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Adds functions to manipulate profiles#14

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Merged
sdpython merged 1 commit intomainfromprof2
May 5, 2023
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2 changes: 2 additions & 0 deletions_doc/api/ort.rst
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Expand Up@@ -23,5 +23,7 @@ OrtTensor
Profiling
+++++++++

.. autofunction:: onnx_array_api.ort.ort_profile.merge_ort_profile

.. autofunction:: onnx_array_api.ort.ort_profile.ort_profile

5 changes: 5 additions & 0 deletions_doc/api/plotting.rst
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Expand Up@@ -8,6 +8,11 @@ Dot

.. autofunction:: onnx_array_api.plotting.dot_plot.to_dot

Statistics
++++++++++

.. autofunction:: onnx_array_api.plotting.stat_plot.plot_ort_profile

Text
++++

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181 changes: 50 additions & 131 deletions_doc/examples/plot_profiling.py
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Expand Up@@ -21,17 +21,22 @@
from onnxruntime import get_available_providers
from onnx_array_api.ext_test_case import example_path
from onnx_array_api.ort.ort_optimizers import ort_optimized_model
from onnx_array_api.ort.ort_profile import ort_profile
from onnx_array_api.ort.ort_profile import ort_profile, merge_ort_profile
from onnx_array_api.plotting.stat_plot import plot_ort_profile


filename = example_path("data/small.onnx")
suffix = ""
filename = example_path(f"data/small{suffix}.onnx")
optimized = filename + ".optimized.onnx"
print(f"model={filename!r}")

if not os.path.exists(optimized):
ort_optimized_model(filename, output=optimized)
print(optimized)
print(f"optimized={optimized!r}")

#############################
# .. _l-example-ort-profiling:
#
# Profiling
# +++++++++

Expand All@@ -43,50 +48,31 @@
disable_optimization=True,
providers=["CPUExecutionProvider"],
)
prof_base.to_excel("prof_base.xlsx", index=False)
prof_base.to_excel(f"prof_base{suffix}.xlsx", index=False)
prof_base

#######################################
# And the optimized model.

prof_opt = ort_profile(
prof_opti = ort_profile(
optimized,
feeds,
repeat=6,
disable_optimization=True,
providers=["CPUExecutionProvider"],
)
prof_opt
prof_opti.to_excel(f"prof_opti{suffix}.xlsx", index=False)
prof_opti

#######################################
# And the graph is:


def plot_profile(df, ax0, ax1=None, title=None):
gr_dur = (
df[["dur", "args_op_name"]].groupby("args_op_name").sum().sort_values("dur")
)
gr_dur.plot.barh(ax=ax0)
if title is not None:
ax0.set_title(title)
if ax1 is not None:
gr_n = (
df[["dur", "args_op_name"]]
.groupby("args_op_name")
.count()
.sort_values("dur")
)
gr_n = gr_n.loc[gr_dur.index, :]
gr_n.plot.barh(ax=ax1)
ax1.set_title("n occurences")


unique_op = set(prof_base["args_op_name"])
fig, ax = plt.subplots(2, 2, figsize=(10, len(unique_op)), sharex="col")
plot_profile(prof_base, ax[0, 0], ax[0, 1], title="baseline")
plot_profile(prof_opt, ax[1, 0], ax[1, 1], title="optimized")

fig.savefig("plot_profiling.png")
plot_ort_profile(prof_base, ax[0, 0], ax[0, 1], title="baseline")
plot_ort_profile(prof_opti, ax[1, 0], ax[1, 1], title="optimized")
fig.tight_layout()
fig.savefig(f"plot_profiling{suffix}.png")

##################################################
# Merging profiles
Expand All@@ -96,103 +82,14 @@ def plot_profile(df, ax0, ax1=None, title=None):
# process the same image and the input and output size are the
# same at every iteration.


def preprocess(df):
groupkey = [
"args_op_name",
"args_output_type_shape",
"args_input_type_shape",
"args_provider",
]

def _idx(row):
"""
There may be multiple node with the same
input/output types and shapes.
This function gives every instance a distinct id.
First unique op with same I/O receives the index 0.
The counter restart when the session goes to the
next image.
"""
if row["cat"] == "Session":
occurences[0] = {}
return -1
assert "idx" not in groupkey
vals = [row[k] for k in groupkey]
key = tuple(map(str, vals))
if key not in occurences[0]:
occurences[0][key] = 0
else:
occurences[0][key] += 1
return occurences[0][key]

df = df.copy()
occurences = [{}]
df["idx"] = df.apply(_idx, axis=1)
df = df[(df["cat"] == "Node") & df["name"].str.contains("kernel_time")]
groupkey.append("idx")
for c in groupkey:
if c != "idx":
df[c] = df[c].apply(str)
gr = df[groupkey + ["dur"]].groupby(groupkey)
return gr.sum()


base = preprocess(prof_base)
opti = preprocess(prof_opt)
merge = base.merge(
opti, how="outer", suffixes=("base", "opti"), left_index=True, right_index=True
)
merge = merge.reset_index(drop=False)
merge.to_excel("plot_profiling_merged.xlsx", index=False)
merge, gr = merge_ort_profile(prof_base, prof_opti)
merge.to_excel(f"plot_profiling_merged{suffix}.xlsx", index=False)
merge


#####################################################
# Aggregation


def classify(row):
if numpy.isnan(row["duropti"]):
return "-"
if numpy.isnan(row["durbase"]):
return "+"
return "="
# More detailed


keys = {"float": "f"}


def process_shape(s):
value = eval(s)
ns = []
for v in value:
if len(v) != 1:
raise NotImplementedError(f"Unexpected value {v} in {s!r}.")
k, v = list(v.items())[0]
n = "-".join([keys[k], "x".join(map(str, v))])
ns.append(n)
return ",".join(ns)


def label(row):
name = row["args_op_name"]
inshape = process_shape(row["args_input_type_shape"])
outshape = process_shape(row["args_output_type_shape"])
side = row["side"][0]
prov = row["args_provider"][:3]
idx = row["idx"]
return f"[{side}{prov}]{name}({inshape})->{outshape}[{idx}]"


df = merge.copy()
df["side"] = df.apply(classify, axis=1)
df["label"] = df.apply(label, axis=1)
gr = (
df[["label", "durbase", "duropti", "idx"]]
.groupby("label")
.agg({"durbase": numpy.sum, "duropti": numpy.sum, "idx": max})
)
gr.to_excel(f"plot_profiling_merged_details{suffix}.xlsx", index=False)
gr

################################
Expand All@@ -210,11 +107,10 @@ def label(row):
gr[["durbase", "duropti"]].plot.barh(ax=ax[0])
ax[0].set_title("Side by side duration")
gr = gr.copy()
gr["idx"] += 1
gr[["idx"]].plot.barh(ax=ax[1])
gr[["countbase", "countopti"]].plot.barh(ax=ax[1])
ax[1].set_title("Side by side count")
fig.tight_layout()
fig.savefig("plot_profiling_side_by_side.png")
fig.savefig(f"plot_profiling_side_by_side{suffix}.png")


########################################
Expand All@@ -231,21 +127,44 @@ def label(row):
disable_optimization=True,
providers=["CUDAExecutionProvider"],
)
prof_base.to_excel(f"prof_cuda_base{suffix}.xlsx", index=False)

prof_opti = ort_profile(
optimized,
feeds,
repeat=6,
disable_optimization=True,
providers=["CUDAExecutionProvider"],
providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
)
prof_opti.to_excel(f"prof_cuda_opti{suffix}.xlsx", index=False)

unique_op = set(prof_base["args_op_name"])
fig, ax = plt.subplots(2, 2, figsize=(10, len(unique_op)), sharex="col")
plot_profile(prof_base, ax[0, 0], ax[0, 1], title="baseline")
plot_profile(prof_opt, ax[1, 0], ax[1, 1], title="optimized")
fig.savefig("plot_profiling_cuda.png")
plot_ort_profile(prof_base, ax[0, 0], ax[0, 1], title="baseline")
plot_ort_profile(prof_opti, ax[1, 0], ax[1, 1], title="optimized")
fig.tight_layout()
fig.savefig(f"plot_profiling_cuda{suffix}.png")

merge, gr = merge_ort_profile(prof_base, prof_opti)
merge.to_excel(f"plot_profiling_merged{suffix}.xlsx", index=False)
gr.to_excel(f"plot_profiling_merged_details{suffix}.xlsx", index=False)

grmax = gr["durbase"] + gr["duropti"]
total = grmax.sum()
grmax /= total
gr = gr[grmax >= 0.01]

fig, ax = plt.subplots(1, 2, figsize=(14, min(gr.shape[0], 500)), sharey=True)
gr[["durbase", "duropti"]].plot.barh(ax=ax[0])
ax[0].set_title("Side by side duration")
gr = gr.copy()
gr[["countbase", "countopti"]].plot.barh(ax=ax[1])
ax[1].set_title("Side by side count")
fig.tight_layout()
fig.savefig(f"plot_profiling_side_by_side_cuda{suffix}.png")

else:
print(f"CUDA not available in {get_available_providers()}")
print(f"CUDA not available in {get_available_providers()}.")
fig, ax = None, None

ax
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31 changes: 29 additions & 2 deletions_unittests/ut_ort/test_ort_profile.py
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@@ -1,10 +1,11 @@
import unittest
import os
import numpy as np
from pandas import DataFrame
from pandas import DataFrame, read_excel
from onnx_array_api.npx import absolute, jit_onnx
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
from onnx_array_api.ort.ort_profile import ort_profile, merge_ort_profile


class TestOrtProfile(ExtTestCase):
Expand All@@ -27,10 +28,36 @@ 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_merge_ort_profile(self):
data = os.path.join(os.path.dirname(__file__), "data")
df1 = read_excel(os.path.join(data, "prof_base.xlsx"))
df2 = read_excel(os.path.join(data, "prof_opti.xlsx"))
merged, gr = merge_ort_profile(df1, df2)
self.assertEqual(merged.shape, (23, 9))
self.assertEqual(
list(merged.columns),
[
"args_op_name",
"args_output_type_shape",
"args_input_type_shape",
"args_provider",
"idx",
"durbase",
"countbase",
"duropti",
"countopti",
],
)
self.assertEqual(gr.shape, (19, 4))
self.assertEqual(
list(gr.columns), ["durbase", "duropti", "countbase", "countopti"]
)


if __name__ == "__main__":
unittest.main(verbosity=2)
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