user provided bound for torchtrt compile when export dimension is unb…

py/torch_tensorrt/dynamo/_compiler.py

@@ -5,11 +5,13 @@
 import os
 import platform
 import warnings
-from typing import Any, Collection, List, Optional, Sequence, Union
+from typing import Any, Collection, Dict, List, Optional, Sequence, Tuple, Union
 
+import sympy
 import torch
 from torch.export import ExportedProgram
 from torch.fx.node import Target
+from torch.utils._sympy.numbers import int_oo
 from torch_tensorrt._Device import Device
 from torch_tensorrt._enums import EngineCapability, dtype
 from torch_tensorrt._features import ENABLED_FEATURES, needs_cross_compile
@@ -874,6 +876,139 @@ def _insert_complex_io_adapters(
         partitioned_module.recompile()
 
 
+def _build_user_symbol_bounds(
+    gm: torch.fx.GraphModule,
+    sample_arg_inputs: Sequence[Input],
+    sample_kwarg_inputs: dict[Any, Any],
+) -> Dict[sympy.Symbol, Tuple[int, int]]:
+    """Map ``sympy.Symbol -> (min, max)`` from dynamic ``Input``s, used to
+    fill ``Dim.DYNAMIC`` upper bounds without mutating ``ShapeEnv``.
+
+    Validates against finite exporter bounds: ``user_max > exp_max`` and
+    ``user_min < exp_min`` raise (TRT would reject those shapes at runtime);
+    a strict subset narrows the engine profile to the user's bounds (info
+    log only); the ``user_min=1, exp_min=2`` case warns -- it's PyTorch's
+    0/1 specialization artifact, not a user error.
+    """
+    placeholders = [n for n in gm.graph.nodes if n.op == "placeholder"]
+
+    sample_by_name: dict[str, Input] = {}
+    for i, node in enumerate(placeholders):
+        if i < len(sample_arg_inputs):
+            inp = sample_arg_inputs[i]
+            if isinstance(inp, Input) and inp.shape_mode == Input._ShapeMode.DYNAMIC:
+                sample_by_name[node.target] = inp
+    for name, inp in sample_kwarg_inputs.items():
+        if isinstance(inp, Input) and inp.shape_mode == Input._ShapeMode.DYNAMIC:
+            sample_by_name[name] = inp
+
+    user_symbol_bounds: Dict[sympy.Symbol, Tuple[int, int]] = {}
+    if not sample_by_name:
+        return user_symbol_bounds
+
+    for node in placeholders:
+        if node.target not in sample_by_name:
+            continue
+        sample_input = sample_by_name[node.target]
+        fake_val = node.meta.get("val")
+        if not isinstance(fake_val, torch.Tensor):
+            continue
+
+        min_shape = sample_input.shape["min_shape"]
+        max_shape = sample_input.shape["max_shape"]
+
+        for d, dim in enumerate(fake_val.size()):
+            if not isinstance(dim, torch.SymInt) or d >= len(min_shape):
+                continue
+            expr = dim.node.expr
+            # Composite exprs (e.g. ``2*s0``) are recomputed by
+            # ``ShapeEnv.bound_sympy``; overriding them directly would lie.
+            if not isinstance(expr, sympy.Symbol):
+                continue
+            if expr in user_symbol_bounds:
+                continue
+            user_min = int(min_shape[d])
+            user_max = int(max_shape[d])
+            user_symbol_bounds[expr] = (user_min, user_max)
+            logger.debug(
+                "Recorded user-supplied bounds for %s: [%d, %d]",
+                expr,
+                user_min,
+                user_max,
+            )
+
+            # If exporter bounds are finite, ``extract_var_range_info`` keeps
+            # them (override is gated on ``max_val is None``). Catch the
+            # mismatch here so the user doesn't hit a runtime "shape outside
+            # profile" error on shapes they explicitly declared.
+            shape_env = getattr(dim.node, "shape_env", None)
+            if shape_env is None:
+                continue
+            exp_range = shape_env.var_to_range.get(expr)
+            if exp_range is None:
+                continue
+            exp_lower = exp_range.lower
+            exp_upper = exp_range.upper
+            exp_max_unbounded = exp_upper is int_oo or exp_upper == sympy.oo
+            if exp_max_unbounded:
+                # Dim.DYNAMIC: user fills the gap (intended use).
+                continue
+            try:
+                exp_min = int(exp_lower)
+                exp_max = int(exp_upper)
+            except (TypeError, ValueError):
+                continue
+            if user_min == exp_min and user_max == exp_max:
+                continue
+
+            mismatch = (
+                f"symbol {expr}: Input({user_min}, {user_max}) vs "
+                f"exporter({exp_min}, {exp_max})."
+            )
+            hint = (
+                f" Re-export with Dim('{expr}', min={user_min}, "
+                f"max={user_max}) or adjust Input to match."
+            )
+
+            if user_max > exp_max:
+                raise ValueError(
+                    f"{mismatch} Input.max_shape exceeds the exporter's max "
+                    f"({user_max} > {exp_max}); TRT will reject shapes above "
+                    f"{exp_max} at runtime.{hint}"
+                )
+
+            if user_min < exp_min:
+                # 1->2 is the 0/1 specialization artifact, not a user error.
+                if user_min == 1 and exp_min == 2:
+                    logger.warning(
+                        "%s Input.min_shape=1 vs exporter min=2 is the "
+                        "PyTorch 0/1 specialization artifact; TRT engine "
+                        "min will be 2.",
+                        mismatch,
+                    )
+                    continue
+                raise ValueError(
+                    f"{mismatch} Input.min_shape is below the exporter's min "
+                    f"({user_min} < {exp_min}); TRT will reject shapes "
+                    f"below {exp_min} at runtime.{hint}"
+                )
+
+            # Strict subset: engine profile narrows to the user's bounds
+            # (applied in ``extract_var_range_info``). Not a warning -- the
+            # user got exactly what they asked for.
+            logger.info(
+                "%s Narrowing engine profile to user bounds [%d, %d] "
+                "(exporter range was [%d, %d]).",
+                mismatch,
+                user_min,
+                user_max,
+                exp_min,
+                exp_max,
+            )
+
+    return user_symbol_bounds
+
+
 @fn_supports_debugger  # type: ignore[misc]
 def compile_module(
     gm: torch.fx.GraphModule,
@@ -905,6 +1040,12 @@ def compile_module(
     if sample_kwarg_inputs is None:
         sample_kwarg_inputs = {}
 
+    # Forwarded to the partitioner to fill Dim.DYNAMIC upper bounds.
+    # Read-only w.r.t. ShapeEnv so range_constraints survive save/re-export.
+    user_symbol_bounds = _build_user_symbol_bounds(
+        gm, sample_arg_inputs, sample_kwarg_inputs
+    )
+
     # Configure user compilation settings to converters.
     CONVERTERS.set_compilation_settings(settings)
 
@@ -1086,7 +1227,9 @@ def preserve_module_specs(
         )
 
         # Get the submodule inputs for min, opt, max shapes of the graph inputs
-        submodule_inputs = partitioning.construct_submodule_inputs(submodule)
+        submodule_inputs = partitioning.construct_submodule_inputs(
+            submodule, user_symbol_bounds=user_symbol_bounds
+        )
 
         assert submodule_inputs is not None
 

py/torch_tensorrt/dynamo/partitioning/common.py

@@ -1,10 +1,10 @@
 import logging
 from typing import Any, Dict, Optional, Sequence, Set, Tuple
 
+import sympy
 import torch
 from torch._subclasses.fake_tensor import FakeTensor
 from torch.fx.experimental.proxy_tensor import unset_fake_temporarily
-
 from torch_tensorrt._Input import Input
 from torch_tensorrt.dynamo.utils import (
     COMPLEX_TO_REAL_DTYPE,
@@ -20,11 +20,14 @@ def construct_dynamic_input(
     input_dtype: torch.dtype,
     name: str = "",
     is_shape_tensor: bool = False,
+    user_symbol_bounds: Optional[Dict[sympy.Symbol, Tuple[int, int]]] = None,
 ) -> Input:
     """
     Constructs a torch_tensorrt.Input based on a symbolic input
     Args:
         input_shape: A symbolic shape / regular shape of a tensor (which can have a  mix of SymInt nodes and static values)
+        user_symbol_bounds: Optional ``{sym: (min, max)}`` map; forwarded to
+            :func:`extract_var_range_info` to fill unbounded exporter uppers.
     Returns:
         A dynamic shaped torch_tensorrt.Input which has the properties of the symbolic shaped input.
     """
@@ -33,7 +36,9 @@ def construct_dynamic_input(
     max_shape = []
     for d, dim in enumerate(input_shape):
         if isinstance(dim, torch.SymInt):
-            min_max_opt = extract_var_range_info(dim)
+            min_max_opt = extract_var_range_info(
+                dim, user_symbol_bounds=user_symbol_bounds
+            )
             unwrapped_min_max_opt: Dict[str, int] = {}
             if "min" not in min_max_opt or min_max_opt["min"] is None:
                 logger.warning(
@@ -85,9 +90,12 @@ def get_input(
     dtype: torch.dtype,
     name: str = "",
     is_shape_tensor: bool = False,
+    user_symbol_bounds: Optional[Dict[sympy.Symbol, Tuple[int, int]]] = None,
 ) -> Input:
     """
-    Based on type of dimensions in the input_shape, construct regular or dynamic shaped inputs
+    Based on type of dimensions in the input_shape, construct regular or dynamic shaped inputs.
+
+    ``user_symbol_bounds`` is forwarded to :func:`construct_dynamic_input`.
     """
     if dtype in COMPLEX_TO_REAL_DTYPE:
         real_dtype = COMPLEX_TO_REAL_DTYPE[dtype]
@@ -106,19 +114,25 @@ def get_input(
             dtype,
             name=name,
             is_shape_tensor=is_shape_tensor,
+            user_symbol_bounds=user_symbol_bounds,
         )
     else:
         return Input(
             shape=input_shape, dtype=dtype, name=name, is_shape_tensor=is_shape_tensor
         )
 
 
-def construct_submodule_inputs(module: torch.fx.GraphModule) -> Sequence[Input]:
+def construct_submodule_inputs(
+    module: torch.fx.GraphModule,
+    user_symbol_bounds: Optional[Dict[sympy.Symbol, Tuple[int, int]]] = None,
+) -> Sequence[Input]:
     """
     Construct torch_tensorrt Inputs based on the module inputs.
     The module inputs will have meta data which has the shape and dtype info
     Args:
         module: Input FX GraphModule
+        user_symbol_bounds: Optional ``{sym: (min, max)}`` map; forwarded to
+            :func:`get_input` to fill unbounded exporter uppers.
     Returns:
         Sequence of torch_tensorrt.Input's representing inputs to given module
     """
@@ -134,7 +148,12 @@ def construct_submodule_inputs(module: torch.fx.GraphModule) -> Sequence[Input]:
                     if isinstance(input_meta, (FakeTensor, torch.Tensor)):
                         input_shape = input_meta.size()
                         torchtrt_inputs.append(
-                            get_input(input_shape, input_meta.dtype, name=input.name)
+                            get_input(
+                                input_shape,
+                                input_meta.dtype,
+                                name=input.name,
+                                user_symbol_bounds=user_symbol_bounds,
+                            )
                         )
                     elif isinstance(input_meta, torch.SymInt):
                         # Assuming sym_integers | shape inputs always have torch.int64 dtype
@@ -144,6 +163,7 @@ def construct_submodule_inputs(module: torch.fx.GraphModule) -> Sequence[Input]:
                                 torch.int64,
                                 name=input.name,
                                 is_shape_tensor=True,
+                                user_symbol_bounds=user_symbol_bounds,
                             )
                         )
                     elif isinstance(input_meta, torch.SymFloat):
@@ -153,6 +173,7 @@ def construct_submodule_inputs(module: torch.fx.GraphModule) -> Sequence[Input]:
                                 torch.float32,
                                 name=input.name,
                                 is_shape_tensor=False,  # Only SymInt inputs are treated as shape tensors
+                                user_symbol_bounds=user_symbol_bounds,
                             )
                         )
                     else:
@@ -164,7 +185,12 @@ def construct_submodule_inputs(module: torch.fx.GraphModule) -> Sequence[Input]:
                     input_meta = input.meta["tensor_meta"]
                     input_shape = input_meta.shape
                     torchtrt_inputs.append(
-                        get_input(input_shape, input_meta.dtype, name=input.name)
+                        get_input(
+                            input_shape,
+                            input_meta.dtype,
+                            name=input.name,
+                            user_symbol_bounds=user_symbol_bounds,
+                        )
                     )
                 else:
                     raise AssertionError(

py/torch_tensorrt/dynamo/utils.py

@@ -406,9 +406,16 @@ def contains_sym_int(tensor: torch.Tensor) -> bool:
     return any(isinstance(dim, torch.SymInt) for dim in tensor)
 
 
-def extract_var_range_info(symbolic_integer: torch.SymInt) -> Dict[str, Optional[int]]:
-    """
-    This function returns the min, max, opt values of a symbolic integer.
+def extract_var_range_info(
+    symbolic_integer: torch.SymInt,
+    user_symbol_bounds: Optional[Dict[sympy.Symbol, Tuple[int, int]]] = None,
+) -> Dict[str, Optional[int]]:
+    """Return ``{min, max, opt}`` for a symbolic integer.
+
+    ``user_symbol_bounds`` (read-only ``{sym: (min, max)}``) is consulted only
+    when the exporter's upper is unbounded; finite exporter bounds always win.
+    The lower is intersected with the exporter's so the 0/1 specialization
+    survives even if the user passes ``min_shape=0``.
     """
     node = symbolic_integer.node
     expr = node.expr
@@ -435,13 +442,42 @@ def extract_var_range_info(symbolic_integer: torch.SymInt) -> Dict[str, Optional
         or expr.xreplace(var_to_val_map)
     )
     assert var_range, var_val
-    min_val, max_val = (
-        int(var_range.lower),
-        int(var_range.upper) if var_range.upper != int_oo else None,
-    )
+
+    # ``var_to_range`` returns ``int_oo`` for unbounded; ``bound_sympy`` (used
+    # for composite exprs like ``s0+s1``) returns ``sympy.oo`` instead. They
+    # are distinct objects -- check both, else ``int(sympy.oo)`` raises.
+    def _bound_to_int_or_none(value: Any) -> Optional[int]:
+        if value is int_oo or value is -int_oo:
+            return None
+        if value == sympy.oo or value == -sympy.oo:
+            return None
+        try:
+            return int(value)
+        except (TypeError, OverflowError, AttributeError):
+            return None
+
+    min_val_opt = _bound_to_int_or_none(var_range.lower)
+    max_val = _bound_to_int_or_none(var_range.upper)
+    # Unbounded lower shouldn't happen for tensor dims; fall back to 1.
+    min_val = min_val_opt if min_val_opt is not None else 1
 
     # Torchdynamo 0/1 specialization outlier
     min_val = 1 if min_val == 2 else min_val
+
+    # Apply user bounds whenever present. ``_build_user_symbol_bounds`` already
+    # rejects user ranges that exceed the exporter, so the only cases reaching
+    # here are: Dim.DYNAMIC (max_val is None), strict subset, or the 1->2
+    # specialization. Clamp defensively in case validation was skipped (no
+    # ShapeEnv access path).
+    if (
+        user_symbol_bounds
+        and isinstance(expr, sympy.Symbol)
+        and expr in user_symbol_bounds
+    ):
+        user_min, user_max = user_symbol_bounds[expr]
+        min_val = max(min_val, int(user_min))
+        max_val = int(user_max) if max_val is None else min(max_val, int(user_max))
+
     min_max_opt: Dict[str, Optional[int]] = {}
     min_max_opt["min"] = min_val
     min_max_opt["max"] = max_val