diff --git a/.devcontainer/Dockerfile_trtllm b/.devcontainer/Dockerfile_trtllm index 21b7114c..239a7bf8 100644 --- a/.devcontainer/Dockerfile_trtllm +++ b/.devcontainer/Dockerfile_trtllm @@ -72,4 +72,4 @@ RUN cargo install cargo-chef COPY --from=trt-builder /usr/local/tensorrt /usr/local/tensorrt COPY --from=mpi-builder /usr/local/mpi /usr/local/mpi -ENV MPI_HOME=/usr/local/mpi \ No newline at end of file +ENV MPI_HOME=/usr/local/mpi diff --git a/Cargo.lock b/Cargo.lock index 74ae6e16..e63d1540 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -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" diff --git a/Dockerfile_amd b/Dockerfile_amd index 7638947a..dc748f49 100644 --- a/Dockerfile_amd +++ b/Dockerfile_amd @@ -234,6 +234,7 @@ FROM kernel-builder AS vllm-builder WORKDIR /usr/src COPY server/Makefile-vllm Makefile +RUN pip install setuptools_scm # Build specific version of vllm RUN make build-vllm-rocm @@ -267,6 +268,15 @@ COPY server/exllamav2_kernels/ . RUN python setup.py build +FROM kernel-builder AS moe-kernels +WORKDIR /usr/src +ENV MOE_KERNELS_BRANCH=a67b35841774b2056a73806c36661134b5054edd +ENV VLLM_TARGET_DEVICE=rocm +RUN git clone https://github.com/danieldk/moe-kernels.git && \ + cd moe-kernels && \ + git checkout ${MOE_KERNELS_BRANCH} && \ + python setup.py install + FROM install_deps AS base-copy # Text Generation Inference base env @@ -289,6 +299,9 @@ 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 moe kernels +COPY --from=moe-kernels /usr/src/moe-kernels/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages + # Install server COPY proto proto COPY server server diff --git a/Dockerfile_intel b/Dockerfile_intel index e024f31a..3b5e4a13 100644 --- a/Dockerfile_intel +++ b/Dockerfile_intel @@ -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 diff --git a/README.md b/README.md index 6d3a9b12..31966ddb 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@
- Making TGI deployment optimal + Making TGI deployment optimal # Text Generation Inference @@ -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=` +2. Copy your CLI READ token +3. Export `HF_TOKEN=` or with Docker: @@ -157,7 +157,7 @@ docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/da ### 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 +196,7 @@ 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 +213,7 @@ 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 ``` diff --git a/backends/trtllm/csrc/backend.hpp b/backends/trtllm/csrc/backend.hpp index f49c437a..40b44a84 100644 --- a/backends/trtllm/csrc/backend.hpp +++ b/backends/trtllm/csrc/backend.hpp @@ -228,4 +228,4 @@ struct fmt::formatter : f } }; -#endif \ No newline at end of file +#endif diff --git a/backends/trtllm/csrc/ffi.hpp b/backends/trtllm/csrc/ffi.hpp index de2333af..d0342d4b 100644 --- a/backends/trtllm/csrc/ffi.hpp +++ b/backends/trtllm/csrc/ffi.hpp @@ -159,4 +159,4 @@ namespace huggingface::tgi::backends::trtllm { ); } } -#endif \ No newline at end of file +#endif diff --git a/backends/trtllm/csrc/hardware.hpp b/backends/trtllm/csrc/hardware.hpp index 8e5fa696..abfb4afd 100644 --- a/backends/trtllm/csrc/hardware.hpp +++ b/backends/trtllm/csrc/hardware.hpp @@ -78,4 +78,4 @@ namespace huggingface::tgi::hardware::cuda { [[nodiscard]] constexpr bool is_at_least_hopper() const { return is_at_least(HOPPER); } }; } -#endif \ No newline at end of file +#endif diff --git a/backends/trtllm/tests/test_backend.cpp b/backends/trtllm/tests/test_backend.cpp index ae097405..14d92b75 100644 --- a/backends/trtllm/tests/test_backend.cpp +++ b/backends/trtllm/tests/test_backend.cpp @@ -149,4 +149,4 @@ TEST_CASE("sampling_params_t to tle::SamplingConfig", "[backend_t]") REQUIRE(config.getTemperature().has_value()); REQUIRE_THAT(*config.getTemperature(), Catch::Matchers::WithinAbs(params.temperature, 1e-6f)); -} \ No newline at end of file +} diff --git a/backends/trtllm/tests/test_hardware.cpp b/backends/trtllm/tests/test_hardware.cpp index 4cb7b562..e14f1f35 100644 --- a/backends/trtllm/tests/test_hardware.cpp +++ b/backends/trtllm/tests/test_hardware.cpp @@ -79,4 +79,4 @@ TEST_CASE("is_at_least") { REQUIRE(HOPPER_CAPABILITIES.is_at_least(AMPERE)); REQUIRE(HOPPER_CAPABILITIES.is_at_least(ADA_LOVELACE)); REQUIRE(HOPPER_CAPABILITIES.is_at_least(HOPPER)); -} \ No newline at end of file +} diff --git a/backends/v2/Cargo.toml b/backends/v2/Cargo.toml index 4d32474e..0decf41a 100644 --- a/backends/v2/Cargo.toml +++ b/backends/v2/Cargo.toml @@ -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" diff --git a/backends/v3/Cargo.toml b/backends/v3/Cargo.toml index 69dad072..996290ed 100644 --- a/backends/v3/Cargo.toml +++ b/backends/v3/Cargo.toml @@ -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" diff --git a/docs/source/backends/trtllm.md b/docs/source/backends/trtllm.md index 8eb37180..be6416b1 100644 --- a/docs/source/backends/trtllm.md +++ b/docs/source/backends/trtllm.md @@ -17,7 +17,7 @@ supported. You can use [Optimum-NVIDIA](https://github.com/huggingface/optimum-nvidia) to compile engines for the models you want to use. -```bash +```bash MODEL_NAME="meta-llama/Llama-3.1-8B-Instruct" # Install huggingface_cli @@ -32,7 +32,7 @@ mkdir -p /tmp/models/$MODEL_NAME # Create a directory to store the compiled engine mkdir -p /tmp/engines/$MODEL_NAME -# Download the model +# Download the model HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download --local-dir /tmp/models/$MODEL_NAME $MODEL_NAME # Compile the engine using Optimum-NVIDIA @@ -69,7 +69,7 @@ docker run \ -e MODEL=$MODEL_NAME \ -e PORT=3000 \ -e HF_TOKEN='hf_XXX' \ - -v /tmp/engines/$MODEL_NAME:/data \ + -v /tmp/engines/$MODEL_NAME:/data \ ghcr.io/huggingface/text-generation-inference:latest-trtllm \ --executor-worker executorWorker \ --model-id /data/$MODEL_NAME @@ -78,4 +78,4 @@ docker run \ ## Development To develop TRTLLM backend, you can use [dev containers](https://containers.dev/) located in -`.devcontainer` directory. \ No newline at end of file +`.devcontainer` directory. diff --git a/docs/source/basic_tutorials/using_guidance.md b/docs/source/basic_tutorials/using_guidance.md index 2d55c952..e389fbbc 100644 --- a/docs/source/basic_tutorials/using_guidance.md +++ b/docs/source/basic_tutorials/using_guidance.md @@ -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 \ diff --git a/docs/source/multi_backend_support.md b/docs/source/multi_backend_support.md index 5899e4b7..c4df15bc 100644 --- a/docs/source/multi_backend_support.md +++ b/docs/source/multi_backend_support.md @@ -1,13 +1,13 @@ # Multi-backend support TGI (Text Generation Inference) offers flexibility by supporting multiple backends for serving large language models (LLMs). -With multi-backend support, you can choose the backend that best suits your needs, -whether you prioritize performance, ease of use, or compatibility with specific hardware. API interaction with +With multi-backend support, you can choose the backend that best suits your needs, +whether you prioritize performance, ease of use, or compatibility with specific hardware. API interaction with TGI remains consistent across backends, allowing you to switch between them seamlessly. **Supported backends:** -* **TGI CUDA backend**: This high-performance backend is optimized for NVIDIA GPUs and serves as the default option +* **TGI CUDA backend**: This high-performance backend is optimized for NVIDIA GPUs and serves as the default option within TGI. Developed in-house, it boasts numerous optimizations and is used in production by various projects, including those by Hugging Face. -* **[TGI TRTLLM backend](./backends/trtllm)**: This backend leverages NVIDIA's TensorRT library to accelerate LLM inference. - It utilizes specialized optimizations and custom kernels for enhanced performance. - However, it requires a model-specific compilation step for each GPU architecture. \ No newline at end of file +* **[TGI TRTLLM backend](./backends/trtllm)**: This backend leverages NVIDIA's TensorRT library to accelerate LLM inference. + It utilizes specialized optimizations and custom kernels for enhanced performance. + However, it requires a model-specific compilation step for each GPU architecture. diff --git a/docs/source/supported_models.md b/docs/source/supported_models.md index 0f39ff28..5ac90351 100644 --- a/docs/source/supported_models.md +++ b/docs/source/supported_models.md @@ -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) diff --git a/flake.lock b/flake.lock index ec87d569..d304e3cc 100644 --- a/flake.lock +++ b/flake.lock @@ -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": { diff --git a/integration-tests/conftest.py b/integration-tests/conftest.py index c9c47766..c702ae70 100644 --- a/integration-tests/conftest.py +++ b/integration-tests/conftest.py @@ -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)) diff --git a/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int/test_compressed_tensors_w8a8_int_all_params.json b/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int/test_compressed_tensors_w8a8_int_all_params.json index 7d35e8f9..771708eb 100644 --- a/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int/test_compressed_tensors_w8a8_int_all_params.json +++ b/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int/test_compressed_tensors_w8a8_int_all_params.json @@ -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" } diff --git a/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_all_params.json b/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_all_params.json index 0db48f3e..6b3f5092 100644 --- a/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_all_params.json +++ b/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_all_params.json @@ -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" } diff --git a/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_load.json b/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_load.json index abcaf876..1fa4e33a 100644 --- a/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_load.json +++ b/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_load.json @@ -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" } diff --git a/integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int/test_compressed_tensors_wna16_all_params.json b/integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int/test_compressed_tensors_wna16_all_params.json index 08c63e79..29709676 100644 --- a/integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int/test_compressed_tensors_wna16_all_params.json +++ b/integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int/test_compressed_tensors_wna16_all_params.json @@ -44,7 +44,7 @@ }, { "id": 38397, - "logprob": -0.12695312, + "logprob": 0.0, "special": false, "text": " subset" }, diff --git a/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_all_params.json b/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_all_params.json index 6306f75e..0f54bbe8 100644 --- a/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_all_params.json +++ b/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_all_params.json @@ -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" } diff --git a/integration-tests/models/__snapshots__/test_flash_starcoder2/test_flash_starcoder2_default_params.json b/integration-tests/models/__snapshots__/test_flash_starcoder2/test_flash_starcoder2_default_params.json index 914e59c0..6674cf50 100644 --- a/integration-tests/models/__snapshots__/test_flash_starcoder2/test_flash_starcoder2_default_params.json +++ b/integration-tests/models/__snapshots__/test_flash_starcoder2/test_flash_starcoder2_default_params.json @@ -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)" } diff --git a/integration-tests/models/__snapshots__/test_idefics3/test_flash_idefics3_next_simple_url.json b/integration-tests/models/__snapshots__/test_idefics3/test_flash_idefics3_next_simple_url.json new file mode 100644 index 00000000..6bf2b93a --- /dev/null +++ b/integration-tests/models/__snapshots__/test_idefics3/test_flash_idefics3_next_simple_url.json @@ -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": "" + } + ], + "top_tokens": null + }, + "generated_text": " There is a statue in the image." +} diff --git a/integration-tests/models/__snapshots__/test_smolvlm/test_flash_smolvlm_next_simple_url.json b/integration-tests/models/__snapshots__/test_smolvlm/test_flash_smolvlm_next_simple_url.json new file mode 100644 index 00000000..17a69d0d --- /dev/null +++ b/integration-tests/models/__snapshots__/test_smolvlm/test_flash_smolvlm_next_simple_url.json @@ -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": "" + } + ], + "top_tokens": null + }, + "generated_text": " A bee on a pink flower." +} diff --git a/integration-tests/models/test_compressed_tensors_w8a8_int_dynamic_weight.py b/integration-tests/models/test_compressed_tensors_w8a8_int_dynamic_weight.py index 7cc82a4e..a0b0416b 100644 --- a/integration-tests/models/test_compressed_tensors_w8a8_int_dynamic_weight.py +++ b/integration-tests/models/test_compressed_tensors_w8a8_int_dynamic_weight.py @@ -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 diff --git a/integration-tests/models/test_idefics3.py b/integration-tests/models/test_idefics3.py new file mode 100644 index 00000000..80be2350 --- /dev/null +++ b/integration-tests/models/test_idefics3.py @@ -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}\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 diff --git a/integration-tests/models/test_smolvlm.py b/integration-tests/models/test_smolvlm.py new file mode 100644 index 00000000..cd105d84 --- /dev/null +++ b/integration-tests/models/test_smolvlm.py @@ -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}\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 diff --git a/launcher/src/main.rs b/launcher/src/main.rs index fb6ba2b2..c092d745 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -1652,7 +1652,11 @@ 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()), } diff --git a/router/Cargo.toml b/router/Cargo.toml index 9258fe03..2e621dfc 100644 --- a/router/Cargo.toml +++ b/router/Cargo.toml @@ -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" diff --git a/router/src/config.rs b/router/src/config.rs index 5d07a293..4d5fcfa0 100644 --- a/router/src/config.rs +++ b/router/src/config.rs @@ -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, diff --git a/router/src/kserve.rs b/router/src/kserve.rs index c53fa481..ea85eb8c 100644 --- a/router/src/kserve.rs +++ b/router/src/kserve.rs @@ -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(), diff --git a/router/src/lib.rs b/router/src/lib.rs index 84e9bc48..21c45241 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -170,6 +170,7 @@ impl TokenizerConfigToken { #[serde(tag = "processor_class")] pub enum HubPreprocessorConfig { Idefics2Processor(Idefics2Preprocessor), + Idefics3Processor(Idefics2Preprocessor), } impl HubPreprocessorConfig { diff --git a/router/src/validation.rs b/router/src/validation.rs index 8137ac58..cdf954de 100644 --- a/router/src/validation.rs +++ b/router/src/validation.rs @@ -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 = ""; + const IMAGE: &str = ""; + const GLOBAL_IMG: &str = ""; + + 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!("", 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) => "".repeat(config.get_number_of_features(height, width)), LlavaNext(config) => "".repeat(config.get_number_of_features(height, width)), Qwen2Vl(config) => format!( @@ -647,7 +711,8 @@ fn prepare_input( static RE: Lazy = 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()); diff --git a/server/Makefile-flash-att-v2 b/server/Makefile-flash-att-v2 index a9cdf782..9a946d97 100644 --- a/server/Makefile-flash-att-v2 +++ b/server/Makefile-flash-att-v2 @@ -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 diff --git a/server/Makefile-flashinfer b/server/Makefile-flashinfer index f0a27622..d5f684ba 100644 --- a/server/Makefile-flashinfer +++ b/server/Makefile-flashinfer @@ -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 diff --git a/server/Makefile-vllm b/server/Makefile-vllm index 45a7980d..90da96d2 100644 --- a/server/Makefile-vllm +++ b/server/Makefile-vllm @@ -1,4 +1,4 @@ -commit_rocm := 4e0929e6e4fa0a3d09d358715c288020ea9dc247 +commit_rocm := de990cd12537f78f74e40b5c8ee1a62d63d734dd build-vllm-rocm: if [ ! -d 'vllm' ]; then \ diff --git a/server/poetry.lock b/server/poetry.lock index 7cf440dd..69133015 100644 --- a/server/poetry.lock +++ b/server/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. [[package]] name = "accelerate" @@ -1289,12 +1289,12 @@ 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 +1302,16 @@ 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 +1319,16 @@ 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 +1336,16 @@ 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 +1353,7 @@ 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 +4097,4 @@ torch = ["torch"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "c7fdcff2b752cd3beb3995c1ecd15f0f4d9b4e117048b06ab991c6d0e0c86ff3" +content-hash = "25f96d5dea777bfa7a959f863e35d2e05e1a6172d0dd45193dbe25ac2f32cc25" diff --git a/server/pyproject.toml b/server/pyproject.toml index 0d56e9c7..bc74a05a 100644 --- a/server/pyproject.toml +++ b/server/pyproject.toml @@ -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 }, diff --git a/server/text_generation_server/layers/attention/cuda.py b/server/text_generation_server/layers/attention/cuda.py index 3038602e..7b5af3c4 100644 --- a/server/text_generation_server/layers/attention/cuda.py +++ b/server/text_generation_server/layers/attention/cuda.py @@ -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, diff --git a/server/text_generation_server/layers/attention/flashinfer.py b/server/text_generation_server/layers/attention/flashinfer.py index 26a72d9b..909eea27 100644 --- a/server/text_generation_server/layers/attention/flashinfer.py +++ b/server/text_generation_server/layers/attention/flashinfer.py @@ -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: diff --git a/server/text_generation_server/layers/attention/kv_cache.py b/server/text_generation_server/layers/attention/kv_cache.py index cad1d98a..93d74732 100644 --- a/server/text_generation_server/layers/attention/kv_cache.py +++ b/server/text_generation_server/layers/attention/kv_cache.py @@ -215,7 +215,9 @@ def paged_reshape_and_cache( raise ImportError( f"Could not import vllm paged attention. Make sure your installation is correct. Complete error: {e}" ) - ops.reshape_and_cache(key, value, key_cache, value_cache, slots, "auto", 1.0) + ops.reshape_and_cache( + key, value, key_cache, value_cache, slots, "auto", 1.0, 1.0 + ) elif SYSTEM == "ipex": import intel_extension_for_pytorch as ipex diff --git a/server/text_generation_server/layers/attention/rocm.py b/server/text_generation_server/layers/attention/rocm.py index ea11c2c2..69a245ad 100644 --- a/server/text_generation_server/layers/attention/rocm.py +++ b/server/text_generation_server/layers/attention/rocm.py @@ -5,27 +5,47 @@ 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 major, minor = torch.cuda.get_device_capability() is_sm75 = major == 7 and minor == 5 -_PARTITION_SIZE_V1V2 = 512 +_PARTITION_SIZE_V1V2 = 1024 _PARTITION_SIZE_CUSTOM = 256 +_GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName +_ON_MI250_MI300 = any( + arch in _GPU_ARCH for arch in ["gfx90a", "gfx940", "gfx941", "gfx942"] +) + use_triton = os.getenv("ROCM_USE_FLASH_ATTN_V2_TRITON", "").lower() in {"true", "1"} ENGINE = "triton" if use_triton else "ck" use_rocm_custom_paged_attn = os.getenv("ROCM_USE_CUSTOM_PAGED_ATTN", "1") != "0" -try: - if use_rocm_custom_paged_attn: - from vllm._custom_C import paged_attention_custom -except ImportError as e: - log_master( - logger.info, - f"Custom Paged Attention not available. Complete error: {e}", + + +def _use_rocm_custom_paged_attention( + qtype: torch.dtype, + head_size: int, + block_size: int, + gqa_ratio: int, + max_seq_len: int, +) -> bool: + # rocm custom page attention not support on navi (gfx1*) + return ( + use_rocm_custom_paged_attn + and _ON_MI250_MI300 + and (qtype == torch.half or qtype == torch.bfloat16) + and (head_size == 64 or head_size == 128) + and (block_size == 16 or block_size == 32) + and (gqa_ratio >= 1 and gqa_ratio <= 16) + and max_seq_len <= 131072 ) - use_rocm_custom_paged_attn = False def paged_attention( @@ -57,22 +77,50 @@ 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] gqa_ratio = num_heads // num_kv_heads - use_custom = ( - use_rocm_custom_paged_attn - and (query.dtype == torch.half or query.dtype == torch.bfloat16) - and (head_size == 128 or head_size == 64) - and (block_size == 16 or block_size == 32) - and (gqa_ratio >= 1 and gqa_ratio <= 16) - and max_s <= 32768 + use_custom = _use_rocm_custom_paged_attention( + query.dtype, head_size, block_size, gqa_ratio, max_s ) if not use_custom: @@ -90,8 +138,6 @@ def paged_attention( # V1 to avoid the overhead of reduction. Also, if the number of # sequences or heads is large, we use V1 since there is enough work # to parallelize. - import vllm._custom_ops as ops - use_v1 = ( max_s <= 8192 and (max_num_partitions == 1 or num_seqs * num_heads > 512) @@ -103,7 +149,7 @@ def paged_attention( query, kv_cache.key, kv_cache.value, - kv_head_mapping, + num_kv_heads, softmax_scale, block_tables, input_lengths, @@ -112,6 +158,7 @@ def paged_attention( None, "auto", 1.0, + 1.0, ) else: # Run PagedAttention V2. @@ -137,7 +184,7 @@ def paged_attention( query, kv_cache.key, kv_cache.value, - kv_head_mapping, + num_kv_heads, softmax_scale, block_tables, input_lengths, @@ -146,9 +193,10 @@ def paged_attention( None, "auto", 1.0, + 1.0, ) else: - paged_attention_custom( + ops.paged_attention_rocm( out, exp_sums, max_logits, @@ -164,6 +212,10 @@ def paged_attention( max_s, None, "auto", + 1.0, + 1.0, + None, + _PARTITION_SIZE, ) return out @@ -232,14 +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, diff --git a/server/text_generation_server/layers/layernorm.py b/server/text_generation_server/layers/layernorm.py index ce5289f9..8c7a2eb0 100644 --- a/server/text_generation_server/layers/layernorm.py +++ b/server/text_generation_server/layers/layernorm.py @@ -72,7 +72,7 @@ if SYSTEM == "cuda": return normed_hidden_states, residual elif SYSTEM == "rocm": - from vllm._C import ops + import vllm._custom_ops as ops class FastLayerNorm(nn.LayerNorm): def forward(self, hidden_states, residual=None): @@ -121,6 +121,27 @@ class FastRMSNorm(nn.Module): residual is not None, ) return out, residual if residual is not None else hidden_states + elif SYSTEM == "rocm": + # We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not. + if residual is not None: + ops.fused_add_rms_norm( + hidden_states, + residual, + self.weight.data, + self.variance_epsilon, + ) + return hidden_states, residual + + residual = hidden_states + + out = torch.empty_like(hidden_states) + ops.rms_norm( + out, + hidden_states, + self.weight.data, + self.variance_epsilon, + ) + return out, residual elif hidden_states.shape[-1] > 8192: if residual is not None: hidden_states += residual @@ -164,20 +185,6 @@ class FastRMSNorm(nn.Module): res = hidden_states return normed_hidden_states, res - elif SYSTEM == "rocm": - # We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not. - if residual is not None: - hidden_states += residual - residual = hidden_states - - out = torch.empty_like(hidden_states) - ops.rms_norm( - out, - hidden_states, - self.weight.data, - self.variance_epsilon, - ) - return out, residual else: raise ValueError( "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction." diff --git a/server/text_generation_server/layers/linear.py b/server/text_generation_server/layers/linear.py index 08306d57..166c2874 100644 --- a/server/text_generation_server/layers/linear.py +++ b/server/text_generation_server/layers/linear.py @@ -11,10 +11,10 @@ if SYSTEM == "rocm": if ROCM_USE_SKINNY_GEMM: try: - from vllm import _custom_C + import vllm._custom_ops as ops except Exception as e: raise ImportError( - f"Could not load `vllm._custom_C` for ROCm skinny gemm. Full error: {e}" + f"Could not load `vllm._custom_ops` for ROCm skinny gemm. Full error: {e}" ) @@ -95,12 +95,12 @@ class FastLinearROCm(torch.nn.Module): out = torch.empty( inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device ) - _custom_C.wvSpltK(weight, inp, out, n, self.cu_count) + ops.wvSpltK(weight, inp, out, n, self.cu_count) elif m % 4 == 0 and n == 1 and k <= 8192: out = torch.empty( inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device ) - _custom_C.LLMM1(weight, inp, out, 4) + ops.LLMM1(weight, inp, out, 4) else: out = F.linear(inp, weight) diff --git a/server/text_generation_server/layers/moe/__init__.py b/server/text_generation_server/layers/moe/__init__.py index a5ae7ff4..be40d78a 100644 --- a/server/text_generation_server/layers/moe/__init__.py +++ b/server/text_generation_server/layers/moe/__init__.py @@ -24,10 +24,7 @@ from text_generation_server.utils.weights import ( UnquantizedWeight, ) -if SYSTEM == "rocm": - from .fused_moe_rocm import grouped_topk - from vllm.model_executor.layers.fused_moe import fused_topk -elif SYSTEM == "ipex": +if SYSTEM == "ipex": from intel_extension_for_pytorch.llm.modules import GatedMLPMOE else: from moe_kernels.fused_moe import fused_topk, grouped_topk diff --git a/server/text_generation_server/layers/moe/fused_moe_rocm.py b/server/text_generation_server/layers/moe/fused_moe_rocm.py deleted file mode 100644 index 68accb99..00000000 --- a/server/text_generation_server/layers/moe/fused_moe_rocm.py +++ /dev/null @@ -1,52 +0,0 @@ -# coding=utf-8 -# Copyright 2023, 2024 DeepSeek-AI and 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. - -from typing import Tuple - -import torch -import torch.distributed - - -# TODO: Remove the functions once moe_kernel are built for ROCM -def grouped_topk( - hidden_states: torch.Tensor, - gating_output: torch.Tensor, - topk: int, - renormalize: bool, - num_expert_group: int = 0, - topk_group: int = 0, -) -> Tuple[torch.Tensor, torch.Tensor]: - scores = torch.softmax(gating_output, dim=-1) - num_token = scores.shape[0] - group_scores = ( - scores.view(num_token, num_expert_group, -1).max(dim=-1).values - ) # [n, n_group] - group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=False)[ - 1 - ] # [n, top_k_group] - group_mask = torch.zeros_like(group_scores) # [n, n_group] - group_mask.scatter_(1, group_idx, 1) # [n, n_group] - score_mask = ( - group_mask.unsqueeze(-1) - .expand(num_token, num_expert_group, scores.shape[-1] // num_expert_group) - .reshape(num_token, -1) - ) # [n, e] - tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0) # [n, e] - topk_weights, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False) - - if renormalize: - topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True) - - return topk_weights, topk_ids diff --git a/server/text_generation_server/layers/moe/unquantized.py b/server/text_generation_server/layers/moe/unquantized.py index 75af0409..3c9bcaba 100644 --- a/server/text_generation_server/layers/moe/unquantized.py +++ b/server/text_generation_server/layers/moe/unquantized.py @@ -6,9 +6,7 @@ import torch.nn as nn from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.utils.weights import UnquantizedWeight, Weights -if SYSTEM == "rocm": - from vllm.model_executor.layers.fused_moe import fused_moe -elif SYSTEM == "ipex": +if SYSTEM == "ipex": from intel_extension_for_pytorch.llm.modules import GatedMLPMOE else: from moe_kernels.fused_moe import fused_moe diff --git a/server/text_generation_server/layers/rotary.py b/server/text_generation_server/layers/rotary.py index 123bbadb..e346d0f8 100644 --- a/server/text_generation_server/layers/rotary.py +++ b/server/text_generation_server/layers/rotary.py @@ -7,7 +7,7 @@ from text_generation_server.utils.import_utils import SYSTEM if SYSTEM == "cuda": import rotary_emb elif SYSTEM == "rocm": - from vllm._C import ops + import vllm._custom_ops as ops elif SYSTEM == "ipex": import intel_extension_for_pytorch as ipex diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index fcc79608..beefeb01 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -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( diff --git a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py index 68719106..ece15e94 100644 --- a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py @@ -75,7 +75,7 @@ class CohereRotary(PositionRotaryEmbedding): rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False) elif SYSTEM == "rocm": - from vllm._C import ops + import vllm._custom_ops as ops # NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems. # Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773 diff --git a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py index 2d1aa96c..aa032782 100644 --- a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py @@ -23,9 +23,7 @@ from typing import Optional, List, Tuple, Any from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.utils.import_utils import SYSTEM -if SYSTEM == "rocm": - from vllm.model_executor.layers.fused_moe import fused_moe -elif SYSTEM == "ipex": +if SYSTEM == "ipex": from intel_extension_for_pytorch.llm.modules import GatedMLPMOE else: from moe_kernels.fused_moe import fused_moe diff --git a/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py index 906a83a4..2fb733cd 100644 --- a/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py @@ -43,9 +43,9 @@ from text_generation_server.utils.weights import Weights if SYSTEM == "rocm": try: - from vllm import _custom_C + import vllm._custom_ops as ops except Exception as e: - raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}") + raise ImportError(f"Could not load `vllm._custom_ops`. Full error: {e}") class DeepseekV2Config(PretrainedConfig): @@ -408,7 +408,7 @@ class DeepseekV2MLP(nn.Module): dtype=hidden_states.dtype, device="cuda", ) - _custom_C.LLMM_Silu(self.gate_up_proj.linear.weight, hidden_states, out, 8) + ops.LLMM_Silu(self.gate_up_proj.linear.weight, hidden_states, out, 8) return self.down_proj(out, reduce=reduce) else: gate_up_states = self.gate_up_proj(hidden_states) diff --git a/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py index 692f8ca3..45b90679 100644 --- a/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py @@ -91,7 +91,7 @@ class GPTJRotary(PositionRotaryEmbedding): rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False) elif SYSTEM == "rocm": - from vllm._C import ops + import vllm._custom_ops as ops # NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems. # Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773 diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py index 2c007d15..28db42fe 100644 --- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py @@ -64,9 +64,9 @@ if SYSTEM != "ipex": if SYSTEM == "rocm": try: - from vllm import _custom_C + import vllm._custom_ops as ops except Exception as e: - raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}") + raise ImportError(f"Could not load `vllm._custom_ops`. Full error: {e}") def load_attention(config, prefix: str, weights, layer_id): @@ -392,7 +392,7 @@ class LlamaMLP(nn.Module): dtype=hidden_states.dtype, device="cuda", ) - _custom_C.LLMM_Silu( + ops.LLMM_Silu( self.gate_up_proj.base_layer.linear.weight, hidden_states, out, 8 ) return self.down_proj(out, adapter_data) @@ -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 diff --git a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py index c66c732f..0fa172d0 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py @@ -49,9 +49,9 @@ from text_generation_server.layers.layernorm import ( if SYSTEM == "rocm": try: - from vllm import _custom_C + import vllm._custom_ops as ops except Exception as e: - raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}") + raise ImportError(f"Could not load `vllm._custom_ops`. Full error: {e}") class MistralConfig(PretrainedConfig): @@ -318,7 +318,7 @@ class MistralMLP(nn.Module): dtype=hidden_states.dtype, device="cuda", ) - _custom_C.LLMM_Silu( + ops.LLMM_Silu( self.gate_up_proj.base_layer.linear.weight, hidden_states, out, 8 ) return self.down_proj(out, adapter_data) diff --git a/server/text_generation_server/models/custom_modeling/idefics3.py b/server/text_generation_server/models/custom_modeling/idefics3.py new file mode 100644 index 00000000..580398cb --- /dev/null +++ b/server/text_generation_server/models/custom_modeling/idefics3.py @@ -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 diff --git a/server/text_generation_server/models/custom_modeling/idefics_modeling.py b/server/text_generation_server/models/custom_modeling/idefics_modeling.py index fc6becc4..9fc9bca6 100644 --- a/server/text_generation_server/models/custom_modeling/idefics_modeling.py +++ b/server/text_generation_server/models/custom_modeling/idefics_modeling.py @@ -52,7 +52,7 @@ from loguru import logger if SYSTEM == "cuda": import dropout_layer_norm elif SYSTEM == "rocm": - from vllm._C import ops + import vllm._custom_ops as ops else: dropout_layer_norm = None diff --git a/server/text_generation_server/models/custom_modeling/qwen2_vl.py b/server/text_generation_server/models/custom_modeling/qwen2_vl.py index ddb4e36d..a8e1e8c1 100644 --- a/server/text_generation_server/models/custom_modeling/qwen2_vl.py +++ b/server/text_generation_server/models/custom_modeling/qwen2_vl.py @@ -450,7 +450,7 @@ class Qwen2VLForConditionalGeneration(nn.Module): width //= self.spatial_merge_size # calculate the length of the text and image tokens - text_length = next_image_pos - current_pos + text_length = next_image_pos start_idx = ( llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0 ) @@ -480,7 +480,7 @@ class Qwen2VLForConditionalGeneration(nn.Module): ) llm_pos_ids_list.append(image_pos_ids) - current_pos = next_image_pos + time_steps * height * width + current_pos += next_image_pos + time_steps * height * width image_index += 1 if current_pos < batch_input_ids.size(1): diff --git a/server/text_generation_server/models/custom_modeling/vlm.py b/server/text_generation_server/models/custom_modeling/vlm.py index 82e409a6..94b8522d 100644 --- a/server/text_generation_server/models/custom_modeling/vlm.py +++ b/server/text_generation_server/models/custom_modeling/vlm.py @@ -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, diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index 5d376990..03fc6147 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -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) @@ -1663,7 +1663,7 @@ 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 +1710,7 @@ 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 +1724,15 @@ 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 +1742,7 @@ 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 +2484,8 @@ 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 +2499,6 @@ 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, ) diff --git a/server/text_generation_server/models/vlm_causal_lm.py b/server/text_generation_server/models/vlm_causal_lm.py index aa0fe107..db78341d 100644 --- a/server/text_generation_server/models/vlm_causal_lm.py +++ b/server/text_generation_server/models/vlm_causal_lm.py @@ -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 = "" IDEFICS2_IMAGE_TOKEN = "" +IDEFICS3_IMAGE_TOKEN = "" +IDEFICS3_FAKE_IMAGE_TOKEN = "" +IDEFICS3_GLOBAL_IMG_TOKEN = "" + + +# 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"" + + 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, @@ -68,7 +119,8 @@ def image_text_replacement(processor, image_input, config, image_id: int) -> str elif config.model_type == "paligemma": return "" * config.text_config.num_image_tokens elif config.model_type == "qwen2_vl": - num_pads = image_input.pixel_values.shape[0] // 4 + grid_t, grid_h, grid_w = image_input["image_grid_thw"][image_id] + num_pads = grid_t * grid_h * grid_w // 4 padding = "<|image_pad|>" * num_pads return f"<|vision_start|>{padding}<|vision_end|>" else: @@ -193,12 +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 = "" @@ -213,16 +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 diff --git a/server/text_generation_server/utils/dist.py b/server/text_generation_server/utils/dist.py index 82aeba6c..1b766ddf 100644 --- a/server/text_generation_server/utils/dist.py +++ b/server/text_generation_server/utils/dist.py @@ -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()