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Commit7675869

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Extend ExtendedReferenceEvaluator (#75)
* update requirements* add more operator to the reference evaluator* extend unit test copverage
1 parenta070da3 commit7675869

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6 files changed

+222
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6 files changed

+222
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lines changed

‎CHANGELOGS.rst‎

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@@ -4,6 +4,7 @@ Change Logs
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0.2.0
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+++++
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*:pr:`75`: add QuickGelu to ExtendedReferenceEvaluator
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*:pr:`71`: adds tools to compare two onnx graphs
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*:pr:`61`: adds function to plot onnx model as graphs
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*:pr:`60`: supports translation of local functions

‎_unittests/ut_reference/test_reference_ops.py‎

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@@ -59,6 +59,88 @@ def test_fused_matmul11(self):
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got=ref.run(None, {"X":a,"Y":a})
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self.assertEqualArray(a.T @a.T,got[0])
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deftest_memcpy(self):
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model=make_model(
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make_graph(
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[
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make_node("MemcpyToHost", ["X"], ["Z"]),
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make_node("MemcpyFromHost", ["X"], ["Z"]),
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],
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"name",
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[make_tensor_value_info("X",TensorProto.FLOAT,None)],
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[make_tensor_value_info("Z",TensorProto.FLOAT,None)],
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),
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opset_imports=[make_opsetid("",18),make_opsetid("com.microsoft",1)],
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ir_version=9,
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)
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a=np.arange(4).reshape(-1,2).astype(np.float32)
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ref=ExtendedReferenceEvaluator(model)
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got=ref.run(None, {"X":a})
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self.assertEqualArray(a,got[0])
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deftest_quick_gelu(self):
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fromonnxruntimeimportInferenceSession
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foralphain [0.0,2.0]:
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model=make_model(
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make_graph(
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[
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make_node(
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"QuickGelu",
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["X"],
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["Z"],
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domain="com.microsoft",
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alpha=alpha,
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)
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],
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"name",
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[make_tensor_value_info("X",TensorProto.FLOAT,None)],
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[make_tensor_value_info("Z",TensorProto.FLOAT,None)],
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),
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opset_imports=[make_opsetid("",18),make_opsetid("com.microsoft",1)],
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ir_version=9,
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)
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sess=InferenceSession(
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model.SerializeToString(),providers=["CPUExecutionProvider"]
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)
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a=np.arange(4).reshape(-1,2).astype(np.float32)
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expected=sess.run(None, {"X":a})
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ref=ExtendedReferenceEvaluator(model)
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got=ref.run(None, {"X":a})
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self.assertEqualArray(expected[0],got[0])
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deftest_scatter_elements(self):
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model=make_model(
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make_graph(
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[
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make_node(
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"ScatterElements",
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["data","indices","updates"],
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["Z"],
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axis=3,
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reduction="add",
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)
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],
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"name",
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[
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make_tensor_value_info("data",TensorProto.FLOAT,None),
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make_tensor_value_info("indices",TensorProto.INT64,None),
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make_tensor_value_info("updates",TensorProto.FLOAT,None),
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],
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[make_tensor_value_info("Z",TensorProto.FLOAT,None)],
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),
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opset_imports=[make_opsetid("",18)],
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)
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data=np.zeros(2**4,dtype=np.float32).reshape((2,2,2,2))
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indices=np.array([[[[0]]]],dtype=np.int64)
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updates=np.array([[[[1]]]],dtype=np.float32)
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y=np.array(
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[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],dtype=np.float32
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).reshape((2,2,2,2))
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ref=ExtendedReferenceEvaluator(model)
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got=ref.run(None, {"data":data,"indices":indices,"updates":updates})
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self.assertEqualArray(y,got[0])
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if__name__=="__main__":
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unittest.main(verbosity=2)

‎onnx_array_api/reference/evaluator.py‎

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@@ -8,6 +8,9 @@
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from .ops.op_concatimportConcat
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from .ops.op_constant_of_shapeimportConstantOfShape
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from .ops.op_fused_matmulimportFusedMatMul
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from .ops.op_memcpy_hostimportMemcpyFromHost,MemcpyToHost
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from .ops.op_quick_geluimportQuickGelu
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from .ops.op_scatter_elementsimportScatterElements
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logger=getLogger("onnx-array-api-eval")
@@ -34,6 +37,10 @@ class ExtendedReferenceEvaluator(ReferenceEvaluator):
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CastLike_19,
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ConstantOfShape,
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FusedMatMul,
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MemcpyFromHost,
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MemcpyToHost,
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QuickGelu,
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ScatterElements,
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]
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@staticmethod
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@@ -0,0 +1,11 @@
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fromonnx.reference.op_runimportOpRun
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classMemcpyFromHost(OpRun):
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def_run(self,x):
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return (x,)
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classMemcpyToHost(OpRun):
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def_run(self,x):
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return (x,)
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importnumpyasnp
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fromonnx.reference.op_runimportOpRun
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defsigmoid(x):# type: ignore
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ifx>0:
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return1/ (1+np.exp(-x))
8+
returnnp.exp(x)/ (1+np.exp(x))
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10+
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classQuickGelu(OpRun):
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op_domain="com.microsoft"
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def__init__(self,onnx_node,run_params):# type: ignore
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OpRun.__init__(self,onnx_node,run_params)
16+
self.vf=np.vectorize(sigmoid)
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def_run(self,X,alpha=1.0):
19+
iflen(X.shape)==0:
20+
return ((X*sigmoid(X*alpha)).astype(X.dtype),)
21+
ifX.size==0:
22+
return (X,)
23+
return ((X*self.vf(X*alpha)).astype(X.dtype),)
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@@ -0,0 +1,98 @@
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importnumpyasnp
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fromonnx.reference.op_runimportOpRun
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defscatter_elements(data,indices,updates,axis=0,reduction=None):# type: ignore
7+
ifreduction=="add":
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9+
deff(x,y):
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returnx+y
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12+
elifreduction=="min":
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deff(x,y):
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returnmin(x,y)
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elifreduction=="max":
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deff(x,y):
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returnmax(x,y)
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else:
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24+
deff(x,y):
25+
returny
26+
27+
ifaxis<0:
28+
axis=data.ndim+axis
29+
30+
iflen(data.shape)==1andaxis==0:
31+
scattered=np.copy(data)
32+
forpos,upinzip(indices,updates):
33+
scattered[pos]=f(scattered[pos],up)
34+
returnscattered
35+
36+
iflen(indices.shape)==2:
37+
scattered=np.copy(data)
38+
ifaxis==0:
39+
foriinrange(indices.shape[0]):
40+
forjinrange(indices.shape[1]):
41+
scattered[indices[i,j],j]=f(
42+
scattered[indices[i,j],j],updates[i,j]
43+
)
44+
else:
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foriinrange(indices.shape[0]):
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forjinrange(indices.shape[1]):
47+
scattered[i,indices[i,j]]=f(
48+
scattered[i,indices[i,j]],updates[i,j]
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)
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returnscattered
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52+
iflen(indices.shape)==3:
53+
scattered=np.copy(data)
54+
ifaxis==0:
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foriinrange(indices.shape[0]):
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forjinrange(indices.shape[1]):
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forkinrange(indices.shape[2]):
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scattered[indices[i,j,k],j,k]=f(
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scattered[indices[i,j,k],j,k],updates[i,j,k]
60+
)
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elifaxis==1:
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foriinrange(indices.shape[0]):
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forjinrange(indices.shape[1]):
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forkinrange(indices.shape[2]):
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scattered[i,indices[i,j,k],k]=f(
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scattered[i,indices[i,j,k],k],updates[i,j,k]
67+
)
68+
elifaxis==2:
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foriinrange(indices.shape[0]):
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forjinrange(indices.shape[1]):
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forkinrange(indices.shape[2]):
72+
scattered[i,j,indices[i,j,k]]=f(
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scattered[i,j,indices[i,j,k]],updates[i,j,k]
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)
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returnscattered
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77+
iflen(indices.shape)==4:
78+
scattered=np.copy(data)
79+
ifaxis==3:
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forainrange(indices.shape[0]):
81+
foriinrange(indices.shape[1]):
82+
forjinrange(indices.shape[2]):
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forkinrange(indices.shape[3]):
84+
scattered[a,i,j,indices[a,i,j,k]]=f(
85+
scattered[a,i,j,indices[a,i,j,k]],
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updates[a,i,j,k],
87+
)
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returnscattered
89+
90+
raiseRuntimeError(
91+
f"Not implemented for indices.shape={indices.shape} and axis={axis}"
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)
93+
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classScatterElements(OpRun):
96+
def_run(self,data,indices,updates,axis=None,reduction=None):# type: ignore
97+
res=scatter_elements(data,indices,updates,axis=axis,reduction=reduction)
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return (res,)

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