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Merge branch 'main' into fp8_kvcache_rocm

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This commit is contained in:
Mohit Sharma 2025-01-17 08:53:31 +00:00
commit 8ffb5b3697
65 changed files with 2642 additions and 488 deletions
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3
.github/workflows/build.yaml vendored
View File

@ -67,7 +67,8 @@ jobs:
export label_extension="-rocm"
export docker_devices="/dev/kfd,/dev/dri"
export docker_volume="/mnt"
export runs_on="amd-gpu-runners"
# This runner was deactivated.
export runs_on="ubuntu-latest"
export platform=""
export extra_pytest="-k test_flash_gemma_gptq_load"
;;

2
.github/workflows/tests.yaml vendored
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@ -31,7 +31,7 @@ jobs:
with:
# Released on: 02 May, 2024
# https://releases.rs/docs/1.78.0/
toolchain: 1.80.0
toolchain: 1.84.0
override: true
components: rustfmt, clippy
- name: Install Protoc

214
Cargo.lock generated
View File

@ -456,18 +456,18 @@ dependencies = [
[[package]]
name = "bit-set"
version = "0.5.3"
version = "0.8.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "0700ddab506f33b20a03b13996eccd309a48e5ff77d0d95926aa0210fb4e95f1"
checksum = "08807e080ed7f9d5433fa9b275196cfc35414f66a0c79d864dc51a0d825231a3"
dependencies = [
"bit-vec",
]
[[package]]
name = "bit-vec"
version = "0.6.3"
version = "0.8.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "349f9b6a179ed607305526ca489b34ad0a41aed5f7980fa90eb03160b69598fb"
checksum = "5e764a1d40d510daf35e07be9eb06e75770908c27d411ee6c92109c9840eaaf7"
[[package]]
name = "bit_field"
@ -502,6 +502,12 @@ dependencies = [
"generic-array",
]
[[package]]
name = "borrow-or-share"
version = "0.2.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3eeab4423108c5d7c744f4d234de88d18d636100093ae04caf4825134b9c3a32"
[[package]]
name = "built"
version = "0.7.5"
@ -1139,6 +1145,15 @@ version = "1.13.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "60b1af1c220855b6ceac025d3f6ecdd2b7c4894bfe9cd9bda4fbb4bc7c0d4cf0"
[[package]]
name = "email_address"
version = "0.2.9"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "e079f19b08ca6239f47f8ba8509c11cf3ea30095831f7fed61441475edd8c449"
dependencies = [
"serde",
]
[[package]]
name = "encode_unicode"
version = "0.3.6"
@ -1196,12 +1211,13 @@ dependencies = [
[[package]]
name = "fancy-regex"
version = "0.11.0"
version = "0.14.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "b95f7c0680e4142284cf8b22c14a476e87d61b004a3a0861872b32ef7ead40a2"
checksum = "6e24cb5a94bcae1e5408b0effca5cd7172ea3c5755049c5f3af4cd283a165298"
dependencies = [
"bit-set",
"regex",
"regex-automata 0.4.9",
"regex-syntax 0.8.5",
]
[[package]]
@ -1247,6 +1263,17 @@ version = "1.0.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "28a80e3145d8ad11ba0995949bbcf48b9df2be62772b3d351ef017dff6ecb853"
[[package]]
name = "fluent-uri"
version = "0.3.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "1918b65d96df47d3591bed19c5cca17e3fa5d0707318e4b5ef2eae01764df7e5"
dependencies = [
"borrow-or-share",
"ref-cast",
"serde",
]
[[package]]
name = "fnv"
version = "1.0.7"
@ -1285,9 +1312,9 @@ dependencies = [
[[package]]
name = "fraction"
version = "0.13.1"
version = "0.15.3"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3027ae1df8d41b4bed2241c8fdad4acc1e7af60c8e17743534b545e77182d678"
checksum = "0f158e3ff0a1b334408dc9fb811cd99b446986f4d8b741bb08f9df1604085ae7"
dependencies = [
"lazy_static",
"num",
@ -1414,10 +1441,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "c4567c8db10ae91089c99af84c68c38da3ec2f087c3f82960bcdbf3656b6f4d7"
dependencies = [
"cfg-if",
"js-sys",
"libc",
"wasi",
"wasm-bindgen",
]
[[package]]
@ -1573,7 +1598,7 @@ dependencies = [
"native-tls",
"num_cpus",
"rand",
"reqwest",
"reqwest 0.11.27",
"serde",
"serde_json",
"thiserror",
@ -2051,15 +2076,6 @@ version = "1.70.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "7943c866cc5cd64cbc25b2e01621d07fa8eb2a1a23160ee81ce38704e97b8ecf"
[[package]]
name = "iso8601"
version = "0.6.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "924e5d73ea28f59011fec52a0d12185d496a9b075d360657aed2a5707f701153"
dependencies = [
"nom",
]
[[package]]
name = "itertools"
version = "0.10.5"
@ -2128,32 +2144,27 @@ dependencies = [
[[package]]
name = "jsonschema"
version = "0.17.1"
version = "0.28.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "2a071f4f7efc9a9118dfb627a0a94ef247986e1ab8606a4c806ae2b3aa3b6978"
checksum = "74d8eb539cdb4222da29bb658cc9881aa2477b33fb1a74c5c31450395fc1a4b2"
dependencies = [
"ahash",
"anyhow",
"base64 0.21.7",
"base64 0.22.1",
"bytecount",
"clap 4.5.21",
"email_address",
"fancy-regex",
"fraction",
"getrandom",
"iso8601",
"idna",
"itoa",
"memchr",
"num-cmp",
"once_cell",
"parking_lot",
"percent-encoding",
"regex",
"reqwest",
"referencing",
"regex-syntax 0.8.5",
"reqwest 0.12.9",
"serde",
"serde_json",
"time",
"url",
"uuid",
"uuid-simd",
]
[[package]]
@ -2984,6 +2995,12 @@ dependencies = [
"serde_json",
]
[[package]]
name = "outref"
version = "0.5.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "4030760ffd992bef45b0ae3f10ce1aba99e33464c90d14dd7c039884963ddc7a"
[[package]]
name = "overload"
version = "0.1.1"
@ -3557,6 +3574,39 @@ dependencies = [
"thiserror",
]
[[package]]
name = "ref-cast"
version = "1.0.23"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ccf0a6f84d5f1d581da8b41b47ec8600871962f2a528115b542b362d4b744931"
dependencies = [
"ref-cast-impl",
]
[[package]]
name = "ref-cast-impl"
version = "1.0.23"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "bcc303e793d3734489387d205e9b186fac9c6cfacedd98cbb2e8a5943595f3e6"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.89",
]
[[package]]
name = "referencing"
version = "0.28.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "093a875008827c0ae15c746189966e162faa05bf347719d06302c548ac63630f"
dependencies = [
"ahash",
"fluent-uri",
"once_cell",
"percent-encoding",
"serde_json",
]
[[package]]
name = "regex"
version = "1.11.1"
@ -3641,6 +3691,42 @@ dependencies = [
"winreg",
]
[[package]]
name = "reqwest"
version = "0.12.9"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a77c62af46e79de0a562e1a9849205ffcb7fc1238876e9bd743357570e04046f"
dependencies = [
"base64 0.22.1",
"bytes",
"futures-channel",
"futures-core",
"futures-util",
"http 1.1.0",
"http-body 1.0.1",
"http-body-util",
"hyper 1.5.1",
"hyper-util",
"ipnet",
"js-sys",
"log",
"mime",
"once_cell",
"percent-encoding",
"pin-project-lite",
"serde",
"serde_json",
"serde_urlencoded",
"sync_wrapper 1.0.2",
"tokio",
"tower-service",
"url",
"wasm-bindgen",
"wasm-bindgen-futures",
"web-sys",
"windows-registry",
]
[[package]]
name = "rgb"
version = "0.8.50"
@ -4220,6 +4306,9 @@ name = "sync_wrapper"
version = "1.0.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "0bf256ce5efdfa370213c1dabab5935a12e49f2c58d15e9eac2870d3b4f27263"
dependencies = [
"futures-core",
]
[[package]]
name = "synstructure"
@ -4404,7 +4493,7 @@ dependencies = [
"once_cell",
"pyo3",
"regex",
"reqwest",
"reqwest 0.11.27",
"serde",
"serde_json",
"thiserror",
@ -4445,7 +4534,7 @@ dependencies = [
"pyo3",
"rand",
"regex",
"reqwest",
"reqwest 0.11.27",
"serde",
"serde_json",
"sysinfo",
@ -4493,7 +4582,7 @@ dependencies = [
"prost-build",
"rand",
"regex",
"reqwest",
"reqwest 0.11.27",
"serde",
"serde_json",
"slotmap",
@ -4544,7 +4633,7 @@ dependencies = [
"prost-build",
"rand",
"regex",
"reqwest",
"reqwest 0.11.27",
"serde",
"serde_json",
"slotmap",
@ -5298,6 +5387,17 @@ dependencies = [
"syn 2.0.89",
]
[[package]]
name = "uuid-simd"
version = "0.8.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "23b082222b4f6619906941c17eb2297fff4c2fb96cb60164170522942a200bd8"
dependencies = [
"outref",
"uuid",
"vsimd",
]
[[package]]
name = "v_frame"
version = "0.3.8"
@ -5349,6 +5449,12 @@ version = "0.9.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a"
[[package]]
name = "vsimd"
version = "0.8.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "5c3082ca00d5a5ef149bb8b555a72ae84c9c59f7250f013ac822ac2e49b19c64"
[[package]]
name = "walkdir"
version = "2.5.0"
@ -5558,6 +5664,36 @@ dependencies = [
"windows-targets 0.52.6",
]
[[package]]
name = "windows-registry"
version = "0.2.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "e400001bb720a623c1c69032f8e3e4cf09984deec740f007dd2b03ec864804b0"
dependencies = [
"windows-result",
"windows-strings",
"windows-targets 0.52.6",
]
[[package]]
name = "windows-result"
version = "0.2.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "1d1043d8214f791817bab27572aaa8af63732e11bf84aa21a45a78d6c317ae0e"
dependencies = [
"windows-targets 0.52.6",
]
[[package]]
name = "windows-strings"
version = "0.1.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "4cd9b125c486025df0eabcb585e62173c6c9eddcec5d117d3b6e8c30e2ee4d10"
dependencies = [
"windows-result",
"windows-targets 0.52.6",
]
[[package]]
name = "windows-sys"
version = "0.45.0"

2
Dockerfile
View File

@ -1,5 +1,5 @@
# Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.80.1 AS chef
FROM lukemathwalker/cargo-chef:latest-rust-1.84.0 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse

14
Dockerfile_amd
View File

@ -1,5 +1,5 @@
# Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.80.1 AS chef
FROM lukemathwalker/cargo-chef:latest-rust-1.84.0 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
@ -268,6 +268,15 @@ COPY server/exllamav2_kernels/ .
RUN python setup.py build
FROM kernel-builder AS marlin-kernels
WORKDIR /usr/src
ENV MARLIN_KERNELS_BRANCH=v0.3.6
ENV VLLM_TARGET_DEVICE=rocm
RUN git clone https://github.com/danieldk/marlin-kernels.git && \
cd marlin-kernels && \
git checkout ${MARLIN_KERNELS_BRANCH} && \
python setup.py install
FROM kernel-builder AS moe-kernels
WORKDIR /usr/src
ENV MOE_KERNELS_BRANCH=a67b35841774b2056a73806c36661134b5054edd
@ -299,6 +308,9 @@ COPY --from=exllama-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-311
# Copy build artifacts from exllamav2 kernels builder
COPY --from=exllamav2-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages
# Copy build artifacts from marlin kernels
COPY --from=marlin-kernels /usr/src/marlin-kernels/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages
# Copy build artifacts from moe kernels
COPY --from=moe-kernels /usr/src/moe-kernels/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages

14
Dockerfile_intel
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@ -1,6 +1,6 @@
ARG PLATFORM=xpu
FROM lukemathwalker/cargo-chef:latest-rust-1.80.1 AS chef
FROM lukemathwalker/cargo-chef:latest-rust-1.84.0 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
@ -97,11 +97,10 @@ ENV HF_HOME=/data \
WORKDIR /usr/src
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/torch-2.5.0a0%2Bgite84e33f-cp311-cp311-linux_x86_64.whl --no-cache-dir
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/torchaudio-2.5.0a0%2B56bc006-cp311-cp311-linux_x86_64.whl --no-cache-dir
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/torchvision-0.20.0a0%2B8e8a208-cp311-cp311-linux_x86_64.whl --no-cache-dir
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/intel_extension_for_pytorch-2.5.10%2Bgit9d489a8-cp311-cp311-linux_x86_64.whl --no-cache-dir
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/oneccl_bind_pt-2.5.0%2Bxpu-cp311-cp311-linux_x86_64.whl --no-cache-dir
RUN pip install https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/torch-2.5.0a0%2Bgite84e33f-cp311-cp311-linux_x86_64.whl --no-cache-dir
RUN pip install https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/torchaudio-2.5.0a0%2B56bc006-cp311-cp311-linux_x86_64.whl --no-cache-dir
RUN pip install https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/torchvision-0.20.0a0%2B8e8a208-cp311-cp311-linux_x86_64.whl --no-cache-dir
RUN pip install https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/oneccl_bind_pt-2.5.0%2Bxpu-cp311-cp311-linux_x86_64.whl --no-cache-dir
RUN pip install triton-xpu==3.0.0b2 --no-cache-dir
@ -119,6 +118,9 @@ ENV CCL_ZE_IPC_EXCHANGE=sockets
#ENV TORCH_LLM_ALLREDUCE=1
#ENV CCL_TOPO_FABRIC_VERTEX_CONNECTION_CHECK=0
RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout 033af6f63745ac748cccdadee5c6140c7971edf6
RUN cd intel-extension-for-pytorch && git submodule update --init --recursive && USE_AOT_DEVLIST='pvc,ats-m150' BUILD_SEPARATE_OPS=OFF BUILD_WITH_CPU=OFF USE_XETLA=ON python setup.py install && rm -rf /usr/src/intel-extension-for-pytorch
# Install benchmarker
COPY --from=builder /usr/src/target/release-opt/text-generation-benchmark /usr/local/bin/text-generation-benchmark
# Install router

2
Dockerfile_trtllm
View File

@ -2,7 +2,7 @@ ARG CUDA_ARCH_LIST="75-real;80-real;86-real;89-real;90-real"
ARG OMPI_VERSION="4.1.7rc1"
# Build dependencies resolver stage
FROM lukemathwalker/cargo-chef:latest AS chef
FROM lukemathwalker/cargo-chef:latest-rust-1.84.0 AS chef
WORKDIR /usr/src/text-generation-inference/backends/trtllm
FROM chef AS planner

17
README.md
View File

@ -1,7 +1,7 @@
<div align="center">
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
<img width=560 width=315 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
<img width=560 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
</a>
# Text Generation Inference
@ -84,7 +84,7 @@ model=HuggingFaceH4/zephyr-7b-beta
volume=$PWD/data
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
3.0.0 ghcr.io/huggingface/text-generation-inference:3.0.0 --model-id $model
ghcr.io/huggingface/text-generation-inference:3.0.0 --model-id $model
```
And then you can make requests like
@ -141,8 +141,8 @@ You have the option to utilize the `HF_TOKEN` environment variable for configuri
For example, if you want to serve the gated Llama V2 model variants:
1. Go to https://huggingface.co/settings/tokens
2. Copy your cli READ token
3. Export `HF_TOKEN=<your cli READ token>`
2. Copy your CLI READ token
3. Export `HF_TOKEN=<your CLI READ token>`
or with Docker:
@ -151,13 +151,14 @@ model=meta-llama/Meta-Llama-3.1-8B-Instruct
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
token=<your cli READ token>
docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.0 --model-id $model
docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/data \
ghcr.io/huggingface/text-generation-inference:3.0.0 --model-id $model
```
### A note on Shared Memory (shm)
[`NCCL`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html) is a communication framework used by
`PyTorch` to do distributed training/inference. `text-generation-inference` make
`PyTorch` to do distributed training/inference. `text-generation-inference` makes
use of `NCCL` to enable Tensor Parallelism to dramatically speed up inference for large language models.
In order to share data between the different devices of a `NCCL` group, `NCCL` might fall back to using the host memory if
@ -196,7 +197,7 @@ Detailed blogpost by Adyen on TGI inner workings: [LLM inference at scale with T
You can also opt to install `text-generation-inference` locally.
First clone the repository and change directoy into it:
First clone the repository and change directory into it:
```shell
git clone https://github.com/huggingface/text-generation-inference
@ -213,7 +214,7 @@ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
conda create -n text-generation-inference python=3.11
conda activate text-generation-inference
#using pyton venv
#using python venv
python3 -m venv .venv
source .venv/bin/activate
```

2
backends/grpc-metadata/src/lib.rs
View File

@ -8,7 +8,7 @@ use tracing_opentelemetry::OpenTelemetrySpanExt;
/// Inject context in the metadata of a gRPC request.
struct MetadataInjector<'a>(pub &'a mut tonic::metadata::MetadataMap);
impl<'a> Injector for MetadataInjector<'a> {
impl Injector for MetadataInjector<'_> {
/// Set a key and value in the MetadataMap. Does nothing if the key or value are not valid inputs
fn set(&mut self, key: &str, value: String) {
if let Ok(key) = tonic::metadata::MetadataKey::from_bytes(key.as_bytes()) {

2
backends/v2/Cargo.toml
View File

@ -23,7 +23,7 @@ clap = { version = "4.4.5", features = ["derive", "env"] }
grpc-metadata = { path = "../grpc-metadata" }
futures = "0.3.28"
hf-hub = { workspace = true }
jsonschema = { version = "0.17.1", features = ["draft202012"] }
jsonschema = { version = "0.28.0" }
metrics = { workspace = true }
metrics-exporter-prometheus = { workspace = true }
nohash-hasher = "0.2.0"

11
backends/v2/src/queue.rs
View File

@ -213,8 +213,7 @@ impl State {
}
// Pad prefill_token_budget to be a multiple of block size
let prefill_token_budget =
((prefill_token_budget + self.block_size - 1) / self.block_size) * self.block_size;
let prefill_token_budget = prefill_token_budget.div_ceil(self.block_size) * self.block_size;
// Create span for this batch to add context to inference calls
let next_batch_span = info_span!(parent: None, "batch", batch_size = tracing::field::Empty);
@ -245,9 +244,8 @@ impl State {
prefill_tokens = (batch_requests.len() + 1) as u32 * max_input_length
} else {
// pad to block size
prefill_tokens += ((entry.request.input_length + self.block_size - 1)
/ self.block_size)
* self.block_size;
prefill_tokens +=
entry.request.input_length.div_ceil(self.block_size) * self.block_size;
}
if self.requires_padding {
@ -262,8 +260,7 @@ impl State {
};
// pad to block size
decode_tokens +=
((max_new_tokens + self.block_size - 1) / self.block_size) * self.block_size;
decode_tokens += max_new_tokens.div_ceil(self.block_size) * self.block_size;
}
if prefill_tokens > prefill_token_budget

2
backends/v3/Cargo.toml
View File

@ -23,7 +23,7 @@ clap = { version = "4.4.5", features = ["derive", "env"] }
grpc-metadata = { path = "../grpc-metadata" }
futures = "0.3.28"
hf-hub = { workspace = true }
jsonschema = { version = "0.17.1", features = ["draft202012"] }
jsonschema = { version = "0.28.0" }
metrics = { workspace = true }
metrics-exporter-prometheus = { workspace = true }
nohash-hasher = "0.2.0"

4
backends/v3/src/block_allocator.rs
View File

@ -165,13 +165,13 @@ impl Allocator for SimpleAllocator {
let (tokens, repeats) = match self.window_size {
None => (tokens, 1),
Some(window_size) => {
let repeats = (tokens + window_size - 1) / window_size;
let repeats = tokens.div_ceil(window_size);
let tokens = core::cmp::min(tokens, window_size);
(tokens, repeats as usize)
}
};
// Pad to a multiple of block size
let required_blocks = (tokens + self.block_size - 1) / self.block_size;
let required_blocks = tokens.div_ceil(self.block_size);
(required_blocks, repeats)
};

3
backends/v3/src/queue.rs
View File

@ -257,8 +257,7 @@ impl State {
}
// Pad prefill_token_budget to be a multiple of block size
let prefill_token_budget =
((prefill_token_budget + self.block_size - 1) / self.block_size) * self.block_size;
let prefill_token_budget = prefill_token_budget.div_ceil(self.block_size) * self.block_size;
// Create span for this batch to add context to inference calls
let next_batch_span = info_span!(parent: None, "batch", batch_size = tracing::field::Empty);

2
backends/v3/src/radix.rs
View File

@ -103,7 +103,7 @@ impl Allocator for RadixAllocator {
let prefix_len = blocks.len() * self.block_size as usize;
let suffix_len = tokens - prefix_len as u32;
let suffix_blocks = (suffix_len + self.block_size - 1) / self.block_size;
let suffix_blocks = suffix_len.div_ceil(self.block_size);
tracing::info!("Prefix {prefix_len} - Suffix {suffix_len}");

2
docs/source/_toctree.yml
View File

@ -13,6 +13,8 @@
title: Using TGI with Intel Gaudi
- local: installation_inferentia
title: Using TGI with AWS Inferentia
- local: installation_tpu
title: Using TGI with Google TPUs
- local: installation_intel
title: Using TGI with Intel GPUs
- local: installation

2
docs/source/basic_tutorials/using_guidance.md
View File

@ -187,8 +187,6 @@ In addition to the grammar parameter, we've also introduced a set of tools and f
Tools are a set of user defined functions that can be used in tandem with the chat functionality to enhance the LLM's capabilities. Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API.
Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API.
```json
curl localhost:3000/v1/chat/completions \
-X POST \

2
docs/source/conceptual/speculation.md
View File

@ -27,7 +27,7 @@ You can check a few existing fine-tunes for popular models:
- [text-generation-inference/Mistral-7B-Instruct-v0.2-medusa](https://huggingface.co/text-generation-inference/Mistral-7B-Instruct-v0.2-medusa)
In order to create your own medusa heads for your own finetune, you should check own the original medusa repo. [../basic_tutorials/train_medusa.md](../basic_tutorials/train_medusa.md)
In order to create your own medusa heads for your own finetune, you should check own the original medusa repo. Read for more in [Train Medusa](../basic_tutorials/train_medusa#training).
In order to use medusa models in TGI, simply point to a medusa enabled model, and everything will load automatically.

3
docs/source/installation_tpu.md Normal file
View File

@ -0,0 +1,3 @@
# Using TGI with Google TPUs
Check out this [guide](https://huggingface.co/docs/optimum-tpu) on how to serve models with TGI on TPUs.

1
docs/source/supported_models.md
View File

@ -5,6 +5,7 @@ Text Generation Inference enables serving optimized models. The following sectio
- [Deepseek V2](https://huggingface.co/deepseek-ai/DeepSeek-V2)
- [Idefics 2](https://huggingface.co/HuggingFaceM4/idefics2-8b) (Multimodal)
- [Idefics 3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (Multimodal)
- [Llava Next (1.6)](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) (Multimodal)
- [Llama](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f)
- [Phi 3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)

18
flake.lock
View File

@ -108,11 +108,11 @@
"pre-commit-hooks": "pre-commit-hooks_3"
},
"locked": {
"lastModified": 1732039290,
"narHash": "sha256-LQKY7bShf2H9kJouxa9ZspfdrulnZF9o4kLTqGqCDYM=",
"lastModified": 1734429562,
"narHash": "sha256-V2XNs3Ir8WXNHdocfzkR/fu0FzkZ9uTDJkVecxJrGmQ=",
"owner": "nix-community",
"repo": "crate2nix",
"rev": "9ff208ce7f5a482272b1bcefbe363c772d7ff914",
"rev": "8537c2d7cb623679aaeff62c4c4c43a91566ab09",
"type": "github"
},
"original": {
@ -853,11 +853,11 @@
]
},
"locked": {
"lastModified": 1732242723,
"narHash": "sha256-NWI8csIK0ujFlFuEXKnoc+7hWoCiEtINK9r48LUUMeU=",
"lastModified": 1736907983,
"narHash": "sha256-fw55wVwpJW36Md2HZBKuxX3YHGeqsGsspPLtCMVr1Y8=",
"owner": "oxalica",
"repo": "rust-overlay",
"rev": "a229311fcb45b88a95fdfa5cecd8349c809a272a",
"rev": "eaa365c911441e07e387ff6acc596619fc50b156",
"type": "github"
},
"original": {
@ -978,11 +978,11 @@
"nixpkgs": "nixpkgs_6"
},
"locked": {
"lastModified": 1732218602,
"narHash": "sha256-BElslL34KjOJCFMPkNtilOz6S/7iY7Vd72FNbRRWKDY=",
"lastModified": 1736436388,
"narHash": "sha256-CIyxVPpM9RrSwthNT/4DQ10YPk/uwzP7AeE83kBNsrE=",
"owner": "huggingface",
"repo": "text-generation-inference-nix",
"rev": "f79638ac4e420e661321261744e745a3a747e182",
"rev": "5103c3fb1f9ad1fd33b6e09ff05e957884b112d5",
"type": "github"
},
"original": {

4
integration-tests/conftest.py
View File

@ -354,6 +354,7 @@ def launcher(event_loop):
kv_cache_dtype: Optional[str] = None,
revision: Optional[str] = None,
max_input_length: Optional[int] = None,
max_input_tokens: Optional[int] = None,
max_batch_prefill_tokens: Optional[int] = None,
max_total_tokens: Optional[int] = None,
lora_adapters: Optional[List[str]] = None,
@ -402,6 +403,9 @@ def launcher(event_loop):
if max_input_length:
args.append("--max-input-length")
args.append(str(max_input_length))
if max_input_tokens:
args.append("--max-input-tokens")
args.append(str(max_input_tokens))
if max_batch_prefill_tokens:
args.append("--max-batch-prefill-tokens")
args.append(str(max_batch_prefill_tokens))

6
integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int/test_compressed_tensors_w8a8_int_all_params.json
View File

@ -32,7 +32,7 @@
},
{
"id": 1101,
"logprob": -1.0947266,
"logprob": -1.0136719,
"special": false,
"text": " also"
},
@ -56,13 +56,13 @@
},
{
"id": 4009,
"logprob": -0.15563965,
"logprob": -0.21923828,
"special": false,
"text": " network"
},
{
"id": 477,
"logprob": -1.4003906,
"logprob": -1.4824219,
"special": false,
"text": " or"
}

36
integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_all_params.json
View File

@ -8,7 +8,7 @@
"tokens": [
{
"id": 1939,
"logprob": -2.2675781,
"logprob": -2.2460938,
"special": false,
"text": "?\n\n"
},
@ -20,13 +20,13 @@
},
{
"id": 20909,
"logprob": -0.37695312,
"logprob": -0.48608398,
"special": false,
"text": " Learning"
},
{
"id": 4102,
"logprob": -1.9316406,
"logprob": -2.265625,
"special": false,
"text": " "
},
@ -38,25 +38,13 @@
},
{
"id": 458,
"logprob": -0.80859375,
"logprob": -0.6328125,
"special": false,
"text": " an"
},
{
"id": 3082,
"logprob": -1.4541016,
"special": false,
"text": " area"
},
{
"id": 315,
"logprob": 0.0,
"special": false,
"text": " of"
},
{
"id": 20443,
"logprob": -0.5136719,
"logprob": -0.1796875,
"special": false,
"text": " artificial"
},
@ -65,9 +53,21 @@
"logprob": 0.0,
"special": false,
"text": " intelligence"
},
{
"id": 320,
"logprob": -0.37695312,
"special": false,
"text": " ("
},
{
"id": 15469,
"logprob": 0.0,
"special": false,
"text": "AI"
}
],
"top_tokens": null
},
"generated_text": "What is deep learning?\n\nDeep Learning is an area of artificial intelligence"
"generated_text": "What is deep learning?\n\nDeep Learning is an artificial intelligence (AI"
}

80
integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_load.json
View File

@ -9,61 +9,61 @@
"tokens": [
{
"id": 18183,
"logprob": -1.6669922,
"logprob": -1.4912109,
"special": false,
"text": " Deep"
},
{
"id": 6832,
"logprob": -0.08959961,
"logprob": -0.075683594,
"special": false,
"text": " learning"
},
{
"id": 374,
"logprob": -0.14685059,
"logprob": -0.12408447,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.125,
"logprob": -0.12768555,
"special": false,
"text": " a"
},
{
"id": 25993,
"logprob": -0.81640625,
"logprob": -0.82128906,
"special": false,
"text": " subset"
},
{
"id": 315,
"logprob": -0.0013418198,
"logprob": -0.0012636185,
"special": false,
"text": " of"
},
{
"id": 5662,
"logprob": -0.16259766,
"logprob": -0.12878418,
"special": false,
"text": " machine"
},
{
"id": 6832,
"logprob": -0.0016393661,
"logprob": -0.0015888214,
"special": false,
"text": " learning"
},
{
"id": 429,
"logprob": -0.4477539,
"logprob": -0.49194336,
"special": false,
"text": " that"
},
{
"id": 5711,
"logprob": -1.2802734,
"logprob": -1.2626953,
"special": false,
"text": " uses"
}
@ -82,61 +82,61 @@
"tokens": [
{
"id": 18183,
"logprob": -1.6669922,
"logprob": -1.4912109,
"special": false,
"text": " Deep"
},
{
"id": 6832,
"logprob": -0.08959961,
"logprob": -0.075683594,
"special": false,
"text": " learning"
},
{
"id": 374,
"logprob": -0.14685059,
"logprob": -0.12408447,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.125,
"logprob": -0.12768555,
"special": false,
"text": " a"
},
{
"id": 25993,
"logprob": -0.81640625,
"logprob": -0.82128906,
"special": false,
"text": " subset"
},
{
"id": 315,
"logprob": -0.0013418198,
"logprob": -0.0012636185,
"special": false,
"text": " of"
},
{
"id": 5662,
"logprob": -0.16259766,
"logprob": -0.12878418,
"special": false,
"text": " machine"
},
{
"id": 6832,
"logprob": -0.0016393661,
"logprob": -0.0015888214,
"special": false,
"text": " learning"
},
{
"id": 429,
"logprob": -0.4477539,
"logprob": -0.49194336,
"special": false,
"text": " that"
},
{
"id": 5711,
"logprob": -1.2802734,
"logprob": -1.2626953,
"special": false,
"text": " uses"
}
@ -155,61 +155,61 @@
"tokens": [
{
"id": 18183,
"logprob": -1.6669922,
"logprob": -1.4912109,
"special": false,
"text": " Deep"
},
{
"id": 6832,
"logprob": -0.08959961,
"logprob": -0.075683594,
"special": false,
"text": " learning"
},
{
"id": 374,
"logprob": -0.14685059,
"logprob": -0.12408447,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.125,
"logprob": -0.12768555,
"special": false,
"text": " a"
},
{
"id": 25993,
"logprob": -0.81640625,
"logprob": -0.82128906,
"special": false,
"text": " subset"
},
{
"id": 315,
"logprob": -0.0013418198,
"logprob": -0.0012636185,
"special": false,
"text": " of"
},
{
"id": 5662,
"logprob": -0.16259766,
"logprob": -0.12878418,
"special": false,
"text": " machine"
},
{
"id": 6832,
"logprob": -0.0016393661,
"logprob": -0.0015888214,
"special": false,
"text": " learning"
},
{
"id": 429,
"logprob": -0.4477539,
"logprob": -0.49194336,
"special": false,
"text": " that"
},
{
"id": 5711,
"logprob": -1.2802734,
"logprob": -1.2626953,
"special": false,
"text": " uses"
}
@ -228,61 +228,61 @@
"tokens": [
{
"id": 18183,
"logprob": -1.6669922,
"logprob": -1.4912109,
"special": false,
"text": " Deep"
},
{
"id": 6832,
"logprob": -0.08959961,
"logprob": -0.075683594,
"special": false,
"text": " learning"
},
{
"id": 374,
"logprob": -0.14685059,
"logprob": -0.12408447,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.125,
"logprob": -0.12768555,
"special": false,
"text": " a"
},
{
"id": 25993,
"logprob": -0.81640625,
"logprob": -0.82128906,
"special": false,
"text": " subset"
},
{
"id": 315,
"logprob": -0.0013418198,
"logprob": -0.0012636185,
"special": false,
"text": " of"
},
{
"id": 5662,
"logprob": -0.16259766,
"logprob": -0.12878418,
"special": false,
"text": " machine"
},
{
"id": 6832,
"logprob": -0.0016393661,
"logprob": -0.0015888214,
"special": false,
"text": " learning"
},
{
"id": 429,
"logprob": -0.4477539,
"logprob": -0.49194336,
"special": false,
"text": " that"
},
{
"id": 5711,
"logprob": -1.2802734,
"logprob": -1.2626953,
"special": false,
"text": " uses"
}

2
integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int/test_compressed_tensors_wna16_all_params.json
View File

@ -44,7 +44,7 @@
},
{
"id": 38397,
"logprob": -0.12695312,
"logprob": 0.0,
"special": false,
"text": " subset"
},

70
integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_all_params.json
View File

@ -14,60 +14,60 @@
},
{
"id": 573,
"logprob": -0.18493652,
"logprob": -0.19030762,
"special": false,
"text": " the"
},
{
"id": 16819,
"logprob": -1.4804688,
"logprob": -1.4863281,
"special": false,
"text": " detection"
},
{
"id": 576,
"logprob": -0.7011719,
"logprob": -0.7089844,
"special": false,
"text": " of"
},
{
"id": 573,
"logprob": -2.0410156,
"special": false,
"text": " the"
},
{
"id": 8566,
"logprob": 0.0,
"special": false,
"text": " presence"
},
{
"id": 689,
"logprob": -0.16491699,
"special": false,
"text": " or"
},
{
"id": 14862,
"logprob": 0.0,
"special": false,
"text": " absence"
},
{
"id": 576,
"logprob": -0.9970703,
"special": false,
"text": " of"
},
{
"id": 671,
"logprob": -2.1738281,
"logprob": -0.5292969,
"special": false,
"text": " an"
},
{
"id": 24646,
"logprob": -3.0449219,
"special": false,
"text": " RNA"
},
{
"id": 12369,
"logprob": -0.19299316,
"special": false,
"text": " virus"
},
{
"id": 575,
"logprob": -0.10632324,
"special": false,
"text": " in"
},
{
"id": 6022,
"logprob": -0.98095703,
"special": false,
"text": " patients"
},
{
"id": 1064,
"logprob": -1.3095703,
"special": false,
"text": " who"
}
],
"top_tokens": null
},
"generated_text": "Test request for the detection of an RNA virus in patients who"
"generated_text": "Test request for the detection of the presence or absence of an"
}

114
integration-tests/models/__snapshots__/test_flash_starcoder2/test_flash_starcoder2_default_params.json
View File

@ -8,7 +8,7 @@
"tokens": [
{
"id": 2284,
"logprob": -0.296875,
"logprob": -0.31323242,
"special": false,
"text": "():"
},
@ -38,13 +38,13 @@
},
{
"id": 10914,
"logprob": -0.7734375,
"logprob": -0.7871094,
"special": false,
"text": " World"
},
{
"id": 16013,
"logprob": -0.61816406,
"logprob": -0.64746094,
"special": false,
"text": "!\")"
},
@ -62,7 +62,7 @@
},
{
"id": 610,
"logprob": -0.4152832,
"logprob": -0.41064453,
"special": false,
"text": "def"
},
@ -92,7 +92,7 @@
},
{
"id": 444,
"logprob": -0.21618652,
"logprob": -0.21655273,
"special": false,
"text": "name"
},
@ -139,28 +139,16 @@
"text": "Hello"
},
{
"id": 925,
"logprob": -3.3476562,
"id": 332,
"logprob": -0.034698486,
"special": false,
"text": " %"
"text": " \""
},
{
"id": 120,
"id": 494,
"logprob": 0.0,
"special": false,
"text": "s"
},
{
"id": 11571,
"logprob": -0.08892822,
"special": false,
"text": "!\""
},
{
"id": 925,
"logprob": 0.0,
"special": false,
"text": " %"
"text": " +"
},
{
"id": 655,
@ -169,10 +157,22 @@
"text": " name"
},
{
"id": 46,
"id": 494,
"logprob": -0.20141602,
"special": false,
"text": " +"
},
{
"id": 332,
"logprob": 0.0,
"special": false,
"text": ")"
"text": " \""
},
{
"id": 16013,
"logprob": 0.0,
"special": false,
"text": "!\")"
},
{
"id": 222,
@ -230,7 +230,7 @@
},
{
"id": 400,
"logprob": -0.074279785,
"logprob": 0.0,
"special": false,
"text": "age"
},
@ -289,22 +289,34 @@
"text": "Hello"
},
{
"id": 925,
"id": 332,
"logprob": 0.0,
"special": false,
"text": " %"
"text": " \""
},
{
"id": 120,
"id": 494,
"logprob": 0.0,
"special": false,
"text": "s"
"text": " +"
},
{
"id": 49,
"logprob": -0.07891846,
"id": 655,
"logprob": 0.0,
"special": false,
"text": ","
"text": " name"
},
{
"id": 494,
"logprob": 0.0,
"special": false,
"text": " +"
},
{
"id": 3021,
"logprob": -0.5761719,
"special": false,
"text": " \","
},
{
"id": 863,
@ -319,55 +331,43 @@
"text": " are"
},
{
"id": 925,
"id": 332,
"logprob": 0.0,
"special": false,
"text": " %"
"text": " \""
},
{
"id": 105,
"id": 494,
"logprob": 0.0,
"special": false,
"text": "d"
"text": " +"
},
{
"id": 11339,
"id": 615,
"logprob": 0.0,
"special": false,
"text": " years"
"text": " str"
},
{
"id": 3627,
"id": 45,
"logprob": 0.0,
"special": false,
"text": " old"
"text": "("
},
{
"id": 11571,
"id": 400,
"logprob": 0.0,
"special": false,
"text": "!\""
"text": "age"
},
{
"id": 925,
"id": 46,
"logprob": 0.0,
"special": false,
"text": " %"
},
{
"id": 327,
"logprob": 0.0,
"special": false,
"text": " ("
},
{
"id": 444,
"logprob": 0.0,
"special": false,
"text": "name"
"text": ")"
}
],
"top_tokens": null
},
"generated_text": "():\n print(\"Hello World!\")\n\ndef print_hello_name(name):\n print(\"Hello %s!\" % name)\n\ndef print_hello_name_age(name, age):\n print(\"Hello %s, you are %d years old!\" % (name"
"generated_text": "():\n print(\"Hello World!\")\n\ndef print_hello_name(name):\n print(\"Hello \" + name + \"!\")\n\ndef print_hello_name_age(name, age):\n print(\"Hello \" + name + \", you are \" + str(age)"
}

73
integration-tests/models/__snapshots__/test_flash_starcoder2_lora/test_flash_starcoder2.json Normal file
View File

@ -0,0 +1,73 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 2284,
"logprob": -0.9355469,
"special": false,
"text": "():"
},
{
"id": 303,
"logprob": -0.40795898,
"special": false,
"text": "\n "
},
{
"id": 1489,
"logprob": -0.27954102,
"special": false,
"text": " print"
},
{
"id": 459,
"logprob": -0.6142578,
"special": false,
"text": "(\""
},
{
"id": 8302,
"logprob": -0.68310547,
"special": false,
"text": "Hello"
},
{
"id": 10914,
"logprob": -1.4599609,
"special": false,
"text": " World"
},
{
"id": 16013,
"logprob": -0.80126953,
"special": false,
"text": "!\")"
},
{
"id": 222,
"logprob": -0.625,
"special": false,
"text": "\n"
},
{
"id": 222,
"logprob": -0.23242188,
"special": false,
"text": "\n"
},
{
"id": 610,
"logprob": -1.2294922,
"special": false,
"text": "def"
}
],
"top_tokens": null
},
"generated_text": "():\n print(\"Hello World!\")\n\ndef"
}

373
integration-tests/models/__snapshots__/test_flash_starcoder2_lora/test_flash_starcoder2_default_params.json Normal file
View File

@ -0,0 +1,373 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 60,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 222,
"logprob": 0.0,
"special": false,
"text": "\n"
},
{
"id": 222,
"logprob": 0.0,
"special": false,
"text": "\n"
},
{
"id": 40,
"logprob": -0.7944336,
"special": false,
"text": "#"
},
{
"id": 494,
"logprob": 0.0,
"special": false,
"text": " +"
},
{
"id": 447,
"logprob": -0.1796875,
"special": false,
"text": " ["
},
{
"id": 9009,
"logprob": 0.0,
"special": false,
"text": "markdown"
},
{
"id": 98,
"logprob": 0.0,
"special": false,
"text": "]"
},
{
"id": 37402,
"logprob": 0.0,
"special": false,
"text": " slideshow"
},
{
"id": 8492,
"logprob": 0.0,
"special": false,
"text": "={\""
},
{
"id": 7277,
"logprob": 0.0,
"special": false,
"text": "slide"
},
{
"id": 100,
"logprob": 0.0,
"special": false,
"text": "_"
},
{
"id": 700,
"logprob": 0.0,
"special": false,
"text": "type"
},
{
"id": 582,
"logprob": 0.0,
"special": false,
"text": "\":"
},
{
"id": 332,
"logprob": 0.0,
"special": false,
"text": " \""
},
{
"id": 7277,
"logprob": -0.06994629,
"special": false,
"text": "slide"
},
{
"id": 3667,
"logprob": 0.0,
"special": false,
"text": "\"}"
},
{
"id": 222,
"logprob": 0.0,
"special": false,
"text": "\n"
},
{
"id": 40,
"logprob": 0.0,
"special": false,
"text": "#"
},
{
"id": 607,
"logprob": -0.8261719,
"special": false,
"text": " #"
},
{
"id": 244,
"logprob": -1.8574219,
"special": false,
"text": " "
},
{
"id": 55,
"logprob": -1.4541016,
"special": false,
"text": "2"
},
{
"id": 51,
"logprob": 0.0,
"special": false,
"text": "."
},
{
"id": 6208,
"logprob": -0.9794922,
"special": false,
"text": " What"
},
{
"id": 458,
"logprob": 0.0,
"special": false,
"text": " is"
},
{
"id": 341,
"logprob": 0.0,
"special": false,
"text": " the"
},
{
"id": 10609,
"logprob": -0.69189453,
"special": false,
"text": " difference"
},
{
"id": 3761,
"logprob": 0.0,
"special": false,
"text": " between"
},
{
"id": 331,
"logprob": 0.0,
"special": false,
"text": " a"
},
{
"id": 1168,
"logprob": -0.27172852,
"special": false,
"text": " list"
},
{
"id": 480,
"logprob": 0.0,
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"text": " and"
},
{
"id": 331,
"logprob": 0.0,
"special": false,
"text": " a"
},
{
"id": 8871,
"logprob": 0.0,
"special": false,
"text": " tuple"
},
{
"id": 68,
"logprob": 0.0,
"special": false,
"text": "?"
},
{
"id": 222,
"logprob": 0.0,
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},
{
"id": 40,
"logprob": -1.3359375,
"special": false,
"text": "#"
},
{
"id": 222,
"logprob": 0.0,
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"text": "\n"
},
{
"id": 40,
"logprob": 0.0,
"special": false,
"text": "#"
},
{
"id": 449,
"logprob": -0.03164673,
"special": false,
"text": " -"
},
{
"id": 418,
"logprob": -1.0947266,
"special": false,
"text": " A"
},
{
"id": 1168,
"logprob": 0.0,
"special": false,
"text": " list"
},
{
"id": 458,
"logprob": 0.0,
"special": false,
"text": " is"
},
{
"id": 331,
"logprob": -0.3305664,
"special": false,
"text": " a"
},
{
"id": 14792,
"logprob": 0.0,
"special": false,
"text": " mutable"
},
{
"id": 6645,
"logprob": -0.40478516,
"special": false,
"text": " sequence"
},
{
"id": 451,
"logprob": 0.0,
"special": false,
"text": " of"
},
{
"id": 4725,
"logprob": -0.50390625,
"special": false,
"text": " elements"
},
{
"id": 49,
"logprob": -2.1269531,
"special": false,
"text": ","
},
{
"id": 2236,
"logprob": -0.1427002,
"special": false,
"text": " while"
},
{
"id": 331,
"logprob": 0.0,
"special": false,
"text": " a"
},
{
"id": 8871,
"logprob": 0.0,
"special": false,
"text": " tuple"
},
{
"id": 458,
"logprob": 0.0,
"special": false,
"text": " is"
},
{
"id": 619,
"logprob": 0.0,
"special": false,
"text": " an"
},
{
"id": 26079,
"logprob": 0.0,
"special": false,
"text": " immutable"
},
{
"id": 6645,
"logprob": 0.0,
"special": false,
"text": " sequence"
},
{
"id": 451,
"logprob": 0.0,
"special": false,
"text": " of"
},
{
"id": 4725,
"logprob": 0.0,
"special": false,
"text": " elements"
},
{
"id": 51,
"logprob": 0.0,
"special": false,
"text": "."
},
{
"id": 222,
"logprob": 0.0,
"special": false,
"text": "\n"
},
{
"id": 40,
"logprob": 0.0,
"special": false,
"text": "#"
},
{
"id": 449,
"logprob": 0.0,
"special": false,
"text": " -"
}
],
"top_tokens": null
},
"generated_text": "\n\n# + [markdown] slideshow={\"slide_type\": \"slide\"}\n# # 2. What is the difference between a list and a tuple?\n#\n# - A list is a mutable sequence of elements, while a tuple is an immutable sequence of elements.\n# -"
}

294
integration-tests/models/__snapshots__/test_flash_starcoder2_lora/test_flash_starcoder2_load.json Normal file
View File

@ -0,0 +1,294 @@
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 222,
"logprob": -1.9091797,
"special": false,
"text": "\n"
},
{
"id": 222,
"logprob": -1.0478516,
"special": false,
"text": "\n"
},
{
"id": 40,
"logprob": -3.015625,
"special": false,
"text": "#"
},
{
"id": 494,
"logprob": -1.4228516,
"special": false,
"text": " +"
},
{
"id": 447,
"logprob": -1.1025391,
"special": false,
"text": " ["
},
{
"id": 9009,
"logprob": -0.0008444786,
"special": false,
"text": "markdown"
},
{
"id": 98,
"logprob": -8.8095665e-05,
"special": false,
"text": "]"
},
{
"id": 37402,
"logprob": -0.5810547,
"special": false,
"text": " slideshow"
},
{
"id": 8492,
"logprob": -0.00022864342,
"special": false,
"text": "={\""
},
{
"id": 7277,
"logprob": -0.00030994415,
"special": false,
"text": "slide"
}
],
"top_tokens": null
},
"generated_text": "\n\n# + [markdown] slideshow={\"slide"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 222,
"logprob": -1.9091797,
"special": false,
"text": "\n"
},
{
"id": 222,
"logprob": -1.0478516,
"special": false,
"text": "\n"
},
{
"id": 40,
"logprob": -3.015625,
"special": false,
"text": "#"
},
{
"id": 494,
"logprob": -1.4228516,
"special": false,
"text": " +"
},
{
"id": 447,
"logprob": -1.1025391,
"special": false,
"text": " ["
},
{
"id": 9009,
"logprob": -0.0008444786,
"special": false,
"text": "markdown"
},
{
"id": 98,
"logprob": -8.8095665e-05,
"special": false,
"text": "]"
},
{
"id": 37402,
"logprob": -0.5810547,
"special": false,
"text": " slideshow"
},
{
"id": 8492,
"logprob": -0.00022864342,
"special": false,
"text": "={\""
},
{
"id": 7277,
"logprob": -0.00030994415,
"special": false,
"text": "slide"
}
],
"top_tokens": null
},
"generated_text": "\n\n# + [markdown] slideshow={\"slide"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 222,
"logprob": -1.9091797,
"special": false,
"text": "\n"
},
{
"id": 222,
"logprob": -1.0478516,
"special": false,
"text": "\n"
},
{
"id": 40,
"logprob": -3.015625,
"special": false,
"text": "#"
},
{
"id": 494,
"logprob": -1.4228516,
"special": false,
"text": " +"
},
{
"id": 447,
"logprob": -1.1025391,
"special": false,
"text": " ["
},
{
"id": 9009,
"logprob": -0.0008444786,
"special": false,
"text": "markdown"
},
{
"id": 98,
"logprob": -8.8095665e-05,
"special": false,
"text": "]"
},
{
"id": 37402,
"logprob": -0.5810547,
"special": false,
"text": " slideshow"
},
{
"id": 8492,
"logprob": -0.00022864342,
"special": false,
"text": "={\""
},
{
"id": 7277,
"logprob": -0.00030994415,
"special": false,
"text": "slide"
}
],
"top_tokens": null
},
"generated_text": "\n\n# + [markdown] slideshow={\"slide"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 222,
"logprob": -1.9091797,
"special": false,
"text": "\n"
},
{
"id": 222,
"logprob": -1.0478516,
"special": false,
"text": "\n"
},
{
"id": 40,
"logprob": -3.015625,
"special": false,
"text": "#"
},
{
"id": 494,
"logprob": -1.4228516,
"special": false,
"text": " +"
},
{
"id": 447,
"logprob": -1.1025391,
"special": false,
"text": " ["
},
{
"id": 9009,
"logprob": -0.0008444786,
"special": false,
"text": "markdown"
},
{
"id": 98,
"logprob": -8.8095665e-05,
"special": false,
"text": "]"
},
{
"id": 37402,
"logprob": -0.5810547,
"special": false,
"text": " slideshow"
},
{
"id": 8492,
"logprob": -0.00022864342,
"special": false,
"text": "={\""
},
{
"id": 7277,
"logprob": -0.00030994415,
"special": false,
"text": "slide"
}
],
"top_tokens": null
},
"generated_text": "\n\n# + [markdown] slideshow={\"slide"
}
]

71
integration-tests/models/__snapshots__/test_flash_starcoder2_lora/test_flash_starcoder2_with_hugcode_adapter.json Normal file
View File

@ -0,0 +1,71 @@
{
"details": {
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 100,
"logprob": -0.9824219,
"special": false,
"text": "_"
},
{
"id": 5879,
"logprob": -0.3017578,
"special": false,
"text": "world"
},
{
"id": 2284,
"logprob": -0.68652344,
"special": false,
"text": "():"
},
{
"id": 303,
"logprob": -0.27734375,
"special": false,
"text": "\n "
},
{
"id": 1489,
"logprob": -0.4482422,
"special": false,
"text": " print"
},
{
"id": 459,
"logprob": -0.54248047,
"special": false,
"text": "(\""
},
{
"id": 8302,
"logprob": -0.4296875,
"special": false,
"text": "Hello"
},
{
"id": 10914,
"logprob": -0.8544922,
"special": false,
"text": " World"
},
{
"id": 16013,
"logprob": -0.7573242,
"special": false,
"text": "!\")"
},
{
"id": 222,
"logprob": -0.81347656,
"special": false,
"text": "\n"
}
]
},
"generated_text": "_world():\n print(\"Hello World!\")\n"
}

67
integration-tests/models/__snapshots__/test_idefics3/test_flash_idefics3_next_simple_url.json Normal file
View File

@ -0,0 +1,67 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 9,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 2684,
"logprob": -0.24902344,
"special": false,
"text": " There"
},
{
"id": 374,
"logprob": -0.0703125,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.23535156,
"special": false,
"text": " a"
},
{
"id": 35372,
"logprob": -0.125,
"special": false,
"text": " statue"
},
{
"id": 304,
"logprob": -0.30273438,
"special": false,
"text": " in"
},
{
"id": 279,
"logprob": -0.20507812,
"special": false,
"text": " the"
},
{
"id": 2217,
"logprob": -0.076171875,
"special": false,
"text": " image"
},
{
"id": 13,
"logprob": -0.053710938,
"special": false,
"text": "."
},
{
"id": 128258,
"logprob": -0.011352539,
"special": true,
"text": "<end_of_utterance>"
}
],
"top_tokens": null
},
"generated_text": " There is a statue in the image."
}

61
integration-tests/models/__snapshots__/test_smolvlm/test_flash_smolvlm_next_simple_url.json Normal file
View File

@ -0,0 +1,61 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 8,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 330,
"logprob": -0.118652344,
"special": false,
"text": " A"
},
{
"id": 11426,
"logprob": -0.28320312,
"special": false,
"text": " bee"
},
{
"id": 335,
"logprob": -0.95703125,
"special": false,
"text": " on"
},
{
"id": 253,
"logprob": -0.06982422,
"special": false,
"text": " a"
},
{
"id": 11986,
"logprob": -0.49414062,
"special": false,
"text": " pink"
},
{
"id": 8525,
"logprob": -0.07763672,
"special": false,
"text": " flower"
},
{
"id": 30,
"logprob": -1.0703125,
"special": false,
"text": "."
},
{
"id": 49154,
"logprob": -0.092285156,
"special": true,
"text": "<end_of_utterance>"
}
],
"top_tokens": null
},
"generated_text": " A bee on a pink flower."
}

2
integration-tests/models/test_compressed_tensors_w8a8_int_dynamic_weight.py
View File

@ -64,7 +64,7 @@ async def test_compressed_tensors_w8a8_int_dynamic_weight_all_params(
assert response.details.generated_tokens == 10
assert (
response.generated_text
== "What is deep learning?\n\nDeep Learning is an area of artificial intelligence"
== "What is deep learning?\n\nDeep Learning is an artificial intelligence (AI"
)
assert response == response_snapshot

79
integration-tests/models/test_flash_starcoder2_lora.py Normal file
View File

@ -0,0 +1,79 @@
import pytest
import requests
@pytest.fixture(scope="module")
def flash_starcoder2_handle(launcher):
with launcher(
"bigcode/starcoder2-3b", lora_adapters=["smangrul/starcoder-3b-hugcoder"]
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_starcoder2(flash_starcoder2_handle):
await flash_starcoder2_handle.health(300)
return flash_starcoder2_handle.client
@pytest.mark.asyncio
async def test_flash_starcoder2(flash_starcoder2, response_snapshot):
response = await flash_starcoder2.generate(
"def print_hello", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_flash_starcoder2_default_params(flash_starcoder2, response_snapshot):
response = await flash_starcoder2.generate(
"who are you?",
max_new_tokens=60,
temperature=0.2,
top_p=0.95,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 60
assert response == response_snapshot
@pytest.mark.asyncio
async def test_flash_starcoder2_load(
flash_starcoder2, generate_load, response_snapshot
):
responses = await generate_load(
flash_starcoder2, "who are you?", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
@pytest.mark.asyncio
async def test_flash_starcoder2_with_hugcode_adapter(
flash_starcoder2, response_snapshot
):
response = requests.post(
f"{flash_starcoder2.base_url}/generate",
headers=flash_starcoder2.headers,
json={
"inputs": "def print_hello",
"parameters": {
"max_new_tokens": 10,
"adapter_id": "smangrul/starcoder-3b-hugcoder",
"details": True,
},
},
)
assert response.status_code == 200
data = response.json()
assert data["generated_text"] == '_world():\n print("Hello World!")\n'
assert data == response_snapshot

31
integration-tests/models/test_idefics3.py Normal file
View File

@ -0,0 +1,31 @@
import pytest
@pytest.fixture(scope="module")
def flash_idefics3_next_handle(launcher):
with launcher("HuggingFaceM4/Idefics3-8B-Llama3") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_idefics3_next(flash_idefics3_next_handle):
await flash_idefics3_next_handle.health(300)
return flash_idefics3_next_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_idefics3_next_simple_url(flash_idefics3_next, response_snapshot):
ny_skyline = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
query = "What is in this image?"
response = await flash_idefics3_next.generate(
f"<|begin_of_text|><|begin_of_text|>User:![]({ny_skyline}){query}<end_of_utterance>\nAssistant:",
max_new_tokens=10,
seed=1337,
)
print(response)
assert (
response.generated_text == " There is a statue in the image."
), f"{repr(response.generated_text)}"
assert response.details.generated_tokens == 9
assert response == response_snapshot

31
integration-tests/models/test_smolvlm.py Normal file
View File

@ -0,0 +1,31 @@
import pytest
@pytest.fixture(scope="module")
def flash_smolvlm_next_handle(launcher):
with launcher("HuggingFaceTB/SmolVLM-Instruct") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_smolvlm_next(flash_smolvlm_next_handle):
await flash_smolvlm_next_handle.health(300)
return flash_smolvlm_next_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_smolvlm_next_simple_url(flash_smolvlm_next, response_snapshot):
ny_skyline = "https://huggingface.co/spaces/merve/chameleon-7b/resolve/main/bee.jpg"
query = "What is in this image?"
response = await flash_smolvlm_next.generate(
f"<|begin_of_text|><|begin_of_text|>User:![]({ny_skyline}){query}<end_of_utterance>\nAssistant:",
max_new_tokens=10,
seed=1337,
)
print(response)
assert (
response.generated_text == " A bee on a pink flower."
), f"{repr(response.generated_text)}"
assert response.details.generated_tokens == 8
assert response == response_snapshot

49
launcher/src/main.rs
View File

@ -5,7 +5,6 @@ use hf_hub::{
};
use nix::sys::signal::{self, Signal};
use nix::unistd::Pid;
use regex::Regex;
use serde::Deserialize;
use std::env;
use std::ffi::OsString;
@ -1652,7 +1651,11 @@ impl From<&str> for Gpu {
"nvidia-l40s" => Gpu::L40S,
"nvidia-a10g" => Gpu::A10G,
"nvidia-h100-80gb-hbm3" => Gpu::H100,
"nvidia-h100-nvl" => Gpu::H100,
"nvidia-h100" => Gpu::H100,
"nvidia-a100-sxm4-80gb" => Gpu::A100,
"nvidia-a100-sxm4-40gb" => Gpu::A100,
"nvidia-a100-80gb-pcie" => Gpu::A100,
"nvidia-a100" => Gpu::A100,
card => Gpu::Unknown(card.to_string()),
}
@ -2075,14 +2078,7 @@ fn main() -> Result<(), LauncherError> {
let cuda_graphs = match (&args.cuda_graphs, &quantize) {
(Some(cuda_graphs), _) => cuda_graphs.iter().cloned().filter(|&c| c > 0).collect(),
#[allow(deprecated)]
(
None,
Some(
Quantization::Bitsandbytes
| Quantization::BitsandbytesNf4
| Quantization::BitsandbytesFp4,
),
) => {
(None, Some(Quantization::Bitsandbytes)) => {
tracing::warn!("Bitsandbytes doesn't work with cuda graphs, deactivating them");
vec![]
}
@ -2172,26 +2168,21 @@ fn main() -> Result<(), LauncherError> {
}
// capture adapter_id, path, revision in format of adapter_id=path@revision
let re = Regex::new(r"^([^=@]+)(?:=([^@]+))?(?:@(.+))?$").unwrap();
if let Some(caps) = re.captures(adapter) {
let adapter_id = caps.get(1).map_or("", |m| m.as_str());
let revision = caps.get(3).map(|m| m.as_str());
download_convert_model(
adapter_id,
revision,
args.trust_remote_code,
args.huggingface_hub_cache.as_deref(),
args.weights_cache_override.as_deref(),
running.clone(),
false, // avoid merging lora adapters if using multi-lora
)?;
} else {
return Err(LauncherError::ArgumentValidation(format!(
"Invalid LoRA adapter format: {}",
adapter
)));
}
// path is disabled beforehand.
let mut splits = adapter.split("@");
let adapter_id = splits.next().ok_or_else(|| {
LauncherError::ArgumentValidation("Missing adapter id".to_string())
})?;
let revision = splits.next();
download_convert_model(
adapter_id,
revision,
args.trust_remote_code,
args.huggingface_hub_cache.as_deref(),
args.weights_cache_override.as_deref(),
running.clone(),
false, // avoid merging lora adapters if using multi-lora
)?;
}
}

4
router/Cargo.toml
View File

@ -17,7 +17,7 @@ clap = { version = "4.4.5", features = ["derive", "env"] }
futures = "0.3.28"
hf-hub = { workspace = true }
itertools = "0.10"
jsonschema = { version = "0.17.1", features = ["draft202012"] }
jsonschema = { version = "0.28.0" }
metrics = { workspace = true }
metrics-exporter-prometheus = { workspace = true }
nohash-hasher = "0.2.0"
@ -25,7 +25,7 @@ opentelemetry = { version = "0.20.0", features = ["rt-tokio"] }
opentelemetry-otlp = "0.13.0"
outlines-core = { git = "https://github.com/dottxt-ai/outlines-core.git", rev = "ba10c619fc9bf3c487e43f49bdecb95a24bb465c" }
rand = "0.8.5"
reqwest = { version = "0.11.20", features = [] }
reqwest = { version = "0.11.20", features = ["blocking"] }
serde = "1.0.188"
serde_json = "1.0.107"
thiserror = "1.0.48"

19
router/src/config.rs
View File

@ -110,6 +110,24 @@ pub struct ClipVisionModel {
patch_size: usize,
}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(rename_all = "snake_case")]
pub struct Idefics3 {}
impl Idefics3 {
pub fn get_max_longest_edge(&self) -> usize {
364
}
pub fn get_number_of_features(&self) -> usize {
169
}
pub fn get_max_longest_edge_for_image_resize(&self) -> usize {
1456
}
}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(rename_all = "snake_case")]
pub struct Idefics2 {}
@ -178,6 +196,7 @@ pub enum Config {
Idefics,
Mllama,
Idefics2(Idefics2),
Idefics3(Idefics3),
Ssm,
GptBigcode,
Granite,

3
router/src/kserve.rs
View File

@ -205,6 +205,7 @@ pub async fn kserve_model_infer(
let generate_request = GenerateRequest {
inputs: str_input.to_string(),
parameters: payload.parameters.clone(),
add_special_tokens: true,
};
let infer = infer.clone();
let compute_type = compute_type.clone();
@ -212,7 +213,7 @@ pub async fn kserve_model_infer(
async move {
generate_internal(infer, compute_type, Json(generate_request), span)
.await
.map(|(_, Json(generation))| {
.map(|(_, _, Json(generation))| {
let generation_as_bytes = generation.generated_text.as_bytes().to_vec();
OutputChunk {
name: output.name.clone(),

3
router/src/lib.rs
View File

@ -79,7 +79,7 @@ impl TokenizerTrait for tokenizers::Tokenizer {
}
}
impl<'a> TokenizerTrait for PyTokenizer<'a> {
impl TokenizerTrait for PyTokenizer<'_> {
fn encode_trait(
&self,
query: String,
@ -170,6 +170,7 @@ impl TokenizerConfigToken {
#[serde(tag = "processor_class")]
pub enum HubPreprocessorConfig {
Idefics2Processor(Idefics2Preprocessor),
Idefics3Processor(Idefics2Preprocessor),
}
impl HubPreprocessorConfig {

90
router/src/validation.rs
View File

@ -7,7 +7,6 @@ use crate::{
use crate::{PyTokenizer, Tokenizer};
use base64::{engine::general_purpose::STANDARD, Engine};
use image::{ImageFormat, ImageReader};
use jsonschema::{Draft, JSONSchema};
use outlines_core::json_schema::to_regex as json_schema_to_regex;
use rand::{thread_rng, Rng};
use serde_json::Value;
@ -355,9 +354,7 @@ impl Validation {
}?;
// Check if the json is a valid JSONSchema
JSONSchema::options()
.with_draft(Draft::Draft202012)
.compile(&json)
jsonschema::draft202012::meta::validate(&json)
.map_err(|e| ValidationError::InvalidGrammar(e.to_string()))?;
// The schema can be valid but lack properties.
@ -614,6 +611,73 @@ fn image_tokens(
image_string
}
Idefics3(config) => {
const FAKE: &str = "<fake_token_around_image>";
const IMAGE: &str = "<image>";
const GLOBAL_IMG: &str = "<global-img>";
let max_longest_edge_for_image_resize = config.get_max_longest_edge_for_image_resize();
// resize image if it is larger than max_longest_edge_for_image_resize keeping aspect ratio
let (height, width) = if height > max_longest_edge_for_image_resize
|| width > max_longest_edge_for_image_resize
{
let aspect_ratio = height as f32 / width as f32;
if height > width {
(
max_longest_edge_for_image_resize,
(max_longest_edge_for_image_resize as f32 / aspect_ratio) as usize,
)
} else {
(
(max_longest_edge_for_image_resize as f32 * aspect_ratio) as usize,
max_longest_edge_for_image_resize,
)
}
} else {
(height, width)
};
let image_seq_len = config.get_number_of_features();
let max_edge = config.get_max_longest_edge();
let (image_rows, image_cols) = if height > max_edge || width > max_edge {
(
(height as f32 / max_edge as f32).ceil() as usize,
(width as f32 / max_edge as f32).ceil() as usize,
)
} else {
(0, 0)
};
let mut image_string = String::new();
if image_rows == 0 && image_cols == 0 {
// Single image case
image_string.push_str(FAKE);
image_string.push_str(GLOBAL_IMG);
image_string.push_str(&IMAGE.repeat(image_seq_len));
image_string.push_str(FAKE);
} else {
// Split image case
for n_h in 0..image_rows {
for n_w in 0..image_cols {
image_string.push_str(FAKE);
image_string.push_str(&format!("<row_{}_col_{}>", n_h + 1, n_w + 1));
image_string.push_str(&IMAGE.repeat(image_seq_len));
}
image_string.push('\n');
}
image_string.push('\n');
image_string.push_str(FAKE);
image_string.push_str(GLOBAL_IMG);
image_string.push_str(&IMAGE.repeat(image_seq_len));
image_string.push_str(FAKE);
}
image_string
}
Paligemma(config) => "<image>".repeat(config.get_number_of_features(height, width)),
LlavaNext(config) => "<image>".repeat(config.get_number_of_features(height, width)),
Qwen2Vl(config) => format!(
@ -647,7 +711,8 @@ fn prepare_input<T: TokenizerTrait>(
static RE: Lazy<Regex> = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap());
let (tokenizer_query, input_chunks) = match config {
Some(
config @ (Idefics | Mllama | Idefics2(_) | Paligemma(_) | LlavaNext(_) | Qwen2Vl(_)),
config @ (Idefics | Mllama | Idefics2(_) | Idefics3(_) | Paligemma(_) | LlavaNext(_)
| Qwen2Vl(_)),
) => {
let mut input_chunks = Vec::new();
let mut tokenizer_query = String::with_capacity(inputs.len());
@ -1164,12 +1229,11 @@ mod tests {
assert!(
chunks
== vec![
Chunk::Text("test".to_string()).into(),
Chunk::Text("test".to_string()),
Chunk::Image(Image {
data: pixel_data.clone(),
mimetype: "image/gif".to_string()
})
.into()
],
"Failed to process images",
);
@ -1224,17 +1288,15 @@ mod tests {
assert!(
chunks
== vec![
Chunk::Text("test".to_string()).into(),
Chunk::Text("test".to_string()),
Chunk::Image(Image {
data: pixel_data.clone(),
mimetype: "image/gif".to_string()
}),
Chunk::Image(Image {
data: pixel_data.clone(),
mimetype: "image/gif".to_string()
})
.into(),
Chunk::Image(Image {
data: pixel_data.clone(),
mimetype: "image/gif".to_string()
})
.into()
],
"Failed to process images",
);

2
rust-toolchain.toml
View File

@ -1,5 +1,5 @@
[toolchain]
# Released on: June 13, 2024
# https://releases.rs/docs/1.79.0/
channel = "1.80.1"
channel = "1.84.0"
components = ["rustfmt", "clippy"]

2
server/Makefile-flash-att-v2
View File

@ -1,5 +1,5 @@
flash_att_v2_commit_cuda := v2.6.1
flash_att_v2_commit_rocm := 2092111b9f975b3347c652ff7fabd431130256c4
flash_att_v2_commit_rocm := 47bd46e0204a95762ae48712fd1a3978827c77fd
build-flash-attention-v2-cuda:
pip install -U packaging wheel

5
server/Makefile-flashinfer
View File

@ -1,2 +1,5 @@
install-flashinfer:
pip install flashinfer==0.1.6 -i https://flashinfer.ai/whl/cu124/torch2.4
# We need fsspec as an additional dependency, but
# `pip install flashinfer` cannot resolve it.
pip install fsspec
pip install flashinfer==0.2.0.post1 -i https://flashinfer.ai/whl/cu124/torch2.4

35
server/poetry.lock generated
View File

@ -290,22 +290,23 @@ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
[[package]]
name = "bitsandbytes"
version = "0.43.3"
version = "0.45.0"
description = "k-bit optimizers and matrix multiplication routines."
optional = true
python-versions = "*"
files = [
{file = "bitsandbytes-0.43.3-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:cc99507c352be0715098b2c7577b690dd158972dc4ea10c7495bac104c7c79f0"},
{file = "bitsandbytes-0.43.3-py3-none-win_amd64.whl", hash = "sha256:257f6552f2144748a84e6c44e1f7a98f3da888f675ed74e18fd7f7eb13c6cafa"},
{file = "bitsandbytes-0.45.0-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:0f0323de1ff1fdf8383e79bdad1283516a4c05a6fd2b44a363bf4e059422305b"},
{file = "bitsandbytes-0.45.0-py3-none-win_amd64.whl", hash = "sha256:ebbf96e0ecb466716a65ecdeaef3fa1983575447b9ab66b74e5211892507c6ff"},
]
[package.dependencies]
numpy = "*"
torch = "*"
typing_extensions = ">=4.8.0"
[package.extras]
benchmark = ["matplotlib", "pandas"]
test = ["scipy"]
test = ["lion_pytorch", "scipy"]
[[package]]
name = "certifi"
@ -1289,12 +1290,12 @@ files = [
[[package]]
name = "marlin-kernels"
version = "0.3.6"
version = "0.3.7"
description = "Marlin quantization kernels"
optional = true
python-versions = ">=3.7"
files = [
{file = "marlin_kernels-0.3.6+cu123torch2.4-cp310-cp310-linux_x86_64.whl", hash = "sha256:afedaa9a15e8991442bc8c81f62833fbf5c1556ae9d7a5a9e13b747ce97beef9"},
{file = "marlin_kernels-0.3.7+cu123torch2.4-cp310-cp310-linux_x86_64.whl", hash = "sha256:bb416d14623dc0ad0eeb2835446c37a41f994555f1baec8701de6d4c1fc17ec8"},
]
[package.dependencies]
@ -1302,16 +1303,16 @@ torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
[[package]]
name = "marlin-kernels"
version = "0.3.6"
version = "0.3.7"
description = "Marlin quantization kernels"
optional = true
python-versions = ">=3.7"
files = [
{file = "marlin_kernels-0.3.6+cu123torch2.4-cp311-cp311-linux_x86_64.whl", hash = "sha256:c0c05621d5e87144415d8a6e439072bd844d5f3cb55e4c4c69eabdc4c94610f4"},
{file = "marlin_kernels-0.3.7+cu123torch2.4-cp311-cp311-linux_x86_64.whl", hash = "sha256:a89bb61d718002d4432158641bce95c6fd68f9ee1a7d5402dd283903397f3185"},
]
[package.dependencies]
@ -1319,16 +1320,16 @@ torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
[[package]]
name = "marlin-kernels"
version = "0.3.6"
version = "0.3.7"
description = "Marlin quantization kernels"
optional = true
python-versions = ">=3.7"
files = [
{file = "marlin_kernels-0.3.6+cu123torch2.4-cp312-cp312-linux_x86_64.whl", hash = "sha256:3be4662c8d25a3cdb1793dafe0e2e76dd600913a69a468e2c68d1fed4e149255"},
{file = "marlin_kernels-0.3.7+cu123torch2.4-cp312-cp312-linux_x86_64.whl", hash = "sha256:ed938d196fc5e9cce9fc44cd2b889d5adc5ca7475c8a23858f1474d29e38bdbf"},
]
[package.dependencies]
@ -1336,16 +1337,16 @@ torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
[[package]]
name = "marlin-kernels"
version = "0.3.6"
version = "0.3.7"
description = "Marlin quantization kernels"
optional = true
python-versions = ">=3.7"
files = [
{file = "marlin_kernels-0.3.6+cu123torch2.4-cp39-cp39-linux_x86_64.whl", hash = "sha256:89eac9d46bc084a256b538afda6053683eb7e505db0e0d4f6dbeca32368caac6"},
{file = "marlin_kernels-0.3.7+cu123torch2.4-cp39-cp39-linux_x86_64.whl", hash = "sha256:113c54f68565ad476ca12366b4de92131fa3e9ddb16cbe8ad63272972a15ac28"},
]
[package.dependencies]
@ -1353,7 +1354,7 @@ torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
[[package]]
name = "mdurl"
@ -4097,4 +4098,4 @@ torch = ["torch"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
content-hash = "c7fdcff2b752cd3beb3995c1ecd15f0f4d9b4e117048b06ab991c6d0e0c86ff3"
content-hash = "0ead8472620eeef6f9ff81f70bcb48403f9c831b6914245efa5e249724d80d0b"

10
server/pyproject.toml
View File

@ -16,7 +16,7 @@ grpcio-reflection = "^1.51.1"
grpc-interceptor = "^0.15.4"
typer = "^0.12.5"
accelerate = {version = "^1.1.0", optional = true}
bitsandbytes = { version = "^0.43.0", optional = true }
bitsandbytes = { version = "^0.45.0", optional = true }
safetensors = "^0.4.5"
loguru = "^0.7.2"
opentelemetry-api = "^1.27.0"
@ -48,10 +48,10 @@ attention-kernels = [
{ url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
]
marlin-kernels = [
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
]
moe-kernels = [
{ url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },

6
server/tests/utils/test_adapter.py
View File

@ -94,6 +94,8 @@ def test_get_mlp_weights_with_gate_up_proj():
# assert the result
expected = {
(3, "c_fc"): ("model.layers.3.mlp.c_fc", mock_layer.mlp.c_fc),
(3, "c_proj"): ("model.layers.3.mlp.c_proj", mock_layer.mlp.c_proj),
(3, "gate_proj"): ("model.layers.3.mlp.gate_proj", mock_layer.mlp.gate_up_proj),
(3, "up_proj"): ("model.layers.3.mlp.up_proj", mock_layer.mlp.gate_up_proj),
(3, "down_proj"): ("model.layers.3.mlp.down_proj", mock_layer.mlp.down_proj),
@ -188,6 +190,8 @@ def test_get_mlp_weights_llama_compatibility():
result = get_mlp_weights(3, mock_layer)
expected = {
(3, "c_fc"): ("model.layers.3.mlp.c_fc", mock_layer.mlp.c_fc),
(3, "c_proj"): ("model.layers.3.mlp.c_proj", mock_layer.mlp.c_proj),
(3, "gate_proj"): ("model.layers.3.mlp.gate_proj", mock_layer.mlp.gate_up_proj),
(3, "up_proj"): ("model.layers.3.mlp.up_proj", mock_layer.mlp.gate_up_proj),
(3, "down_proj"): ("model.layers.3.mlp.down_proj", mock_layer.mlp.down_proj),
@ -240,6 +244,8 @@ def test_get_mlp_weights_gemma_compatibility():
result = get_mlp_weights(3, mock_layer)
expected = {
(3, "c_fc"): ("model.layers.3.mlp.c_fc", mock_layer.mlp.c_fc),
(3, "c_proj"): ("model.layers.3.mlp.c_proj", mock_layer.mlp.c_proj),
(3, "gate_proj"): ("model.layers.3.mlp.gate_proj", mock_layer.mlp.gate_proj),
(3, "up_proj"): ("model.layers.3.mlp.up_proj", mock_layer.mlp.up_proj),
(3, "down_proj"): ("model.layers.3.mlp.down_proj", mock_layer.mlp.down_proj),

11
server/text_generation_server/adapters/lora.py
View File

@ -6,9 +6,11 @@ from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Type, Union
from loguru import logger
import torch
from peft import LoraConfig as _LoraConfig
from torch.distributed import ProcessGroup
from text_generation_server.utils.log import log_master
from text_generation_server.adapters.config import AdapterConfig, ModuleMap
@ -203,8 +205,17 @@ class LoraWeights(AdapterWeights):
lora_a_list = [None] * nlayers
lora_b_list = [None] * nlayers
# import ipdb; ipdb.set_trace()
for layer_id in range(nlayers):
key = (layer_id, layer_type)
if key not in target_to_layer:
# There is no layer of this type in the model
log_master(
logger.warning,
f"Key specified in lora weights but not found in base model: {key}",
)
return None
weight_name, layer = target_to_layer[key]
base_weight = layer.base_layer.linear.weight
base_device = base_weight.device

6
server/text_generation_server/layers/attention/cuda.py
View File

@ -60,8 +60,7 @@ def paged_attention(
from text_generation_server.layers.attention.flashinfer import decode_state
return decode_state.get().forward(
# TODO: remove `contiguous` call once https://github.com/flashinfer-ai/flashinfer/pull/553 is merged.
query.contiguous(),
query,
paged_kv_cache=(kv_cache.key, kv_cache.value),
logits_soft_cap=softcap,
sm_scale=softmax_scale,
@ -231,8 +230,7 @@ def attention(
softcap = 0.0
return prefill_with_paged_kv_state.get().forward(
# TODO: remove `contiguous` call once https://github.com/flashinfer-ai/flashinfer/pull/553 is merged.
query.contiguous(),
query,
causal=causal,
paged_kv_cache=(kv_cache.key, kv_cache.value),
logits_soft_cap=softcap,

49
server/text_generation_server/layers/attention/flashinfer.py
View File

@ -50,7 +50,8 @@ def use_prefill_with_paged_kv_state(
num_kv_heads: int,
head_size: int,
page_size: int,
dtype: torch.dtype,
kv_dtype: torch.dtype,
q_dtype: torch.dtype,
window_left: int,
):
"""
@ -91,9 +92,10 @@ def use_prefill_with_paged_kv_state(
num_qo_heads=num_heads,
num_kv_heads=num_kv_heads,
head_dim=head_size,
q_data_type=dtype,
kv_data_type=kv_dtype,
q_data_type=q_dtype,
page_size=page_size,
window_left=window_left,
window_left=-1 if window_left is None else window_left,
)
yield
finally:
@ -113,41 +115,6 @@ def create_prefill_state(
)
@contextmanager
def use_prefill_state(
*,
state: flashinfer.BatchPrefillWithRaggedKVCacheWrapper,
cu_seqlens: torch.Tensor,
num_heads: int,
num_kv_heads: int,
head_size: int,
dtype: torch.dtype,
window_left: int,
):
"""
Context manager to set the active flashinfer prefill state to the given
`state` and parameters. This state will be used by all calls to the
`attention` function while the context manager is active.
"""
token = prefill_state.set(state)
try:
state.begin_forward(
qo_indptr=cu_seqlens,
kv_indptr=cu_seqlens,
num_qo_heads=num_heads,
num_kv_heads=num_kv_heads,
head_dim=head_size,
q_data_type=dtype,
window_left=window_left,
)
yield
finally:
state.end_forward()
if token is not None:
prefill_state.reset(token)
def create_decode_state(
*,
device: torch.device,
@ -205,7 +172,7 @@ def use_decode_state(
head_size: int,
page_size: int,
kv_cache_dtype: torch.dtype,
dtype: torch.dtype,
q_dtype: torch.dtype,
window_left: int,
):
"""
@ -242,8 +209,8 @@ def use_decode_state(
head_dim=head_size,
page_size=page_size,
data_type=kv_cache_dtype,
q_data_type=dtype,
window_left=window_left,
q_data_type=q_dtype,
window_left=-1 if window_left is None else window_left,
)
yield
finally:

54
server/text_generation_server/layers/attention/rocm.py
View File

@ -5,6 +5,10 @@ from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers.attention import Seqlen
from text_generation_server.utils.log import log_master
from text_generation_server.models.globals import (
ATTENTION,
BLOCK_SIZE,
)
from loguru import logger
import vllm._custom_ops as ops
@ -73,11 +77,44 @@ def paged_attention(
# limitations under the License.
#
if ATTENTION == "flashdecoding":
max_q = 1
max_k = max_s
import flash_attn_2_cuda
if softcap is None:
softcap = 0.0
out = flash_attn_2_cuda.varlen_fwd(
query,
kv_cache.key,
kv_cache.value,
None,
seqlen.cu_seqlen_q,
seqlen.cu_seqlen_k,
None, # pad_k
None,
block_tables,
None,
max_q,
max_k,
0.0, # dropout
softmax_scale,
False, # zero_tensors
True, # causal
-1, # Window_left
-1, # Window right
softcap,
False, # return softmax
None, # generator
)
return out[0]
if softcap is not None:
raise RuntimeError("Paged attention doesn't support softcapping")
# value_cache => [num_blocks, num_heads, head_size, block_size]
block_size = kv_cache.value.shape[3]
# block_size = kv_cache.value.shape[3]
block_size = BLOCK_SIZE
num_seqs, num_heads, head_size = query.shape
num_kv_heads = kv_cache.key.shape[1]
@ -126,17 +163,17 @@ def paged_attention(
else:
# Run PagedAttention V2.
assert _PARTITION_SIZE % block_size == 0
tmp_output = torch.empty(
tmp_output = torch.zeros(
size=(num_seqs, num_heads, max_num_partitions, head_size),
dtype=out.dtype,
device=out.device,
)
exp_sums = torch.empty(
exp_sums = torch.zeros(
size=(num_seqs, num_heads, max_num_partitions),
dtype=torch.float32,
device=out.device,
)
max_logits = torch.empty_like(exp_sums)
max_logits = torch.zeros_like(exp_sums)
if not use_custom:
ops.paged_attention_v2(
@ -247,14 +284,15 @@ def attention(
# We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
return flash_attn_2_cuda.varlen_fwd(
query,
key,
value,
# flashdecoding: pass the KV caches, paged: pass the KV.
kv_cache.key if ATTENTION == "flashdecoding" else key,
kv_cache.value if ATTENTION == "flashdecoding" else value,
out,
seqlen.cu_seqlen_q,
seqlen.cu_seqlen_q,
None,
seqlen.cu_seqlen_k,
None,
None,
block_tables if ATTENTION == "flashdecoding" else None,
None,
seqlen.max_q,
seqlen.max_k,

40
server/text_generation_server/layers/compressed_tensors/w8an_fp.py
View File

@ -3,8 +3,14 @@ from typing import List, Optional, Union
import torch
from compressed_tensors.quantization import QuantizationArgs, QuantizationType
from text_generation_server.layers.fp8 import Fp8Weight, _load_scalar_or_matrix_scale
from text_generation_server.layers.fp8 import (
Fp8Weight,
_load_scalar_or_matrix_scale,
requantize_with_max_scale,
normalize_e4m3fn_to_e4m3fnuz,
)
from text_generation_server.utils.weights import Weights, WeightsLoader
from text_generation_server.utils.import_utils import SYSTEM
class W8ANFpLoader(WeightsLoader):
@ -47,11 +53,10 @@ class W8ANFpLoader(WeightsLoader):
weight_scale = None
if self.load_weight_scale:
weight_scale = (
weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
.reshape(-1)
.expand(w.shape[0])
)
weight_scale = weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
if SYSTEM == "cuda":
weight_scale = weight_scale.reshape(-1).expand(w.shape[0])
input_scale = None
if self.load_input_scale:
@ -87,7 +92,8 @@ class W8ANFpLoader(WeightsLoader):
block_sizes=block_sizes,
to_dtype=False,
)
weight_scale = weight_scale.reshape(-1).expand(w.shape[0])
if SYSTEM == "cuda":
weight_scale = weight_scale.reshape(-1).expand(w.shape[0])
input_scale = None
if self.load_input_scale:
@ -141,6 +147,17 @@ class W8ANFpLoader(WeightsLoader):
else None
)
if self.load_weight_scale and SYSTEM == "rocm":
w, weight_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
w, weight_scale, input_scale
)
if weight_scale.numel() == len(prefixes):
logical_widths = [x[0] for x in shapes]
w, weight_scale = requantize_with_max_scale(
w, weight_scale.to(weights.device), logical_widths, weights.dtype
)
return Fp8Weight(
weight=w,
weight_scale=weight_scale,
@ -153,11 +170,10 @@ class W8ANFpLoader(WeightsLoader):
w = weights.get_sharded(f"{prefix}.weight", dim=1)
weight_scale = None
if self.load_weight_scale:
weight_scale = (
weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
.reshape(-1)
.expand(w.shape[0])
)
weight_scale = weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
if SYSTEM == "cuda":
weight_scale = weight_scale.reshape(-1).expand(w.shape[0])
input_scale = None
if self.load_input_scale:

125
server/text_generation_server/layers/fp8.py
View File

@ -19,6 +19,9 @@ try:
except ImportError:
marlin_kernels = None
quant_dtype: torch.dtype = (
torch.float8_e4m3fnuz if SYSTEM == "rocm" else torch.float8_e4m3fn
)
if SYSTEM == "cuda" and marlin_kernels is not None:
major, minor = torch.cuda.get_device_capability()
@ -60,25 +63,58 @@ def normalize_e4m3fn_to_e4m3fnuz(
weight_scale: torch.Tensor,
input_scale: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
assert weight.dtype == torch.float8_e4m3fn
# The bits pattern 10000000(-128) represents zero in e4m3fn
# but NaN in e4m3fnuz. So here we set it to 0.
# https://onnx.ai/onnx/technical/float8.html
weight_as_int8 = weight.view(torch.int8)
ROCM_FP8_NAN_AS_INT = -128
weight_as_int8[weight_as_int8 == ROCM_FP8_NAN_AS_INT] = 0
weight = weight_as_int8.view(torch.float8_e4m3fnuz)
if weight.dtype == torch.float8_e4m3fn:
# The bits pattern 10000000(-128) represents zero in e4m3fn
# but NaN in e4m3fnuz. So here we set it to 0.
# https://onnx.ai/onnx/technical/float8.html
weight_as_int8 = weight.view(torch.int8)
ROCM_FP8_NAN_AS_INT = -128
weight_as_int8[weight_as_int8 == ROCM_FP8_NAN_AS_INT] = 0
weight = weight_as_int8.view(torch.float8_e4m3fnuz)
# For the same bits representation, e4m3fnuz value is half of
# the e4m3fn value, so we should double the scaling factor to
# get the same dequantized value.
# https://onnx.ai/onnx/technical/float8.html
weight_scale = weight_scale * 2.0
if input_scale is not None:
input_scale = input_scale * 2.0
# For the same bits representation, e4m3fnuz value is half of
# the e4m3fn value, so we should double the scaling factor to
# get the same dequantized value.
# https://onnx.ai/onnx/technical/float8.html
weight_scale = weight_scale * 2.0
if input_scale is not None:
input_scale = input_scale * 2.0
return weight, weight_scale, input_scale
def per_tensor_dequantize(
tensor: torch.Tensor,
inv_scale: Union[float, torch.Tensor],
dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
fake_qweight = tensor.to(dtype)
dq_weight = fake_qweight * inv_scale
return dq_weight
def requantize_with_max_scale(
weight: torch.Tensor,
weight_scale: torch.Tensor,
logical_widths: int,
dtype: torch.dtype,
) -> Tuple[torch.Tensor, torch.Tensor]:
# Max scale to be used for requanitzation.
max_w_scale = weight_scale.max().float()
start = 0
for idx, logical_width in enumerate(logical_widths):
end = start + logical_width
weight_dq = per_tensor_dequantize(
weight[start:end, :], weight_scale[idx], dtype
)
weight[start:end, :], max_w_scale_normalized = fp8_quantize(
weight_dq, max_w_scale
)
start = end
return weight, max_w_scale_normalized
def fp8_quantize(
weight: torch.Tensor,
scale: Optional[torch.Tensor] = None,
@ -96,7 +132,7 @@ def fp8_quantize(
shape = weight.shape
qweight, scale = marlin_kernels.scaled_fp8_quant(
weight.reshape(-1, shape[-1]),
dtype=qdtype,
dtype=quant_dtype,
scale=scale,
scale_ub=scale_upper_bound,
# TODO: don't do this when we have to use the Torch kernel.
@ -116,6 +152,8 @@ def fp8_quantize(
qweight = (weight * scale).clamp(min=finfo.min, max=finfo.max)
scale = scale.float().reciprocal()
else:
if SYSTEM == "rocm":
scale = scale / 2.0
# Use reciprocal to avoid more expensive division.
qweight = (weight * scale.reciprocal()).clamp(min=finfo.min, max=finfo.max)
@ -141,17 +179,18 @@ class HybridFP8UnquantLoader(WeightsLoader):
if w.dtype == torch.float8_e4m3fn:
# FP8 branch
scale = (
weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
.reshape(-1)
.expand(w.shape[0])
)
scale = weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
if SYSTEM == "cuda":
scale.reshape(-1).expand(w.shape[0])
input_scale = None
if weights.has_tensor(f"{prefix}.input_scale"):
input_scale = weights.get_tensor(
f"{prefix}.input_scale", to_dtype=False
).reshape(-1)
input_scale = (
weights.get_tensor(f"{prefix}.input_scale", to_dtype=False)
.reshape(-1)
.max()
)
return Fp8Weight(
weight=w,
@ -178,6 +217,7 @@ class HybridFP8UnquantLoader(WeightsLoader):
if w.dtype == torch.float8_e4m3fn:
# FP8 branch
scale = weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
if scale.numel() > 1:
scale = weights.get_packed_sharded(
f"{prefix}.weight_scale",
@ -185,7 +225,8 @@ class HybridFP8UnquantLoader(WeightsLoader):
block_sizes=block_sizes,
to_dtype=False,
)
scale = scale.reshape(-1).expand(w.shape[0])
if SYSTEM == "cuda":
scale = scale.reshape(-1).expand(w.shape[0])
input_scale = None
if weights.has_tensor(f"{prefix}.input_scale"):
@ -243,6 +284,17 @@ class HybridFP8UnquantLoader(WeightsLoader):
else None
)
if SYSTEM == "rocm":
w, scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
w, scale, input_scale
)
if scale.numel() == len(prefixes):
logical_widths = [x[0] for x in shapes]
w, scale = requantize_with_max_scale(
w, scale.to(weights.device), logical_widths, weights.dtype
)
return Fp8Weight(
weight=w,
weight_scale=scale,
@ -259,16 +311,18 @@ class HybridFP8UnquantLoader(WeightsLoader):
w = weights.get_sharded(f"{prefix}.weight", dim=1)
# FP8 branch
if w.dtype == torch.float8_e4m3fn:
scale = (
weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
.reshape(-1)
.expand(w.shape[0])
)
scale = weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
if SYSTEM == "cuda":
scale = scale.reshape(-1).expand(w.shape[0])
input_scale = None
if weights.has_tensor(f"{prefix}.input_scale"):
input_scale = weights.get_tensor(
f"{prefix}.input_scale", to_dtype=False
).reshape(-1)
input_scale = (
weights.get_tensor(f"{prefix}.input_scale", to_dtype=False)
.reshape(-1)
.max()
)
return Fp8Weight(
weight=w,
@ -326,7 +380,7 @@ class Fp8Linear(torch.nn.Module):
if CUTLASS_FP8_AVAILABLE:
log_once(logger.info, "Using cutlass w8a8 kernels")
if SYSTEM == "rocm" and qweight.dtype == torch.float8_e4m3fn:
qweight, scale, _ = normalize_e4m3fn_to_e4m3fnuz(
qweight, scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
weight=qweight, weight_scale=scale
)
@ -443,6 +497,9 @@ class Fp8Linear(torch.nn.Module):
def _load_scalar_or_matrix_scale(weights: Weights, prefix: str, shape: torch.Size):
scale = weights.get_tensor(prefix, to_dtype=False)
if scale.numel() > 1:
scale = weights.get_sharded(prefix, dim=0, to_dtype=False)
elif SYSTEM == "rocm":
return scale.reshape(-1)
return scale.reshape(-1).expand(shape[0])

30
server/text_generation_server/models/__init__.py
View File

@ -152,6 +152,9 @@ try:
from text_generation_server.models.custom_modeling.idefics2 import (
Idefics2ForConditionalGeneration,
)
from text_generation_server.models.custom_modeling.idefics3 import (
Idefics3ForConditionalGeneration,
)
from text_generation_server.models.custom_modeling.qwen2_vl import (
Qwen2VLForConditionalGeneration,
)
@ -188,6 +191,12 @@ class ModelType(enum.Enum):
"url": "https://huggingface.co/HuggingFaceM4/idefics2-8b",
"multimodal": True,
}
IDEFICS3 = {
"type": "idefics3",
"name": "Idefics 3",
"url": "https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3",
"multimodal": True,
}
LLAVA_NEXT = {
"type": "llava_next",
"name": "Llava Next (1.6)",
@ -1253,6 +1262,24 @@ def get_model(
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == IDEFICS3:
if FLASH_ATTENTION:
return VlmCausalLM(
model_id=model_id,
model_class=Idefics3ForConditionalGeneration,
revision=revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
default_dtype=torch.bfloat16,
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": 1456}},
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == PALIGEMMA:
if FLASH_ATTENTION:
return VlmCausalLM(
@ -1422,6 +1449,9 @@ def get_model_with_lora_adapters(
"up_proj",
"down_proj",
"qkv_proj",
# add c_* layers used in starcoder2
"c_proj",
"c_fc",
]
for layer_name in adapter_layers:

45
server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
View File

@ -515,9 +515,7 @@ class FlashLlamaModel(torch.nn.Module):
self.layers.append(
FlashLlamaLayer(
index=0,
prefix=(
"model.layers.0" if not prefix else f"{prefix}.model.layers.0"
),
prefix=f"{prefix}.layers.0",
config=config,
weights=weights,
)
@ -533,11 +531,7 @@ class FlashLlamaModel(torch.nn.Module):
self.layers.append(
FlashLlamaCrossLayer(
index=layer_id,
prefix=(
f"model.layers.{layer_id}"
if not prefix
else f"{prefix}.model.layers.{layer_id}"
),
prefix=(f"{prefix}.layers.{layer_id}"),
config=config,
weights=weights,
)
@ -546,11 +540,7 @@ class FlashLlamaModel(torch.nn.Module):
self.layers.append(
FlashLlamaLayer(
index=layer_id,
prefix=(
f"model.layers.{layer_id}"
if not prefix
else f"{prefix}.model.layers.{layer_id}"
),
prefix=(f"{prefix}.layers.{layer_id}"),
config=config,
weights=weights,
)
@ -561,18 +551,14 @@ class FlashLlamaModel(torch.nn.Module):
self.layers.append(
FlashLlamaLayer(
index=last_layer_id,
prefix=(
f"model.layers.{last_layer_id}"
if not prefix
else f"{prefix}.model.layers.{last_layer_id}"
),
prefix=(f"{prefix}.layers.{last_layer_id}"),
config=config,
weights=weights,
)
)
self.norm = FastRMSNorm.load(
prefix="model.norm" if not prefix else f"{prefix}.model.norm",
prefix=f"{prefix}.norm",
weights=weights,
eps=config.rms_norm_eps,
)
@ -629,19 +615,24 @@ class FlashLlamaModel(torch.nn.Module):
class FlashLlamaForCausalLM(torch.nn.Module):
def __init__(self, prefix: str, config, weights):
def __init__(self, prefix: str, config, weights, name=None):
if name is None:
name = "model"
super().__init__()
with no_fp8(weights):
self.embed_tokens = TensorParallelEmbedding(
prefix=(
"model.embed_tokens"
f"{name}.embed_tokens"
if not prefix
else f"{prefix}.model.embed_tokens"
else f"{prefix}.{name}.embed_tokens"
),
weights=weights,
)
self.model = FlashLlamaModel(prefix, config, weights)
self.model = FlashLlamaModel(
prefix=name if not prefix else f"{prefix}.{name}",
config=config,
weights=weights,
)
if config.tie_word_embeddings:
suffix = "model.embed_tokens"
else:
@ -652,11 +643,13 @@ class FlashLlamaForCausalLM(torch.nn.Module):
if embedding_multiplier is not None:
self.embed_tokens.weight.data *= embedding_multiplier
prefix = "lm_head" if not prefix or name != "model" else f"{prefix}.{suffix}"
with no_fp8(weights):
self.lm_head = SpeculativeHead.load(
config,
prefix=suffix if not prefix else f"{prefix}.{suffix}",
weights=weights,
prefix,
weights,
)
# Used in Granite

118
server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
View File

@ -32,6 +32,8 @@ from text_generation_server.layers.attention import (
Seqlen,
)
from text_generation_server.layers import (
TensorParallelMultiAdapterLinear,
TensorParallelAdapterRowLinear,
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
@ -109,17 +111,31 @@ class Starcoder2Config(PretrainedConfig):
)
def load_attention(config, prefix, weights):
def load_attention(config, prefix, weights, layer_id):
prefixes = [f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"]
head_size = config.hidden_size // config.num_attention_heads
sizes = [
head_size * config.num_attention_heads,
head_size * config.num_key_value_heads,
head_size * config.num_key_value_heads,
]
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
base_layer = _load_gqa(config, prefix, weights)
else:
return TensorParallelColumnLinear.load_multi(
base_layer = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
prefixes=prefixes,
dim=0,
weights=weights,
bias=config.use_bias,
)
return TensorParallelMultiAdapterLinear.load(
base_layer=base_layer,
layer_id=layer_id,
layer_names=prefixes,
sizes=sizes,
process_group=weights.process_group,
)
def _load_gqa(config, prefix: str, weights):
@ -157,6 +173,7 @@ def _load_gqa(config, prefix: str, weights):
class Starcoder2Attention(torch.nn.Module):
def __init__(
self,
index: int,
prefix: str,
config,
weights,
@ -188,15 +205,23 @@ class Starcoder2Attention(torch.nn.Module):
config.num_key_value_heads // weights.process_group.size()
)
self.query_key_value = load_attention(config, prefix, weights)
self.query_key_value = load_attention(config, prefix, weights, index)
self.kv_scales = get_kv_scales(weights, f"{prefix}")
self.o_proj = TensorParallelRowLinear.load(
o_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.o_proj",
weights=weights,
bias=config.use_bias,
bias=getattr(config, "use_bias", False),
)
self.o_proj = TensorParallelAdapterRowLinear.load(
o_proj,
index,
"o_proj",
process_group=weights.process_group,
)
self.num_groups = self.num_heads // self.num_key_value_heads
self.kv_head_mapping = torch.arange(
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
@ -214,8 +239,9 @@ class Starcoder2Attention(torch.nn.Module):
seqlen,
max_s,
prefill_cache_indices,
adapter_data,
):
qkv = self.query_key_value(hidden_states)
qkv = self.query_key_value(hidden_states, adapter_data)
query, kv = qkv.split(
[
self.head_size * self.num_heads,
@ -267,11 +293,13 @@ class Starcoder2Attention(torch.nn.Module):
kv_scales=self.kv_scales,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
return self.o_proj(
attn_output.view(-1, self.num_heads * self.head_size), adapter_data
)
class Starcoder2MLP(nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, prefix, config, weights, index):
super().__init__()
act = config.hidden_act
self.act = (
@ -285,27 +313,42 @@ class Starcoder2MLP(nn.Module):
)
)
# Fuse gate and up proj
self.c_fc = TensorParallelColumnLinear.load(
c_fc = TensorParallelColumnLinear.load(
config,
prefix=f"{prefix}.c_fc",
weights=weights,
bias=config.use_bias,
)
self.c_proj = TensorParallelRowLinear.load(
c_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.c_proj",
weights=weights,
bias=config.use_bias,
)
def forward(self, hidden_states):
hidden_states = self.c_fc(hidden_states)
self.c_fc = TensorParallelMultiAdapterLinear.load(
c_fc,
layer_id=index,
layer_names=[f"{prefix}.c_fc"],
sizes=[config.intermediate_size, config.intermediate_size],
process_group=weights.process_group,
)
self.c_proj = TensorParallelAdapterRowLinear.load(
c_proj,
index,
"c_proj",
process_group=weights.process_group,
)
def forward(self, hidden_states, adapter_data):
hidden_states = self.c_fc(hidden_states, adapter_data)
hidden_states = self.act(hidden_states)
return self.c_proj(hidden_states)
return self.c_proj(hidden_states, adapter_data)
class Starcoder2GatedMLP(nn.Module):
def __init__(self, prefix, config, weights):
def __init__(self, index, prefix, config, weights):
super().__init__()
act = config.hidden_act
self.act = (
@ -319,27 +362,47 @@ class Starcoder2GatedMLP(nn.Module):
)
)
# Fuse gate and up proj
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
prefixes = [f"{prefix}.gate_proj", f"{prefix}.up_proj"]
sizes = [
config.intermediate_size,
config.intermediate_size,
]
gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
prefixes=prefixes,
weights=weights,
dim=0,
bias=config.use_bias,
)
self.down_proj = TensorParallelRowLinear.load(
self.gate_up_proj = TensorParallelMultiAdapterLinear.load(
gate_up_proj,
index,
layer_names=prefixes,
sizes=sizes,
process_group=weights.process_group,
)
down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=config.use_bias,
)
self.down_proj = TensorParallelAdapterRowLinear.load(
down_proj,
index,
"down_proj",
process_group=weights.process_group,
)
self.intermediate_size = (
config.intermediate_size // weights.process_group.size()
)
def forward(self, hidden_states):
gate_up_states = self.gate_up_proj(hidden_states)
def forward(self, hidden_states, adapter_data):
gate_up_states = self.gate_up_proj(hidden_states, adapter_data)
gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
return self.down_proj(
self.act(gate_up_states[:, 0]) * gate_up_states[:, 1], adapter_data
)
STARCODER2_NORMALIZATION_CLASSES = {
@ -358,11 +421,11 @@ class Starcoder2Layer(nn.Module):
super().__init__()
prefix = f"model.layers.{layer_id}"
self.self_attn = Starcoder2Attention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
prefix=f"{prefix}.self_attn", config=config, weights=weights, index=layer_id
)
self.mlp = STARCODER2_MLP_CLASSES[config.mlp_type](
prefix=f"{prefix}.mlp", config=config, weights=weights
prefix=f"{prefix}.mlp", config=config, weights=weights, index=layer_id
)
self.input_layernorm = STARCODER2_NORMALIZATION_CLASSES[config.norm_type].load(
@ -389,6 +452,7 @@ class Starcoder2Layer(nn.Module):
seqlen,
max_s,
prefill_cache_indices,
adapter_data,
):
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
@ -404,6 +468,7 @@ class Starcoder2Layer(nn.Module):
seqlen,
max_s,
prefill_cache_indices,
adapter_data,
)
# faster post attention rms norm
@ -411,7 +476,7 @@ class Starcoder2Layer(nn.Module):
attn_output, res
)
mlp_output = self.mlp(normed_attn_res_output)
mlp_output = self.mlp(normed_attn_res_output, adapter_data)
return mlp_output, attn_res
@ -458,6 +523,7 @@ class Starcoder2Model(torch.nn.Module):
max_s: int,
true_max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
adapter_data,
) -> torch.Tensor:
hidden_states = self.embed_tokens(input_ids)
@ -481,6 +547,7 @@ class Starcoder2Model(torch.nn.Module):
seqlen,
max_s,
prefill_cache_indices,
adapter_data,
)
hidden_states, _ = self.norm(hidden_states, residual)
@ -552,6 +619,7 @@ class FlashStarcoder2ForCausalLM(torch.nn.Module):
max_s,
true_max_s,
prefill_cache_indices,
adapter_data,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]

584
server/text_generation_server/models/custom_modeling/idefics3.py Normal file
View File

@ -0,0 +1,584 @@
# coding=utf-8
# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Idefics3 model."""
from typing import List, Optional, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.activations import ACT2FN
from text_generation_server.models.custom_modeling.vlm import (
load_text_model,
)
from text_generation_server.layers.attention import Seqlen
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
from text_generation_server.layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
)
from text_generation_server.utils.weights import DefaultWeightsLoader, UnquantizedWeight
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(
batch, num_key_value_heads, n_rep, slen, head_dim
)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class Idefics3VisionEmbeddings(nn.Module):
"""
This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings` to enable images of variable
resolution.
The modifications are adapted from [Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution](https://arxiv.org/abs/2307.06304)
which allows treating images in their native aspect ratio and without the need to resize them to the same
fixed size. In particular, we start from the original pre-trained SigLIP model
(which uses images of fixed-size square images) and adapt it by training on images of variable resolutions.
"""
def __init__(self, prefix, config, weights):
super().__init__()
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
padding="valid",
)
self.patch_embedding.weight = nn.Parameter(
weights.get_tensor(f"{prefix}.patch_embedding.weight"), requires_grad=False
)
self.patch_embedding.bias = nn.Parameter(
weights.get_tensor(f"{prefix}.patch_embedding.bias"), requires_grad=False
)
self.num_patches_per_side = self.image_size // self.patch_size
self.num_patches = self.num_patches_per_side**2
self.num_positions = self.num_patches
self.position_embedding = TensorParallelEmbedding(
prefix=f"{prefix}.position_embedding", weights=weights
)
def forward(
self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor
) -> torch.Tensor:
batch_size, _, max_im_h, max_im_w = pixel_values.shape
patch_embeds = self.patch_embedding(pixel_values)
embeddings = patch_embeds.flatten(2).transpose(1, 2)
max_nb_patches_h, max_nb_patches_w = (
max_im_h // self.patch_size,
max_im_w // self.patch_size,
)
boundaries = torch.arange(
1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side
)
position_ids = torch.full(
size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0
)
for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
nb_patches_h = p_attn_mask[:, 0].sum()
nb_patches_w = p_attn_mask[0].sum()
fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
bucket_coords_h = torch.bucketize(
fractional_coords_h, boundaries, right=True
)
bucket_coords_w = torch.bucketize(
fractional_coords_w, boundaries, right=True
)
pos_ids = (
bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w
).flatten()
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
position_ids = position_ids.to(self.position_embedding.weight.device)
embeddings = embeddings + self.position_embedding(position_ids)
return embeddings
class Idefics3VisionAttention(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_size = self.embed_dim // self.num_heads
if self.head_size * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_size**-0.5
self.dropout = config.attention_dropout
self.num_heads = self.num_heads // weights.process_group.size()
self.embed_dim = self.embed_dim // weights.process_group.size()
self.qkv = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=True,
)
self.out_proj = TensorParallelRowLinear.load(
config=config, prefix=f"{prefix}.out_proj", weights=weights, bias=True
)
self.is_causal = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
batch_size, q_len, _ = hidden_states.size()
qkv = self.qkv(hidden_states)
query_states, key_states, value_states = qkv.split(
[
self.head_size * self.num_heads,
self.head_size * self.num_heads,
self.head_size * self.num_heads,
],
dim=2,
)
query_states = query_states.view(
batch_size, q_len, self.num_heads, self.head_size
).transpose(1, 2)
key_states = key_states.view(
batch_size, q_len, self.num_heads, self.head_size
).transpose(1, 2)
value_states = value_states.view(
batch_size, q_len, self.num_heads, self.head_size
).transpose(1, 2)
k_v_seq_len = key_states.shape[-2]
attn_weights = (
torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale
)
if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
raise ValueError(
f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
raise ValueError(
f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(query_states.dtype)
attn_weights = nn.functional.dropout(
attn_weights, p=self.dropout, training=self.training
)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_size):
raise ValueError(
f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_size)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output
class Idefics3VisionMLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = TensorParallelColumnLinear.load(
prefix=f"{prefix}.fc1", config=config, weights=weights, bias=True
)
self.fc2 = TensorParallelRowLinear.load(
prefix=f"{prefix}.fc2", config=config, weights=weights, bias=True
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class Idefics3EncoderLayer(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Idefics3VisionAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
self.layer_norm1 = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm1", eps=config.layer_norm_eps, weights=weights
)
self.layer_norm2 = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm2", eps=config.layer_norm_eps, weights=weights
)
self.mlp = Idefics3VisionMLP(
prefix=f"{prefix}.mlp", config=config, weights=weights
)
# Copied from transformers.models.siglip.modeling_siglip.SiglipEncoderLayer.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
) -> torch.Tensor:
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states
class Idefics3Encoder(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.layers = nn.ModuleList(
[
Idefics3EncoderLayer(
prefix=f"{prefix}.layers.{i}", config=config, weights=weights
)
for i in range(config.num_hidden_layers)
]
)
# Ignore copy
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
):
hidden_states = inputs_embeds
for encoder_layer in self.layers:
hidden_states = encoder_layer(
hidden_states,
attention_mask,
)
return hidden_states
class Idefics3VisionTransformer(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.embeddings = Idefics3VisionEmbeddings(
prefix=f"{prefix}.embeddings", config=config, weights=weights
)
self.encoder = Idefics3Encoder(
prefix=f"{prefix}.encoder", config=config, weights=weights
)
self.post_layernorm = nn.LayerNorm.load(
prefix=f"{prefix}.post_layernorm",
weights=weights,
eps=config.layer_norm_eps,
)
def forward(
self,
pixel_values,
patch_attention_mask: Optional[torch.BoolTensor] = None,
):
batch_size = pixel_values.size(0)
if patch_attention_mask is None:
patch_size = self.config.patch_size
patch_attention_mask = torch.ones(
(
batch_size,
pixel_values.size(2) // patch_size,
pixel_values.size(3) // patch_size,
)
)
patch_attention_mask = patch_attention_mask.to(
dtype=torch.bool, device=pixel_values.device
)
hidden_states = self.embeddings(
pixel_values=pixel_values, patch_attention_mask=patch_attention_mask
)
patch_attention_mask = patch_attention_mask.view(batch_size, -1)
# The call to `_upad_input` in `_flash_attention_forward` is expensive
# So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
# avoiding passing the attention_mask, which is equivalent to attending to the full sequence
if not torch.any(~patch_attention_mask):
patch_attention_mask = None
else:
patch_attention_mask = _prepare_4d_attention_mask(
patch_attention_mask, hidden_states.dtype
)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=patch_attention_mask,
)
last_hidden_state = encoder_outputs
last_hidden_state = self.post_layernorm(last_hidden_state)
return last_hidden_state
class Idefics3SimpleMLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
input_size = config.vision_config.hidden_size * (config.scale_factor**2)
output_size = config.text_config.hidden_size
proj = nn.Parameter(
weights.get_tensor(f"{prefix}.modality_projection.proj.weight"),
requires_grad=False,
).to(weights.dtype)
self.proj = nn.Linear(input_size, output_size, bias=False)
self.proj.weight = proj
def forward(self, x):
return self.proj(x)
class Idefics3Connector(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.modality_projection = Idefics3SimpleMLP(prefix, config, weights)
self.scale_factor = config.scale_factor
def pixel_shuffle(self, x, scale_factor=2):
bsz, seq, embed_dim = x.size()
height = width = int(seq**0.5)
x = x.view(bsz, height, width, embed_dim)
x = x.view(bsz, height, int(width / scale_factor), embed_dim * scale_factor)
x = x.permute(0, 2, 1, 3)
x = x.reshape(
bsz,
int(width / scale_factor),
int(height / scale_factor),
embed_dim * (scale_factor**2),
)
x = x.permute(0, 2, 1, 3)
x = x.reshape(bsz, int(seq / (scale_factor**2)), embed_dim * (scale_factor**2))
return x
def forward(self, image_hidden_states):
image_hidden_states = self.pixel_shuffle(image_hidden_states, self.scale_factor)
image_hidden_states = self.modality_projection(image_hidden_states)
return image_hidden_states
class Idefics3ForConditionalGeneration(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
config.vision_config.quantize = None
config.vision_config.speculator = config.speculator
config.text_config.quantize = config.quantize
config.text_config.speculator = config.speculator
# set tie_word_embeddings to True to load `.embed_tokens.weight` instead of `.lm_head.weight`
# since Idefics3 uses the `embed_tokens` for the final prediction
# config.text_config.tie_word_embeddings = True
vision_config = config.vision_config
self.text_model = load_text_model(
prefix="model" if not prefix else f"{prefix}.model",
config=config.text_config,
weights=weights,
name="text_model",
)
self.dtype = weights.dtype
# The vision and connector models are not quantized.
with weights.use_loader(DefaultWeightsLoader(UnquantizedWeight)):
self.vision_model = Idefics3VisionTransformer(
prefix=(
f"{prefix}.model.vision_model" if prefix else "model.vision_model"
),
config=vision_config,
weights=weights,
)
config.quantize = None
self.connector = Idefics3Connector(
prefix=f"{prefix}.model.connector" if prefix else "model.connector",
config=config,
weights=weights,
)
self.config = config
self.image_token_id = config.image_token_id
self.pad_token_id = (
config.pad_token_id if config.pad_token_id is not None else -1
)
def _merge_input_ids_with_image_features(
self,
input_ids: torch.Tensor,
inputs_embeds: torch.Tensor,
image_features: torch.Tensor,
):
"""In place merges in vision_embeddings with inputs_embeds."""
# mask = input_ids == self.config.image_token_index
mask = input_ids == self.config.image_token_id
# Let's pray we have enabled enough slots !
inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1])
return inputs_embeds
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
seqlen: Seqlen,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor] = None,
pixel_values: torch.FloatTensor = None,
pixel_attention_mask: Optional[torch.BoolTensor] = None,
# Unused here
image_sizes: Optional[torch.Tensor] = None,
adapter_data: Optional[torch.Tensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
cross_attention_states: Optional[torch.Tensor] = None,
image_indices=None,
):
inputs_embeds = self.text_model.embed_tokens(input_ids)
if pixel_values is not None:
batch_size, num_images, num_channels, height, width = pixel_values.shape
all_states = []
all_pixel_values = pixel_values
all_pixel_mask = pixel_attention_mask
for i in range(batch_size):
pixel_values = all_pixel_values.to(
dtype=self.dtype
) # fp16 compatibility
pixel_values = pixel_values[i : i + 1]
pixel_values = pixel_values.view(num_images, *pixel_values.shape[2:])
# Remove padding images - padding images are full 0.
nb_values_per_image = pixel_values.shape[1:].numel()
real_images_inds = (pixel_values == 0.0).sum(
dim=(-1, -2, -3)
) != nb_values_per_image
pixel_values = pixel_values[real_images_inds].contiguous()
# Handle the vision attention mask
if pixel_attention_mask is None:
pixel_attention_mask = torch.ones(
size=(
pixel_values.size(0),
pixel_values.size(2),
pixel_values.size(3),
),
dtype=torch.bool,
device=pixel_values.device,
)
else:
# Remove padding images from the mask/pP p
pixel_attention_mask = all_pixel_mask[i : i + 1]
pixel_attention_mask = pixel_attention_mask.view(
1 * num_images, *pixel_attention_mask.shape[2:]
)
pixel_attention_mask = pixel_attention_mask[
real_images_inds
].contiguous()
patch_size = self.config.vision_config.patch_size
patches_subgrid = pixel_attention_mask.unfold(
dimension=1, size=patch_size, step=patch_size
)
patches_subgrid = patches_subgrid.unfold(
dimension=2, size=patch_size, step=patch_size
)
patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(
pixel_values=pixel_values,
patch_attention_mask=patch_attention_mask,
)
# Modality projection & resampling
image_hidden_states = self.connector(
image_hidden_states,
)
all_states.append(image_hidden_states)
image_hidden_states = torch.stack(all_states, dim=0)
inputs_embeds = self._merge_input_ids_with_image_features(
input_ids, inputs_embeds, image_hidden_states
)
hidden_states = self.text_model.model(
inputs_embeds=inputs_embeds,
position_ids=position_ids,
cu_seqlen_prefill=cu_seqlen_prefill,
kv_cache=kv_cache,
block_tables=block_tables,
slots=slots,
seqlen=seqlen,
max_s=max_s,
true_max_s=max_s,
prefill_cache_indices=None,
adapter_data=adapter_data,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits, speculative_logits = self.text_model.lm_head(hidden_states)
return logits, speculative_logits

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

@ -4,7 +4,7 @@ def load_text_model(prefix, config, weights, name=None):
FlashLlamaForCausalLM,
)
return FlashLlamaForCausalLM(prefix, config, weights)
return FlashLlamaForCausalLM(prefix, config, weights, name=name)
elif config.model_type == "mistral":
from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
FlashMistralForCausalLM,

25
server/text_generation_server/models/flash_causal_lm.py
View File

@ -1288,7 +1288,7 @@ class FlashCausalLM(Model):
weights_loader=weights_loader,
)
prefix = ""
prefix = None
model = model_class(prefix, config, weights)
torch.distributed.barrier(group=self.process_group)
@ -1595,7 +1595,9 @@ class FlashCausalLM(Model):
if max_total_tokens is None:
if get_support_chunking():
model_max_length = self.tokenizer.model_max_length
max_position_embeddings = self.config.max_position_embeddings
max_position_embeddings = getattr(
self.config, "max_position_embeddings", model_max_length
)
max_total_tokens = min(
num_blocks * BLOCK_SIZE, model_max_length, max_position_embeddings
)
@ -1663,7 +1665,7 @@ class FlashCausalLM(Model):
for seqlen in tuning_sequences:
log_master(logger.info, f"Warming up TunableOp for seqlen={seqlen}")
self.tunableop_warmup(seqlen)
self.tunableop_warmup(seqlen, max_total_tokens)
torch.cuda.tunable.write_file(tunableop_filepath)
if os.environ.get("PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP") != "1":
torch.cuda.tunable.tuning_enable(False)
@ -1710,7 +1712,7 @@ class FlashCausalLM(Model):
assert max_total_tokens is not None
return int(num_blocks * BLOCK_SIZE), max_input_tokens, max_total_tokens
def tunableop_warmup(self, seqlen: int):
def tunableop_warmup(self, seqlen: int, max_bt: int):
input_ids = torch.zeros(seqlen, dtype=torch.int64, device=self.device)
position_ids = torch.zeros(seqlen, dtype=torch.int32, device=self.device)
slots = torch.arange(seqlen, dtype=torch.int64, device=self.device)
@ -1724,11 +1726,15 @@ class FlashCausalLM(Model):
[0, seqlen], device=self.device, dtype=torch.int32
)
max_s = seqlen
block_tables = torch.arange(
max_bt, dtype=torch.int32, device=self.device
).repeat(seqlen)
block_tables = block_tables.reshape((seqlen, max_bt))
seqlen = Seqlen(
input_lengths=input_lengths,
cache_lengths=cache_lengths_tensor,
cu_seqlen_q=cu_seqlen_prefill,
max_q=1,
max_k=seqlen,
)
@ -1738,7 +1744,7 @@ class FlashCausalLM(Model):
position_ids=position_ids,
cu_seqlen_prefill=cu_seqlen_prefill,
kv_cache=self.kv_cache,
block_tables=None,
block_tables=block_tables,
seqlen=seqlen,
slots=slots,
max_s=max_s,
@ -2480,7 +2486,8 @@ class FlashCausalLM(Model):
num_kv_heads=self.num_kv_heads,
head_size=self.head_size,
page_size=BLOCK_SIZE,
dtype=self.dtype,
kv_dtype=self.kv_cache_dtype,
q_dtype=self.dtype,
window_left=self.sliding_window,
)
else:
@ -2494,6 +2501,6 @@ class FlashCausalLM(Model):
head_size=self.head_size,
page_size=BLOCK_SIZE,
kv_cache_dtype=self.kv_cache_dtype,
dtype=self.dtype,
q_dtype=self.dtype,
window_left=self.sliding_window,
)

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

@ -13,6 +13,7 @@ from text_generation_server.models.flash_causal_lm import (
FlashCausalLM,
)
from text_generation_server.models.globals import PREFIX_CACHING, ATTENTION
from loguru import logger
from text_generation_server.utils.log import log_master
from transformers import AutoProcessor
from text_generation_server.layers.attention import Seqlen
@ -23,6 +24,40 @@ tracer = trace.get_tracer(__name__)
IDEFICS2_FAKE_TOKEN = "<fake_token_around_image>"
IDEFICS2_IMAGE_TOKEN = "<image>"
IDEFICS3_IMAGE_TOKEN = "<image>"
IDEFICS3_FAKE_IMAGE_TOKEN = "<fake_token_around_image>"
IDEFICS3_GLOBAL_IMG_TOKEN = "<global-img>"
# copied from: https://github.com/huggingface/transformers/blob/02ed609285c2448b3b54c31e362f2c389fa952ab/src/transformers/models/idefics3/processing_idefics3.py#L44-L60
def _prompt_split_image(
*,
image_seq_len: int,
image_rows: int,
image_cols: int,
fake_token_around_image: str,
image_token: str,
global_img_token: str,
):
"""Prompt with expanded image tokens for when the image is split into patches."""
text_split_images = ""
for n_h in range(image_rows):
for n_w in range(image_cols):
text_split_images += (
f"{fake_token_around_image}"
+ f"<row_{n_h + 1}_col_{n_w + 1}>"
+ f"{image_token}" * image_seq_len
)
text_split_images += "\n"
text_split_images += (
f"\n{fake_token_around_image}"
+ f"{global_img_token}"
+ f"{image_token}" * image_seq_len
+ f"{fake_token_around_image}"
)
return text_split_images
def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
"""
@ -54,10 +89,26 @@ def image_text_replacement(processor, image_input, config, image_id: int) -> str
if processor.image_processor.do_image_splitting:
image_str *= 5
return image_str
if config.model_type == "idefics3":
# TODO: implement this in a more general way
n_rows = image_input["rows"][0][image_id]
n_cols = image_input["cols"][0][image_id]
image_seq_len = int(
((config.vision_config.image_size // config.vision_config.patch_size) ** 2)
/ (config.scale_factor**2)
)
image_str = _prompt_split_image(
image_seq_len=image_seq_len,
image_rows=n_rows,
image_cols=n_cols,
fake_token_around_image=IDEFICS3_FAKE_IMAGE_TOKEN,
image_token=IDEFICS3_IMAGE_TOKEN,
global_img_token=IDEFICS3_GLOBAL_IMG_TOKEN,
)
return image_str
elif config.model_type == "llava_next":
height, width = image_input["image_sizes"][image_id]
num_features = get_number_of_features(height, width, config)
from loguru import logger
log_master(
logger.info,
@ -194,12 +245,21 @@ class VlmCausalLMBatch(FlashCausalLMBatch):
raise RuntimeError(f"Invalid chunk type {chunk_type}")
if images:
image_inputs = processor.image_processor(images, return_tensors="pt")
kwargs = {}
if (
hasattr(processor, "image_processor_class")
and processor.image_processor_class == "Idefics3ImageProcessor"
):
kwargs["return_row_col_info"] = True
image_inputs = processor.image_processor(
images, return_tensors="pt", **kwargs
)
else:
image_inputs = None
batch_inputs = []
max_truncation = 0
batch_tokenized_inputs = []
max_length = 0
image_id = 0
for r in requests:
full_text = ""
@ -214,16 +274,14 @@ class VlmCausalLMBatch(FlashCausalLMBatch):
image_id += 1
full_text = image_text_replacement_fixup(config, full_text)
batch_inputs.append(full_text)
max_truncation = max(max_truncation, r.truncate)
batch_tokenized_inputs = tokenizer(
batch_inputs,
truncation=True,
max_length=max_truncation,
add_special_tokens=not config.model_type == "paligemma",
)["input_ids"]
input_ids = tokenizer(
full_text,
truncation=True,
max_length=r.truncate,
add_special_tokens=r.add_special_tokens,
)["input_ids"]
max_length = max(max_length, len(input_ids))
batch_tokenized_inputs.append(input_ids)
return batch_tokenized_inputs, image_inputs

6
server/text_generation_server/utils/adapter.py
View File

@ -281,6 +281,12 @@ def get_mlp_weights(i, layer):
if hasattr(mlp, "up_proj"):
weights[(i, "up_proj")] = (f"model.layers.{i}.mlp.up_proj", mlp.up_proj)
if hasattr(mlp, "c_fc"):
weights[(i, "c_fc")] = (f"model.layers.{i}.mlp.c_fc", mlp.c_fc)
if hasattr(mlp, "c_proj"):
weights[(i, "c_proj")] = (f"model.layers.{i}.mlp.c_proj", mlp.c_proj)
if hasattr(mlp, "down_proj"):
weights[(i, "down_proj")] = (
f"model.layers.{i}.mlp.down_proj",

5
server/text_generation_server/utils/dist.py
View File

@ -1,6 +1,6 @@
import os
import torch
from torch.distributed import ProcessGroup
from datetime import timedelta
from loguru import logger
from text_generation_server.utils.import_utils import SYSTEM
@ -18,10 +18,11 @@ class FakeBarrier:
pass
class FakeGroup:
class FakeGroup(ProcessGroup):
def __init__(self, rank, size):
self._rank = rank
self._size = size
super().__init__(rank, size)
def allreduce(self, *args, **kwargs):
return FakeBarrier()