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Merge branch 'main' into feature/usage-stats

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This commit is contained in:
ErikKaumk 2024-07-15 10:08:10 +02:00
commit 81c9ad7073
109 changed files with 4328 additions and 3761 deletions
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5
.github/workflows/autodocs.yaml vendored
View File

@ -30,6 +30,10 @@ jobs:
id: install-router
run: cargo install --path router/
- uses: actions/setup-node@v4
with:
node-version: 22
- name: Set up Python
uses: actions/setup-python@v2
with:
@ -37,4 +41,5 @@ jobs:
- name: Check that documentation is up-to-date
run: |
npm install -g swagger-cli
python update_doc.py --check

13
.github/workflows/build.yaml vendored
View File

@ -11,6 +11,11 @@ on:
# - rocm
# - intel
required: true
release-tests:
description: "Run release integration tests"
required: true
default: false
type: boolean
jobs:
build-and-push:
@ -23,7 +28,7 @@ jobs:
group: ${{ github.workflow }}-build-and-push-image-${{ inputs.hardware }}-${{ github.head_ref || github.run_id }}
cancel-in-progress: true
# TODO see with @Glegendre to get CPU runner here instead
runs-on: [self-hosted, nvidia-gpu , multi-gpu, 4-a10, ci]
runs-on: [self-hosted, intel-cpu, 32-cpu, 256-ram, ci]
permissions:
contents: write
packages: write
@ -131,8 +136,8 @@ jobs:
DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}${{ env.LABEL }}
tags: ${{ steps.meta.outputs.tags || steps.meta-pr.outputs.tags }}
labels: ${{ steps.meta.outputs.labels || steps.meta-pr.outputs.labels }}
cache-from: type=registry,ref=registry-push.github-runners.huggingface.tech/api-inference/community/text-generation-inference:cache${{ env.LABEL }},mode=min
cache-to: type=registry,ref=registry-push.github-runners.huggingface.tech/api-inference/community/text-generation-inference:cache${{ env.LABEL }},mode=min
cache-from: type=s3,region=us-east-1,bucket=ci-docker-buildx-cache,name=text-generation-inference-cache${{ env.LABEL }},mode=min,access_key_id=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_ACCESS_KEY_ID }},secret_access_key=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_SECRET_ACCESS_KEY }},mode=min
cache-to: type=s3,region=us-east-1,bucket=ci-docker-buildx-cache,name=text-generation-inference-cache${{ env.LABEL }},mode=min,access_key_id=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_ACCESS_KEY_ID }},secret_access_key=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_SECRET_ACCESS_KEY }},mode=min
- name: Final
id: final
run: |
@ -148,7 +153,7 @@ jobs:
runs-on: ["self-hosted", "${{ needs.build-and-push.outputs.runs_on }}", "multi-gpu"]
if: needs.build-and-push.outputs.runs_on != 'ubuntu-latest'
env:
PYTEST_FLAGS: ${{ (startsWith(github.ref, 'refs/tags/') || github.ref == 'refs/heads/main') && '--release' || '' }}
PYTEST_FLAGS: ${{ (startsWith(github.ref, 'refs/tags/') || github.ref == 'refs/heads/main' || inputs.release-tests == true) && '--release' || '' }}
steps:
- name: Checkout repository
uses: actions/checkout@v4

11
.github/workflows/ci_build.yaml vendored
View File

@ -20,7 +20,14 @@ on:
- "Dockerfile_amd"
- "Dockerfile_intel"
branches:
- 'main'
- "main"
workflow_dispatch:
inputs:
release-tests:
description: "Run release integration tests"
required: true
default: false
type: boolean
jobs:
build:
@ -33,4 +40,6 @@ jobs:
uses: ./.github/workflows/build.yaml # calls the one above ^
with:
hardware: ${{ matrix.hardware }}
# https://github.com/actions/runner/issues/2206
release-tests: ${{ inputs.release-tests == true }}
secrets: inherit

28
Cargo.lock generated
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@ -1935,17 +1935,6 @@ version = "2.7.4"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "78ca9ab1a0babb1e7d5695e3530886289c18cf2f87ec19a575a0abdce112e3a3"
[[package]]
name = "metrics"
version = "0.21.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "fde3af1a009ed76a778cb84fdef9e7dbbdf5775ae3e4cc1f434a6a307f6f76c5"
dependencies = [
"ahash",
"metrics-macros",
"portable-atomic",
]
[[package]]
name = "metrics"
version = "0.23.0"
@ -1969,7 +1958,7 @@ dependencies = [
"hyper-util",
"indexmap 2.2.6",
"ipnet",
"metrics 0.23.0",
"metrics",
"metrics-util",
"quanta",
"thiserror",
@ -1977,17 +1966,6 @@ dependencies = [
"tracing",
]
[[package]]
name = "metrics-macros"
version = "0.7.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "38b4faf00617defe497754acde3024865bc143d44a86799b24e191ecff91354f"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.68",
]
[[package]]
name = "metrics-util"
version = "0.17.0"
@ -1997,7 +1975,7 @@ dependencies = [
"crossbeam-epoch",
"crossbeam-utils",
"hashbrown 0.14.5",
"metrics 0.23.0",
"metrics",
"num_cpus",
"quanta",
"sketches-ddsketch",
@ -3835,7 +3813,7 @@ dependencies = [
"init-tracing-opentelemetry",
"itertools 0.10.5",
"jsonschema",
"metrics 0.21.1",
"metrics",
"metrics-exporter-prometheus",
"minijinja",
"minijinja-contrib",

7
Dockerfile
View File

@ -40,7 +40,9 @@ RUN cargo build --profile release-opt
# Adapted from: https://github.com/pytorch/pytorch/blob/master/Dockerfile
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS pytorch-install
# NOTE: When updating PyTorch version, beware to remove `pip install nvidia-nccl-cu12==2.22.3` below in the Dockerfile. Context: https://github.com/huggingface/text-generation-inference/pull/2099
ARG PYTORCH_VERSION=2.3.0
ARG PYTHON_VERSION=3.10
# Keep in sync with `server/pyproject.toml
ARG CUDA_VERSION=12.1
@ -241,7 +243,10 @@ COPY server/Makefile server/Makefile
RUN cd server && \
make gen-server && \
pip install -r requirements_cuda.txt && \
pip install ".[bnb, accelerate, quantize, peft, outlines]" --no-cache-dir
pip install ".[bnb, accelerate, quantize, peft, outlines]" --no-cache-dir && \
pip install nvidia-nccl-cu12==2.22.3
ENV LD_PRELOAD=/opt/conda/lib/python3.10/site-packages/nvidia/nccl/lib/libnccl.so.2
# Deps before the binaries
# The binaries change on every build given we burn the SHA into them

11
README.md
View File

@ -21,14 +21,15 @@ to power Hugging Chat, the Inference API and Inference Endpoint.
## Table of contents
- [Get Started](#get-started)
- [API Documentation](#api-documentation)
- [Docker](#docker)
- [API documentation](#api-documentation)
- [Using a private or gated model](#using-a-private-or-gated-model)
- [A note on Shared Memory](#a-note-on-shared-memory-shm)
- [A note on Shared Memory (shm)](#a-note-on-shared-memory-shm)
- [Distributed Tracing](#distributed-tracing)
- [Local Install](#local-install)
- [CUDA Kernels](#cuda-kernels)
- [Architecture](#architecture)
- [Local install](#local-install)
- [Optimized architectures](#optimized-architectures)
- [Run Mistral](#run-a-model)
- [Run locally](#run-locally)
- [Run](#run)
- [Quantization](#quantization)
- [Develop](#develop)

2
clients/python/text_generation/types.py
View File

@ -61,7 +61,7 @@ class ChoiceDeltaToolCall(BaseModel):
class ChoiceDelta(BaseModel):
role: str
content: Optional[str] = None
tool_calls: Optional[ChoiceDeltaToolCall]
tool_calls: Optional[ChoiceDeltaToolCall] = None
class Choice(BaseModel):

2
docs/source/_toctree.yml
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@ -11,6 +11,8 @@
title: Using TGI with Intel Gaudi
- local: installation_inferentia
title: Using TGI with AWS Inferentia
- local: installation_intel
title: Using TGI with Intel GPUs
- local: installation
title: Installation from source
- local: supported_models

1
docs/source/architecture.md
View File

@ -103,6 +103,7 @@ Several variants of the model server exist that are actively supported by Huggin
- By default, the model server will attempt building [a server optimized for Nvidia GPUs with CUDA](https://huggingface.co/docs/text-generation-inference/installation_nvidia). The code for this version is hosted in the [main TGI repository](https://github.com/huggingface/text-generation-inference).
- A [version optimized for AMD with ROCm](https://huggingface.co/docs/text-generation-inference/installation_amd) is hosted in the main TGI repository. Some model features differ.
- A [version optimized for Intel GPUs](https://huggingface.co/docs/text-generation-inference/installation_intel) is hosted in the main TGI repository. Some model features differ.
- The [version for Intel Gaudi](https://huggingface.co/docs/text-generation-inference/installation_gaudi) is maintained on a forked repository, often resynchronized with the main [TGI repository](https://github.com/huggingface/tgi-gaudi).
- A [version for Neuron (AWS Inferentia2)](https://huggingface.co/docs/text-generation-inference/installation_inferentia) is maintained as part of [Optimum Neuron](https://github.com/huggingface/optimum-neuron/tree/main/text-generation-inference).
- A version for Google TPUs is maintained as part of [Optimum TPU](https://github.com/huggingface/optimum-tpu/tree/main/text-generation-inference).

19
docs/source/installation_intel.md Normal file
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@ -0,0 +1,19 @@
# Using TGI with Intel GPUs
TGI optimized models are supported on Intel Data Center GPU [Max1100](https://www.intel.com/content/www/us/en/products/sku/232876/intel-data-center-gpu-max-1100/specifications.html), [Max1550](https://www.intel.com/content/www/us/en/products/sku/232873/intel-data-center-gpu-max-1550/specifications.html), the recommended usage is through Docker.
On a server powered by Intel GPUs, TGI can be launched with the following command:
```bash
model=teknium/OpenHermes-2.5-Mistral-7B
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --rm --privileged --cap-add=sys_nice \
--device=/dev/dri \
--ipc=host --shm-size 1g --net host -v $volume:/data \
ghcr.io/huggingface/text-generation-inference:latest-intel \
--model-id $model --cuda-graphs 0
```
The launched TGI server can then be queried from clients, make sure to check out the [Consuming TGI](./basic_tutorials/consuming_tgi) guide.

2
docs/source/quicktour.md
View File

@ -17,7 +17,7 @@ docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
### Supported hardware
TGI supports various hardware. Make sure to check the [Using TGI with Nvidia GPUs](./installation_nvidia), [Using TGI with AMD GPUs](./installation_amd), [Using TGI with Gaudi](./installation_gaudi), [Using TGI with Inferentia](./installation_inferentia) guides depending on which hardware you would like to deploy TGI on.
TGI supports various hardware. Make sure to check the [Using TGI with Nvidia GPUs](./installation_nvidia), [Using TGI with AMD GPUs](./installation_amd), [Using TGI with Intel GPUs](./installation_intel), [Using TGI with Gaudi](./installation_gaudi), [Using TGI with Inferentia](./installation_inferentia) guides depending on which hardware you would like to deploy TGI on.
## Consuming TGI

1
docs/source/supported_models.md
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@ -10,6 +10,7 @@ Text Generation Inference enables serving optimized models on specific hardware
- [Llama](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
- [Phi 3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
- [Gemma](https://huggingface.co/google/gemma-7b)
- [PaliGemma](https://huggingface.co/google/paligemma-3b-pt-224)
- [Gemma2](https://huggingface.co/google/gemma2-9b)
- [Cohere](https://huggingface.co/CohereForAI/c4ai-command-r-plus)
- [Dbrx](https://huggingface.co/databricks/dbrx-instruct)

87
integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq.json
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@ -5,85 +5,80 @@
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83
integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq_all_params.json
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@ -5,85 +5,80 @@
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@ -6,87 +6,82 @@
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"generated_text": "\n \"\"\"\n if request.method == 'POST"
},
{
"details": {
@ -184,87 +174,82 @@
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"id": 2323,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.84375,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.609375,
"id": 1715,
"logprob": -11.34375,
"text": " request"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -2.3261719,
"id": 198,
"logprob": -2.5742188,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.8730469,
"id": 262,
"logprob": -1.6220703,
"special": false,
"text": "Test"
"text": " "
},
{
"id": 2009,
"logprob": -1.2587891,
"id": 3270,
"logprob": -2.0410156,
"special": false,
"text": " \"\"\"\n"
},
{
"id": 262,
"logprob": -0.015281677,
"special": false,
"text": " "
},
{
"id": 422,
"logprob": -2.1445312,
"special": false,
"text": " if"
},
{
"id": 1715,
"logprob": -0.92333984,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -1.6894531,
"id": 13204,
"logprob": -0.07672119,
"special": false,
"text": "\n"
"text": ".method"
},
{
"id": 3057,
"logprob": -1.46875,
"id": 624,
"logprob": -0.021987915,
"special": false,
"text": "Test"
"text": " =="
},
{
"id": 2009,
"logprob": -0.1541748,
"id": 364,
"logprob": -0.39208984,
"special": false,
"text": " request"
"text": " '"
},
{
"id": 13,
"logprob": -0.80322266,
"id": 3019,
"logprob": -0.10638428,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -0.22912598,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.0070495605,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.021606445,
"special": false,
"text": "\n"
"text": "POST"
}
],
"top_tokens": null
},
"generated_text": "\nTest request\nTest request\nTest request\n"
"generated_text": "\n \"\"\"\n if request.method == 'POST"
},
{
"details": {
@ -273,86 +258,81 @@
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"id": 2323,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.84375,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.6015625,
"id": 1715,
"logprob": -11.34375,
"text": " request"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -2.3320312,
"id": 198,
"logprob": -2.5742188,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.875,
"id": 262,
"logprob": -1.6220703,
"special": false,
"text": "Test"
"text": " "
},
{
"id": 2009,
"logprob": -1.2646484,
"id": 3270,
"logprob": -2.0410156,
"special": false,
"text": " \"\"\"\n"
},
{
"id": 262,
"logprob": -0.015281677,
"special": false,
"text": " "
},
{
"id": 422,
"logprob": -2.1445312,
"special": false,
"text": " if"
},
{
"id": 1715,
"logprob": -0.92333984,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -1.6884766,
"id": 13204,
"logprob": -0.07672119,
"special": false,
"text": "\n"
"text": ".method"
},
{
"id": 3057,
"logprob": -1.4589844,
"id": 624,
"logprob": -0.021987915,
"special": false,
"text": "Test"
"text": " =="
},
{
"id": 2009,
"logprob": -0.15185547,
"id": 364,
"logprob": -0.39208984,
"special": false,
"text": " request"
"text": " '"
},
{
"id": 13,
"logprob": -0.79833984,
"id": 3019,
"logprob": -0.10638428,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -0.22827148,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.006996155,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.021560669,
"special": false,
"text": "\n"
"text": "POST"
}
],
"top_tokens": null
},
"generated_text": "\nTest request\nTest request\nTest request\n"
"generated_text": "\n \"\"\"\n if request.method == 'POST"
}
]

48
integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_two_images.json
View File

@ -1,130 +1,124 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 20,
"finish_reason": "eos_token",
"generated_tokens": 19,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 415,
"logprob": -0.039886475,
"logprob": -0.03665161,
"special": false,
"text": " The"
},
{
"id": 12072,
"logprob": -0.1430664,
"logprob": -0.13549805,
"special": false,
"text": " cow"
},
{
"id": 349,
"logprob": -0.056488037,
"logprob": -0.05819702,
"special": false,
"text": " is"
},
{
"id": 6328,
"logprob": -0.6855469,
"logprob": -0.6826172,
"special": false,
"text": " standing"
},
{
"id": 356,
"logprob": -0.1685791,
"logprob": -0.1607666,
"special": false,
"text": " on"
},
{
"id": 272,
"logprob": -0.50097656,
"logprob": -0.5073242,
"special": false,
"text": " the"
},
{
"id": 10305,
"logprob": -0.017303467,
"logprob": -0.016418457,
"special": false,
"text": " beach"
},
{
"id": 304,
"logprob": -1.3564453,
"logprob": -1.3916016,
"special": false,
"text": " and"
},
{
"id": 272,
"logprob": -0.017868042,
"logprob": -0.020217896,
"special": false,
"text": " the"
},
{
"id": 13088,
"logprob": -0.0027103424,
"logprob": -0.0028133392,
"special": false,
"text": " chicken"
},
{
"id": 349,
"logprob": -0.003156662,
"logprob": -0.003145218,
"special": false,
"text": " is"
},
{
"id": 6398,
"logprob": -0.37304688,
"logprob": -0.37060547,
"special": false,
"text": " sitting"
},
{
"id": 356,
"logprob": -0.034576416,
"logprob": -0.034851074,
"special": false,
"text": " on"
},
{
"id": 264,
"logprob": -0.29418945,
"logprob": -0.2878418,
"special": false,
"text": " a"
},
{
"id": 17972,
"logprob": -0.042877197,
"logprob": -0.046051025,
"special": false,
"text": " pile"
},
{
"id": 302,
"logprob": -0.00028443336,
"logprob": -0.00028848648,
"special": false,
"text": " of"
},
{
"id": 2445,
"logprob": -0.023223877,
"logprob": -0.025772095,
"special": false,
"text": " money"
},
{
"id": 28723,
"logprob": -0.018157959,
"logprob": -0.018127441,
"special": false,
"text": "."
},
{
"id": 32002,
"logprob": -0.00018393993,
"logprob": -0.00019824505,
"special": true,
"text": "<end_of_utterance>"
},
{
"id": 2,
"logprob": -1.1920929e-07,
"special": true,
"text": "</s>"
}
],
"top_tokens": null

89
integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized.json Normal file
View File

@ -0,0 +1,89 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.8359375,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.6171875,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -2.3417969,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.8730469,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -1.2626953,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -1.7060547,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.4482422,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.15246582,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.796875,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -0.22766113,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.007045746,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.021759033,
"special": false,
"text": "\n"
}
],
"top_tokens": null
},
"generated_text": "\nTest request\nTest request\nTest request\n"
}

89
integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized_all_params.json Normal file
View File

@ -0,0 +1,89 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.7890625,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.625,
"text": "request"
}
],
"seed": 0,
"tokens": [
{
"id": 29899,
"logprob": -1.4980469,
"special": false,
"text": "-"
},
{
"id": 1454,
"logprob": -0.19433594,
"special": false,
"text": "for"
},
{
"id": 29899,
"logprob": 0.0,
"special": false,
"text": "-"
},
{
"id": 9342,
"logprob": 0.0,
"special": false,
"text": "comment"
},
{
"id": 29901,
"logprob": 0.0,
"special": false,
"text": ":"
},
{
"id": 396,
"logprob": -0.27392578,
"special": false,
"text": " #"
},
{
"id": 29906,
"logprob": -0.49389648,
"special": false,
"text": "2"
},
{
"id": 29900,
"logprob": -0.81103516,
"special": false,
"text": "0"
},
{
"id": 29896,
"logprob": 0.0,
"special": false,
"text": "1"
},
{
"id": 29955,
"logprob": -1.0800781,
"special": false,
"text": "7"
}
],
"top_tokens": null
},
"generated_text": "Test request-for-comment: #2017"
}

358
integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized_load.json Normal file
View File

@ -0,0 +1,358 @@
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.8828125,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.5859375,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -2.3359375,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.8623047,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -1.2451172,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -1.6923828,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.4492188,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.15197754,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.8022461,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -0.22583008,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.007095337,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.021652222,
"special": false,
"text": "\n"
}
],
"top_tokens": null
},
"generated_text": "\nTest request\nTest request\nTest request\n"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.796875,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.625,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -2.3476562,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.8789062,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -1.2734375,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -1.703125,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.4677734,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.15454102,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.7973633,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -0.23278809,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.006980896,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.022033691,
"special": false,
"text": "\n"
}
],
"top_tokens": null
},
"generated_text": "\nTest request\nTest request\nTest request\n"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.9296875,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.5703125,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -2.3203125,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.8486328,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -1.2480469,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -1.7060547,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.4511719,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.1529541,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.81396484,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -0.22180176,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.007133484,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.021835327,
"special": false,
"text": "\n"
}
],
"top_tokens": null
},
"generated_text": "\nTest request\nTest request\nTest request\n"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.84375,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.6171875,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -2.3261719,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.8691406,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -1.2597656,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -1.7070312,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -1.4550781,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.1538086,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.79345703,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": -0.22924805,
"special": false,
"text": "Test"
},
{
"id": 2009,
"logprob": -0.0070266724,
"special": false,
"text": " request"
},
{
"id": 13,
"logprob": -0.021942139,
"special": false,
"text": "\n"
}
],
"top_tokens": null
},
"generated_text": "\nTest request\nTest request\nTest request\n"
}
]

4
integration-tests/models/test_flash_llama_gptq.py
View File

@ -3,7 +3,9 @@ import pytest
@pytest.fixture(scope="module")
def flash_llama_gptq_handle(launcher):
with launcher("huggingface/llama-7b-gptq", num_shard=2, quantize="gptq") as handle:
with launcher(
"astronomer/Llama-3-8B-Instruct-GPTQ-4-Bit", num_shard=2, quantize="gptq"
) as handle:
yield handle

2
integration-tests/models/test_idefics2.py
View File

@ -57,7 +57,7 @@ async def test_flash_idefics2_two_images(flash_idefics2_next, response_snapshot)
response.generated_text
== " The cow is standing on the beach and the chicken is sitting on a pile of money."
), f"{repr(response.generated_text)}"
assert response.details.generated_tokens == 20
assert response.details.generated_tokens == 19
assert response == response_snapshot

2
router/Cargo.toml
View File

@ -24,7 +24,7 @@ futures = "0.3.28"
hf-hub = { workspace = true }
itertools = "0.10"
jsonschema = { version = "0.17.1", features = ["draft202012"] }
metrics = "0.21.1"
metrics = "0.23.0"
metrics-exporter-prometheus = { version = "0.15.1", features = [] }
nohash-hasher = "0.2.0"
opentelemetry = { version = "0.20.0", features = ["rt-tokio"] }

8
router/src/infer/mod.rs
View File

@ -91,14 +91,14 @@ impl Infer {
.limit_concurrent_requests
.try_acquire_owned()
.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "overloaded");
metrics::counter!("tgi_request_failure", "err" => "overloaded").increment(1);
tracing::error!("{err}");
err
})?;
// Validate request
let valid_request = self.validation.validate(request).await.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
tracing::error!("{err}");
err
})?;
@ -140,7 +140,7 @@ impl Infer {
.ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))?
.apply(messages, grammar_with_prompt)
.map_err(|e| {
metrics::increment_counter!("tgi_request_failure", "err" => "template");
metrics::counter!("tgi_request_failure", "err" => "template").increment(1);
tracing::error!("{e}");
e
})
@ -214,7 +214,7 @@ impl Infer {
})
} else {
let err = InferError::IncompleteGeneration;
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
metrics::counter!("tgi_request_failure", "err" => "incomplete").increment(1);
tracing::error!("{err}");
Err(err)
}

8
router/src/infer/v2/queue.rs
View File

@ -111,7 +111,7 @@ async fn queue_task(
match cmd {
QueueCommand::Append(entry, span) => {
span.in_scope(|| state.append(*entry));
metrics::increment_gauge!("tgi_queue_size", 1.0);
metrics::gauge!("tgi_queue_size").increment(1.0);
}
QueueCommand::NextBatch {
min_size,
@ -124,7 +124,7 @@ async fn queue_task(
let next_batch =
state.next_batch(min_size, max_size, prefill_token_budget, token_budget);
response_sender.send(next_batch).unwrap();
metrics::gauge!("tgi_queue_size", state.entries.len() as f64);
metrics::gauge!("tgi_queue_size").set(state.entries.len() as f64);
}),
}
}
@ -226,7 +226,7 @@ impl State {
// Filter entries where the response receiver was dropped (== entries where the request
// was dropped by the client)
if entry.response_tx.is_closed() {
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
metrics::counter!("tgi_request_failure", "err" => "dropped").increment(1);
tracing::debug!("Dropping entry");
continue;
}
@ -336,7 +336,7 @@ impl State {
// Increment batch id
self.next_batch_id += 1;
metrics::histogram!("tgi_batch_next_size", batch.size as f64);
metrics::histogram!("tgi_batch_next_size").record(batch.size as f64);
Some((batch_entries, batch, next_batch_span))
}

61
router/src/infer/v2/scheduler.rs
View File

@ -148,8 +148,8 @@ pub(crate) async fn batching_task(
let batch_size = batch.size;
let batch_max_tokens = batch.max_tokens;
let mut batches = vec![batch];
metrics::gauge!("tgi_batch_current_size", batch_size as f64);
metrics::gauge!("tgi_batch_current_max_tokens", batch_max_tokens as f64);
metrics::gauge!("tgi_batch_current_size").set(batch_size as f64);
metrics::gauge!("tgi_batch_current_max_tokens").set(batch_max_tokens as f64);
let min_size = if waiting_tokens >= max_waiting_tokens {
// If we didn't onboard any new requests since >= max_waiting_tokens, we try
@ -170,9 +170,11 @@ pub(crate) async fn batching_task(
{
// Tracking metrics
if min_size.is_some() {
metrics::increment_counter!("tgi_batch_concat", "reason" => "backpressure");
metrics::counter!("tgi_batch_concat", "reason" => "backpressure")
.increment(1);
} else {
metrics::increment_counter!("tgi_batch_concat", "reason" => "wait_exceeded");
metrics::counter!("tgi_batch_concat", "reason" => "wait_exceeded")
.increment(1);
}
entries.iter_mut().for_each(|(_, entry)| {
@ -219,8 +221,8 @@ pub(crate) async fn batching_task(
.await;
waiting_tokens += 1;
}
metrics::gauge!("tgi_batch_current_size", 0.0);
metrics::gauge!("tgi_batch_current_max_tokens", 0.0);
metrics::gauge!("tgi_batch_current_size").set(0.0);
metrics::gauge!("tgi_batch_current_max_tokens").set(0.0);
}
}
}
@ -234,7 +236,7 @@ async fn prefill(
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_id = batch.id;
metrics::increment_counter!("tgi_batch_inference_count", "method" => "prefill");
metrics::counter!("tgi_batch_inference_count", "method" => "prefill").increment(1);
match client.prefill(batch).await {
Ok((generations, next_batch, timings)) => {
@ -248,11 +250,15 @@ async fn prefill(
// Filter next batch and remove requests that were stopped
let next_batch = filter_batch(client, next_batch, entries).await;
metrics::histogram!("tgi_batch_forward_duration", timings.forward.as_secs_f64(), "method" => "prefill");
metrics::histogram!("tgi_batch_decode_duration", timings.decode.as_secs_f64(), "method" => "prefill");
metrics::histogram!("tgi_batch_filter_duration", start_filtering_time.elapsed().as_secs_f64(), "method" => "prefill");
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "prefill");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "prefill");
metrics::histogram!("tgi_batch_forward_duration","method" => "prefill")
.record(timings.forward.as_secs_f64());
metrics::histogram!("tgi_batch_decode_duration", "method" => "prefill")
.record(timings.decode.as_secs_f64());
metrics::histogram!("tgi_batch_filter_duration", "method" => "prefill")
.record(start_filtering_time.elapsed().as_secs_f64());
metrics::histogram!("tgi_batch_inference_duration","method" => "prefill")
.record(start_time.elapsed().as_secs_f64());
metrics::counter!("tgi_batch_inference_success", "method" => "prefill").increment(1);
next_batch
}
// If we have an error, we discard the whole batch
@ -261,7 +267,7 @@ async fn prefill(
generation_health.store(false, Ordering::SeqCst);
let _ = client.clear_cache(Some(batch_id)).await;
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "prefill");
metrics::counter!("tgi_batch_inference_failure", "method" => "prefill").increment(1);
None
}
}
@ -276,7 +282,7 @@ async fn decode(
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_ids: Vec<u64> = batches.iter().map(|b| b.id).collect();
metrics::increment_counter!("tgi_batch_inference_count", "method" => "decode");
metrics::counter!("tgi_batch_inference_count", "method" => "decode").increment(1);
match client.decode(batches).await {
Ok((generations, next_batch, timings)) => {
@ -291,13 +297,18 @@ async fn decode(
let next_batch = filter_batch(client, next_batch, entries).await;
if let Some(concat_duration) = timings.concat {
metrics::histogram!("tgi_batch_concat_duration", concat_duration.as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_concat_duration", "method" => "decode")
.record(concat_duration.as_secs_f64());
}
metrics::histogram!("tgi_batch_forward_duration", timings.forward.as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_decode_duration", timings.decode.as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_filter_duration", start_filtering_time.elapsed().as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "decode");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "decode");
metrics::histogram!("tgi_batch_forward_duration", "method" => "decode")
.record(timings.forward.as_secs_f64());
metrics::histogram!("tgi_batch_decode_duration", "method" => "decode")
.record(timings.decode.as_secs_f64());
metrics::histogram!("tgi_batch_filter_duration", "method" => "decode")
.record(start_filtering_time.elapsed().as_secs_f64());
metrics::histogram!("tgi_batch_inference_duration", "method" => "decode")
.record(start_time.elapsed().as_secs_f64());
metrics::counter!("tgi_batch_inference_success", "method" => "decode").increment(1);
next_batch
}
// If we have an error, we discard the whole batch
@ -307,7 +318,7 @@ async fn decode(
let _ = client.clear_cache(Some(id)).await;
}
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "decode");
metrics::counter!("tgi_batch_inference_failure", "method" => "decode").increment(1);
None
}
}
@ -365,7 +376,7 @@ fn filter_send_generations(generations: Vec<Generation>, entries: &mut IntMap<u6
// request and we need to stop generating hence why we unwrap_or(true)
let stopped = send_responses(generation, entry).map_err(|err| {
tracing::error!("Entry response channel error.");
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
metrics::counter!("tgi_request_failure", "err" => "dropped").increment(1);
err
}).unwrap_or(true);
if stopped {
@ -381,7 +392,7 @@ fn send_responses(
) -> Result<bool, Box<SendError<Result<InferStreamResponse, InferError>>>> {
// Return directly if the channel is disconnected
if entry.response_tx.is_closed() {
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
metrics::counter!("tgi_request_failure", "err" => "dropped").increment(1);
return Ok(true);
}
@ -407,7 +418,7 @@ fn send_responses(
// Create last Token
let tokens_ = generation.tokens.expect("Non empty tokens in generation");
let n = tokens_.ids.len();
metrics::histogram!("tgi_request_skipped_tokens", (n - 1) as f64);
metrics::histogram!("tgi_request_skipped_tokens").record((n - 1) as f64);
let mut iterator = tokens_
.ids
.into_iter()
@ -472,7 +483,7 @@ fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
// Create and enter a span to link this function back to the entry
let _send_error_span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_error").entered();
let err = InferError::GenerationError(error.to_string());
metrics::increment_counter!("tgi_request_failure", "err" => "generation");
metrics::counter!("tgi_request_failure", "err" => "generation").increment(1);
tracing::error!("{err}");
// unwrap_or is valid here as we don't care if the receiver is gone.

8
router/src/infer/v3/queue.rs
View File

@ -126,7 +126,7 @@ async fn queue_task(
match cmd {
QueueCommand::Append(entry, span) => {
span.in_scope(|| state.append(*entry));
metrics::increment_gauge!("tgi_queue_size", 1.0);
metrics::gauge!("tgi_queue_size").increment(1.0);
}
QueueCommand::NextBatch {
min_size,
@ -141,7 +141,7 @@ async fn queue_task(
.instrument(span)
.await;
response_sender.send(next_batch).unwrap();
metrics::gauge!("tgi_queue_size", state.entries.len() as f64);
metrics::gauge!("tgi_queue_size").set(state.entries.len() as f64);
}
}
}
@ -248,7 +248,7 @@ impl State {
// Filter entries where the response receiver was dropped (== entries where the request
// was dropped by the client)
if entry.response_tx.is_closed() {
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
metrics::counter!("tgi_request_failure", "err" => "dropped").increment(1);
tracing::debug!("Dropping entry");
continue;
}
@ -399,7 +399,7 @@ impl State {
// Increment batch id
self.next_batch_id += 1;
metrics::histogram!("tgi_batch_next_size", batch.size as f64);
metrics::histogram!("tgi_batch_next_size").record(batch.size as f64);
Some((batch_entries, batch, next_batch_span))
}

61
router/src/infer/v3/scheduler.rs
View File

@ -154,8 +154,8 @@ pub(crate) async fn batching_task(
let batch_size = batch.size;
let batch_max_tokens = batch.max_tokens;
let mut batches = vec![batch];
metrics::gauge!("tgi_batch_current_size", batch_size as f64);
metrics::gauge!("tgi_batch_current_max_tokens", batch_max_tokens as f64);
metrics::gauge!("tgi_batch_current_size").set(batch_size as f64);
metrics::gauge!("tgi_batch_current_max_tokens").set(batch_max_tokens as f64);
let min_size = if waiting_tokens >= max_waiting_tokens {
// If we didn't onboard any new requests since >= max_waiting_tokens, we try
@ -176,9 +176,11 @@ pub(crate) async fn batching_task(
{
// Tracking metrics
if min_size.is_some() {
metrics::increment_counter!("tgi_batch_concat", "reason" => "backpressure");
metrics::counter!("tgi_batch_concat", "reason" => "backpressure")
.increment(1);
} else {
metrics::increment_counter!("tgi_batch_concat", "reason" => "wait_exceeded");
metrics::counter!("tgi_batch_concat", "reason" => "wait_exceeded")
.increment(1);
}
entries.iter_mut().for_each(|(_, entry)| {
@ -225,8 +227,8 @@ pub(crate) async fn batching_task(
.await;
waiting_tokens += 1;
}
metrics::gauge!("tgi_batch_current_size", 0.0);
metrics::gauge!("tgi_batch_current_max_tokens", 0.0);
metrics::gauge!("tgi_batch_current_size").set(0.0);
metrics::gauge!("tgi_batch_current_max_tokens").set(0.0);
}
}
}
@ -240,7 +242,7 @@ async fn prefill(
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_id = batch.id;
metrics::increment_counter!("tgi_batch_inference_count", "method" => "prefill");
metrics::counter!("tgi_batch_inference_count", "method" => "prefill").increment(1);
match client.prefill(batch).await {
Ok((generations, next_batch, timings)) => {
@ -254,11 +256,15 @@ async fn prefill(
// Filter next batch and remove requests that were stopped
let next_batch = filter_batch(client, next_batch, entries).await;
metrics::histogram!("tgi_batch_forward_duration", timings.forward.as_secs_f64(), "method" => "prefill");
metrics::histogram!("tgi_batch_decode_duration", timings.decode.as_secs_f64(), "method" => "prefill");
metrics::histogram!("tgi_batch_filter_duration", start_filtering_time.elapsed().as_secs_f64(), "method" => "prefill");
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "prefill");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "prefill");
metrics::histogram!("tgi_batch_forward_duration","method" => "prefill")
.record(timings.forward.as_secs_f64());
metrics::histogram!("tgi_batch_decode_duration", "method" => "prefill")
.record(timings.decode.as_secs_f64());
metrics::histogram!("tgi_batch_filter_duration", "method" => "prefill")
.record(start_filtering_time.elapsed().as_secs_f64());
metrics::histogram!("tgi_batch_inference_duration", "method" => "prefill")
.record(start_time.elapsed().as_secs_f64());
metrics::counter!("tgi_batch_inference_success", "method" => "prefill").increment(1);
next_batch
}
// If we have an error, we discard the whole batch
@ -267,7 +273,7 @@ async fn prefill(
generation_health.store(false, Ordering::SeqCst);
let _ = client.clear_cache(Some(batch_id)).await;
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "prefill");
metrics::counter!("tgi_batch_inference_failure", "method" => "prefill").increment(1);
None
}
}
@ -282,7 +288,7 @@ async fn decode(
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_ids: Vec<u64> = batches.iter().map(|b| b.id).collect();
metrics::increment_counter!("tgi_batch_inference_count", "method" => "decode");
metrics::counter!("tgi_batch_inference_count", "method" => "decode").increment(1);
match client.decode(batches).await {
Ok((generations, next_batch, timings)) => {
@ -297,13 +303,18 @@ async fn decode(
let next_batch = filter_batch(client, next_batch, entries).await;
if let Some(concat_duration) = timings.concat {
metrics::histogram!("tgi_batch_concat_duration", concat_duration.as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_concat_duration", "method" => "decode")
.record(concat_duration.as_secs_f64());
}
metrics::histogram!("tgi_batch_forward_duration", timings.forward.as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_decode_duration", timings.decode.as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_filter_duration", start_filtering_time.elapsed().as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "decode");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "decode");
metrics::histogram!("tgi_batch_forward_duration", "method" => "decode")
.record(timings.forward.as_secs_f64());
metrics::histogram!("tgi_batch_decode_duration", "method" => "decode")
.record(timings.decode.as_secs_f64());
metrics::histogram!("tgi_batch_filter_duration", "method" => "decode")
.record(start_filtering_time.elapsed().as_secs_f64());
metrics::histogram!("tgi_batch_inference_duration", "method" => "decode")
.record(start_time.elapsed().as_secs_f64());
metrics::counter!("tgi_batch_inference_success", "method" => "decode").increment(1);
next_batch
}
// If we have an error, we discard the whole batch
@ -313,7 +324,7 @@ async fn decode(
let _ = client.clear_cache(Some(id)).await;
}
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "decode");
metrics::counter!("tgi_batch_inference_failure", "method" => "decode").increment(1);
None
}
}
@ -371,7 +382,7 @@ fn filter_send_generations(generations: Vec<Generation>, entries: &mut IntMap<u6
// request and we need to stop generating hence why we unwrap_or(true)
let stopped = send_responses(generation, entry).map_err(|err| {
tracing::error!("Entry response channel error.");
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
metrics::counter!("tgi_request_failure", "err" => "dropped").increment(1);
err
}).unwrap_or(true);
if stopped {
@ -387,7 +398,7 @@ fn send_responses(
) -> Result<bool, Box<SendError<Result<InferStreamResponse, InferError>>>> {
// Return directly if the channel is disconnected
if entry.response_tx.is_closed() {
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
metrics::counter!("tgi_request_failure", "err" => "dropped").increment(1);
return Ok(true);
}
@ -413,7 +424,7 @@ fn send_responses(
// Create last Token
let tokens_ = generation.tokens.expect("Non empty tokens in generation");
let n = tokens_.ids.len();
metrics::histogram!("tgi_request_skipped_tokens", (n - 1) as f64);
metrics::histogram!("tgi_request_skipped_tokens").record((n - 1) as f64);
let mut iterator = tokens_
.ids
.into_iter()
@ -478,7 +489,7 @@ fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
// Create and enter a span to link this function back to the entry
let _send_error_span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_error").entered();
let err = InferError::GenerationError(error.to_string());
metrics::increment_counter!("tgi_request_failure", "err" => "generation");
metrics::counter!("tgi_request_failure", "err" => "generation").increment(1);
tracing::error!("{err}");
// unwrap_or is valid here as we don't care if the receiver is gone.

6
router/src/lib.rs
View File

@ -386,7 +386,7 @@ pub struct CompletionRequest {
/// UNUSED
#[schema(example = "mistralai/Mistral-7B-Instruct-v0.2")]
/// ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.
pub model: String,
pub model: Option<String>,
/// The prompt to generate completions for.
#[schema(example = "What is Deep Learning?")]
@ -733,7 +733,7 @@ impl ChatCompletionChunk {
pub(crate) struct ChatRequest {
#[schema(example = "mistralai/Mistral-7B-Instruct-v0.2")]
/// [UNUSED] ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.
pub model: String,
pub model: Option<String>,
/// A list of messages comprising the conversation so far.
#[schema(example = "[{\"role\": \"user\", \"content\": \"What is Deep Learning?\"}]")]
@ -850,7 +850,7 @@ pub enum ToolType {
Function { function: FunctionName },
}
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, ToSchema)]
pub struct FunctionName {
pub name: String,
}

104
router/src/server.rs
View File

@ -11,10 +11,11 @@ use crate::kserve::{
};
use crate::validation::ValidationError;
use crate::{
BestOfSequence, Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest,
GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig, HubTokenizerConfig, Info,
Message, PrefillToken, SimpleToken, StreamDetails, StreamResponse, Token, TokenizeResponse,
Usage, Validation,
BestOfSequence, Details, ErrorResponse, FinishReason, FunctionName, GenerateParameters,
GenerateRequest, GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig,
HubTokenizerConfig, Info, Message, MessageChunk, MessageContent, OutputMessage, PrefillToken,
SimpleToken, StreamDetails, StreamResponse, TextMessage, Token, TokenizeResponse,
ToolCallDelta, ToolCallMessage, Url, Usage, Validation,
};
use crate::{
ChatCompletion, ChatCompletionChoice, ChatCompletionChunk, ChatCompletionComplete,
@ -185,7 +186,7 @@ pub(crate) async fn generate_internal(
span: tracing::Span,
) -> Result<(HeaderMap, Json<GenerateResponse>), (StatusCode, Json<ErrorResponse>)> {
let start_time = Instant::now();
metrics::increment_counter!("tgi_request_count");
metrics::counter!("tgi_request_count").increment(1);
// Do not long ultra long inputs, like image payloads.
tracing::debug!("Input: {}", &req.inputs[..1000.min(req.inputs.len())]);
@ -301,25 +302,15 @@ pub(crate) async fn generate_internal(
);
// Metrics
metrics::increment_counter!("tgi_request_success");
metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
metrics::histogram!(
"tgi_request_validation_duration",
validation_time.as_secs_f64()
);
metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
metrics::histogram!(
"tgi_request_inference_duration",
inference_time.as_secs_f64()
);
metrics::histogram!(
"tgi_request_mean_time_per_token_duration",
time_per_token.as_secs_f64()
);
metrics::histogram!(
"tgi_request_generated_tokens",
response.generated_text.generated_tokens as f64
);
metrics::counter!("tgi_request_success").increment(1);
metrics::histogram!("tgi_request_duration").record(total_time.as_secs_f64());
metrics::histogram!("tgi_request_validation_duration").record(validation_time.as_secs_f64());
metrics::histogram!("tgi_request_queue_duration").record(queue_time.as_secs_f64());
metrics::histogram!("tgi_request_inference_duration").record(inference_time.as_secs_f64());
metrics::histogram!("tgi_request_mean_time_per_token_duration")
.record(time_per_token.as_secs_f64());
metrics::histogram!("tgi_request_generated_tokens")
.record(response.generated_text.generated_tokens as f64);
// Send response
let mut output_text = response.generated_text.text;
@ -399,7 +390,7 @@ async fn generate_stream_internal(
span: tracing::Span,
) -> (HeaderMap, impl Stream<Item = Result<Event, Infallible>>) {
let start_time = Instant::now();
metrics::increment_counter!("tgi_request_count");
metrics::counter!("tgi_request_count").increment(1);
tracing::debug!("Input: {}", req.inputs);
@ -427,12 +418,12 @@ async fn generate_stream_internal(
let best_of = req.parameters.best_of.unwrap_or(1);
if best_of != 1 {
let err = InferError::from(ValidationError::BestOfStream);
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
tracing::error!("{err}");
yield Ok(Event::from(err));
} else if req.parameters.decoder_input_details {
let err = InferError::from(ValidationError::PrefillDetailsStream);
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
tracing::error!("{err}");
yield Ok(Event::from(err));
} else {
@ -500,13 +491,13 @@ async fn generate_stream_internal(
span.record("seed", format!("{:?}", generated_text.seed));
// Metrics
metrics::increment_counter!("tgi_request_success");
metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
metrics::histogram!("tgi_request_validation_duration", validation_time.as_secs_f64());
metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
metrics::histogram!("tgi_request_inference_duration", inference_time.as_secs_f64());
metrics::histogram!("tgi_request_mean_time_per_token_duration", time_per_token.as_secs_f64());
metrics::histogram!("tgi_request_generated_tokens", generated_text.generated_tokens as f64);
metrics::counter!("tgi_request_success").increment(1);
metrics::histogram!("tgi_request_duration").record(total_time.as_secs_f64());
metrics::histogram!("tgi_request_validation_duration").record(validation_time.as_secs_f64());
metrics::histogram!("tgi_request_queue_duration").record(queue_time.as_secs_f64());
metrics::histogram!("tgi_request_inference_duration").record(inference_time.as_secs_f64());
metrics::histogram!("tgi_request_mean_time_per_token_duration").record(time_per_token.as_secs_f64());
metrics::histogram!("tgi_request_generated_tokens").record(generated_text.generated_tokens as f64);
// StreamResponse
end_reached = true;
@ -553,7 +544,7 @@ async fn generate_stream_internal(
// Skip if we already sent an error
if !end_reached && !error {
let err = InferError::IncompleteGeneration;
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
metrics::counter!("tgi_request_failure", "err" => "incomplete").increment(1);
tracing::error!("{err}");
yield Ok(Event::from(err));
}
@ -572,8 +563,8 @@ request_body = CompletionRequest,
responses(
(status = 200, description = "Generated Chat Completion",
content(
("application/json" = Completion),
("text/event-stream" = CompletionCompleteChunk),
("application/json" = CompletionFinal),
("text/event-stream" = Chunk),
)),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})),
@ -604,9 +595,10 @@ async fn completions(
Json(req): Json<CompletionRequest>,
) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
let span = tracing::Span::current();
metrics::increment_counter!("tgi_request_count");
metrics::counter!("tgi_request_count").increment(1);
let CompletionRequest {
model,
max_tokens,
seed,
stop,
@ -625,7 +617,7 @@ async fn completions(
// if suffix is present throw an error
if req.suffix.is_some() {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
return Err((
StatusCode::UNPROCESSABLE_ENTITY,
Json(ErrorResponse {
@ -637,7 +629,7 @@ async fn completions(
}
if req.prompt.0.len() > info.max_client_batch_size {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
return Err((
StatusCode::UNPROCESSABLE_ENTITY,
Json(ErrorResponse {
@ -675,7 +667,7 @@ async fn completions(
seed,
top_n_tokens: None,
grammar: None,
..Default::default()
adapter_id: model.as_ref().filter(|m| *m != "tgi").map(String::from),
},
})
.collect();
@ -820,6 +812,10 @@ async fn completions(
}
};
let stream = stream.chain(futures::stream::once(async {
Ok(Event::default().data("[DONE]"))
}));
let sse = Sse::new(stream).keep_alive(KeepAlive::default());
Ok((headers, sse).into_response())
} else {
@ -1009,8 +1005,9 @@ async fn chat_completions(
Json(req): Json<ChatRequest>,
) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
let span = tracing::Span::current();
metrics::increment_counter!("tgi_request_count");
metrics::counter!("tgi_request_count").increment(1);
let ChatRequest {
model,
logprobs,
max_tokens,
messages,
@ -1039,7 +1036,7 @@ async fn chat_completions(
// response_format and tools are mutually exclusive
if response_format.is_some() && tools.as_ref().is_some() {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
return Err((
StatusCode::UNPROCESSABLE_ENTITY,
Json(ErrorResponse {
@ -1053,7 +1050,7 @@ async fn chat_completions(
let tool_grammar = match ToolGrammar::apply(tools, tool_choice) {
Ok(grammar) => grammar,
Err(err) => {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
tracing::error!("{err}");
return Err((
StatusCode::UNPROCESSABLE_ENTITY,
@ -1082,7 +1079,7 @@ async fn chat_completions(
let inputs = match infer.apply_chat_template(messages, tools_grammar_prompt) {
Ok(inputs) => inputs,
Err(err) => {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
metrics::counter!("tgi_request_failure", "err" => "validation").increment(1);
tracing::error!("{err}");
return Err((
StatusCode::UNPROCESSABLE_ENTITY,
@ -1116,7 +1113,7 @@ async fn chat_completions(
seed,
top_n_tokens: req.top_logprobs,
grammar,
..Default::default()
adapter_id: model.filter(|m| *m != "tgi").map(String::from),
},
};
@ -1178,6 +1175,11 @@ async fn chat_completions(
span,
)
.await;
let response_stream = response_stream.chain(futures::stream::once(async {
Ok(Event::default().data("[DONE]"))
}));
let sse = Sse::new(response_stream).keep_alive(KeepAlive::default());
Ok((headers, sse).into_response())
} else {
@ -1280,7 +1282,7 @@ async fn vertex_compatibility(
Json(req): Json<VertexRequest>,
) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
let span = tracing::Span::current();
metrics::increment_counter!("tgi_request_count");
metrics::counter!("tgi_request_count").increment(1);
// check that theres at least one instance
if req.instances.is_empty() {
@ -1454,6 +1456,14 @@ pub async fn run(
GrammarType,
ChatRequest,
Message,
MessageContent,
MessageChunk,
Url,
FunctionName,
OutputMessage,
TextMessage,
ToolCallMessage,
ToolCallDelta,
ChatCompletionComplete,
ChatCompletionChoice,
ChatCompletionDelta,

4
router/src/validation.rs
View File

@ -157,7 +157,7 @@ impl Validation {
));
}
metrics::histogram!("tgi_request_input_length", input_length as f64);
metrics::histogram!("tgi_request_input_length").record(input_length as f64);
Ok((inputs, input_length, max_new_tokens))
}
// Return inputs without validation
@ -384,7 +384,7 @@ impl Validation {
ignore_eos_token: false,
};
metrics::histogram!("tgi_request_max_new_tokens", max_new_tokens as f64);
metrics::histogram!("tgi_request_max_new_tokens").record(max_new_tokens as f64);
Ok(ValidGenerateRequest {
inputs,

2
server/Makefile
View File

@ -35,5 +35,5 @@ run-dev:
SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
export-requirements:
poetry export -o requirements_cuda.txt --without-hashes
poetry export -o requirements_cuda.txt --without-hashes --with cuda
poetry export -o requirements_rocm.txt --without-hashes

15
server/marlin/marlin_kernels/__init__.pyi
View File

@ -59,3 +59,18 @@ def marlin_gemm(
Matrix multiplication using Marlin kernels.
"""
...
# fp8 marlin
def fp8_marlin_gemm(
a: torch.Tensor,
b_q_weight: torch.Tensor,
b_scales: torch.Tensor,
workspace: torch.Tensor,
num_bits: int,
size_m: int,
size_n: int,
size_k: int,
) -> torch.Tensor:
return torch.ops._C.fp8_marlin_gemm(
a, b_q_weight, b_scales, workspace, num_bits, size_m, size_n, size_k
)

2
server/marlin/marlin_kernels/ext.cpp
View File

@ -9,4 +9,6 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("gptq_marlin_repack", &gptq_marlin_repack,
"Repack GPTQ parameters for Marlin");
m.def("marlin_gemm", &marlin_gemm, "Marlin gemm");
// fp8_marlin Optimized Quantized GEMM for FP8 weight-only.
m.def("fp8_marlin_gemm", &fp8_marlin_gemm);
}

5
server/marlin/marlin_kernels/ext.hh
View File

@ -27,4 +27,9 @@ torch::Tensor marlin_gemm(torch::Tensor &a, torch::Tensor &b_q_weight,
torch::Tensor &b_scales, torch::Tensor &workspace,
int64_t size_m, int64_t size_n, int64_t size_k);
torch::Tensor fp8_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
torch::Tensor& b_scales, torch::Tensor& workspace,
int64_t num_bits, int64_t size_m, int64_t size_n,
int64_t size_k);
#endif

1308
server/marlin/marlin_kernels/fp8_marlin.cu Normal file
View File

File diff suppressed because it is too large Load Diff

1
server/marlin/setup.py
View File

@ -9,6 +9,7 @@ setup(
CUDAExtension(
name="marlin_kernels",
sources=[
"marlin_kernels/fp8_marlin.cu",
"marlin_kernels/gptq_marlin.cu",
"marlin_kernels/gptq_marlin_repack.cu",
"marlin_kernels/marlin_cuda_kernel.cu",

8
server/tests/models/test_bloom.py
View File

@ -8,6 +8,9 @@ from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.utils import weight_hub_files, download_weights
from text_generation_server.models.bloom import BloomCausalLMBatch, BLOOMSharded
from text_generation_server.models.custom_modeling.bloom_modeling import (
BloomForCausalLM,
)
@pytest.fixture(scope="session")
@ -16,7 +19,10 @@ def default_bloom():
revision = "main"
filenames = weight_hub_files(model_id, revision, ".safetensors")
download_weights(filenames, model_id, revision)
return BLOOMSharded(model_id)
return BLOOMSharded(
model_id,
model_class=BloomForCausalLM,
)
@pytest.fixture(scope="session")

2
server/tests/models/test_causal_lm.py
View File

@ -10,7 +10,7 @@ from text_generation_server.models.causal_lm import CausalLM, CausalLMBatch
@pytest.fixture(scope="session")
def default_causal_lm():
return CausalLM("gpt2")
return CausalLM.fallback("gpt2")
@pytest.fixture(scope="session")

5
server/tests/models/test_santacoder.py
View File

@ -1,13 +1,12 @@
import pytest
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.models.santacoder import SantaCoder
from text_generation_server.models.causal_lm import CausalLMBatch, CausalLM
@pytest.fixture(scope="session")
def default_santacoder():
return SantaCoder("bigcode/santacoder")
return CausalLM.fallback(model_id="bigcode/santacoder")
@pytest.fixture

2
server/tests/models/test_seq2seq_lm.py
View File

@ -20,7 +20,7 @@ def mt0_small_tokenizer():
@pytest.fixture(scope="session")
def default_seq2seq_lm():
return Seq2SeqLM("bigscience/mt0-small")
return Seq2SeqLM.fallback("bigscience/mt0-small")
@pytest.fixture

8
server/tests/utils/test_layers.py
View File

@ -2,6 +2,7 @@ import torch
from text_generation_server.layers import (
TensorParallelEmbedding,
)
from text_generation_server.utils.weights import DefaultWeightsLoader
class ProcessGroup:
@ -42,7 +43,12 @@ class Weights:
def test_weight_hub_files_offline_error():
vocab_size = 17
weights = Weights(rank=0, world_size=1, vocab_size=vocab_size, hidden_dim=256)
weights = Weights(
rank=0,
world_size=1,
vocab_size=vocab_size,
hidden_dim=256,
)
embeddings = TensorParallelEmbedding("", weights)
input_ids = torch.arange(vocab_size)

162
server/tests/utils/test_weights.py
View File

@ -1,13 +1,47 @@
import pytest
import torch
from text_generation_server.utils.weights import Weights
from text_generation_server.layers.gptq import GPTQWeight
from text_generation_server.layers.exl2 import Exl2Weight
from text_generation_server.layers.marlin import MarlinWeight
from text_generation_server.utils.weights import (
DefaultWeightsLoader,
Weights,
WeightsLoader,
)
from text_generation_server.layers.gptq import GPTQWeight, GPTQWeightsLoader
from text_generation_server.layers.exl2 import Exl2Weight, Exl2WeightsLoader
from text_generation_server.layers.marlin import MarlinWeight, MarlinWeightsLoader
from types import SimpleNamespace
from typing import List, Optional, Dict, Union
from pathlib import Path
@pytest.fixture
def gptq_weights_loader():
return GPTQWeightsLoader(
bits=4,
groupsize=-1,
desc_act=False,
quant_method="gptq",
quantize="gptq",
sym=True,
)
@pytest.fixture
def gptq_weights_loader_awq():
return GPTQWeightsLoader(
bits=4,
groupsize=-1,
desc_act=False,
quant_method="awq",
quantize="awq",
sym=True,
)
@pytest.fixture
def marlin_weights_loader():
return MarlinWeightsLoader(bits=4, is_marlin_24=False)
dummy_file_system = {
"test_weights": {
"layer.0.weight": torch.tensor(
@ -58,7 +92,7 @@ dummy_file_system = {
dtype=torch.float32,
),
},
"test_get_multi_weights_row": {
"test_get_weights_row": {
"weight.weight": torch.tensor(
[
[1, 2],
@ -101,7 +135,7 @@ dummy_file_system = {
"weight.B": torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
"weight.s": torch.tensor([[0.5000], [0.2500]], dtype=torch.float16),
},
"test_get_multi_weights_row_gptq": {
"test_get_weights_row_gptq": {
"weight.qweight": torch.tensor(
[
[1, 2],
@ -200,7 +234,7 @@ dummy_file_system = {
"weight.q_scale_max": torch.tensor([100], dtype=torch.float16),
"weight.q_groups": torch.tensor([4], dtype=torch.int16),
},
"test_get_multi_weights_row_exl2": {
"test_get_weights_row_exl2": {
"weight.q_weight": torch.tensor(
[
[1, 2],
@ -245,7 +279,7 @@ dummy_file_system = {
"weight.q_scale_max": torch.tensor([100], dtype=torch.float16),
"weight.q_groups": torch.tensor([4], dtype=torch.int16),
},
"test_get_multi_weights_row_marlin": {
"test_get_weights_row_marlin": {
"weight.B": torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
"weight.s": torch.tensor([[0.5], [0.25]], dtype=torch.float16),
},
@ -308,6 +342,7 @@ class MockWeights(Weights):
dummy_fs,
aliases: Optional[Dict[str, List[str]]] = None,
prefix: Optional[str] = None,
weights_loader: Optional[WeightsLoader] = None,
):
routing = {}
self.dummy_fs = dummy_fs
@ -327,6 +362,9 @@ class MockWeights(Weights):
self.dtype = dtype
self.process_group = process_group
self.prefix = prefix
self.weights_loader = (
DefaultWeightsLoader() if weights_loader is None else weights_loader
)
self._handles = {}
def _get_handle(self, filename: Union[Path, str]):
@ -412,12 +450,10 @@ def test_get_weights_col_packed():
)
prefix = "weight"
quantize = None
block_sizes = 1
w = weights.get_weights_col_packed(
prefix=prefix,
quantize=quantize,
block_sizes=block_sizes,
)
@ -448,12 +484,10 @@ def test_get_weights_col_packed_block_size():
)
prefix = "weight"
quantize = None
block_sizes = 2
w = weights.get_weights_col_packed(
prefix=prefix,
quantize=quantize,
block_sizes=block_sizes,
)
@ -484,12 +518,10 @@ def test_get_weights_col_packed_block_size_arr():
)
prefix = "weight"
quantize = None
block_sizes = [1, 1]
w = weights.get_weights_col_packed(
prefix=prefix,
quantize=quantize,
block_sizes=block_sizes,
)
@ -519,11 +551,9 @@ def test_get_multi_weights_col():
)
prefixes = ["weight", "weight"]
quantize = None
w = weights.get_multi_weights_col(
prefixes=prefixes,
quantize=quantize,
dim=0,
)
@ -545,10 +575,10 @@ def test_get_multi_weights_col():
)
def test_get_multi_weights_row():
def test_get_weights_row():
weights = MockWeights(
[
"test_get_multi_weights_row",
"test_get_weights_row",
],
device="cpu",
dtype=torch.float32,
@ -557,11 +587,9 @@ def test_get_multi_weights_row():
)
prefix = "weight"
quantize = None
w = weights.get_multi_weights_row(
w = weights.get_weights_row(
prefix=prefix,
quantize=quantize,
)
assert torch.allclose(
@ -576,7 +604,7 @@ def test_get_multi_weights_row():
# test_get_weights_col
def test_get_weights_col_awq():
def test_get_weights_col_awq(gptq_weights_loader_awq):
weights = MockWeights(
[
"test_get_weights_col_gptq",
@ -585,14 +613,13 @@ def test_get_weights_col_awq():
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=gptq_weights_loader_awq,
)
prefix = "weight"
quantize = "awq"
w = weights.get_weights_col(
prefix=prefix,
quantize=quantize,
)
expected_weight = GPTQWeight(
@ -617,7 +644,7 @@ def test_get_weights_col_awq():
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
def test_get_weights_col_gtpq():
def test_get_weights_col_gtpq(gptq_weights_loader):
weights = MockWeights(
[
"test_get_weights_col_gptq",
@ -626,14 +653,13 @@ def test_get_weights_col_gtpq():
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=gptq_weights_loader,
)
prefix = "weight"
quantize = "gptq"
w = weights.get_weights_col(
prefix=prefix,
quantize=quantize,
)
expected_weight = GPTQWeight(
@ -664,14 +690,13 @@ def test_get_weights_col_exl2():
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=Exl2WeightsLoader(),
)
prefix = "weight"
quantize = "exl2"
w = weights.get_weights_col(
prefix=prefix,
quantize=quantize,
)
scaled_scale_max = 0.3906 * 256
@ -692,7 +717,7 @@ def test_get_weights_col_exl2():
assert torch.allclose(w.q_groups, expected_weight.q_groups), "q_groups mismatch"
def test_get_weights_col_marlin():
def test_get_weights_col_marlin(marlin_weights_loader):
weights = MockWeights(
[
"test_get_weights_col_marlin",
@ -701,14 +726,13 @@ def test_get_weights_col_marlin():
dtype=torch.float16,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=marlin_weights_loader,
)
prefix = "weight"
quantize = "marlin"
w = weights.get_weights_col(
prefix=prefix,
quantize=quantize,
)
expected_weight = MarlinWeight(
@ -723,7 +747,7 @@ def test_get_weights_col_marlin():
# test_get_weights_col_packed
def test_get_weights_col_packed_awq():
def test_get_weights_col_packed_awq(gptq_weights_loader_awq):
weights = MockWeights(
[
"test_get_weights_col_packed_gptq",
@ -732,15 +756,14 @@ def test_get_weights_col_packed_awq():
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=gptq_weights_loader_awq,
)
prefix = "weight"
quantize = "awq"
block_sizes = 1
w = weights.get_weights_col_packed(
prefix=prefix,
quantize=quantize,
block_sizes=block_sizes,
)
@ -773,15 +796,14 @@ def test_get_weights_col_packed_exl2():
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=Exl2WeightsLoader(),
)
prefix = "weight"
quantize = "exl2"
block_sizes = 1
w = weights.get_weights_col_packed(
prefix=prefix,
quantize=quantize,
block_sizes=block_sizes,
)
@ -803,7 +825,7 @@ def test_get_weights_col_packed_exl2():
assert torch.allclose(w.q_groups, expected_weight.q_groups), "q_groups mismatch"
def test_get_weights_col_packed_gptq():
def test_get_weights_col_packed_gptq(gptq_weights_loader):
weights = MockWeights(
[
"test_get_weights_col_packed_gptq",
@ -812,14 +834,13 @@ def test_get_weights_col_packed_gptq():
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=gptq_weights_loader,
)
prefixes = ["weight"]
quantize = "gptq"
w = weights.get_multi_weights_col(
prefixes=prefixes,
quantize=quantize,
dim=0,
)
@ -842,7 +863,7 @@ def test_get_weights_col_packed_gptq():
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
def test_get_weights_col_packed_marlin():
def test_get_weights_col_packed_marlin(marlin_weights_loader):
weights = MockWeights(
[
"test_get_weights_col_packed_marlin",
@ -851,14 +872,13 @@ def test_get_weights_col_packed_marlin():
dtype=torch.float16,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=marlin_weights_loader,
)
prefix = "weight"
quantize = "marlin"
w = weights.get_multi_weights_col(
prefixes=[prefix],
quantize=quantize,
dim=0,
)
@ -876,7 +896,7 @@ def test_get_weights_col_packed_marlin():
# test_get_multi_weights_col
def test_get_multi_weights_col_awq():
def test_get_multi_weights_col_awq(gptq_weights_loader_awq):
weights = MockWeights(
[
"test_get_multi_weights_col_gptq",
@ -885,14 +905,13 @@ def test_get_multi_weights_col_awq():
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=gptq_weights_loader_awq,
)
prefixes = ["weight"]
quantize = "awq"
w = weights.get_multi_weights_col(
prefixes=prefixes,
quantize=quantize,
dim=0,
)
@ -924,22 +943,21 @@ def test_get_multi_weights_col_exl2():
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=Exl2WeightsLoader(),
)
prefix = "weight"
quantize = "exl2"
try:
w = weights.get_multi_weights_col(
prefixes=[prefix],
quantize=quantize,
dim=0,
)
except ValueError as e:
assert e.args[0] == "get_multi_weights_col is not supported for exl2"
def test_get_multi_weights_col_gptq():
def test_get_multi_weights_col_gptq(gptq_weights_loader):
weights = MockWeights(
[
"test_get_multi_weights_col_gptq",
@ -948,14 +966,13 @@ def test_get_multi_weights_col_gptq():
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=gptq_weights_loader,
)
prefixes = ["weight"]
quantize = "gptq"
w = weights.get_multi_weights_col(
prefixes=prefixes,
quantize=quantize,
dim=0,
)
@ -978,7 +995,7 @@ def test_get_multi_weights_col_gptq():
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
def test_get_multi_weights_col_marlin():
def test_get_multi_weights_col_marlin(marlin_weights_loader):
weights = MockWeights(
[
"test_get_multi_weights_col_marlin",
@ -987,14 +1004,13 @@ def test_get_multi_weights_col_marlin():
dtype=torch.float16,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=marlin_weights_loader,
)
prefix = "weight"
quantize = "marlin"
w = weights.get_multi_weights_col(
prefixes=[prefix],
quantize=quantize,
dim=0,
)
@ -1007,26 +1023,25 @@ def test_get_multi_weights_col_marlin():
assert torch.allclose(w.s, expected_weight.s), "s mismatch"
# test_get_multi_weights_row
# test_get_weights_row
def test_get_multi_weights_row_awq():
def test_get_weights_row_awq(gptq_weights_loader_awq):
weights = MockWeights(
[
"test_get_multi_weights_row_gptq",
"test_get_weights_row_gptq",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=gptq_weights_loader_awq,
)
prefix = "weight"
quantize = "awq"
w = weights.get_multi_weights_row(
w = weights.get_weights_row(
prefix=prefix,
quantize=quantize,
)
expected_weight = GPTQWeight(
@ -1048,23 +1063,22 @@ def test_get_multi_weights_row_awq():
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
def test_get_multi_weights_row_exl2():
def test_get_weights_row_exl2():
weights = MockWeights(
[
"test_get_multi_weights_row_exl2",
"test_get_weights_row_exl2",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=Exl2WeightsLoader(),
)
prefix = "weight"
quantize = "exl2"
w = weights.get_multi_weights_row(
w = weights.get_weights_row(
prefix=prefix,
quantize=quantize,
)
print(w)
@ -1086,23 +1100,22 @@ def test_get_multi_weights_row_exl2():
assert torch.allclose(w.q_groups, expected_weight.q_groups), "q_groups mismatch"
def test_get_multi_weights_row_gptq():
def test_get_weights_row_gptq(gptq_weights_loader):
weights = MockWeights(
[
"test_get_multi_weights_row_gptq",
"test_get_weights_row_gptq",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=gptq_weights_loader,
)
prefix = "weight"
quantize = "gptq"
w = weights.get_multi_weights_row(
w = weights.get_weights_row(
prefix=prefix,
quantize=quantize,
)
expected_weight = GPTQWeight(
@ -1124,23 +1137,22 @@ def test_get_multi_weights_row_gptq():
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
def test_get_multi_weights_row_marlin():
def test_get_weights_row_marlin(marlin_weights_loader):
weights = MockWeights(
[
"test_get_multi_weights_row_marlin",
"test_get_weights_row_marlin",
],
device="cpu",
dtype=torch.float16,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
weights_loader=marlin_weights_loader,
)
prefix = "weight"
quantize = "marlin"
w = weights.get_multi_weights_row(
w = weights.get_weights_row(
prefix=prefix,
quantize=quantize,
)
expected_weight = MarlinWeight(

1
server/text_generation_server/cli.py
View File

@ -353,6 +353,7 @@ def quantize(
upload_to_model_id=upload_to_model_id,
percdamp=percdamp,
act_order=act_order,
sym=True,
)

60
server/text_generation_server/layers/exl2.py
View File

@ -1,6 +1,9 @@
import torch
from typing import List, Union
from dataclasses import dataclass
from text_generation_server.utils.weights import WeightsLoader, Weights
@dataclass
class Exl2Weight:
@ -21,3 +24,60 @@ class Exl2Weight:
@property
def device(self) -> torch.device:
return self.q_weight.device
class Exl2WeightsLoader(WeightsLoader):
"""Loader for exl2-quantized weights."""
def get_weights_col_packed(
self,
weights: Weights,
prefix: str,
block_sizes: Union[int, List[int]],
):
raise RuntimeError("Column-packed weights are not supported for exl")
def get_weights_col(self, weights: Weights, prefix: str):
try:
q_weight = weights.get_tensor(f"{prefix}.q_weight")
except RuntimeError:
raise RuntimeError(
"Cannot load `exl2`-quantized weight, make sure the model is already quantized."
)
q_scale = weights.get_tensor(f"{prefix}.q_scale")
q_invperm = weights.get_tensor(f"{prefix}.q_invperm")
q_scale_max = weights.get_tensor(f"{prefix}.q_scale_max")
q_groups = weights.get_tensor(f"{prefix}.q_groups")
return Exl2Weight(
q_weight=q_weight,
q_scale=q_scale,
q_invperm=q_invperm,
q_scale_max=q_scale_max,
q_groups=q_groups,
)
def get_multi_weights_col(self, weights: Weights, prefixes: List[str], dim: int):
raise ValueError("get_multi_weights_col is not supported for exl2")
def get_weights_row(self, weights: Weights, prefix: str):
try:
q_weight = weights.get_tensor(f"{prefix}.q_weight")
except RuntimeError:
raise RuntimeError(
"Cannot load `exl2`-quantized weight, make sure the model is already quantized."
)
q_scale = weights.get_tensor(f"{prefix}.q_scale")
q_invperm = weights.get_tensor(f"{prefix}.q_invperm")
q_scale_max = weights.get_tensor(f"{prefix}.q_scale_max")
q_groups = weights.get_tensor(f"{prefix}.q_groups")
return Exl2Weight(
q_weight=q_weight,
q_scale=q_scale,
q_invperm=q_invperm,
q_scale_max=q_scale_max,
q_groups=q_groups,
)

19
server/text_generation_server/layers/fp8.py
View File

@ -1,4 +1,23 @@
from enum import Enum, auto
import torch
from text_generation_server.utils.import_utils import SYSTEM
def get_fp8_linear() -> torch.nn.Module:
"""
Return an FP8 linear `Module` that is compatible with the current system.
"""
if SYSTEM == "cuda":
major, minor = torch.cuda.get_device_capability()
if major == 8 and minor < 9:
from text_generation_server.layers.marlin import GPTQMarlinFP8Linear
return GPTQMarlinFP8Linear
# On other systems let Torch decide if the hardware supports FP8.
return Fp8Linear
def fp8_quantize(weight, qdtype=torch.float8_e4m3fn):

358
server/text_generation_server/layers/gptq/__init__.py
View File

@ -1,20 +1,14 @@
from dataclasses import dataclass
from loguru import logger
import os
from typing import Optional
from typing import List, Optional, Union
from safetensors import SafetensorError
from text_generation_server.utils.weights import Weights, WeightsLoader
import torch
from text_generation_server.utils.import_utils import (
SYSTEM,
)
@dataclass
class GPTQParams:
bits: int
checkpoint_format: Optional[str]
groupsize: int
desc_act: bool
quant_method: str
sym: bool
from text_generation_server.utils.log import log_once
@dataclass
@ -69,3 +63,345 @@ elif CAN_EXLLAMA:
pass
from text_generation_server.layers.gptq.quant_linear import QuantLinear
class GPTQWeightsLoader(WeightsLoader):
"""
Loader for GPTQ- and AWQ-quantized weights.
"""
def __init__(
self,
*,
bits: int,
desc_act: bool,
groupsize: int,
quant_method: str,
quantize: str,
sym: bool,
):
self.bits = bits
self.desc_act = desc_act
self.groupsize = groupsize
self.quant_method = quant_method
self.quantize = quantize
self.sym = sym
def get_weights_col_packed(
self,
weights: Weights,
prefix: str,
block_sizes: Union[int, List[int]],
):
from text_generation_server.layers.marlin import (
can_use_gptq_marlin,
repack_gptq_for_marlin,
)
try:
qweight = weights.get_packed_sharded(
f"{prefix}.qweight", dim=1, block_sizes=block_sizes
)
except RuntimeError:
raise RuntimeError(
f"Cannot load `{self.quantize}` weight, make sure the model is already quantized."
)
scales = weights.get_packed_sharded(
f"{prefix}.scales", dim=1, block_sizes=block_sizes
)
scales = scales.to(dtype=weights.dtype)
self._get_gptq_params(weights)
if can_use_gptq_marlin(
bits=self.bits,
groupsize=self.groupsize,
quant_method=self.quant_method,
quantize=self.quantize,
sym=self.sym,
):
g_idx = weights.get_tensor(f"{prefix}.g_idx")
return repack_gptq_for_marlin(
qweight=qweight,
scales=scales,
g_idx=g_idx,
bits=self.bits,
desc_act=self.desc_act,
groupsize=self.groupsize,
sym=self.sym,
sharded_infeatures=False,
)
qzeros = weights.get_packed_sharded(
f"{prefix}.qzeros", dim=1, block_sizes=block_sizes
)
if self.quantize == "gptq" and self.quant_method == "gptq":
g_idx = weights.get_tensor(f"{prefix}.g_idx")
elif self.quantize == "gptq" and self.quant_method == "awq":
log_once(
logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
)
from text_generation_server.layers.awq.conversion_utils import (
fast_awq_to_gptq,
)
qweight, qzeros = fast_awq_to_gptq(qweight, qzeros)
g_idx = (
torch.arange(
qweight.shape[0] * (32 // self.bits),
device=qweight.device,
)
// self.groupsize
).to(dtype=torch.int32)
else:
g_idx = None
return GPTQWeight(
qweight=qweight,
qzeros=qzeros,
scales=scales,
g_idx=g_idx,
bits=self.bits,
groupsize=self.groupsize,
use_exllama=False,
)
def get_multi_weights_col(self, weights: Weights, prefixes: List[str], dim: int):
from text_generation_server.layers.marlin import (
can_use_gptq_marlin,
repack_gptq_for_marlin,
)
try:
qweight = torch.cat(
[weights.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1
)
except RuntimeError:
raise RuntimeError(
f"Cannot load `{self.quantize}` weight, make sure the model is already quantized"
)
scales = torch.cat(
[weights.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1
)
self._get_gptq_params(weights)
if can_use_gptq_marlin(
bits=self.bits,
groupsize=self.groupsize,
quant_method=self.quant_method,
quantize=self.quantize,
sym=self.sym,
):
w = [weights.get_tensor(f"{p}.g_idx") for p in prefixes]
for w2 in w[1:]:
torch.testing.assert_close(w2, w[0])
g_idx = w[0]
return repack_gptq_for_marlin(
qweight=qweight,
scales=scales,
g_idx=g_idx,
bits=self.bits,
desc_act=self.desc_act,
groupsize=self.groupsize,
sym=self.sym,
sharded_infeatures=False,
)
qzeros = torch.cat(
[weights.get_sharded(f"{p}.qzeros", dim=1) for p in prefixes], dim=1
)
from text_generation_server.layers.gptq import HAS_EXLLAMA
use_exllama = (
self.bits == 4
and HAS_EXLLAMA
and self.quantize == "gptq"
and not self.desc_act
)
if self.quantize == "gptq" and self.quant_method == "gptq":
w = [weights.get_tensor(f"{p}.g_idx") for p in prefixes]
for w2 in w[1:]:
torch.testing.assert_close(w2, w[0])
g_idx = w[0]
elif self.quantize == "gptq" and self.quant_method == "awq":
log_once(
logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
)
from text_generation_server.layers.awq.conversion_utils import (
fast_awq_to_gptq,
)
qweight, qzeros = fast_awq_to_gptq(qweight, qzeros)
if use_exllama:
g_idx = None
else:
g_idx = (
torch.arange(
qweight.shape[0] * (32 // self.bits),
device=qweight.device,
)
// self.groupsize
).to(dtype=torch.int32)
else:
g_idx = None
return GPTQWeight(
qweight=qweight,
qzeros=qzeros,
scales=scales,
g_idx=g_idx,
bits=self.bits,
groupsize=self.groupsize,
use_exllama=use_exllama,
)
def get_weights_row(self, weights: Weights, prefix: str):
from text_generation_server.layers.marlin import (
can_use_gptq_marlin,
repack_gptq_for_marlin,
)
self._get_gptq_params(weights)
if can_use_gptq_marlin(
bits=self.bits,
groupsize=self.groupsize,
quant_method=self.quant_method,
quantize=self.quantize,
sym=self.sym,
):
log_once(logger.info, "Using GPTQ-Marlin kernels")
try:
qweight = weights.get_sharded(f"{prefix}.qweight", dim=0)
except RuntimeError:
raise RuntimeError(
f"Cannot load `{self.quantize}` weight for GPTQ -> Marlin repacking, make sure the model is already quantized"
)
g_idx = weights.get_sharded(f"{prefix}.g_idx", dim=0)
if self.desc_act or self.groupsize == -1:
scales = weights.get_tensor(f"{prefix}.scales")
else:
scales = weights.get_sharded(f"{prefix}.scales", dim=0)
sharded_in_features = weights.process_group.size() > 1
return repack_gptq_for_marlin(
qweight=qweight,
scales=scales,
g_idx=g_idx,
bits=self.bits,
desc_act=self.desc_act,
groupsize=self.groupsize,
sym=self.sym,
sharded_infeatures=sharded_in_features,
)
use_exllama = True
if self.bits != 4:
use_exllama = False
if self.desc_act:
log_once(logger.warning, "Disabling exllama because desc_act=True")
use_exllama = False
try:
qweight = weights.get_sharded(f"{prefix}.qweight", dim=0)
except RuntimeError:
raise RuntimeError(
"Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
)
if self.quantize == "gptq" and self.quant_method == "gptq":
g_idx = weights.get_sharded(f"{prefix}.g_idx", dim=0)
else:
g_idx = None
if weights.process_group.size() > 1:
if g_idx is not None:
if (
not torch.equal(
g_idx.cpu(),
torch.tensor(
[i // self.groupsize for i in range(g_idx.shape[0])],
dtype=torch.int32,
),
)
and not (g_idx == 0).all()
):
# Exllama implementation does not support row tensor parallelism with act-order, as
# it would require to reorder input activations that are split unto several GPUs
use_exllama = False
from text_generation_server.layers.gptq import (
HAS_EXLLAMA,
CAN_EXLLAMA,
GPTQWeight,
)
if use_exllama:
if not HAS_EXLLAMA:
if CAN_EXLLAMA:
log_once(
logger.warning,
"Exllama GPTQ cuda kernels (which are faster) could have been used, but are not currently installed, try using BUILD_EXTENSIONS=True",
)
use_exllama = False
else:
log_once(logger.info, f"Using exllama kernels v{HAS_EXLLAMA}")
if use_exllama and self.groupsize != -1:
qzeros = weights.get_sharded(f"{prefix}.qzeros", dim=0)
scales = weights.get_sharded(f"{prefix}.scales", dim=0)
else:
qzeros = weights.get_tensor(f"{prefix}.qzeros")
scales = weights.get_tensor(f"{prefix}.scales")
if use_exllama and g_idx is not None:
g_idx = g_idx - g_idx[0]
if self.quantize == "gptq" and self.quant_method == "awq":
log_once(
logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
)
from text_generation_server.layers.awq.conversion_utils import (
fast_awq_to_gptq,
)
qweight, qzeros = fast_awq_to_gptq(qweight, qzeros)
if use_exllama:
g_idx = None
else:
g_idx = (
torch.arange(
qweight.shape[0] * (32 // self.bits),
device=qweight.device,
)
// self.groupsize
).to(dtype=torch.int32)
return GPTQWeight(
qweight=qweight,
qzeros=qzeros,
scales=scales,
g_idx=g_idx,
bits=self.bits,
groupsize=self.groupsize,
use_exllama=use_exllama,
)
def _get_gptq_params(self, weights: Weights):
if weights._has_tensor("gptq_bits") and weights._has_tensor("gptq_groupsize"):
self.bits = weights.get_tensor("gptq_bits").item()
self.groupsize = weights.get_tensor("gptq_groupsize").item()
self.desc_act = False
# `server quantize` used asymmetric quantization unconditionally
# before the `gptq_sym` setting tensor was added.
self.sym = (
weights.get_tensor("gptq_sym").item()
if weights._has_tensor("gptq_sym")
else False
)
self.quant_method = "gptq"

6
server/text_generation_server/layers/gptq/quantize.py
View File

@ -16,6 +16,8 @@ from text_generation_server.layers.gptq.quant_linear import QuantLinear
from loguru import logger
from typing import Optional
from text_generation_server.utils.weights import DefaultWeightsLoader
DEV = torch.device("cuda:0")
@ -869,6 +871,7 @@ def quantize(
upload_to_model_id: Optional[str],
percdamp: float,
act_order: bool,
sym: bool,
):
print("loading model")
config = AutoConfig.from_pretrained(
@ -891,6 +894,7 @@ def quantize(
dtype=torch.float16,
process_group=process_group,
aliases={"embed_tokens.weight": ["lm_head.weight"]},
weights_loader=DefaultWeightsLoader(),
)
hooks = []
for name, module in model.named_modules():
@ -943,6 +947,7 @@ def quantize(
percdamp=percdamp,
act_order=act_order,
hooks=hooks,
sym=sym,
)
print(time.time() - tick)
@ -954,6 +959,7 @@ def quantize(
state_dict = {k: v.cpu().contiguous() for k, v in state_dict.items()}
state_dict["gptq_bits"] = torch.LongTensor([bits])
state_dict["gptq_groupsize"] = torch.LongTensor([groupsize])
state_dict["gptq_sym"] = torch.BoolTensor([sym])
max_shard_size = "10GB"
shards, index = shard_checkpoint(

4
server/text_generation_server/layers/linear.py
View File

@ -106,9 +106,9 @@ def get_linear(weight, bias, quantize):
"Please install EETQ from https://github.com/NetEase-FuXi/EETQ"
)
elif quantize == "fp8":
from text_generation_server.layers.fp8 import Fp8Linear
from text_generation_server.layers.fp8 import get_fp8_linear
linear = Fp8Linear(weight, bias)
linear = get_fp8_linear()(weight, bias)
elif quantize == "bitsandbytes":
try:
from text_generation_server.layers.bnb import (

243
server/text_generation_server/layers/marlin.py
View File

@ -1,11 +1,13 @@
from dataclasses import dataclass
from typing import List, Optional, Tuple
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from text_generation_server.layers.gptq import GPTQParams
from loguru import logger
from text_generation_server.layers.fp8 import fp8_quantize
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.log import log_once
from text_generation_server.utils.weights import Weights, WeightsLoader
try:
import marlin_kernels
@ -24,16 +26,132 @@ GPTQ_MARLIN_GROUP_SIZES = [-1, 32, 64, 128]
MARLIN_TILE_SIZE = 16
def can_use_gptq_marlin(gptq_params: GPTQParams, quantize: str) -> bool:
class MarlinWeightsLoader(WeightsLoader):
"""Loader for Marlin-quantized weights."""
def __init__(self, *, bits: int, is_marlin_24: bool):
self.bits = bits
self.is_marlin_24 = is_marlin_24
def get_weights_col_packed(
self,
weights: Weights,
prefix: str,
block_sizes: Union[int, List[int]],
):
if self.is_marlin_24:
B = weights.get_packed_sharded(
f"{prefix}.B_24", dim=1, block_sizes=block_sizes
)
B_meta = weights.get_packed_sharded(
f"{prefix}.B_meta", dim=1, block_sizes=block_sizes
)
s = weights.get_packed_sharded(
f"{prefix}.s", dim=1, block_sizes=block_sizes
)
weight = GPTQMarlin24Weight(B=B, B_meta=B_meta, s=s, bits=self.bits)
else:
B = weights.get_packed_sharded(
f"{prefix}.B", dim=1, block_sizes=block_sizes
)
s = weights.get_packed_sharded(
f"{prefix}.s", dim=1, block_sizes=block_sizes
)
weight = MarlinWeight(B=B, s=s)
return weight
def get_multi_weights_col(self, weights: Weights, prefixes: List[str], dim: int):
is_marlin_24 = getattr(self, "gptq_checkpoint_format", None) == "marlin_24"
if is_marlin_24:
try:
B = torch.cat(
[weights.get_sharded(f"{p}.B_24", dim=1) for p in prefixes], dim=1
)
except RuntimeError:
raise RuntimeError(
f"Cannot load `marlin` weight, make sure the model is already quantized"
)
B_meta = torch.cat(
[weights.get_sharded(f"{p}.B_meta", dim=1) for p in prefixes], dim=1
)
s = torch.cat(
[weights.get_sharded(f"{p}.s", dim=1) for p in prefixes], dim=1
)
weight = GPTQMarlin24Weight(B=B, B_meta=B_meta, s=s, bits=self.bits)
else:
try:
B = torch.cat(
[weights.get_sharded(f"{p}.B", dim=1) for p in prefixes], dim=1
)
except RuntimeError:
raise RuntimeError(
f"Cannot load `marlin` weight, make sure the model is already quantized"
)
s = torch.cat(
[weights.get_sharded(f"{p}.s", dim=1) for p in prefixes], dim=1
)
weight = MarlinWeight(B=B, s=s)
return weight
def get_weights_row(self, weights: Weights, prefix: str):
is_marlin_24 = getattr(self, "gptq_checkpoint_format", None) == "marlin_24"
if is_marlin_24:
try:
B = weights.get_sharded(f"{prefix}.B_24", dim=0)
except RuntimeError:
raise RuntimeError(
"Cannot load `marlin` 2:4 sparsity weight, make sure the model is already quantized."
)
B_meta = weights.get_sharded(f"{prefix}.B_meta", dim=0)
num_groups = weights._get_slice(f"{prefix}.s").get_shape()[0]
if num_groups == 1:
# The number of groups is 1 when groupsize == -1. share
# scales between all shards in this case.
s = weights.get_tensor(f"{prefix}.s")
else:
s = weights.get_sharded(f"{prefix}.s", dim=0)
weight = GPTQMarlin24Weight(B=B, B_meta=B_meta, s=s, bits=self.bits)
else:
try:
B = weights.get_sharded(f"{prefix}.B", dim=0)
except RuntimeError:
raise RuntimeError(
"Cannot load `marlin` weight, make sure the model is already quantized."
)
num_groups = weights._get_slice(f"{prefix}.s").get_shape()[0]
if num_groups == 1:
# The number of groups is 1 when groupsize == -1. share
# scales between all shards in this case.
s = weights.get_tensor(f"{prefix}.s")
else:
s = weights.get_sharded(f"{prefix}.s", dim=0)
weight = MarlinWeight(B=B, s=s)
return weight
def can_use_gptq_marlin(
*, bits: int, groupsize: int, quant_method: str, quantize: str, sym: bool
) -> bool:
return (
SYSTEM == "cuda"
and marlin_kernels is not None
and has_sm_8_0
and quantize == "gptq"
and gptq_params.quant_method == "gptq"
and gptq_params.bits in GPTQ_MARLIN_BITS
and gptq_params.groupsize in GPTQ_MARLIN_GROUP_SIZES
and gptq_params.sym
and quant_method == "gptq"
and bits in GPTQ_MARLIN_BITS
and groupsize in GPTQ_MARLIN_GROUP_SIZES
and sym
)
@ -339,6 +457,115 @@ class GPTQMarlin24Linear(nn.Module):
return C
class GPTQMarlinFP8Linear(nn.Module):
"""
FP8 GPTQ-Marlin linear layer.
"""
def __init__(
self,
weight: torch.Tensor,
bias: Optional[torch.Tensor],
) -> None:
super().__init__()
_check_marlin_kernels()
assert marlin_kernels is not None
log_once(logger.info, "GPU does not support FP8, using Marlin FP8 kernel")
qweight, scale = fp8_quantize(weight)
scale = scale.to(torch.float16)
qweight, scales = repack_fp8_for_marlin(qweight, scale)
in_features = qweight.shape[0] * MARLIN_TILE_SIZE
out_features = scales.shape[1]
_check_valid_shape(in_features=in_features, out_features=out_features)
self.qweight = qweight
self.scales = scales
self.bias = bias if bias is not None else None
self.workspace = torch.zeros(
out_features // 64 * 16, dtype=torch.int, device=qweight.device
)
def forward(self, A: torch.Tensor) -> torch.Tensor:
assert marlin_kernels is not None
A_flat = A.view(-1, A.shape[-1])
C = marlin_kernels.fp8_marlin_gemm(
A_flat,
self.qweight,
self.scales,
self.workspace,
8,
A_flat.shape[0],
self.scales.shape[1],
A_flat.shape[1],
)
C = C.reshape(A.shape[:-1] + (self.scales.shape[1],))
if self.bias is not None:
C += self.bias
return C
def pack_fp8_as_int32(fp8_tensor: torch.Tensor) -> torch.Tensor:
"""
Repack FP8 weights to gptq format (packed int32 elements).
"""
assert fp8_tensor.dtype == torch.float8_e4m3fn
if fp8_tensor.shape[0] % 4 != 0:
raise ValueError(
f"Leading tensor dimension is not divisable by 4: {fp8_tensor.shape[0]}"
)
# Reshape to prepare for packing
reshaped = fp8_tensor.reshape(-1, 4, *fp8_tensor.shape[1:])
# Convert fp8 to uint8 (byte) representation
byte_tensor = reshaped.view(torch.uint8)
# Pack 4 uint8 values into one int32
packed = torch.zeros(
fp8_tensor.shape[0] // 4,
fp8_tensor.shape[1],
dtype=torch.int32,
device=fp8_tensor.device,
)
for i in range(4):
packed.bitwise_or_(byte_tensor[:, i].to(torch.int32) << i * 8)
return packed
def repack_fp8_for_marlin(weight: torch.Tensor, scale: torch.Tensor):
"""
Repack FP8 tensor for GPTQ-Marlin.
"""
out_features, in_features = weight.shape
# Torch linear layers weights with shape [out_features, in_features],
# GPTQ-quantized weights use [in_feateres/pack_factor, in_features],
# so transpose before packing.
qweight = pack_fp8_as_int32(weight.t())
perm = torch.empty(0, dtype=torch.int, device=qweight.device)
repacked = marlin_kernels.gptq_marlin_repack(
qweight, perm, in_features, out_features, 8
)
scales = scale.reshape(1, 1).repeat(1, out_features)
scales = permute_scales(scales)
return repacked, scales
@dataclass
class MarlinWeight:
"""

4
server/text_generation_server/layers/rotary.py
View File

@ -102,7 +102,7 @@ class PositionRotaryEmbedding(nn.Module):
max_position_embeddings=rope_scaling[
"original_max_position_embeddings"
],
base=10000.0,
base=base,
device=inv_freq.device,
scaling_factor=scaling_factor,
extrapolation_factor=1,
@ -110,7 +110,7 @@ class PositionRotaryEmbedding(nn.Module):
beta_fast=32,
beta_slow=1,
)
elif rope_scaling["type"] == "su":
elif rope_scaling["type"] in ["su", "longrope"]:
short_factor = torch.tensor(
rope_scaling["short_factor"], dtype=torch.float32, device=device
)

17
server/text_generation_server/layers/tensor_parallel.py
View File

@ -52,7 +52,7 @@ class TensorParallelHead(SuperLayer):
weight = weights.get_tensor(f"{prefix}.weight")
except:
# ...otherwise they are quantized.
weight = weights.get_weights_col(prefix, config.quantize)
weight = weights.get_weights_col(prefix)
should_gather = weights.process_group.size() > 1
elif weights.process_group.size() > 1:
try:
@ -129,9 +129,7 @@ class TensorParallelColumnLinear(SuperLayer):
@classmethod
def load_gate_up(cls, config, prefix: str, weights, bias: bool):
"""Specific method when the QKV was joined after the fact"""
weight = weights.get_weights_col_packed_gate_up(
prefix, quantize=config.quantize
)
weight = weights.get_weights_col_packed_gate_up(prefix)
if bias:
raise NotImplementedError("packed_gate_up only implemented without bias")
else:
@ -152,7 +150,6 @@ class TensorParallelColumnLinear(SuperLayer):
"""Specific method when the QKV was joined after the fact"""
weight = weights.get_weights_col_packed_qkv(
prefix,
quantize=config.quantize,
num_heads=num_heads,
num_key_value_heads=num_key_value_heads,
)
@ -165,7 +162,7 @@ class TensorParallelColumnLinear(SuperLayer):
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_weights_col(prefix, config.quantize)
weight = weights.get_weights_col(prefix)
if bias:
bias = weights.get_sharded(f"{prefix}.bias", dim=0)
else:
@ -178,14 +175,12 @@ class TensorParallelColumnLinear(SuperLayer):
if config.quantize == "exl2":
linears = []
for prefix in prefixes:
weight = weights.get_weights_col(prefix, config.quantize)
weight = weights.get_weights_col(prefix)
b = weights.get_tensor(f"{prefix}.bias") if bias else None
linears.append(get_linear(weight, b, config.quantize))
linear = LayerConcat(linears)
else:
weight = weights.get_multi_weights_col(
prefixes, quantize=config.quantize, dim=dim
)
weight = weights.get_multi_weights_col(prefixes, dim=dim)
if bias:
b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes]
bias = torch.cat(b, dim=dim)
@ -202,7 +197,7 @@ class TensorParallelRowLinear(SuperLayer):
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
weight = weights.get_weights_row(prefix)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process

398
server/text_generation_server/models/__init__.py
View File

@ -11,17 +11,27 @@ from pathlib import Path
from text_generation_server.utils.speculate import get_speculate, set_speculate
from text_generation_server.models.model import Model
from text_generation_server.models.causal_lm import CausalLM
from text_generation_server.models.bloom import BLOOMSharded
from text_generation_server.models.mpt import MPTSharded
from text_generation_server.models.causal_lm import CausalLM, CausalLMBatchKeysLast
from text_generation_server.models.custom_modeling.opt_modeling import OPTForCausalLM
from text_generation_server.models.custom_modeling.mpt_modeling import (
MPTForCausalLM,
)
from text_generation_server.models.bloom import BloomCausalLMBatch
from text_generation_server.models.custom_modeling.bloom_modeling import (
BloomForCausalLM,
)
from text_generation_server.models.seq2seq_lm import Seq2SeqLM
from text_generation_server.models.rw import RW
from text_generation_server.models.opt import OPTSharded
from text_generation_server.models.galactica import GalacticaSharded
from text_generation_server.models.santacoder import SantaCoder
from text_generation_server.models.t5 import T5Sharded
from text_generation_server.models.gpt_neox import GPTNeoxSharded
from text_generation_server.models.phi import Phi
from text_generation_server.models.galactica import GalacticaCausalLMBatch
from text_generation_server.models.custom_modeling.neox_modeling import (
GPTNeoxForCausalLM,
)
from text_generation_server.models.custom_modeling.phi_modeling import (
PhiConfig,
PhiForCausalLM,
)
from text_generation_server.models.custom_modeling.t5_modeling import (
T5ForConditionalGeneration,
)
from text_generation_server.utils.import_utils import SYSTEM
@ -41,9 +51,6 @@ __all__ = [
"CausalLM",
"GalacticaSharded",
"Seq2SeqLM",
"SantaCoder",
"OPTSharded",
"T5Sharded",
"get_model",
]
@ -53,38 +60,65 @@ FLASH_ATTENTION = True
try:
from text_generation_server.models.flash_causal_lm import FlashCausalLM
from text_generation_server.models.flash_rw import FlashRWSharded
from text_generation_server.models.flash_gpt2 import FlashGPT2
from text_generation_server.models.flash_neox import FlashNeoXSharded
from text_generation_server.models.flash_llama import (
FlashLlama,
from text_generation_server.models.vlm_causal_lm import VlmCausalLM
from text_generation_server.models.custom_modeling.flash_llama_modeling import (
FlashLlamaForCausalLM,
)
from text_generation_server.models.flash_qwen2 import (
FlashQwen2,
from text_generation_server.models.custom_modeling.flash_cohere_modeling import (
FlashCohereForCausalLM,
)
from text_generation_server.models.flash_cohere import (
FlashCohere,
from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
FlashGemmaForCausalLM,
)
from text_generation_server.models.flash_gemma import (
FlashGemma,
from text_generation_server.models.custom_modeling.flash_gemma2_modeling import (
FlashGemma2ForCausalLM,
)
from text_generation_server.models.flash_gemma2 import (
FlashGemma2,
from text_generation_server.models.custom_modeling.flash_dbrx_modeling import (
FlashDbrxForCausalLM,
DbrxConfig,
)
from text_generation_server.models.custom_modeling.flash_rw_modeling import (
RWConfig,
FlashRWForCausalLM,
)
from text_generation_server.models.custom_modeling.flash_neox_modeling import (
FlashGPTNeoXForCausalLM,
)
from text_generation_server.models.pali_gemma import (
PaliGemma,
PaliGemmaBatch,
)
from text_generation_server.models.flash_santacoder import (
FlashSantacoderSharded,
from text_generation_server.models.custom_modeling.flash_pali_gemma_modeling import (
PaliGemmaForConditionalGeneration,
)
from text_generation_server.models.custom_modeling.flash_phi_modeling import (
FlashPhiForCausalLM,
)
from text_generation_server.models.idefics import IDEFICSSharded
from text_generation_server.models.llava_next import LlavaNext
from text_generation_server.models.idefics2 import Idefics2
from text_generation_server.models.flash_mistral import FlashMistral
from text_generation_server.models.flash_mixtral import FlashMixtral
from text_generation_server.models.flash_phi import FlashPhi
from text_generation_server.models.flash_starcoder2 import FlashStarcoder2
from text_generation_server.models.flash_dbrx import FlashDbrx
from text_generation_server.models.custom_modeling.llava_next import (
LlavaNextForConditionalGeneration,
)
from text_generation_server.models.custom_modeling.flash_santacoder_modeling import (
FlashSantacoderForCausalLM,
)
from text_generation_server.models.custom_modeling.flash_starcoder2_modeling import (
FlashStarcoder2ForCausalLM,
)
from text_generation_server.models.custom_modeling.flash_qwen2_modeling import (
Qwen2ForCausalLM,
)
from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
FlashMistralForCausalLM,
)
from text_generation_server.models.custom_modeling.flash_mixtral_modeling import (
FlashMixtralForCausalLM,
)
from text_generation_server.models.custom_modeling.flash_gpt2_modeling import (
FlashGPT2ForCausalLM,
)
from text_generation_server.models.custom_modeling.idefics2 import (
Idefics2ForConditionalGeneration,
)
from text_generation_server.layers.attention import SUPPORTS_WINDOWING
except ImportError as e:
logger.warning(f"Could not import Flash Attention enabled models: {e}")
@ -93,21 +127,7 @@ except ImportError as e:
if FLASH_ATTENTION:
__all__.append(FlashCausalLM)
__all__.append(FlashGPT2)
__all__.append(FlashNeoXSharded)
__all__.append(FlashRWSharded)
__all__.append(FlashSantacoderSharded)
__all__.append(FlashLlama)
__all__.append(IDEFICSSharded)
__all__.append(FlashMistral)
__all__.append(FlashMixtral)
__all__.append(FlashDbrx)
__all__.append(FlashPhi)
__all__.append(FlashQwen2)
__all__.append(FlashStarcoder2)
__all__.append(FlashGemma)
__all__.append(FlashGemma2)
__all__.append(FlashCohere)
MAMBA_AVAILABLE = True
try:
@ -148,6 +168,11 @@ class ModelType(enum.Enum):
"name": "Gemma",
"url": "https://huggingface.co/google/gemma-7b",
}
PALIGEMMA = {
"type": "paligemma",
"name": "PaliGemma",
"url": "https://huggingface.co/google/paligemma-3b-pt-224",
}
GEMMA2 = {
"type": "gemma2",
"name": "Gemma2",
@ -445,13 +470,16 @@ def get_model(
)
if model_id.startswith("facebook/galactica"):
return GalacticaSharded(
model_id,
revision,
return CausalLM(
model_id=model_id,
# Yes galactica is just an OPT model.
model_class=OPTForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
batch_class=GalacticaCausalLMBatch,
)
if (
@ -460,22 +488,26 @@ def get_model(
and model_id.startswith("bigcode/")
):
if FLASH_ATTENTION:
return FlashSantacoderSharded(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashSantacoderForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
aliases={"transformer.wte.weight": ["lm_head.weight"]},
num_kv_heads=1,
)
elif sharded:
raise NotImplementedError(
FLASH_ATT_ERROR_MESSAGE.format("Sharded Santacoder")
)
else:
return SantaCoder(
model_id,
revision,
return CausalLM.fallback(
model_id=model_id,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
@ -483,38 +515,44 @@ def get_model(
)
if model_type == BLOOM:
return BLOOMSharded(
model_id,
revision,
return CausalLM(
model_id=model_id,
model_class=BloomForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
batch_class=BloomCausalLMBatch,
)
elif model_type == MPT:
return MPTSharded(
model_id,
revision,
return CausalLM(
model_id=model_id,
model_class=MPTForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
batch_class=CausalLMBatchKeysLast,
)
elif model_type == GPT2:
if FLASH_ATTENTION:
try:
return FlashGPT2(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashGPT2ForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
except RuntimeError as e:
# Lots of legacy models with various weight names.
logger.warning(f"Couldn't load flash gpt2 variant: {e}")
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -525,7 +563,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded GPT-2"))
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -535,25 +573,28 @@ def get_model(
)
elif model_type == GPT_NEOX:
if FLASH_ATTENTION:
return FlashNeoXSharded(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashGPTNeoXForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
elif sharded:
return GPTNeoxSharded(
model_id,
revision,
return CausalLM(
model_id=model_id,
model_class=GPTNeoxForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -564,16 +605,18 @@ def get_model(
elif model_type == PHI:
if FLASH_ATTENTION:
return FlashPhi(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashPhiForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -588,9 +631,11 @@ def get_model(
"Legacy phi-msft is not supported with Flash Attention"
)
else:
return Phi(
model_id,
revision,
return CausalLM(
model_id=model_id,
model_class=PhiForCausalLM,
config_class=PhiConfig,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
@ -599,9 +644,10 @@ def get_model(
elif model_type == LLAMA or model_type == BAICHUAN or model_type == PHI3:
if FLASH_ATTENTION:
return FlashLlama(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashLlamaForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
@ -611,7 +657,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -621,18 +667,22 @@ def get_model(
)
if model_type == GEMMA:
if FLASH_ATTENTION:
return FlashGemma(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashGemmaForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
# Works better for these models
default_dtype=torch.bfloat16,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Gemma"))
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -642,18 +692,22 @@ def get_model(
)
elif model_type == GEMMA2:
if FLASH_ATTENTION:
return FlashGemma2(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashGemma2ForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
# Works better for these models
default_dtype=torch.bfloat16,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Gemma2"))
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -664,18 +718,20 @@ def get_model(
if model_type == COHERE:
if FLASH_ATTENTION:
return FlashCohere(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashCohereForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Cohere"))
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -686,18 +742,23 @@ def get_model(
if model_type == DBRX:
if FLASH_ATTENTION:
return FlashDbrx(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashDbrxForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
# Dbrx works better in bfloat16.
default_dtype=torch.bfloat16,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
config_class=DbrxConfig,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded DBRX"))
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -711,27 +772,41 @@ def get_model(
if FLASH_ATTENTION:
if config_dict.get("alibi", False):
raise NotImplementedError("sharded is not supported for this model")
return FlashRWSharded(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashRWForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
aliases={
"lm_head.weight": ["transformer.word_embeddings.weight"],
"transformer.word_embeddings.weight": ["lm_head.weight"],
},
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
config_class=RWConfig,
)
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format(f"Sharded Falcon"))
else:
if FLASH_ATTENTION and not config_dict.get("alibi", False):
return FlashRWSharded(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashRWForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
aliases={
"lm_head.weight": ["transformer.word_embeddings.weight"],
"transformer.word_embeddings.weight": ["lm_head.weight"],
},
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
config_class=RWConfig,
)
else:
return RW(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -742,18 +817,20 @@ def get_model(
if model_type == MISTRAL:
if FLASH_ATTENTION:
return FlashMistral(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashMistralForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mistral"))
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -764,18 +841,20 @@ def get_model(
if model_type == MIXTRAL:
if FLASH_ATTENTION:
return FlashMixtral(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashMixtralForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mixtral"))
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -786,19 +865,22 @@ def get_model(
if model_type == STARCODER2:
if FLASH_ATTENTION:
return FlashStarcoder2(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=FlashStarcoder2ForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
elif sharded:
raise NotImplementedError(
FLASH_ATT_ERROR_MESSAGE.format("Sharded Starcoder2")
)
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -809,17 +891,20 @@ def get_model(
if model_type == QWEN2:
if FLASH_ATTENTION:
return FlashQwen2(
model_id,
revision,
return FlashCausalLM(
model_id=model_id,
model_class=Qwen2ForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Qwen2"))
else:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -829,9 +914,10 @@ def get_model(
)
if model_type == OPT:
return OPTSharded(
model_id,
revision,
return CausalLM(
model_id=model_id,
model_class=OPTForCausalLM,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
@ -839,13 +925,20 @@ def get_model(
)
if model_type == T5:
return T5Sharded(
model_id,
revision,
return Seq2SeqLM(
model_id=model_id,
model_class=T5ForConditionalGeneration,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
aliases={
"shared.weight": [
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
]
},
)
if model_type == IDEFICS:
if FLASH_ATTENTION:
@ -861,34 +954,45 @@ def get_model(
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == IDEFICS2:
if FLASH_ATTENTION:
return Idefics2(
model_id,
revision,
return VlmCausalLM(
model_id=model_id,
model_class=Idefics2ForConditionalGeneration,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
# XXX: Extremely important to cap resolution in order to limit
# VRAM usage.
processor_kwargs={"size": {"longest_edge": 448, "shortest_edge": 378}},
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == "paligemma":
if model_type == PALIGEMMA:
if FLASH_ATTENTION:
return PaliGemma(
model_id,
revision,
return VlmCausalLM(
model_id=model_id,
model_class=PaliGemmaForConditionalGeneration,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
# Works better for these models
default_dtype=torch.bfloat16,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
batch_class=PaliGemmaBatch,
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == LLAVA_NEXT:
if FLASH_ATTENTION:
return LlavaNext(
model_id,
revision,
return VlmCausalLM(
model_class=LlavaNextForConditionalGeneration,
model_id=model_id,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
@ -912,7 +1016,7 @@ def get_model(
elif quantize == "exl2":
raise NotImplementedError("exl2 quantization is not supported for AutoModel")
if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -921,7 +1025,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type in modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:
return Seq2SeqLM(
return Seq2SeqLM.fallback(
model_id,
revision,
quantize=quantize,
@ -933,7 +1037,7 @@ def get_model(
auto_map = config_dict.get("auto_map", None)
if trust_remote_code and auto_map is not None:
if "AutoModelForCausalLM" in auto_map.keys():
return CausalLM(
return CausalLM.fallback(
model_id,
revision,
quantize=quantize,
@ -942,7 +1046,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if "AutoModelForSeq2SeqLM" in auto_map.keys():
return Seq2SeqLM(
return Seq2SeqLM.fallback(
model_id,
revision,
quantize=quantize,

73
server/text_generation_server/models/bloom.py
View File

@ -4,22 +4,12 @@ import torch.distributed
from typing import Optional, Type
from transformers import (
AutoTokenizer,
AutoConfig,
PreTrainedTokenizerBase,
)
from text_generation_server.models.custom_modeling.bloom_modeling import (
BloomForCausalLM,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
class BloomCausalLMBatch(CausalLMBatch):
@ -37,69 +27,6 @@ class BloomCausalLMBatch(CausalLMBatch):
class BLOOMSharded(CausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
slow_but_exact=False,
tp_parallel=True,
trust_remote_code=trust_remote_code,
)
config.pad_token_id = 3
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device=device,
dtype=dtype,
process_group=self.process_group,
prefix="transformer",
)
if config.quantize in ["gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = BloomForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return BloomCausalLMBatch

113
server/text_generation_server/models/causal_lm.py
View File

@ -1,13 +1,26 @@
import torch
import time
import torch.distributed
from dataclasses import dataclass
from opentelemetry import trace
from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
from transformers import (
AutoConfig,
AutoTokenizer,
AutoModelForCausalLM,
PreTrainedTokenizerBase,
)
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.models import Model
from text_generation_server.utils.chunks import concat_text_chunks
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.quantization import get_loader
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_server.models.types import (
Batch,
@ -478,10 +491,93 @@ class CausalLMBatch(Batch):
return len(self.requests)
@dataclass
class CausalLMBatchKeysLast(Batch):
keys_head_dim_last: bool = False
class CausalLM(Model):
def __init__(
self,
model_id: str,
model_class,
revision: Optional[str] = None,
quantize: Optional[str] = None,
speculator: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
default_dtype=torch.float16,
trust_remote_code: bool = False,
tokenizer_class=AutoTokenizer,
config_class=AutoConfig,
batch_class=CausalLMBatch,
):
self.batch_class = batch_class
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = default_dtype if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = default_dtype if dtype is None else dtype
else:
device = torch.device("cpu")
# Float16 doesn't exist on target.
dtype = torch.bfloat16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = tokenizer_class.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = config_class.from_pretrained(
model_id,
revision=revision,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
config.speculator = speculator
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = config.pad_token_id
torch.distributed.barrier(group=self.process_group)
weights_loader = get_loader(
quantize=quantize, model_id=model_id, revision=revision
)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device=device,
dtype=dtype,
process_group=self.process_group,
weights_loader=weights_loader,
)
prefix = ""
model = model_class(prefix, config, weights)
torch.distributed.barrier(group=self.process_group)
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
@classmethod
def fallback(
cls,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
speculator: Optional[str] = None,
@ -537,7 +633,12 @@ class CausalLM(Model):
else:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
super(CausalLM, self).__init__(
self = cls.__new__(
cls,
)
self.batch_class = CausalLMBatch
super().__init__(
self,
model_id=model_id,
model=model,
tokenizer=tokenizer,
@ -545,15 +646,11 @@ class CausalLM(Model):
dtype=dtype,
device=device,
)
return self
@property
def batch_type(self) -> Type[CausalLMBatch]:
return CausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
return self.tokenizer.decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return self.batch_class
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None

2
server/text_generation_server/models/custom_modeling/bloom_modeling.py
View File

@ -816,7 +816,7 @@ class BloomModel(BloomPreTrainedModel):
class BloomForCausalLM(BloomPreTrainedModel):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__(config)
self.transformer = BloomModel(config, weights)

6
server/text_generation_server/models/custom_modeling/clip.py
View File

@ -446,7 +446,7 @@ class CLIPEncoder(nn.Module):
class CLIPTextTransformer(nn.Module):
def __init__(self, config: CLIPTextConfig):
def __init__(self, prefix: str, config: CLIPTextConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
@ -536,9 +536,9 @@ class CLIPTextModel(CLIPPreTrainedModel):
_no_split_modules = ["CLIPTextEmbeddings", "CLIPEncoderLayer"]
def __init__(self, config: CLIPTextConfig):
def __init__(self, prefix, config: CLIPTextConfig):
super().__init__(config)
self.text_model = CLIPTextTransformer(config)
self.text_model = CLIPTextTransformer(prefix, config)
# Initialize weights and apply final processing
self.post_init()

23
server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
View File

@ -163,7 +163,6 @@ def _load_gqa(config, prefix: str, weights):
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
@ -363,9 +362,9 @@ class CohereMLP(nn.Module):
class FlashCohereLayer(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, prefix: str, layer_id, config, weights):
super().__init__()
prefix = f"model.layers.{layer_id}"
prefix = f"{prefix}.layers.{layer_id}"
self.self_attn = FlashCohereAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
@ -416,18 +415,19 @@ class FlashCohereLayer(nn.Module):
class FlashCohereModel(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.embed_tokens = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
prefix=f"{prefix}.embed_tokens", weights=weights
)
self.layers = nn.ModuleList(
[
FlashCohereLayer(
prefix,
layer_id,
config,
weights,
@ -436,7 +436,7 @@ class FlashCohereModel(torch.nn.Module):
]
)
self.norm = FastLayerNorm.load_no_bias(
prefix="model.norm", weights=weights, eps=config.layer_norm_eps
prefix=f"{prefix}.norm", weights=weights, eps=config.layer_norm_eps
)
self.gradient_checkpointing = False
@ -486,10 +486,15 @@ class FlashCohereModel(torch.nn.Module):
class FlashCohereForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.model = FlashCohereModel(config, weights)
if not prefix:
prefix = "model"
else:
prefix = f"{prefix}.model"
self.model = FlashCohereModel(prefix, config, weights)
try:
self.lm_head = SpeculativeHead.load(
config,
@ -499,7 +504,7 @@ class FlashCohereForCausalLM(torch.nn.Module):
except RuntimeError:
self.lm_head = SpeculativeHead.load(
config,
prefix="model.embed_tokens",
prefix=f"{prefix}.embed_tokens",
weights=weights,
)
self.logit_scale = config.logit_scale

32
server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
View File

@ -105,6 +105,12 @@ class DbrxFFNConfig(PretrainedConfig):
class DbrxConfig(PretrainedConfig):
attribute_map = {
"hidden_size": "d_model",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
}
def __init__(
self,
d_model: int = 2048,
@ -157,6 +163,12 @@ class DbrxConfig(PretrainedConfig):
**kwargs,
)
@property
def num_key_value_heads(self):
# We can't use the attribute map, since this the number of KV
# heads is not top-level.
return self.attn_config.kv_n_heads
def promote_scalar(x: torch.Tensor) -> torch.Tensor:
return x.view(1) if len(x.size()) == 0 else x
@ -593,9 +605,9 @@ class DenseMoE(nn.Module):
class DbrxLayer(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, prefix: str, layer_id, config, weights):
super().__init__()
prefix = f"transformer.blocks.{layer_id}"
prefix = f"{prefix}.blocks.{layer_id}"
self.attn = DbrxNormAttentionNorm(
prefix=f"{prefix}.norm_attn_norm", config=config, weights=weights
@ -637,16 +649,17 @@ class DbrxLayer(nn.Module):
class DbrxModel(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.embed_tokens = TensorParallelEmbedding(
prefix="transformer.wte", weights=weights
prefix=f"{prefix}.wte", weights=weights
)
self.layers = nn.ModuleList(
[
DbrxLayer(
prefix,
layer_id,
config,
weights,
@ -655,7 +668,7 @@ class DbrxModel(torch.nn.Module):
]
)
self.norm = FastLayerNorm.load_no_bias(
prefix="transformer.norm_f", weights=weights, eps=1e-5
prefix=f"{prefix}.norm_f", weights=weights, eps=1e-5
)
self.head_size = self.layers[0].attn.self_attn.head_size
@ -702,10 +715,15 @@ class DbrxModel(torch.nn.Module):
class FlashDbrxForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.model = DbrxModel(config, weights)
if not prefix:
prefix = "transformer"
else:
prefix = f"{prefix}.transformer"
self.model = DbrxModel(prefix, config, weights)
self.lm_head = SpeculativeHead.load(
config,
prefix="lm_head",

11
server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
View File

@ -102,7 +102,7 @@ class Gemma2Config(PretrainedConfig):
class Gemma2FastRMSNorm(FastRMSNorm):
@classmethod
def load(cls, prefix, weights, eps=1e-6):
def load(cls, prefix: str, weights, eps=1e-6):
dtype = weights.dtype
weights.dtype = torch.float32
weight = weights.get_tensor(f"{prefix}.weight") + 1
@ -123,7 +123,7 @@ class Gemma2FastRMSNorm(FastRMSNorm):
return hidden_states.to(self.dtype), residual
def load_attention(config, prefix, weights):
def load_attention(config, prefix: str, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
else:
@ -141,7 +141,6 @@ def _load_gqa(config, prefix: str, weights):
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
@ -305,7 +304,7 @@ class Gemma2MLP(nn.Module):
class FlashGemma2Layer(nn.Module):
def __init__(self, prefix, config, weights, causal: bool, is_sliding: bool):
def __init__(self, prefix: str, config, weights, causal: bool, is_sliding: bool):
super().__init__()
self.self_attn = FlashGemma2Attention(
prefix=f"{prefix}.self_attn",
@ -376,7 +375,7 @@ class FlashGemma2Layer(nn.Module):
class FlashGemma2Model(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
def __init__(self, prefix: str, config, weights, causal: bool):
super().__init__()
process_group = weights.process_group
@ -442,7 +441,7 @@ class FlashGemma2Model(torch.nn.Module):
class FlashGemma2ForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
def __init__(self, prefix: str, config, weights, *, causal: bool = True):
super().__init__()
embed_norm = config.hidden_size**0.5

13
server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
View File

@ -102,7 +102,7 @@ class GemmaConfig(PretrainedConfig):
class GemmaFastRMSNorm(FastRMSNorm):
@classmethod
def load(cls, prefix, weights, eps=1e-6):
def load(cls, prefix: str, weights, eps=1e-6):
dtype = weights.dtype
weights.dtype = torch.float32
weight = weights.get_tensor(f"{prefix}.weight") + 1
@ -123,7 +123,7 @@ class GemmaFastRMSNorm(FastRMSNorm):
return hidden_states.to(self.dtype), residual
def load_attention(config, prefix, weights):
def load_attention(config, prefix: str, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
else:
@ -141,7 +141,6 @@ def _load_gqa(config, prefix: str, weights):
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
@ -261,7 +260,7 @@ class FlashGemmaAttention(torch.nn.Module):
class GemmaMLP(nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
act = config.hidden_act
self.act = (
@ -299,7 +298,7 @@ class GemmaMLP(nn.Module):
class FlashGemmaLayer(nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
def __init__(self, prefix: str, config, weights, causal: bool):
super().__init__()
self.self_attn = FlashGemmaAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights, causal=causal
@ -354,7 +353,7 @@ class FlashGemmaLayer(nn.Module):
class FlashGemmaModel(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
def __init__(self, prefix: str, config, weights, causal: bool):
super().__init__()
process_group = weights.process_group
@ -419,7 +418,7 @@ class FlashGemmaModel(torch.nn.Module):
class FlashGemmaForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
def __init__(self, prefix: str, config, weights, *, causal: bool = True):
super().__init__()
embed_norm = config.hidden_size**0.5

15
server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py
View File

@ -61,7 +61,6 @@ def _load_qkv_gptq(config, prefix: str, weights):
# Weights
weight = weights.get_weights_col_packed_qkv(
f"{prefix}.c_attn",
config.quantize,
config.num_attention_heads,
config.num_attention_heads,
)
@ -137,7 +136,7 @@ def load_row(config, prefix: str, weights, bias: bool):
"""load_row, but with transposed weight matrices."""
if config.quantize == "gptq":
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
weight = weights.get_weights_row(prefix)
else:
weight = weights.get_sharded(f"{prefix}.weight", dim=0).T
@ -155,9 +154,7 @@ def load_row(config, prefix: str, weights, bias: bool):
def load_col(config, prefix: str, weights, bias: bool):
"""load_col, but with transposed weight matrices."""
if config.quantize == "gptq":
weight = weights.get_multi_weights_col(
[prefix], quantize=config.quantize, dim=1
)
weight = weights.get_multi_weights_col([prefix], dim=1)
else:
weight = weights.get_sharded(f"{prefix}.weight", dim=1).T
@ -261,7 +258,7 @@ class FlashGPT2Attention(torch.nn.Module):
class GPT2MLP(nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
act = config.activation_function
self.act = (
@ -298,7 +295,7 @@ class GPT2MLP(nn.Module):
class FlashGPT2Layer(nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.self_attn = FlashGPT2Attention(
prefix=f"{prefix}.attn", config=config, weights=weights
@ -350,7 +347,7 @@ class FlashGPT2Layer(nn.Module):
class FlashGPT2Model(torch.nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
process_group = weights.process_group
@ -414,7 +411,7 @@ class FlashGPT2Model(torch.nn.Module):
class FlashGPT2ForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.embed_tokens = TensorParallelEmbedding(

4
server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
View File

@ -54,7 +54,7 @@ if SYSTEM == "rocm":
raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
def load_attention(config, prefix, weights, layer_id):
def load_attention(config, prefix: str, weights, layer_id):
# Only defined in granite.
bias = getattr(config, "attention_bias", False)
head_size = config.hidden_size // config.num_attention_heads
@ -467,7 +467,7 @@ class FlashLlamaModel(torch.nn.Module):
class FlashLlamaForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.embed_tokens = TensorParallelEmbedding(

8
server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
View File

@ -248,7 +248,7 @@ class MistralAttention(torch.nn.Module):
class MistralMLP(nn.Module):
def __init__(self, prefix, config, weights, layer_id):
def __init__(self, prefix: str, config, weights, layer_id):
super().__init__()
self.hidden_act = config.hidden_act
self.act = (
@ -328,7 +328,7 @@ class MistralMLP(nn.Module):
class MistralLayer(nn.Module):
def __init__(self, prefix, config, weights, layer_id):
def __init__(self, prefix: str, config, weights, layer_id):
super().__init__()
self.self_attn = MistralAttention(
prefix=f"{prefix}.self_attn",
@ -392,7 +392,7 @@ class MistralLayer(nn.Module):
class MistralModel(torch.nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
process_group = weights.process_group
@ -462,7 +462,7 @@ class MistralModel(torch.nn.Module):
class FlashMistralForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights, name=None):
def __init__(self, prefix: str, config, weights, name=None):
if name is None:
name = "model"
super().__init__()

11
server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py
View File

@ -116,7 +116,7 @@ def promote_scalar(x: torch.Tensor) -> torch.Tensor:
return x.view(1) if len(x.size()) == 0 else x
def load_attention(config, prefix, weights):
def load_attention(config, prefix: str, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
else:
@ -135,7 +135,6 @@ def _load_gqa(config, prefix: str, weights):
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
@ -155,7 +154,7 @@ def _load_gqa(config, prefix: str, weights):
)
def _load_experts(config, prefix, mat, weights):
def _load_experts(config, prefix: str, mat, weights):
if config.quantize is not None:
raise NotImplementedError("Mixtral does not support weight quantization yet.")
@ -475,7 +474,7 @@ class DenseMoE(nn.Module):
class MixtralLayer(nn.Module):
def __init__(self, prefix, layer_id, config, weights):
def __init__(self, prefix: str, layer_id, config, weights):
super().__init__()
prefix = f"{prefix}.layers.{layer_id}"
@ -536,7 +535,7 @@ class MixtralLayer(nn.Module):
class MixtralModel(torch.nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.embed_tokens = TensorParallelEmbedding(
@ -610,7 +609,7 @@ class MixtralModel(torch.nn.Module):
class FlashMixtralForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.model = MixtralModel(prefix, config, weights)

20
server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
View File

@ -48,7 +48,7 @@ from text_generation_server.layers.rotary import (
def load_row(config, prefix: str, weights, bias: bool):
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
weight = weights.get_weights_row(prefix)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
@ -64,7 +64,7 @@ def load_row(config, prefix: str, weights, bias: bool):
def load_qkv(config, prefix: str, weights, num_heads, head_size, hidden_size):
weight = weights.get_multi_weights_col([prefix], quantize=config.quantize, dim=0)
weight = weights.get_multi_weights_col([prefix], dim=0)
if isinstance(weight, torch.Tensor):
# Only on non quantized versions
weight = (
@ -305,12 +305,12 @@ class FlashGPTNeoXPreTrainedModel(PreTrainedModel):
class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__(config)
self.config = config
self.embed_in = TensorParallelEmbedding(
prefix="gpt_neox.embed_in", weights=weights
prefix=f"{prefix}.embed_in", weights=weights
)
self.layers = nn.ModuleList(
@ -320,7 +320,7 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
]
)
self.final_layer_norm = FastLayerNorm.load(
prefix="gpt_neox.final_layer_norm",
prefix=f"{prefix}.final_layer_norm",
weights=weights,
eps=config.layer_norm_eps,
)
@ -370,9 +370,15 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
def __init__(self, config, weights):
def __init__(self, prefix, config, weights):
super().__init__(config)
self.gpt_neox = FlashGPTNeoXModel(config, weights)
if not prefix:
prefix = "gpt_neox"
else:
prefix = f"{prefix}.gpt_neox"
self.gpt_neox = FlashGPTNeoXModel(prefix, config, weights)
self.embed_out = SpeculativeHead.load(
config, prefix="embed_out", weights=weights

19
server/text_generation_server/models/custom_modeling/flash_phi_modeling.py
View File

@ -85,7 +85,6 @@ def _load_gqa(config, prefix: str, weights):
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
@ -258,9 +257,9 @@ class PhiMLP(nn.Module):
class FlashPhiLayer(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, prefix: str, layer_id, config, weights):
super().__init__()
prefix = f"model.layers.{layer_id}"
prefix = f"{prefix}.layers.{layer_id}"
self.self_attn = FlashPhiAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
@ -307,18 +306,19 @@ class FlashPhiLayer(nn.Module):
class FlashPhiModel(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.embed_tokens = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
prefix=f"{prefix}.embed_tokens", weights=weights
)
self.layers = nn.ModuleList(
[
FlashPhiLayer(
prefix,
layer_id,
config,
weights,
@ -378,10 +378,15 @@ class FlashPhiModel(torch.nn.Module):
class FlashPhiForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.model = FlashPhiModel(config, weights)
if not prefix:
prefix = "model"
else:
prefix = f"{prefix}.model"
self.model = FlashPhiModel(prefix, config, weights)
self.lm_head = SpeculativeHead.load(
config,
prefix="lm_head",

20
server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
View File

@ -203,9 +203,9 @@ class Qwen2MLP(nn.Module):
class Qwen2Layer(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, prefix, layer_id, config, weights):
super().__init__()
prefix = f"model.layers.{layer_id}"
prefix = f"{prefix}.layers.{layer_id}"
self.self_attn = Qwen2Attention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
@ -260,17 +260,18 @@ class Qwen2Layer(nn.Module):
class Qwen2Model(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.embed_tokens = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
prefix=f"{prefix}.embed_tokens", weights=weights
)
self.layers = nn.ModuleList(
[
Qwen2Layer(
prefix,
layer_id,
config,
weights,
@ -279,7 +280,7 @@ class Qwen2Model(torch.nn.Module):
]
)
self.norm = FastRMSNorm.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
@ -331,10 +332,15 @@ class Qwen2Model(torch.nn.Module):
class Qwen2ForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.model = Qwen2Model(config, weights)
if not prefix:
prefix = "model"
else:
prefix = f"{prefix}.model"
self.model = Qwen2Model(prefix, config, weights)
self.lm_head = SpeculativeHead.load(
config,
prefix="lm_head",

37
server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
View File

@ -23,7 +23,7 @@ from text_generation_server.layers.attention import (
def load_row(config, prefix: str, weights, bias: bool):
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
weight = weights.get_weights_row(prefix)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
@ -42,6 +42,7 @@ class RWConfig(PretrainedConfig):
attribute_map = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
"num_key_value_heads": "n_head_kv",
}
def __init__(
@ -127,7 +128,7 @@ class FlashRWAttention(torch.nn.Module):
def __init__(
self,
config,
prefix,
prefix: str,
weights,
):
super().__init__()
@ -236,7 +237,7 @@ class FlashRWLargeAttention(torch.nn.Module):
def __init__(
self,
config,
prefix,
prefix: str,
weights,
):
super().__init__()
@ -358,7 +359,7 @@ class FlashRWLargeAttention(torch.nn.Module):
class FlashMLP(nn.Module):
def __init__(self, config, prefix, weights):
def __init__(self, config, prefix: str, weights):
super().__init__()
self.act = torch.nn.functional.gelu
@ -380,6 +381,7 @@ class FlashRWLayer(nn.Module):
def __init__(
self,
layer_id,
prefix: str,
config,
weights,
):
@ -388,7 +390,7 @@ class FlashRWLayer(nn.Module):
parallel_attn = config.parallel_attn
self.parallel_attn = parallel_attn
prefix = f"transformer.h.{layer_id}"
prefix = f"{prefix}.h.{layer_id}"
self.input_layernorm = FastLayerNorm.load(
prefix=f"{prefix}.input_layernorm",
@ -479,7 +481,7 @@ class FlashRWLayer(nn.Module):
class FlashRWLayerNorm(nn.Module):
def __init__(self, config, prefix, weights):
def __init__(self, config, prefix: str, weights):
super().__init__()
self.num_ln = config.num_ln_in_parallel_attn
@ -518,9 +520,9 @@ class FlashRWLayerNorm(nn.Module):
class FlashRWLargeLayer(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, layer_id, prefix: str, config, weights):
super().__init__()
prefix = f"transformer.h.{layer_id}"
prefix = f"{prefix}.h.{layer_id}"
self.ln_layer = FlashRWLayerNorm(config, prefix, weights)
@ -580,18 +582,18 @@ class FlashRWPreTrainedModel(PreTrainedModel):
class FlashRWModel(FlashRWPreTrainedModel):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__(config)
self.config = config
self.word_embeddings = TensorParallelEmbedding(
prefix="transformer.word_embeddings", weights=weights
prefix=f"{prefix}.word_embeddings", weights=weights
)
if config.new_decoder_architecture:
self.h = nn.ModuleList(
[
FlashRWLargeLayer(layer_id, config, weights)
FlashRWLargeLayer(layer_id, prefix, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
@ -599,14 +601,14 @@ class FlashRWModel(FlashRWPreTrainedModel):
else:
self.h = nn.ModuleList(
[
FlashRWLayer(layer_id, config, weights)
FlashRWLayer(layer_id, prefix, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
self.cache_size = self.h[0].self_attention.num_heads_kv
self.ln_f = FastLayerNorm.load(
prefix="transformer.ln_f",
prefix=f"{prefix}.ln_f",
weights=weights,
eps=config.layer_norm_epsilon,
)
@ -653,10 +655,15 @@ class FlashRWModel(FlashRWPreTrainedModel):
class FlashRWForCausalLM(FlashRWPreTrainedModel):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__(config)
self.transformer = FlashRWModel(config, weights)
if not prefix:
prefix = "transformer"
else:
prefix = f"{prefix}.transformer"
self.transformer = FlashRWModel(prefix, config, weights)
self.lm_head = SpeculativeHead.load(config, prefix="lm_head", weights=weights)

57
server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
View File

@ -17,6 +17,7 @@ from text_generation_server.layers import (
TensorParallelEmbedding,
get_linear,
)
from text_generation_server.layers.gptq import GPTQWeightsLoader
from text_generation_server.layers.layernorm import (
FastLayerNorm,
)
@ -81,11 +82,13 @@ def _load_multi_mqa_gptq(
qzeros = torch.cat([q_tensor, kv_tensor], dim=1)
qzeros = qzeros.to(device=weights.device)
gptq_params = weights._get_gptq_params()
if gptq_params.quant_method == "gptq":
loader = weights.weights_loader
assert isinstance(loader, GPTQWeightsLoader)
loader._get_gptq_params(weights)
if loader.quant_method == "gptq":
g_idx = weights.get_tensor(f"{prefix}.c_attn.g_idx")
g_idx = g_idx.to(device=weights.device)
elif gptq_params.quant_method == "awq":
elif loader.quant_method == "awq":
g_idx = None
from text_generation_server.layers.awq.conversion_utils import (
fast_awq_to_gptq,
@ -100,8 +103,8 @@ def _load_multi_mqa_gptq(
qzeros=qzeros,
scales=scales,
g_idx=g_idx,
bits=gptq_params.bits,
groupsize=gptq_params.groupsize,
bits=loader.bits,
groupsize=loader.groupsize,
use_exllama=HAS_EXLLAMA,
)
@ -197,9 +200,7 @@ def load_col(config, prefix: str, weights, bias: bool):
if config.transpose:
weight = weights.get_sharded(f"{prefix}.weight", dim=1).T
else:
weight = weights.get_multi_weights_col(
[prefix], quantize=config.quantize, dim=0
)
weight = weights.get_multi_weights_col([prefix], dim=0)
if bias:
bias = weights.get_sharded(f"{prefix}.bias", dim=0)
@ -212,7 +213,7 @@ def load_row(config, prefix: str, weights, bias: bool):
if config.transpose:
weight = weights.get_sharded(f"{prefix}.weight", dim=0).T
else:
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
weight = weights.get_weights_row(prefix)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
@ -346,16 +347,16 @@ class MLP(nn.Module):
class Block(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, prefix: str, layer_id, config, weights):
super().__init__()
prefix = f"transformer.h.{layer_id}"
prefix = f"{prefix}.h.{layer_id}"
self.ln_1 = FastLayerNorm.load(
prefix=f"{prefix}.ln_1", weights=weights, eps=config.layer_norm_epsilon
)
self.ln_2 = FastLayerNorm.load(
prefix=f"{prefix}.ln_2", weights=weights, eps=config.layer_norm_epsilon
)
self.attn = FlashMQAttention(
self.self_attn = FlashMQAttention(
prefix=f"{prefix}.attn",
config=config,
weights=weights,
@ -378,7 +379,7 @@ class Block(nn.Module):
max_s,
):
hidden_states, residual = self.ln_1(hidden_states, residual)
hidden_states = self.attn(
hidden_states = self.self_attn(
hidden_states,
cu_seqlen_prefill,
kv_cache,
@ -396,25 +397,26 @@ class Block(nn.Module):
class FlashSantacoderModel(nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.config = config
self.process_group = weights.process_group
self.wte = TensorParallelEmbedding(
prefix="transformer.wte",
prefix=f"{prefix}.wte",
weights=weights,
reduce=False,
)
self.wpe = TensorParallelEmbedding(
prefix="transformer.wpe",
prefix=f"{prefix}.wpe",
weights=weights,
reduce=False,
)
self.h = nn.ModuleList(
self.layers = nn.ModuleList(
[
Block(
prefix,
layer_id,
config,
weights,
@ -426,8 +428,8 @@ class FlashSantacoderModel(nn.Module):
prefix="transformer.ln_f", weights=weights, eps=config.layer_norm_epsilon
)
self.head_size = self.h[0].attn.head_size
self.num_heads = self.h[0].attn.num_heads
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
def forward(
self,
@ -446,7 +448,7 @@ class FlashSantacoderModel(nn.Module):
torch.distributed.all_reduce(hidden_states, group=self.process_group)
residual = None
for i, layer in enumerate(self.h):
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
residual,
@ -464,11 +466,18 @@ class FlashSantacoderModel(nn.Module):
class FlashSantacoderForCausalLM(nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix, config, weights):
super().__init__()
self.transformer = FlashSantacoderModel(config, weights)
if not prefix:
prefix = "transformer"
else:
prefix = f"{prefix}.transformer"
config.transpose = config.architectures[0].startswith("GPT2")
self.model = FlashSantacoderModel(prefix, config, weights)
self.lm_head = SpeculativeHead.load(
config, prefix="transformer.wte", weights=weights
config, prefix=f"{prefix}.wte", weights=weights
)
def forward(
@ -485,7 +494,7 @@ class FlashSantacoderForCausalLM(nn.Module):
lm_head_indices: Optional[torch.Tensor] = None,
adapter_data: Optional[torch.Tensor] = None,
) -> torch.Tensor:
hidden_states = self.transformer(
hidden_states = self.model(
input_ids,
position_ids,
cu_seqlen_prefill,

18
server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
View File

@ -126,7 +126,6 @@ def _load_gqa(config, prefix: str, weights):
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
@ -417,14 +416,14 @@ class Starcoder2Layer(nn.Module):
class Starcoder2Model(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix, config, weights):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.embed_tokens = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
prefix=f"{prefix}.embed_tokens", weights=weights
)
self.layers = nn.ModuleList(
[
@ -437,7 +436,7 @@ class Starcoder2Model(torch.nn.Module):
]
)
self.norm = STARCODER2_NORMALIZATION_CLASSES[config.norm_type].load(
prefix="model.norm", weights=weights, eps=config.norm_epsilon
prefix=f"{prefix}.norm", weights=weights, eps=config.norm_epsilon
)
self.gradient_checkpointing = False
@ -489,10 +488,15 @@ class Starcoder2Model(torch.nn.Module):
class FlashStarcoder2ForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix, config, weights):
super().__init__()
self.model = Starcoder2Model(config, weights)
if not prefix:
prefix = "model"
else:
prefix = f"{prefix}.model"
self.model = Starcoder2Model(prefix, config, weights)
try:
self.lm_head = SpeculativeHead.load(
config,
@ -502,7 +506,7 @@ class FlashStarcoder2ForCausalLM(torch.nn.Module):
except RuntimeError:
self.lm_head = SpeculativeHead.load(
config,
prefix="model.embed_tokens",
prefix=f"{prefix}.embed_tokens",
weights=weights,
)

8
server/text_generation_server/models/custom_modeling/llava_next.py
View File

@ -136,7 +136,7 @@ class LlavaNextForConditionalGeneration(nn.Module):
self.config = config
config.text_config.quantize = config.quantize
config.text_config.speculator = config.speculator
self.language_model = load_text_model(
self.text_model = load_text_model(
prefix="language_model" if not prefix else f"{prefix}.language_model",
config=config.text_config,
weights=weights,
@ -180,7 +180,7 @@ class LlavaNextForConditionalGeneration(nn.Module):
image_sizes: Optional[torch.LongTensor] = None,
adapter_data: Optional[torch.Tensor] = None,
):
inputs_embeds = self.language_model.embed_tokens(input_ids)
inputs_embeds = self.text_model.embed_tokens(input_ids)
if pixel_values is not None and len(pixel_values) > 0:
# num_special_image_tokens = (input_ids == self.config.image_token_index).sum()
# assert num_special_image_tokens == len(pixel_values), f"Received {num_special_image_tokens} for {len(pixel_values)} images, this is invalid"
@ -269,7 +269,7 @@ class LlavaNextForConditionalGeneration(nn.Module):
input_ids, inputs_embeds, image_features
)
hidden_states = self.language_model.model(
hidden_states = self.text_model.model(
inputs_embeds=inputs_embeds,
position_ids=position_ids,
cu_seqlen_prefill=cu_seqlen_prefill,
@ -283,5 +283,5 @@ class LlavaNextForConditionalGeneration(nn.Module):
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits, speculative_logits = self.language_model.lm_head(hidden_states)
logits, speculative_logits = self.text_model.lm_head(hidden_states)
return logits, speculative_logits

20
server/text_generation_server/models/custom_modeling/mpt_modeling.py
View File

@ -783,7 +783,7 @@ class MPTPreTrainedModel(PreTrainedModel):
class MPTModel(MPTPreTrainedModel):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
# config._validate_config()
super().__init__(config)
self.world_size = weights.process_group.size()
@ -809,13 +809,13 @@ class MPTModel(MPTPreTrainedModel):
f"Requested norm type ({config.norm_type}) is not implemented within this repo."
)
self.wte = TensorParallelEmbedding("transformer.wte", weights)
self.wte = TensorParallelEmbedding(f"{prefix}.wte", weights)
if not self.alibi:
self.wpe = TensorParallelEmbedding("transformer.wpe", weights)
self.wpe = TensorParallelEmbedding(f"{prefix}.wpe", weights)
self.blocks = nn.ModuleList(
[
MPTBlock(config, prefix=f"transformer.blocks.{i}", weights=weights)
MPTBlock(config, prefix=f"{prefix}.blocks.{i}", weights=weights)
for i in range(config.n_layers)
]
)
@ -1085,13 +1085,19 @@ class MPTModel(MPTPreTrainedModel):
class MPTForCausalLM(MPTPreTrainedModel):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__(config)
if not prefix:
prefix = "transformer"
else:
prefix = f"{prefix}.transformer"
if not config.tie_word_embeddings:
raise ValueError("MPTForCausalLM only supports tied word embeddings")
self.transformer = MPTModel(config, weights)
self.transformer = MPTModel(prefix, config, weights)
self.lm_head = SpeculativeHead.load(
config, prefix="transformer.wte", weights=weights
config, prefix=f"{prefix}.wte", weights=weights
)
self.logit_scale = None
if config.logit_scale is not None:

28
server/text_generation_server/models/custom_modeling/neox_modeling.py
View File

@ -404,24 +404,24 @@ class GPTNeoXMLP(nn.Module):
class GPTNeoXLayer(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, layer_id, prefix: str, config, weights):
super().__init__()
self.use_parallel_residual = config.use_parallel_residual
self.input_layernorm = nn.LayerNorm.load(
prefix=f"gpt_neox.layers.{layer_id}.input_layernorm",
prefix=f"{prefix}.layers.{layer_id}.input_layernorm",
weights=weights,
eps=config.layer_norm_eps,
)
self.post_attention_layernorm = nn.LayerNorm.load(
prefix=f"gpt_neox.layers.{layer_id}.post_attention_layernorm",
prefix=f"{prefix}.layers.{layer_id}.post_attention_layernorm",
weights=weights,
eps=config.layer_norm_eps,
)
self.attention = GPTNeoXAttention(
config, prefix=f"gpt_neox.layers.{layer_id}.attention", weights=weights
config, prefix=f"{prefix}.layers.{layer_id}.attention", weights=weights
)
self.mlp = GPTNeoXMLP(
config, prefix=f"gpt_neox.layers.{layer_id}.mlp", weights=weights
config, prefix=f"{prefix}.layers.{layer_id}.mlp", weights=weights
)
def forward(
@ -472,23 +472,23 @@ class GPTNeoXLayer(nn.Module):
class GPTNeoXModel(GPTNeoXPreTrainedModel):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__(config)
self.config = config
self.num_attention_heads = config.num_attention_heads
self.embed_in = TensorParallelEmbedding(
prefix="gpt_neox.embed_in", weights=weights
prefix=f"{prefix}.embed_in", weights=weights
)
self.layers = nn.ModuleList(
[
GPTNeoXLayer(layer_id, config, weights)
GPTNeoXLayer(layer_id, prefix, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
self.final_layer_norm = nn.LayerNorm.load(
prefix="gpt_neox.final_layer_norm",
prefix=f"{prefix}.final_layer_norm",
weights=weights,
eps=config.layer_norm_eps,
)
@ -640,9 +640,15 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel):
class GPTNeoxForCausalLM(GPTNeoXPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__(config)
self.gpt_neox = GPTNeoXModel(config, weights)
if not prefix:
prefix = "gpt_neox"
else:
prefix = f"{prefix}.gpt_neox"
self.gpt_neox = GPTNeoXModel(prefix, config, weights)
self.embed_out = SpeculativeHead.load(
config, prefix="embed_out", weights=weights
)

43
server/text_generation_server/models/custom_modeling/opt_modeling.py
View File

@ -94,11 +94,11 @@ class OPTLearnedPositionalEmbedding(nn.Module):
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, weights):
def __init__(self, prefix: str, weights):
super().__init__()
self.offset = 2
self.weight = nn.Parameter(
weights.get_tensor("model.decoder.embed_positions.weight")
weights.get_tensor(f"{prefix}.decoder.embed_positions.weight")
)
def forward(
@ -311,11 +311,11 @@ class OPTAttention(nn.Module):
class OPTDecoderLayer(nn.Module):
def __init__(self, layer_id: int, config: OPTConfig, weights):
def __init__(self, layer_id: int, prefix: str, config: OPTConfig, weights):
super().__init__()
self.process_group = weights.process_group
self.hidden_size = config.hidden_size
prefix = f"model.decoder.layers.{layer_id}"
prefix = f"{prefix}.decoder.layers.{layer_id}"
self.self_attn = OPTAttention(
config,
prefix=f"{prefix}.self_attn",
@ -429,7 +429,7 @@ class OPTPreTrainedModel(PreTrainedModel):
class OPTDecoder(OPTPreTrainedModel):
def __init__(self, config: OPTConfig, weights):
def __init__(self, prefix: str, config: OPTConfig, weights):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.layerdrop
@ -438,20 +438,26 @@ class OPTDecoder(OPTPreTrainedModel):
self.vocab_size = config.vocab_size
self.embed_tokens = TensorParallelEmbedding(
prefix="model.decoder.embed_tokens", weights=weights
prefix=f"{prefix}.decoder.embed_tokens", weights=weights
)
self.embed_positions = OPTLearnedPositionalEmbedding(weights)
self.embed_positions = OPTLearnedPositionalEmbedding(prefix, weights)
if config.word_embed_proj_dim != config.hidden_size:
self.project_out = FastLinear.load(
config, prefix="model.decoder.project_out", weights=weights, bias=False
config,
prefix=f"{prefix}.decoder.project_out",
weights=weights,
bias=False,
)
else:
self.project_out = None
if config.word_embed_proj_dim != config.hidden_size:
self.project_in = FastLinear.load(
config, prefix="model.decoder.project_in", weights=weights, bias=False
config,
prefix=f"{prefix}.decoder.project_in",
weights=weights,
bias=False,
)
else:
self.project_in = None
@ -461,14 +467,14 @@ class OPTDecoder(OPTPreTrainedModel):
# see https://github.com/facebookresearch/metaseq/pull/164
if config.do_layer_norm_before and not config._remove_final_layer_norm:
self.final_layer_norm = nn.LayerNorm.load(
prefix="model.decoder.final_layer_norm", weights=weights, eps=EPS
prefix=f"{prefix}.decoder.final_layer_norm", weights=weights, eps=EPS
)
else:
self.final_layer_norm = None
self.layers = nn.ModuleList(
[
OPTDecoderLayer(layer_id, config, weights)
OPTDecoderLayer(layer_id, prefix, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
@ -686,9 +692,9 @@ class OPTDecoder(OPTPreTrainedModel):
class OPTModel(OPTPreTrainedModel):
def __init__(self, config: OPTConfig, weights):
def __init__(self, prefix: str, config: OPTConfig, weights):
super().__init__(config)
self.decoder = OPTDecoder(config, weights)
self.decoder = OPTDecoder(prefix, config, weights)
# Initialize weights and apply final processing
def forward(
@ -743,13 +749,18 @@ class OPTModel(OPTPreTrainedModel):
class OPTForCausalLM(OPTPreTrainedModel):
def __init__(self, config, weights):
def __init__(self, prefix, config, weights):
super().__init__(config)
self.model = OPTModel(config, weights)
if not prefix:
prefix = "model"
else:
prefix = f"{prefix}.model"
self.model = OPTModel(prefix, config, weights)
self.lm_head = SpeculativeHead.load(
config, prefix="model.decoder.embed_tokens", weights=weights
config, prefix=f"{prefix}.decoder.embed_tokens", weights=weights
)
def forward(

16
server/text_generation_server/models/custom_modeling/phi_modeling.py
View File

@ -248,16 +248,16 @@ class PhiBlock(nn.Module):
# PhiModel implements the embedding layer and the transformer blocks.
class PhiModel(nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.tp_rank = weights.process_group.rank()
self.tp_world_size = weights.process_group.size()
self.embed_tokens = TensorParallelEmbedding(
prefix="transformer.embd.wte", weights=weights
prefix=f"{prefix}.embd.wte", weights=weights
)
self.blocks = nn.ModuleList(
[
PhiBlock(f"transformer.h.{layer_id}", config, weights)
PhiBlock(f"{prefix}.h.{layer_id}", config, weights)
for layer_id in range(config.n_layer)
]
)
@ -289,9 +289,15 @@ class PhiModel(nn.Module):
# PhiForCausalLM wraps the PhiModel and PhiCausalLMHead together and returns a CausalLMOutputWithPast object.
class PhiForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix: str, config, weights):
super().__init__()
self.model = PhiModel(config, weights)
if not prefix:
prefix = "transformer"
else:
prefix = f"{prefix}.transformer"
self.model = PhiModel(prefix, config, weights)
self.lm_head = PhiCausalLMHead(config, weights)
def forward(

207
server/text_generation_server/models/flash_causal_lm.py
View File

@ -10,7 +10,12 @@ import numpy as np
from loguru import logger
from dataclasses import dataclass
from opentelemetry import trace
from transformers import PreTrainedTokenizerBase
from transformers import (
PreTrainedTokenizerBase,
AutoConfig,
AutoTokenizer,
GenerationConfig,
)
from typing import Iterable, Optional, Tuple, List, Type, Dict
from text_generation_server.adapters import AdapterBatchData, AdapterBatchMetadata
@ -21,6 +26,12 @@ from text_generation_server.models import Model
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_server.utils.dist import RANK
from text_generation_server.utils.speculate import get_speculate
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
hub,
)
from text_generation_server.models.types import (
Batch,
Tokens,
@ -39,6 +50,7 @@ from text_generation_server.models.globals import (
from text_generation_server.layers.attention import Seqlen
from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
from text_generation_server.utils.dist import MEMORY_FRACTION
from text_generation_server.utils.quantization import get_loader
from text_generation_server.utils.segments import SegmentConcatBuilder, find_segments
from text_generation_server.utils.import_utils import (
@ -799,29 +811,123 @@ class FlashCausalLMBatch(Batch):
return len(self.requests)
ADAPTER_LAYERS = [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
]
ROW_PARALLEL = {"o_proj", "down_proj", "lm_head"}
class FlashCausalLM(Model):
def __init__(
self,
model_id: str,
model: torch.nn.Module,
tokenizer: PreTrainedTokenizerBase,
num_layers: int,
num_kv_heads: int,
head_size: int,
dtype: torch.dtype,
device: torch.device,
rank: int = 0,
world_size: int = 1,
sliding_window: Optional[int] = None,
model_class,
revision: Optional[str] = None,
quantize: Optional[str] = None,
speculator: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
lora_adapter_ids: Optional[list] = [],
tokenizer_class: PreTrainedTokenizerBase = AutoTokenizer,
config_class: PreTrainedTokenizerBase = AutoConfig,
default_dtype=torch.float16,
aliases=None,
# Used for Santacoder override of config
num_kv_heads=None,
skip_special_tokens: bool = True,
):
self.num_layers = num_layers
self.num_kv_heads = num_kv_heads
self.head_size = head_size
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = default_dtype if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = default_dtype if dtype is None else dtype
else:
device = torch.device("cpu")
# Float16 doesn't exist on target.
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError(f"{model_class} is only available on GPU")
tokenizer = tokenizer_class.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
try:
generation_config = GenerationConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
if isinstance(generation_config.eos_token_id, (list, set)):
# TODO Huge hack
tokenizer._eos_token_ids = set(generation_config.eos_token_id)
except Exception:
pass
config = config_class.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
weights_loader = get_loader(quantize, model_id, revision)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device,
dtype,
process_group=self.process_group,
aliases=aliases,
weights_loader=weights_loader,
)
prefix = ""
model = model_class(prefix, config, weights)
torch.distributed.barrier(group=self.process_group)
# VLM models define the config we care about in their text_config
text_config = getattr(config, "text_config", None)
if text_config is not None:
config = text_config
if getattr(config, "sliding_window", None) is not None:
set_sliding_window(config.sliding_window)
else:
config.sliding_window = None
self.num_layers = config.num_hidden_layers
# Validation is done in the model itself
if num_kv_heads is None:
num_kv_heads = getattr(config, "num_key_value_heads", None)
# GPT-2 workaround
if num_kv_heads is None:
num_kv_heads = getattr(config, "n_head", None)
if num_kv_heads is None:
raise ValueError("Cannot get the number of key/value heads")
self.num_kv_heads = (
num_kv_heads // self.process_group.size()
if num_kv_heads > 1
else num_kv_heads
)
assert self.num_kv_heads > 0
self.head_size = config.hidden_size // config.num_attention_heads
self.cuda_graphs = {}
self.kv_cache = []
super(FlashCausalLM, self).__init__(
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
@ -830,7 +936,7 @@ class FlashCausalLM(Model):
device=device,
rank=rank,
world_size=world_size,
sliding_window=sliding_window,
sliding_window=config.sliding_window,
)
@property
@ -1578,3 +1684,72 @@ class FlashCausalLM(Model):
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, batch, (forward_ns, decode_ns)
@property
def supports_adapter_loading(self) -> bool:
return True
def adapter_target_to_layer(self) -> Dict[str, Tuple[str, torch.Tensor]]:
layer_weights = {}
prefix = "model.layers"
# This accounts for VLMs (e.g. LlavaNext, Idefics2)
# that have a language_model inside of the larger model.
if hasattr(self.model, "language_model"):
_model = self.model.language_model
elif hasattr(self.model, "text_model"):
_model = self.model.text_model
else:
_model = self.model
for i, layer in enumerate(_model.model.layers):
layer_weights[(i, "q_proj")] = (
f"{prefix}.{i}.self_attn.q_proj",
layer.self_attn.query_key_value,
)
layer_weights[(i, "k_proj")] = (
f"{prefix}.{i}.self_attn.k_proj",
layer.self_attn.query_key_value,
)
layer_weights[(i, "v_proj")] = (
f"{prefix}.{i}.self_attn.v_proj",
layer.self_attn.query_key_value,
)
layer_weights[(i, "o_proj")] = (
f"{prefix}.{i}.self_attn.o_proj",
layer.self_attn.o_proj,
)
# TODO: this is a hack to avoid the gate_proj for
# FlashStarcoder2 that doesnt have these layers
if hasattr(layer, "mlp") and hasattr(layer.mlp, "gate_up_proj"):
layer_weights[(i, "gate_proj")] = (
f"{prefix}.{i}.mlp.gate_proj",
layer.mlp.gate_up_proj,
)
layer_weights[(i, "up_proj")] = (
f"{prefix}.{i}.mlp.up_proj",
layer.mlp.gate_up_proj,
)
layer_weights[(i, "down_proj")] = (
f"{prefix}.{i}.mlp.down_proj",
layer.mlp.down_proj,
)
layer_weights[(0, "lm_head")] = ("lm_head", _model.lm_head)
return layer_weights
@property
def adapter_layers(self) -> List[str]:
return ADAPTER_LAYERS
@property
def default_traced_adapter_layers(self) -> List[str]:
return ["q_proj", "v_proj"]
def get_num_layers_for_type(self, layer_type: str) -> int:
return 1 if layer_type == "lm_head" else len(self.model.model.layers)
def is_row_parallel(self, layer_type: str) -> bool:
return layer_type in ROW_PARALLEL

75
server/text_generation_server/models/flash_cohere.py
View File

@ -1,75 +0,0 @@
import torch
import torch.distributed
from opentelemetry import trace
from typing import Optional
from transformers import AutoTokenizer, AutoConfig
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_cohere_modeling import (
FlashCohereForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class FlashCohere(FlashCausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
raise NotImplementedError("FlashCohere is only available on GPU")
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 = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = FlashCohereForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashCohere, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

100
server/text_generation_server/models/flash_dbrx.py
View File

@ -1,100 +0,0 @@
import torch
import torch.distributed
from opentelemetry import trace
from typing import Optional
from transformers import AutoTokenizer
from transformers.models.gpt2 import GPT2TokenizerFast
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_dbrx_modeling import (
FlashDbrxForCausalLM,
DbrxConfig,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class FlashDbrx(FlashCausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashDBRX is only available on GPU")
try:
tokenizer = GPT2TokenizerFast.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
use_fast=True,
from_slow=False,
)
except:
try:
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,
)
except:
# FIXME: change back to model id once the tokenizer.json is merged
tokenizer = GPT2TokenizerFast.from_pretrained(
"Xenova/dbrx-instruct-tokenizer",
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
use_fast=True,
from_slow=False,
)
config = DbrxConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = FlashDbrxForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashDbrx, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

83
server/text_generation_server/models/flash_gemma.py
View File

@ -1,83 +0,0 @@
import torch
import torch.distributed
from opentelemetry import trace
from typing import Optional
from transformers import AutoConfig, AutoTokenizer
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
FlashGemmaForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
tracer = trace.get_tracer(__name__)
class FlashGemma(FlashCausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.bfloat16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashGemma is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
# TODO hardcoded
prefix = ""
model = FlashGemmaForCausalLM(prefix, config, weights, causal=True)
torch.distributed.barrier(group=self.process_group)
super(FlashGemma, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

83
server/text_generation_server/models/flash_gemma2.py
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@ -1,83 +0,0 @@
import torch
import torch.distributed
from opentelemetry import trace
from typing import Optional
from transformers import PretrainedConfig, AutoTokenizer
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_gemma2_modeling import (
FlashGemma2ForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
tracer = trace.get_tracer(__name__)
class FlashGemma2(FlashCausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.bfloat16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashGemma2 is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = PretrainedConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
# TODO hardcoded
prefix = ""
model = FlashGemma2ForCausalLM(prefix, config, weights, causal=True)
torch.distributed.barrier(group=self.process_group)
super(FlashGemma2, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

82
server/text_generation_server/models/flash_gpt2.py
View File

@ -1,82 +0,0 @@
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoConfig, AutoTokenizer, GenerationConfig
from transformers.models.gpt2 import GPT2Tokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_gpt2_modeling import (
FlashGPT2ForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
tracer = trace.get_tracer(__name__)
class FlashGPT2(FlashCausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashGPT2 is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
prefix = ""
model = FlashGPT2ForCausalLM(prefix, config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashGPT2, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

171
server/text_generation_server/models/flash_llama.py
View File

@ -1,171 +0,0 @@
import os
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoConfig, AutoTokenizer, GenerationConfig
from typing import Optional, Tuple, Dict, List
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_llama_modeling import (
FlashLlamaForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
hub,
)
tracer = trace.get_tracer(__name__)
from text_generation_server.utils.import_utils import SYSTEM
ADAPTER_LAYERS = [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
]
ROW_PARALLEL = {"o_proj", "down_proj", "lm_head"}
class FlashLlama(FlashCausalLM):
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,
lora_adapter_ids: Optional[list] = [],
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashLlama is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
try:
generation_config = GenerationConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
if isinstance(generation_config.eos_token_id, (list, set)):
# TODO Huge hack
tokenizer._eos_token_ids = set(generation_config.eos_token_id)
except Exception:
pass
config = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["awq", "exl2", "gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
prefix = ""
model = FlashLlamaForCausalLM(prefix, config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashLlama, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
@property
def supports_adapter_loading(self) -> bool:
return True
def adapter_target_to_layer(self) -> Dict[str, Tuple[str, torch.Tensor]]:
layer_weights = {}
prefix = "model.layers"
# This accounts for VLMs (e.g. LlavaNext, Idefics2)
# that have a language_model inside of the larger model.
if hasattr(self.model, "language_model"):
_model = self.model.language_model
elif hasattr(self.model, "text_model"):
_model = self.model.text_model
else:
_model = self.model
for i, layer in enumerate(_model.model.layers):
layer_weights[(i, "q_proj")] = (
f"{prefix}.{i}.self_attn.q_proj",
layer.self_attn.query_key_value,
)
layer_weights[(i, "k_proj")] = (
f"{prefix}.{i}.self_attn.k_proj",
layer.self_attn.query_key_value,
)
layer_weights[(i, "v_proj")] = (
f"{prefix}.{i}.self_attn.v_proj",
layer.self_attn.query_key_value,
)
layer_weights[(i, "o_proj")] = (
f"{prefix}.{i}.self_attn.o_proj",
layer.self_attn.o_proj,
)
layer_weights[(i, "gate_proj")] = (
f"{prefix}.{i}.mlp.gate_proj",
layer.mlp.gate_up_proj,
)
layer_weights[(i, "up_proj")] = (
f"{prefix}.{i}.mlp.up_proj",
layer.mlp.gate_up_proj,
)
layer_weights[(i, "down_proj")] = (
f"{prefix}.{i}.mlp.down_proj",
layer.mlp.down_proj,
)
layer_weights[(0, "lm_head")] = ("lm_head", _model.lm_head)
return layer_weights
@property
def adapter_layers(self) -> List[str]:
return ADAPTER_LAYERS
@property
def default_traced_adapter_layers(self) -> List[str]:
return ["q_proj", "v_proj"]
def get_num_layers_for_type(self, layer_type: str) -> int:
return 1 if layer_type == "lm_head" else len(self.model.model.layers)
def is_row_parallel(self, layer_type: str) -> bool:
return layer_type in ROW_PARALLEL

126
server/text_generation_server/models/flash_mistral.py
View File

@ -1,24 +1,7 @@
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoTokenizer, AutoConfig
from typing import Optional, Tuple, Dict, List
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.flash_causal_lm import set_sliding_window
from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
FlashMistralForCausalLM,
MistralConfig,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
tracer = trace.get_tracer(__name__)
ADAPTER_LAYERS = [
@ -33,88 +16,7 @@ ADAPTER_LAYERS = [
ROW_PARALLEL = {"o_proj", "down_proj", "lm_head"}
class BaseFlashMistral(FlashCausalLM):
def __init__(
self,
model_cls,
model_id: str,
config_cls=AutoConfig,
revision: Optional[str] = None,
quantize: Optional[str] = None,
speculator: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
tokenizer_class=AutoTokenizer,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashMistral is only available on GPU")
tokenizer = tokenizer_class.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = config_cls.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
# Set context windows
if getattr(config, "sliding_window", None) is not None:
set_sliding_window(config.sliding_window)
else:
config.sliding_window = None
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
prefix = ""
model = model_cls(prefix, config, weights)
self.cuda_graphs = {}
torch.distributed.barrier(group=self.process_group)
num_layers, num_kv_heads, head_size = self.get_layer_config(model)
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=num_layers,
num_kv_heads=num_kv_heads,
head_size=head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
sliding_window=config.sliding_window,
)
def get_layer_config(self, model) -> Tuple[int, int, int]:
return (
len(model.model.layers),
model.model.num_key_value_heads,
model.model.head_size,
)
class FlashMistral(FlashCausalLM):
@property
def supports_adapter_loading(self) -> bool:
return True
@ -126,9 +28,7 @@ class BaseFlashMistral(FlashCausalLM):
# This accounts for VLMs (e.g. LlavaNext, Idefics2)
# that have a language_model inside of the larger model.
if hasattr(self.model, "language_model"):
_model = self.model.language_model
elif hasattr(self.model, "text_model"):
if hasattr(self.model, "text_model"):
_model = self.model.text_model
else:
_model = self.model
@ -183,25 +83,3 @@ class BaseFlashMistral(FlashCausalLM):
def is_row_parallel(self, layer_type: str) -> bool:
return layer_type in ROW_PARALLEL
class FlashMistral(BaseFlashMistral):
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,
):
super(FlashMistral, self).__init__(
config_cls=MistralConfig,
model_cls=FlashMistralForCausalLM,
model_id=model_id,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)

31
server/text_generation_server/models/flash_mixtral.py
View File

@ -1,31 +0,0 @@
import torch
from typing import Optional
from text_generation_server.models.flash_mistral import BaseFlashMistral
from text_generation_server.models.custom_modeling.flash_mixtral_modeling import (
MixtralConfig,
FlashMixtralForCausalLM,
)
class FlashMixtral(BaseFlashMistral):
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,
):
super(FlashMixtral, self).__init__(
config_cls=MixtralConfig,
model_cls=FlashMixtralForCausalLM,
model_id=model_id,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)

82
server/text_generation_server/models/flash_neox.py
View File

@ -1,82 +0,0 @@
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoTokenizer, AutoConfig
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_neox_modeling import (
FlashGPTNeoXForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
tracer = trace.get_tracer(__name__)
class FlashNeoXSharded(FlashCausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashNeoX is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames, device=device, dtype=dtype, process_group=self.process_group
)
if config.quantize in ["gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = FlashGPTNeoXForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashNeoXSharded, self).__init__(
model_id=model_id,
model=model.to(device),
tokenizer=tokenizer,
num_layers=len(model.gpt_neox.layers),
num_kv_heads=model.gpt_neox.num_heads,
head_size=model.gpt_neox.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

111
server/text_generation_server/models/flash_phi.py
View File

@ -1,111 +0,0 @@
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoConfig, AutoTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_phi_modeling import (
FlashPhiForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
tracer = trace.get_tracer(__name__)
class FlashPhi(FlashCausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashPhi is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = FlashPhiForCausalLM(config, weights)
if speculator:
from text_generation_server.utils.medusa import MedusaModel
from huggingface_hub import hf_hub_download
import json
import os
from pathlib import Path
is_local_model = (
Path(speculator).exists() and Path(speculator).is_dir()
) or os.getenv("WEIGHTS_CACHE_OVERRIDE", None) is not None
if not is_local_model:
medusa_config = hf_hub_download(
speculator, revision=revision, filename="config.json"
)
medusa_head = hf_hub_download(
speculator, revision=revision, filename="medusa_lm_head.pt"
)
else:
medusa_config = str(Path(speculator) / "config.json")
medusa_head = str(Path(speculator) / "medusa_lm_head.pt")
with open(medusa_config, "r") as f:
config = json.load(f)
medusa_sf = medusa_head[: -len(".pt")] + ".safetensors"
weights = Weights(
[medusa_sf], device, dtype, process_group=self.process_group
)
lm_head = model.lm_head
model.lm_head = MedusaModel(config, weights, lm_head)
torch.distributed.barrier(group=self.process_group)
super(FlashPhi, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

93
server/text_generation_server/models/flash_qwen2.py
View File

@ -1,93 +0,0 @@
import math
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoTokenizer, AutoConfig
from typing import Optional
from text_generation_server.models.flash_mistral import (
BaseFlashMistral,
set_sliding_window,
)
from text_generation_server.models.custom_modeling.flash_qwen2_modeling import (
Qwen2ForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
tracer = trace.get_tracer(__name__)
class FlashQwen2(BaseFlashMistral):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashQwen2 is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
# Set context windows
if config.sliding_window is not None:
set_sliding_window(config.sliding_window)
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = Qwen2ForCausalLM(config, weights)
self.cuda_graphs = {}
torch.distributed.barrier(group=self.process_group)
super(BaseFlashMistral, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
sliding_window=config.sliding_window,
)

91
server/text_generation_server/models/flash_rw.py
View File

@ -1,91 +0,0 @@
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_rw_modeling import (
RWConfig,
FlashRWForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
tracer = trace.get_tracer(__name__)
class FlashRWSharded(FlashCausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashRW is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = RWConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device,
dtype,
process_group=self.process_group,
aliases={
"lm_head.weight": ["transformer.word_embeddings.weight"],
"transformer.word_embeddings.weight": ["lm_head.weight"],
},
)
config.quantize = quantize
config.speculator = speculator
if config.quantize in ["gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = FlashRWForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashRWSharded, self).__init__(
model_id=model_id,
model=model.to(device),
tokenizer=tokenizer,
num_layers=len(model.transformer.h),
num_kv_heads=model.transformer.cache_size,
head_size=model.transformer.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

99
server/text_generation_server/models/flash_santacoder.py
View File

@ -1,99 +0,0 @@
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoTokenizer, AutoConfig
from typing import Optional, List
import json
import os
from huggingface_hub import hf_hub_download
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_santacoder_modeling import (
FlashSantacoderForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
tracer = trace.get_tracer(__name__)
class FlashSantacoderSharded(FlashCausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif SYSTEM == "ipex":
if hasattr(torch, "xpu") and torch.xpu.is_available():
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashSantacoderSharded is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=True,
)
config.quantize = quantize
config.speculator = speculator
config.transpose = config.architectures[0].startswith("GPT2")
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device=device,
dtype=dtype,
process_group=self.process_group,
aliases={"transformer.wte.weight": ["lm_head.weight"]},
)
if config.quantize in ["gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = FlashSantacoderForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashSantacoderSharded, self).__init__(
model_id=model_id,
model=model.to(device),
tokenizer=tokenizer,
num_layers=len(model.transformer.h),
num_kv_heads=1,
head_size=model.transformer.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
def decode(self, generated_ids: List[int]) -> str:
# Do not skip special tokens as they are used for custom parsing rules of the generated text
return self.tokenizer.decode(
generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
)

84
server/text_generation_server/models/flash_starcoder2.py
View File

@ -1,84 +0,0 @@
import math
import torch
from typing import Optional
from transformers.models.gpt2 import GPT2TokenizerFast
from text_generation_server.models.flash_mistral import (
BaseFlashMistral,
set_sliding_window,
)
from text_generation_server.models.custom_modeling.flash_starcoder2_modeling import (
Starcoder2Config,
FlashStarcoder2ForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
# Starcoder2 has the same base as Mistral
class FlashStarcoder2(BaseFlashMistral):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
raise NotImplementedError("FlashStarcoder2 is only available on GPU")
tokenizer = GPT2TokenizerFast.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = Starcoder2Config.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
# Set context windows
if config.sliding_window is not None:
set_sliding_window(config.sliding_window)
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = FlashStarcoder2ForCausalLM(config, weights)
self.cuda_graphs = {}
torch.distributed.barrier(group=self.process_group)
super(BaseFlashMistral, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
sliding_window=config.sliding_window,
)

80
server/text_generation_server/models/galactica.py
View File

@ -162,83 +162,3 @@ class GalacticaCausalLMBatch(CausalLMBatch):
padding_right_offset=padding_right_offset,
max_tokens=max_tokens,
)
class GalacticaSharded(CausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
tp_parallel=True,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
tokenizer.pad_token_id = config.pad_token_id
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames, device=device, dtype=dtype, process_group=self.process_group
)
if config.quantize in ["gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = OPTForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return GalacticaCausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
# Do not skip special tokens as they are used for custom parsing rules of the generated text
return self.tokenizer.decode(
generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):
outputs, speculative_logits = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
use_cache=True,
)
return outputs.logits, speculative_logits, outputs.past_key_values

89
server/text_generation_server/models/gpt_neox.py
View File

@ -1,89 +0,0 @@
import torch
import torch.distributed
from typing import Optional
from transformers import (
AutoTokenizer,
AutoConfig,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.custom_modeling.neox_modeling import (
GPTNeoxForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
class GPTNeoxSharded(CausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
tokenizer.pad_token = tokenizer.eos_token
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames, device=device, dtype=dtype, process_group=self.process_group
)
if config.quantize in ["gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = GPTNeoxForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):
outputs, speculative_logits = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=True,
)
return outputs.logits, speculative_logits, outputs.past_key_values

5
server/text_generation_server/models/idefics.py
View File

@ -23,6 +23,7 @@ from text_generation_server.utils import (
weight_files,
Weights,
)
from text_generation_server.utils.quantization import get_loader
class IDEFICSSharded(IdeficsCausalLM):
@ -70,6 +71,9 @@ class IDEFICSSharded(IdeficsCausalLM):
trust_remote_code=trust_remote_code,
)
weights_loader = get_loader(
quantize=quantize, model_id=model_id, revision=revision
)
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
@ -77,6 +81,7 @@ class IDEFICSSharded(IdeficsCausalLM):
device=device,
dtype=dtype,
process_group=self.process_group,
weights_loader=weights_loader,
)
model = IdeficsForVisionText2Text(config, weights)

51
server/text_generation_server/models/idefics2.py
View File

@ -1,51 +0,0 @@
import torch
from typing import Optional, Tuple
from transformers import (
AutoProcessor,
)
from text_generation_server.models.custom_modeling.idefics2 import (
Idefics2ForConditionalGeneration,
)
from text_generation_server.models.vlm_causal_lm import VlmCausalLM
class Idefics2(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,
# XXX: Extremely important to cap resolution in order to limit
# VRAM usage.
size={"longest_edge": 448, "shortest_edge": 378},
)
super().__init__(
model_cls=Idefics2ForConditionalGeneration,
model_id=model_id,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
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)

46
server/text_generation_server/models/llava_next.py
View File

@ -1,46 +0,0 @@
import torch
from typing import Optional, Tuple
from transformers import (
AutoProcessor,
)
from text_generation_server.models.custom_modeling.llava_next import (
LlavaNextForConditionalGeneration,
)
from text_generation_server.models.vlm_causal_lm import VlmCausalLM
class LlavaNext(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__(
model_cls=LlavaNextForConditionalGeneration,
model_id=model_id,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
def get_layer_config(self, model) -> Tuple[int, int, int]:
return (
len(model.language_model.model.layers),
model.language_model.model.num_key_value_heads,
model.language_model.model.head_size,
)
def max_past(self) -> Optional[int]:
return getattr(self.model.language_model, "max_past", None)

12
server/text_generation_server/models/mamba.py
View File

@ -28,6 +28,7 @@ from text_generation_server.models.types import (
GeneratedText,
)
from text_generation_server.utils.chunks import concat_text_chunks
from text_generation_server.utils.quantization import get_loader
from text_generation_server.utils.tokens import batch_top_tokens, Sampling
from dataclasses import dataclass
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
@ -448,8 +449,17 @@ class Mamba(Model):
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
weights_loader = get_loader(
quantize=quantize, model_id=model_id, revision=revision
)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
weights = Weights(
filenames,
device,
dtype,
process_group=self.process_group,
weights_loader=weights_loader,
)
model = MambaModel(config, weights)
torch.distributed.barrier(group=self.process_group)
super(Mamba, self).__init__(

4
server/text_generation_server/models/model.py
View File

@ -60,7 +60,7 @@ class Model(ABC):
self.layer_to_adapter_weights: Dict[str, LayerAdapterWeights] = defaultdict(
LayerAdapterWeights
)
self.target_to_layer = self.adapter_target_to_layer()
self.target_to_layer = None
self.loaded_adapters = set()
self.static_adapter_id = adapter_id
@ -187,6 +187,8 @@ class Model(ABC):
into model. Otherwise, the adapter weights are applied during the forward
pass and stored separately from the base model parameters.
"""
if self.target_to_layer is None:
self.target_to_layer = self.adapter_target_to_layer()
if adapter_index in self.loaded_adapters:
# Adapter already loaded
return

105
server/text_generation_server/models/mpt.py
View File

@ -1,105 +0,0 @@
import torch
import torch.distributed
from pathlib import Path
from typing import Optional, Type
from opentelemetry import trace
from transformers import AutoTokenizer, PretrainedConfig, PreTrainedTokenizerBase
from huggingface_hub import hf_hub_download
import json
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.models.custom_modeling.mpt_modeling import (
MPTForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class MPTCausalLMBatch(CausalLMBatch):
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "CausalLMBatch":
batch = super().from_pb(pb=pb, tokenizer=tokenizer, dtype=dtype, device=device)
batch.keys_head_dim_last = False
return batch
class MPTSharded(CausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
tokenizer.pad_token = tokenizer.eos_token
# If model_id is a local path, load the file directly
local_path = Path(model_id, "config.json")
if local_path.exists():
filename = str(local_path.resolve())
else:
filename = hf_hub_download(
model_id, revision=revision, filename="config.json"
)
with open(filename, "r") as f:
config = json.load(f)
config = PretrainedConfig(**config)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
config.quantize = quantize
model = MPTForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return MPTCausalLMBatch

86
server/text_generation_server/models/opt.py
View File

@ -1,86 +0,0 @@
import torch
import torch.distributed
from typing import Optional
from transformers import (
AutoTokenizer,
AutoConfig,
)
from text_generation_server.models.custom_modeling.opt_modeling import OPTForCausalLM
from text_generation_server.models import CausalLM
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
class OPTSharded(CausalLM):
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.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
config.speculator = speculator
tokenizer.pad_token_id = config.pad_token_id
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames, device=device, dtype=dtype, process_group=self.process_group
)
if config.quantize in ["gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = OPTForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):
outputs, speculative_logits = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
use_cache=True,
)
return outputs.logits, speculative_logits, outputs.past_key_values

42
server/text_generation_server/models/pali_gemma.py
View File

@ -74,45 +74,3 @@ class PaliGemmaBatch(VlmCausalLMBatch):
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)

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