,6,7,8,9,10,11,12]"); + y = evaluate("tensor(d0[2]):[2,6]"); + assertEval("reshape", x, y, evaluate("tensor(d0[2],d1[6]):[1,2,3,4,5,6,7,8,9,10,11,12]")); } @Test @@ -435,6 +497,50 @@ public class OnnxOperationsTestCase { } + @Test + public void testTranspose1() throws ParseException { + Tensor x = evaluate("tensor(d0[2],d1[3]):[[1,2,3],[4,5,6]]"); + assertEval("transpose", x, evaluate("tensor(d0[3],d1[2]):[[1,4],[2,5],[3,6]]")); + } + + @Test + public void testTile6() throws ParseException { + Tensor x = evaluate("tensor(d0[2],d1[2]):[1,2,3,4]"); + Tensor y = evaluate("tensor(d0[2]):[1,2]"); + assertEval("tile", x, y, evaluate("tensor(d0[2],d1[4]):[1,2,1,2,3,4,3,4]")); + + x = evaluate("tensor(d0[2],d1[2]):[1,2,3,4]"); + y = evaluate("tensor(d0[2]):[3,1]"); + assertEval("tile", x, y, evaluate("tensor(d0[6],d1[2]):[1,2,3,4,1,2,3,4,1,2,3,4]")); + + x = evaluate("tensor(d0[1],d1[1],d2[1]):[1]"); + y = evaluate("tensor(d0[3]):[1,6,1]"); + assertEval("tile", x, y, evaluate("tensor(d0[1],d1[6],d2[1]):[1,1,1,1,1,1]")); + + } + + + @Test + public void testSplit2() throws ParseException { + Tensor x = evaluate("tensor(d0[6]):[1,2,3,4,5,6]"); + assertEval("split", x, evaluate("tensor(d0[3]):[1,2,3]"), 0); + assertEval("split", x, evaluate("tensor(d0[3]):[4,5,6]"), 1); + assertEval("split", x, evaluate("tensor(d0[2]):[1,2]"), createAttribute("split", new int[] {2}), 0); + assertEval("split", x, evaluate("tensor(d0[4]):[3,4,5,6]"), createAttribute("split", new int[] {2}), 1); + assertEval("split", x, evaluate("tensor(d0[3]):[3,4,5]"), createAttribute("split", new int[] {2,3}), 1); + assertEval("split", x, evaluate("tensor(d0[1]):[6]"), createAttribute("split", new int[] {2,3}), 2); + + x = evaluate("tensor(d0[2],d1[3]):[1,2,3,4,5,6]"); + assertEval("split", x, evaluate("tensor(d0[1],d1[3]):[1,2,3]")); + assertEval("split", x, evaluate("tensor(d0[1],d1[3]):[1,2,3]"), 0); + assertEval("split", x, evaluate("tensor(d0[1],d1[3]):[4,5,6]"), 1); + assertEval("split", x, evaluate("tensor(d0[1],d1[3]):[1,2,3]"), createAttribute("split", new int[] {1}), 0); + assertEval("split", x, evaluate("tensor(d0[1],d1[3]):[4,5,6]"), createAttribute("split", new int[] {1}), 1); + assertEval("split", x, evaluate("tensor(d0[2],d1[1]):[1,4]"), createAttribute("axis", 1), 0); + assertEval("split", x, evaluate("tensor(d0[2],d1[1]):[2,5]"), createAttribute("axis", 1), 1); + assertEval("split", x, evaluate("tensor(d0[2],d1[1]):[3,6]"), createAttribute("axis", 1), 2); + } + private Tensor evaluate(String expr) throws ParseException { return evaluate(expr, null, null, null); } @@ -461,41 +567,49 @@ public class OnnxOperationsTestCase { } private void assertEval(String opName, Tensor x, Tensor expected) { - assertEval(opName, x, null, null, null, null, expected, null); + assertEval(opName, x, null, null, null, null, expected, null, 0); + } + + private void assertEval(String opName, Tensor x, Tensor expected, int output) { + assertEval(opName, x, null, null, null, null, expected, null, output); } private void assertEval(String opName, Tensor x, Tensor expected, AttributeConverter attr) { - assertEval(opName, x, null, null, null, null, expected, attr); + assertEval(opName, x, null, null, null, null, expected, attr, 0); + } + + private void assertEval(String opName, Tensor x, Tensor expected, AttributeConverter attr, int output) { + assertEval(opName, x, null, null, null, null, expected, attr, output); } private void assertEval(String opName, Tensor x, Tensor y, Tensor expected, AttributeConverter attr) { - assertEval(opName, x, y, null, null, null, expected, attr); + assertEval(opName, x, y, null, null, null, expected, attr, 0); } private void assertEval(String opName, Tensor x, Tensor y, Tensor expected) { - assertEval(opName, x, y, null, null, null, expected, null); + assertEval(opName, x, y, null, null, null, expected, null, 0); } private void assertEval(String opName, Tensor x, Tensor y, Tensor z, Tensor expected) { - assertEval(opName, x, y, z, null, null, expected, null); + assertEval(opName, x, y, z, null, null, expected, null, 0); } private void assertEval(String opName, Tensor x, Tensor y, Tensor z, Tensor expected, AttributeConverter attr) { - assertEval(opName, x, y, z, null, null, expected, attr); + assertEval(opName, x, y, z, null, null, expected, attr, 0); } private void assertEval(String opName, Tensor x, Tensor y, Tensor z, Tensor q, Tensor expected) { - assertEval(opName, x, y, z, q, null, expected, null); + assertEval(opName, x, y, z, q, null, expected, null, 0); } private void assertEval(String opName, Tensor x, Tensor y, Tensor z, Tensor q, Tensor r, Tensor expected) { - assertEval(opName, x, y, z, q, r, expected, null); + assertEval(opName, x, y, z, q, r, expected, null, 0); } - private void assertEval(String opName, Tensor x, Tensor y, Tensor z, Tensor q, Tensor r, Tensor expected, AttributeConverter attr) { + private void assertEval(String opName, Tensor x, Tensor y, Tensor z, Tensor q, Tensor r, Tensor expected, AttributeConverter attr, int output) { Context context = new MapContext(DoubleValue.NaN); List<IntermediateOperation> inputs = createInputs(context, x, y, z, q, r); - IntermediateOperation op = mapOperation(opName, inputs, modelName, opName, attr != null ? attr : createAttributes().build()); + IntermediateOperation op = mapOperation(opName, inputs, modelName, opName, attr != null ? attr : createAttributes().build(), output); optimizeAndRename(opName, op); Tensor result = evaluate(op); assertEquals(expected, result); diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/SimpleImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/SimpleImportTestCase.java index 9631bddd93d..abecf4f5cb4 100644 --- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/SimpleImportTestCase.java +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/SimpleImportTestCase.java @@ -35,6 +35,15 @@ public class SimpleImportTestCase { } @Test + public void testConstant() { + ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/simple/const.onnx"); + + MapContext context = new MapContext(); + Tensor result = model.expressions().get("output").evaluate(context).asTensor(); + assertEquals(result, Tensor.from("tensor():0.42")); + } + + @Test public void testGather() { ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/simple/gather.onnx"); @@ -48,6 +57,19 @@ public class SimpleImportTestCase { assertEquals(result, Tensor.from("tensor(d0[2],d1[2],d2[2]):[1, 2, 3, 4, 3, 4, 5, 6]")); } + @Test + public void testConcat() { + ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/simple/concat.onnx"); + + MapContext context = new MapContext(); + context.put("i", new TensorValue(Tensor.from("tensor(d0[1]):[1]"))); + context.put("j", new TensorValue(Tensor.from("tensor(d0[1]):[2]"))); + context.put("k", new TensorValue(Tensor.from("tensor(d0[1]):[3]"))); + + Tensor result = model.expressions().get("y").evaluate(context).asTensor(); + assertEquals(result, Tensor.from("tensor(d0[3]):[1, 2, 3]")); + } + private void evaluateFunction(Context context, ImportedModel model, String functionName) { if (!context.names().contains(functionName)) { RankingExpression e = RankingExpression.from(model.functions().get(functionName)); diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/LesterTensorflowImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/LesterTensorflowImportTestCase.java new file mode 100644 index 00000000000..66af131aa36 --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/LesterTensorflowImportTestCase.java @@ -0,0 +1,162 @@ +// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. +package ai.vespa.rankingexpression.importer.tensorflow; + +import ai.vespa.rankingexpression.importer.ImportedModel; +import ai.vespa.rankingexpression.importer.configmodelview.ImportedMlFunction; +import ai.vespa.rankingexpression.importer.onnx.OnnxImporter; +import com.yahoo.collections.Pair; +import com.yahoo.searchlib.rankingexpression.RankingExpression; +import com.yahoo.searchlib.rankingexpression.evaluation.Context; +import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex; +import com.yahoo.searchlib.rankingexpression.evaluation.ExpressionOptimizer; +import com.yahoo.searchlib.rankingexpression.evaluation.MapContext; +import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue; +import com.yahoo.searchlib.rankingexpression.rule.CompositeNode; +import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode; +import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode; +import com.yahoo.system.ProcessExecuter; +import com.yahoo.tensor.Tensor; +import com.yahoo.tensor.TensorType; +import org.junit.Assert; +import org.junit.Ignore; +import org.junit.Test; +import org.tensorflow.SavedModelBundle; +import org.tensorflow.Session; + +import java.io.IOException; +import java.nio.DoubleBuffer; +import java.nio.FloatBuffer; +import java.util.List; +import java.util.Map; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.junit.Assert.assertTrue; + +public class LesterTensorflowImportTestCase { + + @Test + @Ignore + public void testPyTorchExport() { + ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/pytorch/test.onnx"); + Tensor onnxResult = evaluateVespa(model, "output", model.inputs()); + assertEquals(Tensor.from("tensor(d0[1],d1[2]):[[0.2134095202835272, -0.08556838456161658]]"), onnxResult); + } + + @Test + @Ignore + public void testBERT() { + ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/bert/bertsquad10.onnx"); + } + + private Tensor evaluateVespa(ImportedModel model, String operationName, Map<String, TensorType> inputs) { + Context context = contextFrom(model); + for (Map.Entry<String, TensorType> entry : inputs.entrySet()) { + Tensor argument = vespaInputArgument(1, entry.getValue().dimensions().get(1).size().get().intValue()); + context.put(entry.getKey(), new TensorValue(argument)); + } + model.functions().forEach((k, v) -> evaluateFunction(context, model, k)); + RankingExpression expression = model.expressions().get(operationName); + ExpressionOptimizer optimizer = new ExpressionOptimizer(); + optimizer.optimize(expression, (ContextIndex)context); + return expression.evaluate(context).asTensor(); + } + + @Test + @Ignore + public void testModelImport() { + + // Må endre til tf 2.0 i java! + + String modelDir = "src/test/models/tensorflow/tf2/saved_model/"; + // output function + String operationName = "out"; + + // Import TF + SavedModelBundle tensorFlowModel = SavedModelBundle.load(modelDir, "serve"); + ImportedModel model = new TensorFlowImporter().importModel("test", modelDir, tensorFlowModel); + ImportedModel.Signature signature = model.signature("serving_default"); + assertEquals("Should have no skipped outputs", 0, model.signature("serving_default").skippedOutputs().size()); + ImportedMlFunction output = signature.outputFunction("output", operationName); + assertNotNull(output); + + // Test TF + Session.Runner runner = tensorFlowModel.session().runner(); + runner.feed("x", tensorFlowFloatInputArgument(1, 4)); + List<org.tensorflow.Tensor<?>> results = runner.fetch(operationName).run(); + assertEquals(1, results.size()); + Tensor tfResult = TensorConverter.toVespaTensor(results.get(0)); + + // Test Vespa + Context context = contextFrom(model); + context.put("x", new TensorValue(vespaInputArgument(1, 4))); + model.functions().forEach((k, v) -> evaluateFunction(context, model, k)); + RankingExpression expression = model.expressions().get(operationName); + ExpressionOptimizer optimizer = new ExpressionOptimizer(); + optimizer.optimize(expression, (ContextIndex)context); + Tensor vespaResult = expression.evaluate(context).asTensor(); + + // Equal result? + System.out.println(tfResult); + System.out.println(vespaResult); + assertEquals(tfResult, vespaResult); + } + + private org.tensorflow.Tensor<?> tensorFlowFloatInputArgument(int d0Size, int d1Size) { + FloatBuffer fb1 = FloatBuffer.allocate(d0Size * d1Size); + int i = 0; + for (int d0 = 0; d0 < d0Size; d0++) + for (int d1 = 0; d1 < d1Size; ++d1) + fb1.put(i++, (float)(d1 * 1.0 / d1Size)); + return org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb1); + } + + private Tensor vespaInputArgument(int d0Size, int d1Size) { + Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", d0Size).indexed("d1", d1Size).build()); + for (int d0 = 0; d0 < d0Size; d0++) + for (int d1 = 0; d1 < d1Size; d1++) + b.cell(d1 * 1.0 / d1Size, d0, d1); + return b.build(); + } + + static Context contextFrom(ImportedModel result) { + TestableModelContext context = new TestableModelContext(); + result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor)))); + result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor)))); + return context; + } + + private void evaluateFunction(Context context, ImportedModel model, String functionName) { + if (!context.names().contains(functionName)) { + RankingExpression e = RankingExpression.from(model.functions().get(functionName)); + evaluateFunctionDependencies(context, model, e.getRoot()); + context.put(functionName, new TensorValue(e.evaluate(context).asTensor())); + } + } + + private void evaluateFunctionDependencies(Context context, ImportedModel model, ExpressionNode node) { + if (node instanceof ReferenceNode) { + String name = node.toString(); + if (model.functions().containsKey(name)) { + evaluateFunction(context, model, name); + } + } + else if (node instanceof CompositeNode) { + for (ExpressionNode child : ((CompositeNode)node).children()) { + evaluateFunctionDependencies(context, model, child); + } + } + } + + private static class TestableModelContext extends MapContext implements ContextIndex { + @Override + public int size() { + return bindings().size(); + } + @Override + public int getIndex(String name) { + throw new UnsupportedOperationException(this + " does not support index lookup by name"); + } + } + +} |