add support for multi-gpu rendering

This commit adds speculative support for parallel rendering across
multiple GPUs. The parallelism is at the level of a session. Each
session is given access to a different GPU. When all GPUs are busy,
execution of the session will block until a GPU becomes available.

The code is untested at the current time, and is being posted for
comment.

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -24,8 +24,10 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
     __threadLimit: BoundedSemaphore
 
     def start(self, invoker: Invoker) -> None:
-        # if we do want multithreading at some point, we could make this configurable
-        self.__threadLimit = BoundedSemaphore(1)
+        # LS - this will probably break
+        # but the idea is to enable multithreading up to the number of available
+        # GPUs. Nodes will block on model loading if no GPU is free.
+        self.__threadLimit = BoundedSemaphore(invoker.services.model_manager.gpu_count)
         self.__invoker = invoker
         self.__stop_event = Event()
         self.__invoker_thread = Thread(

invokeai/app/services/model_load/model_load_base.py

@@ -38,3 +38,8 @@ class ModelLoadServiceBase(ABC):
     @abstractmethod
     def convert_cache(self) -> ModelConvertCacheBase:
         """Return the checkpoint convert cache used by this loader."""
+
+    @property
+    @abstractmethod
+    def gpu_count(self) -> int:
+        """Return the number of GPUs we are configured to use."""

invokeai/app/services/model_load/model_load_default.py

@@ -40,6 +40,7 @@ class ModelLoadService(ModelLoadServiceBase):
         self._registry = registry
 
     def start(self, invoker: Invoker) -> None:
+        """Start the service."""
         self._invoker = invoker
 
     @property
@@ -47,6 +48,11 @@ class ModelLoadService(ModelLoadServiceBase):
         """Return the RAM cache used by this loader."""
         return self._ram_cache
 
+    @property
+    def gpu_count(self) -> int:
+        """Return the number of GPUs available for our uses."""
+        return len(self._ram_cache.execution_devices)
+
     @property
     def convert_cache(self) -> ModelConvertCacheBase:
         """Return the checkpoint convert cache used by this loader."""

invokeai/app/services/model_manager/model_manager_base.py

@@ -98,3 +98,8 @@ class ModelManagerServiceBase(ABC):
         context_data: Optional[InvocationContextData] = None,
     ) -> LoadedModel:
         pass
+
+    @property
+    @abstractmethod
+    def gpu_count(self) -> int:
+        """Return the number of GPUs we are configured to use."""

invokeai/app/services/model_manager/model_manager_default.py

@@ -112,6 +112,11 @@ class ModelManagerService(ModelManagerServiceBase):
         else:
             return self.load.load_model(configs[0], submodel, context_data)
 
+    @property
+    def gpu_count(self) -> int:
+        """Return the number of GPUs we are using."""
+        return self.load.gpu_count
+
     @classmethod
     def build_model_manager(
         cls,

invokeai/backend/model_manager/load/model_cache/model_cache_base.py

@@ -10,7 +10,7 @@ model will be cleared and (re)loaded from disk when next needed.
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from logging import Logger
-from typing import Dict, Generic, Optional, TypeVar
+from typing import Dict, Generic, Optional, Set, TypeVar
 
 import torch
 
@@ -89,8 +89,24 @@ class ModelCacheBase(ABC, Generic[T]):
 
     @property
     @abstractmethod
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
+    def execution_devices(self) -> Set[torch.device]:
+        """Return the set of available execution devices."""
+        pass
+
+    @abstractmethod
+    def acquire_execution_device(self, timeout: int = 0) -> torch.device:
+        """
+        Pick the next available execution device.
+
+        If all devices are currently engaged (locked), then
+        block until timeout seconds have passed and raise a
+        TimeoutError if no devices are available.
+        """
+        pass
+
+    @abstractmethod
+    def release_execution_device(self, device: torch.device) -> None:
+        """Release a previously-acquired execution device."""
         pass
 
     @property

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -25,7 +25,8 @@ import sys
 import time
 from contextlib import suppress
 from logging import Logger
-from typing import Dict, List, Optional
+from threading import BoundedSemaphore, Lock
+from typing import Dict, List, Optional, Set
 
 import torch
 
@@ -61,8 +62,8 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self,
         max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
         max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
-        execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
+        execution_devices: Optional[Set[torch.device]] = None,
         precision: torch.dtype = torch.float16,
         sequential_offload: bool = False,
         lazy_offloading: bool = True,
@@ -74,7 +75,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         Initialize the model RAM cache.
 
         :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
-        :param execution_device: Torch device to load active model into [torch.device('cuda')]
+        :param execution_devices: Set of torch device to load active model into [calculated]
         :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
         :param precision: Precision for loaded models [torch.float16]
         :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
@@ -89,7 +90,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self._precision: torch.dtype = precision
         self._max_cache_size: float = max_cache_size
         self._max_vram_cache_size: float = max_vram_cache_size
-        self._execution_device: torch.device = execution_device
+        self._execution_devices: Set[torch.device] = execution_devices or self._get_execution_devices()
         self._storage_device: torch.device = storage_device
         self._logger = logger or InvokeAILogger.get_logger(self.__class__.__name__)
         self._log_memory_usage = log_memory_usage or self._logger.level == logging.DEBUG
@@ -99,6 +100,10 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self._cached_models: Dict[str, CacheRecord[AnyModel]] = {}
         self._cache_stack: List[str] = []
 
+        self._lock = Lock()
+        self._free_execution_device = BoundedSemaphore(len(self._execution_devices))
+        self._busy_execution_devices: Set[torch.device] = set()
+
     @property
     def logger(self) -> Logger:
         """Return the logger used by the cache."""
@@ -115,9 +120,24 @@ class ModelCache(ModelCacheBase[AnyModel]):
         return self._storage_device
 
     @property
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
-        return self._execution_device
+    def execution_devices(self) -> Set[torch.device]:
+        """Return the set of available execution devices."""
+        return self._execution_devices
+
+    def acquire_execution_device(self, timeout: int = 0) -> torch.device:
+        """Acquire and return an execution device (e.g. "cuda" for VRAM)."""
+        with self._lock:
+            self._free_execution_device.acquire(timeout=timeout)
+            free_devices = self.execution_devices - self._busy_execution_devices
+            chosen_device = list(free_devices)[0]
+            self._busy_execution_devices.add(chosen_device)
+        return chosen_device
+
+    def release_execution_device(self, device: torch.device) -> None:
+        """Mark this execution device as unused."""
+        with self._lock:
+            self._free_execution_device.release()
+            self._busy_execution_devices.remove(device)
 
     @property
     def max_cache_size(self) -> float:
@@ -405,3 +425,13 @@ class ModelCache(ModelCacheBase[AnyModel]):
             mps.empty_cache()
 
         self.logger.debug(f"After making room: cached_models={len(self._cached_models)}")
+
+    @staticmethod
+    def _get_execution_devices() -> Set[torch.device]:
+        default_device = choose_torch_device()
+        if default_device != torch.device("cuda"):
+            return {default_device}
+
+        # we get here if the default device is cuda, and return each of the
+        # cuda devices.
+        return {torch.device(f"cuda:{x}") for x in range(0, torch.cuda.device_count())}

invokeai/backend/model_manager/load/model_cache/model_locker.py

@@ -2,10 +2,16 @@
 Base class and implementation of a class that moves models in and out of VRAM.
 """
 
+from typing import Optional
+
+import torch
+
 from invokeai.backend.model_manager import AnyModel
 
 from .model_cache_base import CacheRecord, ModelCacheBase, ModelLockerBase
 
+MAX_GPU_WAIT = 600  # wait up to 10 minutes for a GPU to become free
+
 
 class ModelLocker(ModelLockerBase):
     """Internal class that mediates movement in and out of GPU."""
@@ -19,6 +25,7 @@ class ModelLocker(ModelLockerBase):
         """
         self._cache = cache
         self._cache_entry = cache_entry
+        self._execution_device: Optional[torch.device] = None
 
     @property
     def model(self) -> AnyModel:
@@ -37,10 +44,12 @@ class ModelLocker(ModelLockerBase):
             if self._cache.lazy_offloading:
                 self._cache.offload_unlocked_models(self._cache_entry.size)
 
-            self._cache.move_model_to_device(self._cache_entry, self._cache.execution_device)
+            # We wait for a gpu to be free - may raise a TimeoutError
+            self._execution_device = self._cache.acquire_execution_device(MAX_GPU_WAIT)
+            self._cache.move_model_to_device(self._cache_entry, self._execution_device)
             self._cache_entry.loaded = True
 
-            self._cache.logger.debug(f"Locking {self._cache_entry.key} in {self._cache.execution_device}")
+            self._cache.logger.debug(f"Locking {self._cache_entry.key} in {self._execution_device}")
             self._cache.print_cuda_stats()
 
         except Exception:
@@ -54,6 +63,8 @@ class ModelLocker(ModelLockerBase):
             return
 
         self._cache_entry.unlock()
+        if self._execution_device:
+            self._cache.release_execution_device(self._execution_device)
         if not self._cache.lazy_offloading:
             self._cache.offload_unlocked_models(self._cache_entry.size)
             self._cache.print_cuda_stats()

tests/aa_nodes/test_invoker.py

@@ -65,7 +65,7 @@ def mock_services() -> InvocationServices:
         images=None,  # type: ignore
         invocation_cache=MemoryInvocationCache(max_cache_size=0),
         logger=logging,  # type: ignore
-        model_manager=Mock(),  # type: ignore
+        model_manager=Mock(gpu_count=1),  # type: ignore
         download_queue=None,  # type: ignore
         names=None,  # type: ignore
         performance_statistics=InvocationStatsService(),

tests/backend/model_manager/model_manager_fixtures.py

@@ -112,7 +112,7 @@ def mm2_metadata_store(mm2_record_store: ModelRecordServiceSQL) -> ModelMetadata
 
 
 @pytest.fixture
-def mm2_loader(mm2_app_config: InvokeAIAppConfig, mm2_record_store: ModelRecordServiceBase) -> ModelLoadServiceBase:
+def mm2_loader(mm2_app_config: InvokeAIAppConfig) -> ModelLoadServiceBase:
     ram_cache = ModelCache(
         logger=InvokeAILogger.get_logger(),
         max_cache_size=mm2_app_config.ram_cache_size,