[AutoTVM] Minor bug fixes in AutoTVM for QNN graphs.

apache · Feb 1, 2020 · 5e0c672 · 5e0c672
1 parent 6798ba8
commit 5e0c672
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Showing 4 changed files with 11 additions and 7 deletions.
diff --git a/python/tvm/autotvm/graph_tuner/utils/traverse_graph.py b/python/tvm/autotvm/graph_tuner/utils/traverse_graph.py
@@ -126,10 +126,10 @@ def _traverse_expr(node):
                 for i, input_idx in enumerate(node_entry["inputs"]):
                     input_node_entry = node_list[input_idx[0]]
                     input_type = input_node_entry["types"][input_idx[1]]
-                    if not isinstance(input_node_entry["node"], (Var, Call)):
+                    if not isinstance(input_node_entry["node"], (Var, Constant, Call)):
                         raise RuntimeError("Graph tuner can only tune target "
                                            "operators with input node of type "
-                                           "relay.expr.Var or relay.expr.Call. Now "
+                                           "relay.expr.Var/Constant/Call. Now "
                                            "find a target op %s with input type %s"
                                            % (op_name, str(type(input_node_entry["node"]))))
                     free_var = relay.Var("var_%d" % i, input_type)
@@ -167,7 +167,8 @@ def _traverse_expr(node):
                 else:
                     node_entry["inputs"].append([in_node_idx, 0, 0])
         elif isinstance(node, Constant):
-            pass
+            node_entry["name"] = "Constant_" + str(node_index)
+            node_entry["types"] = [node.checked_type]
         elif isinstance(node, relay.op.op.Op):
             return
         else:

diff --git a/python/tvm/autotvm/graph_tuner/utils/utils.py b/python/tvm/autotvm/graph_tuner/utils/utils.py
@@ -73,7 +73,7 @@ def is_boundary_node(node_entry, input_names):
     # Operators dependent on original layouts.
     _LAYOUT_FIXED_OP = ["batch_flatten", "transpose", "reshape",
                         "multibox_prior", "multibox_transform_loc", "where",
-                        "non_max_suppression", "strided_slice"]
+                        "non_max_suppression"]
 
     out = node_entry["op"] in _LAYOUT_FIXED_OP or \
           ("name" in node_entry and node_entry["name"] in input_names)

diff --git a/python/tvm/autotvm/task/relay_integration.py b/python/tvm/autotvm/task/relay_integration.py
@@ -50,7 +50,8 @@ def _lower(mod,
                 grc.codegen(mod["main"])
     # default case
     compiler = relay.vm.VMCompiler()
-    compiler.set_params(params)
+    if params:
+        compiler.set_params(params)
     compiler.lower(mod, target=target)
 
 
@@ -123,7 +124,9 @@ def extract_from_multiple_program(mods, params, ops, target, target_host=None,
     # relay op -> topi compute
     OP2TOPI = {
         tvm.relay.op.nn.conv2d: [topi.nn.conv2d, topi.nn.depthwise_conv2d_nchw,
-                                 topi.nn.group_conv2d_nchw, topi.nn.conv2d_NCHWc],
+                                 topi.nn.group_conv2d_nchw,
+                                 topi.nn.conv2d_NCHWc,
+                                 topi.nn.conv2d_NCHWc_int8],
         tvm.relay.op.nn.conv2d_transpose: [topi.nn.conv2d_transpose_nchw],
         tvm.relay.op.nn.dense: [topi.nn.dense],
         tvm.relay.op.nn.batch_matmul: [topi.nn.batch_matmul],

diff --git a/python/tvm/relay/backend/vm.py b/python/tvm/relay/backend/vm.py
@@ -422,7 +422,7 @@ def set_params(self, params):
         inputs = {}
         for name, param in params.items():
             if isinstance(param, np.ndarray):
-                param = _nd.array(param)
+                param = tvm.nd.array(param)
             inputs[name] = _expr.const(param)
         self._set_params_func(inputs)