def get_typed_and_scheduled_translation_unit_uncached(
self, entrypoint, arg_to_dtype_set):
from loopy.kernel.tools import add_dtypes
from loopy.kernel import KernelState
from loopy.translation_unit import resolve_callables
program = resolve_callables(self.program)
if arg_to_dtype_set:
var_to_dtype = {}
entry_knl = program[entrypoint]
for var, dtype in arg_to_dtype_set:
if var in entry_knl.impl_arg_to_arg:
dest_name = entry_knl.impl_arg_to_arg[var].name
else:
dest_name = var
var_to_dtype[dest_name] = dtype
program = program.with_kernel(add_dtypes(entry_knl, var_to_dtype))
from loopy.type_inference import infer_unknown_types
program = infer_unknown_types(program, expect_completion=True)
if program.state < KernelState.LINEARIZED:
from loopy.preprocess import preprocess_program
program = preprocess_program(program)
from loopy.schedule import get_one_linearized_kernel
for e in program.entrypoints:
program = program.with_kernel(
get_one_linearized_kernel(program[e], program.callables_table))
return program
def get_typed_and_scheduled_kernel_uncached(self, arg_to_dtype_set):
from loopy.kernel.tools import add_dtypes
kernel = self.kernel
if arg_to_dtype_set:
var_to_dtype = {}
for var, dtype in arg_to_dtype_set:
try:
dest_name = kernel.impl_arg_to_arg[var].name
except KeyError:
dest_name = var
try:
var_to_dtype[dest_name] = dtype
except KeyError:
raise LoopyError("cannot set type for '%s': "
"no known variable/argument with that name"
% var)
kernel = add_dtypes(kernel, var_to_dtype)
from loopy.type_inference import infer_unknown_types
kernel = infer_unknown_types(kernel, expect_completion=True)
if kernel.schedule is None:
from loopy.preprocess import preprocess_kernel
kernel = preprocess_kernel(kernel)
from loopy.schedule import get_one_scheduled_kernel
kernel = get_one_scheduled_kernel(kernel)
return kernel
def get_typed_and_scheduled_kernel_uncached(self, arg_to_dtype_set):
from loopy.kernel.tools import add_dtypes
kernel = self.kernel
if arg_to_dtype_set:
var_to_dtype = {}
for var, dtype in arg_to_dtype_set:
try:
dest_name = kernel.impl_arg_to_arg[var].name
except KeyError:
dest_name = var
try:
var_to_dtype[dest_name] = dtype
except KeyError:
raise LoopyError(
"cannot set type for '%s': "
"no known variable/argument with that name" % var)
kernel = add_dtypes(kernel, var_to_dtype)
from loopy.type_inference import infer_unknown_types
kernel = infer_unknown_types(kernel, expect_completion=True)
if kernel.schedule is None:
from loopy.preprocess import preprocess_kernel
kernel = preprocess_kernel(kernel)
from loopy.schedule import get_one_scheduled_kernel
kernel = get_one_scheduled_kernel(kernel)
return kernel
def add_and_infer_dtypes(knl, dtype_dict):
processed_dtype_dict = {}
for k, v in six.iteritems(dtype_dict):
for subkey in k.split(","):
subkey = subkey.strip()
if subkey:
processed_dtype_dict[subkey] = v
knl = add_dtypes(knl, processed_dtype_dict)
from loopy.type_inference import infer_unknown_types
return infer_unknown_types(knl, expect_completion=True)
def specialize_fortran_division(t_unit):
from loopy.translation_unit import TranslationUnit, resolve_callables
from loopy.kernel.function_interface import CallableKernel
from loopy.type_inference import infer_unknown_types
assert isinstance(t_unit, TranslationUnit)
t_unit = resolve_callables(t_unit)
t_unit = infer_unknown_types(t_unit)
new_callables = {}
for name, clbl in t_unit.callables_table.items():
if isinstance(clbl, CallableKernel):
knl = clbl.subkernel
clbl = clbl.copy(subkernel=_specialize_fortran_division_for_kernel(
knl, t_unit.callables_table))
new_callables[name] = clbl
return t_unit.copy(callables_table=new_callables)
def auto_test_vs_ref(ref_knl,
ctx,
test_knl=None,
op_count=[],
op_label=[],
parameters={},
print_ref_code=False,
print_code=True,
warmup_rounds=2,
dump_binary=False,
fills_entire_output=None,
do_check=True,
check_result=None,
max_test_kernel_count=1,
quiet=False,
blacklist_ref_vendors=[]):
"""Compare results of `ref_knl` to the kernels generated by
scheduling *test_knl*.
:arg check_result: a callable with :class:`numpy.ndarray` arguments
*(result, reference_result)* returning a a tuple (class:`bool`,
message) indicating correctness/acceptability of the result
:arg max_test_kernel_count: Stop testing after this many *test_knl*
"""
import pyopencl as cl
if test_knl is None:
test_knl = ref_knl
do_check = False
if len(ref_knl.args) != len(test_knl.args):
raise LoopyError("ref_knl and test_knl do not have the same number "
"of arguments")
for i, (ref_arg, test_arg) in enumerate(zip(ref_knl.args, test_knl.args)):
if ref_arg.name != test_arg.name:
raise LoopyError(
"ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i + 1))
if ref_arg.dtype != test_arg.dtype:
raise LoopyError(
"ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i + 1))
from loopy.compiled import CompiledKernel
from loopy.target.execution import get_highlighted_code
if isinstance(op_count, (int, float)):
warn("op_count should be a list", stacklevel=2)
op_count = [op_count]
if isinstance(op_label, str):
warn("op_label should be a list", stacklevel=2)
op_label = [op_label]
from time import time
if check_result is None:
check_result = _default_check_result
if fills_entire_output is not None:
warn("fills_entire_output is deprecated",
DeprecationWarning,
stacklevel=2)
# {{{ compile and run reference code
from loopy.type_inference import infer_unknown_types
ref_knl = infer_unknown_types(ref_knl, expect_completion=True)
found_ref_device = False
ref_errors = []
from loopy.kernel.data import ImageArg
need_ref_image_support = any(
isinstance(arg, ImageArg) for arg in ref_knl.args)
for dev in _enumerate_cl_devices_for_ref_test(blacklist_ref_vendors,
need_ref_image_support):
ref_ctx = cl.Context([dev])
ref_queue = cl.CommandQueue(
ref_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
pp_ref_knl = lp.preprocess_kernel(ref_knl)
for knl in lp.generate_loop_schedules(pp_ref_knl):
ref_sched_kernel = knl
break
logger.info("{} (ref): trying {} for the reference calculation".format(
ref_knl.name, dev))
ref_compiled = CompiledKernel(ref_ctx, ref_sched_kernel)
if not quiet and print_ref_code:
print(75 * "-")
print("Reference Code:")
print(75 * "-")
print(get_highlighted_code(ref_compiled.get_code()))
print(75 * "-")
ref_kernel_info = ref_compiled.kernel_info(frozenset())
try:
ref_args, ref_arg_data = \
make_ref_args(ref_sched_kernel,
ref_kernel_info.implemented_data_info,
ref_queue, parameters)
ref_args["out_host"] = False
except cl.RuntimeError as e:
if e.code == cl.status_code.IMAGE_FORMAT_NOT_SUPPORTED:
import traceback
ref_errors.append("\n".join([
75 * "-",
"On %s:" % dev, 75 * "-",
traceback.format_exc(), 75 * "-"
]))
continue
else:
raise
found_ref_device = True
if not do_check:
break
ref_queue.finish()
logger.info("{} (ref): using {} for the reference calculation".format(
ref_knl.name, dev))
logger.info("%s (ref): run" % ref_knl.name)
ref_start = time()
if not AUTO_TEST_SKIP_RUN:
ref_evt, _ = ref_compiled(ref_queue, **ref_args)
else:
ref_evt = cl.enqueue_marker(ref_queue)
ref_queue.finish()
ref_stop = time()
ref_elapsed_wall = ref_stop - ref_start
logger.info("%s (ref): run done" % ref_knl.name)
ref_evt.wait()
ref_elapsed_event = 1e-9 * (ref_evt.profile.END -
ref_evt.profile.START)
break
if not found_ref_device:
raise LoopyError("could not find a suitable device for the "
"reference computation.\n"
"These errors were encountered:\n" +
"\n".join(ref_errors))
# }}}
# {{{ compile and run parallel code
need_check = do_check
queue = cl.CommandQueue(
ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
from loopy.kernel import KernelState
from loopy.target.pyopencl import PyOpenCLTarget
if test_knl.state not in [
KernelState.PREPROCESSED, KernelState.LINEARIZED
]:
if isinstance(test_knl.target, PyOpenCLTarget):
test_knl = test_knl.copy(target=PyOpenCLTarget(ctx.devices[0]))
test_knl = lp.preprocess_kernel(test_knl)
if not test_knl.schedule:
test_kernels = lp.generate_loop_schedules(test_knl)
else:
test_kernels = [test_knl]
test_kernel_count = 0
from loopy.type_inference import infer_unknown_types
for i, kernel in enumerate(test_kernels):
test_kernel_count += 1
if test_kernel_count > max_test_kernel_count:
break
kernel = infer_unknown_types(kernel, expect_completion=True)
compiled = CompiledKernel(ctx, kernel)
kernel_info = compiled.kernel_info(frozenset())
args = make_args(kernel, kernel_info.implemented_data_info, queue,
ref_arg_data, parameters)
args["out_host"] = False
if not quiet:
print(75 * "-")
print("Kernel #%d:" % i)
print(75 * "-")
if print_code:
print(compiled.get_highlighted_code())
print(75 * "-")
if dump_binary:
# {{{ find cl program
for name in dir(kernel_info.cl_kernels):
if name.startswith("__"):
continue
cl_kernel = getattr(kernel_info.cl_kernels, name)
cl_program = cl_kernel.get_info(cl.kernel_info.PROGRAM)
break
else:
assert False, "could not find cl_program"
# }}}
print(type(cl_program))
if hasattr(cl_program, "binaries"):
print(cl_program.binaries[0])
print(75 * "-")
logger.info("%s: run warmup" % (knl.name))
for i in range(warmup_rounds):
if not AUTO_TEST_SKIP_RUN:
compiled(queue, **args)
if need_check and not AUTO_TEST_SKIP_RUN:
for arg_desc in ref_arg_data:
if arg_desc is None:
continue
if not arg_desc.needs_checking:
continue
from pyopencl.compyte.array import as_strided
ref_ary = as_strided(
arg_desc.ref_storage_array.get(),
shape=arg_desc.ref_shape,
strides=arg_desc.ref_numpy_strides).flatten()
test_ary = as_strided(
arg_desc.test_storage_array.get(),
shape=arg_desc.test_shape,
strides=arg_desc.test_numpy_strides).flatten()
common_len = min(len(ref_ary), len(test_ary))
ref_ary = ref_ary[:common_len]
test_ary = test_ary[:common_len]
error_is_small, error = check_result(test_ary, ref_ary)
if not error_is_small:
raise AutomaticTestFailure(error)
need_check = False
events = []
queue.finish()
logger.info("%s: warmup done" % (knl.name))
logger.info("%s: timing run" % (knl.name))
timing_rounds = max(warmup_rounds, 1)
while True:
from time import time
start_time = time()
evt_start = cl.enqueue_marker(queue)
for i in range(timing_rounds):
if not AUTO_TEST_SKIP_RUN:
evt, _ = compiled(queue, **args)
events.append(evt)
else:
events.append(cl.enqueue_marker(queue))
evt_end = cl.enqueue_marker(queue)
queue.finish()
stop_time = time()
for evt in events:
evt.wait()
evt_start.wait()
evt_end.wait()
elapsed_event = (1e-9*events[-1].profile.END
- 1e-9*events[0].profile.START) \
/ timing_rounds
try:
elapsed_event_marker = ((1e-9 * evt_end.profile.START -
1e-9 * evt_start.profile.START) /
timing_rounds)
except cl.RuntimeError:
elapsed_event_marker = None
elapsed_wall = (stop_time - start_time) / timing_rounds
if elapsed_wall * timing_rounds < 0.3:
timing_rounds *= 4
else:
break
logger.info("%s: timing run done" % (knl.name))
rates = ""
for cnt, lbl in zip(op_count, op_label):
rates += " {:g} {}/s".format(cnt / elapsed_wall, lbl)
if not quiet:
def format_float_or_none(v):
if v is None:
return "<unavailable>"
else:
return "%g" % v
print("elapsed: %s s event, %s s marker-event %s s wall "
"(%d rounds)%s" %
(format_float_or_none(elapsed_event),
format_float_or_none(elapsed_event_marker),
format_float_or_none(elapsed_wall), timing_rounds, rates))
if do_check:
ref_rates = ""
for cnt, lbl in zip(op_count, op_label):
ref_rates += " {:g} {}/s".format(cnt / ref_elapsed_event, lbl)
if not quiet:
print("ref: elapsed: {:g} s event, {:g} s wall{}".format(
ref_elapsed_event, ref_elapsed_wall, ref_rates))
# }}}
result_dict = {}
result_dict["elapsed_event"] = elapsed_event
result_dict["elapsed_event_marker"] = elapsed_event_marker
result_dict["elapsed_wall"] = elapsed_wall
result_dict["timing_rounds"] = timing_rounds
if do_check:
result_dict["ref_elapsed_event"] = ref_elapsed_event
result_dict["ref_elapsed_wall"] = ref_elapsed_wall
return result_dict
def generate_code_v2(kernel):
"""
:returns: a :class:`CodeGenerationResult`
"""
from loopy.kernel import kernel_state
if kernel.state == kernel_state.INITIAL:
from loopy.preprocess import preprocess_kernel
kernel = preprocess_kernel(kernel)
if kernel.schedule is None:
from loopy.schedule import get_one_scheduled_kernel
kernel = get_one_scheduled_kernel(kernel)
if kernel.state != kernel_state.SCHEDULED:
raise LoopyError("cannot generate code for a kernel that has not been "
"scheduled")
# {{{ cache retrieval
from loopy import CACHING_ENABLED
if CACHING_ENABLED:
input_kernel = kernel
try:
result = code_gen_cache[input_kernel]
logger.debug("%s: code generation cache hit" % kernel.name)
return result
except KeyError:
pass
# }}}
from loopy.type_inference import infer_unknown_types
kernel = infer_unknown_types(kernel, expect_completion=True)
from loopy.check import pre_codegen_checks
pre_codegen_checks(kernel)
logger.info("%s: generate code: start" % kernel.name)
# {{{ examine arg list
from loopy.kernel.data import ValueArg
from loopy.kernel.array import ArrayBase
implemented_data_info = []
for arg in kernel.args:
is_written = arg.name in kernel.get_written_variables()
if isinstance(arg, ArrayBase):
implemented_data_info.extend(
arg.decl_info(
kernel.target,
is_written=is_written,
index_dtype=kernel.index_dtype))
elif isinstance(arg, ValueArg):
implemented_data_info.append(ImplementedDataInfo(
target=kernel.target,
name=arg.name,
dtype=arg.dtype,
arg_class=ValueArg,
is_written=is_written))
else:
raise ValueError("argument type not understood: '%s'" % type(arg))
allow_complex = False
for var in kernel.args + list(six.itervalues(kernel.temporary_variables)):
if var.dtype.involves_complex():
allow_complex = True
# }}}
seen_dtypes = set()
seen_functions = set()
seen_atomic_dtypes = set()
initial_implemented_domain = isl.BasicSet.from_params(kernel.assumptions)
codegen_state = CodeGenerationState(
kernel=kernel,
implemented_data_info=implemented_data_info,
implemented_domain=initial_implemented_domain,
implemented_predicates=frozenset(),
seen_dtypes=seen_dtypes,
seen_functions=seen_functions,
seen_atomic_dtypes=seen_atomic_dtypes,
var_subst_map={},
allow_complex=allow_complex,
var_name_generator=kernel.get_var_name_generator(),
is_generating_device_code=False,
gen_program_name=(
kernel.target.host_program_name_prefix
+ kernel.name
+ kernel.target.host_program_name_suffix),
schedule_index_end=len(kernel.schedule))
from loopy.codegen.result import generate_host_or_device_program
codegen_result = generate_host_or_device_program(
codegen_state,
schedule_index=0)
device_code_str = codegen_result.device_code()
from loopy.check import check_implemented_domains
assert check_implemented_domains(kernel, codegen_result.implemented_domains,
device_code_str)
# {{{ handle preambles
for arg in kernel.args:
seen_dtypes.add(arg.dtype)
for tv in six.itervalues(kernel.temporary_variables):
seen_dtypes.add(tv.dtype)
preambles = kernel.preambles[:]
preamble_info = PreambleInfo(
kernel=kernel,
seen_dtypes=seen_dtypes,
seen_functions=seen_functions,
# a set of LoopyTypes (!)
seen_atomic_dtypes=seen_atomic_dtypes)
preamble_generators = (kernel.preamble_generators
+ kernel.target.get_device_ast_builder().preamble_generators())
for prea_gen in preamble_generators:
preambles.extend(prea_gen(preamble_info))
codegen_result = codegen_result.copy(device_preambles=preambles)
# }}}
logger.info("%s: generate code: done" % kernel.name)
if CACHING_ENABLED:
code_gen_cache[input_kernel] = codegen_result
return codegen_result
def _add_and_infer_dtypes_overdetermined(knl, dtype_dict):
knl = _add_dtypes_overdetermined(knl, dtype_dict)
from loopy.type_inference import infer_unknown_types
return infer_unknown_types(knl, expect_completion=True)
def preprocess_kernel(kernel, device=None):
if device is not None:
from warnings import warn
warn("passing 'device' to preprocess_kernel() is deprecated",
DeprecationWarning, stacklevel=2)
from loopy.kernel import kernel_state
if kernel.state >= kernel_state.PREPROCESSED:
return kernel
# {{{ cache retrieval
from loopy import CACHING_ENABLED
if CACHING_ENABLED:
input_kernel = kernel
try:
result = preprocess_cache[kernel]
logger.debug("%s: preprocess cache hit" % kernel.name)
return result
except KeyError:
pass
# }}}
logger.info("%s: preprocess start" % kernel.name)
from loopy.check import check_identifiers_in_subst_rules
check_identifiers_in_subst_rules(kernel)
# {{{ check that there are no l.auto-tagged inames
from loopy.kernel.data import AutoLocalIndexTagBase
for iname, tag in six.iteritems(kernel.iname_to_tag):
if (isinstance(tag, AutoLocalIndexTagBase)
and iname in kernel.all_inames()):
raise LoopyError("kernel with automatically-assigned "
"local axes passed to preprocessing")
# }}}
from loopy.transform.subst import expand_subst
kernel = expand_subst(kernel)
# Ordering restriction:
# Type inference and reduction iname uniqueness don't handle substitutions.
# Get them out of the way.
kernel = infer_unknown_types(kernel, expect_completion=False)
check_for_writes_to_predicates(kernel)
check_reduction_iname_uniqueness(kernel)
from loopy.kernel.creation import apply_single_writer_depencency_heuristic
kernel = apply_single_writer_depencency_heuristic(kernel)
# Ordering restrictions:
#
# - realize_reduction must happen after type inference because it needs
# to be able to determine the types of the reduced expressions.
#
# - realize_reduction must happen after default dependencies are added
# because it manipulates the depends_on field, which could prevent
# defaults from being applied.
kernel = realize_reduction(kernel, unknown_types_ok=False)
# Ordering restriction:
# add_axes_to_temporaries_for_ilp because reduction accumulators
# need to be duplicated by this.
from loopy.transform.ilp import add_axes_to_temporaries_for_ilp_and_vec
kernel = add_axes_to_temporaries_for_ilp_and_vec(kernel)
kernel = find_temporary_scope(kernel)
# boostability should be removed in 2017.x.
kernel = find_idempotence(kernel)
kernel = limit_boostability(kernel)
kernel = kernel.target.preprocess(kernel)
logger.info("%s: preprocess done" % kernel.name)
kernel = kernel.copy(
state=kernel_state.PREPROCESSED)
# {{{ prepare for caching
# PicklableDtype instances for example need to know the target they're working
# towards in order to pickle and unpickle them. This is the first pass that
# uses caching, so we need to be ready to pickle. This means propagating
# this target information.
if CACHING_ENABLED:
input_kernel = prepare_for_caching(input_kernel)
kernel = prepare_for_caching(kernel)
# }}}
if CACHING_ENABLED:
preprocess_cache[input_kernel] = kernel
return kernel
def generate_code_v2(kernel):
"""
:returns: a :class:`CodeGenerationResult`
"""
from loopy.kernel import KernelState
if kernel.state == KernelState.INITIAL:
from loopy.preprocess import preprocess_kernel
kernel = preprocess_kernel(kernel)
if kernel.schedule is None:
from loopy.schedule import get_one_scheduled_kernel
kernel = get_one_scheduled_kernel(kernel)
if kernel.state != KernelState.SCHEDULED:
raise LoopyError("cannot generate code for a kernel that has not been "
"scheduled")
# {{{ cache retrieval
from loopy import CACHING_ENABLED
if CACHING_ENABLED:
input_kernel = kernel
try:
result = code_gen_cache[input_kernel]
logger.debug("%s: code generation cache hit" % kernel.name)
return result
except KeyError:
pass
# }}}
from loopy.type_inference import infer_unknown_types
kernel = infer_unknown_types(kernel, expect_completion=True)
from loopy.check import pre_codegen_checks
pre_codegen_checks(kernel)
logger.info("%s: generate code: start" % kernel.name)
# {{{ examine arg list
from loopy.kernel.data import ValueArg
from loopy.kernel.array import ArrayBase
implemented_data_info = []
for arg in kernel.args:
is_written = arg.name in kernel.get_written_variables()
if isinstance(arg, ArrayBase):
implemented_data_info.extend(
arg.decl_info(
kernel.target,
is_written=is_written,
index_dtype=kernel.index_dtype))
elif isinstance(arg, ValueArg):
implemented_data_info.append(ImplementedDataInfo(
target=kernel.target,
name=arg.name,
dtype=arg.dtype,
arg_class=ValueArg,
is_written=is_written))
else:
raise ValueError("argument type not understood: '%s'" % type(arg))
allow_complex = False
for var in kernel.args + list(six.itervalues(kernel.temporary_variables)):
if var.dtype.involves_complex():
allow_complex = True
# }}}
seen_dtypes = set()
seen_functions = set()
seen_atomic_dtypes = set()
initial_implemented_domain = isl.BasicSet.from_params(kernel.assumptions)
codegen_state = CodeGenerationState(
kernel=kernel,
implemented_data_info=implemented_data_info,
implemented_domain=initial_implemented_domain,
implemented_predicates=frozenset(),
seen_dtypes=seen_dtypes,
seen_functions=seen_functions,
seen_atomic_dtypes=seen_atomic_dtypes,
var_subst_map={},
allow_complex=allow_complex,
var_name_generator=kernel.get_var_name_generator(),
is_generating_device_code=False,
gen_program_name=(
kernel.target.host_program_name_prefix
+ kernel.name
+ kernel.target.host_program_name_suffix),
schedule_index_end=len(kernel.schedule))
from loopy.codegen.result import generate_host_or_device_program
codegen_result = generate_host_or_device_program(
codegen_state,
schedule_index=0)
device_code_str = codegen_result.device_code()
from loopy.check import check_implemented_domains
assert check_implemented_domains(kernel, codegen_result.implemented_domains,
device_code_str)
# {{{ handle preambles
for arg in kernel.args:
seen_dtypes.add(arg.dtype)
for tv in six.itervalues(kernel.temporary_variables):
seen_dtypes.add(tv.dtype)
preambles = kernel.preambles[:]
preamble_info = PreambleInfo(
kernel=kernel,
seen_dtypes=seen_dtypes,
seen_functions=seen_functions,
# a set of LoopyTypes (!)
seen_atomic_dtypes=seen_atomic_dtypes,
codegen_state=codegen_state
)
preamble_generators = (kernel.preamble_generators
+ kernel.target.get_device_ast_builder().preamble_generators())
for prea_gen in preamble_generators:
preambles.extend(prea_gen(preamble_info))
codegen_result = codegen_result.copy(device_preambles=preambles)
# }}}
# For faster unpickling in the common case when implemented_domains isn't needed.
from loopy.tools import LazilyUnpicklingDict
codegen_result = codegen_result.copy(
implemented_domains=LazilyUnpicklingDict(
codegen_result.implemented_domains))
logger.info("%s: generate code: done" % kernel.name)
if CACHING_ENABLED:
code_gen_cache.store_if_not_present(input_kernel, codegen_result)
return codegen_result
def auto_test_vs_ref(
ref_knl, ctx, test_knl=None, op_count=[], op_label=[], parameters={},
print_ref_code=False, print_code=True, warmup_rounds=2,
dump_binary=False,
fills_entire_output=None, do_check=True, check_result=None,
max_test_kernel_count=1,
quiet=False, blacklist_ref_vendors=[]):
"""Compare results of `ref_knl` to the kernels generated by
scheduling *test_knl*.
:arg check_result: a callable with :class:`numpy.ndarray` arguments
*(result, reference_result)* returning a a tuple (class:`bool`,
message) indicating correctness/acceptability of the result
:arg max_test_kernel_count: Stop testing after this many *test_knl*
"""
import pyopencl as cl
if test_knl is None:
test_knl = ref_knl
do_check = False
if len(ref_knl.args) != len(test_knl.args):
raise LoopyError("ref_knl and test_knl do not have the same number "
"of arguments")
for i, (ref_arg, test_arg) in enumerate(zip(ref_knl.args, test_knl.args)):
if ref_arg.name != test_arg.name:
raise LoopyError("ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i+1))
if ref_arg.dtype != test_arg.dtype:
raise LoopyError("ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i+1))
from loopy.compiled import CompiledKernel
from loopy.target.execution import get_highlighted_code
if isinstance(op_count, (int, float)):
warn("op_count should be a list", stacklevel=2)
op_count = [op_count]
if isinstance(op_label, str):
warn("op_label should be a list", stacklevel=2)
op_label = [op_label]
from time import time
if check_result is None:
check_result = _default_check_result
if fills_entire_output is not None:
warn("fills_entire_output is deprecated", DeprecationWarning, stacklevel=2)
# {{{ compile and run reference code
from loopy.type_inference import infer_unknown_types
ref_knl = infer_unknown_types(ref_knl, expect_completion=True)
found_ref_device = False
ref_errors = []
for dev in _enumerate_cl_devices_for_ref_test(blacklist_ref_vendors):
ref_ctx = cl.Context([dev])
ref_queue = cl.CommandQueue(ref_ctx,
properties=cl.command_queue_properties.PROFILING_ENABLE)
pp_ref_knl = lp.preprocess_kernel(ref_knl)
for knl in lp.generate_loop_schedules(pp_ref_knl):
ref_sched_kernel = knl
break
logger.info("%s (ref): trying %s for the reference calculation" % (
ref_knl.name, dev))
ref_compiled = CompiledKernel(ref_ctx, ref_sched_kernel)
if not quiet and print_ref_code:
print(75*"-")
print("Reference Code:")
print(75*"-")
print(get_highlighted_code(ref_compiled.get_code()))
print(75*"-")
ref_kernel_info = ref_compiled.kernel_info(frozenset())
try:
ref_args, ref_arg_data = \
make_ref_args(ref_sched_kernel,
ref_kernel_info.implemented_data_info,
ref_queue, parameters)
ref_args["out_host"] = False
except cl.RuntimeError as e:
if e.code == cl.status_code.IMAGE_FORMAT_NOT_SUPPORTED:
import traceback
ref_errors.append("\n".join([
75*"-",
"On %s:" % dev,
75*"-",
traceback.format_exc(),
75*"-"]))
continue
else:
raise
found_ref_device = True
if not do_check:
break
ref_queue.finish()
logger.info("%s (ref): using %s for the reference calculation" % (
ref_knl.name, dev))
logger.info("%s (ref): run" % ref_knl.name)
ref_start = time()
if not AUTO_TEST_SKIP_RUN:
ref_evt, _ = ref_compiled(ref_queue, **ref_args)
else:
ref_evt = cl.enqueue_marker(ref_queue)
ref_queue.finish()
ref_stop = time()
ref_elapsed_wall = ref_stop-ref_start
logger.info("%s (ref): run done" % ref_knl.name)
ref_evt.wait()
ref_elapsed_event = 1e-9*(ref_evt.profile.END-ref_evt.profile.START)
break
if not found_ref_device:
raise LoopyError("could not find a suitable device for the "
"reference computation.\n"
"These errors were encountered:\n"+"\n".join(ref_errors))
# }}}
# {{{ compile and run parallel code
need_check = do_check
queue = cl.CommandQueue(ctx,
properties=cl.command_queue_properties.PROFILING_ENABLE)
from loopy.kernel import KernelState
from loopy.target.pyopencl import PyOpenCLTarget
if test_knl.state not in [
KernelState.PREPROCESSED,
KernelState.SCHEDULED]:
if isinstance(test_knl.target, PyOpenCLTarget):
test_knl = test_knl.copy(target=PyOpenCLTarget(ctx.devices[0]))
test_knl = lp.preprocess_kernel(test_knl)
if not test_knl.schedule:
test_kernels = lp.generate_loop_schedules(test_knl)
else:
test_kernels = [test_knl]
test_kernel_count = 0
from loopy.type_inference import infer_unknown_types
for i, kernel in enumerate(test_kernels):
test_kernel_count += 1
if test_kernel_count > max_test_kernel_count:
break
kernel = infer_unknown_types(kernel, expect_completion=True)
compiled = CompiledKernel(ctx, kernel)
kernel_info = compiled.kernel_info(frozenset())
args = make_args(kernel,
kernel_info.implemented_data_info,
queue, ref_arg_data, parameters)
args["out_host"] = False
if not quiet:
print(75*"-")
print("Kernel #%d:" % i)
print(75*"-")
if print_code:
print(compiled.get_highlighted_code())
print(75*"-")
if dump_binary:
# {{{ find cl program
for name in dir(kernel_info.cl_kernels):
if name.startswith("__"):
continue
cl_kernel = getattr(kernel_info.cl_kernels, name)
cl_program = cl_kernel.get_info(cl.kernel_info.PROGRAM)
break
else:
assert False, "could not find cl_program"
# }}}
print(type(cl_program))
if hasattr(cl_program, "binaries"):
print(cl_program.binaries[0])
print(75*"-")
logger.info("%s: run warmup" % (knl.name))
for i in range(warmup_rounds):
if not AUTO_TEST_SKIP_RUN:
compiled(queue, **args)
if need_check and not AUTO_TEST_SKIP_RUN:
for arg_desc in ref_arg_data:
if arg_desc is None:
continue
if not arg_desc.needs_checking:
continue
from pyopencl.compyte.array import as_strided
ref_ary = as_strided(
arg_desc.ref_storage_array.get(),
shape=arg_desc.ref_shape,
strides=arg_desc.ref_numpy_strides).flatten()
test_ary = as_strided(
arg_desc.test_storage_array.get(),
shape=arg_desc.test_shape,
strides=arg_desc.test_numpy_strides).flatten()
common_len = min(len(ref_ary), len(test_ary))
ref_ary = ref_ary[:common_len]
test_ary = test_ary[:common_len]
error_is_small, error = check_result(test_ary, ref_ary)
if not error_is_small:
raise AutomaticTestFailure(error)
need_check = False
events = []
queue.finish()
logger.info("%s: warmup done" % (knl.name))
logger.info("%s: timing run" % (knl.name))
timing_rounds = warmup_rounds
while True:
from time import time
start_time = time()
evt_start = cl.enqueue_marker(queue)
for i in range(timing_rounds):
if not AUTO_TEST_SKIP_RUN:
evt, _ = compiled(queue, **args)
events.append(evt)
else:
events.append(cl.enqueue_marker(queue))
evt_end = cl.enqueue_marker(queue)
queue.finish()
stop_time = time()
for evt in events:
evt.wait()
evt_start.wait()
evt_end.wait()
elapsed_event = (1e-9*events[-1].profile.END
- 1e-9*events[0].profile.START) \
/ timing_rounds
try:
elapsed_event_marker = ((1e-9*evt_end.profile.START
- 1e-9*evt_start.profile.START)
/ timing_rounds)
except cl.RuntimeError:
elapsed_event_marker = None
elapsed_wall = (stop_time-start_time)/timing_rounds
if elapsed_wall * timing_rounds < 0.3:
timing_rounds *= 4
else:
break
logger.info("%s: timing run done" % (knl.name))
rates = ""
for cnt, lbl in zip(op_count, op_label):
rates += " %g %s/s" % (cnt/elapsed_wall, lbl)
if not quiet:
def format_float_or_none(v):
if v is None:
return "<unavailable>"
else:
return "%g" % v
print("elapsed: %s s event, %s s marker-event %s s wall "
"(%d rounds)%s" % (
format_float_or_none(elapsed_event),
format_float_or_none(elapsed_event_marker),
format_float_or_none(elapsed_wall), timing_rounds, rates))
if do_check:
ref_rates = ""
for cnt, lbl in zip(op_count, op_label):
ref_rates += " %g %s/s" % (cnt/ref_elapsed_event, lbl)
if not quiet:
print("ref: elapsed: %g s event, %g s wall%s" % (
ref_elapsed_event, ref_elapsed_wall, ref_rates))
# }}}
result_dict = {}
result_dict["elapsed_event"] = elapsed_event
result_dict["elapsed_event_marker"] = elapsed_event_marker
result_dict["elapsed_wall"] = elapsed_wall
result_dict["timing_rounds"] = timing_rounds
if do_check:
result_dict["ref_elapsed_event"] = ref_elapsed_event
result_dict["ref_elapsed_wall"] = ref_elapsed_wall
return result_dict
def __call__(self, knl):
insns = knl.instructions[:]
data = knl.args[:]
# we generally need to infer the dtypes for temporary variables at this stage
# in case any of them are atomic
from loopy.type_inference import infer_unknown_types
from loopy.types import to_loopy_type
from pymbolic.primitives import Sum
knl = infer_unknown_types(knl, expect_completion=True)
temps = knl.temporary_variables.copy()
def _check_atomic_data(insn):
# get the kernel arg written by this insn
written = insn.assignee_var_names()[0]
ind = next((i for i, d in enumerate(data) if d.name == written),
None)
# make sure the dtype is atomic, if not update it
if ind is not None and not isinstance(data[ind].dtype, AtomicType):
assert data[ind].dtype is not None, (
"Change of dtype to atomic doesn't work if base dype is not"
" populated")
data[ind] = data[ind].copy(for_atomic=True)
elif ind is None:
assert written in temps, (
'Cannot find written atomic variable: {}'.format(written))
if not isinstance(temps[written].dtype, AtomicType):
temps[written] = temps[written].copy(dtype=to_loopy_type(
temps[written].dtype, for_atomic=True))
return written
for insn_ind, insn in enumerate(insns):
if insn.id in self.atomic_ids:
written = _check_atomic_data(insn)
# and force the insn to an atomic update
insns[insn_ind] = insn.copy(
atomicity=(lp.AtomicUpdate(written), ))
elif insn.id in self.init_ids:
written = _check_atomic_data(insn)
# setup an atomic init
insns[insn_ind] = insn.copy(
atomicity=(lp.AtomicInit(written), ))
elif insn.id in self.split_ids:
if isinstance(insn.expression, Sum) and \
insn.assignee in insn.expression.children:
written = _check_atomic_data(insn)
# get children that are not the assignee and re-sum
others = Sum(
tuple(x for x in insn.expression.children
if x != insn.assignee))
# finally implement the split as a += sum(others) / vec_width
div_size = np.minimum(self.vec_width, self.split_size)
insns[insn_ind] = insn.copy(
expression=insn.assignee + others / div_size,
atomicity=(lp.AtomicUpdate(written), ))
else:
# otherwise can simply divide
div_size = np.minimum(self.vec_width, self.split_size)
insns[insn_ind] = insn.copy(expression=insn.expression /
div_size)
# now force all instructions into inner loop
return super(atomic_deep_specialization, self).__call__(
knl.copy(instructions=insns, args=data, temporary_variables=temps))
