diff --git a/.gitignore b/.gitignore
index f4d6253..6774a18 100644
--- a/.gitignore
+++ b/.gitignore
@@ -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
diff --git a/CHANGELOGS.rst b/CHANGELOGS.rst
index e807b02..9d8d98d 100644
--- a/CHANGELOGS.rst
+++ b/CHANGELOGS.rst
@@ -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
diff --git a/_unittests/ut_ort/test_ort_profile.py b/_unittests/ut_ort/test_ort_profile.py
index 295b7e0..e868860 100644
--- a/_unittests/ut_ort/test_ort_profile.py
+++ b/_unittests/ut_ort/test_ort_profile.py
@@ -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):
@@ -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)
diff --git a/onnx_array_api/ort/ort_profile.py b/onnx_array_api/ort/ort_profile.py
index 37d8092..b61df67 100644
--- a/onnx_array_api/ort/ort_profile.py
+++ b/onnx_array_api/ort/ort_profile.py
@@ -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],
@@ -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]:
"""
@@ -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
"""
@@ -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()
@@ -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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