Adding tl.const annotation to mark and validate that const tensors are not being stored to

python/test/unit/language/test_core.py

@@ -3193,6 +3193,67 @@ def kernel(out_ptr):
     assert re.search(r"arith.constant .* : " + dtype_str, h.asm["ttir"]) is not None
 
 
+@triton.jit
+def pass_const(a, b, choose_b):
+    if choose_b:
+        return b
+    else:
+        return a
+
+
+@pytest.mark.parametrize("choose_const", [True, False])
+@pytest.mark.parametrize("constexpr", [True, False])
+@pytest.mark.parametrize("mode", ["direct", "call", "ternary", "if"])
+def test_const(device, choose_const, constexpr, mode):
+
+    @triton.jit(do_not_specialize=["choose_const"])
+    def kernel(in_ptr: tl.const, out, c_out: tl.const, choose_const, n_elems: tl.int32, BLOCK_SIZE: tl.constexpr):
+        offsets = tl.arange(0, BLOCK_SIZE)
+        mask = offsets < n_elems
+        val = tl.load(in_ptr + offsets, mask=mask)
+        LOSE_TAIL
+        tl.store(final_out + offsets, val, mask=mask)
+
+    @triton.jit
+    def kernel_constexpr(in_ptr: tl.const, out, c_out: tl.const, choose_const: tl.constexpr, n_elems: tl.int32,
+                         BLOCK_SIZE: tl.constexpr):
+        offsets = tl.arange(0, BLOCK_SIZE)
+        mask = offsets < n_elems
+        val = tl.load(in_ptr + offsets, mask=mask)
+        LOSE_TAIL
+        tl.store(final_out + offsets, val, mask=mask)
+
+    if mode == "direct":
+        if choose_const:
+            LOSE_TAIL = "final_out = c_out"
+        else:
+            LOSE_TAIL = "final_out = out"
+    elif mode == "call":
+        LOSE_TAIL = "final_out = pass_const(out, c_out, choose_const)"
+    elif mode == "ternary":
+        LOSE_TAIL = "final_out = c_out if choose_const else out"
+    elif mode == "if":
+        LOSE_TAIL = """
+    if choose_const:
+        final_out = c_out
+    else:
+        final_out = out
+"""
+
+    SIZE = 128
+    input = torch.randn((SIZE, ), dtype=torch.float32, device=device)
+    output = torch.zeros((SIZE, ), dtype=torch.float32, device=device)
+    patched_kernel = patch_kernel(kernel_constexpr if constexpr else kernel, {'LOSE_TAIL': LOSE_TAIL, 'CONSTEXPR': ''})
+
+    expect_fail = (not constexpr and mode != "direct") or choose_const
+    if expect_fail:
+        with pytest.raises(triton.CompilationError) as exc_info:
+            patched_kernel[(1, )](input, output, output, choose_const, SIZE, SIZE)
+    else:
+        patched_kernel[(1, )](input, output, output, choose_const, SIZE, SIZE)
+        assert torch.all(input == output)
+
+
 @pytest.mark.interpreter
 @pytest.mark.parametrize("dtype_str", ['float32', 'float16'])
 def test_dot_without_load(dtype_str, device):

python/triton/language/__init__.py

@@ -45,6 +45,8 @@
     broadcast_to,
     cat,
     clamp,
+    const,
+    const_pointer_type,
     constexpr,
     debug_barrier,
     device_assert,
@@ -142,6 +144,8 @@
     "cat",
     "cdiv",
     "clamp",
+    "const",
+    "const_pointer_type",
     "constexpr",
     "cos",
     "cumprod",
@@ -245,7 +249,12 @@
 
 def str_to_ty(name):
     if name[0] == "*":
-        ty = str_to_ty(name[1:])
+        name = name[1:]
+        if name[0] == "k":
+            name = name[1:]
+            ty = str_to_ty(name)
+            return const_pointer_type(ty)
+        ty = str_to_ty(name)
         return pointer_type(ty)
     tys = {
         "fp8e4nv": float8e4nv,

python/triton/language/core.py

@@ -307,6 +307,10 @@ def is_block():
     def is_ptr():
         return False
 
+    @staticmethod
+    def is_const():
+        return False
+
     def __eq__(self, other: dtype):
         if not isinstance(other, dtype):
             return False
@@ -419,6 +423,23 @@ def scalar(self):
         return self
 
 
+class const_pointer_type(pointer_type):
+
+    def __init__(self, element_ty: dtype, address_space: int = 1):
+        super().__init__(element_ty, address_space)
+
+    def __str__(self):
+        return f'const_pointer<{self.element_ty}>'
+
+    def is_const(self):
+        return True
+
+    def __eq__(self, other) -> bool:
+        if not isinstance(other, const_pointer_type):
+            return False
+        return self.element_ty == other.element_ty and self.address_space == other.address_space
+
+
 class block_type(dtype):
 
     def __init__(self, element_ty: dtype, shape: List):
@@ -514,6 +535,17 @@ def to_ir(self, builder: ir.builder):
 # -----------------------
 
 
+class const:
+    """
+    This class is used as a type annotation to mark pointers to tensors
+    that cannot be modified. Store cannot be called with a const pointer.
+    Const pointers are represented by `const_pointer_type`, and usual
+    Triton type consistency rules apply - you cannot return a const pointer
+    from a function that returns also a non-const pointer, etc.
+    """
+    pass
+
+
 class constexpr:
     """
     This class is used to store a value that is known at compile-time.

python/triton/language/semantic.py

@@ -1123,6 +1123,9 @@ def store(ptr: tl.tensor, val: tl.tensor, mask: Optional[tl.tensor], boundary_ch
     cache = _str_to_store_cache_modifier(cache_modifier)
     eviction = _str_to_eviction_policy(eviction_policy)
 
+    if ptr.type.is_const() or ptr.type.scalar.is_const():
+        raise ValueError("Cannot store to a constant pointer")
+
     if ptr.type.is_ptr() and ptr.type.element_ty.is_block():
         # Store by a block pointer: `pointer_type<block_type<>>`
         return _store_block_pointer(ptr, val, mask, boundary_check, cache, eviction, builder)
@@ -1149,6 +1152,8 @@ def atom_red_typechecking_impl(ptr: tl.tensor, val: tl.tensor, mask: tl.tensor,
                                builder: ir.builder) -> Tuple[tl.tensor, tl.tensor, tl.tensor]:
     if not ptr.type.scalar.is_ptr():
         raise ValueError("Pointer argument of store instruction is " + ptr.type.__repr__())
+    if ptr.type.is_const() or ptr.type.element_ty.is_const():
+        raise ValueError("Cannot store to a constant pointer")
     element_ty = ptr.type.scalar.element_ty
     if element_ty is tl.float16 and op != 'add':
         raise ValueError("atomic_" + op + " does not support fp16")

python/triton/runtime/jit.py

@@ -117,6 +117,10 @@ def annotation(self):
     def is_constexpr(self):
         return "constexpr" in self.annotation
 
+    @cached_property
+    def is_const(self):
+        return "const" in self.annotation and not self.is_constexpr
+
     @property
     def default(self):
         return self._param.default
@@ -140,17 +144,22 @@ def __init__(self, value, param):
     def name(self):
         return self.param.name
 
-    def signature_key(self):
+    def mangled_type(self):
         annotation = self.param.annotation
-        if "Tensor" in annotation:
-            return self.value.dtype
+        const_str = "const " if self.param.is_const else ""
+        is_pointer = False
         for ty1, ty2 in [("uint", 'u'), ("int", 'i')]:
             width = annotation[annotation.find(ty1) + len(ty1):]
             if width and ty1 in annotation:
                 return f"{ty2}{width}"
         if annotation == "bool":
             return "u1"
-        return JITFunction._key_of(self.value)
+
+        if "Tensor" in annotation:
+            key = self.value.dtype
+        else:
+            key = JITFunction._key_of(self.value)
+        return JITFunction._type_of(key, self.param.is_const)
 
     def specialization_key(self):
         assert not self.param.do_not_specialize
@@ -255,7 +264,7 @@ def is_divisible_by_16(x):
         # equal_to_1)
 
     @staticmethod
-    def _type_of(key):
+    def _type_of(key, is_const=False):
         # `None` is nullptr.  Implicitly convert to *i8.
         if key is None:
             return "*i8"
@@ -288,7 +297,8 @@ def _type_of(key):
         # reinterpret can create triton type
         for v in list(tys.values()):
             tys[v] = v
-        return key if isinstance(key, str) else f"*{tys[dtype_str]}"
+        const_str = "k" if is_const else ""
+        return key if isinstance(key, str) else f"*{const_str}{tys[dtype_str]}"
 
     def _make_constants(self, constexpr_key):
         constants = dict(zip(self.constexprs, constexpr_key))
@@ -387,7 +397,7 @@ def run(self, *args, grid, warmup, **kwargs):
         grid_2 = grid[2] if grid_size > 2 else 1
         # compute cache key
         args = [KernelArg(arg_value, param) for (_, arg_value), param in zip(bound_args.arguments.items(), self.params)]
-        sig_key = tuple(arg.signature_key() for arg in args if not arg.param.is_constexpr)
+        sig_key = tuple(arg.mangled_type() for arg in args if not arg.param.is_constexpr)
         spec_key = tuple(arg.specialization_key() for arg in args if not arg.param.do_not_specialize)
         constexpr_key = tuple(arg.value for arg in args if arg.param.is_constexpr)
         key = (sig_key, constexpr_key, spec_key, options)
@@ -405,11 +415,7 @@ def run(self, *args, grid, warmup, **kwargs):
                     raise TypeError(f"Callable constexpr at index {i} is not supported")
 
             # Build kernel signature -- doesn't include constexpr arguments.
-            signature = {
-                arg.param.num: self._type_of(arg.signature_key())
-                for arg in args
-                if not arg.param.is_constexpr
-            }
+            signature = {arg.param.num: arg.mangled_type() for arg in args if not arg.param.is_constexpr}
 
             if self._call_hook(key, signature, device, constants, options, configs):
                 return None