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Replaces long initiliazer by rando values#98

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Merged
sdpython merged 3 commits intomainfromshorten
Mar 9, 2025
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6 changes: 6 additions & 0 deletions_doc/api/translate_api.rst
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Original file line numberDiff line numberDiff line change
Expand Up@@ -39,6 +39,12 @@ InnerEmitter
.. autoclass:: onnx_array_api.translate_api.inner_emitter.InnerEmitter
:members:

InnerEmitterShortInitializer
++++++++++++++++++++++++++++

.. autoclass:: onnx_array_api.translate_api.inner_emitter.InnerEmitterShortInitializer
:members:

LightEmitter
++++++++++++

Expand Down
2 changes: 0 additions & 2 deletions_unittests/ut_ort/test_ort_profile.py
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Expand Up@@ -57,8 +57,6 @@ def myloss(x, y):
prof = ort_profile(optimized, feeds)
events = {
"kernel_time",
"fence_before",
"fence_after",
"SequentialExecutor::Execute",
"model_run",
"model_loading_array",
Expand Down
69 changes: 69 additions & 0 deletions_unittests/ut_translate_api/test_translate_classic.py
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Expand Up@@ -178,6 +178,75 @@ def test_transpose(self):
self.maxDiff = None
self.assertEqual(expected, code)

def test_transpose_short(self):
onx = (
start(opset=19)
.vin("X")
.reshape((-1, 1))
.Transpose(perm=[1, 0])
.rename("Y")
.vout()
.to_onnx()
)
self.assertIsInstance(onx, ModelProto)
self.assertIn("Transpose", str(onx))
ref = ReferenceEvaluator(onx)
a = np.arange(10).astype(np.float32)
got = ref.run(None, {"X": a})[0]
self.assertEqualArray(a.reshape((-1, 1)).T, got)

code = translate(onx, api="onnx-short")
expected = dedent(
"""
opset_imports = [
make_opsetid('', 19),
]
inputs = []
outputs = []
nodes = []
initializers = []
sparse_initializers = []
functions = []
initializers.append(
from_array(
np.array([-1, 1], dtype=np.int64),
name='r'
)
)
inputs.append(make_tensor_value_info('X', TensorProto.FLOAT, shape=[]))
nodes.append(
make_node_extended(
'Reshape',
['X', 'r'],
['r0_0']
)
)
nodes.append(
make_node_extended(
'Transpose',
['r0_0'],
['Y'],
perm=[1, 0]
)
)
outputs.append(make_tensor_value_info('Y', TensorProto.FLOAT, shape=[]))
graph = make_graph(
nodes,
'light_api',
inputs,
outputs,
initializers,
sparse_initializer=sparse_initializers,
)
model = make_model(
graph,
functions=functions,
opset_imports=opset_imports
)"""
).strip("\n")
self.maxDiff = None
self.assertEqual(expected, code)

def test_topk_reverse(self):
onx = (
start(opset=19)
Expand Down
8 changes: 6 additions & 2 deletionsonnx_array_api/translate_api/__init__.py
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Original file line numberDiff line numberDiff line change
@@ -1,6 +1,6 @@
from onnx import ModelProto
from .translate import Translater
from .inner_emitter import InnerEmitter
from .inner_emitter import InnerEmitter, InnerEmitterShortInitializer
from .builder_emitter import BuilderEmitter


Expand All@@ -16,7 +16,8 @@ def translate(proto: ModelProto, single_line: bool = False, api: str = "light")
:class:`onnx_array_api.translate_api.light_emitter.LightEmitter`,
another value is `"onnx"` which is the inner API implemented
in onnx package, `"builder"` follows the syntax for the
class :class:`onnx_array_api.graph_api.GraphBuilder`
class :class:`onnx_array_api.graph_api.GraphBuilder`,
`"onnx-short"` replaces long initializer with random values
:return: code

.. runpython::
Expand DownExpand Up@@ -84,6 +85,9 @@ class :class:`onnx_array_api.graph_api.GraphBuilder`
if api == "onnx":
tr = Translater(proto, emitter=InnerEmitter())
return tr.export(as_str=True)
if api == "onnx-short":
tr = Translater(proto, emitter=InnerEmitterShortInitializer())
return tr.export(as_str=True)
if api == "builder":
tr = Translater(proto, emitter=BuilderEmitter())
return tr.export(as_str=True)
Expand Down
55 changes: 55 additions & 0 deletionsonnx_array_api/translate_api/inner_emitter.py
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Expand Up@@ -106,6 +106,7 @@ def _emit_initializer(self, **kwargs: Dict[str, Any]) -> List[str]:
raise NotImplementedError(f"Unexpected dtype={sdtype}.")
else:
sdtype = f"np.{sdtype}"

return [
"initializers.append(",
f" {fra}(",
Expand DownExpand Up@@ -209,3 +210,57 @@ def _emit_end_function(self, **kwargs: Dict[str, Any]) -> List[str]:
")",
]
return lines


class InnerEmitterShortInitializer(InnerEmitter):
"""
Converts event into proper code.
Initializer are replaced by random values if too big.
"""

def _emit_initializer(self, **kwargs: Dict[str, Any]) -> List[str]:
name = kwargs["name"]
value = kwargs["value"]
repl = {"bool": "bool_", "object": "object_", "str": "str_"}
fra = "from_array"
sdtype = repl.get(str(value.dtype), str(value.dtype))
if sdtype.startswith("("):
from onnx.reference.custom_element_types import float8e4m3fn

if sdtype == str(float8e4m3fn):
sdtype = "float8e4m3fn"
fra = "from_array_extended"
else:
raise NotImplementedError(f"Unexpected dtype={sdtype}.")
else:
sdtype = f"np.{sdtype}"
if value.size <= 16:
return [
"initializers.append(",
f" {fra}(",
f" np.array({value.tolist()}, dtype={sdtype}),",
f" name={name!r}",
" )",
")",
]
if "int" in sdtype:
return [
f"value = np.random.randint(0, 10, size={value.shape})"
f".astype({sdtype})",
"initializers.append(",
f" {fra}(",
f" np.array(value, dtype={sdtype}),",
f" name={name!r}",
" )",
")",
]
return [
f"value = np.random.randn({', '.join(map(str,value.shape))})"
f".astype({sdtype})",
"initializers.append(",
f" {fra}(",
f" np.array(value, dtype={sdtype}),",
f" name={name!r}",
" )",
")",
]
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