static void DocumentResults(LongHistogram accumulatingHistogram, Recorder recorder) { recorder?.RecordValue(GC.GetTotalMemory(false)); var histogram = recorder?.GetIntervalHistogram(); accumulatingHistogram?.Add(histogram); _logWriter?.Append(histogram); RILogManager.Default?.SendDebug($"Accumulated.TotalCount = {accumulatingHistogram.TotalCount,10:G}."); RILogManager.Default?.SendDebug("Mean: " + BytesToString(accumulatingHistogram.GetMean()) + ", StdDev: " + BytesToString(accumulatingHistogram.GetStdDeviation())); }
public void TestGetStdDeviation() { var expectedRawMean = ((10000.0 * 1000) + (1.0 * 100000000)) / 10001; /* direct avg. of raw results */ var expectedRawStdDev = Math.Sqrt( ((10000.0 * Math.Pow((1000.0 - expectedRawMean), 2)) + Math.Pow((100000000.0 - expectedRawMean), 2)) / 10001); const double expectedMean = (1000.0 + 50000000.0) / 2; var expectedSquareDeviationSum = 10000 * Math.Pow((1000.0 - expectedMean), 2); for (long value = 10000; value <= 100000000; value += 10000) { expectedSquareDeviationSum += Math.Pow((value - expectedMean), 2); } var expectedStdDev = Math.Sqrt(expectedSquareDeviationSum / 20000); // We expect to see the standard deviations to be accurate to ~3 decimal points (~0.1%): Assert.AreEqual(expectedRawStdDev, RawHistogram.GetStdDeviation(), expectedRawStdDev * 0.001, "Raw standard deviation is " + expectedRawStdDev + " +/- 0.1%"); Assert.AreEqual(expectedStdDev, LongHistogram.GetStdDeviation(), expectedStdDev * 0.001, "Standard deviation is " + expectedStdDev + " +/- 0.1%"); }
public static void Run() { _outputStream = File.Create(LogPath); _logWriter = new HistogramLogWriter(_outputStream); _logWriter.Write(DateTime.Now); var recorder = HistogramFactory .With64BitBucketSize() ?.WithValuesFrom(1) ?.WithValuesUpTo(2345678912345) ?.WithPrecisionOf(3) ?.WithThreadSafeWrites() ?.WithThreadSafeReads() ?.Create(); var accumulatingHistogram = new LongHistogram(2345678912345, 3); var size = accumulatingHistogram.GetEstimatedFootprintInBytes(); RILogManager.Default?.SendDebug("Histogram size = {0} bytes ({1:F2} MB)", size, size / 1024.0 / 1024.0); RILogManager.Default?.SendDebug("Recorded latencies [in system clock ticks]"); accumulatingHistogram.OutputPercentileDistribution(Console.Out, outputValueUnitScalingRatio: OutputScalingFactor.None, useCsvFormat: true); Console.WriteLine(); RILogManager.Default?.SendDebug("Recorded latencies [in usec]"); accumulatingHistogram.OutputPercentileDistribution(Console.Out, outputValueUnitScalingRatio: OutputScalingFactor.TimeStampToMicroseconds, useCsvFormat: true); Console.WriteLine(); RILogManager.Default?.SendDebug("Recorded latencies [in msec]"); accumulatingHistogram.OutputPercentileDistribution(Console.Out, outputValueUnitScalingRatio: OutputScalingFactor.TimeStampToMilliseconds, useCsvFormat: true); Console.WriteLine(); RILogManager.Default?.SendDebug("Recorded latencies [in sec]"); accumulatingHistogram.OutputPercentileDistribution(Console.Out, outputValueUnitScalingRatio: OutputScalingFactor.TimeStampToSeconds, useCsvFormat: true); DocumentResults(accumulatingHistogram, recorder); RILogManager.Default?.SendDebug("Build Vocabulary."); DocumentResults(accumulatingHistogram, recorder); Vocabulary vocabulary = new Vocabulary(); DocumentResults(accumulatingHistogram, recorder); string trainPath = InternetFileDownloader.Download(DOWNLOAD_URL + TRAIN_FILE, TRAIN_FILE); DocumentResults(accumulatingHistogram, recorder); string validPath = InternetFileDownloader.Download(DOWNLOAD_URL + VALID_FILE, VALID_FILE); DocumentResults(accumulatingHistogram, recorder); string testPath = InternetFileDownloader.Download(DOWNLOAD_URL + TEST_FILE, TEST_FILE); DocumentResults(accumulatingHistogram, recorder); int[] trainData = vocabulary.LoadData(trainPath); DocumentResults(accumulatingHistogram, recorder); int[] validData = vocabulary.LoadData(validPath); DocumentResults(accumulatingHistogram, recorder); int[] testData = vocabulary.LoadData(testPath); DocumentResults(accumulatingHistogram, recorder); int nVocab = vocabulary.Length; RILogManager.Default?.SendDebug("Network Initializing."); FunctionStack model = new FunctionStack("Test10", new EmbedID(nVocab, N_UNITS, name: "l1 EmbedID"), new Dropout(), new LSTM(true, N_UNITS, N_UNITS, name: "l2 LSTM"), new Dropout(), new LSTM(true, N_UNITS, N_UNITS, name: "l3 LSTM"), new Dropout(), new Linear(true, N_UNITS, nVocab, name: "l4 Linear") ); DocumentResults(accumulatingHistogram, recorder); // Do not cease at the given threshold, correct the rate by taking the rate from L2Norm of all parameters GradientClipping gradientClipping = new GradientClipping(threshold: GRAD_CLIP); SGD sgd = new SGD(learningRate: 1); model.SetOptimizer(gradientClipping, sgd); DocumentResults(accumulatingHistogram, recorder); Real wholeLen = trainData.Length; int jump = (int)Math.Floor(wholeLen / BATCH_SIZE); int epoch = 0; Stack <NdArray[]> backNdArrays = new Stack <NdArray[]>(); RILogManager.Default?.SendDebug("Train Start."); double dVal; NdArray x = new NdArray(new[] { 1 }, BATCH_SIZE, (Function)null); NdArray t = new NdArray(new[] { 1 }, BATCH_SIZE, (Function)null); for (int i = 0; i < jump * N_EPOCH; i++) { for (int j = 0; j < BATCH_SIZE; j++) { x.Data[j] = trainData[(int)((jump * j + i) % wholeLen)]; t.Data[j] = trainData[(int)((jump * j + i + 1) % wholeLen)]; } NdArray[] result = model.Forward(true, x); Real sumLoss = new SoftmaxCrossEntropy().Evaluate(result, t); backNdArrays.Push(result); RILogManager.Default?.SendDebug("[{0}/{1}] Loss: {2}", i + 1, jump, sumLoss); //Run truncated BPTT if ((i + 1) % BPROP_LEN == 0) { for (int j = 0; backNdArrays.Count > 0; j++) { RILogManager.Default?.SendDebug("backward" + backNdArrays.Count); model.Backward(true, backNdArrays.Pop()); } model.Update(); model.ResetState(); } if ((i + 1) % jump == 0) { epoch++; RILogManager.Default?.SendDebug("evaluate"); dVal = Evaluate(model, validData); RILogManager.Default?.SendDebug($"validation perplexity: {dVal}"); if (epoch >= 6) { sgd.LearningRate /= 1.2; RILogManager.Default?.SendDebug("learning rate =" + sgd.LearningRate); } } DocumentResults(accumulatingHistogram, recorder); } RILogManager.Default?.SendDebug("test start"); dVal = Evaluate(model, testData); RILogManager.Default?.SendDebug("test perplexity:" + dVal); DocumentResults(accumulatingHistogram, recorder); _logWriter.Dispose(); _outputStream.Dispose(); RILogManager.Default?.SendDebug("Log contents"); RILogManager.Default?.SendDebug(File.ReadAllText(LogPath)); Console.WriteLine(); RILogManager.Default?.SendDebug("Percentile distribution (values reported in milliseconds)"); accumulatingHistogram.OutputPercentileDistribution(Console.Out, outputValueUnitScalingRatio: OutputScalingFactor.TimeStampToMilliseconds, useCsvFormat: true); RILogManager.Default?.SendDebug("Mean: " + BytesToString(accumulatingHistogram.GetMean()) + ", StdDev: " + BytesToString(accumulatingHistogram.GetStdDeviation())); }