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Supports OrtValue in function ort_profile#22

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Merged
sdpython merged 3 commits intomainfromprofo
Jun 22, 2023
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3 changes: 3 additions & 0 deletions.gitignore
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Original file line numberDiff line numberDiff line change
Expand Up@@ -10,8 +10,11 @@ build/*
.eggs/*
.hypothesis/*
*egg-info/*
onnxruntime_profile*
prof
_doc/auto_examples/*
_doc/examples/_cache/*
_doc/examples/onnxruntime_profile*
_doc/examples/plot_*.png
_doc/examples/plot_*.xlsx
_doc/examples/data/*.optimized.onnx
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1 change: 1 addition & 0 deletionsCHANGELOGS.rst
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Expand Up@@ -4,5 +4,6 @@ Change Logs
0.2.0
+++++

* :pr:`22`: support OrtValue in function :func:`ort_profile`
* :pr:`17`: implements ArrayAPI
* :pr:`3`: fixes Array API with onnxruntime and scikit-learn
75 changes: 74 additions & 1 deletion_unittests/ut_ort/test_ort_profile.py
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Expand Up@@ -6,6 +6,10 @@
from onnx_array_api.ext_test_case import ExtTestCase
from onnx_array_api.ort.ort_optimizers import ort_optimized_model
from onnx_array_api.ort.ort_profile import ort_profile, merge_ort_profile
from onnxruntime.capi._pybind_state import (
OrtValue as C_OrtValue,
OrtDevice as C_OrtDevice,
)


class TestOrtProfile(ExtTestCase):
Expand All@@ -28,7 +32,76 @@ def myloss(x, y):
self.assertRaise(lambda: ort_optimized_model(onx, "NO"), ValueError)
optimized = ort_optimized_model(onx)
prof = ort_profile(optimized, feeds)
prof.to_csv("prof.csv", index=False)
self.assertIsInstance(prof, DataFrame)
prof = ort_profile(optimized, feeds, as_df=False)
self.assertIsInstance(prof, list)

def test_ort_profile_first_it_out(self):
def l1_loss(x, y):
return absolute(x - y).sum()

def l2_loss(x, y):
return ((x - y) ** 2).sum()

def myloss(x, y):
return l1_loss(x[:, 0], y[:, 0]) + l2_loss(x[:, 1], y[:, 1])

jitted_myloss = jit_onnx(myloss)
x = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=np.float32)
y = np.array([[0.11, 0.22], [0.33, 0.44]], dtype=np.float32)
jitted_myloss(x, y)
onx = jitted_myloss.get_onnx()
feeds = {"x0": x, "x1": y}
self.assertRaise(lambda: ort_optimized_model(onx, "NO"), ValueError)
optimized = ort_optimized_model(onx)
prof = ort_profile(optimized, feeds)
events = {
"kernel_time",
"fence_before",
"fence_after",
"SequentialExecutor::Execute",
"model_run",
"model_loading_array",
"session_initialization",
}
self.assertEqual(set(prof["event_name"]), events)
agg = ort_profile(optimized, feeds, first_it_out=True, agg=True)
self.assertIsInstance(agg, DataFrame)
self.assertLess(agg.shape[0], prof.shape[0])
self.assertEqual(set(agg.reset_index(drop=False)["event_name"]), events)
agg = ort_profile(
optimized, feeds, first_it_out=True, agg=True, agg_op_name=False
)
self.assertIsInstance(agg, DataFrame)
self.assertLess(agg.shape[0], prof.shape[0])
self.assertEqual(set(agg.reset_index(drop=False)["event_name"]), events)

def test_ort_profile_ort_value(self):
def to_ort_value(m):
device = C_OrtDevice(C_OrtDevice.cpu(), C_OrtDevice.default_memory(), 0)
ort_value = C_OrtValue.ortvalue_from_numpy(m, device)
return ort_value

def l1_loss(x, y):
return absolute(x - y).sum()

def l2_loss(x, y):
return ((x - y) ** 2).sum()

def myloss(x, y):
return l1_loss(x[:, 0], y[:, 0]) + l2_loss(x[:, 1], y[:, 1])

jitted_myloss = jit_onnx(myloss)
x = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=np.float32)
y = np.array([[0.11, 0.22], [0.33, 0.44]], dtype=np.float32)
jitted_myloss(x, y)
onx = jitted_myloss.get_onnx()
np_feeds = {"x0": x, "x1": y}
feeds = {k: to_ort_value(v) for k, v in np_feeds.items()}

self.assertRaise(lambda: ort_optimized_model(onx, "NO"), ValueError)
optimized = ort_optimized_model(onx)
prof = ort_profile(optimized, feeds)
self.assertIsInstance(prof, DataFrame)
prof = ort_profile(optimized, feeds, as_df=False)
self.assertIsInstance(prof, list)
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72 changes: 69 additions & 3 deletionsonnx_array_api/ort/ort_profile.py
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Expand Up@@ -6,6 +6,56 @@
from pandas import DataFrame


def post_process_df_profile(
df: DataFrame,
first_it_out: bool = False,
agg: bool = False,
agg_op_name: bool = True,
) -> DataFrame:
"""
Post-processed a dataframe obtained after profiling onnxruntime.
It adds a column for a more explicit event name and adds
a column for the iteration number

:param agg: aggregate the result
:param first_it_out: leave the first iteration
out of the aggregation
:param agg_op_name: aggregate on operator name or operator index
:return: DataFrame
"""
events = {"kernel_time", "fence_after", "fence_before"}

def sep_event(s):
for e in events:
if s.endswith(e):
return e
return s

df = df.copy()
df["event_name"] = df["name"].apply(sep_event)
df["iteration"] = -1
current = -1
for i in range(df.shape[0]):
if df.loc[i, "name"] == "SequentialExecutor::Execute":
current += 1
df.loc[i, "iteration"] = current

if not agg:
return df

agg_cols = ["cat", "args_node_index", "args_op_name", "args_provider", "event_name"]
if first_it_out:
df["it==0"] = (df["iteration"] <= 0).astype(int)
agg_cols.insert(0, "it==0")
if agg_op_name:
del agg_cols[agg_cols.index("args_node_index")]
for c in agg_cols:
df[c] = df[c].fillna("")
df["dur"] = df["dur"].fillna(0)
agg = df[agg_cols + ["dur"]].groupby(agg_cols).sum()
return agg


def ort_profile(
filename_or_bytes: Union[str, bytes, ModelProto],
feeds: Dict[str, numpy.ndarray],
Expand All@@ -14,6 +64,9 @@ def ort_profile(
repeat: int = 10,
as_df: bool = True,
providers: Optional[List[str]] = None,
first_it_out: bool = False,
agg: bool = False,
agg_op_name: bool = False,
**kwargs,
) -> Union[List, DataFrame]:
"""
Expand All@@ -27,6 +80,9 @@ def ort_profile(
:param as_df: returns the
:param providers: list of providers to use when initializing the inference session,
if None, the default value is `["CPUExecutionProvider"]`
:param first_it_out: if aggregated, leaves the first iteration out
:param agg: aggregate by event
:param agg_op_name: aggregate on operator name or operator index
:param kwargs: additional parameters when initializing the inference session
:return: DataFrame or dictionary
"""
Expand All@@ -45,8 +101,16 @@ def ort_profile(
if providers is None:
providers = ["CPUExecutionProvider"]
sess = InferenceSession(obj, sess_options, providers=providers, **kwargs)
for i in range(repeat):
sess.run(None, feeds)
first = list(feeds.values())[0]

if isinstance(first, numpy.ndarray):
for i in range(repeat):
sess.run(None, feeds)
else:
out_names = [o.name for o in sess.get_outputs()]
for i in range(repeat):
sess._sess.run_with_ort_values(feeds, out_names, None)

prof = sess.end_profiling()
with open(prof, "r") as f:
content = f.read()
Expand All@@ -68,7 +132,9 @@ def ort_profile(
break
rows.append(row)
if as_df:
return DataFrame(rows)
return post_process_df_profile(
DataFrame(rows), first_it_out=first_it_out, agg=agg, agg_op_name=agg_op_name
)
return rows


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