jwfromm commented on a change in pull request #6351: URL: https://github.com/apache/incubator-tvm/pull/6351#discussion_r483107517
########## File path: include/tvm/relay/transform.h ########## @@ -208,6 +208,17 @@ TVM_DLL Pass SimplifyInference(); */ TVM_DLL Pass FastMath(); +/*! + * \brief Find Dynamic ops and make them static + * + * Searches the graph for dynamic ops. If the dynamic inputs to those ops are constants, it replaces + * them with static ops and re-performs type inference and constant folding. The pass repeats + * istself until the graph stops changing or we run too many iterations. Review comment: 'istself' ########## File path: python/tvm/relay/frontend/onnx.py ########## @@ -2056,113 +2015,25 @@ class GraphProto(ExprFunctor): dtype : str or dict of str to str The input types to the graph """ - def __init__(self, shape, dtype): self._nodes = {} self._params = {} + self._inputs = {} self._renames = {} self._num_input = 0 self._num_param = 0 self._shape = shape if shape else {} self._dtype = dtype - #For infering Values - self._tmp_params = {} - self._infer_simulated = True - self._mod = None - super(GraphProto, self).__init__() - - def infer_value(self, input_val, params, mod=None): - self._tmp_params = params - self._infer_simulated = False - self._mod = mod - return self.visit(input_val).data - - def infer_value_simulated(self, input_val, params): - self._tmp_params = params - self._infer_simulated = True - return self.visit(input_val).data - - def infer(self, expr): - if self._infer_simulated: - out = _infer_value_simulated(expr, self._tmp_params) - else: - out = _infer_value(expr, self._tmp_params) - return _expr.const(out.asnumpy()) - - def visit_function(self, fn): - new_params = [self.visit(x) for x in fn.params] - new_body = self.visit(fn.body) - return self.infer(Function( - list(new_params), - new_body, - fn.ret_type, - fn.type_params, - fn.attrs)) - - def visit_let(self, let): - newvar = self.visit(let.var) - newval = self.visit(let.value) - newbody = self.visit(let.body) - return self.infer(Let(newvar, newval, newbody)) - - def visit_call(self, call): - new_fn = self.visit(call.op) - new_args = [self.visit(arg) for arg in call.args] - call = Call(new_fn, new_args, call.attrs) - if new_fn == _op.get("nn.batch_norm"): - return call - return self.infer(call) - - def visit_var(self, var): - return self.infer(var) - - def visit_global_id(self, global_var): - return self.infer(global_var) - - def visit_if(self, ite): - return self.infer(If( - self.visit(ite.cond), - self.visit(ite.true_branch), - self.visit(ite.false_branch))) - - def visit_tuple(self, tup): - return Tuple([self.visit(field) for field in tup.fields]) - - def visit_tuple_getitem(self, op): - tuple_value = self.visit(op.tuple_value) - if not tuple_value.same_as(op.tuple_value): - return self.infer(TupleGetItem(tuple_value, op.index)) - return self.infer(op) - - def visit_global_var(self, gvar): - return self.infer(gvar) - - def visit_op(self, op): - return op - - def visit_constant(self, const): - return const - - def visit_constructor(self, con): - return con - - def visit_match(self, m): - return self.infer(Match( - self.visit(m.data), - [Clause(c.lhs, self.visit(c.rhs)) for c in m.clauses], - complete=m.complete)) - - def visit_ref_create(self, r): - return RefCreate(self.visit(r.value)) - - def visit_ref_write(self, r): - return RefWrite(self.visit(r.ref), self.visit(r.value)) - - def visit_ref_read(self, r): - return RefRead(self.visit(r.ref)) - - def from_onnx(self, graph, opset): + def freeze(self, func, params): + bind_map = {} + for name in params.keys(): + bind_map[self._nodes[name]] = _expr.const(params[name]) + body = _expr.bind(func.body, bind_map) + fn = _function.Function(analysis.free_vars(body), body) + return fn, {} + + def from_onnx(self, graph, opset, freeze_params=False): Review comment: Add a description for `freeze_params`. Specifically it's unclear when we would need or want to use it. ########## File path: tests/python/frontend/onnx/test_forward.py ########## @@ -44,17 +44,19 @@ def get_input_data_shape_dict(graph_def, input_data): return input_names, shape_dict -def get_tvm_output_with_vm(graph_def, input_data, target, ctx, opset=None): +def get_tvm_output_with_vm(graph_def, input_data, target, ctx, opset=None, freeze_params=False): """ Generic function to execute and get tvm output with vm executor""" + if not isinstance(input_data, list): + input_data = [input_data] + input_names, shape_dict = get_input_data_shape_dict(graph_def, input_data) Review comment: `input_names` doesnt seem to be used, maybe put a `_` instead to indicate this. ########## File path: python/tvm/relay/frontend/onnx.py ########## @@ -2372,10 +2248,8 @@ def _fix_outputs(self, op_name, outputs): outputs = outputs[:-1] return outputs -def from_onnx(model, - shape=None, - dtype="float32", - opset=None): + +def from_onnx(model, shape=None, dtype="float32", opset=None, freeze_params=False): Review comment: Also needs argument description of `freeze_params`. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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