@ -1,10 +1,10 @@
def documentation ( task : str ) :
documentation = f """ Create multi-page long and explicit professional pytorch-like documentation for the <MODULE> code below follow the outline for the <MODULE> library,
provide many examples and teach the user about the code , provide examples for every function , make the documentation 10 , 000 words ,
documentation = f """ Create multi-page long and explicit professional pytorch-like documentation for the <MODULE> code below follow the outline for the <MODULE> library,
provide many examples and teach the user about the code , provide examples for every function , make the documentation 10 , 000 words ,
provide many usage examples and note this is markdown docs , create the documentation for the code to document ,
put the arguments and methods in a table in markdown to make it visually seamless
Now make the professional documentation for this code , provide the architecture and how the class works and why it works that way ,
Now make the professional documentation for this code , provide the architecture and how the class works and why it works that way ,
it ' s purpose, provide args, their types, 3 ways of usage examples, in examples show all the code like imports main example etc
BE VERY EXPLICIT AND THOROUGH , MAKE IT DEEP AND USEFUL
@ -45,7 +45,7 @@ def documentation(task: str):
class torch . nn . MultiheadAttention ( embed_dim , num_heads , dropout = 0.0 , bias = True , add_bias_kv = False , add_zero_attn = False , kdim = None , vdim = None , batch_first = False , device = None , dtype = None ) :
` ` `
Creates a multi - head attention module for joint information representation from the different subspaces .
Parameters :
- embed_dim ( int ) : Total dimension of the model .
- num_heads ( int ) : Number of parallel attention heads . The embed_dim will be split across num_heads .
@ -70,7 +70,7 @@ def documentation(task: str):
- value ( Tensor ) : Value embeddings of shape ( S , E_v ) for unbatched input , ( S , N , E_v ) when batch_first = False , or ( N , S , E_v ) when batch_first = True .
- key_padding_mask ( Optional [ Tensor ] ) : If specified , a mask indicating elements to be ignored in key for attention computation .
- need_weights ( bool ) : If specified , returns attention weights in addition to attention outputs . Default : True .
- attn_mask ( Optional [ Tensor ] ) : If specified , a mask preventing attention to certain positions .
- attn_mask ( Optional [ Tensor ] ) : If specified , a mask preventing attention to certain positions .
- average_attn_weights ( bool ) : If true , returns averaged attention weights per head . Otherwise , returns attention weights separately per head . Note that this flag only has an effect when need_weights = True . Default : True .
- is_causal ( bool ) : If specified , applies a causal mask as the attention mask . Default : False .
Kye
1 year ago