Fixed the CI

Author: cehongwang

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

@@ -258,16 +258,15 @@ def index(
             else:
                 dim_tensor_shape_mult_d1 = transpose_tensor_shape[i]
 
-            if isinstance(dim_tensor_shape_mult_d1, TRTTensor):
-                mult_d1 = convert_binary_elementwise(
-                    ctx,
-                    target,
-                    source_ir,
-                    name + f"_shape_{i}",
-                    trt.ElementWiseOperation.PROD,
-                    mult_d1,
-                    dim_tensor_shape_mult_d1,
-                )
+            mult_d1 = convert_binary_elementwise(
+                ctx,
+                target,
+                source_ir,
+                name + f"_shape_{i}",
+                trt.ElementWiseOperation.PROD,
+                mult_d1,
+                dim_tensor_shape_mult_d1,
+            )
 
         concat_tensor_layer = ctx.net.add_concatenation(
             [

tests/py/dynamo/conversion/test_index_put_aten.py

@@ -310,21 +310,16 @@ def forward(self, x, y, z, a, b):
                 {0: seq_len3},
             ),
         )
-        with torchtrt.dynamo.Debugger(
-            log_level="debug",
-            capture_fx_graph_after=["remove_num_users_is_0_nodes"],
-            logging_dir="/home/profile/logging/moe",
-            engine_builder_monitor=False,
-        ):
-            trt_mod = torchtrt.dynamo.compile(
-                ep,
-                inputs,
-                enabled_precisions={torch.float16},
-                min_block_size=1,
-                use_explicit_typing=False,
-                use_fp32_acc=False,
-                disable_tf32=True,
-            )
+
+        trt_mod = torchtrt.dynamo.compile(
+            ep,
+            inputs,
+            enabled_precisions={torch.float16},
+            min_block_size=1,
+            use_explicit_typing=False,
+            use_fp32_acc=False,
+            disable_tf32=True,
+        )
         result = trt_mod(*inputs)
         assert torch.allclose(result, torch_output, atol=1e-4, rtol=1e-4)
 
@@ -350,17 +345,16 @@ def forward(self, source_tensor, indices_tensor, value_tensor):
             (source_tensor, indices_tensor, value_tensor),
             dynamic_shapes=({0: dim1}, {0: dim1}, {0: dim2}),
         )
-        with torchtrt.dynamo.Debugger(log_level="debug"):
-            trt_engine = torchtrt.dynamo.compile(
-                ep,
-                inputs=(source_tensor, indices_tensor, value_tensor),
-                enabled_precisions={torch.float32},
-                min_block_size=1,
-                use_explicit_typing=False,
-                use_fp32_acc=False,
-                disable_tf32=True,
-                use_python_runtime=True,
-            )
+        trt_engine = torchtrt.dynamo.compile(
+            ep,
+            inputs=(source_tensor, indices_tensor, value_tensor),
+            enabled_precisions={torch.float32},
+            min_block_size=1,
+            use_explicit_typing=False,
+            use_fp32_acc=False,
+            disable_tf32=True,
+            use_python_runtime=True,
+        )
         result = trt_engine(source_tensor, indices_tensor, value_tensor)
 
         torch.allclose(result, torch_output, atol=1e-4, rtol=1e-4)