From ea799b0932e3db9dfcb4a0648fc09da8a7b38e85 Mon Sep 17 00:00:00 2001
From: Dominic Kempf <dominic.kempf@iwr.uni-heidelberg.de>
Date: Tue, 4 Apr 2017 14:18:20 +0200
Subject: [PATCH] Refactor treatment of vectorized sumfact kernels
---
python/dune/perftool/loopy/symbolic.py | 11 +-
python/dune/perftool/sumfact/accumulation.py | 23 +-
python/dune/perftool/sumfact/basis.py | 13 +-
python/dune/perftool/sumfact/realization.py | 38 +--
python/dune/perftool/sumfact/symbolic.py | 265 +++++++++++++++---
python/dune/perftool/sumfact/vectorization.py | 45 +--
python/setup.py | 2 +-
7 files changed, 294 insertions(+), 103 deletions(-)
diff --git a/python/dune/perftool/loopy/symbolic.py b/python/dune/perftool/loopy/symbolic.py
index a0a7de01..a592c461 100644
--- a/python/dune/perftool/loopy/symbolic.py
+++ b/python/dune/perftool/loopy/symbolic.py
@@ -3,7 +3,7 @@
Use this module to insert pymbolic nodes and the likes.
"""
from dune.perftool.error import PerftoolError
-from dune.perftool.sumfact.symbolic import SumfactKernel
+from dune.perftool.sumfact.symbolic import SumfactKernel, VectorizedSumfactKernel
from pymbolic.mapper.substitutor import make_subst_func
import loopy as lp
@@ -111,6 +111,15 @@ lp.symbolic.DependencyMapper.map_sumfact_kernel = dependency_map_sumfact_kernel
lp.target.c.codegen.expression.ExpressionToCExpressionMapper.map_sumfact_kernel = needs_resolution
lp.type_inference.TypeInferenceMapper.map_sumfact_kernel = needs_resolution
+# VectorizedSumfactKernel node
+lp.symbolic.IdentityMapper.map_vectorized_sumfact_kernel = identity_map_sumfact_kernel
+lp.symbolic.SubstitutionMapper.map_vectorized_sumfact_kernel = lp.symbolic.SubstitutionMapper.map_variable
+lp.symbolic.WalkMapper.map_vectorized_sumfact_kernel = walk_map_sumfact_kernel
+lp.symbolic.StringifyMapper.map_vectorized_sumfact_kernel = stringify_map_sumfact_kernel
+lp.symbolic.DependencyMapper.map_vectorized_sumfact_kernel = dependency_map_sumfact_kernel
+lp.target.c.codegen.expression.ExpressionToCExpressionMapper.map_vectorized_sumfact_kernel = needs_resolution
+lp.type_inference.TypeInferenceMapper.map_vectorized_sumfact_kernel = needs_resolution
+
# FusedMultiplyAdd node
lp.symbolic.IdentityMapper.map_fused_multiply_add = identity_map_fused_multiply_add
lp.symbolic.WalkMapper.map_fused_multiply_add = walk_map_fused_multiply_add
diff --git a/python/dune/perftool/sumfact/accumulation.py b/python/dune/perftool/sumfact/accumulation.py
index 56ad22cb..d77f8145 100644
--- a/python/dune/perftool/sumfact/accumulation.py
+++ b/python/dune/perftool/sumfact/accumulation.py
@@ -103,24 +103,24 @@ def generate_accumulation_instruction(visitor, accterm, measure, subdomain_id):
)
from dune.perftool.sumfact.vectorization import attach_vectorization_info
- sf = attach_vectorization_info(sf)
+ vsf = attach_vectorization_info(sf)
# Make sure we have a buffer that we can set up the input with
- buffer = sf.buffer
+ buffer = vsf.buffer
if buffer is None:
buffer = get_counted_variable("buffer")
- vectag = frozenset({"gradvec"}) if sf.index is not None else frozenset()
+ vectag = frozenset({"gradvec"}) if vsf.vectorized else frozenset()
temp = get_buffer_temporary(buffer,
- shape=sf.quadrature_shape,
- dim_tags=sf.quadrature_dimtags,
- name=sf.input,
+ shape=vsf.quadrature_shape,
+ dim_tags=vsf.quadrature_dimtags,
+ name=vsf.input,
)
# Those input fields, that are padded need to be set to zero
# in order to do a horizontal_add later on
- for pad in sf.padding:
+ for pad in vsf.padding:
assignee = prim.Subscript(prim.Variable(temp),
tuple(prim.Variable(i) for i in quadrature_inames()) + (pad,))
instruction(assignee=assignee,
@@ -156,7 +156,7 @@ def generate_accumulation_instruction(visitor, accterm, measure, subdomain_id):
# Issue an instruction in the quadrature loop that fills the buffer
# with the evaluation of the contribution at all quadrature points
assignee = prim.Subscript(prim.Variable(temp),
- tuple(prim.Variable(i) for i in quadrature_inames()) + sf.vec_index)
+ tuple(prim.Variable(i) for i in quadrature_inames()) + vsf.vec_index_tuple(sf))
contrib_dep = instruction(assignee=assignee,
expression=expression,
forced_iname_deps=frozenset(quadrature_inames() + visitor.inames),
@@ -174,7 +174,7 @@ def generate_accumulation_instruction(visitor, accterm, measure, subdomain_id):
within_inames=frozenset(visitor.inames))})
inames = tuple(accum_iname((accterm.argument.restriction, restriction), mat.rows, i)
- for i, mat in enumerate(sf.matrix_sequence))
+ for i, mat in enumerate(vsf.matrix_sequence))
# Collect the lfs and lfs indices for the accumulate call
test_lfs.index = flatten_index(tuple(prim.Variable(i) for i in inames),
@@ -196,12 +196,13 @@ def generate_accumulation_instruction(visitor, accterm, measure, subdomain_id):
# Add a sum factorization kernel that implements the multiplication
# with the test function (stage 3)
from dune.perftool.sumfact.realization import realize_sum_factorization_kernel
- result, insn_dep = realize_sum_factorization_kernel(sf.copy(insn_dep=insn_dep))
+ result, insn_dep = realize_sum_factorization_kernel(vsf.copy(insn_dep=insn_dep))
# Determine the expression to accumulate with. This depends on the vectorization strategy!
result = prim.Subscript(result, tuple(prim.Variable(i) for i in inames))
vecinames = ()
- if sf.index is not None:
+ # TODO: evaluate whether the following line would be okay with vsf.vectorized
+ if vsf.vec_index(sf) is not None:
iname = accum_iname((accterm.argument.restriction, restriction), 4, "vec")
vecinames = (iname,)
transform(lp.tag_inames, [(iname, "vec")])
diff --git a/python/dune/perftool/sumfact/basis.py b/python/dune/perftool/sumfact/basis.py
index b4861eeb..3630d9f2 100644
--- a/python/dune/perftool/sumfact/basis.py
+++ b/python/dune/perftool/sumfact/basis.py
@@ -85,16 +85,16 @@ def pymbolic_coefficient_gradient(element, restriction, component, coeff_func, v
)
from dune.perftool.sumfact.vectorization import attach_vectorization_info
- sf = attach_vectorization_info(sf)
+ vsf = attach_vectorization_info(sf)
- if i != sf.index:
+ if i != vsf.vec_index(sf):
direct_indexing_is_possible = False
# Add a sum factorization kernel that implements the
# evaluation of the gradients of basis functions at quadrature
# points (stage 1)
from dune.perftool.sumfact.realization import realize_sum_factorization_kernel
- var, insn_dep = realize_sum_factorization_kernel(sf)
+ var, insn_dep = realize_sum_factorization_kernel(vsf)
buffers.append(var)
@@ -132,17 +132,16 @@ def pymbolic_coefficient(element, restriction, component, coeff_func, visitor):
component=component,
)
- # TODO: Move this away!
from dune.perftool.sumfact.vectorization import attach_vectorization_info
- sf = attach_vectorization_info(sf)
+ vsf = attach_vectorization_info(sf)
# Add a sum factorization kernel that implements the evaluation of
# the basis functions at quadrature points (stage 1)
from dune.perftool.sumfact.realization import realize_sum_factorization_kernel
- var, _ = realize_sum_factorization_kernel(sf)
+ var, _ = realize_sum_factorization_kernel(vsf)
return prim.Subscript(var,
- tuple(prim.Variable(i) for i in quadrature_inames()) + sf.vec_index
+ tuple(prim.Variable(i) for i in quadrature_inames()) + vsf.vec_index_tuple(sf)
), visitor.indices
diff --git a/python/dune/perftool/sumfact/realization.py b/python/dune/perftool/sumfact/realization.py
index dcc06af6..e2e375a2 100644
--- a/python/dune/perftool/sumfact/realization.py
+++ b/python/dune/perftool/sumfact/realization.py
@@ -37,11 +37,13 @@ def realize_sum_factorization_kernel(sf, **kwargs):
if get_global_context_value("dry_run", False):
return sf, sf.insn_dep
else:
- _realize_input(sf)
return _realize_sum_factorization_kernel(sf, **kwargs)
-def _realize_input(sf):
+@generator_factory(item_tags=("sumfactkernel",),
+ context_tags=("kernel",),
+ cache_key_generator=lambda s, **kw: s.cache_key)
+def _realize_sum_factorization_kernel(sf):
# Set up the input for stage 1
if sf.stage == 1 and not get_option("fastdg"):
assert sf.coeff_func
@@ -53,22 +55,24 @@ def _realize_input(sf):
name=sf.input
)
- # Write initial coefficients into buffer
- lfs = name_lfs(sf.element, sf.restriction, sf.component)
- basisiname = sumfact_iname(name_lfs_bound(lfs), "basis")
- container = sf.coeff_func(sf.restriction)
- coeff = pymbolic_coefficient(container, lfs, basisiname)
- assignee = prim.Subscript(prim.Variable(input_setup), (prim.Variable(basisiname),) + sf.vec_index)
- instruction(assignee=assignee,
- expression=coeff,
- depends_on=sf.insn_dep,
- )
-
+ def _write_input(inputsf):
+ # Write initial coefficients into buffer
+ lfs = name_lfs(inputsf.element, inputsf.restriction, inputsf.component)
+ basisiname = sumfact_iname(name_lfs_bound(lfs), "basis")
+ container = inputsf.coeff_func(inputsf.restriction)
+ coeff = pymbolic_coefficient(container, lfs, basisiname)
+ assignee = prim.Subscript(prim.Variable(input_setup), (prim.Variable(basisiname),) + sf.vec_index_tuple(inputsf))
+ instruction(assignee=assignee,
+ expression=coeff,
+ depends_on=inputsf.insn_dep,
+ )
+
+ if sf.vectorized:
+ for inputsf in sf.kernels:
+ _write_input(inputsf)
+ else:
+ _write_input(sf)
-@generator_factory(item_tags=("sumfactkernel",),
- context_tags=("kernel",),
- cache_key_generator=lambda s, **kw: s.cache_key)
-def _realize_sum_factorization_kernel(sf):
# Add a dependency on the input variable
insn_dep = sf.insn_dep
if sf.input:
diff --git a/python/dune/perftool/sumfact/symbolic.py b/python/dune/perftool/sumfact/symbolic.py
index fb03a88d..e0f862b0 100644
--- a/python/dune/perftool/sumfact/symbolic.py
+++ b/python/dune/perftool/sumfact/symbolic.py
@@ -1,13 +1,22 @@
""" A pymbolic node representing a sum factorization kernel """
-from pytools import ImmutableRecord
+from dune.perftool.options import get_option
+from dune.perftool.generation import get_counted_variable
+from dune.perftool.sumfact.tabulation import BasisTabulationMatrixArray
+
+from pytools import ImmutableRecord, product
import pymbolic.primitives as prim
import loopy as lp
+import frozendict
import inspect
-class SumfactKernel(ImmutableRecord, prim.Variable):
+class SumfactKernelBase(object):
+ pass
+
+
+class SumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable):
def __init__(self,
matrix_sequence=None,
buffer=None,
@@ -16,8 +25,6 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
restriction=0,
within_inames=(),
input=None,
- padding=frozenset(),
- index=None,
insn_dep=frozenset(),
coeff_func=None,
element=None,
@@ -92,7 +99,7 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
component: The treepath to the correct component of above element
accumvar: The accumulation variable to accumulate into
"""
- # Check the input and apply defaults where necessary
+ # Assert the inputs!
assert isinstance(matrix_sequence, tuple)
from dune.perftool.sumfact.tabulation import BasisTabulationMatrixBase
assert all(isinstance(m, BasisTabulationMatrixBase) for m in matrix_sequence)
@@ -163,31 +170,25 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
@property
def vectorized(self):
- return next(iter(self.matrix_sequence)).vectorized
+ return False
@property
def transposed(self):
return next(iter(self.matrix_sequence)).transpose
- @property
- def vec_index(self):
- """ A tuple with the vector index
+ def vec_index(self, sf):
+ """ Map an unvectorized sumfact kernel object to its position
+ in the vectorized kernel
+ """
+ return None
- Defaults to the empty tuple to allow addition to any
- existing index tuple """
- if self.index is not None:
- return (self.index,)
- else:
- return ()
+ def vec_index_tuple(self, sf):
+ return ()
@property
def flat_input_shape(self):
""" The 'flat' input tensor shape """
- from pytools import product
- shape = (product(mat.cols for mat in self.matrix_sequence),)
- if self.vectorized:
- shape = shape + (4,)
- return shape
+ return (product(mat.cols for mat in self.matrix_sequence),)
@property
def quadrature_shape(self):
@@ -196,12 +197,9 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
Takes into account the lower dimensionality of faces and vectorization.
"""
if self.transposed:
- shape = tuple(mat.cols for mat in self.matrix_sequence if mat.face is None)
+ return tuple(mat.cols for mat in self.matrix_sequence if mat.face is None)
else:
- shape = tuple(mat.rows for mat in self.matrix_sequence if mat.face is None)
- if self.vectorized:
- shape = shape + (4,)
- return shape
+ return tuple(mat.rows for mat in self.matrix_sequence if mat.face is None)
@property
def quadrature_dimtags(self):
@@ -210,8 +208,6 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
Takes into account the lower dimensionality of faces and vectorization.
"""
tags = ["f"] * len(self.quadrature_shape)
- if self.vectorized:
- tags[-1] = 'c'
return ",".join(tags)
@property
@@ -221,8 +217,6 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
Takes into account vectorization.
"""
shape = tuple(mat.rows for mat in self.matrix_sequence)
- if self.vectorized:
- shape = shape + (4,)
return shape
@property
@@ -232,8 +226,6 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
Takes into account vectorization.
"""
tags = ["f"] * len(self.dof_shape)
- if self.vectorized:
- tags[-1] = 'vec'
return ",".join(tags)
@property
@@ -251,13 +243,216 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
return self.dof_dimtags
def output_to_pymbolic(self, name):
- if self.vectorized:
- return lp.TaggedVariable(name, "vector")
- else:
- return lp.TaggedVariable(name, "sumfac")
+ return lp.TaggedVariable(name, "sumfac")
+
+ #
+ # Define properties for conformity with the interface of VectorizedSumfactKernel
+ #
+
+ @property
+ def padding(self):
+ return set()
# Extract the argument list and store it on the class. This needs to be done
# outside of the class because the SumfactKernel class object needs to be fully
# initialized in order to extract the information from __init__.
SumfactKernel.init_arg_names = tuple(inspect.getargspec(SumfactKernel.__init__)[0][1:])
+
+
+class VectorizedSumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable):
+ def __init__(self,
+ kernels=None,
+ vector_width=get_option('max_vector_width'),
+ buffer=None,
+ input=None,
+ insn_dep=frozenset(),
+ ):
+ # Assert the input data structure
+ assert isinstance(kernels, tuple)
+ assert all(isinstance(k, SumfactKernel) for k in kernels)
+
+ # Assert all the properties that need to be the same across all subkernels
+ assert len(set(k.stage for k in kernels)) == 1
+ assert len(set(k.length for k in kernels)) == 1
+ assert len(set(k.restriction for k in kernels)) == 1
+ assert len(set(k.within_inames for k in kernels)) == 1
+
+ # Assert properties of the matrix sequence of the underlying kernels
+ for i in range(kernels[0].length):
+ assert len(set(tuple(k.matrix_sequence[i].rows for k in kernels))) == 1
+ assert len(set(tuple(k.matrix_sequence[i].cols for k in kernels))) == 1
+ assert len(set(tuple(k.matrix_sequence[i].face for k in kernels))) == 1
+ assert len(set(tuple(k.matrix_sequence[i].transpose for k in kernels))) == 1
+
+ # Join the instruction dependencies of all subkernels
+ insn_dep = insn_dep.union(k.insn_dep for k in kernels)
+
+ # We currently assume that all subkernels are consecutive, 0-based within the vector
+ assert None not in kernels
+
+ ImmutableRecord.__init__(self,
+ kernels=kernels,
+ vector_width=vector_width,
+ buffer=buffer,
+ input=input,
+ insn_dep=insn_dep,
+ )
+
+ prim.Variable.__init__(self, "VecSUMFAC")
+
+ def __getinitargs__(self):
+ return (self.kernels, self.vector_width, self.buffer, self.input, self.insn_dep)
+
+ def stringifier(self):
+ return lp.symbolic.StringifyMapper
+
+ mapper_method = "map_vectorized_sumfact_kernel"
+
+ init_arg_names = ("kernels", "vector_width", "buffer", "input", "insn_dep")
+
+ #
+ # Some cache key definitions
+ # Watch out for the documentation to see which key is used unter what circumstances
+ #
+
+ @property
+ def cache_key(self):
+ """ The cache key that can be used in generation magic
+ Any two sum factorization kernels having the same cache_key
+ are realized simulatenously!
+ """
+ return (self.matrix_sequence, self.restriction, self.stage, self.buffer)
+
+ @property
+ def input_key(self):
+ """ A cache key for the input coefficients
+ Any two sum factorization kernels having the same input_key
+ work on the same input coefficient (and are suitable for simultaneous
+ treatment because of that)
+ """
+ return (self.restriction, self.stage, self.coeff_func, self.element, self.component, self.accumvar)
+
+ #
+ # Deduce all data fields of normal sum factorization kernels from the underlying kernels
+ #
+
+ @property
+ def matrix_sequence(self):
+ return tuple(BasisTabulationMatrixArray(rows=self.kernels[0].matrix_sequence[i].rows,
+ cols=self.kernels[0].matrix_sequence[i].cols,
+ transpose=self.kernels[0].matrix_sequence[i].transpose,
+ face=self.kernels[0].matrix_sequence[i].face,
+ derivative=tuple(k.matrix_sequence[i].derivative for k in self.kernels),
+ )
+ for i in range(self.length))
+
+ @property
+ def stage(self):
+ return self.kernels[0].stage
+
+ @property
+ def restriction(self):
+ return self.kernels[0].restriction
+
+ @property
+ def within_inames(self):
+ return self.kernels[0].within_inames
+
+ @property
+ def coeff_func(self):
+ return tuple(k.coeff_func for k in self.kernels)
+
+ @property
+ def element(self):
+ return tuple(k.element for k in self.kernels)
+
+ @property
+ def component(self):
+ return tuple(k.component for k in self.kernels)
+
+ @property
+ def accumvar(self):
+ return tuple(k.accumvar for k in self.kernels)
+
+ #
+ # Define some properties only needed for this one
+ #
+
+ @property
+ def padding(self):
+ return set(range(self.vector_width)) - set(range(len(self.kernels)))
+
+ #
+ # Define the same properties the normal SumfactKernel defines
+ #
+
+ @property
+ def cache_key(self):
+ return tuple(k.cache_key for k in self.kernels)
+
+ @property
+ def input_key(self):
+ return tuple(k.input_key for k in self.kernels)
+
+ @property
+ def length(self):
+ return self.kernels[0].length
+
+ @property
+ def vectorized(self):
+ return True
+
+ @property
+ def transposed(self):
+ return self.kernels[0].transposed
+
+ def vec_index(self, sf):
+ return self.kernels.index(sf)
+
+ def vec_index_tuple(self, sf):
+ return (self.vec_index(sf),)
+
+ @property
+ def flat_input_shape(self):
+ return (product(mat.cols for mat in self.matrix_sequence), 4)
+
+ @property
+ def quadrature_shape(self):
+ if self.transposed:
+ return tuple(mat.cols for mat in self.matrix_sequence if mat.face is None) + (4,)
+ else:
+ return tuple(mat.rows for mat in self.matrix_sequence if mat.face is None) + (4,)
+
+ @property
+ def quadrature_dimtags(self):
+ tags = ["f"] * len(self.quadrature_shape)
+ tags[-1] = 'c'
+ return ",".join(tags)
+
+ @property
+ def dof_shape(self):
+ return tuple(mat.rows for mat in self.matrix_sequence) + (4,)
+
+ @property
+ def dof_dimtags(self):
+ tags = ["f"] * len(self.dof_shape)
+ tags[-1] = 'vec'
+ return ",".join(tags)
+
+ @property
+ def output_shape(self):
+ if self.stage == 1:
+ return self.quadrature_shape
+ else:
+ return self.dof_shape
+
+ @property
+ def output_dimtags(self):
+ if self.stage == 1:
+ return self.quadrature_dimtags
+ else:
+ return self.dof_dimtags
+
+ def output_to_pymbolic(self, name):
+ return lp.TaggedVariable(name, "vector")
diff --git a/python/dune/perftool/sumfact/vectorization.py b/python/dune/perftool/sumfact/vectorization.py
index ea058464..84c7e615 100644
--- a/python/dune/perftool/sumfact/vectorization.py
+++ b/python/dune/perftool/sumfact/vectorization.py
@@ -1,6 +1,6 @@
""" Sum factorization vectorization """
-from dune.perftool.loopy.symbolic import SumfactKernel
+from dune.perftool.loopy.symbolic import SumfactKernel, VectorizedSumfactKernel
from dune.perftool.generation import (generator_factory,
get_counted_variable,
get_global_context_value,
@@ -57,39 +57,22 @@ def horizontal_vectorization_strategy(sumfacts):
if sumf.preferred_position is None:
position_mapping[sumf] = available.pop()
- # Enable vectorization strategy:
- inp = get_counted_variable("joined_input")
- buf = get_counted_variable("joined_buffer")
-
- # Collect the large matrices!
- large_matrix_sequence = []
- for i in range(len(next(iter(sumfacts)).matrix_sequence)):
- # Assert that the matrices of all sum factorizations have the same size
- assert len(set(tuple(sf.matrix_sequence[i].rows for sf in sumfacts))) == 1
- assert len(set(tuple(sf.matrix_sequence[i].cols for sf in sumfacts))) == 1
-
- # Collect the derivative information
- derivative = [False] * 4
- for sf in sumfacts:
- derivative[position_mapping[sf]] = sf.matrix_sequence[i].derivative
-
- large = BasisTabulationMatrixArray(rows=next(iter(sumfacts)).matrix_sequence[i].rows,
- cols=next(iter(sumfacts)).matrix_sequence[i].cols,
- transpose=next(iter(sumfacts)).matrix_sequence[i].transpose,
- derivative=tuple(derivative),
- face=next(iter(sumfacts)).matrix_sequence[i].face,
- )
- large_matrix_sequence.append(large)
+ # Store the kernels as tuple according to their positions
+ sorting = [None] * len(position_mapping)
+ for sf, pos in position_mapping.items():
+ sorting[pos] = sf
+ kernels = tuple(sorting)
+
+ buffer = get_counted_variable("joined_buffer")
+ input = get_counted_variable("joined_input")
for sumf in sumfacts:
_cache_vectorization_info(sumf,
- sumf.copy(matrix_sequence=tuple(large_matrix_sequence),
- buffer=buf,
- input=inp,
- index=position_mapping[sumf],
- padding=frozenset(available),
- insn_dep=frozenset().union(sf.insn_dep for sf in sumfacts),
- )
+ VectorizedSumfactKernel(kernels=kernels,
+ vector_width=4,
+ buffer=buffer,
+ input=input,
+ )
)
else:
# Disable vectorization strategy
diff --git a/python/setup.py b/python/setup.py
index 00f6f75e..cfc85288 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -39,7 +39,7 @@ setup(name='dune.perftool',
'dune.perftool.ufl',
'dune.perftool.ufl.transformations',
],
- install_requires=['dune.testtools', 'sympy'],
+ install_requires=['dune.testtools', 'sympy', 'frozendict'],
tests_require=['pytest'],
cmdclass={'test': PyTest},
entry_points = {
--
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