o Ei>@sddlZddlmZddlmZddlZddlmZm Z ddl mZ ej dddd^d d Z ejd d d dZejjej ddddd ddedejdede ejedfdede jdejfddZee_ejjdd ddedejde ejedfdede jdejf ddZddZejjdd ddedejde ejedfdede jdejf ddZd d!Zejjdd ddedejde ejedfdede jdejf d"d#Zd$d%Zejjdd ddedejde ejedfdede jdejf d&d'Zd(d)Zejjdd ddedejde ejedfdede jdejf d*d+Z d,d-Z!ejjdd ddedejde ejedfdede jdejf d.d/Z"d0d1Z#ejjdd ddedejde ejedfdede jdejf d2d3Z$d4d5Z%ejjdd ddedejde ejedfdede jdejf d6d7Z&d8d9Z'ejjdd ddedejde ejedfdede jdejf d:d;Z(dd?Z*d@dAZ+ejjdd ddedejde ejedfdede jdejf dBdCZ,dDdEZ-ejjdd ddedejde ejedfdede jdejf dFdGZ.dHdIZ/ejjdd ddedejde ejedfdede jdejf dJdKZ0dLdMZ1ejjdd ddedejde ejedfdede jdejf dNdOZ2dPdQZ3ejjdd ddedejde ejedfdede jdejf dRdSZ4dTdUZ5ejjdd ddedejde ejedfdede jdejf dVdWZ6dXdYZ7ejjdd ddedejde ejedfdede jdejf dZd[Z8d\d]Z9dS)_N)util)partial)CallableUnioncsdfdd S)Ncs|fdd|D||S)Ncg|]}|qSr.0xtrP/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/einx/op/elementwise.py .....r)exprs_in tensors_inexpr_outopbackendcr rr s..Nrr rrrrr sr)tracec st||gD]}|D]}t|tjjjrtdq qtdd|Dr(Jtdd|Dr5Jddfddt ||D} |}tj |||gd \}}t |d kret |d ksgJ|d |d fS) NzConcatenation not allowedcss,|]}|D] }tjj|VqqdSr)alleinxexprstage3 is_marked)r rootr rrr s*z%elementwise_stage3..css|] }tjj|VqdSr)rr r!r"r r rrrr$scSs|dkrdS|dkr dS|S)Naddbiasmultiplyscaler)srrrget_names z$elementwise_stage3..get_namec s8g|]\}}tjj||jttdqS))nameinit)rtracer call_factoryshaper _op_to_str)r tensorr rr+rrrr s z&elementwise_stage3..rrr) listr isinstancerr r! Concatenation ValueErroranyzip all_to_tensorvmap_with_axis_stage3len) rrrrrr#r tensors_out exprs_outrr3relementwise_stage3 s$    r@T)csec Os(tjj||\}}tjj|}t|ddkrHt|dkrHtjjtjj tjj |ddtjj |ddggt |dd}t|dkr|d}dd|D}t }t|D]\}} t|D]\} } || krw| | swnqh|||q`t|dkrtd|dtt|} tjj|dtjj | gg}t|dt|krtdt|dd t|t|ddkrtd t|dtjjd dt|d|Dtj|ddgd d|D|d ddt|dd} | dd| d}} || fS)NrrcSsg|] }dd|DqS)cSs"h|] }t|tjjjr|jqSr)r6rr stage1 NamedAxisr,r%rrr Ms"z#parse...)r)r r#rrrrLszparse..z9Could not implicitly determine output expression for op ''z Expected z input tensors, but got z&Expected 1 output expression, but got cSsg|] \}}tj||qSr)rr Equation)r expr_in tensor_shaperrrres cSs4g|]\}}tjj|t|dtjfdddqS).N)depth1depth2)rr rGnpasarraynewaxis)r kvrrrrns"F)rA cse_concat)rrr!_clean_description_and_parametersr rCparse_opr=OpArgsdemark get_markedr5set enumerateissubsetr&r8nextiter __deepcopy__solver:rGitems) descriptionrA tensor_shapes parametersr input_args in_axis_names valid_parentsiparentjchildrexprsrrrrparse3sn  "     rlcsddfdd S)Nr4cs$|gfdd|DRi|S)Ncrrrr r rrr}rrr)rartensorskwargsrrrr|srrrrrrr|r)rrArarmrrrArcreturnc OsBt|gdd|DRd|i|\}}t|||||d\}}|S)a~ Applies an element-by-element operation over the given tensors. It supports the following shorthand notation: * The output is determined implicitly if one of the input expressions contains the named axes of all other inputs and if this choice is unique. | Example: ``a b, a`` expands to ``a b, a -> a b``. | Example: ``b a, b, a`` expands to ``b a, b, a -> b a``. | Example: ``a b, b a`` raises an exception. | Example: ``a b, a b`` expands to ``a b, a b -> a b``. * Bracket notation can be used when passing two input tensors to indicate that the second input is a subexpression of the first. Example: ``a [b]`` expands to ``a b, b``. Args: description: Description string for the operation in einx notation. tensors: Input tensors or tensor factories matching the description string. op: Backend elemebt-by-element operation. Must accept the same number of tensors as specified in the description string and comply with numpy broadcasting rules. If ``op`` is a string, retrieves the attribute of ``backend`` with the same name. backend: Backend to use for all operations. If None, determines the backend from the input tensors. Defaults to None. cse: Whether to apply common subexpression elimination to the expressions. Defaults to True. graph: Whether to return the graph representation of the operation instead of computing the result. Defaults to False. **parameters: Additional parameters that specify values for single axes, e.g. ``a=4``. Returns: The result of the elementwise operation if ``graph=False``, otherwise the graph representation of the operation. Examples: Compute a sum of two vectors: >>> a, b = np.random.uniform(size=(10,)), np.random.uniform(size=(10,)) >>> einx.elementwise("a, a -> a", a, b, op=np.add).shape (10,) Add a vector on all columns of a matrix: >>> a, b = np.random.uniform(size=(10, 10)), np.random.uniform(size=(10,)) >>> einx.add("a b, a -> a b", a, b).shape (10, 10,) Subtract a vector from all rows of a matrix: >>> a, b = np.random.uniform(size=(10, 10)), np.random.uniform(size=(10,)) >>> einx.subtract("a b, b -> a b", a, b).shape (10, 10,) Select from one of two choices according to a boolean mask: >>> x, mask = ( ... np.random.uniform(size=(10, 10)), ... np.random.uniform(size=(10,)), ... ) >>> einx.where("a, a b, -> a b", mask, x, 0).shape (10, 10,) Add a bias onto all channels of a tensor: >>> x, w = ( ... np.random.uniform(size=(4, 16, 16, 64)), ... np.random.uniform(size=(64,)), ... ) >>> einx.add("b... 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