def test1(model, X_test, number, repeat, name, **data):
res = measure_time('model.predict(X_test)',
div_by_number=True,
number=number,
repeat=repeat,
context={
'X_test': X_test,
'model': model
})
res['name'] = name
res['runtime'] = 'skl'
res['version'] = skl_version
res['batch'] = "y"
res.update(data)
return res
def test5(oinf, X_test, number, repeat, name, **data):
res = measure_time('loop_model2(oinf, X_test)',
div_by_number=True,
number=number,
repeat=repeat,
context={
'oinf': oinf,
'X_test': X_test,
'loop_model2': loop_model2
})
res['name'] = name
res['runtime'] = 'mlprodict'
res['version'] = pyrt_version
res['batch'] = "n"
res.update(data)
return res
def test4(oinf, X_test, number, repeat, name, **data):
input = {'input': X_test}
res = measure_time('oinf.run({"X": X_test})',
div_by_number=True,
number=number,
repeat=repeat,
context={
'oinf': oinf,
'X_test': X_test
})
res['name'] = name
res['runtime'] = 'mlprodict'
res['version'] = pyrt_version
res['batch'] = "y"
res.update(data)
return res
def test3(sess, X_test, number, repeat, name, **data):
res = measure_time('loop_model(sess, X_test)',
div_by_number=True,
number=number,
repeat=repeat,
context={
'sess': sess,
'X_test': X_test,
'loop_model': loop_model
})
res['name'] = name
res['runtime'] = 'onnxruntime'
res['version'] = ort_version
res['batch'] = "n"
res.update(data)
return res
def test2(sess, X_test, number, repeat, name, **data):
input = {'input': X_test}
res = measure_time('sess.run(None, {"X": X_test})',
div_by_number=True,
number=number,
repeat=repeat,
context={
'sess': sess,
'X_test': X_test
})
res['name'] = name
res['runtime'] = 'onnxruntime'
res['version'] = ort_version
res['batch'] = "y"
res.update(data)
return res
def _measure_time(stmt,
*x,
repeat=5,
number=5,
div_by_number=True,
first_run=True,
max_time=None):
"""
Measures a statement and returns the results as a dictionary.
:param stmt: string
:param *x: inputs
:param repeat: average over *repeat* experiment
:param number: number of executions in one row
:param div_by_number: divide by the number of executions
:param first_run: if True, runs the function once before measuring
:param max_time: execute the statement until the total goes
beyond this time (approximatively), *repeat* is ignored,
*div_by_number* must be set to True
:return: dictionary
See `Timer.repeat
<https://docs.python.org/3/library/timeit.html?timeit.Timer.repeat>`_
for a better understanding of parameter *repeat* and *number*.
The function returns a duration corresponding to
*number* times the execution of the main statement.
"""
if first_run:
try:
stmt(*x)
except RuntimeError as e: # pragma: no cover
raise RuntimeError("{}-{}".format(type(x), x.dtype)) from e
def fct():
stmt(*x)
if first_run:
fct()
return measure_time(fct,
context={},
repeat=repeat,
number=number,
div_by_number=div_by_number,
max_time=max_time)
def benchmark_op(axes, repeat=5, number=5, name="ReduceSum", shape_fct=None,
custom_impl=False):
if shape_fct is None:
def shape_fct(dim):
return (3, dim, 1, 128, 64)
ort_fct = build_ort_reducesum(axes)
res = []
for dim in tqdm([8, 16, 32, 64, 100, 128, 200,
256, 400, 512, 1024]):
shape = shape_fct(dim)
n_arrays = 10 if dim < 512 else 4
xs = [numpy.random.rand(*shape).astype(numpy.float32)
for _ in range(n_arrays)]
ys = [numpy.array(axes, dtype=numpy.int64)
for _ in range(n_arrays)]
info = dict(axes=axes, shape=shape)
# numpy
ctx = dict(
xs=xs, ys=ys,
fct=lambda x, y: numpy.sum(x, *y),
loop_fct=loop_fct)
obs = measure_time(
"loop_fct(fct, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'numpy'
obs.update(info)
res.append(obs)
# onnxruntime
ctx['fct'] = ort_fct
obs = measure_time(
"loop_fct(fct, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'ort'
obs.update(info)
res.append(obs)
if custom_impl:
if axes != (0, ):
raise RuntimeError(
"Unexpected axes=%r." % axes)
ctx['fct'] = lambda x, y: custom_reducesum_rk_float(x)
ctx['xs'] = [x.reshape((x.shape[0], -1)).copy() for x in xs]
obs = measure_time(
"loop_fct(fct, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'custom'
obs.update(info)
res.append(obs)
if tf_reduce_sum is not None:
# tensorflow
ctx['fct'] = tf_reduce_sum
ctx['xs'] = [convert_to_tensor(x) for x in xs]
ctx['ys'] = ys
obs = measure_time(
"loop_fct(fct, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'tf'
obs.update(info)
res.append(obs)
if torch_sum is not None:
def torch_sum1(x, y):
return torch_sum(x, y[0])
def torch_sum2(x, y):
return torch_sum(torch_sum(x, y[1]), y[0])
# torch
ctx['fct'] = torch_sum1 if len(axes) == 1 else torch_sum2
ctx['xs'] = [from_numpy(x) for x in xs]
ctx['ys'] = ys # [from_numpy(y) for y in ys]
obs = measure_time(
"loop_fct(fct, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'torch'
obs.update(info)
res.append(obs)
# Dataframes
shape_name = str(shape).replace(str(dim), "N")
df = pandas.DataFrame(res)
df.columns = [_.replace('dim', 'N') for _ in df.columns]
piv = df.pivot('N', 'fct', 'average')
rs = piv.copy()
for c in ['ort', 'torch', 'tf', 'tf_copy']:
if c in rs.columns:
rs[c] = rs['numpy'] / rs[c]
rs['numpy'] = 1.
# Graphs.
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
piv.plot(logx=True, logy=True, ax=ax[0],
title="%s benchmark\n%r - %r"
" lower better" % (name, shape_name, axes))
ax[0].legend(prop={"size": 9})
rs.plot(logx=True, logy=True, ax=ax[1],
title="%s Speedup, baseline=numpy\n%r - %r"
" higher better" % (name, shape_name, axes))
ax[1].plot([min(rs.index), max(rs.index)], [0.5, 0.5], 'g--')
ax[1].plot([min(rs.index), max(rs.index)], [2., 2.], 'g--')
ax[1].legend(prop={"size": 9})
return df, rs, ax
def benchmark_op(axes,
repeat=2,
number=5,
name="ReduceMean",
shape_fct=None,
max_dim=None):
if shape_fct is None:
def shape_fct(dim):
return (3, dim, 1, 128, 64)
ort_fct = build_ort_reducemean(axes)
res = []
for dim in tqdm([4, 8, 16, 32, 64, 100, 128, 200, 256, 400, 512, 1024]):
if max_dim is not None and dim > max_dim:
continue
shape = shape_fct(dim)
n_arrays = 10 if dim < 512 else 4
xs = [
numpy.random.rand(*shape).astype(numpy.float32)
for _ in range(n_arrays)
]
ys = [numpy.array(axes, dtype=numpy.int64) for _ in range(n_arrays)]
info = dict(axes=axes, shape=shape)
# numpy
fct = lambda x, y: numpy.mean(x, axis=tuple(y))
ctx = dict(xs=xs, ys=ys, loop_fct=loop_fct)
obs = measure_time(lambda: loop_fct(fct, xs, ys),
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'numpy'
obs.update(info)
res.append(obs)
# onnxruntime
fct = ort_fct
obs = measure_time(lambda: loop_fct(fct, xs, ys),
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'ort'
obs.update(info)
res.append(obs)
if tf_reduce_mean is not None:
# tensorflow
fct = tf_reduce_mean
ctx['xs'] = [convert_to_tensor(x) for x in xs]
ctx['ys'] = ys
obs = measure_time(lambda: loop_fct(fct, ctx['xs'], ctx['ys']),
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'tf'
obs.update(info)
res.append(obs)
if torch_mean is not None:
def torch_mean1(x, y):
return torch_mean(x, y[0])
def torch_mean2(x, y):
return torch_mean(torch_mean(x, y[1]), y[0])
# torch
fct = torch_mean1 if len(axes) == 1 else torch_mean2
ctx['xs'] = [from_numpy(x) for x in xs]
ctx['ys'] = ys # [from_numpy(y) for y in ys]
obs = measure_time(lambda: loop_fct(fct, ctx['xs'], ctx['ys']),
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'torch'
obs.update(info)
res.append(obs)
# Dataframes
shape_name = str(shape).replace(str(dim), "N")
df = pandas.DataFrame(res)
df.columns = [_.replace('dim', 'N') for _ in df.columns]
piv = df.pivot('N', 'fct', 'average')
rs = piv.copy()
for c in ['ort', 'torch', 'tf', 'tf_copy']:
if c in rs.columns:
rs[c] = rs['numpy'] / rs[c]
rs['numpy'] = 1.
# Graphs.
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
piv.plot(logx=True,
logy=True,
ax=ax[0],
title="%s benchmark\n%r - %r"
" lower better" % (name, shape_name, axes))
ax[0].legend(prop={"size": 9})
rs.plot(logx=True,
logy=True,
ax=ax[1],
title="%s Speedup, baseline=numpy\n%r - %r"
" higher better" % (name, shape_name, axes))
ax[1].plot([min(rs.index), max(rs.index)], [0.5, 0.5], 'g--')
ax[1].plot([min(rs.index), max(rs.index)], [2., 2.], 'g--')
ax[1].legend(prop={"size": 9})
return df, rs, ax
def benchmark_op(repeat=10,
number=10,
name="Slice",
shape_slice_fct=None,
save=None,
opset=14,
repeat_profile=1500,
verbose=1):
if verbose:
print("[benchmark_op] start repeat=%d number=%d repeat_profile=%d"
" opset=%d." % (repeat, number, repeat_profile, opset))
res = []
for dim in tqdm([
8, 16, 32, 64, 100, 128, 200, 256, 400, 512, 600, 784, 800, 1000,
1024, 1200
]):
shape, slices = shape_slice_fct(dim)
onx, ort_fct, npy_fct, ort_fct_gpu = build_ort_op(save=save,
op_version=opset,
slices=slices)
n_arrays = 20
if dim >= 512:
n_arrays = 10
xs = [
numpy.random.rand(*shape).astype(numpy.float32)
for _ in range(n_arrays)
]
info = dict(shape=shape)
ctx = dict(xs=xs, loop_fct=loop_fct)
# numpy
ctx['fct'] = npy_fct
obs = measure_time(lambda: loop_fct(npy_fct, xs),
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'numpy'
obs['shape'] = ",".join(map(str, shape))
obs['slices'] = str(slices)
obs.update(info)
res.append(obs)
# onnxruntime
ctx['fct'] = ort_fct
obs = measure_time(lambda: loop_fct(ort_fct, xs),
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'ort'
obs['shape'] = ",".join(map(str, shape))
obs['slices'] = str(slices)
obs.update(info)
res.append(obs)
if ort_fct_gpu is not None:
# onnxruntime
dev = get_ort_device('cuda:0')
ctx['xs'] = [C_OrtValue.ortvalue_from_numpy(x, dev) for x in xs]
ctx['fct'] = ort_fct_gpu
obs = measure_time(lambda: loop_fct(ort_fct_gpu, ctx['xs']),
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'ort_gpu'
obs['shape'] = ",".join(map(str, shape))
obs['slices'] = str(slices)
obs.update(info)
res.append(obs)
# profiling CPU
if verbose:
print("[benchmark_op] done.")
print("[benchmark_op] profile CPU.")
so = SessionOptions()
so.enable_profiling = True
sess = InferenceSession(onx.SerializeToString(),
so,
providers=["CPUExecutionProvider"])
for i in range(0, repeat_profile):
sess.run(
None,
{'X': xs[-1]},
)
prof = sess.end_profiling()
with open(prof, "r") as f:
js = json.load(f)
dfprof = DataFrame(OnnxWholeSession.process_profiling(js))
dfprof['shape'] = ",".join(map(str, shape))
dfprof['slices'] = str(slices)
if verbose:
print("[benchmark_op] done.")
# profiling CPU
if ort_fct_gpu is not None:
if verbose:
print("[benchmark_op] profile GPU.")
so = SessionOptions()
so.enable_profiling = True
sess = InferenceSession(onx.SerializeToString(),
so,
providers=["CUDAExecutionProvider"])
io_binding = sess.io_binding()._iobinding
device = get_ort_device('cpu')
for i in range(0, repeat_profile):
x = ctx['xs'][-1]
io_binding.bind_input('X', device, numpy.float32, x.shape(),
x.data_ptr())
io_binding.bind_output('Y', device)
sess._sess.run_with_iobinding(io_binding, None)
prof = sess.end_profiling()
with open(prof, "r") as f:
js = json.load(f)
dfprofgpu = DataFrame(OnnxWholeSession.process_profiling(js))
dfprofgpu['shape'] = ",".join(map(str, shape))
dfprofgpu['slices'] = str(slices)
if verbose:
print("[benchmark_op] profile done.")
else:
dfprofgpu = None
# Dataframes
shape_name = str(shape).replace(str(dim), "N")
df = pandas.DataFrame(res)
piv = df.pivot('shape', 'fct', 'average')
rs = piv.copy()
for c in ['numpy', 'ort', 'ort_gpu']:
if c in rs.columns:
rs[f"numpy/{c}"] = rs['numpy'] / rs[c]
rs = rs[[c for c in rs.columns if "/numpy" not in c]].copy()
# Graphs.
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
piv.plot(logx=True,
logy=True,
ax=ax[0],
title=f"{name} benchmark\n{shape_name!r} lower better")
ax[0].legend(prop={"size": 9})
rs.plot(
logx=True,
logy=True,
ax=ax[1],
title=f"{name} Speedup, baseline=numpy\n{shape_name!r} higher better")
ax[1].plot([min(rs.index), max(rs.index)], [0.5, 0.5], 'g--')
ax[1].plot([min(rs.index), max(rs.index)], [2., 2.], 'g--')
ax[1].legend(prop={"size": 9})
return dfprof, dfprofgpu, df, rs, ax
def benchmark_equation(equation):
# equations
ort_einsum = build_ort_einsum(equation)
ort_einsum_decomposed = build_ort_decomposed(equation)
res = []
for dim in tqdm([8, 16, 32, 64, 100, 128, 200, 256, 500, 512]):
xs = [
numpy.random.rand(2, dim, 12, 64).astype(numpy.float32)
for _ in range(5)
]
ys = [
numpy.random.rand(2, dim, 12, 64).astype(numpy.float32)
for _ in range(5)
]
# numpy
ctx = dict(equation=equation,
xs=xs,
ys=ys,
einsum=numpy.einsum,
loop_einsum=loop_einsum,
loop_einsum_eq=loop_einsum_eq,
loop_einsum_eq_th=loop_einsum_eq_th)
obs = measure_time("loop_einsum_eq(einsum, equation, xs, ys)",
div_by_number=True,
context=ctx,
repeat=5,
number=1)
obs['dim'] = dim
obs['fct'] = 'numpy.einsum'
res.append(obs)
# opt-einsum
ctx['einsum'] = contract
obs = measure_time("loop_einsum_eq(einsum, equation, xs, ys)",
div_by_number=True,
context=ctx,
repeat=5,
number=1)
obs['dim'] = dim
obs['fct'] = 'opt-einsum'
res.append(obs)
# onnxruntime
ctx['einsum'] = ort_einsum
obs = measure_time("loop_einsum(einsum, xs, ys)",
div_by_number=True,
context=ctx,
repeat=5,
number=1)
obs['dim'] = dim
obs['fct'] = 'ort_einsum'
res.append(obs)
# onnxruntime decomposed
ctx['einsum'] = ort_einsum_decomposed
obs = measure_time("loop_einsum(einsum, xs, ys)",
div_by_number=True,
context=ctx,
repeat=5,
number=1)
obs['dim'] = dim
obs['fct'] = 'ort_dec'
res.append(obs)
# custom implementation
ctx['einsum'] = custom_einsum_float
obs = measure_time("loop_einsum_eq_th(einsum, equation, xs, ys)",
div_by_number=True,
context=ctx,
repeat=5,
number=1)
obs['dim'] = dim
obs['fct'] = 'c_einsum'
res.append(obs)
# transpose + custom implementation
ctx['einsum'] = custom_einsum_float_tr
obs = measure_time("loop_einsum_eq(einsum, equation, xs, ys)",
div_by_number=True,
context=ctx,
repeat=5,
number=1)
obs['dim'] = dim
obs['fct'] = 'c_einsum_tr'
res.append(obs)
if tf_einsum is not None:
# tensorflow
ctx['einsum'] = tf_einsum
ctx['xs'] = [convert_to_tensor(x) for x in xs]
ctx['ys'] = [convert_to_tensor(y) for y in ys]
obs = measure_time("loop_einsum_eq(einsum, equation, xs, ys)",
div_by_number=True,
context=ctx,
repeat=5,
number=1)
obs['dim'] = dim
obs['fct'] = 'tf_einsum'
res.append(obs)
if torch_einsum is not None:
# torch
ctx['einsum'] = torch_einsum
ctx['xs'] = [from_numpy(x) for x in xs]
ctx['ys'] = [from_numpy(y) for y in ys]
obs = measure_time("loop_einsum_eq(einsum, equation, xs, ys)",
div_by_number=True,
context=ctx,
repeat=5,
number=1)
obs['dim'] = dim
obs['fct'] = 'torch_einsum'
res.append(obs)
# Dataframes
df = pandas.DataFrame(res)
piv = df.pivot('dim', 'fct', 'average')
rs = piv.copy()
rs['c_einsum'] = rs['numpy.einsum'] / rs['c_einsum']
rs['ort_einsum'] = rs['numpy.einsum'] / rs['ort_einsum']
rs['ort_dec'] = rs['numpy.einsum'] / rs['ort_dec']
rs['opt-einsum'] = rs['numpy.einsum'] / rs['opt-einsum']
if 'c_einsum_tr' in rs.columns:
rs['c_einsum_tr'] = rs['numpy.einsum'] / rs['c_einsum_tr']
if 'tf_einsum' in rs.columns:
rs['tf_einsum'] = rs['numpy.einsum'] / rs['tf_einsum']
if 'torch_einsum' in rs.columns:
rs['torch_einsum'] = rs['numpy.einsum'] / rs['torch_einsum']
rs['numpy.einsum'] = 1.
# Graphs.
fig, ax = plt.subplots(1, 2, figsize=(14, 5))
piv.plot(logx=True,
logy=True,
ax=ax[0],
title="Einsum benchmark\n%s -- (2, N, 12, 64)"
" lower better" % equation)
ax[0].legend(prop={"size": 9})
rs.plot(logx=True,
logy=True,
ax=ax[1],
title="Einsum Speedup, baseline=numpy\n%s -- (2, N, 12, 64)"
" higher better" % equation)
ax[1].plot([min(rs.index), max(rs.index)], [0.5, 0.5], 'g--')
ax[1].plot([min(rs.index), max(rs.index)], [2., 2.], 'g--')
ax[1].legend(prop={"size": 9})
return df, rs, ax
def benchmark_op(repeat=5, number=2, name="Add", shape_fcts=None):
if shape_fcts is None:
def shape_fct(dim):
return (5, dim, dim)
shape_fcts = (shape_fct, shape_fct)
ort_fct = build_ort_add()
res = []
for dim in tqdm(
[8, 16, 32, 64, 100, 128, 200, 256, 400, 512, 1024, 1536, 2048, 2560]):
shape1 = shape_fcts[0](dim)
shape2 = shape_fcts[1](dim)
n_arrays = (16 if dim < 512 else 4) if dim < 2048 else 4
if len(shape1) > 3:
n_arrays = int(n_arrays / 4)
xs = [
numpy.random.rand(*shape1).astype(numpy.float32)
for _ in range(n_arrays)
]
ys = [
numpy.random.rand(*shape2).astype(numpy.float32)
for _ in range(n_arrays)
]
info = dict(shape1=shape1, shape2=shape2)
# numpy
ctx = dict(
xs=xs,
ys=ys,
fct=lambda x, y: numpy.add(numpy.add(numpy.add(x, y), y), y),
loop_fct=loop_fct)
obs = measure_time("loop_fct(fct, xs, ys)",
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'numpy'
obs.update(info)
res.append(obs)
# onnxruntime
ctx['fct'] = ort_fct
obs = measure_time("loop_fct(fct, xs, ys)",
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'ort'
obs.update(info)
res.append(obs)
if tf_add is not None:
# tensorflow
ctx['fct'] = lambda x, y: tf_add(tf_add(tf_add(x, y), y), y)
ctx['xs'] = [convert_to_tensor(x) for x in xs]
ctx['ys'] = [convert_to_tensor(y) for y in ys]
obs = measure_time("loop_fct(fct, xs, ys)",
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'tf'
obs.update(info)
res.append(obs)
if torch_add is not None:
# torch
ctx['fct'] = lambda x, y: torch_add(torch_add(torch_add(x, y), y),
y)
ctx['xs'] = [from_numpy(x) for x in xs]
ctx['ys'] = [from_numpy(y) for y in ys]
obs = measure_time("loop_fct(fct, xs, ys)",
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'torch'
obs.update(info)
res.append(obs)
# Dataframes
shape1_name = str(shape1).replace(str(dim), "N")
shape2_name = str(shape2).replace(str(dim), "N")
df = pandas.DataFrame(res)
df.columns = [_.replace('dim', 'N') for _ in df.columns]
piv = df.pivot('N', 'fct', 'average')
rs = piv.copy()
for c in ['ort', 'torch', 'tf']:
if c in rs.columns:
rs[c] = rs['numpy'] / rs[c]
rs['numpy'] = 1.
# Graphs.
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
piv.plot(logx=True,
logy=True,
ax=ax[0],
title="%s benchmark\n%s + %s"
" lower better" % (name, shape1_name, shape2_name))
ax[0].legend(prop={"size": 9})
rs.plot(logx=True,
logy=True,
ax=ax[1],
title="%s Speedup, baseline=numpy\n%s + %s"
" higher better" % (name, shape1_name, shape2_name))
ax[1].plot([min(rs.index), max(rs.index)], [0.5, 0.5], 'g--')
ax[1].plot([min(rs.index), max(rs.index)], [2., 2.], 'g--')
ax[1].legend(prop={"size": 9})
return df, rs, ax
print("profiling...")
txt = profile(runlocal, pyinst_format='text')
print(txt[1])
##########################################
# Now let's measure the performance the average
# computation time per observations for 2 to 100
# observations. The runtime implemented in
# :epkg:`mlprodict` parallizes the computation
# after a given number of observations.
obs = []
for N in tqdm(list(range(2, 21))):
m = measure_time("oinf.run({'X': x})", {
'oinf': oinf,
'x': X32[:N]
},
div_by_number=True,
number=20)
m['N'] = N
m['RT'] = 'ONNX'
obs.append(m)
with config_context(assume_finite=True):
m = measure_time("hgb.predict(x)", {
'hgb': hgb,
'x': X32[:N]
},
div_by_number=True,
number=15)
m['N'] = N
m['RT'] = 'SKL'
r1 = py_topk.run({'X': X})
r1
###########################
#
r2 = ort_topk.run(None, {'X': X})
r2
#################################
# Some figures.
bs = []
bs.append(
measure_time(lambda: py_topk.run({'X': X}),
context=globals(),
div_by_number=True))
bs[-1]['c'] = 'py'
bs[-1]
#################################
#
bs.append(
measure_time(lambda: ort_topk.run(None, {'X': X}),
context=globals(),
div_by_number=True))
bs[-1]['c'] = 'or'
bs[-1]
#####################################
def benchmark_op(perm, repeat=5, number=5, name="Transpose", shape_fct=None):
if shape_fct is None:
def shape_fct(dim):
return (3, dim, 1, 512)
ort_fct = build_ort_transpose(perm)
res = []
for dim in tqdm([8, 16, 32, 64, 100, 128, 200, 256, 400, 512, 1024]):
shape = shape_fct(dim)
n_arrays = 10 if dim < 512 else 4
xs = [
numpy.random.rand(*shape).astype(numpy.float32)
for _ in range(n_arrays)
]
ys = [perm for _ in range(n_arrays)]
equation = perm2eq(perm)
info = dict(perm=perm, shape=shape)
# numpy
ctx = dict(
xs=xs,
ys=ys,
fct=lambda x, y: numpy.ascontiguousarray(numpy.transpose(x, y)),
loop_fct=loop_fct)
obs = measure_time("loop_fct(fct, xs, ys)",
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'numpy'
obs.update(info)
res.append(obs)
# onnxruntime
ctx['fct'] = ort_fct
obs = measure_time("loop_fct(fct, xs, ys)",
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'ort'
obs.update(info)
res.append(obs)
if tf_transpose is not None:
# tensorflow
ctx['fct'] = tf_transpose
ctx['xs'] = [convert_to_tensor(x) for x in xs]
ctx['ys'] = [convert_to_tensor(y) for y in ys]
obs = measure_time("loop_fct(fct, xs, ys)",
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'tf'
obs.update(info)
res.append(obs)
# tensorflow with copy
ctx['fct'] = lambda x, y: tf_transpose(convert_to_tensor(x)).numpy(
)
ctx['xs'] = xs
ctx['ys'] = ys
obs = measure_time("loop_fct(fct, xs, ys)",
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'tf_copy'
obs.update(info)
res.append(obs)
if torch_einsum is not None:
# torch
ctx['fct'] = lambda x, y: torch_einsum(equation, x).contiguous()
ctx['xs'] = [from_numpy(x) for x in xs]
ctx['ys'] = ys # [from_numpy(y) for y in ys]
obs = measure_time("loop_fct(fct, xs, ys)",
div_by_number=True,
context=ctx,
repeat=repeat,
number=number)
obs['dim'] = dim
obs['fct'] = 'torch'
obs.update(info)
res.append(obs)
# Dataframes
shape_name = str(shape).replace(str(dim), "N")
df = pandas.DataFrame(res)
df.columns = [_.replace('dim', 'N') for _ in df.columns]
piv = df.pivot('N', 'fct', 'average')
rs = piv.copy()
for c in ['ort', 'torch', 'tf', 'tf_copy']:
if c in rs.columns:
rs[c] = rs['numpy'] / rs[c]
rs['numpy'] = 1.
# Graphs.
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
piv.plot(logx=True,
logy=True,
ax=ax[0],
title="%s benchmark\n%r - %r - %s"
" lower better" % (name, shape_name, perm, equation))
ax[0].legend(prop={"size": 9})
rs.plot(logx=True,
logy=True,
ax=ax[1],
title="%s Speedup, baseline=numpy\n%r - %r - %s"
" higher better" % (name, shape_name, perm, equation))
ax[1].plot([min(rs.index), max(rs.index)], [0.5, 0.5], 'g--')
ax[1].plot([min(rs.index), max(rs.index)], [2., 2.], 'g--')
ax[1].legend(prop={"size": 9})
return df, rs, ax
def latency(model,
law='normal',
size=1,
number=10,
repeat=10,
max_time=0,
runtime="onnxruntime",
device='cpu',
profiling=None):
"""
Measures the latency of a model (python API).
:param model: ONNX graph
:param law: random law used to generate fake inputs
:param size: batch size, it replaces the first dimension
of every input if it is left unknown
:param number: number of calls to measure
:param repeat: number of times to repeat the experiment
:param max_time: if it is > 0, it runs as many time during
that period of time
:param runtime: available runtime
:param device: device, `cpu`, `cuda:0`
:param profiling: if True, profile the execution of every
node, if can be sorted by name or type,
the value for this parameter should e in `(None, 'name', 'type')`,
:return: dictionary or a tuple (dictionary, dataframe)
if the profiling is enable
.. cmdref::
:title: Measures model latency
:cmd: -m mlprodict latency --help
:lid: l-cmd-latency
The command generates random inputs and call many times the
model on these inputs. It returns the processing time for one
iteration.
Example::
python -m mlprodict latency --model "model.onnx"
"""
if isinstance(model, str) and not os.path.exists(model):
raise FileNotFoundError( # pragma: no cover
"Unable to find model %r." % model)
if profiling not in (None, '', 'name', 'type'):
raise ValueError("Unexpected value for profiling: %r." % profiling)
size = int(size)
number = int(number)
repeat = int(repeat)
if max_time in (None, 0, ""):
max_time = None
else:
max_time = float(max_time)
if max_time <= 0:
max_time = None
if law != "normal":
raise ValueError("Only law='normal' is supported, not %r." % law)
if device in ('cpu', 'CPUExecutionProviders'):
providers = ['CPUExecutionProviders']
elif device in ('cuda:0', 'CUDAExecutionProviders'):
if runtime != 'onnxruntime':
raise NotImplementedError( # pragma: no cover
"Only runtime 'onnxruntime' supports this device or provider "
"%r." % device)
providers = ['CUDAExecutionProviders']
elif ',' in device:
if runtime != 'onnxruntime':
raise NotImplementedError( # pragma: no cover
"Only runtime 'onnxruntime' supports this device or provider "
"%r." % device)
providers = device.split(',')
allp = set(get_all_providers())
for p in providers:
if p not in allp:
raise ValueError(
"One device or provider %r is not supported among %r."
"" % (p, allp))
else:
raise ValueError( # pragma no cover
"Device %r not supported." % device)
if runtime == "onnxruntime":
if profiling in ('name', 'type'):
so = SessionOptions()
so.enable_profiling = True
sess = InferenceSession(model, sess_options=so)
else:
sess = InferenceSession(model)
fct = lambda feeds: sess.run(None, feeds)
inputs = sess.get_inputs()
else:
if profiling in ('name', 'type'):
runtime_options = {"enable_profiling": True}
if runtime != 'onnxruntime1':
raise NotImplementedError( # pragma: no cover
"Profiling is not implemented for runtime=%r." % runtime)
else:
runtime_options = None
oinf = OnnxInference(model,
runtime=runtime,
runtime_options=runtime_options)
fct = lambda feeds: oinf.run(feeds)
inputs = oinf.obj.graph.input
feeds = random_feed(inputs, size)
res = measure_time(lambda: fct(feeds),
number=number,
repeat=repeat,
context={},
max_time=max_time,
div_by_number=True)
for k, v in feeds.items():
res["shape(%s)" % k] = "x".join(map(str, v.shape))
if profiling in ('name', 'type'):
if runtime == 'onnxruntime':
profile_name = sess.end_profiling()
with open(profile_name, 'r', encoding='utf-8') as f:
js = json.load(f)
js = OnnxWholeSession.process_profiling(js)
df = DataFrame(js)
else:
df = oinf.get_profiling(as_df=True)
if profiling == 'name':
gr = df[['dur', "args_op_name",
"name"]].groupby(["args_op_name",
"name"]).sum().sort_values('dur')
else:
gr = df[['dur', "args_op_name"
]].groupby("args_op_name").sum().sort_values('dur')
return res, gr
return res
def benchmark_equation():
# equations
ort_where = build_ort_where()
ort_where_add = build_ort_where_add()
res = []
for dim in tqdm([8, 16, 32, 64, 100, 128, 200,
256, 500, 512, 1024, 2048]):
repeat = 5
number = 10
conds = [(numpy.random.rand(dim, dim) < 0.5).astype(numpy.bool_)
for _ in range(repeat)]
xs = [numpy.random.rand(dim, dim).astype(numpy.float32)
for _ in range(repeat)]
ys = [numpy.random.rand(dim, dim).astype(numpy.float32)
for _ in range(repeat)]
# numpy
ctx = dict(conds=conds, xs=xs, ys=ys, where=numpy.where,
loop_where=loop_where)
obs = measure_time(
"loop_where(where, conds, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'numpy.where'
res.append(obs)
# numpy add
ctx['where'] = numpy_where_add
obs = measure_time(
"loop_where(where, conds, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'numpy_where_add'
res.append(obs)
# onnxruntime
ctx['where'] = ort_where
obs = measure_time(
"loop_where(where, conds, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'ort_where'
res.append(obs)
# onnxruntime - 2
ctx['where'] = ort_where_add
ctx['conds'] = [c.astype(numpy.float32) for c in conds]
obs = measure_time(
"loop_where(where, conds, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'ort_where_add'
res.append(obs)
if tf_where is not None:
# tensorflow
ctx['where'] = tf_where
ctx['conds'] = [convert_to_tensor(c) for c in conds]
ctx['xs'] = [convert_to_tensor(x) for x in xs]
ctx['ys'] = [convert_to_tensor(y) for y in ys]
obs = measure_time(
"loop_where(where, conds, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'tf_where'
res.append(obs)
if torch_where is not None:
# torch
ctx['where'] = torch_where
ctx['conds'] = [from_numpy(c) for c in conds]
ctx['xs'] = [from_numpy(x) for x in xs]
ctx['ys'] = [from_numpy(y) for y in ys]
obs = measure_time(
"loop_where(where, conds, xs, ys)",
div_by_number=True, context=ctx, repeat=repeat, number=number)
obs['dim'] = dim
obs['fct'] = 'torch_where'
res.append(obs)
# Dataframes
df = pandas.DataFrame(res)
piv = df.pivot('dim', 'fct', 'average')
rs = piv.copy()
rs['ort_where'] = rs['numpy.where'] / rs['ort_where']
rs['numpy_where_add'] = rs['numpy.where'] / rs['numpy_where_add']
rs['ort_where_add'] = rs['numpy.where'] / rs['ort_where_add']
if 'tf_where' in rs.columns:
rs['tf_where'] = rs['numpy.where'] / rs['tf_where']
if 'torch_where' in rs.columns:
rs['torch_where'] = rs['numpy.where'] / rs['torch_where']
rs['numpy.where'] = 1.
# Graphs.
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
piv.plot(logx=True, logy=True, ax=ax[0],
title="Where benchmark -- (N, N)\nlower better")
ax[0].legend(prop={"size": 9})
rs.plot(logx=True, logy=True, ax=ax[1],
title="Where Speedup, baseline=numpy -- (N, N)\nhigher better")
ax[1].plot([min(rs.index), max(rs.index)], [0.5, 0.5], 'g--')
ax[1].plot([min(rs.index), max(rs.index)], [2., 2.], 'g--')
ax[1].legend(prop={"size": 9})
return df, rs, ax
