fix: Address PR comments and update logging scheme

Author: gs-olive

py/torch_tensorrt/dynamo/_DryRunTracker.py

@@ -1,9 +1,9 @@
 import logging
 import math
 from dataclasses import dataclass, field
-from typing import List, Tuple
+from typing import Any, Dict, List
 
-import torch
+from torch_tensorrt.dynamo._settings import CompilationSettings
 
 logger = logging.getLogger(__name__)
 
@@ -15,18 +15,18 @@ class PerSubgraphData:
     Args:
         subgraph_name (str): Name of the subgraph in the GraphModule
         subgraph_op_count (int): Number of operations in the subgraph
-        subgraph_input_shapes (List[Tuple[int, ...]]): Shapes of input Tensors of the subgraph
-        subgraph_input_dtypes (List[torch.device]): Input data types of the subgraph
-        subgraph_output_shapes (List[Tuple[int, ...]]): Shapes of output Tensors of the subgraph
-        subgraph_output_dtypes (List[torch.device]): Output data types of the subgraph
+        subgraph_input_shapes (Any): Shapes of input Tensors of the subgraph
+        subgraph_input_dtypes (Any): Input data types of the subgraph
+        subgraph_output_shapes (Any): Shapes of output Tensors of the subgraph
+        subgraph_output_dtypes (Any): Output data types of the subgraph
     """
 
     subgraph_name: str = ""
     subgraph_op_count: int = 0
-    subgraph_input_shapes: List[Tuple[int, ...]] = field(default_factory=list)
-    subgraph_input_dtypes: List[torch.device] = field(default_factory=list)
-    subgraph_output_shapes: List[Tuple[int, ...]] = field(default_factory=list)
-    subgraph_output_dtypes: List[torch.device] = field(default_factory=list)
+    subgraph_input_shapes: Any = field(default_factory=list)
+    subgraph_input_dtypes: Any = field(default_factory=list)
+    subgraph_output_shapes: Any = field(default_factory=list)
+    subgraph_output_dtypes: Any = field(default_factory=list)
 
 
 @dataclass
@@ -36,95 +36,86 @@ class DryRunTracker:
     Args:
         total_ops_in_graph (int): Total number of operators in graph
         supported_ops_in_graph (int): Number of supported operators in graph
-        graph_input_shapes (List[Tuple[int, ...]]): Shapes of input Tensors of the graph
-        graph_input_dtypes (List[torch.device]): Input data types of the graph
-        graph_output_shapes (List[Tuple[int, ...]]): Shapes of output Tensors of the graph
-        graph_output_dtypes (List[torch.device]): Output data types of the graph
+        graph_input_shapes (Any): Shapes of input Tensors of the graph
+        graph_input_dtypes (Any): Input data types of the graph
+        graph_output_shapes (Any): Shapes of output Tensors of the graph
+        graph_output_dtypes (Any): Output data types of the graph
         per_subgraph_data (List[PerSubgraphData]): Per-subgraph data, see above class
         tensorrt_graph_count (int): Number of TensorRT engines to be generated
-        truncated_long_and_double (bool): Whether truncate_long_and_double was enabled
+        compilation_settings (CompilationSettings): User Compilation Settings
+        unsupported_ops (Dict[str, int]): Set of operators not supported in TRT
     """
 
     total_ops_in_graph: int = 0
     supported_ops_in_graph: int = 0
-    graph_input_shapes: List[Tuple[int, ...]] = field(default_factory=list)
-    graph_input_dtypes: List[torch.device] = field(default_factory=list)
-    graph_output_shapes: List[Tuple[int, ...]] = field(default_factory=list)
-    graph_output_dtypes: List[torch.device] = field(default_factory=list)
+    graph_input_shapes: Any = field(default_factory=list)
+    graph_input_dtypes: Any = field(default_factory=list)
+    graph_output_shapes: Any = field(default_factory=list)
+    graph_output_dtypes: Any = field(default_factory=list)
     per_subgraph_data: List[PerSubgraphData] = field(default_factory=list)
     tensorrt_graph_count: int = 0
-    truncated_long_and_double: bool = False
+    compilation_settings: CompilationSettings = field(
+        default_factory=CompilationSettings
+    )
+    unsupported_ops: Dict[str, int] = field(default_factory=dict)
 
 
 def dryrun_stats_display(dryrun_tracker: DryRunTracker, dryrun_enabled: bool) -> None:
-    """Displays statistics about the dryrun either to debug logs or info logs"""
-    # If user specified "dryrun=True", print to info logs, else debug
-    if dryrun_enabled:
-        dryrun_logger = logger.info
-    else:
-        dryrun_logger = logger.debug
-
+    """Displays statistics about the dryrun either to debug logs or stdout"""
     formatted_stats = "\n"
 
     # Print overall stats about the graph, operator counts, etc.
-    formatted_stats += "+" * 50 + " Dry-Run Results for Graph " + "+" * 50 + "\n"
+    formatted_stats += "+" * 50 + " Dry-Run Results for Graph " + "+" * 50 + "\n\n"
     formatted_stats += (
         f"The graph consists of {dryrun_tracker.total_ops_in_graph} Total Operators, "
         f"of which {dryrun_tracker.supported_ops_in_graph} operators are supported, "
-        f"{round(dryrun_tracker.supported_ops_in_graph*100/dryrun_tracker.total_ops_in_graph, 2)}% coverage\n"
-    )
-    formatted_stats += f"Long and double inputs were {'' if dryrun_tracker.truncated_long_and_double else 'not'} truncated (truncate_long_and_double={dryrun_tracker.truncated_long_and_double})\n"
-    formatted_stats += (
-        f"{dryrun_tracker.tensorrt_graph_count} TRT Engine(s) were generated\n"
+        f"{round(dryrun_tracker.supported_ops_in_graph*100/dryrun_tracker.total_ops_in_graph, 2)}% coverage\n\n"
     )
+    formatted_stats += f"The following ops are currently unsupported and set to run in Torch: {dryrun_tracker.unsupported_ops}\n\n"
+    formatted_stats += f"Compiled with: {dryrun_tracker.compilation_settings}\n\n"
 
     assert len(dryrun_tracker.per_subgraph_data) == dryrun_tracker.tensorrt_graph_count
 
     # Print schematic of the graph structure, as in:
     #
-    #   Inputs: [Tensor: (1, 3, 224, 224)@float32]
+    #   Inputs: List[Tensor: (1, 3, 224, 224)@float32]
     #    ...
-    #    TRT Engine #1: _run_on_acc_0
-    #     Engine Inputs: [Tensor: (1, 3, 224, 224)@float32]
-    #     Number of Operators in Engine: 1
-    #     Engine Outputs: [Tensor: (1, 64, 112, 112)@float32]
+    #      TRT Engine #1 - Submodule name: _run_on_acc_0
+    #       Engine Inputs: List[Tensor: (1, 3, 224, 224)@float32]
+    #       Number of Operators in Engine: 1
+    #       Engine Outputs: Tensor: (1, 64, 112, 112)@float32
     #    ...
-    #   Outputs: [Tensor: (1, 1000)@float32]
+    #   Outputs: List[Tensor: (1, 1000)@float32]
     #
     formatted_stats += " " * 2 + "Graph Structure:\n\n"
     formatted_stats += (
         " " * 3
-        + f"Inputs: [{input_formatter(dryrun_tracker.graph_input_shapes, dryrun_tracker.graph_input_dtypes)}]\n"
+        + f"Inputs: {input_formatter(dryrun_tracker.graph_input_shapes, dryrun_tracker.graph_input_dtypes)}\n"
     )
 
     for i, trt_subgraph_data in enumerate(dryrun_tracker.per_subgraph_data):
-        assert len(trt_subgraph_data.subgraph_input_dtypes) == len(
-            trt_subgraph_data.subgraph_input_shapes
-        )
-        assert len(trt_subgraph_data.subgraph_output_dtypes) == len(
-            trt_subgraph_data.subgraph_output_shapes
-        )
         formatted_stats += " " * 4 + "...\n"
         formatted_stats += (
-            " " * 4 + f"TRT Engine #{i+1}: {trt_subgraph_data.subgraph_name}\n"
+            " " * 4
+            + f"TRT Engine #{i+1} - Submodule name: {trt_subgraph_data.subgraph_name}\n"
         )
         formatted_stats += (
             " " * 5
-            + f"Engine Inputs: [{input_formatter(trt_subgraph_data.subgraph_input_shapes, trt_subgraph_data.subgraph_input_dtypes)}]\n"
+            + f"Engine Inputs: {input_formatter(trt_subgraph_data.subgraph_input_shapes, trt_subgraph_data.subgraph_input_dtypes)}\n"
         )
         formatted_stats += (
             " " * 5
             + f"Number of Operators in Engine: {trt_subgraph_data.subgraph_op_count}\n"
         )
         formatted_stats += (
             " " * 5
-            + f"Engine Outputs: [{input_formatter(trt_subgraph_data.subgraph_output_shapes, trt_subgraph_data.subgraph_output_dtypes)}]\n"
+            + f"Engine Outputs: {input_formatter(trt_subgraph_data.subgraph_output_shapes, trt_subgraph_data.subgraph_output_dtypes)}\n"
         )
 
     formatted_stats += " " * 4 + "...\n"
     formatted_stats += (
         " " * 3
-        + f"Outputs: [{input_formatter(dryrun_tracker.graph_output_shapes, dryrun_tracker.graph_output_dtypes)}]\n"
+        + f"Outputs: {input_formatter(dryrun_tracker.graph_output_shapes, dryrun_tracker.graph_output_dtypes)}\n"
     )
 
     # Print aggregate statistics about the graph structure, including recommended "min_block_size" options
@@ -167,23 +158,23 @@ def dryrun_stats_display(dryrun_tracker: DryRunTracker, dryrun_enabled: bool) ->
             + " " * 3
             + "- For minimal graph segmentation, select min_block_size="
             + f"{most_ops_in_an_engine} which would generate "
-            + f"{len([1 for trt_subgraph in dryrun_tracker.per_subgraph_data if trt_subgraph.subgraph_op_count >= most_ops_in_an_engine])} TRT engines"
+            + f"{len([1 for trt_subgraph in dryrun_tracker.per_subgraph_data if trt_subgraph.subgraph_op_count >= most_ops_in_an_engine])} TRT engine(s)"
         )
         if math.ceil(avg_ops_per_engine) != most_ops_in_an_engine:
             formatted_stats += (
                 "\n"
                 + " " * 3
                 + "- For moderate graph segmentation, select min_block_size="
                 + f"{math.ceil(avg_ops_per_engine)} which would generate "
-                + f"{len([1 for trt_subgraph in dryrun_tracker.per_subgraph_data if trt_subgraph.subgraph_op_count >= math.ceil(avg_ops_per_engine)])} TRT engines"
+                + f"{len([1 for trt_subgraph in dryrun_tracker.per_subgraph_data if trt_subgraph.subgraph_op_count >= math.ceil(avg_ops_per_engine)])} TRT engine(s)"
             )
 
         formatted_stats += (
             "\n"
             + " " * 3
             + "- The current level of graph segmentation is equivalent to selecting min_block_size="
             + f"{min_ops_in_an_engine} which generates "
-            + f"{len([1 for trt_subgraph in dryrun_tracker.per_subgraph_data if trt_subgraph.subgraph_op_count >= min_ops_in_an_engine])} TRT engines"
+            + f"{len([1 for trt_subgraph in dryrun_tracker.per_subgraph_data if trt_subgraph.subgraph_op_count >= min_ops_in_an_engine])} TRT engine(s)"
         )
     else:
         formatted_stats += (
@@ -192,14 +183,45 @@ def dryrun_stats_display(dryrun_tracker: DryRunTracker, dryrun_enabled: bool) ->
             + "Aggregate stats not available since no TRT Engines were generated."
         )
 
-    dryrun_logger(formatted_stats)
+    # If user specified "dryrun=True", print to stdout, else debug
+    if dryrun_enabled:
+        print(formatted_stats)
+    else:
+        logger.debug(formatted_stats)
 
 
-def input_formatter(shapes: List[Tuple[int, ...]], dtypes: List[torch.dtype]) -> str:
+def input_formatter(shapes: Any, dtypes: Any) -> str:
     """Format shapes and dtypes of input Tensors into a readable string"""
-    formatted_str = ", "
 
-    for shape, dtype in zip(shapes, dtypes):
-        formatted_str += f"Tensor: {shape}@{str(dtype)[6:]}, "
+    def input_formatter_helper(shapes: Any, dtypes: Any) -> str:
+        """Helper for input formatter"""
+        # Base case - single shape, single dtype
+        if isinstance(shapes, tuple) and all(isinstance(elt, int) for elt in shapes):
+            return f"Tensor: {shapes}@{str(dtypes)[6:]}, "
+
+        # Shapes is a sequence
+        elif isinstance(shapes, (list, tuple)):
+            formatted_str = "List[" if isinstance(shapes, list) else "Tuple("
+            for shape, dtype in zip(shapes, dtypes):
+                formatted_str += input_formatter_helper(shape, dtype)
+            formatted_str = formatted_str[:-2] + (
+                "], " if isinstance(shapes, list) else "), "
+            )
+            return formatted_str
+
+        # Shapes is a dictionary
+        elif isinstance(shapes, dict):
+            formatted_str = "Dict{"
+
+            for key, shape in shapes.items():
+                formatted_str += input_formatter_helper(shape, dtypes[key])
+
+            formatted_str = formatted_str[:-2] + "}, "
+            return formatted_str
+
+        else:
+            raise ValueError(
+                f"Invalid input type {type(shapes)} encountered in parse_complex_tensor_structs parsing."
+            )
 
-    return formatted_str[2:-2]
+    return input_formatter_helper(shapes, dtypes)[:-2]

py/torch_tensorrt/dynamo/_compiler.py

@@ -42,6 +42,7 @@
 from torch_tensorrt.dynamo.lowering import apply_lowering_passes
 from torch_tensorrt.dynamo.utils import (
     get_torch_inputs,
+    parse_complex_tensor_structs,
     prepare_inputs,
     set_log_level,
     to_torch_device,
@@ -234,11 +235,13 @@ def compile_module(
 
     dryrun_tracker.total_ops_in_graph = total_ops
     dryrun_tracker.supported_ops_in_graph = num_supported_ops
-    dryrun_tracker.graph_input_shapes = [
-        tuple(input_.shape) for input_ in sample_inputs
-    ]
-    dryrun_tracker.graph_input_dtypes = [input_.torch_dtype for input_ in sample_inputs]
-    dryrun_tracker.truncated_long_and_double = settings.truncate_long_and_double
+    dryrun_tracker.graph_input_shapes = parse_complex_tensor_structs(
+        sample_inputs, "shape", tuple
+    )
+    dryrun_tracker.graph_input_dtypes = parse_complex_tensor_structs(
+        sample_inputs, "torch_dtype"
+    )
+    dryrun_tracker.compilation_settings = settings
 
     if settings.dryrun and settings.min_block_size > 1:
         logger.info(
@@ -267,7 +270,7 @@ def compile_module(
     # If specified, try using the fast partitioner and fall back to the global one on failure
     if settings.use_fast_partitioner:
         try:
-            partitioned_module = partitioning.fast_partition(
+            partitioned_module, supported_ops = partitioning.fast_partition(
                 gm,
                 verbose=settings.debug,
                 min_block_size=settings.min_block_size,
@@ -284,13 +287,15 @@ def compile_module(
             settings.use_fast_partitioner = False
 
     if not settings.use_fast_partitioner:
-        partitioned_module = partitioning.global_partition(
+        partitioned_module, supported_ops = partitioning.global_partition(
             gm,
             verbose=settings.debug,
             min_block_size=settings.min_block_size,
             torch_executed_ops=settings.torch_executed_ops,
         )
 
+    dryrun_tracker.unsupported_ops = supported_ops.unsupported_operators
+
     # Store TRT replicas of Torch subgraphs
     trt_modules = {}
     # Iterate over all components that can be accelerated
@@ -337,25 +342,23 @@ def compile_module(
                 name,
             )
 
-        subgraph_data.subgraph_input_dtypes = [
-            submodule_input.torch_dtype for submodule_input in submodule_inputs
-        ]
-        subgraph_data.subgraph_input_shapes = [
-            tuple(submodule_input.shape) for submodule_input in submodule_inputs
-        ]
+        subgraph_data.subgraph_input_shapes = parse_complex_tensor_structs(
+            submodule_inputs, "shape", tuple
+        )
+        subgraph_data.subgraph_input_dtypes = parse_complex_tensor_structs(
+            submodule_inputs, "torch_dtype"
+        )
 
         submodule_outputs = submodule(
             *get_torch_inputs(submodule_inputs, to_torch_device(settings.device))
         )
-        if not isinstance(submodule_outputs, (list, tuple)):
-            submodule_outputs = [submodule_outputs]
 
-        subgraph_data.subgraph_output_dtypes = [
-            submodule_output.dtype for submodule_output in submodule_outputs
-        ]
-        subgraph_data.subgraph_output_shapes = [
-            tuple(submodule_output.shape) for submodule_output in submodule_outputs
-        ]
+        subgraph_data.subgraph_output_shapes = parse_complex_tensor_structs(
+            submodule_outputs, "shape", tuple
+        )
+        subgraph_data.subgraph_output_dtypes = parse_complex_tensor_structs(
+            submodule_outputs, "dtype"
+        )
 
         dryrun_tracker.tensorrt_graph_count += 1
         dryrun_tracker.per_subgraph_data.append(subgraph_data)
@@ -378,10 +381,12 @@ def compile_module(
     if not isinstance(sample_outputs, (list, tuple)):
         sample_outputs = [sample_outputs]
 
-    dryrun_tracker.graph_output_shapes = [
-        tuple(output_.shape) for output_ in sample_outputs
-    ]
-    dryrun_tracker.graph_output_dtypes = [output_.dtype for output_ in sample_outputs]
+    dryrun_tracker.graph_output_shapes = parse_complex_tensor_structs(
+        sample_outputs, "shape", tuple
+    )
+    dryrun_tracker.graph_output_dtypes = parse_complex_tensor_structs(
+        sample_outputs, "dtype"
+    )
 
     # Replace all FX Modules with TRT Modules
     for name, trt_module in trt_modules.items():

py/torch_tensorrt/dynamo/partitioning/_adjacency_partitioner.py

@@ -248,7 +248,7 @@ def partition(
     min_block_size: int = MIN_BLOCK_SIZE,
     torch_executed_ops: Collection[Target] = set(),
     require_full_compilation: bool = REQUIRE_FULL_COMPILATION,
-) -> torch.fx.GraphModule:
+) -> Tuple[torch.fx.GraphModule, OpSupportTester]:
     """Partition an FX GraphModule with aten ops into TRT engines
     Partitioning is based on converter operator support
 
@@ -259,7 +259,7 @@ def partition(
         torch_executed_ops: Collection of operations to run in Torch, regardless of converter coverage
         require_full_compilation: Require that all computational operators be run in TRT
     Returns:
-        torch.fx.GraphModule
+        torch.fx.GraphModule, OpSupportTester
     """
     # Ensure graph is clean prior to partitioning
     gm.graph.eliminate_dead_code()
@@ -280,4 +280,4 @@ def partition(
     if verbose:
         supported_ops.print_support_overview(partitioner.num_trt_accelerated_subgraphs)
 
-    return partitioned_graph
+    return partitioned_graph, supported_ops