zhiics commented on a change in pull request #4964: [Torch] Add initial control
flow support
URL: https://github.com/apache/incubator-tvm/pull/4964#discussion_r386065362
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File path: python/tvm/relay/frontend/pytorch.py
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@@ -955,7 +1025,100 @@ def parse_params(graph, state_dict):
return params, param_tensors
-def parse_operators(operators, outputs, output_index_map, ret_name):
+def convert_block(block, outputs, output_index_map):
+ """ Translate Torch "Block", used for prim::If and prim::Loop """
+ ops = _get_operator_nodes(block.nodes())
+ ret_names = _get_input_names(block.returnNode())
+ return convert_operators(ops, outputs, output_index_map, ret_names)
+
+
+def convert_if(if_node, outputs, output_index_map):
+ """ Translate Torch prim::If to Relay If """
+ cond = outputs[output_index_map[if_node.inputsAt(0).debugName()]]
+ blocks = list(if_node.blocks())
+ true_branch = convert_block(blocks[0], outputs, output_index_map)
+ false_branch = convert_block(blocks[1], outputs, output_index_map)
+ assert len(true_branch) == 1 and len(false_branch) == 1
+ return _expr.If(cond, true_branch[0], false_branch[0])
+
+
+def convert_loop(loop_node, outputs, output_index_map):
+ """ Translate Torch prim::Loop to Relay while_loop """
+ def get_input(index):
+ ivalue = loop_node.inputsAt(index)
+ inode = ivalue.node()
+ if inode.kind() == "prim::Constant":
+ return _expr.const(_get_constant(inode))
+ var_name = ivalue.debugName()
+ assert var_name in output_index_map
+ return _wrap_const(outputs[output_index_map[var_name]])
+
+ # Refer to the spec for prim::Loop below
+ #
https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/OVERVIEW.md#loops
+ # The first input: %max_trip_count
+ # The second input: %initial_condition
+ # The rest of input: loop variables
+ max_loop_count = get_input(0)
+ init_cond = get_input(1)
+ num_loop_var = len(list(loop_node.inputs())) - 2
+ init_vals = [get_input(i + 2) for i in range(num_loop_var)]
+
+ # For loop (not while loop) has always %initial_condition being 1
+ is_for_loop = isinstance(init_cond, _expr.Constant)
+
+ if is_for_loop:
+ loop_iter_dtype = "int32"
+ # always count from 0
+ init_loop_iter_val = _expr.const(0, dtype="int32")
+ else:
+ loop_iter_dtype = "bool"
+ init_loop_iter_val = init_cond
+
+ body_block = list(loop_node.blocks())[0]
+ inames = _get_input_names(body_block)
+ loop_input_vals = [init_loop_iter_val] + init_vals
+ name_val_pairs = list(zip(inames, loop_input_vals))
+ _update_outputs_from_pairs(name_val_pairs, outputs, output_index_map)
+
+ def cond(*current_vals):
+ i = current_vals[0]
+
+ if is_for_loop:
+ return _op.less(i, max_loop_count)
+
+ return _op.equal(i, _expr.const(True, 'bool'))
+
+ def body(*current_vals):
+ # Update loop variables using the prev iteration outputs
Review comment:
let's assert the equivalence of the `inames` and `current_vals`
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