Re-land AttrsDescriptor modifications + add updates to repr from upstream

.github/pins/pytorch-upstream.txt

@@ -1 +1 @@
-0efa590d435d2b4aefcbad9014dd5fa75dcf8405
+33dce10ece5b38aa0ab76739b658cd980a6e3d8f

python/test/unit/runtime/test_cache.py

@@ -9,6 +9,7 @@
 import triton
 import triton.language as tl
 from triton.runtime.jit import JITFunction
+from triton._internal_testing import is_hip
 
 
 @triton.jit
@@ -572,3 +573,29 @@ def compiled_hook(*args, **kwargs):
     assert specialization_data is not None and specialization_data_compiled == specialization_data
     assert is_warmup is True
     assert key in kernel_add.cache[getattr(torch, device).current_device()]
+
+
+@pytest.mark.skipif(reason="within_2g is a HIP specific optimization", condition=not is_hip())
+def test_within_2gb(device, fresh_triton_cache) -> None:
+
+    @triton.jit
+    def kernel_add(a):
+        tl.load(a)
+
+    # This is the attribute we want to test
+    pointer_range_32 = None
+
+    def cache_hook(*args, **kwargs):
+        nonlocal pointer_range_32
+        pointer_range_32 = kwargs["compile"]["configs"][0].pointer_range_32
+
+    JITFunction.cache_hook = cache_hook
+    # In warmup we assume that the pointer range is 32 bits
+    kernel_add.warmup(torch.float32, grid=(1, ))
+    assert pointer_range_32 == [0]
+    # Torch tensor > 2GB
+    kernel_add[(1, 0)](torch.empty(2**31, dtype=torch.int8, device=device))
+    assert len(pointer_range_32) == 0
+    # Torch tensor <= 2GB
+    kernel_add[(1, 0)](torch.empty(2**31 - 1, dtype=torch.int8, device=device))
+    assert pointer_range_32 == [0]

python/triton/backends/compiler.py

@@ -8,7 +8,21 @@
 from typing import Dict, Union
 from types import ModuleType
 
+# Table that associates strings to AttrsDescriptor (sub)classes.
+# In this way we can dynamically select the correct class
+# constructor
+_descriptor_table = {}
 
+
+def register_descriptor(cls):
+    """
+    Register a descriptor into the descriptor table
+    """
+    _descriptor_table[cls.__name__] = cls
+    return cls
+
+
+@register_descriptor
 class AttrsDescriptor:
     """
     This class handles compile-time properties for specific function parameters.
@@ -135,18 +149,28 @@ def hash(self):
         return hashlib.sha256(key.encode("utf-8")).hexdigest()
 
     def to_dict(self):
-        return self.arg_properties
+        """
+        Store the fields of this class in a serializable dictionary
+        """
+        # We need to only store the `arg_properties` field. To initialize the
+        # other fields we relay on the class type. We store it as a string in
+        # the dictionary so that we can use it to invoke the appropriate
+        # (sub)class constructor in the `from_dict` method.
+        return {"arg_properties": self.arg_properties, "cls": type(self).__name__}
 
     @staticmethod
     def from_dict(data):
-        attrsDescriptor = AttrsDescriptor()
-        for prop_name, param_ids in data.items():
-            attrsDescriptor.arg_properties[prop_name] = param_ids
-        attrsDescriptor._init_slots()
-        return attrsDescriptor
-
-    @staticmethod
-    def from_hints(hints: list[tuple[int, int]]):
+        """
+        Create the object from a serializable dictionary
+        """
+        attrs_descriptor = _descriptor_table[data["cls"]]()
+        for prop_name, param_ids in data["arg_properties"].items():
+            attrs_descriptor.arg_properties[prop_name] = param_ids
+        attrs_descriptor._init_slots()
+        return attrs_descriptor
+
+    @classmethod
+    def from_hints(cls, hints: list[tuple[int, int]]):
         """
         Create the class from a set of hints that are passed in.
 
@@ -156,11 +180,11 @@ def from_hints(hints: list[tuple[int, int]]):
         then we insert `param_index` into the correct list (e.g., in
         `arg_properties[prop0]`)
         """
-        attrsDescriptor = AttrsDescriptor()
-        for prop_name, prop_val in attrsDescriptor.property_values.items():
-            attrsDescriptor.arg_properties[prop_name] = [i for i, h in hints.items() if h == prop_val]
-        attrsDescriptor._init_slots()
-        return attrsDescriptor
+        attrs_descriptor = cls()
+        for prop_name, prop_val in attrs_descriptor.property_values.items():
+            attrs_descriptor.arg_properties[prop_name] = [i for i, h in hints.items() if h == prop_val]
+        attrs_descriptor._init_slots()
+        return attrs_descriptor
 
     @staticmethod
     def is_divisible_by_16(x):
@@ -187,7 +211,7 @@ def get_property_key(val, align):
         return "N"
 
     def __repr__(self):
-        return f"AttrsDescriptor.from_dict({self.arg_properties})"
+        return f"AttrsDescriptor.from_dict({self.to_dict()!r})"
 
 
 @dataclass(frozen=True)

python/triton/runtime/jit.py

@@ -879,6 +879,10 @@ def __init__(self, dtype):
     def data_ptr():
         return 0  # optimistically assumes multiple of 16
 
+    @staticmethod
+    def ptr_range():
+        return 0  # optimistically assumes 32 bit pointer range
+
 
 class TensorWrapper:
 

third_party/amd/backend/compiler.py

@@ -1,4 +1,4 @@
-from triton.backends.compiler import BaseBackend, GPUTarget
+from triton.backends.compiler import BaseBackend, GPUTarget, AttrsDescriptor, register_descriptor
 from triton._C.libtriton import ir, passes, llvm, amd
 from dataclasses import dataclass
 from typing import Any, Dict, Tuple
@@ -72,6 +72,44 @@ def hash(self):
         return hashlib.sha256(key.encode("utf-8")).hexdigest()
 
 
+@register_descriptor
+class HIPAttrsDescriptor(AttrsDescriptor):
+    # This property asserts if the underlying storage area of a given pointer
+    # can be resepresented as a 32 bit integer. When this is true, we can be
+    # sure that all indices into the tensor behind that pointer can use 32-bit
+    # indexing. That opens the door for the AMD backend to use buffer load/store
+    # instrinsics, which requires this property. Buffer load/store intrinsics
+    # gives direct out-of-bound support and simplifies index calculation for
+    # lower register pressure.
+    __slots__ = ("pointer_range_32")
+
+    def _add_backend_properties(self, params=None, values=None):
+        self.property_values["tt.pointer_range"] = 32
+        if params is None or values is None:
+            return
+
+        self.arg_properties["tt.pointer_range"] = [
+            param.num for param, arg in zip(params, values) if HIPAttrsDescriptor.is_within2gb(arg)
+            and not param.do_not_specialize and not param.do_not_specialize_on_alignment
+        ]
+
+    @staticmethod
+    def is_within2gb(arg):
+        if hasattr(arg, "ptr_range"):
+            return arg.ptr_range() <= 2**31 - 1
+        if "torch.Tensor" in str(type(arg)) and hasattr(arg, "untyped_storage"):
+            # Please note that 2**31-1 is the max int32 positive limit
+            return arg.untyped_storage().size() <= 2**31 - 1
+        return False
+
+    @staticmethod
+    def get_property_key(val, align):
+        generic_key = AttrsDescriptor.get_property_key(val, align)
+        hip_key = "S" if HIPAttrsDescriptor.is_within2gb(val) else "N"
+        key = (generic_key + hip_key).replace("N", "")
+        return key if key else "N"
+
+
 class HIPBackend(BaseBackend):
 
     @staticmethod
@@ -118,6 +156,13 @@ def get_module_map(self) -> Dict[str, ModuleType]:
     def load_dialects(self, ctx):
         amd.load_dialects(ctx)
 
+    def get_attrs_descriptor(self, params, args):
+        return HIPAttrsDescriptor(params, args)
+
+    @staticmethod
+    def compute_spec_key(arg, align):
+        return HIPAttrsDescriptor.get_property_key(arg, align)
+
     @staticmethod
     def path_to_rocm_lld():
         # Check env path for ld.lld