fix: Adding shape distingushing to the engine cache

examples/dynamo/engine_caching_bert_example.py

@@ -52,7 +52,7 @@ def compile_bert(iterations=3):
             "truncate_double": True,
             "debug": False,
             "min_block_size": 1,
-            "make_refitable": True,
+            "make_refittable": True,
             "cache_built_engines": cache_built_engines,
             "reuse_cached_engines": reuse_cached_engines,
             "engine_cache_dir": "/tmp/torch_trt_bert_engine_cache",

examples/dynamo/engine_caching_example.py

@@ -63,7 +63,7 @@ def remove_timing_cache(path=TIMING_CACHE_PATH):
 # in a subsequent compilation, either as part of this session or a new session, the cache will
 # pull the built engine and **refit** the weights which can reduce compilation times by orders of magnitude.
 # As such, in order to insert a new engine into the cache (i.e. ``cache_built_engines=True``),
-# the engine must be refitable (``make_refittable=True``). See :ref:`refit_engine_example` for more details.
+# the engine must be refittable (``make_refittable=True``). See :ref:`refit_engine_example` for more details.
 
 
 def torch_compile(iterations=3):
@@ -97,7 +97,7 @@ def torch_compile(iterations=3):
                 "enabled_precisions": enabled_precisions,
                 "debug": debug,
                 "min_block_size": min_block_size,
-                "make_refitable": True,
+                "make_refittable": True,
                 "cache_built_engines": cache_built_engines,
                 "reuse_cached_engines": reuse_cached_engines,
             },
@@ -157,7 +157,7 @@ def dynamo_compile(iterations=3):
             enabled_precisions=enabled_precisions,
             debug=debug,
             min_block_size=min_block_size,
-            make_refitable=True,
+            make_refittable=True,
             cache_built_engines=cache_built_engines,
             reuse_cached_engines=reuse_cached_engines,
             engine_cache_size=1 << 30,  # 1GB
@@ -268,7 +268,7 @@ def torch_compile_my_cache(iterations=3):
                 "enabled_precisions": enabled_precisions,
                 "debug": debug,
                 "min_block_size": min_block_size,
-                "make_refitable": True,
+                "make_refittable": True,
                 "cache_built_engines": cache_built_engines,
                 "reuse_cached_engines": reuse_cached_engines,
                 "custom_engine_cache": engine_cache,

examples/dynamo/mutable_torchtrt_module_example.py

@@ -31,7 +31,7 @@
 settings = {
     "use_python": False,
     "enabled_precisions": {torch.float32},
-    "make_refitable": True,
+    "make_refittable": True,
 }
 
 model = models.resnet18(pretrained=True).eval().to("cuda")
@@ -80,7 +80,7 @@
         "use_python_runtime": True,
         "enabled_precisions": {torch.float16},
         "debug": True,
-        "make_refitable": True,
+        "make_refittable": True,
     }
 
     model_id = "runwayml/stable-diffusion-v1-5"

examples/dynamo/refit_engine_example.py

@@ -43,11 +43,11 @@
 
 
 # %%
-# Make a Refitable Compilation Program
+# Make a refittable Compilation Program
 # ---------------------------------------
 #
 # The inital step is to compile a module and save it as with a normal. Note that there is an
-# additional parameter `make_refitable` that is set to `True`. This parameter is used to
+# additional parameter `make_refittable` that is set to `True`. This parameter is used to
 # indicate that the engine being built should support weight refitting later. Engines built without
 # these setttings will not be able to be refit.
 #
@@ -69,7 +69,7 @@
     debug=debug,
     min_block_size=min_block_size,
     torch_executed_ops=torch_executed_ops,
-    make_refitable=True,
+    make_refittable=True,
 )  # Output is a torch.fx.GraphModule
 
 # Save the graph module as an exported program

py/torch_tensorrt/dynamo/_compiler.py

@@ -60,7 +60,7 @@ def compile(
         Set[Union[torch.dtype, dtype]], Tuple[Union[torch.dtype, dtype]]
     ] = _defaults.ENABLED_PRECISIONS,
     engine_capability: EngineCapability = _defaults.ENGINE_CAPABILITY,
-    make_refitable: bool = _defaults.MAKE_REFITABLE,
+    make_refittable: bool = _defaults.MAKE_refittable,
     debug: bool = _defaults.DEBUG,
     num_avg_timing_iters: int = _defaults.NUM_AVG_TIMING_ITERS,
     workspace_size: int = _defaults.WORKSPACE_SIZE,
@@ -180,14 +180,14 @@ def compile(
 
     if "refit" in kwargs.keys():
         warnings.warn(
-            "Refit is deprecated. Please use make_refitable=True if you want to enable refitting of the engine.",
+            "Refit is deprecated. Please use make_refittable=True if you want to enable refitting of the engine.",
             DeprecationWarning,
             stacklevel=2,
         )
-        if make_refitable:
-            raise ValueError("Use flag make_refitable only. Flag refit is deprecated.")
+        if make_refittable:
+            raise ValueError("Use flag make_refittable only. Flag refit is deprecated.")
         else:
-            make_refitable = kwargs["refit"]
+            make_refittable = kwargs["refit"]
 
     engine_capability = EngineCapability._from(engine_capability)
 
@@ -238,8 +238,8 @@ def compile(
     engine_cache = None
     if cache_built_engines or reuse_cached_engines:
         assert (
-            make_refitable
-        ), "Engine caching requires make_refitable to be set to True"
+            make_refittable
+        ), "Engine caching requires make_refittable to be set to True"
         engine_cache = (
             custom_engine_cache
             if custom_engine_cache is not None
@@ -270,7 +270,7 @@ def compile(
         "require_full_compilation": require_full_compilation,
         "disable_tf32": disable_tf32,
         "sparse_weights": sparse_weights,
-        "make_refitable": make_refitable,
+        "make_refittable": make_refittable,
         "engine_capability": engine_capability,
         "dla_sram_size": dla_sram_size,
         "dla_local_dram_size": dla_local_dram_size,
@@ -513,7 +513,7 @@ def convert_exported_program_to_serialized_trt_engine(
     require_full_compilation: bool = _defaults.REQUIRE_FULL_COMPILATION,
     disable_tf32: bool = _defaults.DISABLE_TF32,
     sparse_weights: bool = _defaults.SPARSE_WEIGHTS,
-    make_refitable: bool = _defaults.MAKE_REFITABLE,
+    make_refittable: bool = _defaults.MAKE_refittable,
     engine_capability: EngineCapability = _defaults.ENGINE_CAPABILITY,
     num_avg_timing_iters: int = _defaults.NUM_AVG_TIMING_ITERS,
     dla_sram_size: int = _defaults.DLA_SRAM_SIZE,
@@ -600,7 +600,7 @@ def convert_exported_program_to_serialized_trt_engine(
             )
     if "refit" in kwargs.keys():
         warnings.warn(
-            "Refit is deprecated. Please use make_refitable=True if you want to enable refitting of the engine.",
+            "Refit is deprecated. Please use make_refittable=True if you want to enable refitting of the engine.",
             DeprecationWarning,
             stacklevel=2,
         )
@@ -646,7 +646,7 @@ def convert_exported_program_to_serialized_trt_engine(
         "require_full_compilation": require_full_compilation,
         "disable_tf32": disable_tf32,
         "sparse_weights": sparse_weights,
-        "make_refitable": make_refitable,
+        "make_refittable": make_refittable,
         "engine_capability": engine_capability,
         "num_avg_timing_iters": num_avg_timing_iters,
         "dla_sram_size": dla_sram_size,

py/torch_tensorrt/dynamo/_defaults.py

@@ -26,7 +26,7 @@
 USE_PYTHON_RUNTIME = False
 USE_FAST_PARTITIONER = True
 ENABLE_EXPERIMENTAL_DECOMPOSITIONS = False
-MAKE_REFITABLE = False
+MAKE_refittable = False
 REQUIRE_FULL_COMPILATION = False
 DRYRUN = False
 HARDWARE_COMPATIBLE = False

py/torch_tensorrt/dynamo/_engine_cache.py

@@ -1,17 +1,25 @@
 import copy
+from dataclasses import asdict
 import logging
 import os
+import io
 import pickle
+import pickletools
 import shutil
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional, Tuple, cast
+from ast import literal_eval
 
+from sympy.polys.matrices.dense import Sequence
 import torch
-from torch._inductor.codecache import FxGraphCachePickler
+from torch._inductor.codecache import FxGraphCachePickler, sha256_hash
 from torch.fx.experimental.proxy_tensor import unset_fake_temporarily
+from torch_tensorrt.dynamo._settings import CompilationSettings, _SETTINGS_TO_BE_ENGINE_INVARIANT
+from torch_tensorrt._Input import Input
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
 
+UnpackedCacheHit = Tuple[bytes, List[str], List[str], Tuple[Input], CompilationSettings, Optional[Dict[Any, Any]]]
 
 class BaseEngineCache(ABC):
 
@@ -24,7 +32,7 @@ def __init__(
         pass
 
     @staticmethod
-    def get_hash(gm: torch.fx.GraphModule) -> str:
+    def get_hash(gm: torch.fx.GraphModule, input_specs: Sequence[Input], settings: CompilationSettings) -> str:
         """Get the hash value of the GraphModule
 
         Args:
@@ -39,7 +47,22 @@ def get_hash(gm: torch.fx.GraphModule) -> str:
             for name, param in new_gm.named_parameters():
                 param.data.zero_()
 
-            hash_val = cast(str, FxGraphCachePickler.get_hash(new_gm))
+            graph_hash_val = cast(str, FxGraphCachePickler.get_hash(new_gm))
+
+        input_spec_strs = [str(i) for i in input_specs]
+        with io.BytesIO() as stream:
+            input_specs_data = pickle.dumps(input_spec_strs)
+            input_specs_data = pickletools.optimize(input_specs_data)
+        input_specs_hash = sha256_hash(input_specs_data)
+
+        invariant_engine_specs = [str(getattr(settings, field)) for field in _SETTINGS_TO_BE_ENGINE_INVARIANT]
+        with io.BytesIO() as stream:
+            engine_specs_data = pickle.dumps(invariant_engine_specs)
+            engine_specs_data = pickletools.optimize(engine_specs_data)
+        engine_specs_hash = sha256_hash(engine_specs_data)
+
+        # TODO: Super first idea I had hash combination solution @Evan please iterate on this
+        hash_val = graph_hash_val + input_specs_hash + engine_specs_hash
 
         return hash_val
 
@@ -48,6 +71,8 @@ def pack(
         serialized_engine: bytes,
         input_names: List[str],
         output_names: List[str],
+        input_specs: Tuple[Input],
+        compilation_settings: CompilationSettings,
         weight_name_map: Optional[Dict[Any, Any]],
     ) -> bytes:
         """Pack serialized engine, input names, output names, and weight map into a single blob
@@ -61,35 +86,80 @@ def pack(
         Returns:
             bytes: packed blob
         """
+
+        settings = copy.deepcopy(compilation_settings)
+        settings.torch_executed_ops = {
+            f"torch.ops.{op.__str__()}"
+            for op in settings.torch_executed_ops
+        }
+
         return pickle.dumps(
             {
                 "serialized_engine": bytes(serialized_engine),
                 "input_names": input_names,
                 "output_names": output_names,
+                "input_specs": input_specs,
+                "compilation_settings": settings,
                 "weight_name_map": weight_name_map,
             }
         )
 
     @staticmethod
-    def unpack(
-        packed_obj: bytes,
-    ) -> Tuple[bytes, List[str], List[str], Optional[Dict[Any, Any]]]:
+    def unpack(packed_obj: bytes) -> UnpackedCacheHit:
         """Unpack packed blob into serialized engine, input names, output names, and weight map
 
         Args:
             packed_obj (bytes): packed blob
 
         Returns:
-            Tuple[bytes, List[str], List[str], Optional[Dict[str, Any]]]: serialized engine, input names, output names, weight name map
+            Tuple[bytes, List[str], List[str], CompilationSettings, Optional[Dict[str, Any]]]: serialized engine, input names, output names, CompilationSettings, weight name map
         """
         unpacked = pickle.loads(packed_obj)
         return (
             unpacked["serialized_engine"],
             unpacked["input_names"],
             unpacked["output_names"],
+            unpacked["input_specs"],
+            unpacked["compilation_settings"],
             unpacked["weight_name_map"],
         )
 
+    def insert(self, hash: str, entry: UnpackedCacheHit, *args: Any, **kwargs: Any) -> None:
+        """
+        Insert a cache entry into the engine cache.
+
+        Args:
+            hash (str): The hash value of the GraphModule.
+            entry (Tuple[bytes, List[str], List[str], CompilationSettings, Optional[Dict[Any, Any]]]): The cache entry to be inserted.
+            *args: Variable length argument list passed to ``save``.
+            **kwargs: Arbitrary keyword arguments passed to ``save``.
+
+        Returns:
+            None
+        """
+        packed_cache_info = BaseEngineCache.pack(*entry)
+        return self.save(hash, packed_cache_info, *args, **kwargs)
+
+
+    def check(self, hash: str, *args: Any, **kwargs: Any) -> Optional[UnpackedCacheHit]:
+        """
+        Check if a cache entry exists for the given hash.
+
+        Args:
+            hash (str): The hash value of the GraphModule.
+            *args: Variable length argument list passed to ``load``.
+            **kwargs: Arbitrary keyword arguments passed to ``load``.
+
+        Returns:
+            Optional[Tuple[bytes, List[str], List[str], CompilationSettings, Optional[Dict[Any, Any]]]]: The unpacked cache entry if found, None otherwise.
+        """
+        packed_cache_info = self.load(hash, *args, **kwargs)
+
+        if packed_cache_info:
+            return BaseEngineCache.unpack(packed_cache_info)
+        else:
+            return None
+
     @abstractmethod
     def save(self, hash: str, blob: bytes, *args: Any, **kwargs: Any) -> None:
         """Store blob in cache

py/torch_tensorrt/dynamo/_refit.py

@@ -254,7 +254,7 @@ def refit_module_weights(
         ]
         assert (
             encoded_metadata != ""
-        ), "The engine provided is either not refittable or was built with a version of Torch-TensorRT that is too old, please recompile using the latest version with make_refitable=True"
+        ), "The engine provided is either not refittable or was built with a version of Torch-TensorRT that is too old, please recompile using the latest version with make_refittable=True"
         settings = TorchTensorRTModule.decode_metadata(encoded_metadata)["settings"]
         # Handle torch modules
         compiled_submodules_map = dict(compiled_submodules)
@@ -270,7 +270,7 @@ def refit_module_weights(
             settings = submodule.settings
 
     assert (
-        settings.make_refitable
+        settings.make_refittable
     ), "Refitting is not enabled. Please recompile the engine with refit=True."
 
     if settings.debug:

py/torch_tensorrt/dynamo/_settings.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass, field
-from typing import Collection, Optional, Set, Union
+from typing import Collection, Optional, Set, Union, Tuple
 
 from torch.fx.node import Target
 from torch_tensorrt._Device import Device
@@ -18,7 +18,7 @@
     ENGINE_CAPABILITY,
     HARDWARE_COMPATIBLE,
     LAZY_ENGINE_INIT,
-    MAKE_REFITABLE,
+    MAKE_refittable,
     MAX_AUX_STREAMS,
     MIN_BLOCK_SIZE,
     NUM_AVG_TIMING_ITERS,
@@ -98,7 +98,7 @@ class CompilationSettings:
     disable_tf32: bool = DISABLE_TF32
     assume_dynamic_shape_support: bool = ASSUME_DYNAMIC_SHAPE_SUPPORT
     sparse_weights: bool = SPARSE_WEIGHTS
-    make_refitable: bool = MAKE_REFITABLE
+    make_refittable: bool = MAKE_refittable
     engine_capability: EngineCapability = field(
         default_factory=lambda: ENGINE_CAPABILITY
     )
@@ -112,3 +112,28 @@ class CompilationSettings:
     lazy_engine_init: bool = LAZY_ENGINE_INIT
     cache_built_engines: bool = CACHE_BUILT_ENGINES
     reuse_cached_engines: bool = REUSE_CACHED_ENGINES
+
+
+_SETTINGS_TO_BE_ENGINE_INVARIANT =(
+    "enabled_precisions",
+    "max_aux_streams",
+    "version_compatible",
+    "optimization_level",
+    "disable_tf32",
+    "sparse_weights",
+    "make_refittable",
+    "engine_capability",
+    "hardware_compatible",)
+
+
+def settings_are_compatible(set_a: CompilationSettings, set_b: CompilationSettings) -> Tuple[bool, Set[str]]:
+    incompatible_settings: Set[str] = set()
+
+    for field in _SETTINGS_TO_BE_ENGINE_INVARIANT:
+       if getattr(set_a, field) != getattr(set_b, field):
+           incompatible_settings.add(field)
+
+    if len(incompatible_settings) == 0:
+        return True, set()
+    else:
+        return False, incompatible_settings