fix: Remove references to implicit batch for TRT 10

py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py

@@ -58,7 +58,7 @@ def batch_norm(
 
     # For BatchNorm1d, reshape 1d to 2d
     output_shape = input.shape
-    if not ctx.net.has_implicit_batch_dimension and len(input.shape) < 4:
+    if len(input.shape) < 4:
         assert (
             len(get_dynamic_dims(input.shape)) <= 1
         ), "BatchNorm1D with more than one dynamic dims is not currently supported."
@@ -75,7 +75,7 @@ def batch_norm(
     output = layer.get_output(0)
 
     # For BatchNorm1d, reshape output back to 1d
-    if not ctx.net.has_implicit_batch_dimension and len(output_shape) < 4:
+    if len(output_shape) < 4:
         output = impl.shuffle.reshape(
             ctx,
             target,
@@ -411,7 +411,7 @@ def softmax(
     input: TRTTensor,
     dim: Optional[Any] = None,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    input_ranks = len(input.shape) + (1 if ctx.net.has_implicit_batch_dimension else 0)
+    input_ranks = len(input.shape)
 
     if not isinstance(input, TRTTensor):
         raise RuntimeError(
@@ -433,9 +433,6 @@ def get_softmax_dim(ndim: int) -> int:
         dim = cast(int, dim)
 
     dim = get_positive_dim(dim, input_ranks)
-    if ctx.net.has_implicit_batch_dimension:
-        assert dim != 0, "Can't apply softmax on batch dimension when it's implicit."
-        dim -= 1
 
     layer = ctx.net.add_softmax(input)
     layer.axes = 1 << dim

py/torch_tensorrt/dynamo/conversion/impl/select.py

@@ -40,19 +40,12 @@ def select(
             "of the TensorRT region!"
         )
 
-    ranks = len(input.shape) + (1 if ctx.net.has_implicit_batch_dimension else 0)
+    ranks = len(input.shape)
     dim = get_positive_dim(cast(int, dim), ranks)
     dynamic_shape = has_dynamic_shape(input.shape)
-    if ctx.net.has_implicit_batch_dimension:
-        if dim == 0:
-            raise RuntimeError(
-                f"We do not support slice_tensor at batch dim when it's implicit, got {dim}!"
-            )
-        dim = dim - 1
-    else:
-        if dynamic_shape:
-            # Check whether slice target dim is dynamic shape dim
-            assert input.shape[dim] != -1, "Can't select on negative shape dimension!"
+    if dynamic_shape:
+        # Check whether slice target dim is dynamic shape dim
+        assert input.shape[dim] != -1, "Can't select on negative shape dimension!"
     index = index
 
     if index >= input.shape[dim]:

py/torch_tensorrt/dynamo/conversion/impl/squeeze.py

@@ -32,11 +32,8 @@ def squeeze(
     for dim in dims:
         dim = get_positive_dim(
             dim,
-            len(input.shape) + (1 if ctx.net.has_implicit_batch_dimension else 0),
+            len(input.shape),
         )
-        if ctx.net.has_implicit_batch_dimension:
-            assert dim != 0, "We don't support squeeze batch dim when it's implicit."
-            dim -= 1
 
         assert input.shape[dim] != -1, "We don't support squeeze dynamic dim."
         assert (

py/torch_tensorrt/dynamo/conversion/impl/unsqueeze.py

@@ -29,17 +29,9 @@ def unsqueeze(
 
     dim = cast(int, dim)
 
-    input_shape_size = (
-        len(input_val.shape) + 1
-        if ctx.net.has_implicit_batch_dimension
-        else len(input_val.shape)
-    )
+    input_shape_size = len(input_val.shape)
     dim = get_positive_dim(dim, input_shape_size + 1)
 
-    if ctx.net.has_implicit_batch_dimension:
-        assert dim != 0
-        dim -= 1
-
     assert (
         len(get_dynamic_dims(input_val.shape)) <= 1
     ), "Currently we don't support unsqueeze with more than one dynamic dims."