[Hardware][Intel-Gaudi] Add Intel Gaudi (HPU) inference backend (#6143)

Signed-off-by: yuwenzho <yuwen.zhou@intel.com>
Signed-off-by: Chendi.Xue <chendi.xue@intel.com>
Signed-off-by: Bob Zhu <bob.zhu@intel.com>
Signed-off-by: zehao-intel <zehao.huang@intel.com>
Signed-off-by: Konrad Zawora <kzawora@habana.ai>
Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>
Co-authored-by: Sanju C Sudhakaran <scsudhakaran@habana.ai>
Co-authored-by: Michal Adamczyk <madamczyk@habana.ai>
Co-authored-by: Marceli Fylcek <mfylcek@habana.ai>
Co-authored-by: Himangshu Lahkar <49579433+hlahkar@users.noreply.github.com>
Co-authored-by: Vivek Goel <vgoel@habana.ai>
Co-authored-by: yuwenzho <yuwen.zhou@intel.com>
Co-authored-by: Dominika Olszewska <dolszewska@habana.ai>
Co-authored-by: barak goldberg <149692267+bgoldberg-habana@users.noreply.github.com>
Co-authored-by: Michal Szutenberg <37601244+szutenberg@users.noreply.github.com>
Co-authored-by: Jan Kaniecki <jkaniecki@habana.ai>
Co-authored-by: Agata Dobrzyniewicz <160237065+adobrzyniewicz-habana@users.noreply.github.com>
Co-authored-by: Krzysztof Wisniewski <kwisniewski@habana.ai>
Co-authored-by: Dudi Lester <160421192+dudilester@users.noreply.github.com>
Co-authored-by: Ilia Taraban <tarabanil@gmail.com>
Co-authored-by: Chendi.Xue <chendi.xue@intel.com>
Co-authored-by: Michał Kuligowski <mkuligowski@habana.ai>
Co-authored-by: Jakub Maksymczuk <jmaksymczuk@habana.ai>
Co-authored-by: Tomasz Zielinski <85164140+tzielinski-habana@users.noreply.github.com>
Co-authored-by: Sun Choi <schoi@habana.ai>
Co-authored-by: Iryna Boiko <iboiko@habana.ai>
Co-authored-by: Bob Zhu <41610754+czhu15@users.noreply.github.com>
Co-authored-by: hlin99 <73271530+hlin99@users.noreply.github.com>
Co-authored-by: Zehao Huang <zehao.huang@intel.com>
Co-authored-by: Andrzej Kotłowski <Andrzej.Kotlowski@intel.com>
Co-authored-by: Yan Tomsinsky <73292515+Yantom1@users.noreply.github.com>
Co-authored-by: Nir David <ndavid@habana.ai>
Co-authored-by: Yu-Zhou <yu.zhou@intel.com>
Co-authored-by: Ruheena Suhani Shaik <rsshaik@habana.ai>
Co-authored-by: Karol Damaszke <kdamaszke@habana.ai>
Co-authored-by: Marcin Swiniarski <mswiniarski@habana.ai>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: Jacek Czaja <jacek.czaja@intel.com>
Co-authored-by: Jacek Czaja <jczaja@habana.ai>
Co-authored-by: Yuan <yuan.zhou@outlook.com>

vllm/model_executor/layers/layernorm.py

@@ -92,6 +92,25 @@ class RMSNorm(CustomOp):
         )
         return out
 
+    def forward_hpu(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        from vllm_hpu_extension.ops import HPUFusedRMSNorm
+        if HPUFusedRMSNorm is None:
+            return self.forward_native(x, residual)
+        if residual is not None:
+            orig_shape = x.shape
+            residual += x.view(residual.shape)
+            # Note: HPUFusedRMSNorm requires 3D tensors as inputs
+            x = HPUFusedRMSNorm.apply(residual, self.weight,
+                                      self.variance_epsilon)
+            return x.view(orig_shape), residual
+
+        x = HPUFusedRMSNorm.apply(x, self.weight, self.variance_epsilon)
+        return x
+
     def forward_xpu(
         self,
         x: torch.Tensor,

vllm/model_executor/layers/logits_processor.py

@@ -111,8 +111,14 @@ def _prune_hidden_states(
     hidden_states: torch.Tensor,
     sampling_metadata: SamplingMetadata,
 ) -> torch.Tensor:
-    return hidden_states.index_select(0,
-                                      sampling_metadata.selected_token_indices)
+    # NOTE(kzawora): The if guard is needed for Gaudi - in some scenarios
+    # (warmup, profile_run) we might not have selected_token_indices,
+    # so we skip pruning.
+    if sampling_metadata.selected_token_indices is not None:
+        return hidden_states.index_select(
+            0, sampling_metadata.selected_token_indices)
+    else:
+        return hidden_states
 
 
 def _apply_logits_processors(

vllm/model_executor/layers/rotary_embedding.py

@@ -194,6 +194,61 @@ class RotaryEmbedding(CustomOp):
                                  self.cos_sin_cache, self.is_neox_style)
         return query, key
 
+    def forward_hpu(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        from habana_frameworks.torch.hpex.kernels import (
+            RotaryPosEmbeddingMode, apply_rotary_pos_emb)
+        positions = positions.flatten()
+        if offsets is not None:
+            positions = positions + offsets
+        num_tokens = positions.shape[0]
+        cos_sin = self.cos_sin_cache.index_select(0, positions).view(
+            num_tokens, 1, -1)
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        # HPU RoPE kernel requires hidden dimension for cos and sin to be equal
+        # to query hidden dimension, so the original tensors need to be
+        # expanded
+        # GPT-NeoX kernel requires position_ids = None, offset, mode = BLOCKWISE
+        # and expansion of cos/sin tensors via concatenation
+        # GPT-J kernel requires position_ids = None, offset = 0, mode = PAIRWISE
+        # and expansion of cos/sin tensors via repeat_interleave
+        rope_mode: RotaryPosEmbeddingMode
+        if self.is_neox_style:
+            rope_mode = RotaryPosEmbeddingMode.BLOCKWISE
+            cos = torch.cat((cos, cos), dim=-1)
+            sin = torch.cat((sin, sin), dim=-1)
+        else:
+            rope_mode = RotaryPosEmbeddingMode.PAIRWISE
+            sin = torch.repeat_interleave(sin,
+                                          2,
+                                          dim=-1,
+                                          output_size=cos_sin.shape[-1])
+            cos = torch.repeat_interleave(cos,
+                                          2,
+                                          dim=-1,
+                                          output_size=cos_sin.shape[-1])
+
+        query_shape = query.shape
+        query = query.view(num_tokens, -1, self.head_size)
+        query_rot = query[..., :self.rotary_dim]
+        query_pass = query[..., self.rotary_dim:]
+        query_rot = apply_rotary_pos_emb(query_rot, cos, sin, None, 0,
+                                         rope_mode)
+        query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
+
+        key_shape = key.shape
+        key = key.view(num_tokens, -1, self.head_size)
+        key_rot = key[..., :self.rotary_dim]
+        key_pass = key[..., self.rotary_dim:]
+        key_rot = apply_rotary_pos_emb(key_rot, cos, sin, None, 0, rope_mode)
+        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
+        return query, key
+
     def extra_repr(self) -> str:
         s = f"head_size={self.head_size}, rotary_dim={self.rotary_dim}"
         s += f", max_position_embeddings={self.max_position_embeddings}"

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -12,6 +12,7 @@ from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase, method_has_implemented_embedding)
 from vllm.model_executor.parameter import BasevLLMParameter
 from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
 
 DEFAULT_VOCAB_PADDING_SIZE = 64
 
@@ -382,6 +383,18 @@ class VocabParallelEmbedding(torch.nn.Module):
 
         # Copy the data.
         loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
+
+        if current_platform.is_hpu():
+            # FIXME(kzawora): Weight copy with slicing bugs out on Gaudi here,
+            # so we're using a workaround. Remove this when fixed in
+            # HPU PT bridge.
+            padded_weight = torch.cat([
+                loaded_weight,
+                torch.zeros(param.shape[0] - loaded_weight.shape[0],
+                            *loaded_weight.shape[1:])
+            ])
+            param.data.copy_(padded_weight)
+        else:
             param[:loaded_weight.shape[0]].data.copy_(loaded_weight)
             param[loaded_weight.shape[0]:].data.fill_(0)
 

vllm/model_executor/sampling_metadata.py

@@ -284,7 +284,8 @@ def _prepare_seq_groups(
         else:
             # Decode
             prompt_logprob_len = 0
-            query_len = query_lens[i] if query_lens is not None else 1
+            query_len = query_lens[i] if query_lens is not None and len(
+                query_lens) > 0 else 1
             sample_len = len(seq_ids) * query_len if do_sample else 0
 
             if sampling_params.seed is not None and generators is not None:

vllm/platforms/__init__.py

@@ -42,6 +42,13 @@ try:
 except Exception:
     pass
 
+is_hpu = False
+try:
+    from importlib import util
+    is_hpu = util.find_spec('habana_frameworks') is not None
+except Exception:
+    pass
+
 is_xpu = False
 
 try:
@@ -86,6 +93,9 @@ elif is_cuda:
 elif is_rocm:
     from .rocm import RocmPlatform
     current_platform = RocmPlatform()
+elif is_hpu:
+    from .hpu import HpuPlatform
+    current_platform = HpuPlatform()
 elif is_xpu:
     from .xpu import XPUPlatform
     current_platform = XPUPlatform()

vllm/platforms/hpu.py

+import torch
+
+from .interface import Platform, PlatformEnum
+
+
+class HpuPlatform(Platform):
+    _enum = PlatformEnum.HPU
+
+    @staticmethod
+    def inference_mode():
+        return torch.no_grad()

vllm/platforms/interface.py

@@ -10,6 +10,7 @@ class PlatformEnum(enum.Enum):
     CUDA = enum.auto()
     ROCM = enum.auto()
     TPU = enum.auto()
+    HPU = enum.auto()
     XPU = enum.auto()
     CPU = enum.auto()
     NEURON = enum.auto()
@@ -46,6 +47,9 @@ class Platform:
     def is_tpu(self) -> bool:
         return self._enum == PlatformEnum.TPU
 
+    def is_hpu(self) -> bool:
+        return self._enum == PlatformEnum.HPU
+
     def is_xpu(self) -> bool:
         return self._enum == PlatformEnum.XPU
 

vllm/utils.py

@@ -728,6 +728,9 @@ def is_pin_memory_available() -> bool:
     elif current_platform.is_neuron():
         print_warning_once("Pin memory is not supported on Neuron.")
         return False
+    elif current_platform.is_hpu():
+        print_warning_once("Pin memory is not supported on HPU.")
+        return False
     elif current_platform.is_cpu() or current_platform.is_openvino():
         return False
     return True

vllm/worker/hpu_worker.py

+###############################################################################
+# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company
+###############################################################################
+
+import gc
+import os
+from typing import List, Optional, Set, Tuple, Type
+
+import habana_frameworks.torch as htorch  # noqa:F401
+import torch
+import torch.distributed
+from vllm_hpu_extension.profiler import HabanaMemoryProfiler, format_bytes
+
+import vllm.envs as envs
+from vllm.config import ParallelConfig, VllmConfig
+from vllm.distributed import (ensure_model_parallel_initialized,
+                              init_distributed_environment)
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.model_executor import set_random_seed
+from vllm.prompt_adapter.request import PromptAdapterRequest
+from vllm.sequence import ExecuteModelRequest
+from vllm.worker.cache_engine import CacheEngine
+from vllm.worker.hpu_model_runner import HPUModelRunner
+from vllm.worker.model_runner_base import ModelRunnerBase
+from vllm.worker.worker_base import (LocalOrDistributedWorkerBase, WorkerBase,
+                                     WorkerInput)
+
+logger = init_logger(__name__)
+
+
+class HPUWorker(LocalOrDistributedWorkerBase):
+    """A worker class that executes (a partition of) the model on a HPU.
+
+    Each worker is associated with a single HPU. The worker is responsible for
+    maintaining the KV cache and executing the model on the HPU. In case of
+    distributed inference, each worker is assigned a partition of the model.
+    """
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        is_driver_worker: bool = False,
+        model_runner_cls: Optional[Type[ModelRunnerBase]] = None,
+    ) -> None:
+        WorkerBase.__init__(self, vllm_config=vllm_config)
+        self.parallel_config.rank = rank
+        self.local_rank = local_rank
+        self.rank = rank
+        self.distributed_init_method = distributed_init_method
+        self.is_driver_worker = is_driver_worker
+        if self.is_driver_worker:
+            assert self.rank == 0, "The driver worker must have rank 0."
+
+        if self.model_config.trust_remote_code:
+            # note: lazy import to avoid importing torch before initializing
+            from vllm.utils import init_cached_hf_modules
+            init_cached_hf_modules()
+
+        self.model_runner: HPUModelRunner = HPUModelRunner(
+            vllm_config=vllm_config, is_driver_worker=is_driver_worker)
+        # Uninitialized cache engine. Will be initialized by
+        # initialize_cache.
+        self.cache_engine: List[HPUCacheEngine]
+        # Initialize gpu_cache as embedding models don't initialize kv_caches
+        self.hpu_cache: Optional[List[List[torch.tensor]]] = None
+        # Torch profiler. Enabled and configured through env vars:
+        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
+        if envs.VLLM_TORCH_PROFILER_DIR:
+            torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
+            logger.info("Profiling enabled. Traces will be saved to: %s",
+                        torch_profiler_trace_dir)
+            self.profiler = torch.profiler.profile(
+                activities=[
+                    torch.profiler.ProfilerActivity.CPU,
+                    torch.profiler.ProfilerActivity.HPU,
+                ],
+                with_stack=True,
+                on_trace_ready=torch.profiler.tensorboard_trace_handler(
+                    torch_profiler_trace_dir, use_gzip=True))
+        else:
+            self.profiler = None
+
+    def start_profile(self):
+        if self.profiler is None:
+            raise RuntimeError("Profiler is not enabled.")
+        self.profiler.start()
+
+    def stop_profile(self):
+        if self.profiler is None:
+            raise RuntimeError("Profiler is not enabled.")
+        self.profiler.stop()
+
+    def _set_env_vars(self):
+        local_rank = self.local_rank
+        if self.parallel_config.world_size == 1:
+            local_rank = -1
+        import os
+        os.environ["LOCAL_RANK"] = str(local_rank)
+        os.environ["ID"] = str(local_rank)
+        os.environ["WORLD_SIZE"] = str(self.parallel_config.world_size)
+        os.environ["RANK"] = str(self.rank)
+
+    def init_device(self) -> None:
+        if self.device_config.device.type == "hpu":
+            self.device = torch.device("hpu")
+            torch.hpu.set_device(self.device)
+        else:
+            raise RuntimeError(
+                f"Not support device type: {self.device_config.device}")
+        # Initialize the distributed environment.
+        if self.model_config.quantization == 'inc':
+            self._set_env_vars()
+        init_worker_distributed_environment(self.parallel_config, self.rank,
+                                            self.distributed_init_method,
+                                            self.local_rank)
+        # Set random seed.
+        set_random_seed(self.model_config.seed)
+
+    def load_model(self):
+        self.model_runner.load_model()
+
+    @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.
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model.
+        with HabanaMemoryProfiler() as m:
+            self.model_runner.profile_run()
+            torch.hpu.synchronize()
+        msg = ("Model profiling run "
+               f"took {m.get_summary_string()}")
+        logger.info(msg)
+        # At this point we should've allocated the maximum workspace for all
+        # recipes we will use the extra memory for graphs/blocks
+        free_hpu_memory = torch.hpu.mem_get_info()[0]
+
+        cache_block_size = self.get_cache_block_size_bytes()
+        graph_reserved_mem = (float(
+            os.environ.get('VLLM_GRAPH_RESERVED_MEM', '0.1'))
+                              if not self.model_config.enforce_eager else 0)
+        graph_headroom = 1 - graph_reserved_mem
+        available_hpu_memory = free_hpu_memory * \
+            self.cache_config.gpu_memory_utilization
+        hpu_memory_margin = free_hpu_memory * (
+            1 - self.cache_config.gpu_memory_utilization)
+        self.model_runner.mem_margin = hpu_memory_margin
+        cache_size_bytes = available_hpu_memory * graph_headroom
+        graph_headroom_bytes = available_hpu_memory * (1 - graph_headroom)
+        msg = (
+            f"Free device memory: {format_bytes(free_hpu_memory)}, "
+            f"{format_bytes(available_hpu_memory)} usable "
+            f"(gpu_memory_utilization={self.cache_config.gpu_memory_utilization}),"
+            f" {format_bytes(graph_headroom_bytes)} reserved for HPUGraphs "
+            f"(VLLM_GRAPH_RESERVED_MEM={graph_reserved_mem}), "
+            f"{format_bytes(cache_size_bytes)} reserved for KV cache")
+        logger.info(msg)
+        num_hpu_blocks = int(cache_size_bytes // cache_block_size)
+        num_cpu_blocks = int(self.cache_config.swap_space_bytes //
+                             cache_block_size)
+        num_hpu_blocks = max(num_hpu_blocks, 0)
+        num_cpu_blocks = max(num_cpu_blocks, 0)
+
+        if self.model_runner.lora_manager:
+            self.model_runner.remove_all_loras()
+
+        gc.collect()
+        return num_hpu_blocks, num_cpu_blocks
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Allocate GPU and CPU KV cache with the specified number of blocks.
+
+        This also warms up the model, which may record CUDA graphs.
+        """
+        raise_if_cache_size_invalid(num_gpu_blocks,
+                                    self.cache_config.block_size,
+                                    self.model_config.max_model_len)
+
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+        with HabanaMemoryProfiler() as m:
+            self._init_cache_engine()
+            torch.hpu.synchronize()
+        msg = ("Initializing cache engine "
+               f"took {m.get_summary_string()}")
+        logger.info(msg)
+        self._warm_up_model()
+
+    def _init_cache_engine(self):
+        assert self.cache_config.num_gpu_blocks is not None
+        self.cache_engine = [
+            HPUCacheEngine(self.cache_config, self.model_config,
+                           self.parallel_config, self.device_config)
+            for _ in range(self.parallel_config.pipeline_parallel_size)
+        ]
+        self.hpu_cache = [
+            self.cache_engine[ve].gpu_cache
+            for ve in range(self.parallel_config.pipeline_parallel_size)
+        ]
+
+    def _warm_up_model(self) -> None:
+        # NOTE(kzawora): We should use virtual engine index here
+        # for pipeline parallelism. Using 0 for now.
+        assert self.hpu_cache is not None
+        self.model_runner.warmup_model(self.hpu_cache[0])
+        # Reset the seed to ensure that the random state is not affected by
+        # the model initialization and profiling.
+        set_random_seed(self.model_config.seed)
+
+    def finish_measurements(self):
+        self.model_runner.finish_measurements()
+
+    @property
+    def do_metadata_broadcast(self) -> bool:
+        return self.parallel_config.tensor_parallel_size > 1
+
+    @property
+    def kv_cache(self) -> Optional[List[List[torch.Tensor]]]:
+        return self.hpu_cache
+
+    @torch.inference_mode()
+    def prepare_worker_input(
+            self, execute_model_req: ExecuteModelRequest) -> WorkerInput:
+        virtual_engine = execute_model_req.virtual_engine
+        num_seq_groups = len(execute_model_req.seq_group_metadata_list)
+        # `blocks_to_swap_in` and `blocks_to_swap_out` are cpu tensors.
+        # they contain parameters to launch cudamemcpyasync.
+        blocks_to_swap_in = torch.tensor(execute_model_req.blocks_to_swap_in,
+                                         device="cpu",
+                                         dtype=torch.int64).view(-1, 2)
+        blocks_to_swap_out = torch.tensor(execute_model_req.blocks_to_swap_out,
+                                          device="cpu",
+                                          dtype=torch.int64).view(-1, 2)
+        # `blocks_to_copy` is a gpu tensor. The src and tgt of
+        # blocks to copy are in the same device, and `blocks_to_copy`
+        # can be used directly within cuda kernels.
+        blocks_to_copy = torch.tensor(execute_model_req.blocks_to_copy,
+                                      device=self.device,
+                                      dtype=torch.int64).view(-1, 2)
+
+        return WorkerInput(
+            num_seq_groups=num_seq_groups,
+            blocks_to_swap_in=blocks_to_swap_in,
+            blocks_to_swap_out=blocks_to_swap_out,
+            blocks_to_copy=blocks_to_copy,
+            virtual_engine=virtual_engine,
+        )
+
+    @torch.inference_mode()
+    def execute_worker(self, worker_input: WorkerInput) -> None:
+        virtual_engine = worker_input.virtual_engine
+        # Issue cache operations.
+        if (worker_input.blocks_to_swap_in is not None
+                and worker_input.blocks_to_swap_in.numel() > 0):
+            self.cache_engine[virtual_engine].swap_in(
+                worker_input.blocks_to_swap_in)
+        if (worker_input.blocks_to_swap_out is not None
+                and worker_input.blocks_to_swap_out.numel() > 0):
+            self.cache_engine[virtual_engine].swap_out(
+                worker_input.blocks_to_swap_out)
+        if (worker_input.blocks_to_copy is not None
+                and worker_input.blocks_to_copy.numel() > 0):
+            self.cache_engine[virtual_engine].copy(worker_input.blocks_to_copy)
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        return self.model_runner.add_lora(lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        return self.model_runner.remove_lora(lora_id)
+
+    def pin_lora(self, lora_id: int) -> bool:
+        return self.model_runner.pin_lora(lora_id)
+
+    def list_loras(self) -> Set[int]:
+        return self.model_runner.list_loras()
+
+    def add_prompt_adapter(
+            self, prompt_adapter_request: PromptAdapterRequest) -> bool:
+        raise NotImplementedError(
+            "Prompt Adapter is not implemented for HPU backend.")
+
+    def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
+        raise NotImplementedError(
+            "Prompt Adapter is not implemented for HPU backend.")
+
+    def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool:
+        raise NotImplementedError(
+            "Prompt Adapter is not implemented for HPU backend.")
+
+    def list_prompt_adapters(self) -> Set[int]:
+        raise NotImplementedError(
+            "Prompt Adapter is not implemented for HPU backend.")
+
+    def shutdown_inc(self):
+        self.model_runner.shutdown_inc()
+
+    @property
+    def max_model_len(self) -> int:
+        return self.model_config.max_model_len
+
+    @property
+    def vocab_size(self) -> int:
+        return self.model_runner.vocab_size
+
+    def get_cache_block_size_bytes(self) -> int:
+        """Get the size of the KV cache block size in bytes.
+        """
+        return HPUCacheEngine.get_cache_block_size(self.cache_config,
+                                                   self.model_config,
+                                                   self.parallel_config)
+
+
+def init_worker_distributed_environment(
+    parallel_config: ParallelConfig,
+    rank: int,
+    distributed_init_method: Optional[str] = None,
+    local_rank: int = -1,
+) -> None:
+    """Initialize the distributed environment."""
+    init_distributed_environment(parallel_config.world_size,
+                                 rank,
+                                 distributed_init_method,
+                                 local_rank,
+                                 backend='hccl')
+
+    ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
+                                      parallel_config.pipeline_parallel_size)
+
+    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:
+        torch.distributed.init_process_group(
+            backend="hccl",
+            world_size=parallel_config.world_size,
+            rank=rank,
+            init_method=distributed_init_method,
+        )
+
+    # A small all_reduce for warmup & checking conformance.
+    dummy_tensor_hpu = torch.ones(1).to('hpu')
+    torch.distributed.all_reduce(dummy_tensor_hpu)
+    assert dummy_tensor_hpu.item() == parallel_config.world_size
+    ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
+                                      parallel_config.pipeline_parallel_size)
+
+
+def raise_if_cache_size_invalid(num_gpu_blocks, block_size,
+                                max_model_len) -> None:
+    if num_gpu_blocks <= 0:
+        raise ValueError("No available memory for the cache blocks. "
+                         "Try increasing `gpu_memory_utilization` when "
+                         "initializing the engine.")
+    max_seq_len = block_size * num_gpu_blocks
+    if max_model_len > max_seq_len:
+        raise ValueError(
+            f"The model's max seq len ({max_model_len}) "
+            "is larger than the maximum number of tokens that can be "
+            f"stored in KV cache ({max_seq_len}). Try increasing "
+            "`gpu_memory_utilization` or decreasing `max_model_len` when "
+            "initializing the engine.")
+
+
+class HPUCacheEngine(CacheEngine):
+
+    def _allocate_kv_cache(
+        self,
+        num_blocks: int,
+        device: str,
+    ) -> List[Tuple[torch.Tensor, torch.Tensor]]:
+        """Allocates KV cache on the specified device."""
+        kv_cache_shape = self.attn_backend.get_kv_cache_shape(
+            num_blocks, self.block_size, self.num_kv_heads, self.head_size)
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]] = []
+        for _ in range(self.num_attention_layers):
+            key_cache = torch.zeros(kv_cache_shape,
+                                    dtype=self.dtype,
+                                    device=device)
+            value_cache = torch.zeros(kv_cache_shape,
+                                      dtype=self.dtype,
+                                      device=device)
+            kv_layer = (key_cache, value_cache)
+            kv_cache.append(kv_layer)
+        return kv_cache