kevinthesun commented on a change in pull request #6449:
URL: https://github.com/apache/incubator-tvm/pull/6449#discussion_r487322957
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File path: python/tvm/relay/frontend/pytorch.py
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@@ -274,38 +295,91 @@ def _impl(inputs, input_types):
def _slice():
def _impl(inputs, input_types):
+ index_size_limit = 2**63 - 1
data = inputs[0]
- strides = []
+ dshape = _infer_shape(data)
+ ndim = len(dshape)
+ end = []
+ for dim in dshape:
+ if isinstance(dim, tvm.tir.Any):
+ end = _op.shape_of(data)
+ break
+ end.append(int(dim))
- if isinstance(data, _expr.Expr):
- inferred_shape = _infer_shape(data)
- end = []
- for infer in inferred_shape:
- end.append(int(infer))
- if isinstance(data, _expr.Var):
- end = inferred_shape
- end = list(end)
- else:
- end = data.shape
-
- begin = [0] * len(end)
+ begin = [0] * ndim
dim = int(inputs[1])
+ stride = int(inputs[4])
if isinstance(inputs[2], _expr.Call):
- begin[dim] = np.asscalar(_infer_value(inputs[2],
{}).asnumpy().astype(np.int))
+ try:
+ begin[dim] = np.asscalar(_infer_value(inputs[2],
{}).asnumpy().astype(np.int))
+ except Exception:
+ begin[dim] = inputs[2]
else:
begin[dim] = int(inputs[2])
+ # Process begin
+ if not isinstance(begin[dim], int):
+ tmp = []
+ for b in begin:
+ if isinstance(b, int):
+ tmp.append(_op.expand_dims(_expr.const(b, "int64"),
axis=0))
+ else:
+ tmp.append(_op.cast(_op.expand_dims(b, axis=0), "int64"))
+ begin = _op.concatenate(tmp, axis=0)
+ btype = _infer_type(begin).checked_type.dtype
+ if str(btype) != "int32":
+ begin = _op.cast(begin, "int32")
Review comment:
Use int64 now.
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