fix lazy r const folding with variable shape (#4783)

currently not supporting const fold symbolic shape. I think it's possible with a refactor to Tensor.from_node.
also added some failed required tests for symbolic arange.

test/test_symbolic_jit.py

@@ -177,6 +177,17 @@ class TestSymbolicJit(unittest.TestCase):
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
+  def test_ones_sum(self):
+    def f(a): return a.sum().realize()
+    jf = TinyJit(f)
+    for i in range(1, 5):
+      vi = Variable("i", 1, 10).bind(i)
+      # TODO: without contiguous, the CONST shape are different in jit
+      t = Tensor.ones(i).contiguous()
+      symbolic = jf(t.reshape(vi)).item()
+      expected = f(t).item()
+      np.testing.assert_equal(symbolic, expected)
+
   def test_mean(self):
     def f(a): return a.mean().realize()
     def f0(a): return a.mean(0).realize()

test/test_symbolic_ops.py

@@ -141,6 +141,14 @@ class TestSymbolicOps(unittest.TestCase):
       expected = a.shrink(((3,5),(i,i+2))).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
+  def test_ones_sum(self):
+    for i in range(1, 5):
+      vi = Variable("i", 1, 10).bind(i)
+      t = Tensor.ones(i)
+      symbolic = t.reshape(vi).sum().item()
+      expected = t.sum().item()
+      np.testing.assert_equal(symbolic, expected)
+
   def test_mean(self):
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)

test/test_symbolic_shapetracker.py

@@ -173,6 +173,17 @@ class TestSymbolicShrink(unittest.TestCase):
     t = Tensor.rand(3, 5).shrink(((0, 2), (vi, vi+1)))
     assert t.shape == (2, 1)
 
+class TestSymbolicPad(unittest.TestCase):
+  def test_pad(self):
+    v = Variable("v", 1, 100).bind(5)
+    t = Tensor.ones(5).reshape(v).pad(((4, 0),)).reshape(9)
+    assert t.shape == (9,)
+    st = t.lazydata.st
+    print(st)
+    # TODO: fix this, required for symbolic arange
+    with self.assertRaises(RuntimeError):
+      st.expr_idxs()
+
 class TestSymbolicShapeExpr(unittest.TestCase):
   def test_symbolic_expr_idxs(self):
     # taken from symbolic shape llama

tinygrad/lazy.py

@@ -74,6 +74,7 @@ class LazyBuffer:
     return create_lazybuffer(device, ShapeTracker.from_shape(shape), dtype, op, arg, src, enable_cache=enable_cache)
 
   def const(self, val:ConstType, shape:Optional[Tuple[sint,...]]=None) -> LazyBuffer:
+    assert isinstance(val, (int,float,bool)), f"{val=} has {type(val)=}, not a ConstType"
     shape = self.shape if shape is None else shape
     return LazyBuffer.loadop(LoadOps.CONST, tuple(), self.dtype, self.device, arg=val).reshape((1,)*len(shape)).expand(shape)
 
@@ -181,7 +182,8 @@ class LazyBuffer:
     if self.size == 0 and 0 not in new_shape: return self.const({ReduceOps.SUM: 0.0, ReduceOps.MAX: -math.inf}[op], new_shape)
 
     # const folding
-    if self.is_unrealized_unmasked_const():
+    # TODO: fold this for symbolic?
+    if self.is_unrealized_unmasked_const() and all_int(self.shape):
       return self.const(self.base.arg * {ReduceOps.SUM: prod(self.shape[i] for i in axis), ReduceOps.MAX: 1}[op], new_shape)
 
     # TODO: can we split symbolic shape if the reduce axis is not symbolic?