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Improves translation to GraphBuilder#95

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sdpython merged 6 commits intomainfrombugr
Feb 18, 2025
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4 changes: 2 additions & 2 deletions.github/workflows/check-urls.yml
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Expand Up@@ -42,6 +42,6 @@ jobs:
print_all: false
timeout: 2
retry_count# : 2
exclude_urls: https://hal.archives-ouvertes.fr/hal-00990252/document
exclude_patterns: https://www.data.gouv.fr/fr/datasets/r/e3d83ab3-dc52-4c99-abaf-8a38050cc68c,https://dev.azure.com/
exclude_urls: https://hal.archives-ouvertes.fr/hal-00990252/document,https://github.com/onnx/tensorflow-onnx
exclude_patterns: https://www.data.gouv.fr/fr/datasets/r/e3d83ab3-dc52-4c99-abaf-8a38050cc68c,https://dev.azure.com/,https://github.com/onnx/tensorflow-onnx
# force_pass : true
5 changes: 5 additions & 0 deletionsCHANGELOGS.rst
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@@ -1,6 +1,11 @@
Change Logs
===========

0.3.1
+++++

* :pr:`95`: improves translation to GraphBuilder

0.3.0
+++++

Expand Down
67 changes: 62 additions & 5 deletions_unittests/ut_translate_api/test_translate_builder.py
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Expand Up@@ -8,7 +8,8 @@
from onnx_array_api.ext_test_case import ExtTestCase
from onnx_array_api.light_api import start
from onnx_array_api.graph_api import GraphBuilder
from onnx_array_api.translate_api import translate
from onnx_array_api.translate_api import translate, Translater
from onnx_array_api.translate_api.builder_emitter import BuilderEmitter


OPSET_API = min(19, onnx_opset_version() - 1)
Expand All@@ -19,7 +20,7 @@ def setUp(self):
self.maxDiff = None

def test_exp(self):
onx = start(opset=19).vin("X").Exp().rename("Y").vout().to_onnx()
onx = start(opset=19, ir_version=10).vin("X").Exp().rename("Y").vout().to_onnx()
self.assertIsInstance(onx, ModelProto)
self.assertIn("Exp", str(onx))
ref = ReferenceEvaluator(onx)
Expand All@@ -38,7 +39,7 @@ def light_api(
op.Identity(Y, outputs=["Y"])
return Y

g = GraphBuilder({'': 19})
g = GraphBuilder({'': 19}, ir_version=10)
g.make_tensor_input("X", TensorProto.FLOAT, ())
light_api(g.op, "X")
g.make_tensor_output("Y", TensorProto.FLOAT, ())
Expand DownExpand Up@@ -68,7 +69,7 @@ def light_api(

def test_zdoc(self):
onx = (
start(opset=19)
start(opset=19, ir_version=10)
.vin("X")
.reshape((-1, 1))
.Transpose(perm=[1, 0])
Expand All@@ -89,7 +90,7 @@ def light_api(
op.Identity(Y, outputs=["Y"])
return Y

g = GraphBuilder({'': 19})
g = GraphBuilder({'': 19}, ir_version=10)
g.make_tensor_input("X", TensorProto.FLOAT, ())
light_api(g.op, "X")
g.make_tensor_output("Y", TensorProto.FLOAT, ())
Expand DownExpand Up@@ -117,6 +118,62 @@ def light_api(
self.assertNotEmpty(model)
check_model(model)

def test_exp_f(self):
onx = start(opset=19, ir_version=10).vin("X").Exp().rename("Y").vout().to_onnx()
self.assertIsInstance(onx, ModelProto)
self.assertIn("Exp", str(onx))
ref = ReferenceEvaluator(onx)
a = np.arange(10).astype(np.float32)
got = ref.run(None, {"X": a})[0]
self.assertEqualArray(np.exp(a), got)

tr = Translater(onx, emitter=BuilderEmitter("mm"))
code = tr.export(as_str=True)

expected = dedent(
"""
def light_api(
op: "GraphBuilder",
X: "FLOAT[]",
):
Y = op.Exp(X)
op.Identity(Y, outputs=["Y"])
return Y


def mm() -> "ModelProto":
g = GraphBuilder({'': 19}, ir_version=10)
g.make_tensor_input("X", TensorProto.FLOAT, ())
light_api(g.op, "X")
g.make_tensor_output("Y", TensorProto.FLOAT, ())
model = g.to_onnx()
return model


model = mm()
"""
).strip("\n")
self.assertEqual(expected, code.strip("\n"))

def light_api(
op: "GraphBuilder",
X: "FLOAT[]", # noqa: F722
):
Y = op.Exp(X)
op.Identity(Y, outputs=["Y"])
return Y

g2 = GraphBuilder({"": 19})
g2.make_tensor_input("X", TensorProto.FLOAT, ("A",))
light_api(g2.op, "X")
g2.make_tensor_output("Y", TensorProto.FLOAT, ("A",))
onx2 = g2.to_onnx()

ref = ReferenceEvaluator(onx2)
a = np.arange(10).astype(np.float32)
got = ref.run(None, {"X": a})[0]
self.assertEqualArray(np.exp(a), got)


if __name__ == "__main__":
unittest.main(verbosity=2)
2 changes: 1 addition & 1 deletiononnx_array_api/__init__.py
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Expand Up@@ -2,5 +2,5 @@
APIs to create ONNX Graphs.
"""

__version__ = "0.3.0"
__version__ = "0.3.1"
__author__ = "Xavier Dupré"
60 changes: 52 additions & 8 deletionsonnx_array_api/translate_api/builder_emitter.py
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Expand Up@@ -4,10 +4,17 @@
from .base_emitter import BaseEmitter

_types = {
TensorProto.DOUBLE: "DOUBLE",
TensorProto.FLOAT: "FLOAT",
TensorProto.FLOAT16: "FLOAT16",
TensorProto.INT64: "INT64",
TensorProto.INT32: "INT32",
TensorProto.INT16: "INT16",
TensorProto.UINT64: "UINT64",
TensorProto.UINT32: "UINT32",
TensorProto.UINT16: "UINT16",
TensorProto.STRING: "STRING",
TensorProto.BOOL: "BOOL",
}


Expand All@@ -20,6 +27,10 @@ class BuilderEmitter(BaseEmitter):
Converts event into proper code.
"""

def __init__(self, make_model_function: str = ""):
super().__init__()
self.make_model_function = make_model_function

def join(self, rows: List[str], single_line: bool = False) -> str:
"Join the rows"
assert (
Expand All@@ -29,6 +40,7 @@ def join(self, rows: List[str], single_line: bool = False) -> str:

def _emit_start(self, **kwargs: Dict[str, Any]) -> List[str]:
self.opsets = kwargs.get("opsets", {})
self.ir_version = kwargs.get("ir_version", None)
return []

def _emit_to_onnx_model(self, **kwargs: Dict[str, Any]) -> List[str]:
Expand All@@ -43,12 +55,27 @@ def _emit_to_onnx_model(self, **kwargs: Dict[str, Any]) -> List[str]:
)
rows = [
"",
f"g = GraphBuilder({self.opsets})",
(
f"g = GraphBuilder({self.opsets}, ir_version={self.ir_version})"
if self.ir_version
else f"GraphBuilder({self.opsets})"
),
*inputs,
f"{self.name}({inps})",
*outputs,
"model = g.to_onnx()",
]
if self.make_model_function:
rows = [
"",
"",
f'def {self.make_model_function}() -> "ModelProto":',
*[" " + _ for _ in rows[1:]],
" return model",
"",
"",
f"model = {self.make_model_function}()",
]
return rows

def _emit_begin_graph(self, **kwargs: Dict[str, Any]) -> List[str]:
Expand DownExpand Up@@ -78,13 +105,16 @@ def _emit_input(self, **kwargs: Dict[str, Any]) -> List[str]:
name = kwargs["name"]
itype = kwargs.get("elem_type", 0)
shape = kwargs.get("shape", None)
name = self._clean_result_name(name)
if itype == 0:
inp = "X"
inp =name or"X"
else:
if shape is None:
inp = f'X: "{_itype_to_string(itype)}"'
inp = f'{name}: "{_itype_to_string(itype)}"'
else:
inp = f'X: "{_itype_to_string(itype)}[{", ".join(map(str, shape))}]"'
inp = (
f'{name}: "{_itype_to_string(itype)}[{", ".join(map(str, shape))}]"'
)
self.inputs_full.append(inp)
self.inputs.append(name)
self.inputs_full_.append((name, _itype_to_string(itype), shape))
Expand DownExpand Up@@ -113,6 +143,7 @@ def _emit_end_return(self, **kwargs: Dict[str, Any]) -> List[str]:

def _emit_output(self, **kwargs: Dict[str, Any]) -> List[str]:
name = kwargs["name"]
name = self._clean_result_name(name)
itype = kwargs.get("elem_type", 0)
shape = kwargs.get("shape", None)
self.outputs.append(name)
Expand All@@ -126,6 +157,8 @@ def _emit_node(self, **kwargs: Dict[str, Any]) -> List[str]:
if kwargs.get("domain", "") != "":
domain = kwargs["domain"]
op_type = f"{domain}.{op_type}"
else:
domain = ""
atts = kwargs.get("atts", {})
args = []
for k, v in atts.items():
Expand All@@ -134,11 +167,22 @@ def _emit_node(self, **kwargs: Dict[str, Any]) -> List[str]:
raise NotImplementedError("Graph attribute not supported yet.")
args.append(f"{k}={vatt}")

outs = ", ".join(outputs)
inps = ", ".join(inputs)
outs = ", ".join(map(self._clean_result_name, outputs))
inps = ", ".join(map(self._clean_result_name, inputs))
op_type = self._emit_node_type(op_type, domain)
sdomain = "" if not domain else f", domain={domain!r}"
if args:
sargs = ", ".join(args)
row = f" {outs} = op.{op_type}({inps}, {sargs})"
if inps:
row = f" {outs} = op.{op_type}({inps}, {sargs}{sdomain})"
else:
row = f" {outs} = op.{op_type}({sargs}{sdomain})"
else:
row = f" {outs} = op.{op_type}({inps})"
row = f" {outs} = op.{op_type}({inps}{sdomain})"
return [row]

def _clean_result_name(self, name):
return name

def _emit_node_type(self, op_type, domain):
return op_type
6 changes: 5 additions & 1 deletiononnx_array_api/translate_api/translate.py
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Expand Up@@ -35,7 +35,11 @@ def export(self, as_str, single_line: bool = False) -> Union[str, List[str]]:
last_event = None
if isinstance(self.proto_, ModelProto):
opsets = {d.domain: d.version for d in self.proto_.opset_import}
rows.extend(self.emitter(EventType.START, opsets=opsets))
rows.extend(
self.emitter(
EventType.START, opsets=opsets, ir_version=self.proto_.ir_version
)
)
inputs = self.proto_.graph.input
outputs = self.proto_.graph.output
nodes = self.proto_.graph.node
Expand Down
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