From e02aef1dedc15e9eed9410b54c5024832e9f3164 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 Apr 2024 11:48:54 -0700
Subject: [PATCH] Bump transformers from 4.36.0 to 4.38.0 in
 /onnxruntime/python/tools/transformers/models/stable_diffusion (#20271)
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Bumps [transformers](https://github.com/huggingface/transformers) from
4.36.0 to 4.38.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/huggingface/transformers/releases">transformers's
releases</a>.</em></p>
<blockquote>
<h2>v4.38: Gemma, Depth Anything, Stable LM; Static Cache, HF Quantizer,
AQLM</h2>
<h2>New model additions</h2>
<h3>💎 Gemma 💎</h3>
<p>Gemma is a new opensource Language Model series from Google AI that
comes with a 2B and 7B variant. The release comes with the pre-trained
and instruction fine-tuned versions and you can use them via
<code>AutoModelForCausalLM</code>, <code>GemmaForCausalLM</code> or
<code>pipeline</code> interface!</p>
<p>Read more about it in the Gemma release blogpost: <a
href="https://hf.co/blog/gemma">https://hf.co/blog/gemma</a></p>
<pre lang="python"><code>from transformers import AutoTokenizer,
AutoModelForCausalLM
<p>tokenizer =
AutoTokenizer.from_pretrained(&quot;google/gemma-2b&quot;)
model =
AutoModelForCausalLM.from_pretrained(&quot;google/gemma-2b&quot;,
device_map=&quot;auto&quot;, torch_dtype=torch.float16)</p>
<p>input_text = &quot;Write me a poem about Machine Learning.&quot;
input_ids = tokenizer(input_text,
return_tensors=&quot;pt&quot;).to(&quot;cuda&quot;)</p>
<p>outputs = model.generate(**input_ids)
</code></pre></p>
<p>You can use the model with Flash Attention, SDPA, Static cache and
quantization API for further optimizations !</p>
<ul>
<li>Flash Attention 2</li>
</ul>
<pre lang="python"><code>from transformers import AutoTokenizer,
AutoModelForCausalLM
<p>tokenizer =
AutoTokenizer.from_pretrained(&quot;google/gemma-2b&quot;)</p>
<p>model = AutoModelForCausalLM.from_pretrained(
&quot;google/gemma-2b&quot;, device_map=&quot;auto&quot;,
torch_dtype=torch.float16,
attn_implementation=&quot;flash_attention_2&quot;
)</p>
<p>input_text = &quot;Write me a poem about Machine Learning.&quot;
input_ids = tokenizer(input_text,
return_tensors=&quot;pt&quot;).to(&quot;cuda&quot;)</p>
<p>outputs = model.generate(**input_ids)
</code></pre></p>
<ul>
<li>bitsandbytes-4bit</li>
</ul>
<pre lang="python"><code>from transformers import AutoTokenizer,
AutoModelForCausalLM
<p>tokenizer =
AutoTokenizer.from_pretrained(&quot;google/gemma-2b&quot;)</p>
<p>model = AutoModelForCausalLM.from_pretrained(
&quot;google/gemma-2b&quot;, device_map=&quot;auto&quot;,
load_in_4bit=True
)
&lt;/tr&gt;&lt;/table&gt;
</code></pre></p>
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/huggingface/transformers/commit/08ab54ada594f8f4cc1e458b1217bf8c53f04dd8"><code>08ab54a</code></a>
[ <code>gemma</code>] Adds support for Gemma 💎 (<a
href="https://redirect.github.com/huggingface/transformers/issues/29167">#29167</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/2de9314197a12e134f3fff2710f6d86e97b774a3"><code>2de9314</code></a>
[<code>Maskformer</code>] safely get backbone config (<a
href="https://redirect.github.com/huggingface/transformers/issues/29166">#29166</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/476957b5b471fe8a3be9968485118c67a739a044"><code>476957b</code></a>
🚨 Llama: update rope scaling to match static cache changes (<a
href="https://redirect.github.com/huggingface/transformers/issues/29143">#29143</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/7a4bec6e8f6f81f47228092b9f6b9b7e2b55ebe7"><code>7a4bec6</code></a>
Release: 4.38.0</li>
<li><a
href="https://github.com/huggingface/transformers/commit/ee3af60be0d21044692211d97dfd858aa3e4b418"><code>ee3af60</code></a>
Add support for fine-tuning CLIP-like models using
contrastive-image-text exa...</li>
<li><a
href="https://github.com/huggingface/transformers/commit/0996a10077219de0556281511fc02f3ab68002d5"><code>0996a10</code></a>
Revert low cpu mem tie weights (<a
href="https://redirect.github.com/huggingface/transformers/issues/29135">#29135</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/15cfe38942e4012f5476e7f45dfacf26791b0ccc"><code>15cfe38</code></a>
[<code>Core tokenization</code>] <code>add_dummy_prefix_space</code>
option to help with latest is...</li>
<li><a
href="https://github.com/huggingface/transformers/commit/efdd436663436e78d8ad3213d11325d86578db95"><code>efdd436</code></a>
FIX [<code>PEFT</code> / <code>Trainer</code> ] Handle better peft +
quantized compiled models (<a
href="https://redirect.github.com/huggingface/transformers/issues/29">#29</a>...</li>
<li><a
href="https://github.com/huggingface/transformers/commit/5e95dcabe1d3d522a8bc5a45990c53d9d4e9f2eb"><code>5e95dca</code></a>
[<code>cuda kernels</code>] only compile them when initializing (<a
href="https://redirect.github.com/huggingface/transformers/issues/29133">#29133</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/a7755d24096306c84a3557394b54a95db7a0f76f"><code>a7755d2</code></a>
Generate: unset GenerationConfig parameters do not raise warning (<a
href="https://redirect.github.com/huggingface/transformers/issues/29119">#29119</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/huggingface/transformers/compare/v4.36.0...v4.38.0">compare
view</a></li>
</ul>
</details>
<br />


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---
 .../tools/transformers/models/stable_diffusion/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/onnxruntime/python/tools/transformers/models/stable_diffusion/requirements.txt b/onnxruntime/python/tools/transformers/models/stable_diffusion/requirements.txt
index ee9b9bfeef..0798b65930 100644
--- a/onnxruntime/python/tools/transformers/models/stable_diffusion/requirements.txt
+++ b/onnxruntime/python/tools/transformers/models/stable_diffusion/requirements.txt
@@ -1,5 +1,5 @@
 diffusers==0.24.0
-transformers==4.36.0
+transformers==4.38.0
 numpy>=1.24.1
 accelerate
 onnx==1.14.1