pytorch · gs-olive · Jun 18, 2024 · Oct 20, 2023 · Dec 27, 2023 · Jun 14, 2024
diff --git a/py/torch_tensorrt/dynamo/_compiler.py b/py/torch_tensorrt/dynamo/_compiler.py
@@ -313,6 +313,7 @@ def contains_metadata(gm: torch.fx.GraphModule) -> bool:
     # If specified, try using the fast partitioner and fall back to the global one on failure
     if settings.use_fast_partitioner:
         try:
+            logger.info("Partitioning the graph via the fast partitioner")
             partitioned_module, supported_ops = partitioning.fast_partition(
                 gm,
                 verbose=settings.debug,
@@ -322,14 +323,15 @@ def contains_metadata(gm: torch.fx.GraphModule) -> bool:
         except torch.fx.passes.splitter_base.FxNetSplitterInternalError:
             logger.error(
                 "Partitioning failed on the subgraph with fast partition. See trace above. "
-                + "Retrying with global partition.",
+                "Retrying with global partition.",
                 exc_info=True,
             )
 
             fast_partitioner_failed = True
             settings.use_fast_partitioner = False
 
     if not settings.use_fast_partitioner:
+        logger.info("Partitioning the graph via the global partitioner")
         partitioned_module, supported_ops = partitioning.global_partition(
             gm,
             verbose=settings.debug,
@@ -367,14 +369,15 @@ def contains_metadata(gm: torch.fx.GraphModule) -> bool:
         # Get the submodule inputs for min, opt, max shapes of the graph inputs
         submodule_inputs = partitioning.construct_submodule_inputs(submodule)
 
+        assert submodule_inputs is not None
+
         logger.debug(
-            "Submodule name: %s\n Input shapes: %s\n %s",
+            "Converting submodule: %s\n Input shapes: %s\n %s",
             str(name),
             [input.shape for input in submodule_inputs],
             str(submodule.graph),
         )
 
-        assert submodule_inputs is not None
         # Handle long/double inputs if requested by the user
         if settings.truncate_double:
             submodule_inputs = repair_double_inputs(

diff --git a/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py b/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
@@ -2,7 +2,7 @@
 import os
 import warnings
 from datetime import datetime
-from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence, Set
+from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple
 
 import numpy as np
 import tensorrt as trt
@@ -21,6 +21,7 @@
 )
 from torch_tensorrt.dynamo.conversion._ConverterRegistry import CallingConvention
 from torch_tensorrt.dynamo.conversion.converter_utils import (
+    get_node_io,
     get_node_name,
     get_trt_tensor,
 )
@@ -106,6 +107,9 @@ def __init__(
             [dtype._from(o) for o in output_dtypes] if output_dtypes else None
         )
 
+        # Mapping of constants to shapes and dtypes
+        self.const_mapping: Dict[str, Tuple[Sequence[int], str]] = {}
+
     def validate_conversion(self) -> Set[str]:
         missing_converters: Set[str] = set()
 
@@ -361,8 +365,19 @@ def run_node(self, n: torch.fx.Node) -> torch.fx.Node:
         n.kwargs = kwargs
 
         # run the node
+        _LOGGER.debug(
+            f"Running node {self._cur_node_name}, a {self._cur_node.op} node "
+            f"with target {self._cur_node.target} in the TensorRT Interpreter"
+        )
         trt_node: torch.fx.Node = super().run_node(n)
 
+        if n.op == "get_attr":
+            self.const_mapping[str(n)] = (tuple(trt_node.shape), str(trt_node.dtype))
+
+        _LOGGER.debug(
+            f"Ran node {self._cur_node_name} with properties: {get_node_io(n, self.const_mapping)}"
+        )
+
         # remove "_itensor_to_tensor_meta"
         kwargs = dict(n.kwargs)
         del kwargs["_itensor_to_tensor_meta"]

diff --git a/py/torch_tensorrt/dynamo/conversion/converter_utils.py b/py/torch_tensorrt/dynamo/conversion/converter_utils.py
@@ -1,3 +1,4 @@
+import collections
 import functools
 import logging
 import re
@@ -8,6 +9,7 @@
 import torch
 import torch_tensorrt.dynamo.conversion.impl as impl
 from torch.fx.node import Argument, Target
+from torch.fx.passes.shape_prop import TensorMetadata
 from torch_tensorrt import _enums
 from torch_tensorrt.dynamo._SourceIR import SourceIR
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
@@ -44,6 +46,75 @@ def get_node_name(node: torch.fx.Node) -> str:
     return node_name
 
 
+def get_node_io(
+    node: torch.fx.Node, constant_mapping: Dict[str, Tuple[Sequence[int], str]]
+) -> str:
+    """Gets a string representing the node inputs and outputs including tensor shapes and dtypes"""
+
+    def format_tensor_metadata(metadata: Union[Any, Sequence[Any]]) -> str:
+        """Formats the metadata for a single node"""
+        # If the provided data is a simple TensorMetadata object, parse it
+        if isinstance(metadata, TensorMetadata) or issubclass(
+            type(metadata), torch.Tensor
+        ):
+            return f"{tuple(metadata.shape)}@{metadata.dtype}"  # type: ignore
+        # If the provided data is a scalar, return it as is
+        elif isinstance(metadata, (int, float, bool)):
+            return f"{metadata}@Python-{type(metadata)}"
+        # If the provided data is a sequence, recursively parse it
+        elif isinstance(metadata, collections.abc.Sequence):
+            formatted_str = "("
+            for meta in metadata:
+                formatted_str += format_tensor_metadata(meta) + ", "
+
+            return formatted_str[:-2] + ")"
+        else:
+            _LOGGER.warning(
+                f"Detected unparseable type in node formatting: {type(metadata)}"
+            )
+            return ""
+
+    # Format input tensors
+    metadata_string = "Inputs: ("
+
+    # For each input argument, format it accordingly
+    for arg in node.args:
+        if isinstance(arg, torch.fx.Node):
+            if arg.op == "get_attr":
+                shape, dtype = constant_mapping[str(arg)]
+                arg_repr = f"{shape}@{dtype}"
+            elif arg.meta.get("tensor_meta") is not None:
+                arg_repr = format_tensor_metadata(arg.meta["tensor_meta"])
+            elif arg.meta.get("val") is not None:
+                arg_repr = format_tensor_metadata(arg.meta["val"])
+            else:
+                arg_repr = ""
+
+            metadata_string += f"{arg}: {arg_repr}, "
+        else:
+            metadata_string += f"{arg}, "
+
+    metadata_string = (
+        metadata_string[:-2] if metadata_string[-1] != "(" else metadata_string
+    ) + ")"
+
+    # Format output tensors and arguments
+    metadata_string += " | Outputs: ("
+    if node.op == "get_attr":
+        shape, dtype = constant_mapping[str(node)]
+        node_repr = f"{shape}@{dtype}"
+    elif node.meta.get("tensor_meta") is not None:
+        node_repr = format_tensor_metadata(node.meta["tensor_meta"])
+    elif node.meta.get("val") is not None:
+        node_repr = format_tensor_metadata(node.meta["val"])
+    else:
+        node_repr = ""
+    metadata_string += f"{node}: {node_repr}, "
+    metadata_string = metadata_string[:-2] + ")"
+
+    return metadata_string
+
+
 def is_only_operator_on_placeholder(node: torch.fx.Node) -> bool:
     """Detects whether a call_function node is the only operator on a placeholder"""
     # Returns true if the node operates on a placeholder and is a direct output

diff --git a/py/torch_tensorrt/dynamo/conversion/truncate_double.py b/py/torch_tensorrt/dynamo/conversion/truncate_double.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 from typing import Optional, Sequence, Set
 
 import torch
@@ -8,6 +9,8 @@
 from torch_tensorrt._Input import Input
 from torch_tensorrt.dynamo.utils import get_torch_inputs
 
+logger = logging.getLogger(__name__)
+
 
 def _extract_downstream_get_nodes(
     module_node: torch.fx.Node, output_indices: Set[int]
@@ -62,6 +65,10 @@ def _repair_64bit_input(
         torch.float64,
     ), f"dtype argument must be torch.float64, got {dtype}"
 
+    logger.info(
+        f"Downcasting a 64-bit input at position {position} of submodule {submodule_name}"
+    )
+
     # Determine target data type in 32 and 64 bit forms
     dtype_64bit = dtype
     dtype_32bit = torch.float32

diff --git a/py/torch_tensorrt/dynamo/lowering/passes/replace_max_pool_with_indices.py b/py/torch_tensorrt/dynamo/lowering/passes/replace_max_pool_with_indices.py
@@ -43,6 +43,7 @@ def replace_max_pool_with_indices(
                     args=node.args,
                     kwargs=node.kwargs,
                 )
+                maxpool_fused.meta = node.meta
 
             logger.debug(
                 f"Replacing all uses of nodes {node}, {getitem_node} with fused maxpool node {maxpool_fused} "

diff --git a/py/torch_tensorrt/dynamo/utils.py b/py/torch_tensorrt/dynamo/utils.py
@@ -191,7 +191,7 @@ def parse_complex_tensor_structs(
 
     else:
         raise ValueError(
-            f"Invalid input type {type(inputs)} encountered in parse_complex_tensor_structs parsing. "
+            f"Invalid input type {type(inputs)} encountered during Dynamo input parsing. "
             + "Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}"
         )