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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/09/03 21:41:34 UTC
[GitHub] [incubator-tvm] jwfromm commented on a change in pull request #6351: Dynamic ONNX Importer
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`.
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