import{S as Ta,i as Ja,s as da,e as a,k as c,w as J,t as s,M as ha,c as r,d as t,m as u,a as n,x as d,h as o,b as f,G as e,g as i,y as h,L as Ua,q as U,o as M,B as w,v as Ma}from"../chunks/vendor-hf-doc-builder.js";import{I as Me}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as Q}from"../chunks/CodeBlock-hf-doc-builder.js";import{D as wa}from"../chunks/DocNotebookDropdown-hf-doc-builder.js";function ya(ro){let v,we,k,G,Bl,Y,Tt,$l,Jt,ye,q,be,C,dt,S,ht,Ut,_e,vl,Mt,Ee,m,wt,Wl,yt,bt,H,Ll,_t,Et,xl,jt,vt,zl,kt,gt,Dl,Rt,Xt,Nl,At,It,Ol,Zt,Vt,Yl,Qt,Gt,je,y,Ct,P,Ft,Bt,K,$t,Wt,ll,Lt,xt,el,zt,Dt,ve,kl,Nt,ke,g,F,ql,tl,Ot,Sl,Yt,ge,gl,qt,Re,sl,Xe,B,St,Hl,Ht,Pt,Ae,ol,Ie,Rl,Kt,Ze,al,Ve,R,Pl,ls,es,Kl,ts,ss,Qe,rl,Ge,X,$,le,nl,os,ee,as,Ce,W,rs,te,ns,ps,Fe,pl,Be,Xl,is,$e,il,We,L,fs,se,cs,us,Le,A,x,oe,fl,ms,ae,Ts,xe,_,Js,re,ds,hs,cl,Us,Ms,ze,z,ws,ne,ys,bs,De,I,pe,_s,Es,ie,js,vs,Ne,ul,Oe,D,ks,fe,gs,Rs,Ye,ml,qe,Al,Xs,Se,Tl,He,Jl,ce,As,Is,Pe,Il,Zs,Ke,Z,N,ue,dl,Vs,me,Qs,lt,Zl,Gs,et,b,E,hl,Cs,Fs,Ul,Bs,$s,Ml,Ws,Ls,xs,Vl,wl,zs,Ds,Ns,Ql,yl,Os,Ys,qs,Gl,Ss,V,Te,bl,Hs,Ps,Je,_l,Ks,lo,de,El,eo,tt;return Y=new Me({}),q=new wa({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tf_xla.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/tf_xla.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/tf_xla.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tf_xla.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/tf_xla.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/tf_xla.ipynb"}]}}),tl=new Me({}),sl=new Q({props:{code:"aW1wb3J0JTIwdGVuc29yZmxvdyUyMGFzJTIwdGYlMEElMEFtb2RlbCUyMCUzRCUyMHRmLmtlcmFzLlNlcXVlbnRpYWwoJTBBJTIwJTIwJTIwJTIwJTVCdGYua2VyYXMubGF5ZXJzLkRlbnNlKDEwJTJDJTIwaW5wdXRfc2hhcGUlM0QoMTAlMkMpJTJDJTIwYWN0aXZhdGlvbiUzRCUyMnJlbHUlMjIpJTJDJTIwdGYua2VyYXMubGF5ZXJzLkRlbnNlKDUlMkMlMjBhY3RpdmF0aW9uJTNEJTIyc29mdG1heCUyMiklNUQlMEEp",highlighted:`<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

model = tf.keras.Sequential(
    [tf.keras.layers.Dense(<span class="hljs-number">10</span>, input_shape=(<span class="hljs-number">10</span>,), activation=<span class="hljs-string">&quot;relu&quot;</span>), tf.keras.layers.Dense(<span class="hljs-number">5</span>, activation=<span class="hljs-string">&quot;softmax&quot;</span>)]
)`}}),ol=new Q({props:{code:"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",highlighted:`<span class="hljs-comment"># \uBAA8\uB378\uC5D0 \uB300\uD55C \uC784\uC758\uC758 \uC785\uB825\uC744 \uC0DD\uC131\uD569\uB2C8\uB2E4.</span>
batch_size = <span class="hljs-number">16</span>
input_vector_dim = <span class="hljs-number">10</span>
random_inputs = tf.random.normal((batch_size, input_vector_dim))

<span class="hljs-comment"># \uC21C\uC804\uD30C\uB97C \uC2E4\uD589\uD569\uB2C8\uB2E4.</span>
_ = model(random_inputs)`}}),al=new Q({props:{code:"eGxhX2ZuJTIwJTNEJTIwdGYuZnVuY3Rpb24obW9kZWwlMkMlMjBqaXRfY29tcGlsZSUzRFRydWUpJTBBXyUyMCUzRCUyMHhsYV9mbihyYW5kb21faW5wdXRzKQ==",highlighted:`xla_fn = tf.function(model, jit_compile=<span class="hljs-literal">True</span>)
_ = xla_fn(random_inputs)`}}),rl=new Q({props:{code:"bXlfeGxhX2ZuJTIwJTNEJTIwdGYuZnVuY3Rpb24obW9kZWwubXlfeGxhX2ZuJTJDJTIwaml0X2NvbXBpbGUlM0RUcnVlKQ==",highlighted:'my_xla_fn = tf.function(model.my_xla_fn, jit_compile=<span class="hljs-literal">True</span>)'}}),nl=new Me({}),pl=new Q({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyUyMC0tdXBncmFkZQ==",highlighted:"pip install transformers --upgrade"}}),il=new Q({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForCausalLM

<span class="hljs-comment"># \uCD5C\uC18C \uBC84\uC804\uC758 Transformers\uAC00 \uC124\uCE58\uB418\uC5B4 \uC788\uC9C0 \uC54A\uB2E4\uBA74 \uC624\uB958\uAC00 \uBC1C\uC0DD\uD569\uB2C8\uB2E4.</span>
<span class="hljs-keyword">from</span> transformers.utils <span class="hljs-keyword">import</span> check_min_version

check_min_version(<span class="hljs-string">&quot;4.21.0&quot;</span>)


tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>, padding_side=<span class="hljs-string">&quot;left&quot;</span>, pad_token=<span class="hljs-string">&quot;&lt;/s&gt;&quot;</span>)
model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)
input_string = [<span class="hljs-string">&quot;TensorFlow is&quot;</span>]

<span class="hljs-comment"># XLA \uC0DD\uC131 \uD568\uC218\uB97C \uB9CC\uB4E4\uAE30 \uC704\uD55C \uD55C \uC904</span>
xla_generate = tf.function(model.generate, jit_compile=<span class="hljs-literal">True</span>)

tokenized_input = tokenizer(input_string, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
generated_tokens = xla_generate(**tokenized_input, num_beams=<span class="hljs-number">2</span>)

decoded_text = tokenizer.decode(generated_tokens[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Generated -- <span class="hljs-subst">{decoded_text}</span>&quot;</span>)
<span class="hljs-comment"># Generated -- TensorFlow is an open-source, open-source, distributed-source application # framework for the</span>`}}),fl=new Me({}),ul=new Q({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>, padding_side=<span class="hljs-string">&quot;left&quot;</span>, pad_token=<span class="hljs-string">&quot;&lt;/s&gt;&quot;</span>)
model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)
input_string = [<span class="hljs-string">&quot;TensorFlow is&quot;</span>]

xla_generate = tf.function(model.generate, jit_compile=<span class="hljs-literal">True</span>)

<span class="hljs-comment"># \uC5EC\uAE30\uC11C, padding \uC635\uC158\uC774 \uC788\uB294 \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uD638\uCD9C\uD569\uB2C8\uB2E4.</span>
tokenized_input = tokenizer(input_string, pad_to_multiple_of=<span class="hljs-number">8</span>, padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

generated_tokens = xla_generate(**tokenized_input, num_beams=<span class="hljs-number">2</span>)
decoded_text = tokenizer.decode(generated_tokens[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Generated -- <span class="hljs-subst">{decoded_text}</span>&quot;</span>)`}}),ml=new Q({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> time
<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>, padding_side=<span class="hljs-string">&quot;left&quot;</span>, pad_token=<span class="hljs-string">&quot;&lt;/s&gt;&quot;</span>)
model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)

xla_generate = tf.function(model.generate, jit_compile=<span class="hljs-literal">True</span>)

<span class="hljs-keyword">for</span> input_string <span class="hljs-keyword">in</span> [<span class="hljs-string">&quot;TensorFlow is&quot;</span>, <span class="hljs-string">&quot;TensorFlow is a&quot;</span>, <span class="hljs-string">&quot;TFLite is a&quot;</span>]:
    tokenized_input = tokenizer(input_string, pad_to_multiple_of=<span class="hljs-number">8</span>, padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
    start = time.time_ns()
    generated_tokens = xla_generate(**tokenized_input, num_beams=<span class="hljs-number">2</span>)
    end = time.time_ns()
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Execution time -- <span class="hljs-subst">{(end - start) / <span class="hljs-number">1e6</span>:<span class="hljs-number">.1</span>f}</span> ms\\n&quot;</span>)`}}),Tl=new Q({props:{code:"RXhlY3V0aW9uJTIwdGltZSUyMC0tJTIwMzA4MTkuNiUyMG1zJTBBJTBBRXhlY3V0aW9uJTIwdGltZSUyMC0tJTIwNzkuMCUyMG1zJTBBJTBBRXhlY3V0aW9uJTIwdGltZSUyMC0tJTIwNzguOSUyMG1z",highlighted:`Execution time -- 30819.6 ms

Execution time -- 79.0 ms

Execution time -- 78.9 ms`}}),dl=new Me({}),{c(){v=a("meta"),we=c(),k=a("h1"),G=a("a"),Bl=a("span"),J(Y.$$.fragment),Tt=c(),$l=a("span"),Jt=s("TensorFlow \uBAA8\uB378\uC744 \uC704\uD55C XLA \uD1B5\uD569"),ye=c(),J(q.$$.fragment),be=c(),C=a("p"),dt=s("XLA(Accelerated Linear Algebra)\uB294 TensorFlow \uBAA8\uB378\uC758 \uC2E4\uD589 \uC2DC\uAC04\uC744 \uAC00\uC18D\uD654\uD558\uAE30 \uC704\uD55C \uCEF4\uD30C\uC77C\uB7EC\uC785\uB2C8\uB2E4. 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