add intel xpu support for TGI (#1475)

# What does this PR do?

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Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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      to it if that's the case.
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- [ ] Did you write any new necessary tests?


## Who can review?

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---------

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

.github/workflows/build.yaml

@@ -274,12 +274,105 @@ jobs:
           cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache-rocm,mode=min
           cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache-rocm,mode=min
 
+  build-and-push-image-intel:
+    concurrency:
+      group: ${{ github.workflow }}-build-and-push-image-intel-${{ github.head_ref || github.run_id }}
+      cancel-in-progress: true
+    needs:
+      - start-runner
+      - build-and-push-image # Wait for the main docker image to be built
+      - integration-tests # Wait for the main integration-tests
+    runs-on: ${{ needs.start-runner.outputs.label }} # run the job on the newly created runner
+    permissions:
+      contents: write
+      packages: write
+      # This is used to complete the identity challenge
+      # with sigstore/fulcio when running outside of PRs.
+      id-token: write
+      security-events: write
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+      - name: Initialize Docker Buildx
+        uses: docker/setup-buildx-action@v2.0.0
+        with:
+          install: true
+      - name: Inject slug/short variables
+        uses: rlespinasse/github-slug-action@v4.4.1
+      - name: Tailscale
+        uses: tailscale/github-action@7bd8039bf25c23c4ab1b8d6e2cc2da2280601966
+        with:
+          authkey: ${{ secrets.TAILSCALE_AUTHKEY }}
+      - name: Login to GitHub Container Registry
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@v2
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - name: Login to internal Container Registry
+        uses: docker/login-action@v2.1.0
+        with:
+          username: ${{ secrets.TAILSCALE_DOCKER_USERNAME }}
+          password: ${{ secrets.TAILSCALE_DOCKER_PASSWORD }}
+          registry: registry.internal.huggingface.tech
+      - name: Login to Azure Container Registry
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@v2.1.0
+        with:
+          username: ${{ secrets.AZURE_DOCKER_USERNAME }}
+          password: ${{ secrets.AZURE_DOCKER_PASSWORD }}
+          registry: db4c2190dd824d1f950f5d1555fbadf0.azurecr.io
+      # If pull request
+      - name: Extract metadata (tags, labels) for Docker
+        if: ${{ github.event_name == 'pull_request' }}
+        id: meta-pr
+        uses: docker/metadata-action@v4.3.0
+        with:
+          images: |
+            registry.internal.huggingface.tech/api-inference/community/text-generation-inference
+          tags: |
+            type=raw,value=sha-${{ env.GITHUB_SHA_SHORT }}-intel
+      # If main, release or tag
+      - name: Extract metadata (tags, labels) for Docker
+        if: ${{ github.event_name != 'pull_request' }}
+        id: meta
+        uses: docker/metadata-action@v4.3.0
+        with:
+          flavor: |
+            latest=false
+          images: |
+            registry.internal.huggingface.tech/api-inference/community/text-generation-inference
+            ghcr.io/huggingface/text-generation-inference
+            db4c2190dd824d1f950f5d1555fbadf0.azurecr.io/text-generation-inference
+          tags: |
+            type=semver,pattern={{version}}-intel
+            type=semver,pattern={{major}}.{{minor}}-intel
+            type=raw,value=latest-intel,enable=${{ github.ref == format('refs/heads/{0}', github.event.repository.default_branch) }}
+            type=raw,value=sha-${{ env.GITHUB_SHA_SHORT }}-intel
+      - name: Build and push Docker image
+        id: build-and-push
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: Dockerfile_intel
+          push: true
+          platforms: 'linux/amd64'
+          build-args: |
+            GIT_SHA=${{ env.GITHUB_SHA }}
+            DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}-intel
+          tags: ${{ steps.meta.outputs.tags || steps.meta-pr.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels || steps.meta-pr.outputs.labels }}
+          cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache-intel,mode=min
+          cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache-intel,mode=min
+
   stop-runner:
     name: Stop self-hosted EC2 runner
     needs:
       - start-runner
       - build-and-push-image
       - build-and-push-image-rocm
+      - build-and-push-image-intel
       - integration-tests
     runs-on: ubuntu-latest
     env:

Dockerfile_intel

@@ -0,0 +1,105 @@
+FROM lukemathwalker/cargo-chef:latest-rust-1.75 AS chef
+WORKDIR /usr/src
+
+ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
+
+FROM chef as planner
+COPY Cargo.toml Cargo.toml
+COPY rust-toolchain.toml rust-toolchain.toml
+COPY proto proto
+COPY benchmark benchmark
+COPY router router
+COPY launcher launcher
+RUN cargo chef prepare --recipe-path recipe.json
+
+FROM chef AS builder
+
+ARG GIT_SHA
+ARG DOCKER_LABEL
+
+RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \
+    curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP && \
+    unzip -o $PROTOC_ZIP -d /usr/local bin/protoc && \
+    unzip -o $PROTOC_ZIP -d /usr/local 'include/*' && \
+    rm -f $PROTOC_ZIP
+
+COPY --from=planner /usr/src/recipe.json recipe.json
+RUN cargo chef cook --release --recipe-path recipe.json
+
+COPY Cargo.toml Cargo.toml
+COPY rust-toolchain.toml rust-toolchain.toml
+COPY proto proto
+COPY benchmark benchmark
+COPY router router
+COPY launcher launcher
+RUN cargo build --release
+
+
+# Text Generation Inference base image for Intel
+FROM intel/intel-extension-for-pytorch:2.1.10-xpu as base
+
+USER root
+# libssl.so.1.1 is not installed on Ubuntu 22.04 by default, install it
+RUN wget http://nz2.archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_amd64.deb && \
+    dpkg -i ./libssl1.1_1.1.1f-1ubuntu2_amd64.deb
+
+
+RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
+| gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list
+
+RUN apt-get update && apt install -y intel-basekit xpu-smi cmake python3-dev ninja-build
+
+# Text Generation Inference base env
+ENV HUGGINGFACE_HUB_CACHE=/data \
+    HF_HUB_ENABLE_HF_TRANSFER=1 \
+    PORT=80
+
+
+WORKDIR /usr/src
+# Build pytorch and ipex
+RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout -b xpu_main origin/xpu-main
+RUN git clone https://github.com/pytorch/pytorch.git && cd pytorch && git checkout 209f2fa8ff86652f67d75c2f19bf9cb9942fd018 && git apply /usr/src/intel-extension-for-pytorch/torch_patches/00*.patch
+
+# Install server
+COPY proto proto
+COPY server server
+COPY server/Makefile server/Makefile
+RUN cd server && \
+    make gen-server && \
+    pip install -r requirements_cuda.txt && \
+    pip install ".[accelerate, peft, outlines]" --no-cache-dir
+
+ENV CCL_ROOT=/opt/intel/oneapi/ccl/latest
+ENV I_MPI_ROOT=/opt/intel/oneapi/mpi/latest
+ENV FI_PROVIDER_PATH=/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/lib/prov:/usr/lib/x86_64-linux-gnu/libfabric
+ENV DIAGUTIL_PATH=/opt/intel/oneapi/compiler/latest/etc/compiler/sys_check/sys_check.sh
+ENV CCL_CONFIGURATION=cpu_gpu_dpcpp
+ENV MANPATH=/opt/intel/oneapi/mpi/latest/share/man:/opt/intel/oneapi/mpi/latest/share/man:/opt/intel/oneapi/compiler/latest/share/man
+ENV CMAKE_PREFIX_PATH=/opt/intel/oneapi/mkl/latest/lib/cmake:/opt/intel/oneapi/compiler/latest
+ENV CMPLR_ROOT=/opt/intel/oneapi/compiler/latest
+ENV LIBRARY_PATH=/opt/intel/oneapi/mpi/latest/lib:/opt/intel/oneapi/ccl/latest/lib/:/opt/intel/oneapi/mkl/latest/lib/:/opt/intel/oneapi/compiler/latest/lib
+ENV OCL_ICD_FILENAMES=libintelocl_emu.so:libalteracl.so:/opt/intel/oneapi/compiler/latest/lib/libintelocl.so
+ENV CLASSPATH=/opt/intel/oneapi/mpi/latest/share/java/mpi.jar:/opt/intel/oneapi/mpi/latest/share/java/mpi.jar
+ENV LD_LIBRARY_PATH=/opt/intel/oneapi/ccl/latest/lib/:/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/lib:/opt/intel/oneapi/mpi/latest/lib:/opt/intel/oneapi/mkl/latest/lib:/opt/intel/oneapi/compiler/latest/opt/compiler/lib:/opt/intel/oneapi/compiler/latest/lib:/opt/intel/oneapi/lib:/opt/intel/oneapi/lib/intel64:
+ENV MKLROOT=/opt/intel/oneapi/mkl/latest
+ENV NLSPATH=/opt/intel/oneapi/mkl/latest/share/locale/%l_%t/%N:/opt/intel/oneapi/compiler/latest/lib/locale/%l_%t/%N
+ENV PATH=/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/bin:/opt/intel/oneapi/mpi/latest/bin:/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/bin:/opt/intel/oneapi/mkl/latest/bin/:/opt/intel/oneapi/compiler/latest/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+ENV CPATH=/opt/intel/oneapi/mpi/latest/include:/opt/intel/oneapi/ccl/latest/include:/opt/intel/oneapi/mkl/latest/include
+ENV CCL_ZE_IPC_EXCHANGE=sockets
+
+
+RUN pip uninstall -y torch && cd pytorch && git submodule update --init --recursive && python setup.py install
+RUN pip uninstall -y intel-extension-for-pytorch && cd intel-extension-for-pytorch && git submodule update --init --recursive && USE_AOT_DEVLIST='pvc' BUILD_SEPARATE_OPS=ON BUILD_WITH_CPU=ON USE_XETLA=ON python setup.py install
+
+# Install benchmarker
+COPY --from=builder /usr/src/target/release/text-generation-benchmark /usr/local/bin/text-generation-benchmark
+# Install router
+COPY --from=builder /usr/src/target/release/text-generation-router /usr/local/bin/text-generation-router
+# Install launcher
+COPY --from=builder /usr/src/target/release/text-generation-launcher /usr/local/bin/text-generation-launcher
+
+# Final image
+FROM base
+
+ENTRYPOINT ["text-generation-launcher"]
+CMD ["--json-output"]

launcher/src/env_runtime.rs

@@ -7,14 +7,17 @@ pub(crate) struct Env {
     git_sha: &'static str,
     docker_label: &'static str,
     nvidia_env: String,
+    xpu_env: String,
 }
 
 impl Env {
     pub fn new() -> Self {
         let nvidia_env = nvidia_smi();
+        let xpu_env = xpu_smi();
 
         Self {
             nvidia_env: nvidia_env.unwrap_or("N/A".to_string()),
+            xpu_env: xpu_env.unwrap_or("N/A".to_string()),
             cargo_target: env!("VERGEN_CARGO_TARGET_TRIPLE"),
             cargo_version: env!("VERGEN_RUSTC_SEMVER"),
             git_sha: option_env!("VERGEN_GIT_SHA").unwrap_or("N/A"),
@@ -31,7 +34,8 @@ impl fmt::Display for Env {
         writeln!(f, "Cargo version: {}", self.cargo_version)?;
         writeln!(f, "Commit sha: {}", self.git_sha)?;
         writeln!(f, "Docker label: {}", self.docker_label)?;
-        write!(f, "nvidia-smi:\n{}", self.nvidia_env)?;
+        writeln!(f, "nvidia-smi:\n{}", self.nvidia_env)?;
+        write!(f, "xpu-smi:\n{}", self.xpu_env)?;
 
         Ok(())
     }
@@ -43,3 +47,10 @@ fn nvidia_smi() -> Option<String> {
     let output = nvidia_smi.replace('\n', "\n   ");
     Some(output.trim().to_string())
 }
+
+fn xpu_smi() -> Option<String> {
+    let output = Command::new("xpu-smi").arg("discovery").output().ok()?;
+    let xpu_smi = String::from_utf8(output.stdout).ok()?;
+    let output = xpu_smi.replace('\n', "\n   ");
+    Some(output.trim().to_string())
+}

server/text_generation_server/models/cache_manager.py

@@ -2,6 +2,7 @@ import math
 import torch
 
 from typing import Optional, List, Tuple
+from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
 
 BLOCK_SIZE: int = 16
 # Will be set in warmup
@@ -24,6 +25,9 @@ class CacheManager:
         self.repeat_slots = repeat_slots
 
         element_size = torch.tensor([], dtype=dtype).element_size()
+        if IS_XPU_SYSTEM:
+            x = 1
+        else:
             x = self.block_size // element_size
 
         self.kv_cache = [

server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py

@@ -21,7 +21,9 @@ from transformers.activations import ACT2FN
 from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple, Any
 from loguru import logger
+from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
 
+if not IS_XPU_SYSTEM:
     from vllm.model_executor.layers.fused_moe import fused_moe
 from text_generation_server.utils import paged_attention, flash_attn
 from text_generation_server.utils.layers import (

server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py

@@ -24,6 +24,9 @@ import torch.distributed
 import numpy as np
 
 from torch import nn
+from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
+
+if not IS_XPU_SYSTEM:
     from vllm.model_executor.layers.fused_moe import fused_moe
 from transformers.activations import ACT2FN
 from transformers.configuration_utils import PretrainedConfig

server/text_generation_server/models/flash_causal_lm.py

@@ -33,7 +33,7 @@ from text_generation_server.utils import StoppingCriteria, HeterogeneousNextToke
 from text_generation_server.utils.dist import MEMORY_FRACTION
 
 tracer = trace.get_tracer(__name__)
-
+from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM, IS_XPU_SYSTEM
 
 @dataclass
 class FlashCausalLMBatch(Batch):
@@ -752,7 +752,10 @@ class FlashCausalLM(Model):
 
     def warmup(self, batch: FlashCausalLMBatch):
         # The warmup batch is the biggest batch we could ever receive
+        if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
             torch.cuda.empty_cache()
+        elif IS_XPU_SYSTEM:
+            torch.xpu.empty_cache()
         try:
             cache_manager = set_cache_manager(
                 batch.blocks,
@@ -772,7 +775,10 @@ class FlashCausalLM(Model):
                 f"You need to decrease `--max-batch-prefill-tokens`"
             ) from e
 
+        if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
             torch.cuda.synchronize(self.device)
+        elif IS_XPU_SYSTEM:
+            torch.xpu.synchronize(self.device)
 
         # Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm)
         # Calculate the number of blocks that can be allocated with the free memory
@@ -780,12 +786,18 @@ class FlashCausalLM(Model):
         cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size
         total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size
 
+        if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
             total_free_memory, _ = torch.cuda.mem_get_info(self.device)
             total_gpu_memory = torch.cuda.get_device_properties(self.device).total_memory
 
             free_memory = max(
                 0, total_free_memory - (1 - MEMORY_FRACTION) * total_gpu_memory
             )
+        elif IS_XPU_SYSTEM:
+            total_gpu_memory = torch.xpu.get_device_properties(self.device).total_memory
+            free_memory = int(total_gpu_memory *0.5)
+        else:
+            raise NotImplementedError("FlashModel is only available on GPU")
 
         num_blocks = (
             # Leave 5% for some wiggle room

server/text_generation_server/models/flash_llama.py

@@ -18,6 +18,7 @@ from text_generation_server.utils import (
 
 tracer = trace.get_tracer(__name__)
 
+from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
 
 class FlashLlama(FlashCausalLM):
     def __init__(
@@ -33,6 +34,9 @@ class FlashLlama(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
+        elif IS_XPU_SYSTEM:
+            device = torch.device(f"xpu:{rank}")
+            dtype = torch.float16 if dtype is None else dtype
         else:
             raise NotImplementedError("FlashLlama is only available on GPU")
 

server/text_generation_server/models/flash_mistral.py

@@ -33,8 +33,9 @@ tracer = trace.get_tracer(__name__)
 # Will be set in init
 SLIDING_WINDOW: Optional[int] = None
 SLIDING_WINDOW_BLOCKS: Optional[int] = None
+from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
 
-MEM_POOL = torch.cuda.graph_pool_handle()
+MEM_POOL = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None
 
 
 def set_sliding_window(sliding_window: int, sliding_window_blocks: int):
@@ -316,6 +317,9 @@ class BaseFlashMistral(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
+        elif IS_XPU_SYSTEM:
+            device = torch.device(f"xpu:{rank}")
+            dtype = torch.float16 if dtype is None else dtype
         else:
             raise NotImplementedError("FlashMistral is only available on GPU")
 

server/text_generation_server/models/flash_neox.py

@@ -14,7 +14,7 @@ from text_generation_server.utils import (
     weight_files,
     Weights,
 )
-
+from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
 tracer = trace.get_tracer(__name__)
 
 
@@ -32,6 +32,9 @@ class FlashNeoXSharded(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
+        elif IS_XPU_SYSTEM:
+            device = torch.device(f"xpu:{rank}")
+            dtype = torch.float16 if dtype is None else dtype
         else:
             raise NotImplementedError("FlashNeoX is only available on GPU")
 

server/text_generation_server/models/flash_rw.py

@@ -15,7 +15,7 @@ from text_generation_server.utils import (
     weight_files,
     Weights,
 )
-
+from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
 tracer = trace.get_tracer(__name__)
 
 
@@ -33,6 +33,9 @@ class FlashRWSharded(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
+        elif IS_XPU_SYSTEM:
+            device = torch.device(f"xpu:{rank}")
+            dtype = torch.float16 if dtype is None else dtype
         else:
             raise NotImplementedError("FlashRW is only available on GPU")
 

server/text_generation_server/models/flash_santacoder.py

@@ -18,6 +18,7 @@ from text_generation_server.utils import (
     Weights,
 )
 
+from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
 tracer = trace.get_tracer(__name__)
 
 
@@ -35,6 +36,9 @@ class FlashSantacoderSharded(FlashCausalLM):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
+        elif IS_XPU_SYSTEM:
+            device = torch.device(f"xpu:{rank}")
+            dtype = torch.float16 if dtype is None else dtype
         else:
             raise NotImplementedError("FlashSantacoderSharded is only available on GPU")
 

server/text_generation_server/models/globals.py

@@ -1,10 +1,10 @@
 import torch
 import os
 
-MEM_POOL = torch.cuda.graph_pool_handle()
+MEM_POOL = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None
 # This is overridden by the cli
 cuda_graphs = os.getenv("CUDA_GRAPHS")
-if cuda_graphs is not None and cuda_graphs != "0":
+if torch.cuda.is_available() and cuda_graphs is not None and cuda_graphs != "0":
     try:
         cuda_graphs = [int(item) for item in cuda_graphs.split(",")]
     except Exception as e:

server/text_generation_server/utils/dist.py

@@ -57,6 +57,13 @@ def initialize_torch_distributed():
         options.is_high_priority_stream = True
         options._timeout = timedelta(seconds=60)
     else:
+        try:
+            import oneccl_bindings_for_pytorch
+
+            backend = "ccl"
+            if os.getenv("CCL_WORKER_COUNT", None) is None:
+                os.environ["CCL_WORKER_COUNT"] = str(1)
+        except ImportError:
             backend = "gloo"
         options = None
 

server/text_generation_server/utils/flash_attn.py

@@ -2,12 +2,24 @@ import os
 import torch
 
 from loguru import logger
+import math
 
-from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
+from text_generation_server.utils.import_utils import (
+    IS_CUDA_SYSTEM,
+    IS_ROCM_SYSTEM,
+    IS_XPU_SYSTEM,
+)
 
 if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
     raise ImportError("`USE_FLASH_ATTENTION` is false.")
+HAS_FLASH_ATTN = True
+HAS_FLASH_ATTN_V2_CUDA = False
+HAS_FLASH_ATTN_V2_ROCM = False
 
+if IS_XPU_SYSTEM:
+    import intel_extension_for_pytorch as ipex
+
+if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
     if not torch.cuda.is_available():
         raise ImportError("CUDA is not available")
 
@@ -80,6 +92,28 @@ def attention(
     if window_size_left <= 0 and window_size_left != -1:
         raise ValueError("`window_size_left` must be > 0 or -1")
 
+    if IS_XPU_SYSTEM:
+        if window_size_left != -1:
+            raise ValueError(
+                f"XPU version of Flash Attention does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
+            )
+        return ipex.llm.functional.varlen_attention(
+            q,
+            k,
+            v,
+            out,
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            0.0,
+            softmax_scale,
+            False,
+            True,
+            False,
+            None,
+        )
+
     if HAS_FLASH_ATTN_V2_CUDA:
         return flash_attn_2_cuda.varlen_fwd(
             q,

server/text_generation_server/utils/import_utils.py

@@ -1,4 +1,13 @@
 import torch
 
+def is_xpu_available():
+    try:
+        import intel_extension_for_pytorch
+    except ImportError:
+        return False
+
+    return hasattr(torch, "xpu") and torch.xpu.is_available()
+
 IS_ROCM_SYSTEM = torch.version.hip is not None
 IS_CUDA_SYSTEM = torch.version.cuda is not None
+IS_XPU_SYSTEM = is_xpu_available()

server/text_generation_server/utils/layers.py

@@ -18,7 +18,14 @@ except ImportError:
 from accelerate import init_empty_weights
 
 from text_generation_server.utils.gptq.quant_linear import QuantLinear
-from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
+from text_generation_server.utils.import_utils import (
+    IS_CUDA_SYSTEM,
+    IS_ROCM_SYSTEM,
+    IS_XPU_SYSTEM,
+)
+
+if IS_XPU_SYSTEM:
+    import intel_extension_for_pytorch as ipex
 
 HAS_AWQ = True
 try:
@@ -812,7 +819,15 @@ try:
 
     class FastLayerNorm(nn.LayerNorm):
         def forward(self, hidden_states, residual=None):
-            if hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM:
+            if IS_XPU_SYSTEM:
+                res_out = hidden_states
+                out = ipex.llm.functional.add_layer_norm(
+                    residual, hidden_states, self.weight, self.bias, self.eps, True
+                )
+                if residual is not None:
+                    res_out = residual
+                return out, res_out
+            elif hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM:
                 if residual is not None:
                     hidden_states += residual
                 residual = hidden_states
@@ -858,7 +873,20 @@ try:
             return cls(weight, eps)
 
         def forward(self, hidden_states, residual=None):
-            if hidden_states.shape[-1] > 8192:
+            if IS_XPU_SYSTEM:
+                residual_out = hidden_states
+                out = ipex.llm.functional.add_rms_norm(
+                    residual,
+                    hidden_states,
+                    self.weight,
+                    None,
+                    self.variance_epsilon,
+                    True,
+                )
+                if residual is not None:
+                    residual_out = residual
+                return out, residual_out
+            elif hidden_states.shape[-1] > 8192:
                 if residual is not None:
                     hidden_states += residual
                 residual = hidden_states
@@ -984,6 +1012,10 @@ try:
 
                 # Inplace operation, updating query and key.
                 pos_encoding_ops.rotary_embedding(query, key, head_size, cos, sin, True)
+            elif IS_XPU_SYSTEM:
+                ipex.llm.functional.rotary_embedding(
+                    query, key, sin, cos, query.size(-1), True
+                )
             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."
@@ -1103,6 +1135,7 @@ try:
 
             cos = torch.index_select(self._cos_cached, 0, position_ids)
             sin = torch.index_select(self._sin_cached, 0, position_ids)
+
             # Note: this unsqueeze is not necessary on RoCm + VLLM ROPE implementation, but we leave it as is to avoid yet an other controlflow.
             return cos.unsqueeze(1), sin.unsqueeze(1)
 

server/text_generation_server/utils/paged_attention.py

@@ -1,9 +1,15 @@
 import torch
-
+from text_generation_server.utils.import_utils import (
-from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
+    IS_CUDA_SYSTEM,
+    IS_ROCM_SYSTEM,
+    IS_XPU_SYSTEM,
+)
 
 _PARTITION_SIZE = 512
 
+if IS_XPU_SYSTEM:
+    import intel_extension_for_pytorch as ipex
+
 
 def reshape_and_cache(
     key: torch.Tensor,
@@ -22,6 +28,10 @@ def reshape_and_cache(
         from vllm import cache_ops
 
         cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots)
+    elif IS_XPU_SYSTEM:
+        ipex.llm.modules.PagedAttention.reshape_and_cache(
+            key, value, key_cache, value_cache, slots
+        )
     else:
         raise ValueError("vllm is not supported on your system")
 
@@ -58,6 +68,22 @@ def attention(
     block_size = value_cache.shape[3]
     num_seqs, num_heads, head_size = query.shape
     max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
+    if IS_XPU_SYSTEM:
+        query = query.contiguous()
+        return ipex.llm.modules.PagedAttention.single_query_cached_kv_attention(
+            out,
+            query,
+            key_cache,
+            value_cache,
+            kv_head_mapping,
+            softmax_scale,
+            block_tables,
+            input_lengths,
+            block_size,
+            max_s,
+            None,
+        )
+
     # NOTE(woosuk): We use a simple heuristic to decide whether to use
     # PagedAttention V1 or V2. If the number of partitions is 1, we use
     # V1 to avoid the overhead of reduction. Also, if the number of