[INTERPRETER] Correct None tensor check logic (#5049)

In the interpreter mode, we cannot use `not tensor` to check if `tensor`
is None or not because the interpreter directly evaluates the tensor.

Also consolidated the test cases for `tl.store`.

python/test/unit/language/test_core.py

@@ -1740,47 +1740,34 @@ def kernel(X, Y, Z, N: tl.constexpr):
 
 @pytest.mark.interpreter
 @pytest.mark.parametrize("dtype_str", list(torch_dtypes))
+@pytest.mark.parametrize("constant_field", ["value", "mask"])
 @pytest.mark.parametrize("num_ctas", num_ctas_list)
-def test_store_constant(dtype_str, num_ctas, device):
+def test_store_constant(num_ctas, dtype_str, constant_field, device):
     check_type_supported(dtype_str, device)
-    """Tests that boolean True is stored as 1"""
 
     @triton.jit
-    def kernel(output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
+    def kernel(output_ptr, n_elements, BLOCK_SIZE: tl.constexpr, CONSTANT_FIELD: tl.constexpr):
         offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-        mask = offsets < n_elements
-        output = GENERATE_TEST_HERE
+        if CONSTANT_FIELD == "value":
+            value = 1
+            output = tl.full([BLOCK_SIZE], value=value, dtype=value.dtype)
+            mask = offsets < n_elements
+        elif CONSTANT_FIELD == "mask":
+            output = offsets < n_elements
+            mask = False
         tl.store(output_ptr + offsets, output, mask=mask)
 
-    triton_dtype_str = 'uint8' if dtype_str == 'bool' else dtype_str
-    kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.zeros([BLOCK_SIZE], dtype=tl.{triton_dtype_str}) + 1'})
     block_size = 128
     ref = torch.ones([block_size], dtype=getattr(torch, dtype_str), device=device)
     output = torch.zeros([block_size], dtype=getattr(torch, dtype_str), device=device)
-    kernel[(1, )](output, block_size, BLOCK_SIZE=block_size, num_ctas=num_ctas)
-
-    assert torch.all(output == ref)
 
+    kernel[(1, )](output, block_size, BLOCK_SIZE=block_size, num_ctas=num_ctas, CONSTANT_FIELD=constant_field)
 
-@pytest.mark.interpreter
-@pytest.mark.parametrize("num_ctas", num_ctas_list)
-def test_store_constant_default_dtype(num_ctas, device):
-    """Tests that boolean True is stored as 1"""
-
-    @triton.jit
-    def kernel(output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
-        offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-        mask = offsets < n_elements
-        value = 1
-        output = tl.full([BLOCK_SIZE], value=value, dtype=value.dtype)
-        tl.store(output_ptr + offsets, output, mask=mask)
-
-    block_size = 128
-    ref = torch.ones([block_size], dtype=getattr(torch, 'int32'), device=device)
-    output = torch.zeros([block_size], dtype=getattr(torch, 'int32'), device=device)
-    kernel[(1, )](output, block_size, BLOCK_SIZE=block_size, num_ctas=num_ctas)
-
-    assert torch.all(output == ref)
+    if constant_field == "value":
+        print(output, ref)
+        assert torch.all(output == ref)
+    else:
+        assert torch.all(output == 0)
 
 
 def test_load_store_same_ptr(device):

python/triton/language/semantic.py

@@ -1253,7 +1253,7 @@ def _store_legacy(ptr, val, mask, boundary_check, cache, eviction, builder):
     val = cast(val, elt_ty, builder)
 
     # Build IR
-    if not mask:
+    if mask is None:
         return tl.tensor(builder.create_store(ptr.handle, val.handle, cache, eviction), tl.void)
     if not mask.type.scalar.is_bool():
         raise ValueError("Mask must have boolean scalar type")
@@ -1308,7 +1308,7 @@ def atom_red_typechecking_impl(ptr: tl.tensor, val: tl.tensor, mask: tl.tensor,
         if val is not None:
             val = broadcast_impl_shape(val, ptr.type.get_block_shapes(), builder)
     val = cast(val, ptr.type.scalar.element_ty, builder)
-    if not mask:
+    if mask is None:
         mask_ir = builder.get_int1(True)
         mask_ty = tl.int1
         if ptr.type.is_block():