manupak commented on code in PR #12447:
URL: https://github.com/apache/tvm/pull/12447#discussion_r950678863
##########
python/tvm/relay/backend/contrib/uma/api/lower.py:
##########
@@ -60,27 +60,7 @@ def _lower_relay_to_tir(self, relay_prim_func:
relay.Function) -> tvm.tir.PrimFu
"""
def _get_tensors(te_cached_func):
- outputs = list(te_cached_func.outputs)
- stack = []
- visited = set()
- for output_ in outputs:
- if output_ not in visited:
- visited.add(output_)
- stack.append(output_)
-
- args = []
- while len(stack) != 0:
- tensor = stack.pop()
- if isinstance(tensor.op, tvm.te.tensor.PlaceholderOp):
- args.append(tensor)
- elif isinstance(tensor.op, tvm.te.tensor.ComputeOp):
- inputs = tensor.op.input_tensors
- for input_ in inputs:
- if input_ not in visited:
- visited.add(input_)
- stack.append(input_)
-
- return args + outputs
+ return list(te_cached_func.inputs) + list(te_cached_func.outputs)
Review Comment:
This make sense to me because the input visitation done here to discover the
inputs might not be the ordering where the call from relay main wants it to be.
##########
python/tvm/testing/aot.py:
##########
@@ -931,20 +930,23 @@ def generate_ref_data(mod, input_data, params=None,
target="llvm"):
return dict(zip(output_tensor_names, out))
-def create_relay_module_and_inputs_from_tflite_file(tflite_model_file):
+def create_relay_module_and_inputs_from_tflite_file(tflite_model_file,
bind_params_by_name=True):
"""A helper function to create a Relay IRModule with inputs
and params from a tflite file"""
with open(tflite_model_file, "rb") as f:
tflite_model_buf = f.read()
- mod, params = convert_to_relay(tflite_model_buf)
+ mod, params = convert_to_relay(tflite_model_buf, bind_params_by_name)
inputs = dict()
for param in mod["main"].params:
name = str(param.name_hint)
data_shape = [int(i) for i in param.type_annotation.shape]
dtype = str(param.type_annotation.dtype)
- in_min, in_max = (np.iinfo(dtype).min, np.iinfo(dtype).max)
- data = np.random.randint(in_min, high=in_max, size=data_shape,
dtype=dtype)
+ if dtype == "float32":
Review Comment:
I think we can use np.finfo to make it "generally" working for float dtypes
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