Squash the commits

Author: cehongwang

docsrc/contributors/resource_management.rst

@@ -0,0 +1,77 @@
+.. _resource_management:
+
+Resource Management
+===================
+
+Overview
+--------
+
+Efficient control of CPU and GPU memory is essential for successful model compilation, 
+especially when working with large models such as LLMs or diffusion models. 
+Uncontrolled memory growth can cause compilation failures or process termination. 
+This guide describes the symptoms of excessive memory usage and provides methods 
+to reduce both CPU and GPU memory consumption.
+
+Memory Usage Control
+--------------------
+
+CPU Memory
+^^^^^^^^^^
+
+By default, Torch-TensorRT may consume up to **5×** the model size in CPU memory.  
+This can exceed system limits when compiling large models.
+
+**Common symptoms of high CPU memory usage:**
+
+- Program freeze  
+- Process terminated by the operating system  
+
+**Ways to lower CPU memory usage:**
+
+1. **Enable memory trimming**
+
+   Set the following environment variable:
+
+   .. code-block:: bash
+
+      export TRIM_CPU_MEMORY=1
+
+   This reduces approximately **2×** of redundant model copies, limiting 
+   total CPU memory usage to up to **3×** the model size.
+
+2. **Disable CPU offloading**
+
+   In compilation settings, set:
+
+   .. code-block:: python
+
+      offload_module_to_cpu = False
+
+   This removes another **1×** model copy, reducing peak CPU memory 
+   usage to about **2×** the model size.
+
+GPU Memory
+^^^^^^^^^^
+
+By default, Torch-TensorRT may consume up to **2×** the model size in GPU memory.
+
+**Common symptoms of high GPU memory usage:**
+
+- CUDA out-of-memory errors  
+- TensorRT compilation errors  
+
+**Ways to lower GPU memory usage:**
+
+1. **Enable offloading to CPU**
+
+   In compilation settings, set:
+
+   .. code-block:: python
+
+      offload_module_to_cpu = True
+
+   This shifts one model copy from GPU to CPU memory.  
+   As a result, peak GPU memory usage decreases to about **1×** 
+   the model size, while CPU memory usage increases by roughly **1×**.
+
+

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,
@@ -681,7 +682,7 @@ def compile(
         "offload_module_to_cpu": offload_module_to_cpu,
         "use_distributed_mode_trace": use_distributed_mode_trace,
     }
-
+    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)
@@ -695,14 +696,17 @@ 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:
@@ -868,6 +872,11 @@ 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
@@ -1243,7 +1252,7 @@ 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}
 
@@ -1330,7 +1339,7 @@ def convert_exported_program_to_serialized_trt_engine(
             )
 
     flattened_input_list = get_flat_args_with_check(
-        exported_program, list(trt_arg_inputs), trt_kwarg_inputs
+        exported_program, list(trt_arg_inputs), trt_kwarg_inputs  # type: ignore
     )[0]
 
     try:

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
     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.debug(
+            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

@@ -1,24 +1,34 @@
 from __future__ import annotations
 
+import io
 import logging
-from typing import Any, List, Optional, Sequence
+from typing import Any, List, NamedTuple, Optional, Sequence
 
 import torch
 from torch_tensorrt._enums import dtype
 from torch_tensorrt._features import ENABLED_FEATURES
 from torch_tensorrt._Input import Input
 from torch_tensorrt.dynamo._engine_cache import BaseEngineCache
 from torch_tensorrt.dynamo._settings import CompilationSettings
-from torch_tensorrt.dynamo.conversion._TRTInterpreter import (
-    TRTInterpreter,
-    TRTInterpreterResult,
-)
+from torch_tensorrt.dynamo.conversion._TRTInterpreter import TRTInterpreter
 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__)
 
 
+class SerializedInterpreterResult(NamedTuple):
+    serialized_engine: bytes
+    input_names: Sequence[str]
+    output_names: Sequence[str]
+    weight_name_map: Optional[dict[Any, Any]]
+    requires_output_allocator: bool
+
+
 def infer_module_output_dtypes(
     module: torch.fx.GraphModule,
     truncate_double: bool = False,
@@ -29,7 +39,7 @@ 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(
@@ -39,7 +49,7 @@ def interpret_module_to_result(
     arg_inputs: Optional[Sequence[Input]] = None,
     kwarg_inputs: Optional[dict[str, Any]] = None,
     engine_cache: Optional[BaseEngineCache] = None,
-) -> TRTInterpreterResult:
+) -> SerializedInterpreterResult:
     """Interpret an FX module to a TRTInterpreterResult
     Args:
         module: FX GraphModule to interpret
@@ -65,7 +75,32 @@ def interpret_module_to_result(
     )
 
     interpreter_result = interpreter.run()
-    return interpreter_result
+    # 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()
+        logger.debug(
+            f"CPU memory usage after serializing engine: {get_cpu_memory_usage()} MB"
+        )
+    serialized_interpreter_result = SerializedInterpreterResult(
+        serialized_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 serialized_interpreter_result
 
 
 def convert_module(

py/torch_tensorrt/dynamo/debug/_Debugger.py

@@ -220,6 +220,7 @@ 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 = []