importnumpyasnpfromonnx_array_api.npximportabsolute,jit_onnxfromonnx_array_api.ort.ort_tensorsimportJitOrtTensor,OrtTensordefl1_loss(x,y):returnabsolute(x-y).sum()defl2_loss(x,y):return((x-y)**2).sum()defmyloss(x,y):l1=l1_loss(x[:,0],y[:,0])l2=l2_loss(x[:,1],y[:,1])returnl1+l2ort_myloss=jit_onnx(myloss,JitOrtTensor,target_opsets={"":17},ir_version=8)x=np.array([[0.1,0.2],[0.3,0.4]],dtype=np.float32)y=np.array([[0.11,0.22],[0.33,0.44]],dtype=np.float32)xort=OrtTensor.from_array(x)yort=OrtTensor.from_array(y)res=ort_myloss(xort,yort)print(res.numpy())

0.042

fromonnx_array_api.profilingimportprofile,profile2graphx=np.random.randn(10000,2).astype(np.float32)y=np.random.randn(10000,2).astype(np.float32)xort=OrtTensor.from_array(x)yort=OrtTensor.from_array(y)defloop_ort(n):for_inrange(n):ort_myloss(xort,yort)defloop_numpy(n):for_inrange(n):myloss(x,y)defloop(n=1000):loop_numpy(n)loop_ort(n)ps=profile(loop)[0]root,nodes=profile2graph(ps,clean_text=lambdax:x.split("/")[-1])text=root.to_text()print(text)

var                                                          --    2000    2000 -- 0.00304 0.02256 -- npx_core_api.py:54:var (var)    __init__                                                 --    2000    2000 -- 0.01280 0.01952 -- npx_var.py:287:__init__ (__init__) +++get_cst_var                                                  --    2000    2000 -- 0.00300 0.00494 -- npx_var.py:216:get_cst_var (get_cst_var)    parent                                                   --    2000    2000 -- 0.00115 0.00194 -- <frozen importlib._bootstrap>:645:parent (parent)        <method 'rpartition' of 'str' objects>               --    2000    2000 -- 0.00078 0.00078 -- ~:0:<method 'rpartition' of 'str' objects> (<method 'rpartition' of 'str' objects>)__init__                                                     --    3000    3000 -- 0.02129 0.03181 -- npx_var.py:287:__init__ (__init__)    __init__                                                 --    3000    3000 -- 0.00080 0.00080 -- npx_var.py:281:__init__ (__init__)    self_var                                                 --    2000    2000 -- 0.00067 0.00100 -- npx_var.py:375:self_var (self_var) +++    <method 'items' of 'dict' objects>                       --    3000    3000 -- 0.00067 0.00067 -- ~:0:<method 'items' of 'dict' objects> (<method 'items' of 'dict' objects>) +++    <built-in method builtins.hasattr>                       --    3000    3000 -- 0.00056 0.00056 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++    <built-in method builtins.isinstance>                    --   23000   23000 -- 0.00587 0.00587 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++    <built-in method builtins.len>                           --    6000    6000 -- 0.00117 0.00117 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++    <method 'ravel' of 'numpy.ndarray' objects>              --    1000    1000 -- 0.00046 0.00046 -- ~:0:<method 'ravel' of 'numpy.ndarray' objects> (<method 'ravel' of 'numpy.ndarray' objects>)self_var                                                     --    4000    4000 -- 0.00139 0.00211 -- npx_var.py:375:self_var (self_var)    <built-in method builtins.hasattr>                       --    4000    4000 -- 0.00072 0.00072 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++loop_numpy                                                   --       1       1 -- 0.00043 0.09823 -- plot_onnxruntime.py:61:loop_numpy (loop_numpy)    myloss                                                   --    1000    1000 -- 0.00299 0.09780 -- plot_onnxruntime.py:28:myloss (myloss)        __add__                                              --    1000    1000 -- 0.00064 0.01949 -- npx_var.py:645:__add__ (__add__)            _binary_op                                       --    1000    1000 -- 0.00256 0.01885 -- npx_var.py:615:_binary_op (_binary_op)                var                                          --    1000    1000 -- 0.00184 0.01280 -- npx_core_api.py:54:var (var) +++                get_cst_var                                  --    1000    1000 -- 0.00147 0.00249 -- npx_var.py:216:get_cst_var (get_cst_var) +++                self_var                                     --    1000    1000 -- 0.00036 0.00055 -- npx_var.py:375:self_var (self_var) +++                <built-in method builtins.isinstance>        --    1000    1000 -- 0.00045 0.00045 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++        l1_loss                                              --    1000    1000 -- 0.00972 0.05069 -- plot_onnxruntime.py:20:l1_loss (l1_loss)            wrapper                                          --    1000    1000 -- 0.00902 0.02593 -- npx_core_api.py:142:wrapper (wrapper)                annotation                                   --    1000    1000 -- 0.00013 0.00013 -- inspect.py:2773:annotation (annotation)                kind                                         --    2000    2000 -- 0.00025 0.00025 -- inspect.py:2777:kind (kind)                parameters                                   --    2000    2000 -- 0.00028 0.00028 -- inspect.py:3058:parameters (parameters)                return_annotation                            --    1000    1000 -- 0.00013 0.00013 -- inspect.py:3062:return_annotation (return_annotation)                __init__                                     --    1000    1000 -- 0.00849 0.01229 -- npx_var.py:287:__init__ (__init__) +++                <method 'items' of 'mappingproxy' objects>   --    1000    1000 -- 0.00038 0.00038 -- ~:0:<method 'items' of 'mappingproxy' objects> (<method 'items' of 'mappingproxy' objects>)                <method 'append' of 'list' objects>          --    1000    1000 -- 0.00028 0.00028 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++                <method 'items' of 'dict' objects>           --    1000    1000 -- 0.00023 0.00023 -- ~:0:<method 'items' of 'dict' objects> (<method 'items' of 'dict' objects>) +++                <built-in method builtins.any>               --    1000    1000 -- 0.00094 0.00184 -- ~:0:<built-in method builtins.any> (<built-in method builtins.any>)                    <genexpr>                                --    2000    2000 -- 0.00066 0.00089 -- npx_core_api.py:143:<genexpr> (<genexpr>)                        <built-in method ...tins.isinstance> --    1000    1000 -- 0.00024 0.00024 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++                <built-in method builtins.isinstance>        --    2000    2000 -- 0.00042 0.00042 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++                <built-in method builtins.issubclass>        --    2000    2000 -- 0.00047 0.00047 -- ~:0:<built-in method builtins.issubclass> (<built-in method builtins.issubclass>) +++                <built-in method builtins.len>               --    1000    1000 -- 0.00019 0.00019 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++            sum                                              --    1000    1000 -- 0.00062 0.01504 -- npx_var.py:890:sum (sum)                reduce_function                              --    1000    1000 -- 0.00166 0.01442 -- npx_var.py:871:reduce_function (reduce_function)                    var                                      --    1000    1000 -- 0.00120 0.00976 -- npx_core_api.py:54:var (var) +++                    get_cst_var                              --    1000    1000 -- 0.00153 0.00244 -- npx_var.py:216:get_cst_var (get_cst_var) +++                    self_var                                 --    1000    1000 -- 0.00037 0.00056 -- npx_var.py:375:self_var (self_var) +++        l2_loss                                              --    1000    1000 -- 0.01685 0.02464 -- plot_onnxruntime.py:24:l2_loss (l2_loss)            <method 'sum' of 'numpy.ndarray' objects>        --    1000    1000 -- 0.00082 0.00779 -- ~:0:<method 'sum' of 'numpy.ndarray' objects> (<method 'sum' of 'numpy.ndarray' objects>)                _sum                                         --    1000    1000 -- 0.00061 0.00696 -- _methods.py:49:_sum (_sum)                    <method 'reduce' of...py.ufunc' objects> --    1000    1000 -- 0.00635 0.00635 -- ~:0:<method 'reduce' of 'numpy.ufunc' objects> (<method 'reduce' of 'numpy.ufunc' objects>) +++<built-in method builtins.isinstance>                        --   59000   59000 -- 0.01767 0.01767 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>)<method 'append' of 'list' objects>                          --   13000   13000 -- 0.00433 0.00433 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>)<method 'items' of 'dict' objects>                           --   16000   16000 -- 0.00505 0.00505 -- ~:0:<method 'items' of 'dict' objects> (<method 'items' of 'dict' objects>)<built-in method builtins.issubclass>                        --   38000   38000 -- 0.01102 0.01102 -- ~:0:<built-in method builtins.issubclass> (<built-in method builtins.issubclass>)<built-in method builtins.len>                               --  576000  576000 -- 0.07698 0.07698 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>)<built-in method builtins.hasattr>                           --    7000    7000 -- 0.00128 0.00128 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>)<method 'reduce' of 'numpy.ufunc' objects>                   --    9000    9000 -- 0.04297 0.04297 -- ~:0:<method 'reduce' of 'numpy.ufunc' objects> (<method 'reduce' of 'numpy.ufunc' objects>)

frompandasimportDataFramefromtqdmimporttqdmfromonnx_array_api.ext_test_caseimportmeasure_timedata=[]fornintqdm([1,10,100,1000,10000,100000]):x=np.random.randn(n,2).astype(np.float32)y=np.random.randn(n,2).astype(np.float32)obs=measure_time(lambdax=x,y=y:myloss(x,y))obs["name"]="numpy"obs["n"]=ndata.append(obs)xort=OrtTensor.from_array(x)yort=OrtTensor.from_array(y)obs=measure_time(lambdaxort=xort,yort=yort:ort_myloss(xort,yort))obs["name"]="ort"obs["n"]=ndata.append(obs)df=DataFrame(data)piv=df.pivot(index="n",columns="name",values="average")piv

  0%|          | 0/6 [00:00<?, ?it/s] 17%|█▋        | 1/6 [00:00<00:02,  1.75it/s] 33%|███▎      | 2/6 [00:01<00:02,  1.41it/s] 50%|█████     | 3/6 [00:04<00:05,  1.69s/it] 67%|██████▋   | 4/6 [00:04<00:02,  1.32s/it] 83%|████████▎ | 5/6 [00:05<00:01,  1.17s/it]100%|██████████| 6/6 [00:07<00:00,  1.16s/it]100%|██████████| 6/6 [00:07<00:00,  1.17s/it]

importmatplotlib.pyplotaspltfig,ax=plt.subplots(1,2,figsize=(12,4))piv.plot(title="Comparison between numpy and onnxruntime",logx=True,logy=True,ax=ax[0])piv["ort/numpy"]=piv["ort"]/piv["numpy"]piv["ort/numpy"].plot(title="Ratio ort/numpy",logx=True,ax=ax[1])fig.savefig("plot_onnxruntime.png")

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