def test_Testbed_GetOrAdd_rollback(session):
deeplearning.deepsmith.testbed.Testbed.GetOrAdd(
session,
deepsmith_pb2.Testbed(
toolchain='opencl',
name='nvidia',
opts={
'opencl': '1.2',
'devtype': 'GPU',
},
))
assert session.query(deeplearning.deepsmith.testbed.Testbed).count() == 1
assert session.query(
deeplearning.deepsmith.testbed.TestbedOpt).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptSet).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptName).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptValue).count() == 2
session.rollback()
assert session.query(deeplearning.deepsmith.testbed.Testbed).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOpt).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptSet).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptName).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptValue).count() == 0
def test_Testbed_GetOrAdd(session):
proto = deepsmith_pb2.Testbed(
toolchain='cpp',
name='clang',
opts={
'arch': 'x86_64',
'build': 'debug+assert'
},
)
testbed = deeplearning.deepsmith.testbed.Testbed.GetOrAdd(session, proto)
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptSet).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOpt).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptName).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptValue).count() == 2
assert testbed.toolchain.string == 'cpp'
assert testbed.name == 'clang'
assert len(testbed.optset) == 2
assert len(testbed.opts) == 2
assert testbed.opts['arch'] == 'x86_64'
assert testbed.opts['build'] == 'debug+assert'
def ToProto(self) -> deepsmith_pb2.Testbed:
"""Create protocol buffer representation.
Returns:
A Testbed message.
"""
proto = deepsmith_pb2.Testbed()
return self.SetProto(proto)
def test_duplicate_testcase_testbed_ignored(session):
"""Test that result is ignored if testbed and testcase are not unique."""
proto = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain='cpp',
generator=deepsmith_pb2.Generator(name='generator'),
harness=deepsmith_pb2.Harness(name='harness'),
inputs={
'src': 'void main() {}',
'data': '[1,2]',
},
invariant_opts={
'config': 'opt',
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='generate',
duration_ms=100,
event_start_epoch_ms=1123123123,
),
]),
testbed=deepsmith_pb2.Testbed(
toolchain='cpp',
name='clang',
opts={'arch': 'x86_64'},
),
returncode=0,
outputs={'stdout': 'Hello, world!'},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='exec',
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
outcome=deepsmith_pb2.Result.PASS,
)
r1 = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto)
session.add(r1)
session.flush()
# Attempt to add a new result which is identical to the first in all fields
# except for the outputs.
proto.outputs['stdout'] = '!'
r2 = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto)
session.add(r2)
session.flush()
# Check that only one result was added.
assert session.query(deeplearning.deepsmith.result.Result).count() == 1
# Check that only the first result was added.
r3 = session.query(deeplearning.deepsmith.result.Result).first()
assert r3.outputs['stdout'] == 'Hello, world!'
def test_Generator_GetOrAdd_ToProto_equivalence(session):
proto_in = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain="cpp",
generator=deepsmith_pb2.Generator(name="generator"),
harness=deepsmith_pb2.Harness(name="harness"),
inputs={"src": "void main() {}", "data": "[1,2]",},
invariant_opts={"config": "opt",},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="generate",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="foo",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
),
testbed=deepsmith_pb2.Testbed(
toolchain="cpp",
name="clang",
opts={"arch": "x86_64", "build": "debug+assert",},
),
returncode=0,
outputs={"stdout": "Hello, world!", "stderr": "",},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="exec",
duration_ms=500,
event_start_epoch_ms=1123123123,
),
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="overhead",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
outcome=deepsmith_pb2.Result.PASS,
)
result = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto_in)
# NOTE: We have to flush so that SQLAlchemy resolves all of the object IDs.
session.flush()
proto_out = result.ToProto()
assert proto_in == proto_out
proto_out.ClearField("outputs")
assert proto_in != proto_out # Sanity check.
def test_Testbed_GetOrAdd_only_different_optset(session):
testbed_a = deeplearning.deepsmith.testbed.Testbed.GetOrAdd(
session,
deepsmith_pb2.Testbed(
toolchain='toolchain',
name='name',
opts={
'a': 'A',
'b': 'B',
'c': 'C',
},
))
testbed_b = deeplearning.deepsmith.testbed.Testbed.GetOrAdd(
session,
deepsmith_pb2.Testbed(
toolchain='toolchain',
name='name',
opts={
'd': 'D',
},
))
testbed_c = deeplearning.deepsmith.testbed.Testbed.GetOrAdd(
session,
deepsmith_pb2.Testbed(
toolchain='toolchain',
name='name',
opts={},
))
assert session.query(deeplearning.deepsmith.testbed.Testbed).count() == 3
assert session.query(
deeplearning.deepsmith.testbed.TestbedOpt).count() == 4
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptSet).count() == 4
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptName).count() == 4
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptValue).count() == 4
assert len(testbed_a.optset) == 3
assert len(testbed_b.optset) == 1
assert len(testbed_c.optset) == 0
def _AddExistingTestbed(session):
deeplearning.deepsmith.testbed.Testbed.GetOrAdd(
session,
deepsmith_pb2.Testbed(
toolchain='toolchain',
name='name',
opts={
'a': 'a',
'b': 'b',
'c': 'c',
},
))
session.flush()
def _AddRandomNewTestbed(session):
deeplearning.deepsmith.testbed.Testbed.GetOrAdd(
session,
deepsmith_pb2.Testbed(
toolchain=str(random.random()),
name=str(random.random()),
opts={
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
},
))
session.flush()
def test_duplicate_testcase_testbed_ignored(session):
"""Test that result is ignored if testbed and testcase are not unique."""
proto = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain="cpp",
generator=deepsmith_pb2.Generator(name="generator"),
harness=deepsmith_pb2.Harness(name="harness"),
inputs={"src": "void main() {}", "data": "[1,2]",},
invariant_opts={"config": "opt",},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="generate",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
),
testbed=deepsmith_pb2.Testbed(
toolchain="cpp", name="clang", opts={"arch": "x86_64"},
),
returncode=0,
outputs={"stdout": "Hello, world!"},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="exec",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
outcome=deepsmith_pb2.Result.PASS,
)
r1 = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto)
session.add(r1)
session.flush()
# Attempt to add a new result which is identical to the first in all fields
# except for the outputs.
proto.outputs["stdout"] = "!"
r2 = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto)
session.add(r2)
session.flush()
# Check that only one result was added.
assert session.query(deeplearning.deepsmith.result.Result).count() == 1
# Check that only the first result was added.
r3 = session.query(deeplearning.deepsmith.result.Result).first()
assert r3.outputs["stdout"] == "Hello, world!"
def test_Testbed_GetOrAdd_no_opts(session):
testbed = deeplearning.deepsmith.testbed.Testbed.GetOrAdd(
session,
deepsmith_pb2.Testbed(
toolchain='toolchain',
name='name',
opts={},
))
empty_md5 = hashlib.md5().digest()
assert testbed.optset_id == empty_md5
assert session.query(deeplearning.deepsmith.testbed.Testbed).count() == 1
assert session.query(
deeplearning.deepsmith.testbed.TestbedOpt).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptSet).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptName).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptValue).count() == 0
def test_Testbed_GetOrAdd_ToProto_equivalence(session):
proto_in = deepsmith_pb2.Testbed(
toolchain='cpp',
name='clang',
opts={
'arch': 'x86_64',
'build': 'debug+assert'
},
)
testbed = deeplearning.deepsmith.testbed.Testbed.GetOrAdd(
session, proto_in)
# NOTE: We have to flush before constructing a proto so that SQLAlchemy
# resolves all of the object IDs.
session.flush()
proto_out = testbed.ToProto()
assert proto_in == proto_out
proto_out.ClearField('toolchain')
assert proto_in != proto_out # Sanity check.
def OpenClEnvironmentToTestbed(
opencl_environment: env.OpenCLEnvironment) -> deepsmith_pb2.Testbed:
"""Instantiate a DeepSmith testbed from an OpenCL environment.
Args:
opencl_environment: A cldrive OpenCLEnvironment instance.
Returns:
A Testbed proto instance.
"""
testbed = deepsmith_pb2.Testbed()
testbed.toolchain = 'opencl'
testbed.name = opencl_environment.name
testbed.opts['platform'] = opencl_environment.platform_name
testbed.opts['device'] = opencl_environment.device_name
testbed.opts['driver'] = opencl_environment.driver_version
testbed.opts['device_type'] = opencl_environment.device_type
testbed.opts['opencl_version'] = opencl_environment.opencl_version
testbed.opts['opencl_opt'] = ('enabled' if opencl_environment.opencl_opt
else 'disabled')
return testbed
def OpenClEnvironmentToTestbed(
opencl_environment: env.OpenCLEnvironment, ) -> deepsmith_pb2.Testbed:
"""Instantiate a DeepSmith testbed from an OpenCL environment.
Args:
opencl_environment: A cldrive OpenCLEnvironment instance.
Returns:
A Testbed proto instance.
"""
testbed = deepsmith_pb2.Testbed()
testbed.toolchain = "opencl"
testbed.name = opencl_environment.name
testbed.opts["platform"] = opencl_environment.platform_name
testbed.opts["device"] = opencl_environment.device_name
testbed.opts["driver"] = opencl_environment.driver_version
testbed.opts["device_type"] = opencl_environment.device_type
testbed.opts["opencl_version"] = opencl_environment.opencl_version
testbed.opts["opencl_opt"] = ("enabled" if opencl_environment.opencl_opt
else "disabled")
return testbed
def test_Testbed_GetOrAdd_duplicates(session):
assert session.query(deeplearning.deepsmith.testbed.Testbed).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOpt).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptSet).count() == 0
assert session.query(
deeplearning.deepsmith.toolchain.Toolchain).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptName).count() == 0
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptValue).count() == 0
proto_a1 = deepsmith_pb2.Testbed(
toolchain='cpp',
name='clang',
opts={
'arch': 'x86_64',
'build': 'debug+assert',
},
)
proto_a2 = deepsmith_pb2.Testbed(
toolchain='cpp',
name='clang',
opts={
'arch': 'x86_64',
'build': 'debug+assert',
},
)
proto_b = deepsmith_pb2.Testbed(
toolchain='cpp',
name='gcc',
opts={
'arch': 'x86_64',
'build': 'opt',
},
)
assert proto_a1 == proto_a2 # Sanity check.
deeplearning.deepsmith.testbed.Testbed.GetOrAdd(session, proto_a1)
assert session.query(deeplearning.deepsmith.testbed.Testbed).count() == 1
assert session.query(
deeplearning.deepsmith.testbed.TestbedOpt).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptSet).count() == 2
assert session.query(
deeplearning.deepsmith.toolchain.Toolchain).count() == 1
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptName).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptValue).count() == 2
deeplearning.deepsmith.testbed.Testbed.GetOrAdd(session, proto_a2)
# proto_a1 == proto_a2, so the counts should remain unchanged.
assert session.query(deeplearning.deepsmith.testbed.Testbed).count() == 1
assert session.query(
deeplearning.deepsmith.testbed.TestbedOpt).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptSet).count() == 2
assert session.query(
deeplearning.deepsmith.toolchain.Toolchain).count() == 1
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptName).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptValue).count() == 2
deeplearning.deepsmith.testbed.Testbed.GetOrAdd(session, proto_b)
# proto_b adds a new testbed, new opt (note the duplicate arch), and
# two new entries in the TestbedOptSet table.
assert session.query(deeplearning.deepsmith.testbed.Testbed).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOpt).count() == 3
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptSet).count() == 4
assert session.query(
deeplearning.deepsmith.toolchain.Toolchain).count() == 1
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptName).count() == 2
assert session.query(
deeplearning.deepsmith.testbed.TestbedOptValue).count() == 3
def _ExportSolidityResults(cursor, start_id, proto_dir):
batch_size = 1000
result_id = start_id
while True:
cursor.execute(
"""
SELECT
results.id,
platforms.platform,
platforms.version,
platforms.host,
testbeds.optimizations,
programs.generator,
programs.date,
programs.generation_time,
programs.src,
testcases.harness,
testcases.timeout,
results.date,
results.returncode,
results.runtime,
stdouts.stdout,
stderrs.stderr
FROM results
LEFT JOIN testbeds ON results.testbed_id = testbeds.id
LEFT JOIN platforms ON testbeds.platform_id = platforms.id
LEFT JOIN testcases ON results.testcase_id = testcases.id
LEFT JOIN programs ON testcases.program_id = programs.id
LEFT JOIN stdouts ON results.stdout_id = stdouts.id
LEFT JOIN stderrs ON results.stderr_id = stderrs.id
WHERE results.id >= %s
ORDER BY results.id
LIMIT %s
""", (result_id, batch_size))
i = 0
for row in cursor:
i += 1
(result_id, platform_name, platform_version, host_os,
optimizations, generator_id, program_date,
program_generation_time, program_src, harness_id, harness_timeout,
result_date, returncode, runtime, stdout, stderr) = row
assert harness_id == 2
proto = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain='solidity',
generator=_GetSolidityGenerator(generator_id),
harness=deepsmith_pb2.Harness(
name='solc',
opts={
'timeout_seconds':
str(int(harness_timeout)),
'url':
'https://github.com/ChrisCummins/dsmith/blob/5181c7c95575d428b5144a25549e5a5a55a3da31/dsmith/sol/harnesses.py#L117',
},
),
inputs={
"src": program_src,
},
invariant_opts={},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="cc1",
type="generation",
duration_ms=int(program_generation_time * 1000),
event_start_epoch_ms=dateutil.
MillisecondsTimestamp(program_date),
),
]),
testbed=deepsmith_pb2.Testbed(
toolchain='solidity',
name=platform_name,
opts={
'version':
platform_version,
'optimizations':
'enabled' if optimizations else 'disabled',
},
),
returncode=returncode,
outputs={
"stdout": stdout,
"stderr": stderr,
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='cc1',
type="runtime",
duration_ms=int(runtime * 1000),
event_start_epoch_ms=dateutil.MillisecondsTimestamp(
result_date),
),
],
)
with open(proto_dir / 'sol' / 'results' / str(result_id),
'wb') as f:
f.write(proto.SerializeToString())
if i < batch_size:
return
def _ExportOpenCLResults(cursor, start_id, proto_dir):
batch_size = 1000
result_id = start_id
while True:
cursor.execute(
"""
SELECT
results.id,
platforms.platform,
platforms.device,
platforms.driver,
platforms.opencl,
platforms.devtype,
platforms.host,
testbeds.optimizations,
programs.generator,
programs.date,
programs.generation_time,
programs.src,
testcases.harness,
testcases.timeout,
results.date,
results.returncode,
results.runtime,
stdouts.stdout,
stderrs.stderr,
stderrs.truncated,
threads.gsize_x,
threads.gsize_y,
threads.gsize_z,
threads.lsize_x,
threads.lsize_y,
threads.lsize_z,
clsmith_testcase_metas.oclverified,
dsmith_testcase_metas.gpuverified,
dsmith_testcase_metas.oclverified,
dsmith_program_metas.contains_floats,
dsmith_program_metas.vector_inputs,
dsmith_program_metas.compiler_warnings
FROM results
LEFT JOIN testbeds ON results.testbed_id = testbeds.id
LEFT JOIN platforms ON testbeds.platform_id = platforms.id
LEFT JOIN testcases ON results.testcase_id = testcases.id
LEFT JOIN programs ON testcases.program_id = programs.id
LEFT JOIN threads ON testcases.threads_id = threads.id
LEFT JOIN stdouts ON results.stdout_id = stdouts.id
LEFT JOIN stderrs ON results.stderr_id = stderrs.id
LEFT JOIN clsmith_testcase_metas ON testcases.id=clsmith_testcase_metas.id
LEFT JOIN dsmith_testcase_metas ON testcases.id=dsmith_testcase_metas.id
LEFT JOIN dsmith_program_metas ON programs.id=dsmith_program_metas.id
WHERE results.id >= %s
ORDER BY results.id
LIMIT %s
""", (result_id, batch_size))
i = 0
for row in cursor:
i += 1
(result_id, platform_name, device_name, driver_version,
opencl_version, devtype, host_os, cl_opt, generator_id,
program_date, program_generation_time, program_src, harness_id,
harness_timeout, result_date, returncode, runtime, stdout, stderr,
truncated_stderr, gsize_x, gsize_y, gsize_z, lsize_x, lsize_y,
lsize_z, clsmith_oclverified, dsmith_gpuverified,
dsmith_oclverified, dsmith_program_contains_floats,
dsmith_program_vector_inputs,
dsmith_program_compiler_warnings) = row
inputs = {
'src': program_src,
}
if harness_id != -1:
inputs['gsize'] = f'{gsize_x},{gsize_y},{gsize_z}'
inputs['lsize'] = f'{lsize_x},{lsize_y},{lsize_z}'
testbed_name = OPENCL_DEVICE_MAP[device_name]
testbed_opts = {}
_SetIf(testbed_opts, 'opencl_device', device_name.strip())
_SetIf(testbed_opts, 'opencl_version', opencl_version.strip())
_SetIf(testbed_opts, 'host', HOSTS_MAP.get(host_os, host_os))
if testbed_name == "clang":
_SetIf(testbed_opts, 'llvm_version', driver_version.strip())
else:
_SetIf(testbed_opts, 'driver_version', driver_version.strip())
_SetIf(testbed_opts, 'opencl_devtype',
OPENCL_DEVTYPE_MAP.get(devtype, devtype))
_SetIf(testbed_opts, 'opencl_platform', platform_name.strip())
_SetIf(testbed_opts, 'opencl_opt',
'enabled' if cl_opt else 'disabled')
invariant_opts = {}
if clsmith_oclverified == 0:
invariant_opts['oclverify'] = 'fail'
elif clsmith_oclverified == 1:
invariant_opts['oclverify'] = 'pass'
elif dsmith_oclverified == 0:
invariant_opts['oclverify'] = 'fail'
elif dsmith_oclverified == 1:
invariant_opts['oclverify'] = 'pass'
if dsmith_gpuverified == 0:
invariant_opts['gpuverify'] = 'fail'
elif dsmith_gpuverified == 1:
invariant_opts['gpuverify'] = 'pass'
if dsmith_program_contains_floats == 0:
invariant_opts['kernel_uses_floats'] = 'false'
elif dsmith_program_contains_floats == 1:
invariant_opts['kernel_uses_floats'] = 'true'
if dsmith_program_vector_inputs == 0:
invariant_opts['kernel_has_vector_inputs'] = 'false'
elif dsmith_program_vector_inputs == 1:
invariant_opts['kernel_has_vector_inputs'] = 'true'
if dsmith_program_compiler_warnings == 0:
invariant_opts['kernel_throws_compiler_warning'] = 'false'
elif dsmith_program_compiler_warnings == 1:
invariant_opts['kernel_throws_compiler_warning'] = 'true'
testbed = deepsmith_pb2.Testbed(
toolchain='opencl',
name=testbed_name,
opts=testbed_opts,
)
proto = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain="opencl",
generator=_GetOpenCLGenerator(generator_id),
harness=_GetOpenCLHarness(harness_id, harness_timeout),
inputs=inputs,
invariant_opts=invariant_opts,
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="cc1",
type="generation",
duration_ms=int(program_generation_time * 1000),
event_start_epoch_ms=dateutil.
MillisecondsTimestamp(program_date),
),
]),
testbed=testbed,
returncode=returncode,
outputs={
"stdout": stdout,
"stderr": stderr,
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client={
'Ubuntu 16.04 64bit': 'cc1',
'CentOS Linux 7.1.1503 64bit': 'fuji',
'openSUSE 13.1 64bit': 'kobol',
}[host_os],
type="runtime",
duration_ms=int(runtime * 1000),
event_start_epoch_ms=dateutil.MillisecondsTimestamp(
result_date),
),
],
)
with open(proto_dir / 'opencl' / 'results' / str(result_id),
'wb') as f:
f.write(proto.SerializeToString())
if i < batch_size:
return
def _ExportOpenCLResults(cursor, start_id, proto_dir):
batch_size = 1000
result_id = start_id
while True:
cursor.execute("""
SELECT
results.id,
platforms.platform,
platforms.device,
platforms.driver,
platforms.opencl,
platforms.devtype,
platforms.host,
testbeds.optimizations,
programs.generator,
programs.date,
programs.generation_time,
programs.src,
testcases.harness,
testcases.timeout,
results.date,
results.returncode,
results.runtime,
stdouts.stdout,
stderrs.stderr,
stderrs.truncated,
threads.gsize_x,
threads.gsize_y,
threads.gsize_z,
threads.lsize_x,
threads.lsize_y,
threads.lsize_z
FROM results
LEFT JOIN testbeds ON results.testbed_id = testbeds.id
LEFT JOIN platforms ON testbeds.platform_id = platforms.id
LEFT JOIN testcases on results.testcase_id = testcases.id
LEFT JOIN programs on testcases.program_id = programs.id
LEFT JOIN threads on testcases.threads_id = threads.id
LEFT JOIN stdouts on results.stdout_id = stdouts.id
LEFT JOIN stderrs on results.stderr_id = stderrs.id
WHERE results.id >= %s
ORDER BY results.id
LIMIT %s
""", (result_id, batch_size))
i = 0
for row in cursor:
i += 1
(
result_id,
platform_name,
device_name,
driver_version,
opencl_version,
devtype,
host_os,
cl_opt,
generator_id,
program_date,
program_generation_time,
program_src,
harness_id,
harness_timeout,
result_date,
returncode,
runtime,
stdout,
stderr,
truncated_stderr,
gsize_x,
gsize_y,
gsize_z,
lsize_x,
lsize_y,
lsize_z
) = row
inputs = {
"src": program_src,
}
if harness_id != -1:
inputs["gsize"] = f"{gsize_x},{gsize_y},{gsize_z}"
inputs["lsize"] = f"{lsize_x},{lsize_y},{lsize_z}"
testbed_name = OPENCL_DEVICE_MAP[device_name]
testbed_opts = {}
_SetIf(testbed_opts, 'opencl_device', device_name.strip())
_SetIf(testbed_opts, 'opencl_version', opencl_version.strip())
_SetIf(testbed_opts, 'host', HOSTS_MAP.get(host_os, host_os))
if testbed_name == "clang":
_SetIf(testbed_opts, 'llvm_version', driver_version.strip())
else:
_SetIf(testbed_opts, 'driver_version', driver_version.strip())
_SetIf(testbed_opts, 'opencl_devtype', OPENCL_DEVTYPE_MAP.get(devtype, devtype))
_SetIf(testbed_opts, 'opencl_platform', platform_name.strip())
_SetIf(testbed_opts, 'opencl_opt', 'enabled' if cl_opt else 'disabled')
testbed = deepsmith_pb2.Testbed(
toolchain='opencl',
name=testbed_name,
opts=testbed_opts,
)
proto = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain="opencl",
generator=_GetOpenCLGenerator(generator_id),
harness=_GetOpenCLHarness(harness_id, harness_timeout),
inputs=inputs,
invariant_opts={},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="cc1",
type="generation",
duration_seconds=program_generation_time,
date_epoch_seconds=int(program_date.strftime('%s')),
),
]
),
testbed=testbed,
returncode=returncode,
outputs={
"stdout": stdout,
"stderr": stderr,
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client={
'Ubuntu 16.04 64bit': 'cc1',
'CentOS Linux 7.1.1503 64bit': 'fuji',
'openSUSE 13.1 64bit': 'kobol',
}[host_os],
type="runtime",
duration_seconds=runtime,
date_epoch_seconds=int(result_date.strftime('%s')),
),
],
)
with open(proto_dir / 'opencl' / 'results' / str(result_id), 'wb') as f:
f.write(proto.SerializeToString())
if i < batch_size:
return
def test_Generator_GetOrAdd_ToProto_equivalence(session):
proto_in = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain='cpp',
generator=deepsmith_pb2.Generator(name='generator'),
harness=deepsmith_pb2.Harness(name='harness'),
inputs={
'src': 'void main() {}',
'data': '[1,2]',
},
invariant_opts={
'config': 'opt',
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='generate',
duration_ms=100,
event_start_epoch_ms=1123123123,
),
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='foo',
duration_ms=100,
event_start_epoch_ms=1123123123,
),
]),
testbed=deepsmith_pb2.Testbed(
toolchain='cpp',
name='clang',
opts={
'arch': 'x86_64',
'build': 'debug+assert',
},
),
returncode=0,
outputs={
'stdout': 'Hello, world!',
'stderr': '',
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='exec',
duration_ms=500,
event_start_epoch_ms=1123123123,
),
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='overhead',
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
outcome=deepsmith_pb2.Result.PASS,
)
result = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto_in)
# NOTE: We have to flush so that SQLAlchemy resolves all of the object IDs.
session.flush()
proto_out = result.ToProto()
assert proto_in == proto_out
proto_out.ClearField('outputs')
assert proto_in != proto_out # Sanity check.
