From 569cc1d834d009abbe72747ea6e14902d527d05f Mon Sep 17 00:00:00 2001
From: Martin Bauer <martin.bauer@fau.de>
Date: Fri, 26 Jul 2019 12:14:24 +0200
Subject: [PATCH] [API] New interface for lattice model generation
- previously a LatticeBoltzmannMethod object was required to generate
a lattice model for waLBerla
- problem: output fields, smagorinsky, entropic additions are only done
when coverting the method to a collision rule
- the new interface now takes a collision rule instead of a LB method
to adapt your app have a look at example usage in
walberla/tests/lbm/codegen
---
lbmpy_walberla/templates/LatticeModel.tmpl.h | 1 -
lbmpy_walberla/walberla_lbm_generation.py | 153 +++----------------
2 files changed, 23 insertions(+), 131 deletions(-)
diff --git a/lbmpy_walberla/templates/LatticeModel.tmpl.h b/lbmpy_walberla/templates/LatticeModel.tmpl.h
index e3246f3..e76e2dd 100644
--- a/lbmpy_walberla/templates/LatticeModel.tmpl.h
+++ b/lbmpy_walberla/templates/LatticeModel.tmpl.h
@@ -101,7 +101,6 @@ public:
static const real_t wInv[{{Q}}];
static const bool compressible = {% if compressible %}true{% else %}false{% endif %};
- static const int equilibriumAccuracyOrder = {{equilibrium_order}};
class Sweep
{
diff --git a/lbmpy_walberla/walberla_lbm_generation.py b/lbmpy_walberla/walberla_lbm_generation.py
index c5d36fc..0fe73b6 100644
--- a/lbmpy_walberla/walberla_lbm_generation.py
+++ b/lbmpy_walberla/walberla_lbm_generation.py
@@ -1,14 +1,16 @@
+import warnings
+
import numpy as np
import sympy as sp
from jinja2 import Environment, PackageLoader, Template
from sympy.tensor import IndexedBase
+import pystencils as ps
from lbmpy.creationfunctions import create_lb_update_rule, update_with_default_parameters
-from lbmpy.fieldaccess import StreamPullTwoFieldsAccessor, CollideOnlyInplaceAccessor
+from lbmpy.fieldaccess import CollideOnlyInplaceAccessor, StreamPullTwoFieldsAccessor
from lbmpy.relaxationrates import relaxation_rate_scaling
from lbmpy.stencils import get_stencil
-from lbmpy.updatekernels import create_stream_pull_only_kernel, create_lbm_kernel
-import pystencils as ps
+from lbmpy.updatekernels import create_lbm_kernel, create_stream_pull_only_kernel
from pystencils import AssignmentCollection, create_kernel
from pystencils.astnodes import SympyAssignment
from pystencils.backends.cbackend import CBackend, CustomSympyPrinter, get_headers
@@ -24,39 +26,13 @@ cpp_printer = CustomSympyPrinter()
REFINEMENT_SCALE_FACTOR = sp.Symbol("level_scale_factor")
-def generate_lattice_model2(generation_context, class_name, collision_rule, refinement_scaling=None,
- **create_kernel_params):
-
- # usually a numpy layout is chosen by default i.e. xyzf - which is bad for waLBerla where at least the spatial
- # coordinates should be ordered in reverse direction i.e. zyx
- params, opt_params = update_with_default_parameters({}, {'field_layout': 'fzyx'})
-
- is_float = not generation_context.double_accuracy
- dtype = np.float32 if is_float else np.float64
- lb_method = collision_rule.method
+def __lattice_model(generation_context, class_name, lb_method, stream_collide_ast, collide_ast, stream_ast,
+ refinement_scaling):
stencil_name = get_stencil_name(lb_method.stencil)
if not stencil_name:
raise ValueError("lb_method uses a stencil that is not supported in waLBerla")
-
- q = len(lb_method.stencil)
-
- create_kernel_params = default_create_kernel_parameters(generation_context, create_kernel_params)
- if create_kernel_params['target'] == 'gpu':
- raise ValueError("Lattice Models can only be generated for CPUs. To generate LBM on GPUs use sweeps directly")
-
- src_field, dst_field = ps.fields("pdfs({q}), pdfs_tmp({q})".format(q=q), layout='fzyx')
-
- stream_collide_update_rule = create_lbm_kernel(collision_rule, src_field, dst_field, StreamPullTwoFieldsAccessor())
- stream_collide_ast = create_kernel(stream_collide_update_rule, **create_kernel_params)
- stream_collide_ast.function_name = 'kernel_streamCollide'
-
- collide_update_rule = create_lbm_kernel(collision_rule, src_field, dst_field, CollideOnlyInplaceAccessor())
- collide_ast = create_kernel(collide_update_rule, **create_kernel_params)
- collide_ast.function_name = 'kernel_collide'
-
- stream_update_rule = create_stream_pull_only_kernel(lb_method.stencil, None, 'pdfs', 'pdfs_tmp', 'fzyx', dtype)
- stream_ast = create_kernel(stream_update_rule, **create_kernel_params)
- stream_ast.function_name = 'kernel_stream'
+ is_float = not generation_context.double_accuracy
+ dtype = np.float32 if is_float else np.float64
vel_symbols = lb_method.conserved_quantity_computation.first_order_moment_symbols
rho_sym = sp.Symbol("rho")
@@ -99,8 +75,6 @@ def generate_lattice_model2(generation_context, class_name, collision_rule, refi
'weights': ",".join(str(w.evalf()) + constant_suffix for w in lb_method.weights),
'inverse_weights': ",".join(str((1/w).evalf()) + constant_suffix for w in lb_method.weights),
- 'equilibrium_order': params['equilibrium_order'],
-
'equilibrium_from_direction': stencil_switch_statement(lb_method.stencil, equilibrium),
'symmetric_equilibrium_from_direction': stencil_switch_statement(lb_method.stencil, symmetric_equilibrium),
'asymmetric_equilibrium_from_direction': stencil_switch_statement(lb_method.stencil, asymmetric_equilibrium),
@@ -129,124 +103,43 @@ def generate_lattice_model2(generation_context, class_name, collision_rule, refi
header = env.get_template('LatticeModel.tmpl.h').render(**jinja_context)
source = env.get_template('LatticeModel.tmpl.cpp').render(**jinja_context)
- source_extension = "cpp" if create_kernel_params.get("target", "cpu") == "cpu" else "cu"
generation_context.write_file("{}.h".format(class_name), header)
- generation_context.write_file("{}.{}".format(class_name, source_extension), source)
+ generation_context.write_file("{}.cpp".format(class_name), source)
-def generate_lattice_model(generation_context, class_name, lb_method, refinement_scaling=None, update_rule_params={},
+def generate_lattice_model(generation_context, class_name, collision_rule, refinement_scaling=None,
**create_kernel_params):
# usually a numpy layout is chosen by default i.e. xyzf - which is bad for waLBerla where at least the spatial
# coordinates should be ordered in reverse direction i.e. zyx
- optimization = {'field_layout': 'fzyx'}
+ is_float = not generation_context.double_accuracy
+ dtype = np.float32 if is_float else np.float64
+ lb_method = collision_rule.method
+
+ q = len(lb_method.stencil)
create_kernel_params = default_create_kernel_parameters(generation_context, create_kernel_params)
if create_kernel_params['target'] == 'gpu':
raise ValueError("Lattice Models can only be generated for CPUs. To generate LBM on GPUs use sweeps directly")
- is_float = not generation_context.double_accuracy
- update_rule_params['lb_method'] = lb_method
- params, opt_params = update_with_default_parameters(update_rule_params, optimization)
+ src_field = ps.Field.create_generic('pdfs', 3, dtype, index_dimensions=1, layout='fzyx', index_shape=(q,))
+ dst_field = ps.Field.create_generic('pdfs_tmp', 3, dtype, index_dimensions=1, layout='fzyx', index_shape=(q,))
- stencil_name = get_stencil_name(lb_method.stencil)
- if not stencil_name:
- raise ValueError("lb_method uses a stencil that is not supported in waLBerla")
-
- params['field_name'] = 'pdfs'
- params['temporary_field_name'] = 'pdfs_tmp'
-
- stream_collide_update_rule = create_lb_update_rule(optimization=opt_params, **params)
+ stream_collide_update_rule = create_lbm_kernel(collision_rule, src_field, dst_field, StreamPullTwoFieldsAccessor())
stream_collide_ast = create_kernel(stream_collide_update_rule, **create_kernel_params)
stream_collide_ast.function_name = 'kernel_streamCollide'
- params['kernel_type'] = 'collide_only'
- collide_update_rule = create_lb_update_rule(optimization=opt_params, **params)
+ collide_update_rule = create_lbm_kernel(collision_rule, src_field, dst_field, CollideOnlyInplaceAccessor())
collide_ast = create_kernel(collide_update_rule, **create_kernel_params)
collide_ast.function_name = 'kernel_collide'
- dtype = np.float32 if is_float else np.float64
- stream_update_rule = create_stream_pull_only_kernel(lb_method.stencil, src_field_name=params['field_name'],
- dst_field_name=params['temporary_field_name'],
- generic_field_type=dtype,
- generic_layout=opt_params['field_layout'])
+ stream_update_rule = create_stream_pull_only_kernel(lb_method.stencil, None, 'pdfs', 'pdfs_tmp', 'fzyx', dtype)
stream_ast = create_kernel(stream_update_rule, **create_kernel_params)
stream_ast.function_name = 'kernel_stream'
-
- vel_symbols = lb_method.conserved_quantity_computation.first_order_moment_symbols
- rho_sym = sp.Symbol("rho")
- pdfs_sym = sp.symbols("f_:%d" % (len(lb_method.stencil),))
- vel_arr_symbols = [IndexedBase(sp.Symbol('u'), shape=(1,))[i] for i in range(len(vel_symbols))]
- momentum_density_symbols = [sp.Symbol("md_%d" % (i,)) for i in range(len(vel_symbols))]
-
- equilibrium = lb_method.get_equilibrium()
- equilibrium = equilibrium.new_with_substitutions({a: b for a, b in zip(vel_symbols, vel_arr_symbols)})
- _, _, equilibrium = add_types(equilibrium.main_assignments, "float32" if is_float else "float64", False)
- equilibrium = sp.Matrix([e.rhs for e in equilibrium])
-
- symmetric_equilibrium = get_symmetric_part(equilibrium, vel_arr_symbols)
- asymmetric_equilibrium = sp.expand(equilibrium - symmetric_equilibrium)
-
- force_model = lb_method.force_model
- macroscopic_velocity_shift = None
- if force_model:
- if hasattr(force_model, 'macroscopic_velocity_shift'):
- macroscopic_velocity_shift = [expression_to_code(e, "lm.", ["rho"])
- for e in force_model.macroscopic_velocity_shift(rho_sym)]
-
- cqc = lb_method.conserved_quantity_computation
-
- eq_input_from_input_eqs = cqc.equilibrium_input_equations_from_init_values(sp.Symbol("rho_in"), vel_arr_symbols)
- density_velocity_setter_macroscopic_values = equations_to_code(eq_input_from_input_eqs, dtype=dtype,
- variables_without_prefix=['rho_in', 'u'])
- momentum_density_getter = cqc.output_equations_from_pdfs(pdfs_sym, {'density': rho_sym,
- 'momentum_density': momentum_density_symbols})
- constant_suffix = "f" if is_float else ""
-
- required_headers = get_headers(stream_collide_ast)
-
- jinja_context = {
- 'class_name': class_name,
- 'stencil_name': stencil_name,
- 'D': lb_method.dim,
- 'Q': len(lb_method.stencil),
- 'compressible': lb_method.conserved_quantity_computation.compressible,
- 'weights': ",".join(str(w.evalf()) + constant_suffix for w in lb_method.weights),
- 'inverse_weights': ",".join(str((1/w).evalf()) + constant_suffix for w in lb_method.weights),
+ __lattice_model(generation_context, class_name, lb_method, stream_collide_ast, collide_ast, stream_ast,
+ refinement_scaling)
- 'equilibrium_order': params['equilibrium_order'],
- 'equilibrium_from_direction': stencil_switch_statement(lb_method.stencil, equilibrium),
- 'symmetric_equilibrium_from_direction': stencil_switch_statement(lb_method.stencil, symmetric_equilibrium),
- 'asymmetric_equilibrium_from_direction': stencil_switch_statement(lb_method.stencil, asymmetric_equilibrium),
- 'equilibrium': [cpp_printer.doprint(e) for e in equilibrium],
-
- 'macroscopic_velocity_shift': macroscopic_velocity_shift,
- 'density_getters': equations_to_code(cqc.output_equations_from_pdfs(pdfs_sym, {"density": rho_sym}),
- variables_without_prefix=[e.name for e in pdfs_sym], dtype=dtype),
- 'momentum_density_getter': equations_to_code(momentum_density_getter, variables_without_prefix=pdfs_sym,
- dtype=dtype),
- 'density_velocity_setter_macroscopic_values': density_velocity_setter_macroscopic_values,
-
- 'refinement_scaling': refinement_scaling,
-
- 'stream_collide_kernel': KernelInfo(stream_collide_ast, ['pdfs_tmp'], [('pdfs', 'pdfs_tmp')], []),
- 'collide_kernel': KernelInfo(collide_ast, [], [], []),
- 'stream_kernel': KernelInfo(stream_ast, ['pdfs_tmp'], [('pdfs', 'pdfs_tmp')], []),
- 'target': 'cpu',
- 'namespace': 'lbm',
- 'headers': required_headers,
- }
-
- env = Environment(loader=PackageLoader('lbmpy_walberla'))
- add_pystencils_filters_to_jinja_env(env)
-
- header = env.get_template('LatticeModel.tmpl.h').render(**jinja_context)
- source = env.get_template('LatticeModel.tmpl.cpp').render(**jinja_context)
-
- source_extension = "cpp" if create_kernel_params.get("target", "cpu") == "cpu" else "cu"
- generation_context.write_file("{}.h".format(class_name), header)
- generation_context.write_file("{}.{}".format(class_name, source_extension), source)
class RefinementScaling:
--
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