fix issues from comments, add more unit tests

py/torch_tensorrt/dynamo/_compiler.py

@@ -86,8 +86,8 @@ def compile(
     lazy_engine_init: bool = _defaults.LAZY_ENGINE_INIT,
     cache_built_engines: bool = _defaults.CACHE_BUILT_ENGINES,
     reuse_cached_engines: bool = _defaults.REUSE_CACHED_ENGINES,
-    engine_cache_dir: str = _defaults.ENGINE_CACHE_DIR,
-    engine_cache_size: int = _defaults.ENGINE_CACHE_SIZE,
+    engine_cache_dir: Optional[str] = _defaults.ENGINE_CACHE_DIR,
+    engine_cache_size: Optional[int] = _defaults.ENGINE_CACHE_SIZE,
     custom_engine_cache: Optional[BaseEngineCache] = _defaults.CUSTOM_ENGINE_CACHE,
     **kwargs: Any,
 ) -> torch.fx.GraphModule:
@@ -156,8 +156,8 @@ def compile(
         lazy_engine_init (bool): Defer setting up engines until the compilation of all engines is complete. Can allow larger models with multiple graph breaks to compile but can lead to oversubscription of GPU memory at runtime.
         cache_built_engines (bool): Whether to save the compiled TRT engines to storage
         reuse_cached_engines (bool): Whether to load the compiled TRT engines from storage
-        engine_cache_dir (str): Directory to store the cached TRT engines
-        engine_cache_size (int): Maximum hard-disk space to use for the engine cache
+        engine_cache_dir (Optional[str]): Directory to store the cached TRT engines
+        engine_cache_size (Optional[int]): Maximum hard-disk space (bytes) to use for the engine cache, default is 1GB. If the cache exceeds this size, the oldest engines will be removed by default
         custom_engine_cache (Optional[BaseEngineCache]): Engine cache instance to use for saving and loading engines. Users can provide their own engine cache by inheriting from BaseEngineCache. If used, engine_cache_dir and engine_cache_size will be ignored.
         **kwargs: Any,
     Returns:
@@ -235,12 +235,16 @@ def compile(
     gm = post_lowering(gm)
     logger.debug("Lowered Input graph: " + str(gm.graph))
 
+    engine_cache = None
     if cache_built_engines or reuse_cached_engines:
         assert (
             make_refitable
         ), "Engine caching requires make_refitable to be set to True"
-        if custom_engine_cache is None:
-            custom_engine_cache = DiskEngineCache(engine_cache_dir, engine_cache_size)
+        engine_cache = (
+            custom_engine_cache
+            if custom_engine_cache is not None
+            else DiskEngineCache(engine_cache_dir, engine_cache_size)
+        )
 
     compilation_options = {
         "enabled_precisions": (
@@ -277,12 +281,13 @@ def compile(
         "lazy_engine_init": lazy_engine_init,
         "cache_built_engines": cache_built_engines,
         "reuse_cached_engines": reuse_cached_engines,
-        "custom_engine_cache": custom_engine_cache,
     }
 
     settings = CompilationSettings(**compilation_options)
     logger.info("Compilation Settings: %s\n", settings)
-    trt_gm = compile_module(gm, trt_arg_inputs, trt_kwarg_inputs, settings)
+    trt_gm = compile_module(
+        gm, trt_arg_inputs, trt_kwarg_inputs, settings, engine_cache
+    )
     return trt_gm
 
 
@@ -291,6 +296,7 @@ def compile_module(
     sample_arg_inputs: Sequence[Input],
     sample_kwarg_inputs: Optional[dict[Any, Any]] = None,
     settings: CompilationSettings = CompilationSettings(),
+    engine_cache: Optional[BaseEngineCache] = None,
 ) -> torch.fx.GraphModule:
     """Compile a traced FX module
 
@@ -301,6 +307,7 @@ def compile_module(
         arg_inputs: Inputs to the module
         kwarg_inputs: kwargs to the module
         settings: Compilation settings
+        engine_cache: Engine cache instance to store/load compiled engines
     Returns:
         Compiled FX GraphModule
     """
@@ -480,6 +487,7 @@ def contains_metadata(gm: torch.fx.GraphModule) -> bool:
                 submodule_inputs,
                 settings=settings,
                 name=name,
+                engine_cache=engine_cache,
             )
 
             trt_modules[name] = trt_module

py/torch_tensorrt/dynamo/_settings.py

@@ -7,7 +7,6 @@
 from torch_tensorrt.dynamo._defaults import (
     ASSUME_DYNAMIC_SHAPE_SUPPORT,
     CACHE_BUILT_ENGINES,
-    CUSTOM_ENGINE_CACHE,
     DEBUG,
     DISABLE_TF32,
     DLA_GLOBAL_DRAM_SIZE,
@@ -36,7 +35,6 @@
     WORKSPACE_SIZE,
     default_device,
 )
-from torch_tensorrt.dynamo._engine_caching import BaseEngineCache
 
 
 @dataclass
@@ -80,7 +78,6 @@ class CompilationSettings:
         timing_cache_path (str): Path to the timing cache if it exists (or) where it will be saved after compilation
         cache_built_engines (bool): Whether to save the compiled TRT engines to storage
         reuse_cached_engines (bool): Whether to load the compiled TRT engines from storage
-        custom_engine_cache (Optional[BaseEngineCache]): Engine cache instance to use for saving and loading engines. Users can provide their own engine cache by inheriting from BaseEngineCache
     """
 
     enabled_precisions: Set[dtype] = field(default_factory=lambda: ENABLED_PRECISIONS)
@@ -115,4 +112,3 @@ class CompilationSettings:
     lazy_engine_init: bool = LAZY_ENGINE_INIT
     cache_built_engines: bool = CACHE_BUILT_ENGINES
     reuse_cached_engines: bool = REUSE_CACHED_ENGINES
-    custom_engine_cache: Optional[BaseEngineCache] = CUSTOM_ENGINE_CACHE

py/torch_tensorrt/dynamo/backend/backends.py

@@ -48,21 +48,23 @@ def torch_tensorrt_backend(
 def aot_torch_tensorrt_aten_backend(
     gm: torch.fx.GraphModule, sample_inputs: Sequence[Any], **kwargs: Any
 ) -> torch.nn.Module:
-    settings = parse_dynamo_kwargs(kwargs)
-    return _pretraced_backend(gm, sample_inputs, settings)
+    settings, engine_cache = parse_dynamo_kwargs(kwargs)
+    return _pretraced_backend(gm, sample_inputs, settings, engine_cache)
 
 
 def _pretraced_backend(
     gm: torch.fx.GraphModule,
     sample_inputs: Sequence[Any],
     settings: CompilationSettings = CompilationSettings(),
+    engine_cache: Any = None,
 ) -> torch.fx.GraphModule | Callable[..., Any]:
     """Helper function to manage translation of traced FX module to TRT engines
 
     Args:
         module: FX GraphModule to convert
         inputs: Inputs to the module
         settings: Compilation settings
+        engine_cache: Engine cache instance
     Returns:
         Compiled FX GraphModule
     """
@@ -109,6 +111,7 @@ def _pretraced_backend(
                 gm,
                 torchtrt_inputs,
                 settings=settings,
+                engine_cache=engine_cache,
             )
             return trt_compiled
     except (AssertionError, RuntimeError):

py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py

@@ -27,6 +27,7 @@
 from torch_tensorrt._enums import dtype
 from torch_tensorrt._Input import Input
 from torch_tensorrt.dynamo import _defaults
+from torch_tensorrt.dynamo._engine_caching import BaseEngineCache
 from torch_tensorrt.dynamo._settings import CompilationSettings
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
 from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
@@ -71,6 +72,7 @@ def __init__(
         logger_level: trt.ILogger.Severity = trt.ILogger.Severity.WARNING,
         output_dtypes: Optional[Sequence[dtype]] = None,
         compilation_settings: CompilationSettings = CompilationSettings(),
+        engine_cache: Optional[BaseEngineCache] = None,
     ):
         super().__init__(module)
 
@@ -126,6 +128,9 @@ def __init__(
         self.const_mapping: Dict[str, Tuple[Sequence[int], str]] = {}
         self.weight_name_map: Optional[dict[str, Any]] = None
 
+        # Engine cache for storing and reusing TRT engines
+        self.engine_cache = engine_cache
+
     def validate_conversion(self) -> Set[str]:
         missing_converters: Set[str] = set()
 
@@ -521,22 +526,22 @@ def run(
         Return:
             TRTInterpreterResult
         """
-        if (
-            self.compilation_settings.custom_engine_cache is not None
-        ):  # custom_engine_cache could be None if this function is called from convert_exported_program_to_serialized_trt_engine etc.
+        # self.engine_cache could be None if:
+        # 1) engine_cache is not passed in when calling this function like convert_exported_program_to_serialized_trt_engine etc., or
+        # 2) both cache_built_engines and reuse_cached_engines are False
+        if self.engine_cache is not None:
             if (
                 self.compilation_settings.cache_built_engines
                 or self.compilation_settings.reuse_cached_engines
             ):
-                engine_cache = self.compilation_settings.custom_engine_cache
-                hash_val = engine_cache.get_hash(self.module)
+                hash_val = self.engine_cache.get_hash(self.module)
 
             if self.compilation_settings.reuse_cached_engines:
                 # query the cached TRT engine
-                blob = engine_cache.load(hash_val)
+                blob = self.engine_cache.load(hash_val)
                 if blob is not None:  # hit the cache
                     serialized_engine, input_names, output_names, weight_name_map = (
-                        engine_cache.unpack(blob)
+                        self.engine_cache.unpack(blob)
                     )
                     self._input_names = input_names
                     self._output_names = output_names
@@ -605,16 +610,16 @@ def run(
             builder_config, self.compilation_settings.timing_cache_path
         )
         if (
-            self.compilation_settings.custom_engine_cache is not None
+            self.engine_cache is not None
             and self.compilation_settings.cache_built_engines
         ):
-            blob = engine_cache.pack(
+            blob = self.engine_cache.pack(
                 serialized_engine,
                 self._input_names,
                 self._output_names,
                 self.weight_name_map,
             )
-            engine_cache.save(hash_val, blob)
+            self.engine_cache.save(hash_val, blob)
 
         with io.BytesIO() as engine_bytes:
             engine_bytes.write(serialized_engine)

py/torch_tensorrt/dynamo/conversion/_conversion.py

@@ -10,6 +10,7 @@
 from torch_tensorrt._enums import dtype
 from torch_tensorrt._features import ENABLED_FEATURES
 from torch_tensorrt._Input import Input
+from torch_tensorrt.dynamo._engine_caching import BaseEngineCache
 from torch_tensorrt.dynamo._settings import CompilationSettings
 from torch_tensorrt.dynamo.conversion._TRTInterpreter import (
     TRTInterpreter,
@@ -70,6 +71,7 @@ def interpret_module_to_result(
     settings: CompilationSettings = CompilationSettings(),
     arg_inputs: Optional[Sequence[Input]] = None,
     kwarg_inputs: Optional[dict[str, Any]] = None,
+    engine_cache: Optional[BaseEngineCache] = None,
 ) -> TRTInterpreterResult:
     """Interpret an FX module to a TRTInterpreterResult
     Args:
@@ -79,6 +81,7 @@ def interpret_module_to_result(
         arg_inputs: Sequence of Tensors representing inputs to the module.
         kwarg_inputs: A dictionary of Tensors representing inputs to the module.
         settings: Compilation settings
+        engine_cache: Engine cache instance
     Returns:
         TRTInterpreterResult
     """
@@ -105,6 +108,7 @@ def interpret_module_to_result(
         logger_level=(trt.Logger.VERBOSE if settings.debug else trt.Logger.WARNING),
         output_dtypes=output_dtypes,
         compilation_settings=settings,
+        engine_cache=engine_cache,
     )
     interpreter_result = interpreter.run()
     return interpreter_result
@@ -115,17 +119,21 @@ def convert_module(
     inputs: Sequence[Input],
     settings: CompilationSettings = CompilationSettings(),
     name: str = "",
+    engine_cache: Optional[BaseEngineCache] = None,
 ) -> PythonTorchTensorRTModule | TorchTensorRTModule:
     """Convert an FX module to a TRT module
     Args:
         module: FX GraphModule to convert
         inputs: Sequence of Tensors representing inputs to the module
         settings: Compilation settings
         name: TRT engine name
+        engine_cache: Engine cache instance
     Returns:
         PythonTorchTensorRTModule or TorchTensorRTModule
     """
-    interpreter_result = interpret_module_to_result(module, inputs, settings)
+    interpreter_result = interpret_module_to_result(
+        module, inputs, settings, engine_cache=engine_cache
+    )
 
     rt_cls = PythonTorchTensorRTModule
 

py/torch_tensorrt/dynamo/utils.py

@@ -3,7 +3,7 @@
 import logging
 from dataclasses import fields, replace
 from enum import Enum
-from typing import Any, Callable, Dict, Optional, Sequence, Union
+from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import tensorrt as trt
@@ -12,6 +12,7 @@
 from torch_tensorrt._enums import dtype
 from torch_tensorrt._Input import Input
 from torch_tensorrt.dynamo import _defaults
+from torch_tensorrt.dynamo._engine_caching import BaseEngineCache
 from torch_tensorrt.dynamo._settings import CompilationSettings
 
 from packaging import version
@@ -301,7 +302,9 @@ def to_torch_tensorrt_device(
     return Device._from(device)
 
 
-def parse_dynamo_kwargs(kwargs: Any) -> CompilationSettings:
+def parse_dynamo_kwargs(
+    kwargs: Any,
+) -> Tuple[CompilationSettings, Optional[BaseEngineCache]]:
     """Parses the kwargs field of a Dynamo backend
 
     Args:
@@ -360,11 +363,15 @@ def parse_dynamo_kwargs(kwargs: Any) -> CompilationSettings:
 
     # If cache_built_engines and reuse_cached_engines are True but custom_engine_cache is not provided,
     # then create a default disk engine cache
+    engine_cache = None
     if kwargs.get("cache_built_engines") or kwargs.get("reuse_cached_engines"):
         assert kwargs.get(
             "make_refitable"
         ), "Engine caching requires make_refitable to be set to True"
-        if settings.custom_engine_cache is None:
+
+        if kwargs.get("custom_engine_cache") is not None:
+            engine_cache = kwargs.get("custom_engine_cache")
+        else:
             from torch_tensorrt.dynamo._engine_caching import DiskEngineCache
 
             engine_cache_dir = kwargs.get(
@@ -373,13 +380,11 @@ def parse_dynamo_kwargs(kwargs: Any) -> CompilationSettings:
             engine_cache_size = kwargs.get(
                 "engine_cache_size", _defaults.ENGINE_CACHE_SIZE
             )
-            settings.custom_engine_cache = DiskEngineCache(
-                engine_cache_dir, engine_cache_size
-            )
+            engine_cache = DiskEngineCache(engine_cache_dir, engine_cache_size)
 
     logger.info("Compilation Settings: %s\n", settings)
 
-    return settings
+    return settings, engine_cache
 
 
 def req_torch_version(min_torch_version: str = "2.dev") -> Callable[..., Any]: