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64 lines
6.3 KiB
Plaintext
64 lines
6.3 KiB
Plaintext
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<00>p0d\ <00> @ s<> d Z ddlZddlmZ ddlZddlmZ ddlmZ G dd<06> dej <09>Z
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G dd<08> dej <09>Z G d d
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<EFBFBD> d
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ej <09>Z G d d <0C> d ej <09>Z
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d<0E> d<0E><02>ZG dd<10> dej <09>ZdS )zc
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Much of this code is adapted from Andrej Karpathy's NanoGPT
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(https://github.com/karpathy/nanoGPT)
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<EFBFBD> N)<01> dataclass)<01>
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functionalc s( e Zd ZdZ<03> fdd<03>Zdd<05> Z<05> ZS )<06> LayerNormzP LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False c s: t <00> <00><01> t<02>t<04>|<01><01>| _|r0t<02>t<04>|<01><01>nd | _d S <00>N) <09>super<65>__init__<5F>nn<6E> Parameter<65>torch<63>ones<65>weight<68>zeros<6F>bias)<03>self<6C>ndimr <00><01> __class__<5F> <00>$/Users/georg/code/bark/bark/model.pyr s
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zLayerNorm.__init__c C s t <00>|| jj| j| jd<01>S )Ng<4E>h㈵<68><E388B5>>)<05>F<>
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layer_normr <00>shaper )r <00>inputr r r <00>forward s zLayerNorm.forward)<07>__name__<5F>
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__module__<EFBFBD> __qualname__<5F>__doc__r r <00>
__classcell__r r r r r s r c s$ e Zd Z<02> fdd<02>Zdd<04> Z<04> ZS )<05>CausalSelfAttentionc s<> t <00> <00><01> |j|j dks t<04>tj|jd|j |jd<03>| _tj|j|j|jd<03>| _ t<05>
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|j <0B>| _ t<05>
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|j| _|j| _|j | _ ttjjd<04>| _| js<>| <00>dt<0F>t<0F>|j|j<15><02><01>dd|j|j<15><04> d S )Nr <00> <00>r <00> scaled_dot_product_attentionr <00> )r r <00>n_embd<62>n_head<61>AssertionErrorr <00>Linearr <00>c_attn<74>c_proj<6F>Dropout<75>dropout<75> attn_dropout<75>
resid_dropout<75>hasattrr
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r <00>flash<73>register_buffer<65>trilr <00>
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block_size<EFBFBD>view<65>r <00>configr r r r s"
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<04>z CausalSelfAttention.__init__c
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C sR |<01> <00> \}}}| <00>|<01>j| jdd<02>\}}}|<06>||| j|| j <00><04>dd<01>}|<05>||| j|| j <00><04>dd<01>}|<07>||| j|| j <00><04>dd<01>}| jr<>tj j
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j |||d | j dd<05>}nv||<06>dd<07> dt
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<EFBFBD><01>} tj| dd<02>} | <00>| <09>} | | }|<08>dd<01><02><15> <00>|||<04>}| <00>| <00>|<08><01>}|S ) N<> <00><01>dimr# T)<03> attn_mask<73> dropout_p<5F> is_causal<61><6C><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>g <00>?r z-inf)<18>sizer( <00>splitr$ r3 r% <00> transposer/ r
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r r r" r+ <00>math<74>sqrt<72> masked_fillr <00>floatr <00>softmaxr, <00>
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contiguousr- r) )
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r <00>x<>B<>T<>C<>q<>k<>v<>yZattr r r r . s $2
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zCausalSelfAttention.forward<72>r r r r r r r r r r r s r c s$ e Zd Z<02> fdd<02>Zdd<04> Z<04> ZS )<05>MLPc s^ t <00> <00><01> tj|jd|j |jd<02>| _tjd|j |j|jd<02>| _t<02>|j <09>| _ t<02>
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<EFBFBD> | _ d S )N<> r! ) r r r r' r$ r <00>c_fcr) r* r+ <00>GELU<4C>gelur4 r r r r J s
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z MLP.__init__c C s, | <00> |<01>}| <00>|<01>}| <00>|<01>}| <00>|<01>}|S r )rR rT r) r+ <00>r rG r r r r Q s
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z MLP.forwardrO r r r r rP H s rP c s$ e Zd Z<02> fdd<02>Zdd<04> Z<04> ZS )<05>Blockc sL t <00> <00><01> t|j|jd<01>| _t|<01>| _t|j|jd<01>| _t |<01>| _
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|| _ d S )Nr! ) r r r r$ r <00>ln_1r <00>attn<74>ln_2rP <00>mlp<6C> layer_idx)r r5 r[ r r r r Z s
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zBlock.__init__c C s, || <00> | <00>|<01><01> }|| <00>| <00>|<01><01> }|S r )rX rW rZ rY rU r r r r b s z
Block.forwardrO r r r r rV X s rV c @ sn e Zd ZU dZeed< dZeed< dZeed< dZeed< dZ eed< d Z
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eed
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< d Z e ed < d
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eed< dS )<10> GPTConfigi r2 i@' <00>input_vocab_size<7A>output_vocab_size<7A> <00>n_layerr% i r$ g r+ Tr N)r r r r2 <00>int<6E>__annotations__r] r^ r` r% r$ r+ rD r <00>boolr r r r r\ g s
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r\ c s0 e Zd Z<02> fdd<02>Zd dd<05>Zd
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dd<08>Z<05> ZS ) <0B>GPTc s<> t <00> <00><01> <00> jd k st<03><01> jd k s&t<03><01> jd k s4t<03><01> | _t<07>t t<07>
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<EFBFBD> j<02> j <0B>t<07>
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<EFBFBD> j<05> j <0B>t<07> <0C> j
<0A>t<07><0E> fdd<02>t<0F> j<10>D <00><01>t<11> j <0B> jd<03>d<04><05>| _tj<14> j <0B> jdd<03>| _d S )Nc s g | ]}t <00> |<01><02>qS r )rV )<02>.0<EFBFBD>idx<64>r5 r r <00>
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<listcomp> s z GPT.__init__.<locals>.<listcomp>r! )<05>wte<74>wpe<70>drop<6F>h<>ln_fF)r r r] r& r^ r2 r5 r <00>
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ModuleDict<EFBFBD>dict<63> Embeddingr$ r* r+ <00>
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ModuleList<EFBFBD>ranger` r r <00> transformerr' <00>lm_headr4 r rg r r t s
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<0E>
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z GPT.__init__Tc C sB t dd<02> | <00><01> D <00><01>}|r>|| jjj<04><05> 8 }|| jjj<04><05> 8 }|S )a8
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Return the number of parameters in the model.
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For non-embedding count (default), the position embeddings get subtracted.
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The token embeddings would too, except due to the parameter sharing these
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params are actually used as weights in the final layer, so we include them.
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c s s | ]}|<01> <00> V qd S r )<01>numel)re <00>pr r r <00> <genexpr><3E> s z%GPT.get_num_params.<locals>.<genexpr>)<07>sum<75>
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parametersrs ri r ru rj )r Z
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zGPT.get_num_paramsFc C sH |j }|<01><01> \}}|r8|jd dks(t<03>|jd d }n$|| jjks\td|<05> d| jj<05> <00><04><01>|r<>tj| j<08> |d d <00>d d<03>f <00>| j<08> |d d <00>dd<06>f <00> | j<08> |d d <00>dd <00>f <00>gdd<07>}n | j<08> |<01>}tj
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d|tj |d <09><04> d<08>}| j<08>
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gd d <00>f <00>} | S ) Nr# i <00> z"Cannot forward sequence of length z, block size is only i r7 r )<02>dtype<70>devicer= )r~ r> r r& r5 r2 r
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<00>catrs ri <00>arange<67>long<6E> unsqueezerj rk rl rm rt ) r rf <00>
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rd )r rA <00> dataclassesr r
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<00>torch.nnr r r <00>Moduler r rP rV r\ rd r r r r <00><module> s 1
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