Merge pull request #191 from piotrbartman/thrust_storage

rollback indexed_storage.py changes

Author: piotrbartman

PySDM/backends/numba/impl/_algorithmic_methods.py

@@ -30,7 +30,7 @@ def calculate_displacement(dim, scheme, displacement, courant, cell_origin, posi
     @staticmethod
     @numba.njit(int64(int64[:], float64[:], int64[:], int64, float64[:, :], float64[:, :], float64[:], int64[:], numba.boolean, int64, float64[:]),
                 **{**conf.JIT_FLAGS, **{'parallel': False}})
-    # TODO: waits for https://github.com/numba/numba/issues/5279
+    # TODO: reopen https://github.com/numba/numba/issues/5279 with minimal rep. ex.
     def coalescence_body(n, volume, idx, length, intensive, extensive, gamma, healthy, adaptive, subs, adaptive_memory):
         result = 1
         for i in prange(length - 1):
@@ -86,10 +86,6 @@ def compute_gamma_body(prob, rand):
         """
         for i in prange(len(prob)):
             prob[i] = np.ceil(prob[i] - rand[i // 2])
-            # TODO: same in Thrust?
-#             prob[i] *= -1.
-#             prob[i] += rand[i // 2]
-#             prob[i] = -np.floor(prob[i])
 
     @staticmethod
     def compute_gamma(prob, rand):

PySDM/backends/numba/impl/_algorithmic_step_methods.py

@@ -25,7 +25,7 @@ def amin(row, idx, length):
         return result
 
     @staticmethod
-    # @numba.njit(**conf.JIT_FLAGS)  # TODO: "np.dot() only supported on float and complex arrays"
+    # @numba.njit(**conf.JIT_FLAGS)  # Note: in Numba 0.51 "np.dot() only supported on float and complex arrays"
     def cell_id(cell_id, cell_origin, strides):
         cell_id.data[:] = np.dot(strides.data, cell_origin.data)
 

PySDM/backends/numba/impl/_maths_methods.py

@@ -35,7 +35,7 @@ def floor(output):
     @staticmethod
     @numba.njit(void(int64[:, :], float64[:, :]), **conf.JIT_FLAGS)
     def floor_out_of_place(output, input_data):
-        output[:] = np.floor(input_data)  # TODO: Try input_data//1 instead of np.floor(input_data)
+        output[:] = np.floor(input_data)
 
     @staticmethod
     @numba.njit(**{**conf.JIT_FLAGS, **{'parallel': False}})

PySDM/backends/numba/storage/indexed_storage.py

@@ -64,7 +64,8 @@ def to_ndarray(self):
         return self.data[:self.length].copy()
 
     def read_row(self, i):
-        result = IndexedStorage(self.idx, self.data[i, :], (1, *self.shape[1:]), self.dtype)
+        # TODO: shape like in ThrustRTC
+        result = IndexedStorage(self.idx, self.data[i, :], *self.shape[1:], self.dtype)
         return result
 
     def remove_zeros(self):

PySDM/backends/numba/storage/storage.py

@@ -166,6 +166,6 @@ def to_ndarray(self):
     def upload(self, data):
         np.copyto(self.data, data, casting='safe')
 
-    # TODO: optimize
+    # TODO: remove
     def write_row(self, i, row):
         self.data[i, :] = row.data

PySDM/backends/thrustRTC/impl/_maths_methods.py

@@ -2,9 +2,7 @@
 Created at 10.12.2019
 """
 
-import numpy as np
 import ThrustRTC as trtc
-import CURandRTC as rndrtc
 from ._storage_methods import StorageMethods
 from PySDM.backends.thrustRTC.nice_thrust import nice_thrust
 from PySDM.backends.thrustRTC.conf import NICE_THRUST_FLAGS
@@ -119,31 +117,3 @@ def power(output, exponent):
     @nice_thrust(**NICE_THRUST_FLAGS)
     def subtract(output, subtrahend):
         MathsMethods.__subtract_body.launch_n(output.size(), [output, subtrahend])
-        # trtc.Transform_Binary(output, subtrahend, output, trtc.Minus())
-
-    __urand_init_rng_state_body = trtc.For(['rng', 'states', 'seed'], 'i', '''
-        rng.state_init(1234, i, 0, states[i]);
-        ''')
-
-    __urand_body = trtc.For(['states', 'vec_rnd'], 'i', '''
-        vec_rnd[i]=states[i].rand01();
-        ''')
-
-    __rng = rndrtc.DVRNG()
-    states = trtc.device_vector('RNGState', 2**19)
-    __urand_init_rng_state_body.launch_n(states.size(), [__rng, states, trtc.DVInt64(12)])
-
-    @staticmethod
-    @nice_thrust(**NICE_THRUST_FLAGS)
-    def urand(data, seed=None):
-        # TODO: print("Numpy import!: ThrustRTC.urand(...)")
-
-        seed = seed or np.random.randint(2**16)
-        dseed = trtc.DVInt64(seed)
-        # MathsMethods.__urand_init_rng_state_body.launch_n(MathsMethods.states.size(), [MathsMethods.__rng, MathsMethods.states, dseed])
-        MathsMethods.__urand_body.launch_n(data.size(), [MathsMethods.states, data])
-        # hdata = data.to_host()
-        # print(np.mean(hdata))
-        # np.random.seed(seed)
-        # output = np.random.uniform(0, 1, data.shape)
-        # StorageMethods.upload(output, data)

PySDM/backends/thrustRTC/impl/_storage_methods.py

@@ -9,7 +9,6 @@
 
 
 class StorageMethods:
-    # TODO check static For
     storage = trtc.DVVector.DVVector
     integer = np.int64
     double = np.float64
@@ -27,7 +26,6 @@ def array(shape, dtype):
             raise NotImplementedError
 
         data = trtc.device_vector(elem_cls, int(np.prod(shape)))
-        # TODO: trtc.Fill(data, trtc.DVConstant(np.nan))
 
         StorageMethods.__equip(data, shape, elem_dtype)
         return data
@@ -109,19 +107,10 @@ def shuffle_global(idx, length, u01):
     @nice_thrust(**NICE_THRUST_FLAGS)
     def shuffle_local(idx, u01, cell_start):
         StorageMethods.__shuffle_local_body.launch_n(cell_start.size() - 1, [cell_start, u01, idx])
-        # TODO: print("Numba import!: ThrustRTC.shuffle_local(...)")
-        # from PySDM.backends.numba.numba import Numba
-        # host_idx = StorageMethods.to_ndarray(idx)
-        # host_u01 = StorageMethods.to_ndarray(u01)
-        # host_cell_start = StorageMethods.to_ndarray(cell_start)
-        # Numba.shuffle_local(host_idx, host_u01, host_cell_start)
-        # device_idx = StorageMethods.from_ndarray(host_idx)
-        # trtc.Copy(device_idx, idx)
 
     @staticmethod
     @nice_thrust(**NICE_THRUST_FLAGS)
     def to_ndarray(data):
-        # TODO: move to __equip??
         if isinstance(data, StorageMethods.storage):
             pass
         elif isinstance(data, trtc.DVVector.DVRange):

PySDM/dynamics/condensation/condensation.py

@@ -42,7 +42,7 @@ def register(self, builder):
         self.max_substeps = int(self.core.dt)
         self.max_substeps = int(self.core.dt)
         self.substeps = self.core.Storage.empty(self.core.mesh.n_cell, dtype=int)
-        self.substeps[:] = np.maximum(1, int(self.core.dt))  # TODO: reset substeps
+        self.substeps[:] = np.maximum(1, int(self.core.dt))  # TODO: min substep length
         self.ripening_flags = self.core.Storage.empty(self.core.mesh.n_cell, dtype=int)
         self.ripening_flags[:] = 0
 

PySDM/mesh.py

@@ -37,7 +37,7 @@ def mesh_0d(dv=None):
 
     @staticmethod
     def __strides(grid):
-        domain = np.empty(tuple(grid))  # TODO optimize
+        domain = np.empty(tuple(grid))
         strides = np.array(domain.strides).reshape(1, -1) // domain.itemsize
         return strides
 

PySDM/state/products/aerosol_specific_concentration.py

@@ -22,7 +22,7 @@ def __init__(self, radius_threshold):
     def get(self):
         self.download_moment_to_buffer('volume', rank=0,
                                        filter_range=[0, phys.volume(self.radius_threshold)])
-        result = self.buffer.copy()  # TODO !!!
+        result = self.buffer.copy()  # TODO
         self.download_to_buffer(self.core.environment['rhod'])
         result[:] /= self.core.mesh.dv
         result[:] /= self.buffer