Deep Learning HMC

Building Topological Samplers for Lattice QCD

Sam Foreman

May, 2021

saforem2/l2hmc-qcd
 arXiv:2105.03418

Acknowledgements

This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.

Collaborators:

  • Xiao-Yong Jin
  • James C. Osborn

Huge thank you to:

  • Norman Christ
  • Akio Tomiya
  • Luchang Jin
  • Chulwoo Jung
  • Peter Boyle
  • Taku Izubuchi
  • Critical Slowing Down group (ECP)
  • ALCF Staff + Datascience group

MCMC in Lattice QCD

  • Generating independent gauge configurations is a MAJOR bottleneck for LatticeQCD.
  • As the lattice spacing, \(a \rightarrow 0\), the MCMC updates tend to get stuck in sectors of fixed gauge topology.
    • This causes the number of steps needed to adequately sample different topological sectors to increase exponentially.

Critical slowing down!

Markov Chain Monte Carlo (MCMC)

  • Goal: Draw independent samples from a target distribution, \(p(x)\)
x^{\prime} = x_{0} + \delta,\quad \delta\sim\mathcal{N}(0, \mathbb{1})
  • Starting from some initial state \(x_{0}\sim \mathcal{N}(0, \mathbb{1})\) , we generate proposal configurations \(x^{\prime}\)
  • Use Metropolis-Hastings acceptance criteria
x_{i+1} = \begin{cases} x^{\prime},\quad\text{with probability } A(x^{\prime}|x)\\ x,\quad\text{with probability } 1 - A(x^{\prime}|x) \end{cases}
A(x^{\prime}|x) = \min\left\{1, \frac{p(x^{\prime})}{p(x)}\left|\frac{\partial x^{\prime}}{\partial x^{T}}\right|\right\}

Inefficient!

Issues with MCMC

saved

dropped

\(x_{0}\rightarrow x_{1}\rightarrow x_{2}\rightarrow\cdots\rightarrow x_{m-1}\rightarrow x_{m}\rightarrow x_{m+1}\rightarrow\cdots\rightarrow x_{n-2}\rightarrow x_{n-1}\rightarrow x_{n}\)

 1. Construct chain:

Goal: Generate an ensemble of independent configurations

random walk

  • Generate proposal \(x^{\prime}\):

\(x^{\prime} = x  + \delta\), where \(\delta \sim \mathcal{N}(0, \mathbb{1})\)

\(x_{0}\rightarrow x_{1}\rightarrow x_{2}\rightarrow\cdots\rightarrow x_{m-1}\rightarrow x_{m}\rightarrow x_{m+1}\rightarrow\cdots\rightarrow x_{n-2}\rightarrow x_{n-1}\rightarrow x_{n}\)

 2. Thermalize ("burn-in"):

 3. Drop correlated samples ("thinning"):

\(x_{0}\rightarrow x_{1}\rightarrow x_{2}\rightarrow\cdots\rightarrow x_{m-1}\rightarrow x_{m}\rightarrow x_{m+1}\rightarrow\cdots\rightarrow x_{n-2}\rightarrow x_{n-1}\rightarrow x_{n}\)

Hamiltonian Monte Carlo (HMC)

  • Introduce fictitious momentum:

\(v\sim\mathcal{N}(0, 1)\)

  • Target distribution:

\(p(x)\propto e^{-S(x)}\)

  • Joint target distribution:

p(x, v) = p(x)\cdot p(v) = e^{-S(x)}\cdot e^{-\frac{1}{2}v^{T}v} = e^{-\mathcal{H(x,v)}}
p(x, v) = p(x|v)p(v)

lift to phase space

v\sim \mathcal{N}(0, \mathbb{1})
\mathcal{H}=\mathrm{const}

  • Hamilton's Equations

\dot{x} = \frac{\partial\mathcal{H}}{\partial v},
\dot{v} = -\frac{\partial\mathcal{H}}{\partial x}

HMC: Leapfrog Integrator

(trajectory)

  • Hamilton's Eqs:

\dot{x}=\frac{\partial\mathcal{H}}{\partial v}, \dot{v}=-\frac{\partial{\mathcal{H}}}{\partial x}
\mathcal{H}(x, v) = S(x) + \frac{1}{2}v^{T}v

  • Hamiltonian:

(x_{0}, v_{0})\rightarrow \cdots \rightarrow(x_{N_{\mathrm{LF}}}, v_{N_{\mathrm{LF}}})

  • \(N_{\mathrm{LF}}\) leapfrog steps:

\Longrightarrow

Leapfrog Integrator

x^{\prime} = x + \varepsilon v^{1/2}
v^{\prime} = v^{1/2} - \frac{\varepsilon}{2}\partial_{x}S(x^{\prime})
v^{1/2} = v - \frac{\varepsilon}{2}\partial_{x}S(x)

2. Full-step \(x\)-update:

3. Half-step \(v\)-update:

1. Half-step \(v\)-update:

HMC: Issues

  • Cannot easily traverse low-density zones.
  • What do we want in a good sampler?
  • Fast mixing
  • Fast burn-in
  • Mix across energy levels
  • Mix between modes
  • Energy levels selected randomly \(\longrightarrow\) slow mixing!

Stuck!

Leapfrog Layer

  • Introduce a persistent direction \(d \sim \mathcal{U}(+,-)\)  (forward/backward)
  • Introduce a discrete index \(k \in \{1, 2, \ldots, N_{\mathrm{LF}}\}\) to denote the current leapfrog step
  • Let \(\xi = (x, v, \pm)\) denote a complete state, then the target distribution is given by
p(\xi) = p(x)\cdot p(v)\cdot p(d)
  • Each leapfrog step transforms \(\xi_{k} = (x_{k}, v_{k}, \pm) \rightarrow (x''_{k}, v''_{k}, \pm) = \xi''_{k}\) by passing it through the \(k^{\mathrm{th}}\) leapfrog layer

Leapfrog Layer

  • Each leapfrog step transforms \(\xi_{k}=(x_{k}, v_{k}, \pm)\rightarrow (x''_{k}, v''_{k}, \pm) = \xi''_{k}\) by passing it through the \(k^{\mathrm{th}}\) leapfrog layer.
  • \(x\)-update \((d = +)\):
x^{\prime}_{k} = \Lambda^{+}_{k}(x_{k};\zeta_{x_{k}})
\zeta_{x_{k}}\equiv \big(\quad\odot x_{k}, \partial_{x}S(x_{k})\big)
\bar{m}_{t}

(\(m_{t}\)\(\odot  x\)) -independent

\,\quad= x_{k}\odot\exp\left(\varepsilon^{k}_{x}s^{k}_{x}(\zeta_{x_{k}})\right) + \varepsilon^{k}_{x}\left[v^{\prime}_{k}\odot\exp\left(\varepsilon^{k}_{x}q^{k}_{x}(\zeta_{x_{k}})\right)+t^{k}_{x}(\zeta_{x_{k}})\right]

masks:

m_{t}
\bar{m}_{t}
= \mathbb{1}
+

Momentum (\(v_{k}\)) scaling

Gradient \(\partial_{x}S(x_{k})\) scaling

Translation

\,\quad\equiv\, v_{k}\odot \exp\left(\frac{\varepsilon^{k}_{v}}{2}s^{k}_{v}(\zeta_{v_{k}})\right) - \frac{\varepsilon^{k}_{v}}{2}\left[\partial_{x}S(x_{k})\odot\exp\left(\varepsilon^{k}_{v}q^{k}_{v}(\zeta_{v_{k}})\right) \,+\,t^{k}_{v}(\zeta_{v_{k}})\right]
\zeta_{v_{k}}\equiv \big(x_{k}, \partial_{x}S(x_{k})\big)
  • \(v\)-update \((d = +)\):
v^{\prime}_{k} = \Gamma^{+}_{k}(v_{k};\zeta_{v_{k}} )
\overbrace{\hspace{80px}}
\overbrace{\hspace{100px}}
\overbrace{\hspace{30px}}

(\(v\)-independent)

where \((s_{v}^{k}, q^{k}_{v}, t^{k}_{v})\), and \((s_{x}^{k}, q^{k}_{x}, t^{k}_{x})\), are parameterized by neural networks

L2HMC: Generalized Leapfrog

  • Complete (generalized) update:
    1. Half-step \(v\) update:
    2. Full-step \(\frac{1}{2} x\) update:
    3. Full-step \(\frac{1}{2} x\) update:
    4. Half-step \(v\) update:
v^{\prime}_{k} = \Gamma^{\pm}(v_{k};\zeta_{v_{k}})
x^{\prime}_{k} = \hspace{14px}\odot x_{k} + \hspace{14px}\odot \Lambda^{\pm}(x_{k};\zeta_{x_{k}})
\bar{m}^{t}
m^{t}
x^{\prime\prime}_{k} = \hspace{14px}\odot \Lambda^{\pm}_{k}(x^{\prime}_{k};\zeta_{x^{\prime}_{k}}) + \hspace{14px}\odot x^{\prime}_{k}
\bar{m}^{t}
m^{t}
v^{\prime\prime}_{k} = \Gamma^{\pm}(v^{\prime}_{k};\zeta_{v^{\prime}_{k}})

Leapfrog Layer

masks:

Stack of fully-connected layers

\(x_{k} \in U(1) \longrightarrow x_{k} = \left[\cos\theta, \sin\theta\right]\)

Training Algorithm

     construct
trajectory
Compute loss
  + backprop
 Metropolis-Hastings 
       accept/reject

     re-sample    
      momentum
   + direction

Annealing Schedule

\left\{\gamma_{t}\right\}_{t=0}^{N} = \left\{\gamma_{0}, \gamma_{1}, \ldots, \gamma_{N-1}, \gamma_{N}\right\},
\gamma_{0} < \gamma_{1} < \ldots < \gamma_{N} \equiv 1
\gamma_{t+1} - \gamma_{t} \ll 1
  • Introduce an annealing schedule during the training phase:

(varied slowly)

 e.g. \( \{0.1, 0.2, \ldots, 0.9, 1.0\}\)

(increasing)

p_{t}(x)\propto e^{-\gamma_{t}S(x)},\quad\text{for}\quad t=0, 1,\ldots, N
  • Target distribution becomes:
  • For \(\|\gamma_{t}\| < 1\), this helps to rescale (shrink) the energy barriers between isolated modes
    • Allows our sampler to explore previously inaccessible regions of the target distribution

Example: GMM \(\in\mathbb{R}^{2}\)

Note:

\(A(\xi',\xi)\) = acceptance probability

\(A(\xi'|\xi)\cdot\delta(\xi',\xi)\)= avg. distance

\(\xi\) = initial state

\(\xi\) = initial state

  • Define the squared jump distance:
\delta(\xi^{\prime}, \xi) = \|x^{\prime} - x\|^{2}_{2}

HMC

L2HMC

  • Maximize
\mathcal{L}_{\theta}\left(\theta\right) \equiv \mathbb{E}_{p(\xi)}\left[A(\xi'|\xi)\cdot \delta(\xi',\xi)\right]

expected squared jump distance:

  • Wilson action:
S_{\beta}(x)=\beta \sum_{P}1-\cos x_{P}
x_{P} = x_{\mu}(n) + x_{\nu}(n+\hat{\mu}) - x_{\mu}(n+\hat{\nu}) - x_{\nu}(n)
  • Link variables:
U_{\mu}(x) = e^{i x_{\mu}(n)} \in U(1)
x_{\mu}(n) \in [-\pi,\pi]

Lattice Gauge Theory

  • Topological charge:
\left\lfloor x_{P}\right\rfloor = x_{P} - 2\pi\left\lfloor\frac{x_{P}+\pi}{2\pi}\right\rfloor
\mathcal{Q}_{\mathbb{Z}} = \frac{1}{2\pi}\sum_{P}\left\lfloor x_{P}\right\rfloor\hspace{6pt} \in\mathbb{Z}
\mathcal{Q}_{\mathbb{R}} = \frac{1}{2\pi}\sum_{P}\sin x_{P}\in\mathbb{R}

continuous, differentiable

discrete, hard to work with

Non-Compact Projection

 [1.] "Normalizing Flows on Tori and Spheres" arXiv:2002.02428

[1.]

  • Project \([-\pi, \pi]\) onto \(\mathbb{R}\) using a transformation: \(z = g(x)\), \(g: [-\pi, \pi] \rightarrow \mathbb{R}\)
    • \(z = \tan\left(\frac{x}{2}\right)\)
  • Perform the update in \(\mathbb{R}\)
    • \(z' = m^{t}\odot z + \bar{m}^{t}\odot [\alpha z + \beta]\)
  • Project back to \([-\pi, \pi]\) using the inverse transformation \(x = g^{-1}(z)\), \(g^{-1}: \mathbb{R}\rightarrow [-\pi, \pi]\)
    • \(x = 2\tan^{-1}(z)\)
  • These steps can be combined into a single update equation
    • \(x' = m^{t}\odot x + \bar{m}^{t}\odot\left[2\tan^{-1}\left(\alpha\tan\left(\frac{x}{2}\right)\right) + \beta\right]\)
    • with corresponding Jacobian factor
      • \(\frac{\partial x'}{\partial x} = \frac{\exp(\varepsilon s_{x})}{\cos^{2}(x/2) + \exp(2\varepsilon s_{x})\sin(x/2)}\)

\(x_{k} \in U(1) \longrightarrow x_{k} = \left[\cos\theta, \sin\theta\right]\)

  • We maximize the expected squared charge difference:

Loss function: \(\mathcal{L}(\theta)\)

\delta \mathcal{Q}_{\mathbb{R}}^{2}(\xi',\xi) \equiv \left(\mathcal{Q}_{\mathbb{R}}(x') - \mathcal{Q}_{\mathbb{R}}(x)\right)^{2}
\mathcal{L}(\theta) = \mathbb{E}_{p(\xi)}\left[-\delta\mathcal{Q}^{2}_{\mathbb{R}}(\xi',\xi)\cdot A(\xi'|\xi)\right]
A(\xi'|\xi) = \min\left\{1, \frac{p(\xi')}{p(\xi)} \left|\frac{\partial\xi'}{\partial\xi^{T}}\right|\right\}

\(\beta = 5\)

\(\beta = 6\)

\(\beta = 7\)

Results: \(\tau^{\mathcal{Q}_{\mathbb{Z}}}_{\mathrm{int}}\)

  • Instead, we account for the autocorrelation, so the variance becomes: \(\sigma^{2} = \frac{\tau^{\mathcal{O}}_{int}}{N}\mathrm{Var}\left[\mathcal{O}(x)\right]\)

Rescale: \(N_{\mathrm{LF}}\cdot\tau^{\mathcal{Q}_{\mathbb{Z}}}_{\mathrm{int}}\) to account for different trajectory lengths

  • If we had independent configurations, we could approximate by \(\langle\mathcal{O}\rangle \simeq \frac{1}{N}\sum_{n=1}^{N} \mathcal{O}(x_{n})\longrightarrow \sigma^{2}=\frac{1}{N}\mathrm{Var}\left[\mathcal{O}(x)\right]\propto\frac{1}{N}\)
  • Want to calculate: \(\langle \mathcal{O}\rangle\propto \int \left[\mathcal{D} x\right] \mathcal{O}(x)e^{-S[x]}\)

Results: \(\tau^{\mathcal{Q}_{\mathbb{Z}}}_{\mathrm{int}}\)

  • We maximize the expected squared charge difference:
\delta \mathcal{Q}_{\mathbb{R}}^{2}(\xi',\xi) \equiv \left(\mathcal{Q}_{\mathbb{R}}(x') - \mathcal{Q}_{\mathbb{R}}(x)\right)^{2}
\mathcal{L}(\theta) = \mathbb{E}_{p(\xi)}\left[-\delta\mathcal{Q}^{2}_{\mathbb{R}}(\xi',\xi)\cdot A(\xi'|\xi)\right]
A(\xi'|\xi) = \min\left\{1, \frac{p(\xi')}{p(\xi)} \left|\frac{\partial\xi'}{\partial\xi^{T}}\right|\right\}

Interpretation

  • Look at how different quantities evolve over a single trajectory
    • See that the sampler artificially increases the energy during the first half of the trajectory (before returning to original value)

Leapfrog step

variation in the avg plaquette

continuous topological charge

shifted energy

\langle x_{P}-x^{*}_{p}\rangle
\text{leapfrog step}

Interpretation

  • Look at how the variation in \(\langle\delta x_{P}\rangle\) varies for different values of \(\beta\)

 \(\beta = 7\) 

\(\simeq \beta = 3\)

\langle x_{P}-x^{*}_{p}\rangle
\text{leapfrog step}

 \(\beta = 7\) 

\(\simeq \beta = 3\)

Training Costs

  • We trained our model(s) using Horovod with TensorFlow on the ThetaGPU supercomputer at the Argonne Leadership Computing Facility.
  • A typical training run:
    • 1 node (8\(\times\) NVIDIA A100 GPUs)
    •  Batch size \(M = 2048\)
    • Hidden layer shapes \(=\{256, 256, 256\}\)
    • Leapfrog layers \(N_{\mathrm{LF}}=10\)
    • Lattice volume \(=16\times 16\)
    • Training steps \(=5\times 10^{5}\)
    • \(\simeq\) 24 hours to complete.

 

Next Steps

  • Going forward, we plan to:
    • Reduce training cost
    • Continue testing on larger lattice volumes to better understand scaling efficiency
    • Generalize to 2D / 4D \(SU(3)\)
    • Test alternative network architectures
    • Gauge Equivariant layers

Thanks for listening!

Interested?

 

arXiv:2105.03418
saforem2/l2hmc-qcd
slides.com/samforeman/dlhmc

Leapfrog Layer

  • Each leapfrog step transforms \(\xi_{k}=(x_{k}, v_{k}, \pm)\rightarrow (x''_{k}, v''_{k}, \pm) = \xi''_{k}\) by passing it through the \(k^{\mathrm{th}}\) leapfrog layer.
  • \(x\)-update \((d = +)\):
x^{\prime}_{k} = \Lambda^{+}_{k}(x_{k};\zeta_{x_{k}})
\,\quad= x_{k}\odot\exp\left(\varepsilon^{k}_{x}s^{k}_{x}(\zeta_{x_{k}})\right) + \varepsilon^{k}_{x}\left[v^{\prime}_{k}\odot\exp\left(\varepsilon^{k}_{x}q^{k}_{x}(\zeta_{x_{k}})\right)+t^{k}_{x}(\zeta_{x_{k}})\right]

Momentum (\(v_{k}\)) scaling

Gradient \(\partial_{x}S(x_{k})\) scaling

Translation

\,\quad\equiv\, v_{k}\odot \exp\left(\frac{\varepsilon^{k}_{v}}{2}s^{k}_{v}(\zeta_{v_{k}})\right) - \frac{\varepsilon^{k}_{v}}{2}\left[\partial_{x}S(x_{k})\odot\exp\left(\varepsilon^{k}_{v}q^{k}_{v}(\zeta_{v_{k}})\right) \,+\,t^{k}_{v}(\zeta_{v_{k}})\right]
  • \(v\)-update \((d = +)\):
v^{\prime}_{k} = \Gamma^{+}_{k}(v_{k};\zeta_{v_{k}} )
\overbrace{\hspace{80px}}
\overbrace{\hspace{100px}}
\overbrace{\hspace{30px}}

where \((s_{v}^{k}, q^{k}_{v}, t^{k}_{v})\), and \((s_{x}^{k}, q^{k}_{x}, t^{k}_{x})\), are parameterized by neural networks

\(\alpha_{s^{k}_{v}}\cdot s_{v}^{k}(\zeta_{v_{k}})\)

\(\alpha_{q^{k}_{v}}\cdot q_{v}^{k}(\zeta_{v_{k}})\)

\(\alpha_{t^{k}_{v}}\cdot t_{v}^{k}(\zeta_{v_{k}})\)

\(\alpha_{s^{k}_{x}}\cdot s_{x}^{k}(\zeta_{x_{k}})\)

\(\alpha_{q^{k}_{x}}\cdot q_{x}^{k}(\zeta_{x_{k}})\)

\(\alpha_{t^{k}_{x}}\cdot t_{x}^{k}(\zeta_{x_{k}})\)

\(\alpha \in (0, 1)\)

4096

8192

1024

2048

512

Scaling test: Training

\(4096 \sim 1.73\times\)

\(8192 \sim 2.19\times\)

\(1024 \sim 1.04\times\)

\(2048 \sim 1.29\times\)

\(512\sim 1\times\)

Scaling test: Training

Scaling test: Training

\(8192\sim \times\)

4096

1024

2048

512

Scaling test: Inference

dlhmc

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