o
    Xi~                     @  s   d dl mZ d dlmZ d dlZd dlZd dlZd dlZ	d dl
mZ d#d	d
Zde	jjdddfd$ddZd%ddZd&d!d"ZdS )'    )annotations)AnyN)irmodelonnx.ModelProtoreturndict[str, Any]c                 C  s   i }| j jD ]S}|jj}t|jj}tdd |D s$td|j	 dtdd |D }|j
tjjkrR|rDtjj| tj||j	< qtjdtj||j	< qtd|j
 |S )Nc                 s  s    | ]}t |d V  qdS )	dim_valueN)hasattr.0d r   O/home/ubuntu/.local/lib/python3.10/site-packages/onnxscript/rewriter/testing.py	<genexpr>   s    z)generate_random_inputs.<locals>.<genexpr>zInput z has dynamic shape dimensions.c                 s  s    | ]}|j V  qd S )N)r	   r   r   r   r   r      s       zNot implemented for input type )graphinputtypetensor_typetupleshapedimall
ValueErrorname	elem_typeonnxTensorProtoFLOATnprandomrandnastypefloat32)r   feedsr   
input_typer   r   r   r   generate_random_inputs   s   r'   r   gMbP?Foriginal_model_protoonnx.ModelProto | ir.Modelrewritten_model_protoargs'tuple[Any, ...] | dict[str, Any] | Noneort_optimization_levelort.GraphOptimizationLevelrtolfloatatoluse_referenceboolc                 C  s(  t | tjrtj| } t |tjrtj|}|du r#t| }|}n"t |tr-|}|}ndd t| jj	|D }dd t|jj	|D }|rbt
|  |}	|	d|}
t
| |}|d|}n%t|  |}t }d|_|jd||d}
t| |}|jd||d}tjj|
|||dd dS )	a  Assert that the two models are numerically equal.

    Args:
        original_model_proto: The original model proto or ir.Model.
        rewritten_model_proto: The rewritten by the rules model proto or ir.Model.
        args: The positional arguments to pass to the model.
        ort_optimization_level: Onnxruntime optimization level.
        rtol: Relative tolerance.
        atol: Absolute tolerance.
        use_reference: If True, use ONNX reference implementation instead of ONNXRuntime.
    Nc                 S     i | ]\}}|j |qS r   r   r   kvr   r   r   
<dictcomp>B       z,assert_numerically_equal.<locals>.<dictcomp>c                 S  r4   r   r5   r6   r   r   r   r9   E   r:      )run_optionsT)r/   r1   	equal_nan)
isinstancer   Modelserdeserialize_modelr'   dictzipr   r   _reference_sessionSerializeToStringrun_ort_session_initializerort
RunOptionslog_severity_levelr    testingassert_allclose)r(   r*   r+   r-   r/   r1   r2   original_proto_ort_inputsthe_rewritten_proto_ort_inputsoriginal_evaluatororiginal_outputsrewritten_evaluatorthe_rewritten_outputs$original_proto_ort_inference_sessionr<   )the_rewritten_proto_ort_inference_sessionr   r   r   assert_numerically_equal!   sT   


rU   str | bytesort.InferenceSessionc                   sN   ddl }| }d|_||_d}t|   fdd|D }|j| ||dS )zFInitialize an ONNX Runtime inference session with the specified model.r   Nr;   )CUDAExecutionProviderCPUExecutionProviderc                   s   g | ]}| v r|qS r   r   )r   provideravailable_providersr   r   
<listcomp>y   s    z,_ort_session_initializer.<locals>.<listcomp>)	providerssess_options)onnxruntimeSessionOptionsrJ   graph_optimization_levelsetget_available_providersInferenceSession)r   r-   rH   session_optionspossible_providersr^   r   r[   r   rG   k   s   
rG   !onnx.reference.ReferenceEvaluatorc                 C  s*   t | ttfrt| }n| }tj|S )z@Initialize an ONNX reference evaluator with the specified model.)r>   strbytesr   load_from_string	referenceReferenceEvaluator)r   r-   model_protor   r   r   rD      s   rD   )r   r   r   r   )r(   r)   r*   r)   r+   r,   r-   r.   r/   r0   r1   r0   r2   r3   )r   rV   r-   r.   r   rW   )r   rV   r-   r.   r   rh   )
__future__r   typingr   numpyr    r   onnx.referencer`   rH   
onnxscriptr   r'   GraphOptimizationLevelORT_ENABLE_ALLrU   rG   rD   r   r   r   r   <module>   s    

J