Addressing review comments

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

@@ -81,30 +81,20 @@ def index(
     source_ir: Optional[SourceIR],
     name: str,
     input: TRTTensor,
-    index: Union[
-        TRTTensor,
-        Sequence[TRTTensor],
-        np.ndarray,
-        Sequence[np.ndarray],
-        torch.Tensor,
-        Sequence[torch.Tensor],
-    ],
+    index: Sequence[Union[TRTTensor, np.ndarray, torch.Tensor]],
 ) -> TRTTensor:
     adv_indx_indices = []
     tensor_indices = []
-    # _LOGGER.debug(f"The index shape is {index.shape}")
     # check if the input is dynamic
     dynamic_shape = has_dynamic_shape(input.shape)
     # is_numpy is a flag to specify if all the indices are numpy or torchTensor.
     # If any is not this flag will be set to False
-    is_numpy = True
-    _LOGGER.debug(f"Checking for the is_numpy flag")
-    for i, ind in enumerate(index):
-        if ind is None:
-            continue
-        if not (isinstance(ind, torch.Tensor) or isinstance(ind, np.ndarray)):
-            is_numpy = False
-            break
+    _LOGGER.debug(
+        f"Determining whether aten.index constant-index optimization can be invoked"
+    )
+    is_numpy = all(
+        isinstance(ind, (torch.Tensor, np.ndarray)) for ind in index if ind is not None
+    )
     # here we need to check if all the index are broadcastable
     # if no, then we need to broadcast
     last_index = None
@@ -117,7 +107,6 @@ def index(
             # other cases are kept as TRTTensor
             if is_numpy:
                 ind = to_numpy(ind)
-                is_numpy = True
             else:
                 ind = get_trt_tensor(ctx, ind, name + f"_parameter_to_fp32_tensor_{i}")
             if last_index is not None:
@@ -156,9 +145,7 @@ def index(
         for i in range(rank):
             dim = input_shape[i]
             dim_tensor = get_trt_tensor(ctx, dim, name + f"_individual_dim_{i}")
-            # dim_tensor_list is a list of tensors or numpy
-            if is_numpy:
-                dim_list.append(dim)
+            # dim_tensor_list is a list of tensors
             dim_tensor_list.append(dim_tensor)
 
         # for cases like
@@ -211,12 +198,12 @@ def index(
         # tensor index = \sum_{i=1}^m (ind_i * \prod_{j=i+1}^m (x_j)),  ind_i is input indices[i], x_j is the
         # // j dimension of input x.
         if is_numpy:
-            multiplier = dim_list[adv_indx_indices[adv_indx_count - 1]]
+            multiplier = input_shape[adv_indx_indices[adv_indx_count - 1]]
             cum_adv_index = tensor_indices[adv_indx_count - 1]
             for i in range(adv_indx_count - 2, -1, -1):
                 adv_index = multiplier * tensor_indices[i]
                 cum_adv_index = cum_adv_index + adv_index
-                multiplier = multiplier * dim_list[adv_indx_indices[i]]
+                multiplier = multiplier * input_shape[adv_indx_indices[i]]
             cum_adv_index = get_trt_tensor(
                 ctx, cum_adv_index, name + f"_index_sum_intermediate"
             )