Zfit Performance: Analyzing Slow Toy Fits Vs ROOFIT

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Hey everyone, I've been digging into zfit for some toy fitting exercises, and I've hit a bit of a snag. I'm seeing some significant performance differences compared to ROOFIT, and I'm hoping to get some insights from the community. Let's dive in!

The Performance Puzzle: Zfit vs. ROOFIT

I'm running a straightforward toy test involving a Gaussian signal plus an exponential background. With zfit, simulating 1000 toys takes around 3.5 minutes. Now, here's the kicker: ROOFIT completes the same task in just 6 seconds! That's a difference of two whole orders of magnitude. Something seems off, and I'm trying to figure out if I'm missing something crucial in my zfit setup.

To illustrate, here’s the Python code I’m using with zfit:

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

import tqdm
import zfit

obs     = zfit.Space('x', limits=(0, 10))
mu      = zfit.Parameter('mu', 5.0, 0, 10)
sg      = zfit.Parameter('sg', 1.0, 0, 10)
gaus    = zfit.pdf.Gauss(obs=obs, mu=mu, sigma=sg)
nsig    = zfit.Parameter('nsig', 1000, 0, 10_000)
gaus    = gaus.create_extended(nsig)

lam     = zfit.Parameter('lb', -0.01, -1, 0)
exp     = zfit.pdf.Exponential(lam=lam, obs=obs)
nbkg    = zfit.Parameter('nbkg', 1000, 0, 10_000)
exp     = exp.create_extended(nbkg)

model   = zfit.pdf.SumPDF([gaus, exp]) 
sampler = model.create_sampler(n=2000)

nll     = zfit.loss.ExtendedUnbinnedNLL(model=model, data=sampler)
minimizer = zfit.minimize.Minuit()

nruns = 1000
for run_number in tqdm.tqdm(range(nruns), ascii=' -'):
    mu.set_value(5.0)
    sg.set_value(1.0)

    sampler.resample()
    res = minimizer.minimize(nll)

And here's the equivalent C++ code for ROOFIT:

#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooChebychev.h"
#include "RooAddPdf.h"
#include "RooMCStudy.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH2.h"
#include "RooFitResult.h"
#include "TStyle.h"
#include "TDirectory.h"
 
using namespace RooFit;
 
void rf801_mcstudy()
{
   RooRealVar x("x", "x", 0, 10);
   x.setBins(40);
 
   // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
   RooRealVar mean("mean", "mean of gaussians", 5, 0, 10);
   RooRealVar sigma("sigma", "width of gaussians", 1.0, 0.0, 10.0);
   RooGaussian sig("sig", "Signal component 1", x, mean, sigma);
 
   RooRealVar lm("lm", "lm", -0.01, -1, 0.);
   RooExponential bkg("bkg", "Background", x, lm);
 
 
   RooRealVar nbkg("nbkg", "number of background events,", 1000, 0, 10000);
   RooRealVar nsig("nsig", "number of signal events", 1000, 0, 10000);
   RooAddPdf model("model", "model", RooArgList(bkg, sig), RooArgList(nbkg, nsig));
 
   RooMCStudy *mcstudy =
      new RooMCStudy(
              model, 
              x, 
              Binned(false), 
              Silence(), 
              Extended(), 
              FitOptions(Save(true), PrintEvalErrors(0)));
 
   mcstudy->generateAndFit(1000);

   RooPlot *frame = mcstudy->plotPull(mean, Bins(40), FitGauss(true));
   gStyle->SetOptStat(0);
   TCanvas *c = new TCanvas("rf801_mcstudy", "rf801_mcstudy", 900, 900);
   frame->Draw();
}

Both codes are designed to do essentially the same thing, with the core difference being the underlying fitting engine. Given this, I'd expect the performance to be at least within a reasonable range of each other. Right now, the zfit performance is significantly lagging, and I'm wondering if I've overlooked something in my implementation.

What I Expected

Ideally, the zfit version should perform within a factor of 2 of ROOFIT. The current discrepancy is far too large to be explained by minor differences in the fitting algorithms.

Environment Details

For context, here are the details of my environment:

  • zfit version: 0.27.1
  • Python version: 3.12.11
  • Package Management: micromamba
  • Operating System: AlmaLinux9
  • Tensorflow version: 2.19.0

Diving Deeper: Potential Bottlenecks and Optimizations in Zfit

Okay, guys, so let's break down what might be causing this massive slowdown in zfit compared to ROOFIT. There are a few key areas where performance bottlenecks often creep in, and we'll try to tackle them one by one.

1. Graph Compilation Overhead

Zfit, being built on TensorFlow, relies heavily on graph compilation. Every time you define a model, loss function, or perform a minimization, TensorFlow might be recompiling parts of the graph. This compilation step can introduce a significant overhead, especially when you're running many toy fits.

Possible Solutions:

  • Pre-compilation: One potential optimization is to try and pre-compile the graph as much as possible before entering the main loop. For instance, you could perform a dummy fit with a small dataset to force TensorFlow to compile the graph. This way, subsequent fits should be faster because the graph is already compiled.
  • Function Tracing: TensorFlow's function tracing can sometimes help optimize performance. By wrapping your fitting routine in a tf.function, you allow TensorFlow to trace the function and optimize its execution. However, be aware that this can also introduce overhead if not used carefully.

2. Data Handling and Sampling

The way you handle data and sampling can also impact performance. Creating and resampling the dataset in each iteration of the loop might be a bottleneck.

Possible Solutions:

  • Efficient Data Structures: Ensure that you're using efficient data structures for your data. For example, converting your data to TensorFlow tensors before the loop can speed up computations.
  • Vectorized Operations: Whenever possible, use vectorized operations instead of looping through individual data points. TensorFlow is highly optimized for vectorized computations, and leveraging this can significantly improve performance.
  • Sampler Optimization: Check if the create_sampler method in zfit is optimized for repeated resampling. If not, consider alternative methods for generating toy datasets.

3. Minimization Algorithm and Settings

The choice of minimization algorithm and its settings can also play a role. Some algorithms might be more efficient than others for your specific problem.

Possible Solutions:

  • Experiment with Minimizers: Try different minimizers available in zfit, such as Minuit, Adam, or Scipy. Each minimizer has its own strengths and weaknesses, so it's worth experimenting to see which one performs best for your problem.
  • Tune Minimizer Settings: Adjust the settings of the minimizer, such as the tolerance and maximum number of iterations. Sometimes, the default settings might not be optimal for your problem.

4. PDF Evaluation

Zfit needs to evaluate the PDFs (Gaussian and Exponential in this case) many times during the fitting process. Optimizing the PDF evaluation can lead to substantial performance gains.

Possible Solutions:

  • Caching: If possible, cache the results of PDF evaluations to avoid redundant computations. However, be careful with caching, as it can consume memory and might not always be beneficial.
  • Simplify PDFs: If your PDFs are overly complex, consider simplifying them if possible. For example, you might be able to approximate the Gaussian with a simpler function without sacrificing accuracy.

5. CPU vs. GPU

Although you've disabled CUDA in your code (os.environ["CUDA_VISIBLE_DEVICES"] = "-1"), it's worth ensuring that TensorFlow is indeed running on the CPU and not accidentally trying to use the GPU.

Possible Solutions:

  • Verify Device Placement: Use TensorFlow's device placement logging to verify that computations are being performed on the CPU. You can enable device placement logging by setting tf.config.set_soft_device_placement(True).

6. Zfit Internals and Overheads

Sometimes, the overhead might be coming from within zfit itself. There could be internal checks, data conversions, or other operations that are adding to the execution time.

Possible Solutions:

  • Profile the Code: Use profiling tools to identify the parts of the code that are taking the most time. This can help you pinpoint the exact source of the overhead.
  • Consult Zfit Developers: If you've exhausted all other options, consider reaching out to the zfit developers for assistance. They might be aware of specific performance issues or have suggestions for optimizing your code.

Next Steps: Investigating and Optimizing

So, what's the plan of action? I'm going to start by implementing some of the optimization strategies I've outlined above. Specifically, I'll focus on pre-compiling the graph, optimizing data handling, and experimenting with different minimizers. I'll also profile the code to identify any hidden bottlenecks.

I'll keep you guys updated on my progress. If anyone has experience with optimizing zfit for toy fits, I'd love to hear your suggestions! Let's crack this performance puzzle together!

Update: I managed to improve the performance by doing x, y, and z. It turns out that the issue was with a, b, and c.

Next Steps: If you're facing similar performance issues with zfit, I highly recommend going through the optimization strategies I've outlined above. Profiling your code is crucial for identifying bottlenecks, and experimenting with different settings can lead to significant performance gains. Good luck, and happy fitting!