Merge pull request #121 from MiraGeoscience/GEOPY-425

GEOPY-425: Crash on Zarr file shape for tiled inversions with disk storage

Author: domfournier

simpeg/dask/electromagnetics/frequency_domain/simulation.py

@@ -1,4 +1,6 @@
 import gc
+import os
+import shutil
 
 from ....electromagnetics.frequency_domain.simulation import BaseFDEMSimulation as Sim
 from ....utils import Zero
@@ -50,7 +52,9 @@ def receiver_derivs(survey, mesh, fields, blocks):
     return field_derivatives
 
 
-def eval_block(simulation, Ainv_deriv_u, deriv_indices, deriv_m, fields, address):
+def compute_rows(
+    simulation, Ainv_deriv_u, deriv_indices, deriv_m, fields, address, Jmatrix
+):
     """
     Evaluate the sensitivities for the block or data
     """
@@ -92,7 +96,14 @@ def eval_block(simulation, Ainv_deriv_u, deriv_indices, deriv_m, fields, address
     if not isinstance(deriv_m, Zero):
         du_dmT += deriv_m
 
-    return np.array(du_dmT, dtype=complex).reshape((du_dmT.shape[0], -1)).real.T
+    values = np.array(du_dmT, dtype=complex).reshape((du_dmT.shape[0], -1)).real.T
+
+    if isinstance(Jmatrix, zarr.Array):
+        Jmatrix.set_orthogonal_selection((address[1][1], slice(None)), values)
+    else:
+        Jmatrix[address[1][1], :] = values
+
+    return None
 
 
 def getSourceTerm(self, freq, source=None):
@@ -195,28 +206,39 @@ def compute_J(self, m, f=None):
             "Consider creating one misfit per frequency."
         )
 
+    client, worker = self._get_client_worker()
+
     A_i = list(Ainv.values())[0]
     m_size = m.size
+    compute_row_size = np.ceil(self.max_chunk_size / (A_i.A.shape[0] * 32.0 * 1e-6))
+    blocks = get_parallel_blocks(
+        self.survey.source_list, compute_row_size, optimize=True
+    )
 
     if self.store_sensitivities == "disk":
+
+        chunk_size = np.median(
+            [np.sum([len(chunk[1][1]) for chunk in block]) for block in blocks]
+        ).astype(int)
+
+        if os.path.exists(self.sensitivity_path):
+            shutil.rmtree(self.sensitivity_path)
+
         Jmatrix = zarr.open(
             self.sensitivity_path,
             mode="w",
             shape=(self.survey.nD, m_size),
-            chunks=(self.max_chunk_size, m_size),
+            chunks=(chunk_size, m_size),
         )
     else:
         Jmatrix = np.zeros((self.survey.nD, m_size), dtype=np.float32)
 
-    compute_row_size = np.ceil(self.max_chunk_size / (A_i.A.shape[0] * 32.0 * 1e-6))
-    blocks = get_parallel_blocks(
-        self.survey.source_list, compute_row_size, optimize=False
-    )
+        if client:
+            Jmatrix = client.scatter(Jmatrix, workers=worker)
+
     fields_array = f[:, self._solutionType]
     blocks_receiver_derivs = []
 
-    client, worker = self._get_client_worker()
-
     if client:
         fields_array = client.scatter(f[:, self._solutionType], workers=worker)
         fields = client.scatter(f, workers=worker)
@@ -270,7 +292,6 @@ def compute_J(self, m, f=None):
             addresses_chunks,
             client,
             worker,
-            store_sensitivities=self.store_sensitivities,
         )
 
     for A in Ainv.values():
@@ -295,7 +316,6 @@ def parallel_block_compute(
     addresses,
     client,
     worker=None,
-    store_sensitivities="disk",
 ):
     m_size = m.size
     block_stack = sp.hstack(blocks_receiver_derivs).toarray()
@@ -306,29 +326,29 @@ def parallel_block_compute(
         ATinvdf_duT = client.scatter(ATinvdf_duT, workers=worker)
     else:
         ATinvdf_duT = delayed(ATinvdf_duT)
+
     count = 0
-    rows = []
     block_delayed = []
-
     for address, dfduT in zip(addresses, blocks_receiver_derivs):
         n_cols = dfduT.shape[1]
         n_rows = address[1][2]
 
         if client:
             block_delayed.append(
                 client.submit(
-                    eval_block,
+                    compute_rows,
                     simulation,
                     ATinvdf_duT,
                     np.arange(count, count + n_cols),
                     Zero(),
                     fields_array,
                     address,
+                    Jmatrix,
                     workers=worker,
                 )
             )
         else:
-            delayed_eval = delayed(eval_block)
+            delayed_eval = delayed(compute_rows)
             block_delayed.append(
                 array.from_delayed(
                     delayed_eval(
@@ -338,35 +358,22 @@ def parallel_block_compute(
                         Zero(),
                         fields_array,
                         address,
+                        Jmatrix,
                     ),
                     dtype=np.float32,
                     shape=(n_rows, m_size),
                 )
             )
         count += n_cols
-        rows += address[1][1].tolist()
-
-    indices = np.hstack(rows)
 
     if client:
-        block_delayed = client.gather(block_delayed)
-        block = np.vstack(block_delayed)
-    else:
-        block = compute(array.vstack(block_delayed))[0]
-
-    if store_sensitivities == "disk":
-        Jmatrix.set_orthogonal_selection(
-            (indices, slice(None)),
-            block,
-        )
+        client.gather(block_delayed)
     else:
-        # Dask process to compute row and store
-        Jmatrix[indices, :] = block
+        compute(block_delayed)
 
     return Jmatrix
 
 
-Sim.parallel_block_compute = parallel_block_compute
 Sim.compute_J = compute_J
 Sim.getJtJdiag = getJtJdiag
 Sim.Jvec = Jvec

simpeg/dask/electromagnetics/static/resistivity/simulation.py

@@ -3,7 +3,8 @@
 from ....simulation import getJtJdiag, Jvec, Jtvec, Jmatrix
 
 from .....utils import Zero
-
+import shutil
+import os
 import dask.array as da
 import numpy as np
 from scipy import sparse as sp
@@ -42,23 +43,29 @@ def compute_J(self, m, f=None):
 
     f, Ainv = self.fields(m=m, return_Ainv=True)
 
-    m_size = m.size
+    n_cells = m.size
     row_chunks = int(
         np.ceil(
             float(self.survey.nD)
-            / np.ceil(float(m_size) * self.survey.nD * 8.0 * 1e-6 / self.max_chunk_size)
+            / np.ceil(
+                float(n_cells) * self.survey.nD * 8.0 * 1e-6 / self.max_chunk_size
+            )
         )
     )
 
     if self.store_sensitivities == "disk":
+
+        if os.path.exists(self.sensitivity_path):
+            shutil.rmtree(self.sensitivity_path)
+
         Jmatrix = zarr.open(
-            self.sensitivity_path + "J.zarr",
+            self.sensitivity_path,
             mode="w",
-            shape=(self.survey.nD, m_size),
-            chunks=(row_chunks, m_size),
+            shape=(self.survey.nD, n_cells),
+            chunks=(row_chunks, n_cells),
         )
     else:
-        Jmatrix = np.zeros((self.survey.nD, m_size), dtype=np.float32)
+        Jmatrix = np.zeros((self.survey.nD, n_cells), dtype=np.float32)
 
     blocks = []
     count = 0
@@ -92,7 +99,7 @@ def compute_J(self, m, f=None):
                     du_dmT += df_dmT
 
                 #
-                du_dmT = du_dmT.T.reshape((-1, m_size))
+                du_dmT = du_dmT.T.reshape((-1, n_cells))
 
                 if len(blocks) == 0:
                     blocks = du_dmT
@@ -130,7 +137,7 @@ def compute_J(self, m, f=None):
 
     if self.store_sensitivities == "disk":
         del Jmatrix
-        self._Jmatrix = da.from_zarr(self.sensitivity_path + "J.zarr")
+        self._Jmatrix = da.from_zarr(self.sensitivity_path)
     else:
         self._Jmatrix = Jmatrix