Add fake HPU mode to Habana components with dummy habana_frameworks m…

…odule. (HabanaAI#250)

Co-authored-by: Konrad Zawora <kzawora@habana.ai>

.github/workflows/cpu-test.yml

@@ -0,0 +1,34 @@
+name: cpu-test
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the habana_main branch
+  push:
+    branches:
+      - habana_main
+  pull_request:
+    branches:
+      - habana_main
+
+
+jobs:
+  cputest:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements-hpu.txt
+        VLLM_TARGET_DEVICE=hpu python setup.py develop
+    - name: cpu-test
+      run: |
+        VLLM_SKIP_WARMUP=true VLLM_PROMPT_SEQ_BUCKET_MAX=128 VLLM_USE_FAKE_HPU=1 python examples/offline_inference_fakehpu.py

examples/offline_inference_fakehpu.py

@@ -0,0 +1,38 @@
+import os
+
+from vllm import LLM, SamplingParams
+
+if os.environ.get('VLLM_USE_FAKE_HPU', '0') != '0':
+    from vllm.utils import migrate_to_cpu
+    migrate_to_cpu()
+
+# Sample prompts.
+prompts = [
+    "Berlin is the capital city of ",
+    "Louvre is located in the city of ",
+    "Barack Obama was the 44th president of ",
+    "Warsaw is the capital city of ",
+    "Gniezno is a city in ",
+    "San Francisco is located in the state of ",
+    "Llanfairpwllgwyngyll is located in country of ",
+]
+ref_answers = [
+    "Germany", "Paris", "United States", "Poland", "Poland", "California",
+    "Wales"
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0, n=1, use_beam_search=False)
+
+# Create an LLM.
+llm = LLM(model="facebook/opt-125m", max_model_len=32, max_num_seqs=4)
+# Generate texts from the prompts. The output is a list of RequestOutput objects
+# that contain the prompt, generated text, and other information.
+outputs = llm.generate(prompts, sampling_params)
+# Print the outputs.
+for output, answer in zip(outputs, ref_answers):
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    assert answer in generated_text, (
+        f"The generated text does not contain the correct answer: {answer}")
+print('PASSED')

vllm/__init__.py

@@ -1,4 +1,8 @@
 """vLLM: a high-throughput and memory-efficient inference engine for LLMs"""
+from vllm.utils import is_fake_hpu, migrate_to_cpu
+
+if is_fake_hpu():
+    migrate_to_cpu()
 
 from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine

vllm/executor/ray_habana_executor.py

@@ -13,7 +13,7 @@
 from vllm.utils import (_run_task_with_lock,
                         error_on_invalid_device_count_status,
                         get_distributed_init_method, get_ip, get_open_port,
-                        get_vllm_instance_id, make_async)
+                        get_vllm_instance_id, is_fake_hpu, make_async)
 
 if ray is not None:
     from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
@@ -87,18 +87,20 @@ def _init_workers_ray(self, placement_group: "PlacementGroup",
         driver_ip = get_ip()
         worker_wrapper_kwargs = self._get_worker_wrapper_args()
         for bundle_id, bundle in enumerate(placement_group.bundle_specs):
-            if not bundle.get("HPU", 0):
+            resource_name = "HPU" if not is_fake_hpu() else "CPU"
+            if not bundle.get(resource_name, 0):
                 continue
             scheduling_strategy = PlacementGroupSchedulingStrategy(
                 placement_group=placement_group,
                 placement_group_capture_child_tasks=True,
                 placement_group_bundle_index=bundle_id,
             )
-
+            resources = {'HPU': num_gpus} if not is_fake_hpu() else {}
+            num_cpus = 0 if not is_fake_hpu() else num_gpus
             worker = ray.remote(
-                num_cpus=0,
+                num_cpus=num_cpus,
                 num_gpus=0,
-                resources={'HPU': num_gpus},
+                resources=resources,
                 scheduling_strategy=scheduling_strategy,
                 **ray_remote_kwargs,
             )(RayWorkerWrapper).remote(**worker_wrapper_kwargs)

vllm/executor/ray_utils.py

@@ -3,7 +3,8 @@
 from vllm.config import ParallelConfig
 from vllm.logger import init_logger
 from vllm.sequence import ExecuteModelRequest
-from vllm.utils import get_ip, is_hip, is_hpu, is_tpu, is_xpu
+from vllm.utils import (get_ip, hpu_device_string, is_hip, is_hpu, is_tpu,
+                        is_xpu)
 from vllm.worker.worker_base import WorkerWrapperBase
 
 logger = init_logger(__name__)
@@ -97,7 +98,7 @@ def initialize_ray_cluster(
     if is_tpu():
         device_str = "TPU"
     elif is_hpu():
-        device_str = "HPU"
+        device_str = hpu_device_string()
     # Create placement group for worker processes
     current_placement_group = ray.util.get_current_placement_group()
     if current_placement_group:

vllm/model_executor/model_loader/loader.py

@@ -37,7 +37,7 @@
                                                    supports_vision)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
-from vllm.utils import is_hpu, is_tpu
+from vllm.utils import is_fake_hpu, is_hpu, is_tpu
 
 logger = init_logger(__name__)
 
@@ -277,7 +277,10 @@ def load_model(self, *, model_config: ModelConfig,
                    scheduler_config: SchedulerConfig,
                    cache_config: CacheConfig) -> nn.Module:
         with set_default_torch_dtype(model_config.dtype):
-            with torch.device(self.load_config.device):
+            _device = torch.device(
+                device_config.device) if is_fake_hpu() else torch.device(
+                    self.load_config.device)
+            with _device:
                 model = _initialize_model(model_config, self.load_config,
                                           lora_config, multimodal_config,
                                           cache_config, scheduler_config)

vllm/model_executor/models/opt.py

@@ -254,7 +254,6 @@ def forward(
         if self.project_in is not None:
             inputs_embeds, _ = self.project_in(inputs_embeds)
         hidden_states = inputs_embeds + pos_embeds
-
         for i in range(len(self.layers)):
             layer = self.layers[i]
             hidden_states = layer(hidden_states, kv_caches[i], attn_metadata)

vllm/utils.py

@@ -208,10 +208,41 @@ def is_neuron() -> bool:
 
 @lru_cache(maxsize=None)
 def is_hpu() -> bool:
+    return _is_habana_frameworks_installed() or _is_built_for_hpu()
+
+
+@lru_cache(maxsize=None)
+def is_fake_hpu() -> bool:
+    return os.environ.get('VLLM_USE_FAKE_HPU', '0') != '0'
+
+
+@lru_cache(maxsize=None)
+def hpu_device_string():
+    device_string = 'hpu' if not is_fake_hpu() else 'cpu'
+    return device_string
+
+
+@lru_cache(maxsize=None)
+def hpu_backend_string():
+    backend_string = 'hccl' if not is_fake_hpu() else 'gloo'
+    return backend_string
+
+
+@lru_cache(maxsize=None)
+def _is_habana_frameworks_installed() -> bool:
     from importlib import util
     return util.find_spec('habana_frameworks') is not None
 
 
+@lru_cache(maxsize=None)
+def _is_built_for_hpu() -> bool:
+    from importlib.metadata import PackageNotFoundError, version
+    try:
+        return "gaudi" in version("vllm")
+    except PackageNotFoundError:
+        return False
+
+
 @lru_cache(maxsize=None)
 def is_tpu() -> bool:
     try:
@@ -624,18 +655,24 @@ def __init__(self, device=None):
 
     @staticmethod
     def current_device_memory_usage() -> float:
+        if is_fake_hpu():
+            return 0
         # Return the device memory usage in bytes.
         free_hpu_memory, total_hpu_memory = torch.hpu.mem_get_info()
         return total_hpu_memory - free_hpu_memory
 
     @staticmethod
     def current_free_device_memory() -> float:
+        if is_fake_hpu():
+            return 0
         # Return the device memory usage in bytes.
         free_hpu_memory, _ = torch.hpu.mem_get_info()
         return free_hpu_memory
 
     @staticmethod
     def total_device_memory() -> float:
+        if is_fake_hpu():
+            return 0
         # Return the device memory usage in bytes.
         _, total_hpu_memory = torch.hpu.mem_get_info()
         return total_hpu_memory
@@ -1088,3 +1125,29 @@ async def _run_task_with_lock(task: Callable, lock: asyncio.Lock, *args,
     """Utility function to run async task in a lock"""
     async with lock:
         return await task(*args, **kwargs)
+
+
+def migrate_to_cpu():
+    import importlib
+    from unittest.mock import MagicMock
+
+    torch.hpu = MagicMock(name="torch.hpu")
+
+    # Adding dummy submodules to habana_frameworks.torch for cpu-test,
+    # functions from dummy modules will do nothing by default
+    spec = importlib.util.spec_from_loader('habana_frameworks', loader=None)
+    sys.modules['habana_frameworks'] = MagicMock()
+    sys.modules['habana_frameworks'].__spec__ = spec
+
+    builtin_import = __builtins__['__import__']  # type: ignore
+
+    def import_wrapper(name, *args, **kwargs):
+        if 'habana_frameworks' in name:
+            sys.modules[name] = MagicMock()
+        return builtin_import(name, *args, **kwargs)
+
+    __builtins__['__import__'] = import_wrapper
+
+    # In case you want to mock a function to actually do something
+    import habana_frameworks.torch as htorch
+    htorch.utils.internal.is_lazy.return_value = False

vllm/worker/cache_engine.py

@@ -6,7 +6,7 @@
 from vllm.attention import get_attn_backend
 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,
+from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, get_dtype_size, is_fake_hpu,
                         is_pin_memory_available)
 
 logger = init_logger(__name__)
@@ -78,7 +78,7 @@ def _allocate_kv_cache(
         pin_memory = is_pin_memory_available() if device == "cpu" else False
         kv_cache: List[torch.Tensor] = []
         for _ in range(self.num_attention_layers):
-            if device == 'hpu':
+            if device == 'hpu' or is_fake_hpu():
                 key_cache = torch.zeros(kv_cache_shape,
                                         dtype=self.dtype,
                                         device=device)

vllm/worker/habana_model_runner.py

@@ -37,7 +37,7 @@
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import (IntermediateTensors, SamplerOutput, SequenceData,
                            SequenceGroupMetadata)
-from vllm.utils import (HabanaMemoryProfiler, format_bytes,
+from vllm.utils import (HabanaMemoryProfiler, format_bytes, is_fake_hpu,
                         is_pin_memory_available, make_tensor_with_pad)
 from vllm.worker.model_runner_base import (
     ModelRunnerBase, ModelRunnerInputBase,
@@ -246,7 +246,8 @@ def __init__(self, model, block_size, dtype, enforce_eager):
                                                '0').lower() in ['1', 'true']
         self.block_size = block_size
         self.dtype = dtype
-        if not htorch.utils.internal.is_lazy() and not enforce_eager:
+        if not is_fake_hpu() and not htorch.utils.internal.is_lazy(
+        ) and not enforce_eager:
             self.model = torch.compile(self.model,
                                        backend='hpu_backend',
                                        dynamic=False)
@@ -509,7 +510,9 @@ def __init__(
                                if model_config is not None else None)
         self.device_config = (device_config
                               if device_config is not None else DeviceConfig())
-
+        if is_fake_hpu():
+            device_config.device = torch.device('cpu')
+            device_config.device_type = 'cpu'
         self.device = self.device_config.device
         self.enforce_eager = self.model_config.enforce_eager
         self.max_num_seqs = self.scheduler_config.max_num_seqs
@@ -618,7 +621,7 @@ def load_model(self) -> None:
                                           mark_only_scales_as_const=True)
                 logger.info("Preparing model with INC took %s",
                             m_inc.get_summary_string())
-            else:
+            elif not is_fake_hpu():
                 self.model = self.model.to("hpu")
                 htcore.mark_step()
             torch.hpu.synchronize()

vllm/worker/habana_worker.py

@@ -21,7 +21,8 @@
 from vllm.model_executor import set_random_seed
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sequence import ExecuteModelRequest
-from vllm.utils import HabanaMemoryProfiler, format_bytes
+from vllm.utils import (HabanaMemoryProfiler, format_bytes, hpu_backend_string,
+                        hpu_device_string, is_fake_hpu)
 from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.habana_model_runner import HabanaModelRunner
 from vllm.worker.worker_base import LocalOrDistributedWorkerBase, WorkerInput
@@ -105,6 +106,8 @@ def init_device(self) -> None:
         if self.device_config.device.type == "hpu":
             self.device = torch.device("hpu")
             torch.hpu.set_device(self.device)
+        elif self.device_config.device_type == "cpu":
+            self.device = torch.device("cpu")
         else:
             raise RuntimeError(
                 f"Not support device type: {self.device_config.device}")
@@ -138,6 +141,10 @@ def determine_num_available_blocks(self) -> Tuple[int, int]:
 
         # Execute a forward pass with dummy inputs to profile the memory usage
         # of the model.
+        if is_fake_hpu():
+            cache_block_size = self.get_cache_block_size_bytes()
+            fake_hpu_cache_alloc = 4 * 2**30  # take 4 GiB flat on fake hpu
+            return fake_hpu_cache_alloc // cache_block_size, 0
         with HabanaMemoryProfiler() as m:
             self.model_runner.profile_run()
             torch.hpu.synchronize()
@@ -335,11 +342,12 @@ def init_worker_distributed_environment(
     local_rank: int = -1,
 ) -> None:
     """Initialize the distributed environment."""
+    backend = hpu_backend_string()
     init_distributed_environment(parallel_config.world_size,
                                  rank,
                                  distributed_init_method,
                                  local_rank,
-                                 backend='hccl')
+                                 backend=backend)
 
     ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
                                       parallel_config.pipeline_parallel_size)
@@ -356,15 +364,17 @@ def init_worker_distributed_environment(
             "distributed_init_method must be set if torch.distributed "
             "is not already initialized")
     else:
+        backend = hpu_backend_string()
         torch.distributed.init_process_group(
-            backend="hccl",
+            backend=backend,
             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')
+    device = hpu_device_string()
+    dummy_tensor_hpu = torch.ones(1).to(device)
     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,