Cpu memory optimization

py/torch_tensorrt/dynamo/_compiler.py

@@ -42,6 +42,7 @@
 )
 from torch_tensorrt.dynamo.utils import (
     deallocate_module,
+    get_cpu_memory_usage,
     get_flat_args_with_check,
     get_output_metadata,
     parse_graph_io,
@@ -675,7 +676,7 @@ def compile(
         "l2_limit_for_tiling": l2_limit_for_tiling,
         "offload_module_to_cpu": offload_module_to_cpu,
     }
-
+    logger.debug(f"CPU memory usage before lowering: {get_cpu_memory_usage()} MB")
     settings = CompilationSettings(**compilation_options)
     logger.info("Compilation Settings: %s\n", settings)
     exported_program = pre_export_lowering(exported_program, settings)
@@ -689,14 +690,17 @@ def compile(
 
     # Apply lowering on the graph module
     gm = post_lowering(gm, settings)
+    logger.debug(f"CPU memory usage after post_lowering: {get_cpu_memory_usage()} MB")
     logger.debug("Lowered Input graph: " + str(gm.graph))
 
     # Move the weights in the state_dict to CPU
     if offload_module_to_cpu:
+        deallocate_module(gm, delete_module=False)
         deallocate_module(exported_program.module(), delete_module=False)
         logger.info(
             "The PyTorch model was moved to the CPU to allocate all GPU memory to TensorRT. To retain the model on the GPU, set offload_module_to_cpu=False"
         )
+        logger.debug(f"CPU memory usage after CPU offload: {get_cpu_memory_usage()} MB")
     else:
         remaining_memory, total_memory = torch.cuda.mem_get_info()
         if remaining_memory < total_memory // 2:
@@ -858,6 +862,11 @@ def preserve_module_specs(
     # Iterate over all components that can be accelerated
     # Generate the corresponding TRT Module for those
 
+    # Here we delete the frozen parameters from the graph module. Note this does not affect the submodules. We are going to delete the frozen parameters from the submodules in the convert_module function.
+    # This is done to release CPU memory.
+    for attr in dir(gm):
+        if attr.startswith("_frozen_param"):
+            delattr(gm, attr)
     for name, _ in partitioned_module.named_children():
         submodule = getattr(partitioned_module, name)
         # filter on the GraphModule
@@ -1231,7 +1240,7 @@ def convert_exported_program_to_serialized_trt_engine(
 
     # Prepare torch_trt inputs
     trt_arg_inputs: Sequence[Input] = prepare_inputs(arg_inputs)
-    trt_kwarg_inputs: Optional[dict[Any, Any]] = prepare_inputs(kwarg_inputs)
+    trt_kwarg_inputs: Optional[dict[str, Any]] = prepare_inputs(kwarg_inputs)
     device = to_torch_tensorrt_device(device)
     enabled_precisions = {dtype._from(p) for p in enabled_precisions}
 

py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py

@@ -50,7 +50,12 @@
 from torch_tensorrt.dynamo.debug._DebuggerConfig import DebuggerConfig
 from torch_tensorrt.dynamo.debug._supports_debugger import cls_supports_debugger
 from torch_tensorrt.dynamo.observer import Observer
-from torch_tensorrt.dynamo.utils import DYNAMIC_DIM, deallocate_module, to_torch_device
+from torch_tensorrt.dynamo.utils import (
+    DYNAMIC_DIM,
+    deallocate_module,
+    get_cpu_memory_usage,
+    to_torch_device,
+)
 from torch_tensorrt.logging import TRT_LOGGER
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
@@ -65,7 +70,7 @@ class UnsupportedOperatorException(RuntimeError):
 
 
 class TRTInterpreterResult(NamedTuple):
-    serialized_engine: bytes
+    engine: trt.ICudaEngine | bytes
     input_names: Sequence[str]
     output_names: Sequence[str]
     weight_name_map: Optional[dict[Any, Any]]
@@ -512,8 +517,7 @@ def _save_weight_mapping(self) -> None:
         _LOGGER.info("Building weight name mapping...")
         # Stage 1: Name mapping
         torch_device = to_torch_device(self.compilation_settings.device)
-        self.module.to(torch_device)
-        sd = self.module.state_dict()
+        sd = {k: v.to(torch_device) for k, v in self.module.state_dict().items()}
         weight_name_map: dict[str, Any] = {}
         weight_refit_map = self.ctx.weight_refit_map
         constant_mapping = {k: v for k, v in weight_refit_map.items() if v.size == 1}
@@ -592,13 +596,11 @@ def _save_weight_mapping(self) -> None:
         torch.cuda.empty_cache()
 
     @needs_refit  # type: ignore[misc]
-    def _insert_engine_to_cache(self, hash_val: str, serialized_engine: bytes) -> None:
+    def _insert_engine_to_cache(self, hash_val: str, engine: trt.ICudaEngine) -> None:
+        serialized_engine = engine.serialize()
         # TODO: @Evan is waiting for TRT's feature to cache the weight-stripped engine
         # if not self.compilation_settings.strip_engine_weights:
         #     # set EXCLUDE_WEIGHTS flag to strip weights
-        #     runtime = trt.Runtime(TRT_LOGGER)
-        #     engine = runtime.deserialize_cuda_engine(serialized_engine)
-
         #     serialization_config = engine.create_serialization_config()
         #     serialization_config.set_flag(trt.SerializationFlag.EXCLUDE_WEIGHTS)
         #     serialized_engine = engine.serialize_with_config(
@@ -733,6 +735,9 @@ def run(
                         return interpreter_result  # type: ignore[no-any-return]
 
         self._construct_trt_network_def()
+        _LOGGER.info(
+            f"CPU memory usage after network construction: {get_cpu_memory_usage()} MB"
+        )
 
         if not self.compilation_settings.immutable_weights:
             self._save_weight_mapping()
@@ -750,16 +755,19 @@ def run(
         self._create_timing_cache(
             builder_config, self.compilation_settings.timing_cache_path
         )
-        serialized_engine = self.builder.build_serialized_network(
+
+        cuda_engine = self.builder.build_engine_with_config(
             self.ctx.net, builder_config
         )
-        assert serialized_engine
+        assert cuda_engine
+
+        _LOGGER.debug(
+            f"CPU memory usage after engine building: {get_cpu_memory_usage()} MB"
+        )
 
         _LOGGER.info(
             f"Build TRT engine elapsed time: {datetime.now() - build_engine_start_time}"
         )
-        _LOGGER.info(f"TRT Engine uses: {serialized_engine.nbytes} bytes of Memory")
-
         self.ctx.clear_cpu_weights_reference_holder()
 
         self._save_timing_cache(
@@ -772,14 +780,10 @@ def run(
             and self.compilation_settings.cache_built_engines
             and self.engine_cache is not None
         ):
-            self._insert_engine_to_cache(hash_val, serialized_engine)
-
-        with io.BytesIO() as engine_bytes:
-            engine_bytes.write(serialized_engine)
-            engine_str = engine_bytes.getvalue()
+            self._insert_engine_to_cache(hash_val, cuda_engine)
 
         return TRTInterpreterResult(
-            engine_str,
+            cuda_engine,
             self._input_names,
             self._output_names,
             self.weight_name_map,

py/torch_tensorrt/dynamo/conversion/_conversion.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import io
 import logging
 from typing import Any, List, Optional, Sequence
 
@@ -14,7 +15,11 @@
     TRTInterpreterResult,
 )
 from torch_tensorrt.dynamo.runtime import PythonTorchTensorRTModule, TorchTensorRTModule
-from torch_tensorrt.dynamo.utils import get_output_dtypes
+from torch_tensorrt.dynamo.utils import (
+    get_cpu_memory_usage,
+    get_output_dtypes,
+    release_memory,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -29,7 +34,7 @@ def infer_module_output_dtypes(
     """
     outputs = [node for node in module.graph.nodes if node.op == "output"]
     outputs = outputs[0].args
-    return get_output_dtypes(outputs, truncate_double)  # type: ignore[no-any-return]
+    return get_output_dtypes(outputs, truncate_double)
 
 
 def interpret_module_to_result(
@@ -65,6 +70,29 @@ def interpret_module_to_result(
     )
 
     interpreter_result = interpreter.run()
+    # Delete the frozen parameters from the module to release CPU memory
+    del interpreter
+    for attr in dir(module):
+        if attr.startswith("_frozen_param"):
+            delattr(module, attr)
+    release_memory()
+    logger.debug(
+        f"CPU memory usage after clearing frozen parameters and building memory in conversion: {get_cpu_memory_usage()} MB"
+    )
+
+    serialized_engine = interpreter_result.engine.serialize()
+    with io.BytesIO() as engine_bytes:
+        engine_bytes.write(serialized_engine)
+        serialized_engine = engine_bytes.getvalue()
+
+    interpreter_result = TRTInterpreterResult(
+        engine=serialized_engine,
+        input_names=interpreter_result.input_names,
+        output_names=interpreter_result.output_names,
+        weight_name_map=interpreter_result.weight_name_map,
+        requires_output_allocator=interpreter_result.requires_output_allocator,
+    )
+
     return interpreter_result
 
 
@@ -104,7 +132,7 @@ def convert_module(
         )
 
     return rt_cls(
-        serialized_engine=interpreter_result.serialized_engine,
+        serialized_engine=interpreter_result.engine,
         input_binding_names=list(interpreter_result.input_names),
         output_binding_names=list(interpreter_result.output_names),
         name=name,

py/torch_tensorrt/dynamo/debug/_Debugger.py

@@ -197,6 +197,7 @@ def get_logging_config(self, log_level: Optional[int] = None) -> dict[str, Any]:
                 "class": "logging.FileHandler",
                 "filename": f"{self.cfg.logging_dir}/torch_tensorrt_logging.log",
                 "formatter": "standard",
+                "mode": "w",  # This will clear the previous content
             }
             config["loggers"][""]["handlers"].append("file")
         return config

py/torch_tensorrt/dynamo/lowering/passes/constant_folding.py

@@ -37,7 +37,9 @@ def constant_fold(
     # For TRT INetwork construction the constants are moved to CPU in get_attr call.
     for node, constant in cf.node_replacements.items():
         replace_node_with_constant(
-            gm, node, torch.nn.Parameter(constant, requires_grad=False)
+            gm,
+            node,
+            torch.nn.Parameter(constant.cpu().contiguous(), requires_grad=False),
         )
 
     erased_params = []

py/torch_tensorrt/dynamo/utils.py

@@ -1,13 +1,16 @@
 from __future__ import annotations
 
+import ctypes
 import gc
 import logging
+import platform
 import warnings
 from dataclasses import fields, replace
 from enum import Enum
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
+import psutil
 import sympy
 import tensorrt as trt
 import torch
@@ -858,3 +861,24 @@ def is_thor() -> bool:
     if torch.cuda.get_device_capability() in [(11, 0)]:
         return True
     return False
+
+
+def get_cpu_memory_usage() -> Any:
+    return psutil.Process().memory_info().rss / 1024 / 1024
+
+
+def release_memory() -> None:
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+        torch.cuda.synchronize()
+
+    if platform.system() == "Linux":
+        try:
+            libc = ctypes.CDLL("libc.so.6")
+            if libc.malloc_trim(0) != 1:
+                logger.warning("Failed to release CPU memory.")
+        except Exception:
+            logger.warning("Failed to release CPU memory.")

tests/py/dynamo/models/test_models.py

@@ -54,6 +54,52 @@ def test_resnet18(ir):
     torch._dynamo.reset()
 
 
+def compile_one(idx: int, ir: str):
+    model = models.resnet18(pretrained=True).eval().to("cuda")
+    input = torch.randn((idx + 1, 3, 224, 224)).to("cuda")
+
+    compile_spec = {
+        "inputs": [
+            torchtrt.Input(
+                input.shape, dtype=torch.float, format=torch.contiguous_format
+            )
+        ],
+        "device": torchtrt.Device("cuda:0"),
+        "enabled_precisions": {torch.float},
+        "ir": ir,
+        "pass_through_build_failures": True,
+        "optimization_level": 1,
+        "cache_built_engines": False,
+        "reuse_cached_engines": False,
+    }
+
+    trt_mod = torchtrt.compile(model, **compile_spec)
+    cos_sim = cosine_similarity(model(input), trt_mod(input))
+    assertions.assertTrue(
+        cos_sim > COSINE_THRESHOLD,
+        msg=f"In multiprocess compilation test, process {idx} failed: Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+    )
+
+
+@pytest.mark.unit
+@unittest.skipIf(
+    not importlib.util.find_spec("torchvision"),
+    "torchvision is not installed",
+)
+def test_resnet18_multiprocess(ir):
+    import torch.multiprocessing as mp
+
+    mp.set_start_method("spawn", force=True)
+    procs = []
+    for i in range(3):
+        p = mp.Process(target=compile_one, args=(i, ir))
+        p.start()
+        procs.append(p)
+    for p in procs:
+        p.join()
+    torch._dynamo.reset()
+
+
 @pytest.mark.unit
 @unittest.skipIf(
     not importlib.util.find_spec("torchvision"),