[Hardware][Intel GPU] Add Intel GPU(XPU) inference backend (#3814)

Co-authored-by: Jiang Li <jiang1.li@intel.com>
Co-authored-by: Abhilash Majumder <abhilash.majumder@intel.com>
Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>

.buildkite/run-xpu-test.sh

@@ -0,0 +1,14 @@
+# This script build the CPU docker image and run the offline inference inside the container.
+# It serves a sanity check for compilation and basic model usage.
+set -ex
+
+# Try building the docker image
+docker build -t xpu-test -f Dockerfile.xpu .
+
+# Setup cleanup
+remove_docker_container() { docker rm -f xpu-test || true; }
+trap remove_docker_container EXIT
+remove_docker_container
+
+# Run the image and launch offline inference
+docker run --network host --name xpu-test --device /dev/dri -v /dev/dri/by-path:/dev/dri/by-path xpu-test python3 examples/offline_inference.py

.buildkite/test-template.j2

@@ -45,6 +45,11 @@ steps:
       queue: intel
     command: bash .buildkite/run-cpu-test.sh
 
+  - label: "XPU Test"
+    agents:
+      queue: intel
+    command: bash .buildkite/run-xpu-test.sh
+
   {% for step in steps %}
   - label: "{{ step.label }}"
     agents:

Dockerfile.xpu

@@ -0,0 +1,22 @@
+FROM intel/oneapi-basekit:2024.1.0-devel-ubuntu22.04
+
+RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
+    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
+    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
+    rm /etc/apt/sources.list.d/intel-graphics.list && \
+    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
+    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
+    chmod 644 /usr/share/keyrings/intel-graphics.gpg
+
+RUN apt-get update  -y \
+&& apt-get install -y curl libicu70 lsb-release git wget vim numactl python3 python3-pip
+
+COPY ./ /workspace/vllm
+
+WORKDIR /workspace/vllm
+
+RUN pip install -v -r requirements-xpu.txt
+
+RUN VLLM_TARGET_DEVICE=xpu python3 setup.py install
+
+CMD ["/bin/bash"]

benchmarks/benchmark_latency.py

@@ -191,7 +191,7 @@ if __name__ == '__main__':
         "--device",
         type=str,
         default="cuda",
-        choices=["cuda", "cpu", "tpu"],
+        choices=["cuda", "cpu", "tpu", "xpu"],
         help='device type for vLLM execution, supporting CUDA and CPU.')
     parser.add_argument('--block-size',
                         type=int,

benchmarks/benchmark_throughput.py

@@ -349,7 +349,7 @@ if __name__ == "__main__":
         "--device",
         type=str,
         default="cuda",
-        choices=["cuda", "cpu", "tpu"],
+        choices=["cuda", "cpu", "tpu", "xpu"],
         help='device type for vLLM execution, supporting CUDA and CPU.')
     parser.add_argument(
         "--enable-prefix-caching",

docs/source/getting_started/xpu-installation.rst

@@ -0,0 +1,61 @@
+.. _installation_xpu:
+
+Installation with XPU
+========================
+
+vLLM initially supports basic model inferencing and serving on Intel GPU platform.
+
+Table of contents:
+
+#. :ref:`Requirements <xpu_backend_requirements>`
+#. :ref:`Quick start using Dockerfile <xpu_backend_quick_start_dockerfile>`
+#. :ref:`Build from source <build_xpu_backend_from_source>`
+
+.. _xpu_backend_requirements:
+
+Requirements
+------------
+
+* OS: Linux
+* Supported Hardware: Intel Data Center GPU (Intel ARC GPU WIP)
+* OneAPI requirements: oneAPI 2024.1 
+
+.. _xpu_backend_quick_start_dockerfile:
+
+Quick start using Dockerfile
+----------------------------
+
+.. code-block:: console
+
+    $ docker build -f Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
+    $ docker run -it \
+                 --rm \
+                 --network=host \
+                 --device /dev/dri \
+                 -v /dev/dri/by-path:/dev/dri/by-path \
+                 vllm-xpu-env
+
+.. _build_xpu_backend_from_source:
+
+Build from source
+-----------------
+
+- First, install required driver and intel OneAPI 2024.1.
+
+- Second, install Python packages for vLLM XPU backend building:
+
+.. code-block:: console
+
+    $ pip install --upgrade pip
+    $ pip install -v -r requirements-xpu.txt 
+
+- Finally, build and install vLLM XPU backend: 
+
+.. code-block:: console
+
+    $ VLLM_TARGET_DEVICE=xpu python setup.py install
+
+.. note::
+    - FP16 is the default data type in the current XPU backend. The BF16 data
+      type will be supported in the future.
+

docs/source/index.rst

@@ -66,6 +66,7 @@ Documentation
    getting_started/cpu-installation
    getting_started/neuron-installation
    getting_started/tpu-installation
+   getting_started/xpu-installation
    getting_started/quickstart
    getting_started/debugging
    getting_started/examples/examples_index

requirements-xpu.txt

@@ -0,0 +1,11 @@
+# Common dependencies
+-r requirements-common.txt
+
+setuptools < 70.0.0 # IPEX's torch have some dependency. to be removed.
+
+torch @ https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/xpu/torch-2.1.0.post1%2Bcxx11.abi-cp310-cp310-linux_x86_64.whl
+intel_extension_for_pytorch @ https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/xpu/intel_extension_for_pytorch-2.1.30a0-cp310-cp310-linux_x86_64.whl
+oneccl_bind_pt @ https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/oneccl_bind_pt-2.1.200%2Bxpu-cp310-cp310-linux_x86_64.whl
+
+triton @ https://github.com/intel/intel-xpu-backend-for-triton/releases/download/v2.1.0/triton-2.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+

setup.py

@@ -233,6 +233,10 @@ def _is_cpu() -> bool:
     return VLLM_TARGET_DEVICE == "cpu"
 
 
+def _is_xpu() -> bool:
+    return VLLM_TARGET_DEVICE == "xpu"
+
+
 def _build_custom_ops() -> bool:
     return _is_cuda() or _is_hip() or _is_cpu()
 
@@ -337,6 +341,8 @@ def get_vllm_version() -> str:
         version += "+tpu"
     elif _is_cpu():
         version += "+cpu"
+    elif _is_xpu():
+        version += "+xpu"
     else:
         raise RuntimeError("Unknown runtime environment")
 
@@ -386,6 +392,8 @@ def get_requirements() -> List[str]:
         requirements = _read_requirements("requirements-tpu.txt")
     elif _is_cpu():
         requirements = _read_requirements("requirements-cpu.txt")
+    elif _is_xpu():
+        requirements = _read_requirements("requirements-xpu.txt")
     else:
         raise ValueError(
             "Unsupported platform, please use CUDA, ROCm, Neuron, or CPU.")

vllm/_custom_ops.py

@@ -373,7 +373,8 @@ def reshape_and_cache_flash(
                                                    kv_cache_dtype)
 
 
-def copy_blocks(key_caches: torch.Tensor, value_caches: torch.Tensor,
+def copy_blocks(key_caches: List[torch.Tensor],
+                value_caches: List[torch.Tensor],
                 block_mapping: torch.Tensor) -> None:
     torch.ops._C_cache_ops.copy_blocks(key_caches, value_caches, block_mapping)
 

vllm/_ipex_ops.py

@@ -0,0 +1,241 @@
+from typing import List, Optional, Tuple
+
+import torch
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+try:
+    import intel_extension_for_pytorch as ipex
+except ImportError as e:
+    logger.warning("Import error msg: %s", e.msg)
+
+
+class ipex_ops:
+
+    @staticmethod
+    def _reshape_activation_tensor(
+            x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        num = x.size(0)
+        d = x.size(1) // 2
+        x = x.reshape(num, 2, d)
+        x1, x2 = torch.chunk(x, chunks=2, dim=1)
+        x1 = x1.reshape(num, d)
+        x2 = x2.reshape(num, d)
+        return x1, x2
+
+    def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
+        x1, x2 = ipex_ops._reshape_activation_tensor(x)
+        ipex.llm.functional.silu_mul(x1, x2, out)
+
+    def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
+        x1, x2 = ipex_ops._reshape_activation_tensor(x)
+        ipex.llm.functional.gelu_mul(x1, x2, out, "none")
+
+    def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
+        x1, x2 = ipex_ops._reshape_activation_tensor(x)
+        ipex.llm.functional.gelu_mul(x1, x2, out, "tanh")
+
+    def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
+        out.copy_(torch.nn.functional.gelu(x))
+
+    def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
+        out.copy_(torch.nn.functional.gelu(x))
+
+    def paged_attention_v1(
+        out: torch.Tensor,
+        query: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        num_kv_heads: int,
+        scale: float,
+        block_tables: torch.Tensor,
+        context_lens: torch.Tensor,
+        block_size: int,
+        max_context_len: int,
+        alibi_slopes: Optional[torch.Tensor],
+        kv_cache_dtype: str,
+        kv_scale: float,
+        tp_rank: int = 0,
+        blocksparse_local_blocks: int = 0,
+        blocksparse_vert_stride: int = 0,
+        blocksparse_block_size: int = 64,
+        blocksparse_head_sliding_step: int = 0,
+    ) -> None:
+        assert kv_cache_dtype == "auto"
+        num_heads = out.size(1)
+        num_queries_per_tokens = num_heads // num_kv_heads
+        head_mapping = torch.arange(
+            0,
+            num_kv_heads,
+            device=query.device,
+            dtype=torch.int32,
+        ).view(num_kv_heads,
+               1).repeat_interleave(num_queries_per_tokens).flatten()
+        # todo: ipex will refactor namespace
+        torch.xpu.paged_attention_v1(out, query.contiguous(),
+                                     key_cache.view_as(value_cache),
+                                     value_cache, head_mapping, scale,
+                                     block_tables, context_lens, block_size,
+                                     max_context_len, alibi_slopes)
+
+    def paged_attention_v2(
+        out: torch.Tensor,
+        exp_sum: torch.Tensor,
+        max_logits: torch.Tensor,
+        tmp_out: torch.Tensor,
+        query: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        num_kv_heads: int,
+        scale: float,
+        block_tables: torch.Tensor,
+        context_lens: torch.Tensor,
+        block_size: int,
+        max_context_len: int,
+        alibi_slopes: Optional[torch.Tensor],
+        kv_cache_dtype: str,
+        kv_scale: float,
+        tp_rank: int = 0,
+        blocksparse_local_blocks: int = 0,
+        blocksparse_vert_stride: int = 0,
+        blocksparse_block_size: int = 64,
+        blocksparse_head_sliding_step: int = 0,
+    ) -> None:
+        assert kv_cache_dtype == "auto"
+        num_heads = out.size(1)
+        num_queries_per_tokens = num_heads // num_kv_heads
+        head_mapping = torch.arange(
+            0,
+            num_kv_heads,
+            dtype=torch.int32,
+            device=query.device,
+        ).view(num_kv_heads,
+               1).repeat_interleave(num_queries_per_tokens).flatten()
+        # todo: ipex will refactor namespace
+        torch.xpu.paged_attention_v2(out, exp_sum, max_logits, tmp_out,
+                                     query.contiguous(),
+                                     key_cache.view_as(value_cache),
+                                     value_cache, head_mapping, block_tables,
+                                     context_lens, scale, block_size,
+                                     max_context_len, alibi_slopes)
+
+    def rotary_embedding(
+        positions: torch.Tensor,  # [batch_size, seq_len]
+        query: torch.Tensor,  # [batch_size, seq_len, num_heads*head_size]
+        key: torch.Tensor,  # [batch_size, seq_len, num_kv_heads*head_size]
+        head_size: int,
+        cos_sin_cache: torch.Tensor,  # [cos_sin_dim, rot_dim]
+        is_neox: bool,
+    ) -> None:
+        if positions.dim() == 1:
+            positions = positions.unsqueeze(0)
+            query = query.unsqueeze(0)
+            key = key.unsqueeze(0)
+
+        rotary_dim = cos_sin_cache.size(1)
+        query = query.view(*query.shape[:-1], -1, head_size)
+        key = key.view(*key.shape[:-1], -1, head_size)
+
+        query_rot = query[..., :rotary_dim]
+        key_rot = key[..., :rotary_dim]
+
+        cos_sin = cos_sin_cache[positions.long()]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+
+        if is_neox:
+            cos = cos.repeat(1, 1, 2).unsqueeze(-2)
+            sin = sin.repeat(1, 1, 2).unsqueeze(-2)
+        else:
+            cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2)
+            sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2)
+        ipex.llm.functional.rotary_embedding(query_rot, key_rot, sin, cos,
+                                             rotary_dim, is_neox, positions)
+
+    def batched_rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
+                                 key: torch.Tensor, head_size: int,
+                                 cos_sin_cache: torch.Tensor, is_neox: bool,
+                                 rot_dim: int,
+                                 cos_sin_cache_offsets: torch.Tensor) -> None:
+        if positions.dim() == 1:
+            positions = positions.unsqueeze(0)
+            query = query.unsqueeze(0)
+            key = key.unsqueeze(0)
+        cos_sin_cache_offsets = cos_sin_cache_offsets.view_as(positions)
+        rotary_dim = cos_sin_cache.size(1)
+        query = query.view(*query.shape[:-1], -1, head_size)
+        key = key.view(*key.shape[:-1], -1, head_size)
+
+        query_rot = query[..., :rotary_dim]
+        key_rot = key[..., :rotary_dim]
+
+        cos_sin = cos_sin_cache[torch.add(positions,
+                                          cos_sin_cache_offsets).long()]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+
+        if is_neox:
+            cos = cos.repeat(1, 1, 2).unsqueeze(-2)
+            sin = sin.repeat(1, 1, 2).unsqueeze(-2)
+        else:
+            cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2)
+            sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2)
+
+        ipex.llm.functional.rotary_embedding(query_rot, key_rot, sin, cos,
+                                             rotary_dim, is_neox, positions)
+
+    def rms_norm(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
+                 epsilon: float) -> None:
+        tmp = ipex.llm.functional.rms_norm(input, weight, epsilon)
+        out.copy_(tmp)
+
+    def fused_add_rms_norm(input: torch.Tensor, residual: torch.Tensor,
+                           weight: torch.Tensor, epsilon: float) -> None:
+        tmp = ipex.llm.functional.add_rms_norm(residual, input, weight, None,
+                                               epsilon, True)
+        input.copy_(tmp)
+
+    def varlen_attention(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        out: torch.Tensor,
+        seqlen_q: torch.Tensor,
+        seqlen_k: torch.Tensor,
+        max_seqlen_q: int,
+        max_seqlen_k: int,
+        pdropout: float,
+        softmax_scale: float,
+        zero_tensors: bool,
+        is_causal: bool,
+        return_softmax: bool,
+        gen_: torch.Generator,
+    ) -> None:
+        ipex.llm.functional.varlen_attention(query, key, value, out, seqlen_q,
+                                             seqlen_k, max_seqlen_q,
+                                             max_seqlen_k, pdropout,
+                                             softmax_scale, zero_tensors,
+                                             is_causal, return_softmax, gen_)
+
+    def reshape_and_cache(
+        key: torch.Tensor,
+        value: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        slot_mapping: torch.Tensor,
+        kv_cache_dtype: str,
+        kv_scale: float,
+    ) -> None:
+        assert kv_cache_dtype == "auto"
+        ipex.llm.modules.PagedAttention.reshape_and_cache(
+            key, value, key_cache, value_cache, slot_mapping)
+
+    @staticmethod
+    def copy_blocks(key_caches: List[torch.Tensor],
+                    value_caches: List[torch.Tensor],
+                    block_mapping: torch.Tensor) -> None:
+        torch.xpu.copy_blocks(key_caches, value_caches, block_mapping)
+
+    def swap_blocks(src: torch.Tensor, dst: torch.Tensor,
+                    block_mapping: torch.Tensor) -> None:
+        torch.xpu.swap_blocks(src, dst, block_mapping)

vllm/attention/backends/ipex_attn.py

@@ -0,0 +1,355 @@
+""" Attention layer with torch scaled_dot_product_attention
+    and PagedAttention."""
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Type
+
+import torch
+
+from vllm._ipex_ops import ipex_ops
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata)
+from vllm.attention.ops.paged_attn import (PagedAttention,
+                                           PagedAttentionMetadata)
+
+_PARTITION_SIZE = 512
+
+
+class IpexAttnBackend(AttentionBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "ipex-attn"
+
+    @staticmethod
+    def get_impl_cls() -> Type["IpexAttnBackendImpl"]:
+        return IpexAttnBackendImpl
+
+    @staticmethod
+    def make_metadata(*args, **kwargs) -> "IpexAttnMetadata":
+        return IpexAttnMetadata(*args, **kwargs)
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[int, ...]:
+        return PagedAttention.get_kv_cache_shape(num_blocks, block_size,
+                                                 num_kv_heads, head_size)
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: torch.Tensor,
+    ) -> None:
+        PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: List[torch.Tensor],
+        src_to_dists: torch.Tensor,
+    ) -> None:
+        PagedAttention.copy_blocks(kv_caches, src_to_dists)
+
+
+@dataclass
+class IpexAttnMetadata(AttentionMetadata, PagedAttentionMetadata):
+    """Metadata for IpexAttnBackend.
+    """
+    # Currently, input sequences can only contain all prompts
+    # or all decoding. True if all sequences are prompts.
+    is_prompt: bool
+    slot_mapping: torch.Tensor
+    seq_lens: Optional[List[int]]
+    seqlen_q: Optional[torch.Tensor]
+    max_seqlen: Optional[int]
+
+    def __post_init__(self):
+        # Set during the execution of the first attention op.
+        # It is a list because it is needed to set per prompt
+        # when alibi slopes is used. It is because of the limitation
+        # from xformer API.
+        # will not appear in the __repr__ and __init__
+        self.attn_bias: Optional[List[torch.Tensor]] = None
+
+    @property
+    def prefill_metadata(self) -> Optional["IpexAttnMetadata"]:
+        # Currently chunked prefill is not supported
+        if self.num_decode_tokens == 0:
+            assert self.num_prefills > 0
+            return self
+
+        return None
+
+    @property
+    def decode_metadata(self) -> Optional["IpexAttnMetadata"]:
+        # Currently chunked prefill is not supported
+        if self.num_prefills > 0:
+            assert self.num_decode_tokens == 0
+            return None
+
+        return self
+
+
+class IpexAttnBackendImpl(AttentionImpl[IpexAttnMetadata]):
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: int,
+        alibi_slopes: Optional[List[float]],
+        sliding_window: Optional[int],
+        kv_cache_dtype: str,
+        blocksparse_params: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        assert blocksparse_params is None, ValueError(
+            "Torch SPDA does not support block-sparse attention.")
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_kv_heads
+        if alibi_slopes is not None:
+            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
+        self.alibi_slopes = alibi_slopes
+        self.sliding_window = sliding_window
+        self.kv_cache_dtype = kv_cache_dtype
+
+        assert self.num_heads % self.num_kv_heads == 0
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+        self.need_mask = (self.alibi_slopes is not None
+                          or self.sliding_window is not None)
+
+        supported_head_sizes = PagedAttention.get_supported_head_sizes()
+        if head_size not in supported_head_sizes:
+            raise ValueError(
+                f"Head size {head_size} is not supported by PagedAttention. "
+                f"Supported head sizes are: {supported_head_sizes}.")
+        if kv_cache_dtype != "auto":
+            raise NotImplementedError(
+                "IPEX backend does not support FP8 KV cache. "
+                "Please use xFormers backend instead.")
+
+    def split_kv_cache(
+        self,
+        kv_cache: torch.Tensor,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        x = 1
+        num_blocks = kv_cache.shape[1]
+
+        key_cache = kv_cache[0]
+        key_cache = key_cache.view(num_blocks, num_kv_heads, head_size // x,
+                                   -1, x)
+        value_cache = kv_cache[1]
+        value_cache = value_cache.view(num_blocks, num_kv_heads, head_size, -1)
+        return key_cache, value_cache
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: Optional[torch.Tensor],
+        attn_metadata: IpexAttnMetadata,  # type: ignore
+        kv_scale: float = 1.0,
+    ) -> torch.Tensor:
+        """Forward pass with IPEX varlen_attention and PagedAttention.
+
+        Args:
+            query: shape = [num_tokens, num_heads * head_size]
+            key: shape = [num_tokens, num_kv_heads * head_size]
+            value: shape = [num_tokens, num_kv_heads * head_size]
+            kv_cache = [2, num_blocks, block_size * num_kv_heads * head_size]
+            attn_metadata: Metadata for attention.
+        Returns:
+            shape = [num_tokens, num_heads * head_size]
+        """
+        assert kv_scale == 1.0
+        num_tokens, hidden_size = query.shape
+        # Reshape the query, key, and value tensors.
+        query = query.view(-1, self.num_heads, self.head_size)
+        key = key.view(-1, self.num_kv_heads, self.head_size)
+        value = value.view(-1, self.num_kv_heads, self.head_size)
+
+        if kv_cache is not None:
+            key_cache, value_cache = self.split_kv_cache(
+                kv_cache, self.num_kv_heads, self.head_size)
+            ipex_ops.reshape_and_cache(
+                key,
+                value,
+                key_cache,
+                value_cache,
+                attn_metadata.slot_mapping.flatten(),
+                self.kv_cache_dtype,
+                kv_scale,
+            )
+
+        if attn_metadata.is_prompt:
+            assert attn_metadata.seq_lens is not None
+            if (kv_cache is None or attn_metadata.block_tables.numel() == 0):
+                if self.num_kv_heads != self.num_heads:
+                    key = key.repeat_interleave(self.num_queries_per_kv, dim=1)
+                    value = value.repeat_interleave(self.num_queries_per_kv,
+                                                    dim=1)
+
+                if attn_metadata.attn_bias is None:
+                    if self.alibi_slopes is not None:
+                        att_masks = _make_alibi_bias(
+                            self.alibi_slopes, query.dtype,
+                            attn_metadata.seq_lens)  # type: ignore
+                    elif self.sliding_window is not None:
+                        att_masks = _make_sliding_window_bias(
+                            attn_metadata.seq_lens, self.sliding_window,
+                            query.dtype)  # type: ignore
+                    else:
+                        att_masks = _make_sliding_window_bias(
+                            attn_metadata.seq_lens, None, dtype=query.dtype)
+                    attn_metadata.attn_bias = att_masks
+
+                output = torch.empty(
+                    (num_tokens, self.num_heads, self.head_size),
+                    dtype=query.dtype,
+                    device=query.device)
+                ipex_ops.varlen_attention(query,
+                                          key,
+                                          value,
+                                          output,
+                                          attn_metadata.seqlen_q,
+                                          attn_metadata.seqlen_q,
+                                          attn_metadata.max_seqlen,
+                                          attn_metadata.max_seqlen,
+                                          pdropout=0.0,
+                                          softmax_scale=self.scale,
+                                          zero_tensors=False,
+                                          is_causal=True,
+                                          return_softmax=False,
+                                          gen_=None)
+            else:
+                # prefix-enabled attention
+                raise RuntimeError(
+                    "IPEX backend doesn't support prefix decoding.")
+
+        else:
+            # Decoding run.
+            max_seq_len = attn_metadata.max_decode_seq_len
+            output = torch.empty_like(query)
+            block_size = value_cache.shape[3]
+            num_seqs, num_heads, head_size = query.shape
+            max_num_partitions = ((max_seq_len + _PARTITION_SIZE - 1) //
+                                  _PARTITION_SIZE)
+            # 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
+            # sequences or heads is large, we use V1 since there is enough work
+            # to parallelize.
+            # TODO(woosuk): Tune this heuristic.
+            # For context len > 8192, use V2 kernel to avoid shared memory
+            # shortage.
+            use_v1 = (max_seq_len <= 8192 and
+                      (max_num_partitions == 1 or num_seqs * num_heads > 512))
+            if use_v1:
+                # Run PagedAttention V1.
+                ipex_ops.paged_attention_v1(
+                    output,
+                    query,
+                    key_cache,
+                    value_cache,
+                    self.num_kv_heads,
+                    self.scale,
+                    attn_metadata.block_tables,
+                    attn_metadata.seq_lens_tensor,
+                    block_size,
+                    max_seq_len,
+                    self.alibi_slopes,
+                    self.kv_cache_dtype,
+                    kv_scale,
+                )
+            else:
+                # Run PagedAttention V2.
+                assert _PARTITION_SIZE % block_size == 0
+                tmp_output = torch.empty(
+                    size=(num_seqs, num_heads, max_num_partitions, head_size),
+                    dtype=output.dtype,
+                    device=output.device,
+                )
+                exp_sums = torch.empty(
+                    size=(num_seqs, num_heads, max_num_partitions),
+                    dtype=torch.float32,
+                    device=output.device,
+                )
+                max_logits = torch.empty_like(exp_sums)
+                ipex_ops.paged_attention_v2(
+                    output,
+                    exp_sums,
+                    max_logits,
+                    tmp_output,
+                    query,
+                    key_cache,
+                    value_cache,
+                    self.num_kv_heads,
+                    self.scale,
+                    attn_metadata.block_tables,
+                    attn_metadata.seq_lens_tensor,
+                    block_size,
+                    max_seq_len,
+                    self.alibi_slopes,
+                    self.kv_cache_dtype,
+                    kv_scale,
+                )
+
+            # Reshape the output tensor.
+        return output.view(-1, self.num_heads * self.head_size)
+
+
+def _make_alibi_bias(
+    alibi_slopes: torch.Tensor,
+    dtype: torch.dtype,
+    seq_lens: List[int],
+) -> List[torch.Tensor]:
+    attn_biases = []
+    for seq_len in seq_lens:
+        bias = torch.arange(seq_len, dtype=dtype, device=alibi_slopes.device)
+        # NOTE(zhuohan): HF uses
+        #     `bias = bias[None, :].repeat(seq_len, 1)`
+        # here. We find that both biases give the same results, but
+        # the bias below more accurately follows the original ALiBi
+        # paper.
+        bias = bias[None, :] - bias[:, None]
+
+        num_heads = alibi_slopes.shape[0]
+        bias = bias[None, :].repeat((num_heads, 1, 1))
+        bias.mul_(alibi_slopes[:, None, None])
+        inf_mask = torch.empty(
+            (1, seq_len, seq_len),
+            dtype=bias.dtype,
+            device=alibi_slopes.device).fill_(-torch.inf).triu_(diagonal=1)
+        attn_biases.append((bias + inf_mask).to(dtype))
+
+    return attn_biases
+
+
+def _make_sliding_window_bias(
+    seq_lens: List[int],
+    window_size: Optional[int],
+    dtype: torch.dtype,
+) -> List[torch.Tensor]:
+    attn_biases = []
+    for seq_len in seq_lens:
+        tensor = torch.full(
+            (1, seq_len, seq_len),
+            dtype=dtype,
+            fill_value=1,
+        )
+        shift = 0
+        mask = torch.tril(tensor, diagonal=shift).to(dtype)  # type: ignore
+        if window_size is not None:
+            mask = torch.triu(mask, diagonal=shift - window_size + 1)
+        mask = torch.log(mask)
+        attn_biases.append(mask.to(dtype))
+
+    return attn_biases

vllm/attention/selector.py

@@ -7,7 +7,7 @@ import torch
 import vllm.envs as envs
 from vllm.attention.backends.abstract import AttentionBackend
 from vllm.logger import init_logger
-from vllm.utils import is_cpu, is_hip, is_tpu
+from vllm.utils import is_cpu, is_hip, is_tpu, is_xpu
 
 logger = init_logger(__name__)
 
@@ -19,6 +19,7 @@ class _Backend(enum.Enum):
     TORCH_SDPA = enum.auto()
     FLASHINFER = enum.auto()
     PALLAS = enum.auto()
+    IPEX = enum.auto()
 
 
 @lru_cache(maxsize=None)
@@ -58,12 +59,17 @@ def get_attn_backend(
             ROCmFlashAttentionBackend)
         return ROCmFlashAttentionBackend
     elif backend == _Backend.TORCH_SDPA:
-        # TODO: make XPU backend available here.
         assert is_cpu(), RuntimeError(
             "Torch SDPA backend is only used for the CPU device.")
         logger.info("Using Torch SDPA backend.")
         from vllm.attention.backends.torch_sdpa import TorchSDPABackend
         return TorchSDPABackend
+    elif backend == _Backend.IPEX:
+        assert is_xpu(), RuntimeError(
+            "IPEX attention backend is only used for the XPU device.")
+        logger.info("Using IPEX attention backend.")
+        from vllm.attention.backends.ipex_attn import IpexAttnBackend
+        return IpexAttnBackend
     elif backend == _Backend.FLASHINFER:
         logger.info("Using Flashinfer backend.")
         logger.warning("Eager mode is required for the Flashinfer backend. "
@@ -107,6 +113,11 @@ def which_attn_to_use(
             logger.info("Cannot use %s backend on CPU.", selected_backend)
         return _Backend.TORCH_SDPA
 
+    if is_xpu():
+        if selected_backend != _Backend.IPEX:
+            logger.info("Cannot use %s backend on XPU.", selected_backend)
+        return _Backend.IPEX
+
     if is_tpu():
         if selected_backend != _Backend.PALLAS:
             logger.info("Cannot use %s backend on TPU.", selected_backend)

vllm/config.py

@@ -12,7 +12,7 @@ from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.model_executor.models import ModelRegistry
 from vllm.transformers_utils.config import get_config, get_hf_text_config
 from vllm.utils import (cuda_device_count_stateless, get_cpu_memory, is_cpu,
-                        is_hip, is_neuron, is_tpu)
+                        is_hip, is_neuron, is_tpu, is_xpu)
 
 if TYPE_CHECKING:
     from ray.util.placement_group import PlacementGroup
@@ -757,6 +757,8 @@ class DeviceConfig:
                 self.device_type = "tpu"
             elif is_cpu():
                 self.device_type = "cpu"
+            elif is_xpu():
+                self.device_type = "xpu"
             else:
                 # We don't call torch.cuda.is_available() here to
                 # avoid initializing CUDA before workers are forked

vllm/distributed/parallel_state.py

@@ -58,7 +58,7 @@ def _split_tensor_dict(
             # because it contains not only the device type but also the device
             # index (e.g. "cuda:0"). We only need the device type.
             # receiving side will set the device index.
-            device = "cpu" if value.is_cpu else "cuda"
+            device = value.device.type
             metadata_list.append(
                 (key, TensorMetadata(device, value.dtype, value.size())))
             tensor_list.append(value)

vllm/engine/arg_utils.py

@@ -501,10 +501,11 @@ class EngineArgs:
                   'Enabling this will use the fully sharded layers. '
                   'At high sequence length, max rank or '
                   'tensor parallel size, this is likely faster.'))
-        parser.add_argument("--device",
+        parser.add_argument(
+            "--device",
             type=str,
             default=EngineArgs.device,
-                            choices=["auto", "cuda", "neuron", "cpu", "tpu"],
+            choices=["auto", "cuda", "neuron", "cpu", "tpu", "xpu"],
             help='Device type for vLLM execution.')
 
         # Related to Vision-language models such as llava

vllm/engine/async_llm_engine.py

@@ -383,6 +383,17 @@ class AsyncLLMEngine:
                 "Distributed execution is not supported with the CPU backend.")
             from vllm.executor.cpu_executor import CPUExecutorAsync
             executor_class = CPUExecutorAsync
+        elif engine_config.device_config.device_type == "xpu":
+            if distributed_executor_backend is None:
+                from vllm.executor.xpu_executor import XPUExecutorAsync
+                executor_class = XPUExecutorAsync
+            elif distributed_executor_backend == "ray":
+                initialize_ray_cluster(engine_config.parallel_config)
+                from vllm.executor.ray_xpu_executor import RayXPUExecutorAsync
+                executor_class = RayXPUExecutorAsync
+            else:
+                raise RuntimeError(
+                    "Not supported distributed execution model on XPU device.")
         elif distributed_executor_backend == "ray":
             initialize_ray_cluster(engine_config.parallel_config)
             from vllm.executor.ray_gpu_executor import RayGPUExecutorAsync

vllm/engine/llm_engine.py

@@ -347,6 +347,14 @@ class LLMEngine:
         elif engine_config.device_config.device_type == "cpu":
             from vllm.executor.cpu_executor import CPUExecutor
             executor_class = CPUExecutor
+        elif engine_config.device_config.device_type == "xpu":
+            if distributed_executor_backend == "ray":
+                initialize_ray_cluster(engine_config.parallel_config)
+                from vllm.executor.ray_xpu_executor import RayXPUExecutor
+                executor_class = RayXPUExecutor
+            else:
+                from vllm.executor.xpu_executor import XPUExecutor
+                executor_class = XPUExecutor
         elif distributed_executor_backend == "ray":
             initialize_ray_cluster(engine_config.parallel_config)
             from vllm.executor.ray_gpu_executor import RayGPUExecutor

vllm/executor/ray_utils.py

@@ -3,7 +3,7 @@ from typing import List, Optional, Tuple
 
 from vllm.config import ParallelConfig
 from vllm.logger import init_logger
-from vllm.utils import get_ip, is_hip
+from vllm.utils import get_ip, is_hip, is_xpu
 from vllm.worker.worker_base import WorkerWrapperBase
 
 logger = init_logger(__name__)
@@ -71,7 +71,7 @@ def initialize_ray_cluster(
             "serving.")
 
     # Connect to a ray cluster.
-    if is_hip():
+    if is_hip() or is_xpu():
         ray.init(address=ray_address,
                  ignore_reinit_error=True,
                  num_gpus=parallel_config.world_size)

vllm/executor/ray_xpu_executor.py

@@ -0,0 +1,401 @@
+import asyncio
+import os
+import pickle
+from collections import defaultdict
+from itertools import islice, repeat
+from typing import (TYPE_CHECKING, Any, Awaitable, Dict, List, Optional, Set,
+                    Tuple, Union)
+
+from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
+                         ModelConfig, ParallelConfig, SchedulerConfig,
+                         SpeculativeConfig, VisionLanguageConfig)
+from vllm.executor.distributed_gpu_executor import (  # yapf: disable
+    DistributedGPUExecutor, DistributedGPUExecutorAsync)
+from vllm.executor.ray_utils import RayWorkerWrapper, ray
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
+                        make_async)
+
+if ray is not None:
+    from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
+
+if TYPE_CHECKING:
+    from ray.util.placement_group import PlacementGroup
+
+logger = init_logger(__name__)
+
+# If the env var is set, it uses the Ray's compiled DAG API
+# which optimizes the control plane overhead.
+# Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
+USE_RAY_COMPILED_DAG = bool(os.getenv("VLLM_USE_RAY_COMPILED_DAG", 0))
+
+
+class RayXPUExecutor(DistributedGPUExecutor):
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        cache_config: CacheConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        load_config: LoadConfig,
+        lora_config: Optional[LoRAConfig],
+        vision_language_config: Optional[VisionLanguageConfig],
+        speculative_config: Optional[SpeculativeConfig],
+    ) -> None:
+        assert device_config.device_type == "xpu"
+        assert (not speculative_config
+                ), "Speculative decoding not yet supported for XPU backend"
+
+        self.model_config = model_config
+        self.cache_config = cache_config
+        self.load_config = load_config
+        self.lora_config = lora_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+        self.vision_language_config = vision_language_config
+
+        placement_group = self.parallel_config.placement_group
+
+        # Disable Ray usage stats collection.
+        ray_usage = os.environ.get("RAY_USAGE_STATS_ENABLED", "0")
+        if ray_usage != "1":
+            os.environ["RAY_USAGE_STATS_ENABLED"] = "0"
+
+        # Create the parallel GPU workers.
+        self._init_workers_ray(placement_group)
+
+        # Profile the memory usage and initialize the cache.
+        self.forward_dag = None
+        if USE_RAY_COMPILED_DAG:
+            self.forward_dag = self._compiled_ray_dag()
+
+        # This is non-None when the execute model loop is running
+        # in the parallel workers. It's a coroutine in the AsyncLLMEngine case.
+        self.parallel_worker_tasks: Optional[Union[Any, Awaitable[Any]]] = None
+        # Updated by implementations that require additional args to be passed
+        # to the _run_workers execute_model call
+        self.extra_execute_model_run_workers_kwargs: Dict[str, Any] = {}
+
+    def _init_executor(self) -> None:
+        pass
+
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """Determine the number of available KV blocks.
+
+        This invokes `determine_num_available_blocks` on each worker and takes
+        the min of the results, guaranteeing that the selected cache sizes are
+        compatible with all workers.
+
+        Returns:
+            - Tuple[num_gpu_blocks, num_cpu_blocks]
+        """
+        # Get the maximum number of blocks that can be allocated on GPU and CPU.
+        num_blocks = self._run_workers("determine_num_available_blocks", )
+
+        # Since we use a shared centralized controller, we take the minimum
+        # number of blocks across all workers to make sure all the memory
+        # operators can be applied to all workers.
+        num_gpu_blocks = min(b[0] for b in num_blocks)
+        num_cpu_blocks = min(b[1] for b in num_blocks)
+
+        return num_gpu_blocks, num_cpu_blocks
+
+    def _init_workers_ray(self, placement_group: "PlacementGroup",
+                          **ray_remote_kwargs):
+        if self.parallel_config.tensor_parallel_size == 1:
+            # For single GPU case, we use a ray worker with constrained memory.
+            num_gpus = self.cache_config.gpu_memory_utilization
+        else:
+            # Otherwise, the ray workers are allocated with a full GPU.
+            num_gpus = 1
+
+        # The driver dummy worker does not actually use any resources.
+        # It holds the resource for the driver worker.
+        self.driver_dummy_worker: Optional[RayWorkerWrapper] = None
+        # The remaining workers are the actual ray actors.
+        self.workers: List[RayWorkerWrapper] = []
+
+        # Create the workers.
+        driver_ip = get_ip()
+        for bundle_id, bundle in enumerate(placement_group.bundle_specs):
+            if not bundle.get("GPU", 0):
+                continue
+            scheduling_strategy = PlacementGroupSchedulingStrategy(
+                placement_group=placement_group,
+                placement_group_capture_child_tasks=True,
+                placement_group_bundle_index=bundle_id,
+            )
+            worker = ray.remote(
+                num_cpus=0,
+                num_gpus=num_gpus,
+                scheduling_strategy=scheduling_strategy,
+                **ray_remote_kwargs,
+            )(RayWorkerWrapper).remote(
+                worker_module_name="vllm.worker.xpu_worker",
+                worker_class_name="XPUWorker",
+                trust_remote_code=self.model_config.trust_remote_code,
+            )
+
+            worker_ip = ray.get(worker.get_node_ip.remote())
+            if worker_ip == driver_ip and self.driver_dummy_worker is None:
+                # If the worker is on the same node as the driver, we use it
+                # as the resource holder for the driver process.
+                self.driver_dummy_worker = worker
+                self.driver_worker = RayWorkerWrapper(
+                    worker_module_name="vllm.worker.xpu_worker",
+                    worker_class_name="XPUWorker",
+                    trust_remote_code=self.model_config.trust_remote_code,
+                )
+            else:
+                # Else, added to the list of workers.
+                self.workers.append(worker)
+        if self.driver_dummy_worker is None:
+            raise ValueError(
+                "Ray does not allocate any GPUs on the driver node. Consider "
+                "adjusting the Ray placement group or running the driver on a "
+                "GPU node.")
+
+        # Get the set of GPU IDs used on each node.
+        worker_node_and_gpu_ids = self._run_workers("get_node_and_gpu_ids",
+                                                    use_dummy_driver=True)
+
+        node_workers = defaultdict(list)
+        node_gpus = defaultdict(list)
+
+        for i, (node_id, gpu_ids) in enumerate(worker_node_and_gpu_ids):
+            node_workers[node_id].append(i)
+            node_gpus[node_id].extend(gpu_ids)
+        for node_id, gpu_ids in node_gpus.items():
+            node_gpus[node_id] = sorted(gpu_ids)
+
+        # TODO: add env var for xpu
+
+        distributed_init_method = get_distributed_init_method(
+            driver_ip, get_open_port())
+
+        def collect_arg_helper_func(**kwargs):
+            # avoid writing `{"name": value}` manually
+            return kwargs
+
+        init_worker_all_kwargs = []
+
+        # Initialize the actual workers inside worker wrapper.
+        for rank, (node_id, _) in enumerate(worker_node_and_gpu_ids, ):
+            local_rank = node_workers[node_id].index(rank)
+            init_worker_all_kwargs.append(
+                collect_arg_helper_func(
+                    model_config=self.model_config,
+                    parallel_config=self.parallel_config,
+                    scheduler_config=self.scheduler_config,
+                    device_config=self.device_config,
+                    cache_config=self.cache_config,
+                    load_config=self.load_config,
+                    local_rank=local_rank,
+                    rank=rank,
+                    distributed_init_method=distributed_init_method,
+                    lora_config=self.lora_config,
+                    vision_language_config=self.vision_language_config,
+                    is_driver_worker=rank == 0,
+                ))
+        self._run_workers("init_worker", all_kwargs=init_worker_all_kwargs)
+
+        self._run_workers("init_device")
+        self._run_workers(
+            "load_model",
+            max_concurrent_workers=self.parallel_config.
+            max_parallel_loading_workers,
+        )
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Initialize the KV cache in all workers.
+        """
+
+        # NOTE: We log here to avoid multiple logs when number of workers is
+        # greater than one. We could log in the engine, but not all executors
+        # have GPUs.
+        logger.info("# GPU blocks: %d, "
+                    "# CPU blocks: %d", num_gpu_blocks, num_cpu_blocks)
+
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+        self._run_workers("initialize_cache",
+                          num_gpu_blocks=num_gpu_blocks,
+                          num_cpu_blocks=num_cpu_blocks)
+
+    def _driver_execute_model(
+        self,
+        execute_model_req: Optional[ExecuteModelRequest] = None
+    ) -> List[SamplerOutput]:
+        """Run execute_model in the driver worker.
+
+        Passing None will cause the driver to stop the model execution
+        loop running in each of the remote workers.
+        """
+        return self.driver_worker.execute_method("execute_model",
+                                                 execute_model_req)
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        assert lora_request.lora_int_id > 0, "lora_id must be greater than 0."
+        return self._run_workers(
+            "add_lora",
+            lora_request=lora_request,
+        )
+
+    def remove_lora(self, lora_id: int) -> bool:
+        assert lora_id > 0, "lora_id must be greater than 0."
+        return self._run_workers(
+            "remove_lora",
+            lora_id=lora_id,
+        )
+
+    def list_loras(self) -> Set[int]:
+        return self._run_workers("list_loras")
+
+    def _run_workers(
+        self,
+        method: str,
+        *args,
+        async_run_remote_workers_only: bool = False,
+        all_args: Optional[List[Tuple[Any, ...]]] = None,
+        all_kwargs: Optional[List[Dict[str, Any]]] = None,
+        use_dummy_driver: bool = False,
+        max_concurrent_workers: Optional[int] = None,
+        use_ray_compiled_dag: bool = False,
+        **kwargs,
+    ) -> Any:
+        """Runs the given method on all workers. Can be used in the following
+        ways:
+
+        - args/kwargs: All workers share the same args/kwargs
+        - args/kwargs and driver_args/driver_kwargs: Driver worker has
+          different args
+        - all_args/all_kwargs: args/kwargs for each worker are specified
+          individually
+        """
+
+        if max_concurrent_workers:
+            raise NotImplementedError(
+                "max_concurrent_workers is not supported yet.")
+
+        count = len(self.workers)
+        all_worker_args = repeat(args, count) if all_args is None \
+            else islice(all_args, 1, None)
+        all_worker_kwargs = repeat(kwargs, count) if all_kwargs is None \
+            else islice(all_kwargs, 1, None)
+
+        if use_ray_compiled_dag:
+            # Right now, compiled DAG can only accept a single
+            # input. TODO(sang): Fix it.
+            assert self.forward_dag is not None
+            output_channels = self.forward_dag.execute(1)
+        else:
+            # Start the ray workers first.
+            ray_worker_outputs = [
+                worker.execute_method.remote(method, *worker_args,
+                                             **worker_kwargs)
+                for (worker, worker_args, worker_kwargs
+                     ) in zip(self.workers, all_worker_args, all_worker_kwargs)
+            ]
+        if async_run_remote_workers_only:
+            # Just return futures
+            return ray_worker_outputs
+
+        driver_args = args if all_args is None else all_args[0]
+        driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0]
+
+        # Start the driver worker after all the ray workers.
+        if not use_dummy_driver:
+            driver_worker_output = self.driver_worker.execute_method(
+                method, *driver_args, **driver_kwargs)
+        else:
+            assert self.driver_dummy_worker is not None
+            driver_worker_output = ray.get(
+                self.driver_dummy_worker.execute_method.remote(
+                    method, *driver_args, **driver_kwargs))
+        # Get the results of the ray workers.
+        if self.workers:
+            if use_ray_compiled_dag:
+                try:
+                    ray_worker_outputs = [
+                        pickle.loads(chan.begin_read())
+                        for chan in output_channels
+                    ]
+                finally:
+                    # Has to call end_read in order to reuse the DAG.
+                    for chan in output_channels:
+                        chan.end_read()
+            else:
+                ray_worker_outputs = ray.get(ray_worker_outputs)
+
+        return [driver_worker_output] + ray_worker_outputs
+
+    def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
+        """Wait for futures returned from _run_workers() with
+        async_run_remote_workers_only to complete."""
+        ray.get(parallel_worker_tasks)
+
+    def _compiled_ray_dag(self):
+        import pkg_resources
+        required_version = "2.9"
+        current_version = pkg_resources.get_distribution("ray").version
+        if current_version < required_version:
+            raise ValueError(f"Ray version {required_version} or greater is "
+                             f"required, but found {current_version}")
+
+        from ray.dag import InputNode, MultiOutputNode
+        assert self.parallel_config.worker_use_ray
+
+        # Right now, compiled DAG requires at least 1 arg. We send
+        # a dummy value for now. It will be fixed soon.
+        with InputNode() as input_data:
+            forward_dag = MultiOutputNode([
+                worker.execute_model_compiled_dag_remote.
+                bind(  # type: ignore[attr-defined]
+                    input_data) for worker in self.workers
+            ])
+        return forward_dag.experimental_compile()
+
+    def check_health(self) -> None:
+        """Raises an error if engine is unhealthy."""
+        self._check_if_any_actor_is_dead()
+
+    def _check_if_any_actor_is_dead(self):
+        if not self.workers:
+            return
+
+        dead_actors = []
+        for actor in self.workers:
+            actor_state = ray.state.actors(actor._ray_actor_id.hex())  # pylint: disable=protected-access
+            if actor_state["State"] == "DEAD":
+                dead_actors.append(actor)
+        if dead_actors:
+            raise RuntimeError("At least one Worker is dead. "
+                               f"Dead Workers: {dead_actors}. ")
+
+
+class RayXPUExecutorAsync(RayXPUExecutor, DistributedGPUExecutorAsync):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.driver_exec_method = make_async(self.driver_worker.execute_method)
+
+    async def _driver_execute_model_async(
+        self,
+        execute_model_req: Optional[ExecuteModelRequest] = None
+    ) -> List[SamplerOutput]:
+        return await self.driver_exec_method("execute_model",
+                                             execute_model_req)
+
+    async def _start_worker_execution_loop(self):
+        coros = [
+            worker.execute_method.remote("start_worker_execution_loop")
+            for worker in self.workers
+        ]
+        return await asyncio.gather(*coros)

vllm/executor/xpu_executor.py

@@ -0,0 +1,98 @@
+from typing import List, Optional
+
+import torch
+
+from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
+                         ModelConfig, ParallelConfig, SchedulerConfig,
+                         SpeculativeConfig, VisionLanguageConfig)
+from vllm.executor.executor_base import ExecutorAsyncBase
+from vllm.executor.gpu_executor import GPUExecutor
+from vllm.logger import init_logger
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.utils import make_async
+from vllm.worker.worker_base import WorkerWrapperBase
+
+logger = init_logger(__name__)
+
+
+class XPUExecutor(GPUExecutor):
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        cache_config: CacheConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        load_config: LoadConfig,
+        lora_config: Optional[LoRAConfig],
+        vision_language_config: Optional[VisionLanguageConfig],
+        speculative_config: Optional[SpeculativeConfig],
+    ) -> None:
+        assert device_config.device_type == "xpu"
+        assert (not speculative_config
+                ), "Speculative decoding not yet supported for XPU backend"
+
+        model_config = _verify_and_get_model_config(model_config)
+
+        self.model_config = model_config
+        self.cache_config = cache_config
+        self.load_config = load_config
+        self.lora_config = lora_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+        self.vision_language_config = vision_language_config
+        self.speculative_config = None
+
+        # Instantiate the worker and load the model to GPU.
+        self._init_executor()
+
+    def _create_worker(self,
+                       local_rank: int = 0,
+                       rank: int = 0,
+                       distributed_init_method: Optional[str] = None):
+        if self.speculative_config is None:
+            worker_module_name = "vllm.worker.xpu_worker"
+            worker_class_name = "XPUWorker"
+        else:
+            raise NotImplementedError(
+                "XPU does not support speculative decoding")
+
+        wrapper = WorkerWrapperBase(
+            worker_module_name=worker_module_name,
+            worker_class_name=worker_class_name,
+        )
+        wrapper.init_worker(**self._get_worker_kwargs(local_rank, rank,
+                                                      distributed_init_method))
+        return wrapper.worker
+
+    def execute_model(
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        output = self.driver_worker.execute_model(execute_model_req)
+        return output
+
+
+class XPUExecutorAsync(XPUExecutor, ExecutorAsyncBase):
+
+    async def execute_model_async(
+        self,
+        execute_model_req: ExecuteModelRequest,
+    ) -> List[SamplerOutput]:
+        output = await make_async(self.driver_worker.execute_model
+                                  )(execute_model_req=execute_model_req)
+        return output
+
+
+def _verify_and_get_model_config(config: ModelConfig) -> ModelConfig:
+    if config.dtype == torch.bfloat16:
+        logger.warning(
+            "bfloat16 is not fully supported on XPU, casting to float16.")
+        config.dtype = torch.float16
+    if not config.enforce_eager:
+        logger.warning(
+            "CUDA graph is not supported on XPU, fallback to the eager "
+            "mode.")
+        config.enforce_eager = True
+    return config

vllm/model_executor/custom_op.py

@@ -1,6 +1,6 @@
 import torch.nn as nn
 
-from vllm.utils import is_cpu, is_hip, is_tpu
+from vllm.utils import is_cpu, is_hip, is_tpu, is_xpu
 
 
 class CustomOp(nn.Module):
@@ -29,9 +29,7 @@ class CustomOp(nn.Module):
         return self.forward_cuda(*args, **kwargs)
 
     def forward_xpu(self, *args, **kwargs):
-        # By default, we assume that XPU ops are compatible with CUDA ops.
+        raise NotImplementedError
-        # NOTE(woosuk): This is a placeholder for future extensions.
-        return self.forward_cuda(*args, **kwargs)
 
     def forward_cpu(self, *args, **kwargs):
         # By default, we assume that CPU ops are compatible with CUDA ops.
@@ -58,5 +56,7 @@ class CustomOp(nn.Module):
             return self.forward_cpu
         elif is_tpu():
             return self.forward_tpu
+        elif is_xpu():
+            return self.forward_xpu
         else:
             return self.forward_cuda

vllm/model_executor/layers/activation.py

@@ -37,6 +37,15 @@ class SiluAndMul(CustomOp):
         ops.silu_and_mul(out, x)
         return out
 
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.silu_and_mul(out, x)
+        return out
+
 
 class GeluAndMul(CustomOp):
     """An activation function for GeGLU.
@@ -71,6 +80,18 @@ class GeluAndMul(CustomOp):
             ops.gelu_tanh_and_mul(out, x)
         return out
 
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        if self.approximate == "none":
+            ops.gelu_and_mul(out, x)
+        elif self.approximate == "tanh":
+            ops.gelu_tanh_and_mul(out, x)
+        return out
+
     def extra_repr(self) -> str:
         return f'approximate={repr(self.approximate)}'
 
@@ -90,6 +111,13 @@ class NewGELU(CustomOp):
         ops.gelu_new(out, x)
         return out
 
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        out = torch.empty_like(x)
+        ops.gelu_new(out, x)
+        return out
+
 
 class FastGELU(CustomOp):
 
@@ -105,6 +133,13 @@ class FastGELU(CustomOp):
         ops.gelu_fast(out, x)
         return out
 
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        out = torch.empty_like(x)
+        ops.gelu_fast(out, x)
+        return out
+
 
 class ScaledActivation(nn.Module):
     """An activation function with post-scale parameters.

vllm/model_executor/layers/layernorm.py

@@ -67,6 +67,30 @@ class RMSNorm(CustomOp):
         )
         return out
 
+    def forward_xpu(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        if residual is not None:
+            ops.fused_add_rms_norm(
+                x,
+                residual,
+                self.weight.data,
+                self.variance_epsilon,
+            )
+            return x, residual
+        out = torch.empty_like(x)
+        ops.rms_norm(
+            out,
+            x,
+            self.weight.data,
+            self.variance_epsilon,
+        )
+        return out
+
     def extra_repr(self) -> str:
         s = f"hidden_size={self.weight.data.size(0)}"
         s += f", eps={self.variance_epsilon}"

vllm/model_executor/layers/rotary_embedding.py

@@ -221,6 +221,29 @@ class RotaryEmbedding(CustomOp):
                                  self.cos_sin_cache, self.is_neox_style)
         return query, key
 
+    def forward_xpu(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        self.cos_sin_cache = self.cos_sin_cache.to(positions.device,
+                                                   dtype=query.dtype)
+        # ops.rotary_embedding()/batched_rotary_embedding()
+        # are in-place operations that update the query and key tensors.
+        if offsets is not None:
+            ops.batched_rotary_embedding(positions, query, key, self.head_size,
+                                         self.cos_sin_cache,
+                                         self.is_neox_style, self.rotary_dim,
+                                         offsets)
+        else:
+            ops.rotary_embedding(positions, query, key, self.head_size,
+                                 self.cos_sin_cache, self.is_neox_style)
+        return query, key
+
     def forward_tpu(
         self,
         positions: torch.Tensor,

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -307,7 +307,7 @@ class VocabParallelEmbedding(torch.nn.Module):
         else:
             masked_input = input_
             # Get the embeddings.
-        output_parallel = F.embedding(masked_input, self.weight)
+        output_parallel = F.embedding(masked_input.long(), self.weight)
         # Mask the output embedding.
         if self.tp_size > 1:
             output_parallel.masked_fill_(input_mask.unsqueeze(1), 0)

vllm/utils.py

@@ -160,6 +160,26 @@ def is_tpu() -> bool:
     return libtpu is not None
 
 
+@lru_cache(maxsize=None)
+def is_xpu() -> bool:
+    from importlib.metadata import version
+    is_xpu_flag = "xpu" in version("vllm")
+    # vllm is not build with xpu
+    if not is_xpu_flag:
+        return False
+    try:
+        import intel_extension_for_pytorch as ipex  # noqa: F401
+        _import_ipex = True
+    except ImportError as e:
+        logger.warning("Import Error for IPEX: %s", e.msg)
+        _import_ipex = False
+    # ipex dependency is not ready
+    if not _import_ipex:
+        logger.warning("not found ipex lib")
+        return False
+    return hasattr(torch, "xpu") and torch.xpu.is_available()
+
+
 @lru_cache(maxsize=None)
 def get_max_shared_memory_bytes(gpu: int = 0) -> int:
     """Returns the maximum shared memory per thread block in bytes."""
@@ -482,6 +502,9 @@ def is_pin_memory_available() -> bool:
         print_warning_once("Using 'pin_memory=False' as WSL is detected. "
                            "This may slow down the performance.")
         return False
+    elif is_xpu():
+        print_warning_once("Pin memory is not supported on XPU.")
+        return False
     elif is_neuron():
         print_warning_once("Pin memory is not supported on Neuron.")
         return False
@@ -497,8 +520,12 @@ class CudaMemoryProfiler:
 
     def current_memory_usage(self) -> float:
         # Return the memory usage in bytes.
+        if torch.cuda.is_available():
             torch.cuda.reset_peak_memory_stats(self.device)
             mem = torch.cuda.max_memory_allocated(self.device)
+        elif is_xpu():
+            torch.xpu.reset_peak_memory_stats(self.device)
+            mem = torch.xpu.max_memory_allocated(self.device)
         return mem
 
     def __enter__(self):

vllm/worker/cache_engine.py

@@ -4,7 +4,7 @@ from typing import List
 import torch
 
 from vllm.attention import get_attn_backend
-from vllm.config import CacheConfig, ModelConfig, ParallelConfig
+from vllm.config import CacheConfig, DeviceConfig, ModelConfig, ParallelConfig
 from vllm.logger import init_logger
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, get_dtype_size,
                         is_pin_memory_available)
@@ -25,10 +25,12 @@ class CacheEngine:
         cache_config: CacheConfig,
         model_config: ModelConfig,
         parallel_config: ParallelConfig,
+        device_config: DeviceConfig,
     ) -> None:
         self.cache_config = cache_config
         self.model_config = model_config
         self.parallel_config = parallel_config
+        self.device_config = device_config
 
         self.head_size = model_config.get_head_size()
         self.num_layers = model_config.get_num_layers(parallel_config)
@@ -55,7 +57,8 @@ class CacheEngine:
         )
 
         # Initialize the cache.
-        self.gpu_cache = self._allocate_kv_cache(self.num_gpu_blocks, "cuda")
+        self.gpu_cache = self._allocate_kv_cache(
+            self.num_gpu_blocks, self.device_config.device_type)
         self.cpu_cache = self._allocate_kv_cache(self.num_cpu_blocks, "cpu")
 
     def _allocate_kv_cache(

vllm/worker/worker.py

@@ -205,7 +205,8 @@ class Worker(WorkerBase):
     def _init_cache_engine(self):
         assert self.cache_config.num_gpu_blocks is not None
         self.cache_engine = CacheEngine(self.cache_config, self.model_config,
-                                        self.parallel_config)
+                                        self.parallel_config,
+                                        self.device_config)
         self.gpu_cache = self.cache_engine.gpu_cache
 
     def _warm_up_model(self) -> None:

vllm/worker/xpu_model_runner.py

@@ -0,0 +1,417 @@
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+
+from vllm.attention import get_attn_backend
+from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
+                         ModelConfig, ParallelConfig, SchedulerConfig,
+                         VisionLanguageConfig)
+from vllm.distributed import broadcast_tensor_dict
+from vllm.logger import init_logger
+from vllm.model_executor.model_loader import get_model
+from vllm.sampling_params import SamplingParams
+from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
+from vllm.utils import CudaMemoryProfiler, make_tensor_with_pad
+from vllm.worker.model_runner import AttentionMetadata, SamplingMetadata
+
+logger = init_logger(__name__)
+
+_PAD_SLOT_ID = -1
+_BATCH_SIZE_ALIGNMENT = 8
+_BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [
+    _BATCH_SIZE_ALIGNMENT * i for i in range(1, 33)
+]
+
+
+class XPUModelRunner:
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        cache_config: CacheConfig,
+        load_config: LoadConfig,
+        lora_config: Optional[LoRAConfig],
+        vision_language_config: Optional[VisionLanguageConfig],
+        kv_cache_dtype: Optional[str] = "auto",
+        is_driver_worker: bool = False,
+        *args,
+        **kwargs,
+    ):
+        self.model_config = model_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.lora_config = lora_config
+        self.load_config = load_config
+        self.cache_config = cache_config
+        self.vision_language_config = vision_language_config
+        self.is_driver_worker = is_driver_worker
+
+        self.sliding_window = model_config.get_sliding_window()
+        self.device_config = device_config
+        self.device = self.device_config.device
+
+        self.kv_cache_dtype = kv_cache_dtype
+        self.block_size = cache_config.block_size
+        self.max_context_len_to_capture = (
+            self.model_config.max_context_len_to_capture
+            if self.model_config is not None else 0)
+
+        self.attn_backend = get_attn_backend(
+            self.model_config.get_num_attention_heads(self.parallel_config),
+            self.model_config.get_head_size(),
+            self.model_config.get_num_kv_heads(self.parallel_config),
+            self.model_config.get_sliding_window(),
+            self.model_config.dtype,
+            self.kv_cache_dtype,
+            self.block_size,
+        )
+
+        # Lazy initialization.
+        self.model: nn.Module  # Set after init_Model
+
+    def load_model(self) -> None:
+        with CudaMemoryProfiler() as m:
+            self.model = get_model(
+                model_config=self.model_config,
+                device_config=self.device_config,
+                load_config=self.load_config,
+                lora_config=self.lora_config,
+                vision_language_config=self.vision_language_config,
+                parallel_config=self.parallel_config,
+                scheduler_config=self.scheduler_config,
+                cache_config=self.cache_config,
+            )
+
+        self.model_memory_usage = m.consumed_memory
+        logger.info("Loading model weights took %.4f GB",
+                    self.model_memory_usage / float(2**30))
+
+    @property
+    def vocab_size(self) -> int:
+        return self.model_config.get_vocab_size()
+
+    @torch.inference_mode()
+    def profile_run(self) -> None:
+        # Enable top-k sampling to reflect the accurate memory usage.
+        sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1)
+        max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
+        max_num_seqs = self.scheduler_config.max_num_seqs
+
+        # Profile memory usage with max_num_sequences sequences and the total
+        # number of tokens equal to max_num_batched_tokens.
+        seqs: List[SequenceGroupMetadata] = []
+        # Additional GPU memory may be needed for vision encoding, which needs
+        # to be accounted for when calculating the GPU blocks for
+        # vLLM blocker manager.
+        # To exercise the worst scenario for GPU memory consumption,
+        # the number of seqs (batch_size) is chosen to maximize the number
+        # of images processed.
+        for group_id in range(max_num_seqs):
+            seq_len = (max_num_batched_tokens // max_num_seqs +
+                       (group_id < max_num_batched_tokens % max_num_seqs))
+
+            seq_data = SequenceData([0] * seq_len)
+            dummy_multi_modal_data = None
+            seq = SequenceGroupMetadata(
+                request_id=str(group_id),
+                is_prompt=True,
+                seq_data={group_id: seq_data},
+                sampling_params=sampling_params,
+                block_tables=None,
+                lora_request=None,
+                multi_modal_data=dummy_multi_modal_data,
+            )
+            seqs.append(seq)
+
+        # Run the model with the dummy inputs.
+        num_layers = self.model_config.get_num_layers(self.parallel_config)
+        kv_caches = [None] * num_layers
+        self.execute_model(seqs, kv_caches)
+        torch.xpu.synchronize()
+        return
+
+    def prepare_input_tensors(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+    ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata,
+               Optional[torch.Tensor]]:
+        multi_modal_input = None
+        if self.is_driver_worker:
+            # NOTE: We assume that all sequences in the group are all prompts or
+            # all decodes.
+            is_prompt = seq_group_metadata_list[0].is_prompt
+            # Prepare input tensors.
+            if is_prompt:
+                (input_tokens, input_positions, attn_metadata, seq_lens,
+                 multi_modal_input
+                 ) = self._prepare_prompt(seq_group_metadata_list)
+            else:
+                (input_tokens, input_positions,
+                 attn_metadata) = self._prepare_decode(seq_group_metadata_list)
+                seq_lens = []
+            sampling_metadata = SamplingMetadata.prepare(
+                seq_group_metadata_list,
+                seq_lens,
+                # subquery_lens is not needed if chunked prefill is not
+                # supported. Since CPU worker doesn't support chunked prefill
+                # just use seq_lens instead.
+                seq_lens,
+                self.device,
+                pin_memory=False)
+            # Broadcast the metadata.
+            metadata_dict = {
+                "input_tokens": input_tokens,
+                "input_positions": input_positions,
+                "selected_token_indices":
+                sampling_metadata.selected_token_indices,
+            }
+            metadata_dict.update(attn_metadata.asdict_zerocopy())
+            broadcast_tensor_dict(metadata_dict, src=0)
+        else:
+            metadata_dict = broadcast_tensor_dict(src=0)
+            input_tokens = metadata_dict.pop("input_tokens")
+            input_positions = metadata_dict.pop("input_positions")
+            selected_token_indices = metadata_dict.pop(
+                "selected_token_indices")
+            attn_metadata = self.attn_backend.make_metadata(**metadata_dict)
+            sampling_metadata = SamplingMetadata(
+                seq_groups=None,
+                selected_token_indices=selected_token_indices,
+                categorized_sample_indices=None,
+                num_prompts=0,
+            )
+
+        return (input_tokens, input_positions, attn_metadata,
+                sampling_metadata, multi_modal_input)
+
+    def _prepare_decode(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+    ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata]:
+        assert len(seq_group_metadata_list) > 0
+        input_tokens: List[int] = []
+        input_positions: List[int] = []
+        slot_mapping: List[int] = []
+        seq_lens: List[int] = []
+        block_tables: List[List[int]] = []
+
+        for seq_group_metadata in seq_group_metadata_list:
+            assert not seq_group_metadata.is_prompt
+            assert seq_group_metadata.token_chunk_size == 1
+
+            seq_ids = list(seq_group_metadata.seq_data.keys())
+
+            for seq_id in seq_ids:
+                seq_data = seq_group_metadata.seq_data[seq_id]
+                generation_token = seq_data.get_last_token_id()
+                input_tokens.append(generation_token)
+
+                seq_len = seq_data.get_len()
+                position = seq_len - 1
+                input_positions.append(position)
+
+                seq_len = seq_len if self.sliding_window is None else min(
+                    seq_len, self.sliding_window)
+                seq_lens.append(seq_len)
+
+                block_table = seq_group_metadata.block_tables[seq_id]
+                block_number = block_table[position // self.block_size]
+                block_offset = position % self.block_size
+                slot = block_number * self.block_size + block_offset
+                slot_mapping.append(slot)
+
+                if self.sliding_window is not None:
+                    sliding_window_blocks = (self.sliding_window //
+                                             self.block_size)
+                    block_table = block_table[-sliding_window_blocks:]
+                block_tables.append(block_table)
+
+        max_decode_seq_len = max(seq_lens)
+
+        input_tokens = torch.tensor(input_tokens,
+                                    dtype=torch.long,
+                                    device=self.device)
+        input_positions = torch.tensor(input_positions,
+                                       dtype=torch.long,
+                                       device=self.device)
+        slot_mapping = torch.tensor(slot_mapping,
+                                    dtype=torch.long,
+                                    device=self.device)
+        seq_lens_tensor = torch.tensor(seq_lens,
+                                       dtype=torch.int,
+                                       device=self.device)
+
+        max_block_table_len = max(
+            len(block_table) for block_table in block_tables)
+        block_tables = make_tensor_with_pad(
+            block_tables,
+            max_len=max_block_table_len,
+            pad=0,
+            dtype=torch.int,
+            device=self.device,
+        )
+
+        attn_metadata = self.attn_backend.make_metadata(
+            is_prompt=False,
+            slot_mapping=slot_mapping,
+            seq_lens=seq_lens,
+            seqlen_q=None,
+            max_seqlen=None,
+            seq_lens_tensor=seq_lens_tensor,
+            max_decode_seq_len=max_decode_seq_len,
+            num_prefill_tokens=0,
+            num_decode_tokens=len(input_tokens),
+            num_prefills=0,
+            block_tables=block_tables,
+        )
+        return (
+            input_tokens,
+            input_positions,
+            attn_metadata,
+        )
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        kv_caches: List[torch.Tensor],
+    ) -> Optional[SamplerOutput]:
+        (input_tokens, input_positions, attn_metadata, sampling_metadata,
+         multi_modal_input
+         ) = self.prepare_input_tensors(seq_group_metadata_list)
+
+        model_executable = self.model
+        execute_model_kwargs = {
+            "input_ids": input_tokens,
+            "positions": input_positions,
+            "kv_caches": kv_caches,
+            "attn_metadata": attn_metadata,
+        }
+        if self.vision_language_config:
+            execute_model_kwargs.update({"image_input": multi_modal_input})
+
+        hidden_states = model_executable(**execute_model_kwargs)
+
+        # Compute the logits.
+        logits = self.model.compute_logits(hidden_states, sampling_metadata)
+
+        # Only perform sampling in the driver worker.
+        if not self.is_driver_worker:
+            return None
+
+        # Sample the next token.
+        output = self.model.sample(
+            logits=logits,
+            sampling_metadata=sampling_metadata,
+        )
+        return output
+
+    def _prepare_prompt(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+    ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, List[int],
+               Optional[torch.Tensor]]:
+        assert len(seq_group_metadata_list) > 0
+        input_tokens: List[int] = []
+        input_positions: List[int] = []
+        slot_mapping: List[int] = []
+        seq_lens: List[int] = []
+        multi_modal_input_list: List[torch.Tensor] = []
+
+        for seq_group_metadata in seq_group_metadata_list:
+            assert seq_group_metadata.is_prompt
+            seq_ids = list(seq_group_metadata.seq_data.keys())
+            assert len(seq_ids) == 1
+            seq_id = seq_ids[0]
+
+            seq_data = seq_group_metadata.seq_data[seq_id]
+            prompt_tokens = seq_data.get_token_ids()
+            computed_len = seq_data.get_num_computed_tokens()
+            seq_len = len(prompt_tokens)
+
+            seq_lens.append(seq_len)  # Prompt token num
+            input_tokens.extend(prompt_tokens)  # Token ids
+
+            # Token position ids
+            # NOTE(woosuk): Here we assume that the first token in the prompt
+            # is always the first token in the sequence.
+            input_positions.extend(list(range(computed_len, seq_len)))
+
+            if seq_group_metadata.multi_modal_data:
+                multi_modal_input_list.append(
+                    seq_group_metadata.multi_modal_data.data)
+
+            if seq_group_metadata.block_tables is None:
+                # During memory profiling, the block tables are not initialized
+                # yet. In this case, we just use a dummy slot mapping.
+                slot_mapping.extend([_PAD_SLOT_ID] * seq_len)
+                continue
+
+            # Compute the slot mapping.
+            block_table = seq_group_metadata.block_tables[seq_id]
+            # Mask the [0, start_idx) tokens of the prompt with _PAD_SLOT_ID,
+            # where start_idx is max(0, seq_len - sliding_window).
+            # For example, if the prompt len is 10, sliding window is 8, and
+            # block size is 4, the first two tokens are masked and the slot
+            # mapping will be [-1, -1, 2, 3, 4, 5, 6, 7, 0, 1].
+            start_idx = 0
+            if self.sliding_window is not None:
+                start_idx = max(0, seq_len - self.sliding_window)
+
+            for i in range(computed_len, seq_len):
+                if i < start_idx:
+                    slot_mapping.append(_PAD_SLOT_ID)
+                    continue
+
+                block_number = block_table[i //
+                                           self.block_size]  # type: ignore
+                block_offset = i % self.block_size  # type: ignore
+                slot = block_number * self.block_size + block_offset
+                slot_mapping.append(slot)
+
+        if multi_modal_input_list:
+            assert self.vision_language_config, (
+                "Multi-modal inputs are only supported by "
+                "vision language models.")
+            multi_modal_input = torch.cat(multi_modal_input_list,
+                                          dim=0).to(self.device)
+        else:
+            multi_modal_input = None
+
+        num_prompt_tokens = len(input_tokens)
+
+        input_tokens = torch.tensor(input_tokens,
+                                    dtype=torch.long,
+                                    device=self.device)  # type: ignore
+        input_positions = torch.tensor(input_positions,
+                                       dtype=torch.long,
+                                       device=self.device)  # type: ignore
+        slot_mapping = torch.tensor(slot_mapping,
+                                    dtype=torch.long,
+                                    device=self.device)  # type: ignore
+
+        max_seqlen = max(seq_lens)
+        tmp = [0]
+        tmp.extend(seq_lens)
+        seqlen = torch.tensor(tmp)
+        seqlen_q = torch.cumsum(seqlen, dim=0).to(device=self.device)
+
+        attn_metadata = self.attn_backend.make_metadata(
+            is_prompt=True,
+            slot_mapping=slot_mapping,
+            seq_lens=seq_lens,
+            seqlen_q=seqlen_q,
+            max_seqlen=max_seqlen,
+            seq_lens_tensor=None,
+            max_decode_seq_len=None,
+            num_prefills=len(seq_lens),
+            num_prefill_tokens=num_prompt_tokens,
+            num_decode_tokens=0,
+            block_tables=torch.tensor([], device=self.device, dtype=torch.int),
+        )
+        return (input_tokens, input_positions, attn_metadata, seq_lens,
+                multi_modal_input)

vllm/worker/xpu_worker.py

@@ -0,0 +1,193 @@
+"""A XPU worker class."""
+import gc
+import os
+from typing import List, Optional, Tuple
+
+import intel_extension_for_pytorch  # noqa: F401
+import oneccl_bindings_for_pytorch  # noqa: F401
+import torch
+import torch.distributed
+
+from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
+                         ModelConfig, ParallelConfig, SchedulerConfig,
+                         SpeculativeConfig, VisionLanguageConfig)
+from vllm.distributed import (ensure_model_parallel_initialized,
+                              init_distributed_environment)
+from vllm.logger import init_logger
+from vllm.model_executor import set_random_seed
+from vllm.utils import is_xpu
+from vllm.worker.cache_engine import CacheEngine
+from vllm.worker.worker import Worker
+from vllm.worker.worker_base import LoraNotSupportedWorkerBase
+from vllm.worker.xpu_model_runner import XPUModelRunner
+
+logger = init_logger(__name__)
+
+
+class XPUWorker(LoraNotSupportedWorkerBase, Worker):
+    """A worker class that executes (a partition of) the model on a GPU.
+    
+    Each worker is associated with a single XPU device. The worker is 
+    responsible for maintaining the KV cache and executing the model on the 
+    XPU. In case of distributed inference, each worker is assigned a partition
+    of the model.
+    """
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        cache_config: CacheConfig,
+        load_config: LoadConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        lora_config: Optional[LoRAConfig] = None,
+        vision_language_config: Optional[VisionLanguageConfig] = None,
+        speculative_config: Optional[SpeculativeConfig] = None,
+        is_driver_worker: bool = False,
+    ) -> None:
+        assert device_config.device_type == "xpu"
+        assert is_xpu()
+
+        self.model_config = model_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+        self.cache_config = cache_config
+        self.load_config = load_config
+        self.local_rank = local_rank
+        self.rank = rank
+        self.distributed_init_method = distributed_init_method
+        self.lora_config = lora_config
+        self.is_driver_worker = is_driver_worker
+        if self.is_driver_worker:
+            assert self.rank == 0, "The driver worker must have rank 0."
+
+        self.vision_language_config = vision_language_config
+        if self.vision_language_config:
+            assert not self.lora_config, (
+                "To be tested: vision language model with LoRA settings.")
+
+        self.model_runner = XPUModelRunner(  # type: ignore
+            model_config,
+            parallel_config,
+            scheduler_config,
+            device_config,
+            cache_config,
+            load_config=self.load_config,
+            lora_config=self.lora_config,
+            kv_cache_dtype=self.cache_config.cache_dtype,
+            is_driver_worker=is_driver_worker,
+            vision_language_config=vision_language_config,
+        )
+        # Uninitialized cache engine. Will be initialized by
+        # initialize_cache.
+        self.cache_engine: CacheEngine
+        self.gpu_cache: List[torch.Tensor]
+
+    def init_device(self) -> None:
+        if self.device_config.device.type == "xpu" and is_xpu():
+            self.device = torch.device(f"xpu:{self.local_rank}")
+            torch.xpu.set_device(self.device)
+            torch.xpu.empty_cache()
+            self.init_gpu_memory = torch.xpu.get_device_properties(
+                self.local_rank).total_memory
+        else:
+            raise RuntimeError(
+                f"Not support device type: {self.device_config.device}")
+        # Initialize the distributed environment.
+        self.init_worker_distributed_environment()
+        # Initialize the model.
+        set_random_seed(self.model_config.seed)
+
+    # keep this method for `empty_cache` and `synchronize` api
+    @torch.inference_mode()
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """Profiles the peak memory usage of the model to determine how many
+        KV blocks may be allocated without OOMs.
+
+        The engine will first conduct a profiling of the existing memory usage.
+        Then, it calculate the maximum possible number of GPU and CPU blocks
+        that can be allocated with the remaining free memory.
+
+        .. tip::
+            You may limit the usage of GPU memory
+            by adjusting the `gpu_memory_utilization` parameter.
+        """
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.xpu.empty_cache()
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model.
+        self.model_runner.profile_run()
+
+        # Calculate the number of blocks that can be allocated with the
+        # profiled peak memory.
+        torch.xpu.synchronize()
+        used_memory = torch.xpu.memory_allocated()
+        total_gpu_memory = torch.xpu.get_device_properties(
+            self.local_rank).total_memory
+        free_gpu_memory = total_gpu_memory - used_memory
+
+        # NOTE(woosuk): Here we assume that the other processes using the same
+        # GPU did not change their memory usage during the profiling.
+        peak_memory = self.init_gpu_memory - free_gpu_memory
+        assert peak_memory > 0, (
+            "Error in memory profiling. This happens when the GPU memory was "
+            "not properly cleaned up before initializing the vLLM instance.")
+
+        cache_block_size = self.get_cache_block_size_bytes()
+        num_gpu_blocks = int(
+            (total_gpu_memory * self.cache_config.gpu_memory_utilization -
+             peak_memory) // cache_block_size)
+        num_cpu_blocks = int(self.cache_config.swap_space_bytes //
+                             cache_block_size)
+        num_gpu_blocks = max(num_gpu_blocks, 0)
+        num_cpu_blocks = max(num_cpu_blocks, 0)
+        gc.collect()
+        torch.xpu.empty_cache()
+        return num_gpu_blocks, num_cpu_blocks
+
+    def _warm_up_model(self) -> None:
+        # IPEX don't support capture graph yet
+        pass
+
+    def init_worker_distributed_environment(self) -> None:
+        """Initialize the distributed environment."""
+
+        parallel_config = self.parallel_config
+        rank = self.rank
+        distributed_init_method = self.distributed_init_method
+
+        if torch.distributed.is_initialized():
+            torch_world_size = torch.distributed.get_world_size()
+            if torch_world_size != parallel_config.world_size:
+                raise RuntimeError(
+                    "torch.distributed is already initialized but the torch "
+                    "world size does not match parallel_config.world_size "
+                    f"({torch_world_size} vs. {parallel_config.world_size}).")
+        elif not distributed_init_method:
+            raise ValueError(
+                "distributed_init_method must be set if torch.distributed "
+                "is not already initialized")
+        else:
+            # use sockets as default Level zero IPC exchange backend. By
+            # default oneccl will use `drmfd` as mechanism which need extra
+            # dependency (libdrm and drm headers) on your system.
+            ENV_CCL_ZE_IPC_EXCHANGE = os.getenv("CCL_ZE_IPC_EXCHANGE",
+                                                "sockets")
+            os.environ['CCL_ZE_IPC_EXCHANGE'] = ENV_CCL_ZE_IPC_EXCHANGE
+            init_distributed_environment(
+                world_size=parallel_config.world_size,
+                rank=rank,
+                distributed_init_method=distributed_init_method,
+                local_rank=self.local_rank,
+                backend="ccl")
+
+        ensure_model_parallel_initialized(
+            parallel_config.tensor_parallel_size,
+            parallel_config.pipeline_parallel_size)