TRT Dynamic Reshape Fix (#7412)

* Dynamic Reshape

* Changes

* Add test cases

* Add test cases

* PR COmments

* CI Error

* EmptyCommitCIError

Co-authored-by: Ubuntu <ubuntu@ip-172-31-42-251.us-east-2.compute.internal>

Author: codeislife99

python/tvm/relay/op/contrib/tensorrt.py

@@ -615,7 +615,6 @@ def layout_transform_annotate_fn(expr):  # pylint: disable=unused-variable
 @_register_external_dynamic_check_func("reshape")
 def reshape_annotate_fn(expr):  # pylint: disable=unused-variable
     """Check if reshape is supported by TensorRT."""
-
     attrs, args = expr.attrs, expr.args
     if args[0].checked_type.dtype != "float32":
         logger.info("Only float32 inputs are supported for TensorRT.")
@@ -629,23 +628,23 @@ def reshape_annotate_fn(expr):  # pylint: disable=unused-variable
         if len(new_shape) == 0 or len(shape) == 0:
             logger.info("reshape: Can't reshape to or from scalar.")
             return False
-
         dynamic_reshape = any([isinstance(x, tvm.tir.expr.Any) for x in shape])
 
         if dynamic_reshape:
             # Make sure that the batch dim is unmodified.
             if int(new_shape[0]) < 0:
-                for shape_val, new_shape_val in enumerate(shape[1:], new_shape[1:]):
+                for shape_val, new_shape_val in zip(shape[1:], new_shape[1:]):
                     if not (
-                        isinstance(shape_val, int)
-                        and isinstance(new_shape_val, int)
+                        isinstance(shape_val, (int, tvm.tir.expr.IntImm))
+                        and isinstance(new_shape_val, (int, tvm.tir.expr.IntImm))
                         and int(shape_val) == int(new_shape_val)
                     ):
                         return False
             elif int(new_shape[0]) > 0:
+                # Currently we only allow dim[0] to be Any, so this branch will always be False
                 if not (
-                    isinstance(shape[0], int)
-                    and isinstance(new_shape[0], int)
+                    isinstance(shape[0], (int, tvm.tir.expr.IntImm))
+                    and isinstance(new_shape[0], (int, tvm.tir.expr.IntImm))
                     and int(shape[0]) == int(new_shape[0])
                 ):
                     return False

tests/python/contrib/test_tensorrt.py

@@ -27,6 +27,7 @@
 from tvm.contrib import graph_runtime, utils
 from tvm.runtime.vm import VirtualMachine
 from tvm.relay import Any, GlobalVar, transform
+from tvm.relay.expr_functor import ExprVisitor
 from typing import Dict, Tuple, Union
 from tvm.contrib.download import download
 from tvm.relay.op.contrib import tensorrt
@@ -631,6 +632,106 @@ def get_graph(x_shape, new_shape):
     run_and_verify_func(get_graph((1, 1, 2, 3), (1, 6)))
 
 
+class AreOpsOnGraph(ExprVisitor):
+    """
+    Visits the Graph recursively and checks if it contains ops in the op_list
+    """
+
+    def __init__(self, op_list):
+        ExprVisitor.__init__(self)
+        self.op_list = op_list
+        self.on_graph = False
+
+    def visit_call(self, call):
+        if isinstance(call.op, tvm.tir.op.Op):
+            if str(call.op) in self.op_list:
+                self.on_graph = True
+
+        return super().visit_call(call)
+
+    def are_ops_on_graph(self, subgraph) -> bool:
+        """
+        This function recursively visits the graph and checks if op_list ops are ongraph"
+        """
+        self.visit(subgraph)
+        return self.on_graph
+
+
+def are_ops_on_trt(mod, op_list):
+    for subgraph in mod.get_global_vars():
+        name = subgraph.name_hint
+        op_on_trt = False
+        op_on_tvm = True
+        if name == "main":
+            op_on_tvm = AreOpsOnGraph(op_list).are_ops_on_graph(mod[name].body)
+        elif mod[name].attrs and mod[name].attrs["Compiler"] == "tensorrt":
+            op_on_trt = AreOpsOnGraph(op_list).are_ops_on_graph(mod[name].body)
+        else:
+            op_on_tvm &= AreOpsOnGraph(op_list).are_ops_on_graph(mod[name].body)
+
+        if not op_on_trt or op_on_tvm:
+            return False
+
+    return True
+
+
+def test_dynamic_reshape():
+    if skip_codegen_test():
+        return
+
+    def test_run(x_data_list, x_shape, new_shape, should_offload_to_trt):
+        result_arr = [{} for _ in range(len(x_data_list))]
+        for use_trt in [True, False]:
+            x = relay.var("x", shape=x_shape, dtype="float32")
+            out = relay.reshape(x, new_shape)
+            f = relay.Function([x], out)
+            mod = tvm.IRModule()
+            mod["main"] = f
+            if use_trt:
+                mod, _ = tensorrt.partition_for_tensorrt(
+                    mod, params={}, remove_no_mac_subgraphs=False
+                )
+                assert are_ops_on_trt(mod, op_list=["reshape"]) == should_offload_to_trt
+            if not skip_runtime_test():
+                with relay.build_config(opt_level=3):
+                    relay_exec = relay.create_executor("vm", mod=mod, ctx=tvm.cpu(0), target="llvm")
+
+                for i, x_data in enumerate(x_data_list):
+                    result_arr[i][use_trt] = relay_exec.evaluate()(x_data)
+
+        if not skip_runtime_test():
+            for i in range(len(x_data_list)):
+                assert_result_dict_holds(result_arr[i])
+
+    dim_values = [1, 1, 0, 2, 3, 0, 1, 3, 2]
+    x_shape = (relay.Any(), 3, 2, 3)
+    x_data_list = [
+        np.ones([dim_value] + list(x_shape)[1:]).astype("float32") for dim_value in dim_values
+    ]
+    new_shape = (-1, 3, 2, 3)
+    should_offload_to_trt = True
+    test_run(x_data_list, x_shape, new_shape, should_offload_to_trt)
+
+    dim_values = [1, 1, 0, 2, 3, 0, 1, 3, 2]
+    x_shape = (relay.Any(), 3, 2, 3)
+    x_data_list = [
+        np.ones([dim_value] + list(x_shape)[1:]).astype("float32") for dim_value in dim_values
+    ]
+    new_shape = (-1, 1, 2, 3)
+    should_offload_to_trt = False
+    test_run(x_data_list, x_shape, new_shape, should_offload_to_trt)
+
+    dim_values = [1, 1, 0, 2, 3, 0, 1, 3, 2]
+    x_shape = (1, relay.Any(), 2, 3)
+    x_data_list = [
+        np.ones(list(x_shape[:1]) + [dim_value] + list(x_shape)[2:]).astype("float32")
+        for dim_value in dim_values
+    ]
+    new_shape = (1, -1, 2, 3)
+    should_offload_to_trt = False
+    test_run(x_data_list, x_shape, new_shape, should_offload_to_trt)
+
+
 def test_transpose():
     def get_graph(x_shape, order):
         x = relay.var("x", shape=(x_shape), dtype="float32")