def optimize_chain_inner(dot_shapes):
print "In optimize_chain_inner! Looking to optimize these dot calls/shapes ", dot_shapes
dot_call_dfg_nodes = {dotcall_ni.dfg_node: shape for (dotcall_ni, shape) in dot_shapes.items()}
for (dotcall_ni, shape) in dot_shapes.iteritems():
if len(shape) == 2:
chain_for(dotcall_ni.dfg_node, shape, dot_call_dfg_nodes)
# Remove incomplete chains
for d in subchains:
done_chains.pop(d, None)
print "And here are the chains: "
pprint(done_chains)
for (end, inputs) in done_chains.iteritems():
for (source, shape) in inputs:
assumptions[source] = "{1}.shape == %s" % str(shape)
chain_inputs = [inp for (inp, shp) in inputs]
chain_shapes = [shp for (inp, shp) in inputs]
optimal_order = matrix_chain.matrix_chain_tree(chain_shapes)
print "Optimal order", optimal_order
placeholders = ["{%i}" % i for i in range(len(chain_inputs))]
new_chain_expr = mult_order_to_expr(placeholders, optimal_order, end.ast_node.func.as_string())
nodes_to_replace, edges_to_replace = dfg.subgraph_between(chain_inputs, end)
cprint("Going to replace a subgraph, saved as 'last_subgraph', with %s" % new_chain_expr, "red")
mainvar("last_subgraph", nodes_to_replace)
mainvar("input_nodes", chain_inputs)
mainvar("new_chain_expr", new_chain_expr)
mainvar("assumptions", assumptions)
if not DRYRUN:
modcode.replace_subgraph_and_code(dfg, nodes_to_replace, chain_inputs, new_chain_expr, assumptions)
def cythonify_if_numeric(func_and_var_info):
try:
print "cythonify_if_numeric called with", func_and_var_info
var_types = {}
for (ni, val) in func_and_var_info.items():
if isinstance(val, tuple): # It's variable info
typ, dtype, shape = val
if not is_numerical_type(typ, dtype):
cprint("Can't do numerical optimization of loop because of non-numerical " +\
'var %s (%s)'%(ni.expr, str(typ)), 'red')
return False
var_types[ni.expr] = val
elif callable(val):
if not is_numerical_callable(val):
cprint("Can't do numerical optimization of loop " +\
"because of non-numerical callable " + \
str(val), 'red')
return False
loopfn, argnames, retnames = cythonify.loop_to_cython(dfg, loopnode, var_types)
__builtin__.myloopfn = loopfn
if retnames:
newexpr = (''.join([r+',' for r in retnames]) + ' = ' +
'myloopfn(' + ','.join(argnames) + ')' )
else:
newexpr = 'myloopfn(' + ','.join(argnames) + ')'
cprint("new expression is "+ newexpr, 'blue')
in_edges = dfg.get_incoming_edges(loopnode)
modcode.replace_subgraph_and_code(dfg, [loopnode], [e.n1 for e in in_edges],
newexpr, assumptions={})
except:
logger.exception("Oh crap!")
def optimize_chain_inner(dot_shapes):
print "In optimize_chain_inner! Looking to optimize these dot calls/shapes ", dot_shapes
dot_call_dfg_nodes = {
dotcall_ni.dfg_node: shape
for (dotcall_ni, shape) in dot_shapes.items()
}
for (dotcall_ni, shape) in dot_shapes.iteritems():
if len(shape) == 2:
chain_for(dotcall_ni.dfg_node, shape, dot_call_dfg_nodes)
# Remove incomplete chains
for d in subchains:
done_chains.pop(d, None)
print "And here are the chains: "
pprint(done_chains)
for (end, inputs) in done_chains.iteritems():
for (source, shape) in inputs:
assumptions[source] = "{1}.shape == %s" % str(shape)
chain_inputs = [inp for (inp, shp) in inputs]
chain_shapes = [shp for (inp, shp) in inputs]
optimal_order = matrix_chain.matrix_chain_tree(chain_shapes)
print "Optimal order", optimal_order
placeholders = ['{%i}' % i for i in range(len(chain_inputs))]
new_chain_expr = mult_order_to_expr(
placeholders, optimal_order, end.ast_node.func.as_string())
nodes_to_replace, edges_to_replace = dfg.subgraph_between(
chain_inputs, end)
cprint(
"Going to replace a subgraph, saved as 'last_subgraph', with %s"
% new_chain_expr, 'red')
mainvar('last_subgraph', nodes_to_replace)
mainvar('input_nodes', chain_inputs)
mainvar('new_chain_expr', new_chain_expr)
mainvar('assumptions', assumptions)
if not DRYRUN:
modcode.replace_subgraph_and_code(dfg, nodes_to_replace,
chain_inputs,
new_chain_expr,
assumptions)
def cythonify_if_numeric(func_and_var_info):
try:
print "cythonify_if_numeric called with", func_and_var_info
var_types = {}
for (ni, val) in func_and_var_info.items():
if isinstance(val, tuple): # It's variable info
typ, dtype, shape = val
if not is_numerical_type(typ, dtype):
cprint("Can't do numerical optimization of loop because of non-numerical " +\
'var %s (%s)'%(ni.expr, str(typ)), 'red')
return False
var_types[ni.expr] = val
elif callable(val):
if not is_numerical_callable(val):
cprint("Can't do numerical optimization of loop " +\
"because of non-numerical callable " + \
str(val), 'red')
return False
loopfn, argnames, retnames = cythonify.loop_to_cython(
dfg, loopnode, var_types)
__builtin__.myloopfn = loopfn
if retnames:
newexpr = (''.join([r + ',' for r in retnames]) + ' = ' +
'myloopfn(' + ','.join(argnames) + ')')
else:
newexpr = 'myloopfn(' + ','.join(argnames) + ')'
cprint("new expression is " + newexpr, 'blue')
in_edges = dfg.get_incoming_edges(loopnode)
modcode.replace_subgraph_and_code(dfg, [loopnode],
[e.n1 for e in in_edges],
newexpr,
assumptions={})
except:
logger.exception("Oh crap!")
