fix: cherry pick PR of 3445

.github/workflows/build-test-linux.yml

@@ -174,6 +174,7 @@ jobs:
         python -m pip install -r requirements.txt
         cd dynamo
         python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dyn_models_export.xml --ir dynamo models/
+        python -m pytest -ra --junitxml=${RUNNER_TEST_RESULTS_DIR}/dyn_models_export.xml test_modelopt_models.py
         popd
 
   tests-py-dynamo-serde:

py/torch_tensorrt/dynamo/_refit.py

@@ -9,6 +9,7 @@
 import tensorrt as trt
 import torch
 from torch.export import ExportedProgram
+from torch.fx.experimental.proxy_tensor import unset_fake_temporarily
 from torch_tensorrt._enums import dtype
 from torch_tensorrt._Input import Input
 from torch_tensorrt.dynamo import partitioning
@@ -144,71 +145,72 @@ def _refit_single_trt_engine_with_gm(
     Refit a TensorRT Engine in place
     """
 
-    refitted = set()
-    torch_device = get_model_device(new_gm)
-    refitter = trt.Refitter(old_engine, TRT_LOGGER)
-    weight_list = refitter.get_all_weights()
-
-    if weight_name_map:
-        # Get the refitting mapping
-        trt_wt_location = (
-            trt.TensorLocation.DEVICE
-            if torch_device.type == "cuda"
-            else trt.TensorLocation.HOST
-        )
+    with unset_fake_temporarily():
+        refitted = set()
+        torch_device = get_model_device(new_gm)
+        refitter = trt.Refitter(old_engine, TRT_LOGGER)
+        weight_list = refitter.get_all_weights()
+
+        if weight_name_map:
+            # Get the refitting mapping
+            trt_wt_location = (
+                trt.TensorLocation.DEVICE
+                if torch_device.type == "cuda"
+                else trt.TensorLocation.HOST
+            )
 
-        constant_mapping: dict[str, Any] = weight_name_map.pop(
-            "constant_mapping", {}
-        )  # type: ignore
-        mapping = construct_refit_mapping_from_weight_name_map(
-            weight_name_map, new_gm.state_dict()
-        )
-        constant_mapping_with_type = {}
-
-        for constant_name, val in constant_mapping.items():
-            np_weight_type = val.dtype
-            val_tensor = torch.from_numpy(val).cuda()
-            trt_dtype = dtype.try_from(np_weight_type).to(trt.DataType)
-            torch_dtype = dtype.try_from(np_weight_type).to(torch.dtype)
-            constant_mapping_with_type[constant_name] = (
-                val_tensor.clone().reshape(-1).contiguous().to(torch_dtype),
-                trt_dtype,
+            constant_mapping: dict[str, Any] = weight_name_map.pop(
+                "constant_mapping", {}
+            )  # type: ignore
+            mapping = construct_refit_mapping_from_weight_name_map(
+                weight_name_map, new_gm.state_dict()
             )
+            constant_mapping_with_type = {}
+
+            for constant_name, val in constant_mapping.items():
+                np_weight_type = val.dtype
+                val_tensor = torch.from_numpy(val).cuda()
+                trt_dtype = dtype.try_from(np_weight_type).to(trt.DataType)
+                torch_dtype = dtype.try_from(np_weight_type).to(torch.dtype)
+                constant_mapping_with_type[constant_name] = (
+                    val_tensor.clone().reshape(-1).contiguous().to(torch_dtype),
+                    trt_dtype,
+                )
 
-        mapping.update(constant_mapping_with_type)
+            mapping.update(constant_mapping_with_type)
 
-        for layer_name in weight_list:
-            if layer_name not in mapping:
-                logger.warning(f"{layer_name} is not found in weight mapping.")
-                continue
-            # Use Numpy to create weights
-            weight, weight_dtype = mapping[layer_name]
-            trt_wt_tensor = trt.Weights(
-                weight_dtype, weight.data_ptr(), torch.numel(weight)
-            )
-            refitter.set_named_weights(layer_name, trt_wt_tensor, trt_wt_location)
-        assert (
-            len(refitter.get_missing_weights()) == 0
-        ), "Fast refitting failed due to incomplete mapping"
+            for layer_name in weight_list:
+                if layer_name not in mapping:
+                    logger.warning(f"{layer_name} is not found in weight mapping.")
+                    continue
+                # Use Numpy to create weights
+                weight, weight_dtype = mapping[layer_name]
+                trt_wt_tensor = trt.Weights(
+                    weight_dtype, weight.data_ptr(), torch.numel(weight)
+                )
+                refitter.set_named_weights(layer_name, trt_wt_tensor, trt_wt_location)
+            assert (
+                len(refitter.get_missing_weights()) == 0
+            ), "Fast refitting failed due to incomplete mapping"
 
-    else:
-        mapping = construct_refit_mapping(new_gm, input_list, settings)
-        trt_wt_location = trt.TensorLocation.HOST
-        for layer_name in weight_list:
-            if layer_name not in mapping:
-                raise AssertionError(f"{layer_name} is not found in weight mapping")
-            # Use Numpy to create weights
-            weight, datatype = mapping[layer_name]
-            trt_wt_tensor = trt.Weights(datatype, weight.ctypes.data, weight.size)
-            refitter.set_named_weights(layer_name, trt_wt_tensor, trt_wt_location)
-            refitted.add(layer_name)
-
-        if len(refitted) != len(weight_list):
-            logger.warning("Not all weights have been refitted!!!")
-
-    if not refitter.refit_cuda_engine():
-        logger.error("Error: failed to refit new weights.")
-        raise AssertionError("Refitting failed.")
+        else:
+            mapping = construct_refit_mapping(new_gm, input_list, settings)
+            trt_wt_location = trt.TensorLocation.HOST
+            for layer_name in weight_list:
+                if layer_name not in mapping:
+                    raise AssertionError(f"{layer_name} is not found in weight mapping")
+                # Use Numpy to create weights
+                weight, datatype = mapping[layer_name]
+                trt_wt_tensor = trt.Weights(datatype, weight.ctypes.data, weight.size)
+                refitter.set_named_weights(layer_name, trt_wt_tensor, trt_wt_location)
+                refitted.add(layer_name)
+
+            if len(refitted) != len(weight_list):
+                logger.warning("Not all weights have been refitted!!!")
+
+        if not refitter.refit_cuda_engine():
+            logger.error("Error: failed to refit new weights.")
+            raise AssertionError("Refitting failed.")
 
 
 def refit_module_weights(

py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py

@@ -21,6 +21,7 @@
 import tensorrt as trt
 import torch
 import torch.fx
+from torch.fx.experimental.proxy_tensor import unset_fake_temporarily
 from torch.fx.node import _get_qualified_name
 from torch.fx.passes.shape_prop import TensorMetadata
 from torch.utils._python_dispatch import _disable_current_modes
@@ -41,6 +42,7 @@
     get_node_io,
     get_node_name,
     get_trt_tensor,
+    to_torch,
 )
 from torch_tensorrt.dynamo.utils import DYNAMIC_DIM, get_model_device, to_torch_device
 from torch_tensorrt.fx.observer import Observer
@@ -408,27 +410,29 @@ def find_weight(
         np_map: the map from weight name to np values in INetworkDefinition
         state_dict: state of the graph module
         """
-        network_weight = torch.from_numpy(np_map[weight_name]).to(device)
-        for sd_w_name, sd_weight in state_dict.items():
-            if TRTInterpreter.check_weight_equal(sd_weight, network_weight, device):
-                del state_dict[sd_w_name]
-                return sd_w_name
-        return ""
+        with unset_fake_temporarily():
+            network_weight = torch.from_numpy(np_map[weight_name]).to(device)
+            for sd_w_name, sd_weight in state_dict.items():
+                if TRTInterpreter.check_weight_equal(sd_weight, network_weight, device):
+                    del state_dict[sd_w_name]
+                    return sd_w_name
+            return ""
 
     @staticmethod
     def check_weight_equal(
         sd_weight: torch.tensor,
         network_weight: Union[torch.Tensor, np.ndarray],
         device: torch.device,
     ) -> Any:
-        if not isinstance(network_weight, torch.Tensor):
-            network_weight = torch.from_numpy(network_weight).to(device)
-        try:
-            return sd_weight.shape == network_weight.shape and torch.all(
-                torch.abs(sd_weight - network_weight) < 0.01
-            )
-        except Exception:
-            return torch.all(sd_weight == network_weight)
+        with unset_fake_temporarily():
+            if not isinstance(network_weight, torch.Tensor):
+                network_weight = torch.from_numpy(network_weight).to(device)
+            try:
+                return sd_weight.shape == network_weight.shape and torch.all(
+                    torch.abs(sd_weight - network_weight) < 0.01
+                )
+            except Exception:
+                return torch.all(sd_weight == network_weight)
 
     def _save_weight_mapping(self) -> None:
         """
@@ -887,19 +891,15 @@ def call_function(self, target: str, args: Any, kwargs: Any) -> Any:
             return converter(self.ctx, target, args, kwargs, self._cur_node_name)
 
     def get_attr(self, target: str, args: Any, kwargs: Any) -> np.ndarray:
-        with _disable_current_modes():
-            from torch_tensorrt.dynamo.conversion.converter_utils import to_numpy
-
+        with _disable_current_modes(), unset_fake_temporarily():
             frozen_attr = self.fetch_attr(target)
 
             if isinstance(frozen_attr, torch.nn.Parameter):
                 constant_tensor = frozen_attr.data
             else:
                 constant_tensor = frozen_attr
 
-            network_constant = to_numpy(constant_tensor)
-
-        return network_constant
+        return to_torch(constant_tensor)
 
     def call_method(self, target: str, args: Any, kwargs: Any) -> Any:
         assert isinstance(target, str)

py/torch_tensorrt/dynamo/conversion/converter_utils.py

@@ -9,6 +9,7 @@
 import tensorrt as trt
 import torch
 import torch_tensorrt.dynamo.conversion.impl as impl
+from torch.fx.experimental.proxy_tensor import unset_fake_temporarily
 from torch.fx.node import Argument, Target
 from torch.fx.passes.shape_prop import TensorMetadata
 from torch_tensorrt import _enums
@@ -340,17 +341,47 @@ def create_constant(
     Returns:
         A TensorRT ITensor that represents the given value.
     """
-    shape = (1,)
-    # Rank 0 constant is required in IFillLayer inputs.
-    if min_rank == 0:
-        shape = trt.Dims()
-    numpy_value = to_numpy(value, dtype)
-    constant = ctx.net.add_constant(
-        shape if isinstance(value, (int, float, bool)) else value.shape,
-        numpy_value.copy() if isinstance(numpy_value, np.ndarray) else numpy_value,
-    )
-    constant.name = name
-    return constant.get_output(0)
+    with unset_fake_temporarily():
+
+        torch_value = to_torch(value, dtype)
+        if torch_value.dtype == torch.float64:
+            raise ValueError(
+                "TensorRT does not support float64 (double) precision. To resolve this, please set truncate_double=True in your compilation settings and re-run the model."
+            )
+        # Rank 0 constant is required in IFillLayer inputs.
+        if min_rank == 0 and isinstance(value, (int, float, bool)):
+            shape = trt.Dims()
+        elif list(torch_value.shape) == []:
+            shape = trt.Dims()
+        else:
+            shape = list(torch_value.shape)
+
+        if torch_value is not None:
+            if torch_value.dtype == torch.bfloat16:
+                torch_value_fp32 = torch_value.to(torch.float32)
+                numpy_value = torch_value_fp32.numpy()
+            else:
+                numpy_value = torch_value.numpy()
+
+            constant = ctx.net.add_constant(
+                shape,
+                numpy_value,
+            )
+            constant.name = name
+
+            if torch_value.dtype == torch.bfloat16:
+                return cast_trt_tensor(
+                    ctx,
+                    constant.get_output(0),
+                    trt.DataType.BF16,
+                    name + "_bf16_cast",
+                )
+
+            return constant.get_output(0)
+        else:
+            raise ValueError(
+                f"Cannot convert tensor '{name}' to a TensorRT constant because its value is None."
+            )
 
 
 def get_trt_tensor(
@@ -564,6 +595,9 @@ def to_numpy(
             value = value.dequantize()
         elif value.dtype == torch.bfloat16:
             # TODO: Remove when numpy has a BF16 type
+            _LOGGER.warning(
+                "Requested a conversion of bfloat16 tensor from torch to numpy which isn't supported. Casting this tensor to FP32 precision currently. Please use to_torch() API for better data representation",
+            )
             value = value.to(torch.float)
 
         output = value.cpu().detach().contiguous().numpy()
@@ -589,6 +623,53 @@ def to_numpy(
         )
 
 
+def to_torch(
+    value: Optional[Union[torch.Tensor, np.ndarray, int, float, bool]],
+    dtype: Optional[Union[torch.dtype, np.dtype, TRTDataType, _enums.dtype]] = None,
+) -> Optional[torch.Tensor]:
+    """
+    Convert a Numpy array, or scalar to a PyTorch tensor and move it to CPU
+    Args:
+        value (Optional[Union[torch.Tensor, np.ndarray, int, float, bool]]):
+            A PyTorch tensor, Numpy array, int, float, or bool
+        dtype (Optional[Union[torch.dtype, np.dtype, TRTDataType]]):
+            If a dtype is given, we will convert the type of the given `value` to this dtype.
+    Returns:
+        A PyTorch tensor or None, if the input was None.
+    """
+
+    cpu_device = torch.device("cpu")
+    torch_dtype = (
+        _enums.dtype._from(dtype).to(torch.dtype, use_default=True) if dtype else None
+    )
+
+    with unset_fake_temporarily():
+        if value is None:
+            return None
+
+        elif isinstance(value, torch.Tensor):
+            output = value.to(cpu_device).contiguous()
+
+        elif isinstance(value, np.ndarray):
+            output = torch.from_numpy(value).to(cpu_device).contiguous()
+
+        elif isinstance(value, int):
+            output = torch.tensor([value], device=cpu_device, dtype=torch.int32)
+
+        elif isinstance(value, float):
+            output = torch.tensor([value], device=cpu_device, dtype=torch.float32)
+
+        elif isinstance(value, bool):
+            output = torch.tensor([value], device=cpu_device, dtype=torch.bool)
+
+        else:
+            raise AssertionError(
+                f"to_torch can only be called on None, bool, int, float, np.ndarray, or torch.Tensor, got an object of type: {type(value)}"
+            )
+
+        return output.to(torch_dtype) if torch_dtype else output
+
+
 def flatten_dims(
     input: Sequence[Union[TRTTensor, torch.Tensor, np.ndarray]],
     start_dim: int,