o
    Ih'J                     @   sZ  d dl Z d dlZd dlZd dlZd dlZd dlmZ d dlmZmZm	Z	m
Z
 d dlZd dlmZ d dlmZmZmZmZmZ d dlmZmZmZmZmZ e
eef Ze
eeedf f Zdd	 Zd
ee	e  dedefddZde
eeedf f defddZ dededeg e!f defddZ"dedededeeee ee ef fddZ#d
ee dee dedefddZ$dd  Z%de
eeedf f d!eded"ededefd#d$Z&d%d& Z'd!ededdfd'd(Z(defd)d*Z)d+a*e+ Z,da-d,d- Z.d.d/ Z/d0d1 Z0d2d3 Z1d4d5 Z2d6d7 Z3d8d9 Z4d:d; Z5e j6d<d= Z7d>d? Z8d@dA Z9dBdC Z:dDdE Z;dS )F    N)partial)AnyCallableOptionalUnion)Tensor)_add_batch_dim_remove_batch_dim_vmap_decrement_nesting_vmap_increment_nestingis_batchedtensor)_broadcast_to_and_flattentree_flatten	tree_map_tree_unflattenTreeSpec.c                    s    dt   fdd}|S )Nzvtorch.func.{grad, vjp, jacrev, hessian} don't yet support saved tensor hooks. Please open an issue with your use case.c                     s@   t jj  | i |W  d    S 1 sw   Y  d S N)torchautogradgraphdisable_saved_tensors_hooks)argskwargsfmessage I/var/www/vscode/kcb/lib/python3.10/site-packages/torch/_functorch/vmap.pyfn-   s   $z.doesnt_support_saved_tensors_hooks.<locals>.fn)	functoolswraps)r   r   r   r   r   "doesnt_support_saved_tensors_hooks'   s
   r!   flat_in_dims	flat_argsreturnc                    sZ   dd t | |D  t dkrtd r)t fdd D r)td  d d S )	Nc                 S   s"   g | ]\}}|d ur| |qS r   )size.0in_dimargr   r   r   
<listcomp>9   s
    z0_validate_and_get_batch_size.<locals>.<listcomp>r   z/vmap: Expected at least one Tensor to vmap overc                 3   s    | ]	}| d  kV  qdS )r   Nr   )r'   r%   batch_sizesr   r   	<genexpr>@   s    z/_validate_and_get_batch_size.<locals>.<genexpr>zTvmap: Expected all tensors to have the same size in the mapped dimension, got sizes z for the mapped dimension)ziplen
ValueErrorany)r"   r#   r   r+   r   _validate_and_get_batch_size6   s   r2   batched_outputsc                 C   s   t | tr	t| S dS )N   )
isinstancetupler/   )r3   r   r   r   _num_outputsH   s   
r7   valuenum_elementserror_message_lambdac                 C   s.   t | ts
| f| S t| |krt| | S r   )r5   r6   r/   r0   )r8   r9   r:   r   r   r   	_as_tupleR   s
   


r;   in_dimsr   funcc           	      C   s  t | tst | tstdt| d|  dt|  dt|dkr,tdt| dt|\}}t| |}|d u rRtdt| d|  dt| d  d	| d	t	t
||D ]~\}\}}t |tsx|d urxtdt| d|  d
| dt |trt |tstdt| d|  d
| dt| d	|d ur||  k s|| krtdt| d|  d
| d|  d|  d|  d|d ur|dk r||  ||< qYt|||||fS )Nvmap(z
, in_dims=zv, ...)(<inputs>): expected `in_dims` to be int or a (potentially nested) tuple matching the structure of inputs, got: .r   z)(<inputs>): got no inputs. Maybe you forgot to add inputs, or you are trying to vmap over a function with no inputs. The latter is unsupported.zb, ...)(<inputs>): in_dims is not compatible with the structure of `inputs`. in_dims has structure r4   z but inputs has structure z, ...)(<inputs>): Got in_dim=zE for an input but in_dim must be either an integer dimension or None.z' for an input but the input is of type zT. We cannot vmap over non-Tensor arguments, please use None as the respective in_dimz> for some input, but that input is a Tensor of dimensionality z  so expected in_dim to satisfy -z <= in_dim < )r5   intr6   r0   	_get_nametyper/   r   r   	enumerater.   r   dimr2   )	r<   r   r=   r#   	args_specr"   ir)   r(   r   r   r   _process_batched_inputs\   sn   

"rG   
vmap_levelc                    s"    fddt | |D }t||S )Nc                    s(   g | ]\}}|d u r|nt || qS r   )r   r&   rH   r   r   r*      s    z*_create_batched_inputs.<locals>.<listcomp>)r.   r   )r"   r#   rH   rE   batched_inputsr   rI   r   _create_batched_inputs   s   

rK   c                 C   sp   |d u rt |tjrt|rtd|  d|  d|S t |tjs1td|  d|  dt| dt||||S )Nr>   z	, ...): `z5` can not return a BatchedTensor when out_dim is Nonez%` must only return Tensors, got type z3. Did you mean to set out_dims= to None for output?)r5   r   r   r   r0   rB   r	   )namebatched_outputrH   
batch_sizeout_dimr   r   r   _maybe_remove_batch_dim   s   rP   out_dimsrN   c           	         s   t | \}fdd}t| tjr7ttrg}n&ttr+tdkr+}nd u r3g}n|  nt}|d u rC|   fddt||D }t	|S )Nc                
      s.   t dt  d dtd  d d	)Nr>   , ..., out_dims=z`)(<inputs>): out_dims is not compatible with the structure of `outputs`. out_dims has structure r4   z but outputs has structure r?   )r0   rA   r   r   )r=   rQ   output_specr   r   incompatible_error   s   
z+_unwrap_batched.<locals>.incompatible_errorr4   c                    s$   g | ]\}}t t| |qS r   )rP   rA   )r'   rM   rO   )rN   r=   rH   r   r   r*      s    z#_unwrap_batched.<locals>.<listcomp>)
r   r5   r   r   r@   r6   r/   r   r.   r   )	r3   rQ   rH   rN   r=   flat_batched_outputsrT   flat_out_dimsflat_outputsr   )rN   r=   rQ   rS   rH   r   _unwrap_batched   s"   


rX   c                 C   s4   t | trd S | d u rd S tdt| d| d)Nr>   rR   z): `out_dims` must be an int, None or a python collection of ints representing where in the outputs the vmapped dimension should appear.)r5   r@   r0   rA   )xr=   rQ   r   r   r   _check_int_or_none   s   
rZ   c                 C   s&   t | trd S ttt|| d|  d S )N)r=   rQ   )r5   r@   r   r   rZ   )rQ   r=   r   r   r   $_check_out_dims_is_int_or_int_pytree   s   
r[   c                 C   s6   t | dr| jS t| tjrdt| j dS t| S )N__name__zfunctools.partial(z, ...))hasattrr\   r5   r   r   rA   r=   repr)r=   r   r   r   rA      s
   
rA   Fc                     s  t rd S t t r	 W d    d S tjdddkrnda 	 W d    d S tjdddaddl	m
   fd	d
} | tjjjj | tjjjj | tjjjj | tjjjj | tjjjj | tjjjj | tjjjj | tjjjj da W d    d S 1 sw   Y  d S )NPYTORCH_JIT1TatenIMPLFuncTorchBatchedr   decomposition_tablec                    s*   |  v rt |  |   d S td|  )Nz!could not find decomposition for )VMAP_DECOMPOSITIONS_LIBimplRuntimeError)decomprd   r   r   #_register_python_decomposition_vmap$  s   zElazy_load_decompositions.<locals>._register_python_decomposition_vmap)DECOMPOSITIONS_LOADEDDECOMPOSITIONS_LOCKosenvirongetr   libraryLibraryrf   torch._decompre   opsra   mse_loss_backwarddefaultsmooth_l1_loss_backwardhuber_loss_backwardnll_loss_forwardnll_loss2d_forwardnll_loss_backwardnll_loss2d_backwardaddr)rj   r   rd   r   lazy_load_decompositions  s6   
"r}   c                 O   sp   t   t||  t||| \}}}	}
|d ur*t|	|||}t| |||
||fi |S t| |||	|
||fi |S r   )r}   r[   rG   _get_chunked_inputs_chunked_vmap
_flat_vmap)r=   r<   rQ   
randomness
chunk_sizer   r   rN   r"   r#   rE   chunks_flat_argsr   r   r   	vmap_impl8  s<   
r   c                 C   s4   | |  }}|g| }| | }|dkr| | |S )Nr   )append)total_elemsr   n_chunkschunk_sizes	remainderr   r   r   get_chunk_sizesZ  s   

r   c                    sN   |f |d urt ||}tt| t fddt| |D }t| }|S )Nc                 3   s8    | ]\}}|d ur|j  |dn|gt  V  qd S N)rD   )tensor_splitr/   )r'   tr(   
split_idxsr   r   r-   j  s    	
z&_get_chunked_inputs.<locals>.<genexpr>)r   r6   	itertools
accumulater.   )r#   r"   rN   r   r   flat_args_chunksr   r   r   r   r~   d  s   
	r~   c                 C   sH   g }d }| D ]}t |\}}|| |d u r|}qtt| }||fS r   )r   r   listr.   )chunks_output_flat_chunks_outputarg_specoutputflat_output	arg_specsflat_output_chunksr   r   r   _flatten_chunks_output|  s   
r   c                 C   sX   t | |}t|t|ksJ g }t|D ]\}}|tj|| |d d ||< q|S r   )r   r/   rC   r   r   cat)rQ   r   r   rV   r   idxrO   r   r   r   _concat_chunked_outputs  s   

r   c                 K   s   g }|dkr
t  nd }|D ]&}	t||	}
|
dkrq|d ur#t | |t| |
||	|||fi | qt|\}}~t|||}t||S )Nsamer   )	r   get_rng_stater2   set_rng_stater   r   r   r   r   )r=   r"   r   rE   rQ   r   r   chunks_outputrsr#   rN   r   r   r   r   r   r   r     s2   


r   c                 C   s   | dvrt d|  d S )N)error	differentr   zLOnly allowed values for randomness are 'error', 'different', or 'same'. Got )rh   )r   r   r   r   _check_randomness_arg  s
   r   c                 c   s(    zt | |}|V  W t  d S t  w r   )r   r
   )rN   r   rH   r   r   r   vmap_increment_nesting  s
   
r   c                 K   sZ   t ||}t||||}	| |	i |}
t|
|||| W  d    S 1 s&w   Y  d S r   )r   rK   rX   )r=   rN   r"   r#   rE   rQ   r   r   rH   rJ   r3   r   r   r   r     s   $r   c                    s    fdd}|S )Nc                     sR   t  }t| |}|i |}t||W  d    S 1 s"w   Y  d S r   )r   wrap_batchedunwrap_batched)r   r   rH   rJ   r3   rN   r=   r<   r   r   r   inner  s
   $zrestore_vmap.<locals>.innerr   )r=   r<   rN   r   r   r   r   r   restore_vmap  s   r   c                 C   s4   t | \}}t||}|d usJ t||||}|S r   )r   r   rK   )r   bdimslevelr#   spec
flat_bdimsresultr   r   r   r     s
   
r   c                    sR   t | \}}t|dkr| dfS  fdd|D }t| \}}t||t||fS )Nr   r   c                    s0   g | ]}t |tjrtjj| n|d fqS r   )r5   r   r   _C
_functorchrX   )r'   r)   r   r   r   r*     s    
z"unwrap_batched.<locals>.<listcomp>)r   r/   r.   r   )r   r   r#   r   r   r   r   r   r   r   r     s   
r   )<
contextlibr   r   rm   	threadingr   typingr   r   r   r   r   r   torch._C._functorchr   r	   r
   r   r   torch.utils._pytreer   r   r   r   r   r@   r6   	in_dims_t
out_dims_tr!   r   r2   r7   strr;   rG   rK   rP   rX   rZ   r[   rA   rk   Lockrl   rf   r}   r   r   r~   r   r   r   r   contextmanagerr   r   r   r   r   r   r   r   r   <module>   s   	

"




>

*,"
2

