[FX] Sync to OSS

py/torch_tensorrt/fx/lower.py

@@ -92,7 +92,18 @@ def __call__(self, mod, input, split_name) -> TRTInterpreterResult:
         input_specs_val = (
             self.lower_setting.input_specs
             if self.lower_setting.input_specs
-            else InputTensorSpec.from_tensors(input)
+            else (
+                InputTensorSpec.from_tensors_with_dynamic_batch_size(
+                    input,
+                    (
+                        0,
+                        self.lower_setting.max_batch_size,
+                        self.lower_setting.max_batch_size,
+                    ),
+                )
+                if self.lower_setting.explicit_batch_dimension
+                else InputTensorSpec.from_tensors(input)
+            )
         )
 
         # Prepare algorithm selector and timing_cache for TRTInterpreter

py/torch_tensorrt/fx/lower_setting.py

@@ -63,6 +63,12 @@ class LowerSetting(LowerSettingBasic):
     save_timing_cache: Save updated timing cache data into timing cache file if the timing
     cache file is provided.
     cuda_graph_batch_size (int): Cuda graph batch size, default to be -1.
+    preset_lowerer (str): when specified, use a preset logic to build the
+    instance of Lowerer. Refer to
+    `caffe2.torch.fb.model_transform.fx2trt.presets.LowererPresetsManager` on
+    how presets are applied. Refer to
+    `caffe2.torch.fb.model_transform.fx2trt.presets.ESUHMLowererPreset` on how
+    to add a preset.
     """
 
     input_specs: List[InputTensorSpec] = dc.field(default_factory=list)
@@ -79,3 +85,4 @@ class LowerSetting(LowerSettingBasic):
     timing_cache_prefix: str = ""
     save_timing_cache: bool = False
     cuda_graph_batch_size: int = -1
+    preset_lowerer: str = ""

py/torch_tensorrt/fx/passes/lower_basic_pass.py

@@ -31,7 +31,9 @@ def skip_folding_quant_dequant(node: torch.fx.Node):
                 return True
         return False
 
-    const_split_mod = split_const_subgraphs(traced_mod, skip_folding_quant_dequant)
+    const_split_mod = split_const_subgraphs(
+        traced_mod, skip_folding_quant_dequant, device_for_folded_attrs="cuda"
+    )
     const_split_mod.run_folding()
     return const_split_mod
 

py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py

@@ -2576,5 +2576,6 @@ def test_all_acc_ops_registered(self):
                 acc_ops.new_ones,
                 acc_ops.einsum,
                 acc_ops.as_strided,
+                acc_ops.var,
             },
         )

py/torch_tensorrt/fx/tracer/acc_tracer/acc_ops.py

@@ -2864,3 +2864,18 @@ def as_strided(*, input, size, stride, storage_offset=0):
     return torch.as_strided(
         input=input, size=size, stride=stride, storage_offset=storage_offset
     )
+
+
+@register_acc_op_mapping(op_and_target=("call_function", torch.var))
+@register_acc_op_mapping(
+    op_and_target=("call_method", "var"),
+    arg_replacement_tuples=[
+        ("input", "input"),
+        ("dim", "dim"),
+        ("unbiased", "unbiased"),
+        ("keepdim", "keepdim"),
+    ],
+)
+@register_acc_op
+def var(*, input, dim, unbiased, keepdim=False):
+    return torch.var(input=input, dim=dim, unbiased=unbiased, keepdim=keepdim)