tutorials for LSM and MDD using NCCL

tutorials_nccl/lsm_nccl.py

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+r"""
+Least-squares Migration with NCCL
+=================================
+This tutorial is an extension of the :ref:`sphx_glr_tutorials_lsm.py`
+tutorial where PyLops-MPI is run in multi-GPU setting with GPUs communicating
+via NCCL.
+"""
+
+import warnings
+warnings.filterwarnings('ignore')
+
+import numpy as np
+import cupy as cp
+from matplotlib import pyplot as plt
+from mpi4py import MPI
+
+from pylops.utils.wavelets import ricker
+from pylops.waveeqprocessing.lsm import LSM
+
+import pylops_mpi
+
+###############################################################################
+# NCCL communication can be easily initialized with
+# :py:func:`pylops_mpi.utils._nccl.initialize_nccl_comm` operator.
+# One can think of this as GPU-counterpart of :code:`MPI.COMM_WORLD`
+
+np.random.seed(42)
+plt.close("all")
+nccl_comm = pylops_mpi.utils._nccl.initialize_nccl_comm()
+rank = MPI.COMM_WORLD.Get_rank()
+
+###############################################################################
+# Let's start by defining all the parameters required by the
+# :py:class:`pylops.waveeqprocessing.LSM` operator.
+# Note that this section is exactly the same as the one in the MPI example
+# as we will keep using MPI for transfering metadata (i.e., shapes, dims, etc.)
+
+# Velocity Model
+nx, nz = 81, 60
+dx, dz = 4, 4
+x, z = np.arange(nx) * dx, np.arange(nz) * dz
+v0 = 1000  # initial velocity
+kv = 0.0  # gradient
+vel = np.outer(np.ones(nx), v0 + kv * z)
+
+# Reflectivity Model
+refl = np.zeros((nx, nz))
+refl[:, 30] = -1
+refl[:, 50] = 0.5
+
+# Receivers
+nr = 11
+rx = np.linspace(10 * dx, (nx - 10) * dx, nr)
+rz = 20 * np.ones(nr)
+recs = np.vstack((rx, rz))
+
+# Sources
+ns = 10
+# Total number of sources at all ranks
+nstot = MPI.COMM_WORLD.allreduce(ns, op=MPI.SUM)
+sxtot = np.linspace(dx * 10, (nx - 10) * dx, nstot)
+sx = sxtot[rank * ns: (rank + 1) * ns]
+sztot = 10 * np.ones(nstot)
+sz = 10 * np.ones(ns)
+sources = np.vstack((sx, sz))
+sources_tot = np.vstack((sxtot, sztot))
+
+if rank == 0:
+    plt.figure(figsize=(10, 5))
+    im = plt.imshow(vel.T, cmap="summer", extent=(x[0], x[-1], z[-1], z[0]))
+    plt.scatter(recs[0], recs[1], marker="v", s=150, c="b", edgecolors="k")
+    plt.scatter(sources_tot[0], sources_tot[1], marker="*", s=150, c="r", edgecolors="k")
+    cb = plt.colorbar(im)
+    cb.set_label("[m/s]")
+    plt.axis("tight")
+    plt.xlabel("x [m]"), plt.ylabel("z [m]")
+    plt.title("Velocity")
+    plt.xlim(x[0], x[-1])
+    plt.tight_layout()
+
+    plt.figure(figsize=(10, 5))
+    im = plt.imshow(refl.T, cmap="gray", extent=(x[0], x[-1], z[-1], z[0]))
+    plt.scatter(recs[0], recs[1], marker="v", s=150, c="b", edgecolors="k")
+    plt.scatter(sources_tot[0], sources_tot[1], marker="*", s=150, c="r", edgecolors="k")
+    plt.colorbar(im)
+    plt.axis("tight")
+    plt.xlabel("x [m]"), plt.ylabel("z [m]")
+    plt.title("Reflectivity")
+    plt.xlim(x[0], x[-1])
+    plt.tight_layout()
+
+###############################################################################
+# We create a :py:class:`pylops.waveeqprocessing.LSM` at each rank and then push them
+# into a :py:class:`pylops_mpi.basicoperators.MPIVStack` to perform a matrix-vector
+# product with the broadcasted reflectivity at every location on the subsurface.
+# Also, we must pass `nccl_comm` to `refl` in order to use NCCL for communications.
+# Noted that we allocate some arrays (wav, lsm.Demop.trav_srcs, and lsm.Demop.trav.recs)
+# to GPU upfront. Because we want a fair performace comparison, we avoid having
+# LSM internally copying arrays.
+
+# Wavelet
+nt = 651
+dt = 0.004
+t = np.arange(nt) * dt
+wav, wavt, wavc = ricker(t[:41], f0=20)
+
+lsm = LSM(
+    z,
+    x,
+    t,
+    sources,
+    recs,
+    v0,
+    cp.asarray(wav.astype(np.float32)),
+    wavc,
+    mode="analytic",
+    engine="cuda",
+    dtype=np.float32
+)
+lsm.Demop.trav_srcs = cp.asarray(lsm.Demop.trav_srcs.astype(np.float32))
+lsm.Demop.trav_recs = cp.asarray(lsm.Demop.trav_recs.astype(np.float32))
+
+VStack = pylops_mpi.MPIVStack(ops=[lsm.Demop, ])
+refl_dist = pylops_mpi.DistributedArray(global_shape=nx * nz,
+                                        partition=pylops_mpi.Partition.BROADCAST,
+                                        base_comm_nccl=nccl_comm,
+                                        engine="cupy")
+refl_dist[:] = cp.asarray(refl.flatten())
+d_dist = VStack @ refl_dist
+d = d_dist.asarray().reshape((nstot, nr, nt))
+
+###############################################################################
+# We calculate now the adjoint and model the data using the adjoint reflectivity
+# as input.
+madj_dist = VStack.H @ d_dist
+madj = madj_dist.asarray().reshape((nx, nz))
+d_adj_dist = VStack @ madj_dist
+d_adj = d_adj_dist.asarray().reshape((nstot, nr, nt))
+
+###############################################################################
+# We calculate the inverse using the :py:func:`pylops_mpi.optimization.basic.cgls`
+# solver. Here, we pass the `nccl_comm` to `x0` to use NCCL as a communicator.
+# In this particular case, the local computation will be done in GPU.
+# Collective communication calls will be carried through NCCL GPU-to-GPU.
+
+# Inverse
+# Initializing x0 to zeroes
+x0 = pylops_mpi.DistributedArray(VStack.shape[1],
+                                 partition=pylops_mpi.Partition.BROADCAST,
+                                 base_comm_nccl=nccl_comm,
+                                 engine="cupy")
+x0[:] = 0
+minv_dist = pylops_mpi.cgls(VStack, d_dist, x0=x0, niter=100, show=True)[0]
+minv = minv_dist.asarray().reshape((nx, nz))
+d_inv_dist = VStack @ minv_dist
+d_inv = d_inv_dist.asarray().reshape(nstot, nr, nt)
+
+##############################################################################
+# Finally we visualize the results. Note that the array must be copied back
+# to the CPU by calling the :code:`get()` method on the CuPy arrays.
+
+if rank == 0:
+    # Visualize
+    fig1, axs = plt.subplots(1, 3, figsize=(10, 3))
+    axs[0].imshow(refl.T, cmap="gray", vmin=-1, vmax=1)
+    axs[0].axis("tight")
+    axs[0].set_title(r"$m$")
+    axs[1].imshow(madj.T.get(), cmap="gray", vmin=-madj.max(), vmax=madj.max())
+    axs[1].set_title(r"$m_{adj}$")
+    axs[1].axis("tight")
+    axs[2].imshow(minv.T.get(), cmap="gray", vmin=-1, vmax=1)
+    axs[2].axis("tight")
+    axs[2].set_title(r"$m_{inv}$")
+    plt.tight_layout()
+    fig1.savefig("model.png")
+
+    fig2, axs = plt.subplots(1, 3, figsize=(10, 3))
+    axs[0].imshow(d[0, :, :300].T.get(), cmap="gray", vmin=-d.max(), vmax=d.max())
+    axs[0].set_title(r"$d$")
+    axs[0].axis("tight")
+    axs[1].imshow(d_adj[0, :, :300].T.get(), cmap="gray", vmin=-d_adj.max(), vmax=d_adj.max())
+    axs[1].set_title(r"$d_{adj}$")
+    axs[1].axis("tight")
+    axs[2].imshow(d_inv[0, :, :300].T.get(), cmap="gray", vmin=-d.max(), vmax=d.max())
+    axs[2].set_title(r"$d_{inv}$")
+    axs[2].axis("tight")
+    fig2.savefig("data1.png")
+
+    fig3, axs = plt.subplots(1, 3, figsize=(10, 3))
+    axs[0].imshow(d[nstot // 2, :, :300].T.get(), cmap="gray", vmin=-d.max(), vmax=d.max())
+    axs[0].set_title(r"$d$")
+    axs[0].axis("tight")
+    axs[1].imshow(d_adj[nstot // 2, :, :300].T.get(), cmap="gray", vmin=-d_adj.max(), vmax=d_adj.max())
+    axs[1].set_title(r"$d_{adj}$")
+    axs[1].axis("tight")
+    axs[2].imshow(d_inv[nstot // 2, :, :300].T.get(), cmap="gray", vmin=-d.max(), vmax=d.max())
+    axs[2].set_title(r"$d_{inv}$")
+    axs[2].axis("tight")
+    plt.tight_layout()
+    fig3.savefig("data2.png")