Pali gemma modeling (#1895)
This PR adds paligemma modeling code Blog post: https://huggingface.co/blog/paligemma Transformers PR: https://github.com/huggingface/transformers/pull/30814 install the latest changes and run with ```bash # get the weights # text-generation-server download-weights gv-hf/PaliGemma-base-224px-hf # run TGI text-generation-launcher --model-id gv-hf/PaliGemma-base-224px-hf ``` basic example sending various requests ```python from huggingface_hub import InferenceClient client = InferenceClient("http://127.0.0.1:3000") images = [ "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png", ] prompts = [ "What animal is in this image?", "Name three colors in this image.", "What are 10 colors in this image?", "Where is the cow standing?", "answer en Where is the cow standing?", "Is there a bird in the image?", "Is ther a cow in the image?", "Is there a rabbit in the image?", "how many birds are in the image?", "how many rabbits are in the image?", ] for img in images: print(f"\nImage: {img.split('/')[-1]}") for prompt in prompts: inputs = f"{prompt}\n" json_data = { "inputs": inputs, "parameters": { "max_new_tokens": 30, "do_sample": False, }, } generated_output = client.text_generation(prompt, max_new_tokens=30, stream=False) print([f"{prompt}\n{generated_output}"]) ``` --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
This commit is contained in:
drbh
GitHub
parent
6c715f8183
commit
40213c957f
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@ -27,7 +27,7 @@ jobs:
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runs-on: ubuntu-latest
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env:
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AWS_REGION: us-east-1
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EC2_AMI_ID: ami-03cfed9ea28f4b002
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EC2_AMI_ID: ami-0789b6925c11b1fb2
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EC2_INSTANCE_TYPE: g5.12xlarge
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EC2_SUBNET_ID: subnet-931b34f5,subnet-ecb993cd,subnet-943dc2d8,subnet-45371f1a,subnet-ee93e0df,subnet-fddc3dfc
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EC2_SECURITY_GROUP: sg-030175c435ac141d6
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@ -43,7 +43,7 @@ ARG PYTORCH_VERSION=2.3.0
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ARG PYTHON_VERSION=3.10
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# Keep in sync with `server/pyproject.toml
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ARG CUDA_VERSION=12.1
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ARG MAMBA_VERSION=23.3.1-1
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ARG MAMBA_VERSION=24.3.0-0
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ARG CUDA_CHANNEL=nvidia
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ARG INSTALL_CHANNEL=pytorch
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# Automatically set by buildx
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@ -181,6 +181,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins
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ca-certificates \
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make \
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curl \
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git \
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&& rm -rf /var/lib/apt/lists/*
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# Copy conda with PyTorch installed
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After Width: | Height: | Size: 66 KiB |
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@ -0,0 +1,25 @@
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{
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"details": {
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"best_of_sequences": null,
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"finish_reason": "eos_token",
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"generated_tokens": 2,
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"prefill": [],
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"seed": null,
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"tokens": [
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{
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"id": 54901,
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"logprob": -0.72753906,
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"special": false,
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"text": "beach"
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},
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{
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"id": 1,
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"logprob": -0.011009216,
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"special": true,
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"text": "<eos>"
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}
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],
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"top_tokens": null
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},
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"generated_text": "beach"
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}
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@ -0,0 +1,39 @@
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import pytest
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import requests
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import io
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import base64
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@pytest.fixture(scope="module")
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def flash_pali_gemma_handle(launcher):
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with launcher(
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"google/paligemma-3b-pt-224",
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num_shard=1,
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revision="float16",
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max_input_length=4000,
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max_total_tokens=4096,
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) as handle:
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yield handle
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@pytest.fixture(scope="module")
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async def flash_pali_gemma(flash_pali_gemma_handle):
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await flash_pali_gemma_handle.health(300)
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return flash_pali_gemma_handle.client
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def get_cow_beach():
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with open("integration-tests/images/cow_beach.png", "rb") as image_file:
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encoded_string = base64.b64encode(image_file.read())
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return f"data:image/png;base64,{encoded_string.decode('utf-8')}"
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_pali_gemma(flash_pali_gemma, response_snapshot):
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cow = get_cow_beach()
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inputs = f"Where is the cow standing?\n"
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response = await flash_pali_gemma.generate(inputs, max_new_tokens=20)
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assert response.generated_text == "beach"
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assert response == response_snapshot
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@ -100,7 +100,6 @@ impl LlavaNext {
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}
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#[derive(Clone, Debug, Serialize, Deserialize)]
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#[serde(tag = "model_type")]
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#[serde(rename_all = "snake_case")]
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pub struct ClipVisionModel {
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image_size: usize,
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@ -108,7 +107,6 @@ pub struct ClipVisionModel {
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}
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#[derive(Clone, Debug, Serialize, Deserialize)]
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#[serde(tag = "model_type")]
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#[serde(rename_all = "snake_case")]
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pub struct Idefics2 {}
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@ -118,6 +116,24 @@ impl Idefics2 {
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}
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}
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#[derive(Clone, Debug, Serialize, Deserialize)]
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#[serde(rename_all = "snake_case")]
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pub struct PaliTextConfig {
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num_image_tokens: usize,
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}
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#[derive(Clone, Debug, Serialize, Deserialize)]
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#[serde(rename_all = "snake_case")]
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pub struct Paligemma {
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text_config: PaliTextConfig,
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}
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impl Paligemma {
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pub fn get_number_of_features(&self, _height: usize, _width: usize) -> usize {
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self.text_config.num_image_tokens
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}
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}
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#[derive(Clone, Debug, Serialize, Deserialize)]
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#[serde(tag = "model_type")]
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#[serde(rename_all = "snake_case")]
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@ -140,6 +156,7 @@ pub enum Config {
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Phi3,
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Llama,
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Baichuan,
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Paligemma(Paligemma),
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Gemma,
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Cohere,
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Drbx,
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@ -544,6 +544,30 @@ fn prepare_input(
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inputs = modified_inputs;
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tokenizer_query
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}
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Some(Config::Paligemma(config)) => {
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let mut modified_inputs = String::with_capacity(inputs.len());
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let mut tokenizer_query = String::with_capacity(inputs.len());
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let mut start = 0;
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for chunk in RE.find_iter(&inputs) {
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let chunk_start = chunk.start();
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let chunk_end = chunk.end();
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if chunk_start != start {
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modified_inputs.push_str(&inputs[start..chunk_start]);
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tokenizer_query.push_str(&inputs[start..chunk_start]);
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}
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let (image_uri, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?;
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let slots = config.get_number_of_features(height, width);
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tokenizer_query.push_str(&"<image>".repeat(slots));
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modified_inputs.push_str(&image_uri);
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start = chunk_end;
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}
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if start != inputs.len() - 1 {
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modified_inputs.push_str(&inputs[start..]);
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tokenizer_query.push_str(&inputs[start..]);
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}
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inputs = modified_inputs;
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tokenizer_query
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}
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Some(Config::Idefics2(config)) => {
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let mut modified_inputs = String::with_capacity(inputs.len());
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let mut tokenizer_query = String::with_capacity(inputs.len());
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@ -359,43 +359,45 @@ files = [
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[[package]]
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name = "datasets"
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version = "2.14.4"
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version = "2.19.1"
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description = "HuggingFace community-driven open-source library of datasets"
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optional = true
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python-versions = ">=3.8.0"
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files = [
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{file = "datasets-2.14.4-py3-none-any.whl", hash = "sha256:29336bd316a7d827ccd4da2236596279b20ca2ac78f64c04c9483da7cbc2459b"},
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{file = "datasets-2.14.4.tar.gz", hash = "sha256:ef29c2b5841de488cd343cfc26ab979bff77efa4d2285af51f1ad7db5c46a83b"},
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{file = "datasets-2.19.1-py3-none-any.whl", hash = "sha256:f7a78d15896f45004ccac1c298f3c7121f92f91f6f2bfbd4e4f210f827e6e411"},
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{file = "datasets-2.19.1.tar.gz", hash = "sha256:0df9ef6c5e9138cdb996a07385220109ff203c204245578b69cca905eb151d3a"},
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]
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[package.dependencies]
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aiohttp = "*"
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dill = ">=0.3.0,<0.3.8"
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fsspec = {version = ">=2021.11.1", extras = ["http"]}
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huggingface-hub = ">=0.14.0,<1.0.0"
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dill = ">=0.3.0,<0.3.9"
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filelock = "*"
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fsspec = {version = ">=2023.1.0,<=2024.3.1", extras = ["http"]}
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huggingface-hub = ">=0.21.2"
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multiprocess = "*"
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numpy = ">=1.17"
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packaging = "*"
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pandas = "*"
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pyarrow = ">=8.0.0"
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pyarrow = ">=12.0.0"
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pyarrow-hotfix = "*"
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pyyaml = ">=5.1"
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requests = ">=2.19.0"
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tqdm = ">=4.62.1"
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xxhash = "*"
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[package.extras]
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apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
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apache-beam = ["apache-beam (>=2.26.0)"]
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audio = ["librosa", "soundfile (>=0.12.1)"]
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benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
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dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "black (>=23.1,<24.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "ruff (>=0.0.241)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
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docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
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jax = ["jax (>=0.2.8,!=0.3.2,<=0.3.25)", "jaxlib (>=0.1.65,<=0.3.25)"]
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dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
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docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"]
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jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
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metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
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quality = ["black (>=23.1,<24.0)", "pyyaml (>=5.3.1)", "ruff (>=0.0.241)"]
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quality = ["ruff (>=0.3.0)"]
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s3 = ["s3fs"]
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tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
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tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
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tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
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tensorflow = ["tensorflow (>=2.6.0)"]
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tensorflow-gpu = ["tensorflow (>=2.6.0)"]
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tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
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torch = ["torch"]
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vision = ["Pillow (>=6.2.1)"]
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@ -418,17 +420,18 @@ dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
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[[package]]
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name = "dill"
|
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version = "0.3.7"
|
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version = "0.3.8"
|
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description = "serialize all of Python"
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optional = true
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python-versions = ">=3.7"
|
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python-versions = ">=3.8"
|
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files = [
|
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{file = "dill-0.3.7-py3-none-any.whl", hash = "sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e"},
|
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{file = "dill-0.3.7.tar.gz", hash = "sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03"},
|
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{file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
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{file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
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]
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[package.extras]
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graph = ["objgraph (>=1.7.2)"]
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profile = ["gprof2dot (>=2022.7.29)"]
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[[package]]
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name = "diskcache"
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@ -871,13 +874,13 @@ files = [
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[[package]]
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name = "huggingface-hub"
|
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version = "0.19.4"
|
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version = "0.23.0"
|
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description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
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optional = false
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python-versions = ">=3.8.0"
|
||||
files = [
|
||||
{file = "huggingface_hub-0.19.4-py3-none-any.whl", hash = "sha256:dba013f779da16f14b606492828f3760600a1e1801432d09fe1c33e50b825bb5"},
|
||||
{file = "huggingface_hub-0.19.4.tar.gz", hash = "sha256:176a4fc355a851c17550e7619488f383189727eab209534d7cef2114dae77b22"},
|
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{file = "huggingface_hub-0.23.0-py3-none-any.whl", hash = "sha256:075c30d48ee7db2bba779190dc526d2c11d422aed6f9044c5e2fdc2c432fdb91"},
|
||||
{file = "huggingface_hub-0.23.0.tar.gz", hash = "sha256:7126dedd10a4c6fac796ced4d87a8cf004efc722a5125c2c09299017fa366fa9"},
|
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]
|
||||
|
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[package.dependencies]
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@ -890,16 +893,17 @@ tqdm = ">=4.42.1"
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typing-extensions = ">=3.7.4.3"
|
||||
|
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[package.extras]
|
||||
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
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all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
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cli = ["InquirerPy (==0.3.4)"]
|
||||
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||||
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||||
tensorflow-testing = ["keras (<3.0)", "tensorflow"]
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||||
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[[package]]
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@ -1282,31 +1286,27 @@ files = [
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[package.dependencies]
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numpy = ">=1.16.6"
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[[package]]
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name = "pyarrow-hotfix"
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version = "0.6"
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description = ""
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optional = true
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python-versions = ">=3.5"
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files = [
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[[package]]
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@ -3016,18 +3027,16 @@ telegram = ["requests"]
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[[package]]
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files = []
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develop = false
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[package.dependencies]
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huggingface-hub = ">=0.23.0,<1.0"
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pyyaml = ">=5.1"
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@ -3040,27 +3049,25 @@ tqdm = ">=4.27"
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|
||||
dev-torch = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
|
||||
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
ftfy = ["ftfy"]
|
||||
integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
|
||||
ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
|
||||
ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)"]
|
||||
modelcreation = ["cookiecutter (==1.7.3)"]
|
||||
natten = ["natten (>=0.14.6,<0.15.0)"]
|
||||
onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
|
||||
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
|
||||
optuna = ["optuna"]
|
||||
quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
|
||||
quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
|
||||
ray = ["ray[tune] (>=2.7.0)"]
|
||||
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
|
||||
sagemaker = ["sagemaker (>=2.31.0)"]
|
||||
|
|
@ -3069,19 +3076,25 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
|
|||
sigopt = ["sigopt"]
|
||||
sklearn = ["scikit-learn"]
|
||||
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
|
||||
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
|
||||
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
timm = ["timm"]
|
||||
tokenizers = ["tokenizers (>=0.19,<0.20)"]
|
||||
torch = ["accelerate (>=0.21.0)", "torch"]
|
||||
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
|
||||
video = ["av (==9.2.0)", "decord (==0.6.0)"]
|
||||
vision = ["Pillow (>=10.0.1,<=15.0)"]
|
||||
|
||||
[package.source]
|
||||
type = "git"
|
||||
url = "https://github.com/huggingface/transformers.git"
|
||||
reference = "b8aee2e"
|
||||
resolved_reference = "b8aee2e918d7ba2d5e9e80162ae26b4806873307"
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "2.3.0"
|
||||
|
|
@ -3488,4 +3501,4 @@ torch = ["torch"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<3.13"
|
||||
content-hash = "df83b265d0263870b5d1ae8bfd847f406abef90868fdf528ff38527b512f86c0"
|
||||
content-hash = "b2a29b0b6e32d0e7043e94b984c5731f2c27c5d95feccbeb80bd890db22d6c4a"
|
||||
|
|
|
|||
|
|
@ -25,8 +25,9 @@ opentelemetry-instrumentation-grpc = "^0.36b0"
|
|||
hf-transfer = "^0.1.2"
|
||||
sentencepiece = "^0.1.97"
|
||||
tokenizers = "^0.19.1"
|
||||
huggingface-hub = "^0.19.3"
|
||||
transformers = "^4.40"
|
||||
huggingface-hub = "^0.23"
|
||||
# transformers = "^4.40"
|
||||
transformers = { git = "https://github.com/huggingface/transformers.git", rev="b8aee2e" }
|
||||
einops = "^0.6.1"
|
||||
texttable = { version = "^1.6.7", optional = true }
|
||||
datasets = { version = "^2.14.0", optional = true }
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
|
|||
grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
grpcio==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13"
|
||||
huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
huggingface-hub==0.23.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
idna==3.7 ; python_version >= "3.9" and python_version < "3.13"
|
||||
loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
numpy==1.26.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
|
|
@ -40,7 +40,7 @@ sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
|
|||
setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
tqdm==4.66.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
transformers==4.40.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
transformers @ git+https://github.com/huggingface/transformers.git@b8aee2e918d7ba2d5e9e80162ae26b4806873307 ; python_version >= "3.9" and python_version < "3.13"
|
||||
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
|
|||
grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
grpcio==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13"
|
||||
huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
huggingface-hub==0.23.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
idna==3.7 ; python_version >= "3.9" and python_version < "3.13"
|
||||
loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
numpy==1.26.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
|
|
@ -40,7 +40,7 @@ sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
|
|||
setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
tqdm==4.66.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
transformers==4.40.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
transformers @ git+https://github.com/huggingface/transformers.git@b8aee2e918d7ba2d5e9e80162ae26b4806873307 ; python_version >= "3.9" and python_version < "3.13"
|
||||
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
|
|
|
|||
|
|
@ -10,9 +10,9 @@ class FastLinear(torch.nn.Module):
|
|||
bias,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.weight = torch.nn.Parameter(weight)
|
||||
self.weight = torch.nn.Parameter(weight, requires_grad=False)
|
||||
if bias is not None:
|
||||
self.bias = torch.nn.Parameter(bias)
|
||||
self.bias = torch.nn.Parameter(bias, requires_grad=False)
|
||||
else:
|
||||
self.bias = None
|
||||
|
||||
|
|
|
|||
|
|
@ -65,6 +65,9 @@ try:
|
|||
from text_generation_server.models.flash_gemma import (
|
||||
FlashGemma,
|
||||
)
|
||||
from text_generation_server.models.pali_gemma import (
|
||||
PaliGemma,
|
||||
)
|
||||
from text_generation_server.models.flash_santacoder import (
|
||||
FlashSantacoderSharded,
|
||||
)
|
||||
|
|
@ -676,6 +679,18 @@ def get_model(
|
|||
)
|
||||
else:
|
||||
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
|
||||
if model_type == "paligemma":
|
||||
if FLASH_ATTENTION:
|
||||
return PaliGemma(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
speculator=speculator,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
|
||||
|
||||
if model_type == "llava_next":
|
||||
if FLASH_ATTENTION:
|
||||
|
|
|
|||
|
|
@ -99,8 +99,13 @@ class GemmaConfig(PretrainedConfig):
|
|||
class GemmaFastRMSNorm(FastRMSNorm):
|
||||
@classmethod
|
||||
def load(cls, prefix, weights, eps=1e-6):
|
||||
dtype = weights.dtype
|
||||
weights.dtype = torch.float32
|
||||
weight = weights.get_tensor(f"{prefix}.weight") + 1
|
||||
return cls(weight, eps)
|
||||
weights.dtype = dtype
|
||||
new = cls(weight, eps)
|
||||
new.dtype = dtype
|
||||
return new
|
||||
|
||||
# perform the multiplication in full precision and downcast after
|
||||
def forward(self, hidden_states, residual=None):
|
||||
|
|
@ -111,7 +116,7 @@ class GemmaFastRMSNorm(FastRMSNorm):
|
|||
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
||||
hidden_states = hidden_states * self.weight
|
||||
return hidden_states.to(self.weight.dtype), residual
|
||||
return hidden_states.to(self.dtype), residual
|
||||
|
||||
|
||||
def load_attention(config, prefix, weights):
|
||||
|
|
@ -153,15 +158,11 @@ def _load_gqa(config, prefix: str, weights):
|
|||
|
||||
|
||||
class FlashGemmaAttention(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
prefix: str,
|
||||
config,
|
||||
weights,
|
||||
):
|
||||
def __init__(self, prefix: str, config, weights, causal: bool):
|
||||
super().__init__()
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.head_size = config.head_dim
|
||||
self.causal = causal
|
||||
|
||||
self.rotary_emb = PositionRotaryEmbedding.static(
|
||||
config=config,
|
||||
|
|
@ -238,6 +239,7 @@ class FlashGemmaAttention(torch.nn.Module):
|
|||
cu_seqlen_prefill,
|
||||
max_s,
|
||||
self.softmax_scale,
|
||||
causal=self.causal,
|
||||
)
|
||||
# Decode
|
||||
else:
|
||||
|
|
@ -295,11 +297,10 @@ class GemmaMLP(nn.Module):
|
|||
|
||||
|
||||
class FlashGemmaLayer(nn.Module):
|
||||
def __init__(self, layer_id, config, weights):
|
||||
def __init__(self, prefix, config, weights, causal: bool):
|
||||
super().__init__()
|
||||
prefix = f"model.layers.{layer_id}"
|
||||
self.self_attn = FlashGemmaAttention(
|
||||
prefix=f"{prefix}.self_attn", config=config, weights=weights
|
||||
prefix=f"{prefix}.self_attn", config=config, weights=weights, causal=causal
|
||||
)
|
||||
self.mlp = GemmaMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
|
||||
|
||||
|
|
@ -351,30 +352,25 @@ class FlashGemmaLayer(nn.Module):
|
|||
|
||||
|
||||
class FlashGemmaModel(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
def __init__(self, prefix, config, weights, causal: bool):
|
||||
super().__init__()
|
||||
|
||||
process_group = weights.process_group
|
||||
self.tp_rank = process_group.rank()
|
||||
self.tp_world_size = process_group.size()
|
||||
embed_norm = config.hidden_size**0.5
|
||||
self.embed_tokens = TensorParallelEmbedding(
|
||||
prefix="model.embed_tokens", weights=weights
|
||||
)
|
||||
self.embed_tokens.weight *= embed_norm
|
||||
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
FlashGemmaLayer(
|
||||
layer_id,
|
||||
config,
|
||||
weights,
|
||||
prefix=f"{prefix}.layers.{layer_id}",
|
||||
config=config,
|
||||
weights=weights,
|
||||
causal=causal,
|
||||
)
|
||||
for layer_id in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
self.norm = GemmaFastRMSNorm.load(
|
||||
prefix="model.norm", weights=weights, eps=config.rms_norm_eps
|
||||
prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps
|
||||
)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
|
@ -385,7 +381,7 @@ class FlashGemmaModel(torch.nn.Module):
|
|||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
inputs_embeds: torch.Tensor,
|
||||
position_ids: torch.Tensor,
|
||||
cu_seqlen_prefill: Optional[torch.Tensor],
|
||||
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
||||
|
|
@ -394,7 +390,7 @@ class FlashGemmaModel(torch.nn.Module):
|
|||
input_lengths: torch.Tensor,
|
||||
max_s: int,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
# Get rotary cos and sin for this forward
|
||||
# Avoid to index in each layer
|
||||
|
|
@ -423,13 +419,30 @@ class FlashGemmaModel(torch.nn.Module):
|
|||
|
||||
|
||||
class FlashGemmaForCausalLM(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
def __init__(self, prefix, config, weights, causal: bool):
|
||||
super().__init__()
|
||||
|
||||
self.model = FlashGemmaModel(config, weights)
|
||||
embed_norm = config.hidden_size**0.5
|
||||
if prefix is None:
|
||||
prefix = "model"
|
||||
else:
|
||||
prefix = f"{prefix}.model"
|
||||
|
||||
self.embed_tokens = TensorParallelEmbedding(
|
||||
prefix=f"{prefix}.embed_tokens", weights=weights
|
||||
)
|
||||
self.embed_tokens.weight *= embed_norm
|
||||
|
||||
self.model = FlashGemmaModel(
|
||||
prefix=prefix, config=config, weights=weights, causal=causal
|
||||
)
|
||||
self.lm_head = SpeculativeHead.load(
|
||||
config,
|
||||
prefix="model.embed_tokens" if config.tie_word_embeddings else "lm_head",
|
||||
prefix=(
|
||||
f"{prefix}.embed_tokens"
|
||||
if config.tie_word_embeddings
|
||||
else f"{prefix}.lm_head"
|
||||
),
|
||||
config=config,
|
||||
weights=weights,
|
||||
)
|
||||
|
||||
|
|
@ -445,8 +458,9 @@ class FlashGemmaForCausalLM(torch.nn.Module):
|
|||
max_s: int,
|
||||
lm_head_indices: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
||||
input_embeds = self.embed_tokens(input_ids)
|
||||
hidden_states = self.model(
|
||||
input_ids,
|
||||
input_embeds,
|
||||
position_ids,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,110 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import torch
|
||||
import torch.distributed
|
||||
from torch import nn
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from typing import Optional, List, Tuple
|
||||
|
||||
from text_generation_server.layers.tensor_parallel import TensorParallelColumnLinear
|
||||
from text_generation_server.models.custom_modeling.vlm import (
|
||||
load_text_model,
|
||||
load_vision_model,
|
||||
)
|
||||
|
||||
|
||||
class PaliGemmaForConditionalGeneration(nn.Module):
|
||||
def __init__(self, prefix, config, weights):
|
||||
super().__init__()
|
||||
config.vision_config.quantize = config.quantize
|
||||
self.vision_tower = load_vision_model(
|
||||
prefix="vision_tower" if not prefix else f"{prefix}.vision_tower",
|
||||
config=config.vision_config,
|
||||
weights=weights,
|
||||
)
|
||||
|
||||
self.multi_modal_projector = TensorParallelColumnLinear.load(
|
||||
config,
|
||||
prefix="multi_modal_projector.linear",
|
||||
weights=weights,
|
||||
bias=True,
|
||||
)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.config = config
|
||||
|
||||
text_config = config.text_config
|
||||
text_config.speculator = config.speculator
|
||||
text_config.quantize = config.quantize
|
||||
self.text_model = load_text_model(
|
||||
prefix="language_model" if not prefix else f"{prefix}.language_model",
|
||||
config=config.text_config,
|
||||
weights=weights,
|
||||
)
|
||||
self.pad_token_id = (
|
||||
config.pad_token_id if config.pad_token_id is not None else -1
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
position_ids: torch.Tensor,
|
||||
cu_seqlen_prefill: Optional[torch.Tensor],
|
||||
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
||||
block_tables: torch.Tensor,
|
||||
slots: torch.Tensor,
|
||||
input_lengths: torch.Tensor,
|
||||
max_s: int,
|
||||
prefill_cache_indices: Optional[torch.Tensor] = None,
|
||||
lm_head_indices: Optional[torch.Tensor] = None,
|
||||
pixel_values: torch.FloatTensor = None,
|
||||
# Unused here
|
||||
pixel_attention_mask: Optional[torch.BoolTensor] = None,
|
||||
image_sizes: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
||||
inputs_embeds = self.text_model.embed_tokens(input_ids)
|
||||
# TODO This is odd but apparently pali gemma position ids start at 1.
|
||||
if cu_seqlen_prefill is not None:
|
||||
max_s += 1
|
||||
position_ids += 1
|
||||
|
||||
if pixel_values is not None:
|
||||
pixel_values = pixel_values.to(dtype=inputs_embeds.dtype)
|
||||
image_outputs = self.vision_tower(pixel_values)
|
||||
image_features = self.multi_modal_projector(image_outputs.last_hidden_state)
|
||||
|
||||
# mask where image or padding tokens
|
||||
mask = input_ids == self.config.image_token_index
|
||||
|
||||
# insert image features into input embeddings
|
||||
inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1])
|
||||
|
||||
hidden_states = self.text_model.model(
|
||||
inputs_embeds=inputs_embeds,
|
||||
position_ids=position_ids,
|
||||
cu_seqlen_prefill=cu_seqlen_prefill,
|
||||
kv_cache=kv_cache,
|
||||
block_tables=block_tables,
|
||||
slots=slots,
|
||||
input_lengths=input_lengths,
|
||||
max_s=max_s,
|
||||
)
|
||||
|
||||
if lm_head_indices is not None:
|
||||
hidden_states = hidden_states[lm_head_indices]
|
||||
logits, speculative_logits = self.text_model.lm_head(hidden_states)
|
||||
|
||||
return logits, speculative_logits
|
||||
|
|
@ -0,0 +1,565 @@
|
|||
from typing import Optional, Tuple, Union
|
||||
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.modeling_attn_mask_utils import (
|
||||
_create_4d_causal_attention_mask,
|
||||
_prepare_4d_attention_mask,
|
||||
)
|
||||
from transformers.modeling_outputs import (
|
||||
BaseModelOutput,
|
||||
BaseModelOutputWithPooling,
|
||||
ImageClassifierOutput,
|
||||
)
|
||||
from transformers import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
|
||||
|
||||
from text_generation_server.layers.tensor_parallel import (
|
||||
TensorParallelEmbedding,
|
||||
TensorParallelColumnLinear,
|
||||
TensorParallelRowLinear,
|
||||
)
|
||||
|
||||
|
||||
class SiglipVisionEmbeddings(nn.Module):
|
||||
def __init__(self, prefix, config: SiglipVisionConfig, weights):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.embed_dim = config.hidden_size
|
||||
self.image_size = config.image_size
|
||||
self.patch_size = config.patch_size
|
||||
self.patch_embedding = nn.Conv2d(
|
||||
in_channels=config.num_channels,
|
||||
out_channels=self.embed_dim,
|
||||
kernel_size=self.patch_size,
|
||||
stride=self.patch_size,
|
||||
padding="valid",
|
||||
)
|
||||
self.patch_embedding.weight = nn.Parameter(
|
||||
weights.get_tensor(f"{prefix}.patch_embedding.weight"), requires_grad=False
|
||||
)
|
||||
self.patch_embedding.bias = nn.Parameter(
|
||||
weights.get_tensor(f"{prefix}.patch_embedding.bias"), requires_grad=False
|
||||
)
|
||||
self.num_patches = (self.image_size // self.patch_size) ** 2
|
||||
self.num_positions = self.num_patches
|
||||
self.position_embedding = TensorParallelEmbedding(
|
||||
prefix=f"{prefix}.position_embedding", weights=weights
|
||||
)
|
||||
self.register_buffer(
|
||||
"position_ids",
|
||||
torch.arange(self.num_positions, device=weights.device).expand((1, -1)),
|
||||
persistent=False,
|
||||
)
|
||||
|
||||
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
|
||||
patch_embeds = self.patch_embedding(
|
||||
pixel_values
|
||||
) # shape = [*, width, grid, grid]
|
||||
embeddings = patch_embeds.flatten(2).transpose(1, 2)
|
||||
|
||||
embeddings = embeddings + self.position_embedding(self.position_ids)
|
||||
return embeddings
|
||||
|
||||
|
||||
class SiglipTextEmbeddings(nn.Module):
|
||||
def __init__(self, config: SiglipTextConfig):
|
||||
super().__init__()
|
||||
embed_dim = config.hidden_size
|
||||
|
||||
self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
|
||||
self.position_embedding = nn.Embedding(
|
||||
config.max_position_embeddings, embed_dim
|
||||
)
|
||||
|
||||
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
||||
self.register_buffer(
|
||||
"position_ids",
|
||||
torch.arange(config.max_position_embeddings).expand((1, -1)),
|
||||
persistent=False,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
) -> torch.Tensor:
|
||||
seq_length = (
|
||||
input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
|
||||
)
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = self.position_ids[:, :seq_length]
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.token_embedding(input_ids)
|
||||
|
||||
position_embeddings = self.position_embedding(position_ids)
|
||||
embeddings = inputs_embeds + position_embeddings
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
class SiglipAttention(nn.Module):
|
||||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||||
|
||||
def __init__(self, prefix, config, weights):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.embed_dim = config.hidden_size
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.head_dim = self.embed_dim // self.num_heads
|
||||
self.head_size = self.head_dim
|
||||
if self.head_dim * self.num_heads != self.embed_dim:
|
||||
raise ValueError(
|
||||
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
|
||||
f" {self.num_heads})."
|
||||
)
|
||||
self.num_heads = self.num_heads // weights.process_group.size()
|
||||
self.embed_dim = self.embed_dim // weights.process_group.size()
|
||||
self.scale = self.head_dim**-0.5
|
||||
self.dropout = config.attention_dropout
|
||||
|
||||
self.k_proj = TensorParallelColumnLinear.load(
|
||||
config, prefix=f"{prefix}.k_proj", weights=weights, bias=True
|
||||
)
|
||||
self.v_proj = TensorParallelColumnLinear.load(
|
||||
config, prefix=f"{prefix}.v_proj", weights=weights, bias=True
|
||||
)
|
||||
self.q_proj = TensorParallelColumnLinear.load(
|
||||
config, prefix=f"{prefix}.q_proj", weights=weights, bias=True
|
||||
)
|
||||
self.out_proj = TensorParallelRowLinear.load(
|
||||
config, prefix=f"{prefix}.out_proj", weights=weights, bias=True
|
||||
)
|
||||
|
||||
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
||||
return (
|
||||
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
|
||||
.transpose(1, 2)
|
||||
.contiguous()
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
output_attentions: Optional[bool] = False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
"""Input shape: Batch x Time x Channel"""
|
||||
|
||||
bsz, tgt_len, _ = hidden_states.size()
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
|
||||
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
|
||||
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
|
||||
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
|
||||
key_states = key_states.view(*proj_shape)
|
||||
value_states = value_states.view(*proj_shape)
|
||||
|
||||
src_len = key_states.size(1)
|
||||
# scale post matmul
|
||||
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) * self.scale
|
||||
|
||||
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
|
||||
raise ValueError(
|
||||
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
|
||||
f" {attn_weights.size()}"
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
|
||||
raise ValueError(
|
||||
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
|
||||
)
|
||||
attn_weights = (
|
||||
attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
||||
+ attention_mask
|
||||
)
|
||||
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
||||
|
||||
# upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(
|
||||
attn_weights, dim=-1, dtype=torch.float32
|
||||
).to(attn_weights.dtype)
|
||||
attn_weights = nn.functional.dropout(
|
||||
attn_weights, p=self.dropout, training=self.training
|
||||
)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_size):
|
||||
raise ValueError(
|
||||
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_size)}, but is"
|
||||
f" {attn_output.size()}"
|
||||
)
|
||||
|
||||
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_size)
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
|
||||
|
||||
attn_output = self.out_proj(attn_output)
|
||||
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class SiglipMLP(nn.Module):
|
||||
def __init__(self, prefix, config, weights):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.activation_fn = ACT2FN[config.hidden_act]
|
||||
self.fc1 = TensorParallelColumnLinear.load( # config.hidden_size, config.intermediate_size
|
||||
prefix=f"{prefix}.fc1", config=config, weights=weights, bias=True
|
||||
)
|
||||
self.fc2 = TensorParallelRowLinear.load( # config.intermediate_size, config.hidden_size
|
||||
prefix=f"{prefix}.fc2", config=config, weights=weights, bias=True
|
||||
)
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
hidden_states = self.fc1(hidden_states)
|
||||
hidden_states = self.activation_fn(hidden_states)
|
||||
hidden_states = self.fc2(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class SiglipEncoderLayer(nn.Module):
|
||||
def __init__(self, prefix, config: SiglipConfig, weights):
|
||||
super().__init__()
|
||||
self.embed_dim = config.hidden_size
|
||||
self.self_attn = SiglipAttention(
|
||||
prefix=f"{prefix}.self_attn", config=config, weights=weights
|
||||
)
|
||||
self.layer_norm1 = nn.LayerNorm.load(
|
||||
prefix=f"{prefix}.layer_norm1", weights=weights, eps=config.layer_norm_eps
|
||||
)
|
||||
self.mlp = SiglipMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
|
||||
self.layer_norm2 = nn.LayerNorm.load(
|
||||
prefix=f"{prefix}.layer_norm2", weights=weights, eps=config.layer_norm_eps
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: torch.Tensor,
|
||||
output_attentions: Optional[bool] = False,
|
||||
) -> Tuple[torch.FloatTensor]:
|
||||
"""
|
||||
Args:
|
||||
hidden_states (`torch.FloatTensor`):
|
||||
Input to the layer of shape `(batch, seq_len, embed_dim)`.
|
||||
attention_mask (`torch.FloatTensor`):
|
||||
Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
|
||||
output_attentions (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||||
returned tensors for more detail.
|
||||
"""
|
||||
residual = hidden_states
|
||||
hidden_states = self.layer_norm1(hidden_states)
|
||||
hidden_states, attn_weights = self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
output_attentions=output_attentions,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
residual = hidden_states
|
||||
hidden_states = self.layer_norm2(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
if output_attentions:
|
||||
return hidden_states, attn_weights
|
||||
return hidden_states, None
|
||||
|
||||
|
||||
class SiglipMultiheadAttentionPoolingHead(nn.Module):
|
||||
"""Multihead Attention Pooling."""
|
||||
|
||||
def __init__(self, prefix, config: SiglipVisionConfig, weights):
|
||||
super().__init__()
|
||||
|
||||
self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
|
||||
self.attention = torch.nn.MultiheadAttention(
|
||||
config.hidden_size, config.num_attention_heads, batch_first=True
|
||||
)
|
||||
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
||||
self.mlp = SiglipMLP(prefix, config, weights)
|
||||
|
||||
def forward(self, hidden_state):
|
||||
batch_size = hidden_state.shape[0]
|
||||
probe = self.probe.repeat(batch_size, 1, 1)
|
||||
|
||||
hidden_state = self.attention(probe, hidden_state, hidden_state)[0]
|
||||
|
||||
residual = hidden_state
|
||||
hidden_state = self.layernorm(hidden_state)
|
||||
hidden_state = residual + self.mlp(hidden_state)
|
||||
|
||||
return hidden_state[:, 0]
|
||||
|
||||
|
||||
import warnings
|
||||
|
||||
|
||||
def _trunc_normal_(tensor, mean, std, a, b):
|
||||
# Cut & paste from PyTorch official master until it's in a few official releases - RW
|
||||
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
|
||||
def norm_cdf(x):
|
||||
# Computes standard normal cumulative distribution function
|
||||
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
|
||||
|
||||
if (mean < a - 2 * std) or (mean > b + 2 * std):
|
||||
warnings.warn(
|
||||
"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
|
||||
"The distribution of values may be incorrect.",
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
# Values are generated by using a truncated uniform distribution and
|
||||
# then using the inverse CDF for the normal distribution.
|
||||
# Get upper and lower cdf values
|
||||
l = norm_cdf((a - mean) / std)
|
||||
u = norm_cdf((b - mean) / std)
|
||||
|
||||
# Uniformly fill tensor with values from [l, u], then translate to
|
||||
# [2l-1, 2u-1].
|
||||
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
||||
|
||||
# Use inverse cdf transform for normal distribution to get truncated
|
||||
# standard normal
|
||||
tensor.erfinv_()
|
||||
|
||||
# Transform to proper mean, std
|
||||
tensor.mul_(std * math.sqrt(2.0))
|
||||
tensor.add_(mean)
|
||||
|
||||
# Clamp to ensure it's in the proper range
|
||||
tensor.clamp_(min=a, max=b)
|
||||
|
||||
|
||||
def trunc_normal_tf_(
|
||||
tensor: torch.Tensor,
|
||||
mean: float = 0.0,
|
||||
std: float = 1.0,
|
||||
a: float = -2.0,
|
||||
b: float = 2.0,
|
||||
) -> torch.Tensor:
|
||||
"""Fills the input Tensor with values drawn from a truncated
|
||||
normal distribution. The values are effectively drawn from the
|
||||
normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
|
||||
with values outside :math:`[a, b]` redrawn until they are within
|
||||
the bounds. The method used for generating the random values works
|
||||
best when :math:`a \\leq \text{mean} \\leq b`.
|
||||
|
||||
NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
|
||||
bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
|
||||
and the result is subsquently scaled and shifted by the mean and std args.
|
||||
|
||||
Args:
|
||||
tensor: an n-dimensional `torch.Tensor`
|
||||
mean: the mean of the normal distribution
|
||||
std: the standard deviation of the normal distribution
|
||||
a: the minimum cutoff value
|
||||
b: the maximum cutoff value
|
||||
"""
|
||||
with torch.no_grad():
|
||||
_trunc_normal_(tensor, 0, 1.0, a, b)
|
||||
tensor.mul_(std).add_(mean)
|
||||
|
||||
|
||||
from torch.nn.init import _calculate_fan_in_and_fan_out
|
||||
|
||||
|
||||
def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
|
||||
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
|
||||
if mode == "fan_in":
|
||||
denom = fan_in
|
||||
elif mode == "fan_out":
|
||||
denom = fan_out
|
||||
elif mode == "fan_avg":
|
||||
denom = (fan_in + fan_out) / 2
|
||||
|
||||
variance = scale / denom
|
||||
|
||||
if distribution == "truncated_normal":
|
||||
# constant is stddev of standard normal truncated to (-2, 2)
|
||||
trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
|
||||
elif distribution == "normal":
|
||||
with torch.no_grad():
|
||||
tensor.normal_(std=math.sqrt(variance))
|
||||
elif distribution == "uniform":
|
||||
bound = math.sqrt(3 * variance)
|
||||
with torch.no_grad():
|
||||
tensor.uniform_(-bound, bound)
|
||||
else:
|
||||
raise ValueError(f"invalid distribution {distribution}")
|
||||
|
||||
|
||||
def lecun_normal_(tensor):
|
||||
variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
|
||||
|
||||
|
||||
def default_flax_embed_init(tensor):
|
||||
variance_scaling_(tensor, mode="fan_in", distribution="normal")
|
||||
|
||||
|
||||
from transformers import PreTrainedModel
|
||||
|
||||
|
||||
class SiglipPreTrainedModel(PreTrainedModel):
|
||||
"""
|
||||
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
||||
models.
|
||||
"""
|
||||
|
||||
config_class = SiglipConfig
|
||||
base_model_prefix = "siglip"
|
||||
supports_gradient_checkpointing = True
|
||||
|
||||
def _init_weights(self, module):
|
||||
"""Initialize the weights"""
|
||||
if isinstance(module, SiglipVisionEmbeddings):
|
||||
width = (
|
||||
self.config.vision_config.hidden_size
|
||||
if isinstance(self.config, SiglipConfig)
|
||||
else self.config.hidden_size
|
||||
)
|
||||
nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
|
||||
elif isinstance(module, nn.Embedding):
|
||||
default_flax_embed_init(module.weight)
|
||||
elif isinstance(module, SiglipAttention):
|
||||
nn.init.xavier_uniform_(module.q_proj.weight)
|
||||
nn.init.xavier_uniform_(module.k_proj.weight)
|
||||
nn.init.xavier_uniform_(module.v_proj.weight)
|
||||
nn.init.xavier_uniform_(module.out_proj.weight)
|
||||
nn.init.zeros_(module.q_proj.bias)
|
||||
nn.init.zeros_(module.k_proj.bias)
|
||||
nn.init.zeros_(module.v_proj.bias)
|
||||
nn.init.zeros_(module.out_proj.bias)
|
||||
elif isinstance(module, SiglipMLP):
|
||||
nn.init.xavier_uniform_(module.fc1.weight)
|
||||
nn.init.xavier_uniform_(module.fc2.weight)
|
||||
nn.init.normal_(module.fc1.bias, std=1e-6)
|
||||
nn.init.normal_(module.fc2.bias, std=1e-6)
|
||||
elif isinstance(module, SiglipMultiheadAttentionPoolingHead):
|
||||
nn.init.xavier_uniform_(module.probe.data)
|
||||
nn.init.xavier_uniform_(module.attention.in_proj_weight.data)
|
||||
nn.init.zeros_(module.attention.in_proj_bias.data)
|
||||
elif isinstance(module, SiglipModel):
|
||||
logit_scale_init = torch.log(torch.tensor(1.0))
|
||||
module.logit_scale.data.fill_(logit_scale_init)
|
||||
module.logit_bias.data.zero_()
|
||||
elif isinstance(module, (nn.Linear, nn.Conv2d)):
|
||||
lecun_normal_(module.weight)
|
||||
if module.bias is not None:
|
||||
nn.init.zeros_(module.bias)
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.bias.data.zero_()
|
||||
module.weight.data.fill_(1.0)
|
||||
|
||||
|
||||
class SiglipEncoder(nn.Module):
|
||||
"""
|
||||
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
||||
[`SiglipEncoderLayer`].
|
||||
|
||||
Args:
|
||||
config: SiglipConfig
|
||||
"""
|
||||
|
||||
def __init__(self, prefix, config: SiglipConfig, weights):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
SiglipEncoderLayer(
|
||||
prefix=f"{prefix}.layers.{i}", config=config, weights=weights
|
||||
)
|
||||
for i in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
inputs_embeds,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
output_attentions: Optional[torch.Tensor] = None,
|
||||
):
|
||||
r"""
|
||||
Args:
|
||||
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
||||
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
||||
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
||||
than the model's internal embedding lookup matrix.
|
||||
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
||||
|
||||
- 1 for tokens that are **not masked**,
|
||||
- 0 for tokens that are **masked**.
|
||||
|
||||
[What are attention masks?](../glossary#attention-mask)
|
||||
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Causal mask for the text model. Mask values selected in `[0, 1]`:
|
||||
|
||||
- 1 for tokens that are **not masked**,
|
||||
- 0 for tokens that are **masked**.
|
||||
|
||||
[What are attention masks?](../glossary#attention-mask)
|
||||
"""
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
for idx, encoder_layer in enumerate(self.layers):
|
||||
hidden_states, _ = encoder_layer(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
output_attentions=output_attentions,
|
||||
)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class SiglipVisionTransformer(nn.Module):
|
||||
def __init__(self, prefix, config: SiglipVisionConfig, weights):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
embed_dim = config.hidden_size
|
||||
|
||||
self.embeddings = SiglipVisionEmbeddings(
|
||||
prefix=f"{prefix}.embeddings", config=config, weights=weights
|
||||
)
|
||||
self.encoder = SiglipEncoder(
|
||||
prefix=f"{prefix}.encoder", config=config, weights=weights
|
||||
)
|
||||
self.post_layernorm = nn.LayerNorm.load(
|
||||
prefix=f"{prefix}.post_layernorm",
|
||||
weights=weights,
|
||||
eps=config.layer_norm_eps,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
pixel_values: Optional[torch.FloatTensor] = None,
|
||||
):
|
||||
r"""
|
||||
Returns:
|
||||
|
||||
"""
|
||||
if pixel_values is None:
|
||||
raise ValueError("You have to specify pixel_values")
|
||||
|
||||
hidden_states = self.embeddings(pixel_values)
|
||||
|
||||
# NOTE: up until this point, the code logits are exactly
|
||||
# the same as the transformers code. The values evaulate
|
||||
# slightly differently in our encoder layer.
|
||||
encoder_outputs = self.encoder(
|
||||
inputs_embeds=hidden_states,
|
||||
)
|
||||
last_hidden_state = encoder_outputs
|
||||
post_last_hidden_state = self.post_layernorm(last_hidden_state)
|
||||
|
||||
return BaseModelOutputWithPooling(
|
||||
last_hidden_state=post_last_hidden_state,
|
||||
# pooler_output=pooled_output,
|
||||
# hidden_states=encoder_outputs,
|
||||
)
|
||||
|
|
@ -11,6 +11,18 @@ def load_text_model(prefix, config, weights, name=None):
|
|||
)
|
||||
|
||||
return FlashMistralForCausalLM(prefix, config, weights, name=name)
|
||||
elif config.model_type == "gemma":
|
||||
from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
|
||||
FlashGemmaForCausalLM,
|
||||
)
|
||||
|
||||
return FlashGemmaForCausalLM(prefix, config, weights, causal=False)
|
||||
elif config.model_type == "paligemma":
|
||||
from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
|
||||
FlashGemmaForCausalLM,
|
||||
)
|
||||
|
||||
return FlashGemmaForCausalLM(prefix, config, weights)
|
||||
else:
|
||||
raise RuntimeError(f"Unsupported model type {config.model_type}")
|
||||
|
||||
|
|
@ -24,5 +36,13 @@ def load_vision_model(prefix, config, weights):
|
|||
return CLIPVisionTransformer(
|
||||
prefix=f"{prefix}.vision_model", config=config, weights=weights
|
||||
)
|
||||
if config.model_type == "siglip_vision_model":
|
||||
from text_generation_server.models.custom_modeling.siglip import (
|
||||
SiglipVisionTransformer,
|
||||
)
|
||||
|
||||
return SiglipVisionTransformer(
|
||||
prefix=f"vision_tower.vision_model", config=config, weights=weights
|
||||
)
|
||||
else:
|
||||
raise RuntimeError(f"Unsupported model type {config.model_type}")
|
||||
|
|
|
|||
|
|
@ -133,6 +133,17 @@ class FlashCausalLMBatch(Batch):
|
|||
device: torch.device,
|
||||
) -> "FlashCausalLMBatch":
|
||||
batch_tokenized_inputs = cls.batch_tokenized_inputs(pb.requests, tokenizer)
|
||||
return cls.from_tokenized(pb, tokenizer, batch_tokenized_inputs, dtype, device)
|
||||
|
||||
@classmethod
|
||||
def from_tokenized(
|
||||
cls,
|
||||
pb: generate_pb2.Batch,
|
||||
tokenizer: PreTrainedTokenizerBase,
|
||||
batch_tokenized_inputs,
|
||||
dtype: torch.dtype,
|
||||
device: torch.device,
|
||||
) -> "FlashCausalLMBatch":
|
||||
position_ids = []
|
||||
speculative_ids = []
|
||||
cu_seqlen_prefill = [0]
|
||||
|
|
@ -207,6 +218,7 @@ class FlashCausalLMBatch(Batch):
|
|||
# Paged attention
|
||||
# Remove one as the first token des not have a past
|
||||
speculative_length = get_speculate()
|
||||
speculative_length = 0 if speculative_length is None else speculative_length
|
||||
total_tokens = input_length + max_new_tokens - 1 + speculative_length
|
||||
needed_blocks = math.ceil(total_tokens / BLOCK_SIZE)
|
||||
blocks += needed_blocks
|
||||
|
|
|
|||
|
|
@ -3,12 +3,11 @@ import torch.distributed
|
|||
|
||||
from opentelemetry import trace
|
||||
from typing import Optional
|
||||
from transformers.models.gemma import GemmaTokenizerFast
|
||||
from transformers import AutoConfig, AutoTokenizer
|
||||
|
||||
from text_generation_server.models import FlashCausalLM
|
||||
from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
|
||||
FlashGemmaForCausalLM,
|
||||
GemmaConfig,
|
||||
)
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
|
|
@ -36,17 +35,15 @@ class FlashGemma(FlashCausalLM):
|
|||
else:
|
||||
raise NotImplementedError("FlashGemma is only available on GPU")
|
||||
|
||||
tokenizer = GemmaTokenizerFast.from_pretrained(
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
use_fast=True,
|
||||
from_slow=False,
|
||||
)
|
||||
|
||||
config = GemmaConfig.from_pretrained(
|
||||
config = AutoConfig.from_pretrained(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
config.quantize = quantize
|
||||
|
|
@ -59,7 +56,9 @@ class FlashGemma(FlashCausalLM):
|
|||
if config.quantize in ["gptq", "awq"]:
|
||||
weights._set_gptq_params(model_id, revision)
|
||||
|
||||
model = FlashGemmaForCausalLM(config, weights)
|
||||
# TODO hardcoded
|
||||
prefix = "language_model"
|
||||
model = FlashGemmaForCausalLM(prefix, config, weights, causal=True)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashGemma, self).__init__(
|
||||
|
|
|
|||
|
|
@ -0,0 +1,123 @@
|
|||
import torch
|
||||
import torch.distributed
|
||||
from opentelemetry import trace
|
||||
from typing import Optional, Tuple
|
||||
from text_generation_server.models.vlm_causal_lm import (
|
||||
VlmCausalLM,
|
||||
VlmCausalLMBatch,
|
||||
image_text_replacement,
|
||||
load_data_uri,
|
||||
split,
|
||||
)
|
||||
from text_generation_server.models.custom_modeling.flash_pali_gemma_modeling import (
|
||||
PaliGemmaForConditionalGeneration,
|
||||
)
|
||||
from transformers import AutoProcessor, AutoConfig, AutoImageProcessor
|
||||
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
|
||||
class PaliGemmaBatch(VlmCausalLMBatch):
|
||||
@classmethod
|
||||
def batch_tokenized_inputs(cls, requests, tokenizer, processor, config):
|
||||
batch_inputs = []
|
||||
image_inputs = []
|
||||
max_truncation = 0
|
||||
for r in requests:
|
||||
chunks = split(r.inputs)
|
||||
full_text = ""
|
||||
image_id = 0
|
||||
for chunk in chunks:
|
||||
if chunk["type"] == "text":
|
||||
full_text += "<bos>" + chunk["content"] + "\n"
|
||||
elif chunk["type"] == "image":
|
||||
image = chunk["content"]
|
||||
# Should never receive URLs anymore, processing should be done
|
||||
# On the rust layer.
|
||||
# This avoid making n queries per TP
|
||||
# if image.startswith("https://") or image.startswith("http://"):
|
||||
# image = processor.image_processor.fetch_images(image)
|
||||
if image.startswith("data:"):
|
||||
image = load_data_uri(image)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Cannot process input image not starting with data:"
|
||||
)
|
||||
# TODO do_convert_RGB should be on by default ?
|
||||
image = image.convert("RGB")
|
||||
image_input = processor.image_processor(image, return_tensors="pt")
|
||||
full_text += image_text_replacement(image_input, config, image_id)
|
||||
image_inputs.append(image_input)
|
||||
else:
|
||||
raise RuntimeError(f"Invalid chunk type {chunk['type']}")
|
||||
|
||||
batch_inputs.append(full_text)
|
||||
max_truncation = max(max_truncation, r.truncate)
|
||||
|
||||
batch_tokenized_inputs = tokenizer(
|
||||
batch_inputs,
|
||||
truncation=True,
|
||||
max_length=max_truncation,
|
||||
add_special_tokens=False,
|
||||
)["input_ids"]
|
||||
if image_inputs:
|
||||
image_input = image_inputs[0]
|
||||
new_image_inputs = {
|
||||
"pixel_values": torch.cat(
|
||||
[img["pixel_values"] for img in image_inputs], dim=0
|
||||
),
|
||||
}
|
||||
if "pixel_attention_mask" in image_input:
|
||||
new_image_inputs["pixel_attention_mask"] = torch.cat(
|
||||
[img["pixel_attention_mask"] for img in image_inputs], dim=0
|
||||
)
|
||||
if "image_sizes" in image_input:
|
||||
new_image_inputs["image_sizes"] = torch.cat(
|
||||
[img["image_sizes"] for img in image_inputs], dim=0
|
||||
)
|
||||
image_inputs = new_image_inputs
|
||||
else:
|
||||
image_inputs = None
|
||||
return batch_tokenized_inputs, image_inputs
|
||||
|
||||
|
||||
class PaliGemma(VlmCausalLM):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
speculator: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
self.processor = AutoProcessor.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
super().__init__(
|
||||
config_cls=AutoConfig,
|
||||
model_cls=PaliGemmaForConditionalGeneration,
|
||||
model_id=model_id,
|
||||
revision=revision,
|
||||
quantize=quantize,
|
||||
speculator=speculator,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
@property
|
||||
def batch_type(self):
|
||||
return PaliGemmaBatch
|
||||
|
||||
def get_layer_config(self, model) -> Tuple[int, int, int]:
|
||||
return (
|
||||
len(model.text_model.model.layers),
|
||||
model.text_model.model.num_key_value_heads,
|
||||
model.text_model.model.head_size,
|
||||
)
|
||||
|
||||
def max_past(self) -> Optional[int]:
|
||||
return getattr(self.model.text_model, "max_past", None)
|
||||
|
|
@ -15,6 +15,7 @@ from text_generation_server.models.flash_mistral import (
|
|||
BaseFlashMistral,
|
||||
FlashMistralBatch,
|
||||
)
|
||||
from text_generation_server.models.flash_causal_lm import FlashCausalLMBatch
|
||||
from text_generation_server.models.cache_manager import (
|
||||
get_cache_manager,
|
||||
)
|
||||
|
|
@ -80,6 +81,9 @@ def image_text_replacement(image_input, config, image_id) -> str:
|
|||
|
||||
logger.info(f"Found {num_features} in image of resolution {height}x{width}")
|
||||
return "<image>" * num_features
|
||||
|
||||
elif config.model_type == "paligemma":
|
||||
return "<image>" * config.text_config.num_image_tokens
|
||||
else:
|
||||
raise RuntimeError(f"Unknown config {config.model_type} for multimodal")
|
||||
|
||||
|
|
@ -193,7 +197,10 @@ class VlmCausalLMBatch(FlashMistralBatch):
|
|||
max_truncation = max(max_truncation, r.truncate)
|
||||
|
||||
batch_tokenized_inputs = tokenizer(
|
||||
batch_inputs, truncation=True, max_length=max_truncation
|
||||
batch_inputs,
|
||||
truncation=True,
|
||||
max_length=max_truncation,
|
||||
add_special_tokens=not config.model_type == "paligemma",
|
||||
)["input_ids"]
|
||||
if image_inputs:
|
||||
image_input = image_inputs[0]
|
||||
|
|
|
|||
|
|
@ -14,7 +14,10 @@ from typing import List, Optional
|
|||
from text_generation_server.cache import Cache
|
||||
from text_generation_server.interceptor import ExceptionInterceptor
|
||||
from text_generation_server.models import Model, get_model
|
||||
from text_generation_server.models.vlm_causal_lm import VlmCausalLMBatch
|
||||
from text_generation_server.models.pali_gemma import PaliGemmaBatch
|
||||
from text_generation_server.models.vlm_causal_lm import (
|
||||
VlmCausalLMBatch,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2_grpc, generate_pb2
|
||||
from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
|
||||
from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
|
||||
|
|
@ -98,6 +101,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
|
|||
if self.model.batch_type in {
|
||||
IdeficsCausalLMBatch,
|
||||
VlmCausalLMBatch,
|
||||
PaliGemmaBatch,
|
||||
}: # Hack, i would rather use kwargs in the `from_pb` call
|
||||
batch = self.model.batch_type.from_pb_processor(
|
||||
request.batch,
|
||||
|
|
@ -122,6 +126,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
|
|||
if self.model.batch_type in {
|
||||
IdeficsCausalLMBatch,
|
||||
VlmCausalLMBatch,
|
||||
PaliGemmaBatch,
|
||||
}: # Hack, i would rather use kwargs in the `from_pb` call
|
||||
batch = self.model.batch_type.from_pb_processor(
|
||||
request.batch,
|
||||
|
|
|
|||
|
|
@ -116,6 +116,7 @@ if HAS_FLASH_ATTN_V2_CUDA:
|
|||
max_s,
|
||||
softmax_scale,
|
||||
window_size_left=-1,
|
||||
causal=True,
|
||||
):
|
||||
if window_size_left <= 0 and window_size_left != -1:
|
||||
raise ValueError("`window_size_left` must be > 0 or -1")
|
||||
|
|
@ -134,7 +135,7 @@ if HAS_FLASH_ATTN_V2_CUDA:
|
|||
0.0,
|
||||
softmax_scale,
|
||||
False,
|
||||
True,
|
||||
causal,
|
||||
window_size_left,
|
||||
0,
|
||||
False,
|
||||
|
|
|
|||
Loading…
Reference in New Issue