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Better handling of float 8 in onnx_simple_text_plot#27

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sdpython merged 4 commits intomainfromtplot
Jul 2, 2023
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5 changes: 4 additions & 1 deletionCHANGELOGS.rst
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Expand Up@@ -4,7 +4,10 @@ Change Logs
0.2.0
+++++

* :pr:`24`: add ExtendedReferenceEvaluator to support scenario for the Array API onnx does not support
* :pr:`27`: add function from_array_extended to convert
an array to a TensorProto, including bfloat16 and float 8 types
* :pr:`24`: add ExtendedReferenceEvaluator to support scenario
for the Array API onnx does not support
* :pr:`22`: support OrtValue in function :func:`ort_profile`
* :pr:`17`: implements ArrayAPI
* :pr:`3`: fixes Array API with onnxruntime and scikit-learn
44 changes: 44 additions & 0 deletions_unittests/ut_plotting/test_text_plot.py
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Expand Up@@ -306,6 +306,50 @@ def test_function_plot(self):
self.assertIn("type=? shape=?", text)
self.assertIn("LinearRegression[custom]", text)

def test_function_plot_f8(self):
new_domain = "custom"
opset_imports = [make_opsetid("", 14), make_opsetid(new_domain, 1)]

node1 = make_node("MatMul", ["X", "A"], ["XA"])
node2 = make_node("Add", ["XA", "B"], ["Y"])

linear_regression = make_function(
new_domain, # domain name
"LinearRegression", # function name
["X", "A", "B"], # input names
["Y"], # output names
[node1, node2], # nodes
opset_imports, # opsets
[],
) # attribute names

X = make_tensor_value_info("X", TensorProto.FLOAT8E4M3FN, [None, None])
A = make_tensor_value_info("A", TensorProto.FLOAT8E5M2, [None, None])
B = make_tensor_value_info("B", TensorProto.FLOAT8E4M3FNUZ, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT8E5M2FNUZ, None)

graph = make_graph(
[
make_node(
"LinearRegression", ["X", "A", "B"], ["Y1"], domain=new_domain
),
make_node("Abs", ["Y1"], ["Y"]),
],
"example",
[X, A, B],
[Y],
)

onnx_model = make_model(
graph, opset_imports=opset_imports, functions=[linear_regression]
) # functions to add)

text = onnx_simple_text_plot(onnx_model)
self.assertIn("function name=LinearRegression domain=custom", text)
self.assertIn("MatMul(X, A) -> XA", text)
self.assertIn("type=? shape=?", text)
self.assertIn("LinearRegression[custom]", text)

def test_onnx_text_plot_tree_simple(self):
iris = load_iris()
X, y = iris.data.astype(numpy.float32), iris.target
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56 changes: 56 additions & 0 deletions_unittests/ut_reference/test_array_tensor.py
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@@ -0,0 +1,56 @@
import unittest
import numpy as np
from onnx import TensorProto
from onnx.helper import make_graph, make_model, make_node, make_tensor_value_info
from onnx_array_api.ext_test_case import ExtTestCase
from onnx_array_api.reference import (
to_array_extended,
from_array_extended,
ExtendedReferenceEvaluator,
)


class TestArrayTensor(ExtTestCase):
def test_from_array(self):
for dt in (np.float32, np.float16, np.uint16, np.uint8):
with self.subTest(dtype=dt):
a = np.array([0, 1, 2], dtype=dt)
t = from_array_extended(a, "a")
b = to_array_extended(t)
self.assertEqualArray(a, b)
t2 = from_array_extended(b, "a")
self.assertEqual(t.SerializeToString(), t2.SerializeToString())

def test_from_array_f8(self):
def make_model_f8(fr, to):
model = make_model(
make_graph(
[make_node("Cast", ["X"], ["Y"], to=to)],
"cast",
[make_tensor_value_info("X", fr, None)],
[make_tensor_value_info("Y", to, None)],
)
)
return model

for dt in (np.float32, np.float16, np.uint16, np.uint8):
with self.subTest(dtype=dt):
a = np.array([0, 1, 2], dtype=dt)
b = from_array_extended(a, "a")
for to in [
TensorProto.FLOAT8E4M3FN,
TensorProto.FLOAT8E4M3FNUZ,
TensorProto.FLOAT8E5M2,
TensorProto.FLOAT8E5M2FNUZ,
TensorProto.BFLOAT16,
]:
with self.subTest(fr=b.data_type, to=to):
model = make_model_f8(b.data_type, to)
ref = ExtendedReferenceEvaluator(model)
got = ref.run(None, {"X": a})[0]
back = from_array_extended(got, "a")
self.assertEqual(to, back.data_type)


if __name__ == "__main__":
unittest.main(verbosity=2)
2 changes: 1 addition & 1 deletiononnx_array_api/npx/npx_functions.py
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Expand Up@@ -3,7 +3,7 @@
import numpy as np
from onnx import FunctionProto, ModelProto, NodeProto, TensorProto
from onnx.helper import make_tensor, tensor_dtype_to_np_dtype
fromonnx.numpy_helper import from_array
from..reference import from_array_extended as from_array
from .npx_constants import FUNCTION_DOMAIN
from .npx_core_api import cst, make_tuple, npxapi_inline, npxapi_no_inline, var
from .npx_types import (
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2 changes: 1 addition & 1 deletiononnx_array_api/npx/npx_graph_builder.py
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Expand Up@@ -24,11 +24,11 @@
make_opsetid,
make_tensor_value_info,
)
from onnx.numpy_helper import from_array
from onnx.onnx_cpp2py_export.checker import ValidationError
from onnx.onnx_cpp2py_export.shape_inference import InferenceError
from onnx.shape_inference import infer_shapes

from ..reference import from_array_extended as from_array
from .npx_constants import _OPSET_TO_IR_VERSION, FUNCTION_DOMAIN, ONNX_DOMAIN
from .npx_function_implementation import get_function_implementation
from .npx_helper import (
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2 changes: 1 addition & 1 deletiononnx_array_api/npx/npx_helper.py
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Expand Up@@ -9,8 +9,8 @@
make_operatorsetid,
make_value_info,
)
from onnx.numpy_helper import from_array
from onnx.version_converter import convert_version
from ..reference import from_array_extended as from_array


def rename_in_onnx_graph(
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19 changes: 16 additions & 3 deletionsonnx_array_api/plotting/_helper.py
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Expand Up@@ -10,7 +10,7 @@
ValueInfoProto,
)
from onnx.helper import tensor_dtype_to_np_dtype
fromonnx.numpy_helper import to_array
from..reference import to_array_extended as to_array
from ..npx.npx_types import DType


Expand DownExpand Up@@ -136,12 +136,25 @@ def _get_type(obj0):
return tensor_dtype_to_np_dtype(TensorProto.DOUBLE)
if obj.data_type == TensorProto.INT64 and hasattr(obj, "int64_data"):
return tensor_dtype_to_np_dtype(TensorProto.INT64)
if obj.data_type == TensorProto.INT32 and hasattr(obj, "int32_data"):
if obj.data_type in (
TensorProto.INT8,
TensorProto.UINT8,
TensorProto.UINT16,
TensorProto.INT16,
TensorProto.INT32,
TensorProto.FLOAT8E4M3FN,
TensorProto.FLOAT8E4M3FNUZ,
TensorProto.FLOAT8E5M2,
TensorProto.FLOAT8E5M2FNUZ,
) and hasattr(obj, "int32_data"):
return tensor_dtype_to_np_dtype(TensorProto.INT32)
if hasattr(obj, "raw_data") and len(obj.raw_data) > 0:
arr = to_array(obj)
return arr.dtype
raise RuntimeError(f"Unable to guess type from {obj0!r}.")
raise RuntimeError(
f"Unable to guess type from obj.data_type={obj.data_type} "
f"and obj={obj0!r} - {TensorProto.__dict__}."
)
if hasattr(obj, "type"):
obj = obj.type
if hasattr(obj, "tensor_type"):
Expand Down
2 changes: 1 addition & 1 deletiononnx_array_api/plotting/dot_plot.py
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Expand Up@@ -3,8 +3,8 @@

from onnx import GraphProto, ModelProto
from onnx.helper import tensor_dtype_to_string
from onnx.numpy_helper import to_array

from ..reference import to_array_extended as to_array
from ._helper import Graph, _get_shape, attributes_as_dict


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4 changes: 1 addition & 3 deletionsonnx_array_api/plotting/text_plot.py
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@@ -1,10 +1,8 @@
import pprint
from collections import OrderedDict

import numpy
from onnx import AttributeProto
from onnx.numpy_helper import to_array

from ..reference import to_array_extended as to_array
from ._helper import _get_shape, _get_type, attributes_as_dict


Expand Down
44 changes: 44 additions & 0 deletionsonnx_array_api/reference/__init__.py
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@@ -1 +1,45 @@
from typing import Optional
import numpy as np
from onnx import TensorProto
from onnx.numpy_helper import from_array as onnx_from_array
from onnx.reference.ops.op_cast import (
bfloat16,
float8e4m3fn,
float8e4m3fnuz,
float8e5m2,
float8e5m2fnuz,
)
from onnx.reference.op_run import to_array_extended
from .evaluator import ExtendedReferenceEvaluator


def from_array_extended(tensor: np.array, name: Optional[str] = None) -> TensorProto:
"""
Converts an array into a TensorProto.

:param tensor: numpy array
:param name: name
:return: TensorProto
"""
dt = tensor.dtype
if dt == float8e4m3fn and dt.descr[0][0] == "e4m3fn":
to = TensorProto.FLOAT8E4M3FN
dt_to = np.uint8
elif dt == float8e4m3fnuz and dt.descr[0][0] == "e4m3fnuz":
to = TensorProto.FLOAT8E4M3FNUZ
dt_to = np.uint8
elif dt == float8e5m2 and dt.descr[0][0] == "e5m2":
to = TensorProto.FLOAT8E5M2
dt_to = np.uint8
elif dt == float8e5m2fnuz and dt.descr[0][0] == "e5m2fnuz":
to = TensorProto.FLOAT8E5M2FNUZ
dt_to = np.uint8
elif dt == bfloat16 and dt.descr[0][0] == "bfloat16":
to = TensorProto.BFLOAT16
dt_to = np.uint16
else:
return onnx_from_array(tensor, name)

t = onnx_from_array(tensor.astype(dt_to), name)
t.data_type = to
return t
2 changes: 1 addition & 1 deletiononnx_array_api/validation/tools.py
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Expand Up@@ -16,7 +16,7 @@
make_node,
set_model_props,
)
fromonnx.numpy_helper import from_array, to_array
from..reference importfrom_array_extended asfrom_array, to_array_extended as to_array


def randomize_proto(
Expand Down
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