t-vi commented on a change in pull request #6449:
URL: https://github.com/apache/incubator-tvm/pull/6449#discussion_r487440253



##########
File path: python/tvm/relay/frontend/pytorch.py
##########
@@ -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")
+
         if isinstance(inputs[3], str) and inputs[3].isdigit():
-            end[dim] = min(end[dim], int(inputs[3]))
+            target_end = int(inputs[3])
         else:
-            if isinstance(inputs[3], _expr.Call):
-                target_end = np.asscalar(_infer_value(inputs[3], 
{}).asnumpy().astype(np.int))
+            if isinstance(inputs[3], _expr.Expr):
+                try:
+                    target_end = np.asscalar(_infer_value(inputs[3], 
{}).asnumpy().astype(np.int))
+                except Exception:

Review comment:
       I'd have a strong preference for that, yeah.




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