Run unit tests across Python versions 3.10-3.12.

.github/workflows/ci.yaml

@@ -22,11 +22,17 @@ concurrency:
 
 jobs:
   test:
-    name: "Unit Tests and Type Checking"
+    name: "${{ matrix.os }} :: ${{ matrix.version }} :: Unit Tests and Type Checking "
     strategy:
       fail-fast: false
       matrix:
-        version: [3.11]
+        version:
+          - "3.10"
+          - "3.11"
+          - "3.12"
+
+          # Support for Python 3.13 depends on https://github.com/pytorch/pytorch/issues/130249
+          # - "3.13"
         os: [ubuntu-22.04]
     runs-on: ${{matrix.os}}
     env:

iree/turbine/kernel/compiler/kernel_codegen.py

@@ -279,10 +279,12 @@ def only_write_dependencies(node):
             # Create new Memory type with the correct usage
             memory_type = self.bindings[index].kernel_buffer_type
             self.bindings[index].kernel_buffer_type = Memory[
-                *memory_type.symbolic_shape,
-                memory_type.address_space,
-                memory_type.dtype,
-                usage,
+                (
+                    *memory_type.symbolic_shape,
+                    memory_type.address_space,
+                    memory_type.dtype,
+                    usage,
+                )
             ]
         return
 

iree/turbine/kernel/lang/wave_types.py

@@ -4,7 +4,6 @@
     ClassVar,
     Iterable,
     Optional,
-    Self,
     Type,
     TypeAlias,
     TypeVar,
@@ -17,6 +16,7 @@
 
 from sympy import Symbol
 from sympy.core.expr import Expr
+from typing_extensions import Self
 
 from itertools import chain
 

iree/turbine/kernel/ops/wave_ops.py

@@ -9,12 +9,12 @@
     Any,
     Callable,
     Optional,
-    Self,
     Sequence,
     Type,
     TypeVar,
     final,
 )
+from typing_extensions import Self
 import torch.fx as fx
 
 from ..lang.wave_types import Memory, Register, IndexMapping
@@ -792,7 +792,7 @@ def indexing_dims(self) -> list[IndexSymbol]:
 
     @property
     def type(self) -> "Memory":
-        return Memory[*self.shape, self.address_space, self.dtype]
+        return Memory[(*self.shape, self.address_space, self.dtype)]
 
 
 @define_op("shared_memory_barrier")
@@ -855,7 +855,7 @@ def indexing_dims(self) -> list[IndexSymbol]:
         return list(self.shape)
 
     def infer_type(self):
-        self.type = Register[*self.shape, self.dtype]
+        self.type = Register[(*self.shape, self.dtype)]
 
 
 @define_op("mma")
@@ -960,7 +960,7 @@ def indexing_dims(self) -> list[IndexSymbol]:
 
     def infer_type(self):
         dtype = self.memory_type.dtype
-        self.type = Register[*self.indexing_dims, dtype]
+        self.type = Register[(*self.indexing_dims, dtype)]
 
     @property
     def memory_type(self) -> "Memory":
@@ -1168,7 +1168,7 @@ def indexing_dims(self) -> list[IndexSymbol]:
     def infer_type(self):
         address_space = self.memory_type.address_space
         dtype = self.memory_type.dtype
-        self.type = Memory[*self.indexing_dims, address_space, dtype]
+        self.type = Memory[(*self.indexing_dims, address_space, dtype)]
 
     @property
     def memory_type(self) -> "Memory":
@@ -1304,7 +1304,7 @@ def infer_type(self):
         dst_shape = list(src_type.symbolic_shape)
         dim_to_remove = dst_shape[-1] if not non_unit_dim else non_unit_dim[0]
         dst_shape.remove(dim_to_remove)
-        dst_type = Register[*dst_shape, src_type.dtype]
+        dst_type = Register[(*dst_shape, src_type.dtype)]
         self.type = dst_type
 
 
@@ -1354,7 +1354,7 @@ def indexing_dims(self) -> list[IndexSymbol]:
 
     def infer_type(self):
         src_dtype = get_custom(self.arg).type.dtype
-        self.type = Register[*self.target_shape, src_dtype]
+        self.type = Register[(*self.target_shape, src_dtype)]
 
 
 @define_interface_op("max")
@@ -1406,7 +1406,7 @@ def infer_type(self):
         else:
             src_type = get_custom(self.arg).type
         reduced_dims = [dims for dims in src_type.symbolic_shape if dims != self.dim]
-        dst_type = Register[*reduced_dims, src_type.dtype]
+        dst_type = Register[(*reduced_dims, src_type.dtype)]
         self.type = dst_type
 
     @property
@@ -1465,7 +1465,7 @@ def indexing_dims(self) -> list[IndexSymbol]:
 
     def infer_type(self):
         src_shape = get_custom(self.arg).type.symbolic_shape
-        self.type = Register[*src_shape, self.dtype]
+        self.type = Register[(*src_shape, self.dtype)]
 
 
 @define_op("permute")
@@ -1488,7 +1488,7 @@ def infer_type(self):
         assert set(src_type.symbolic_shape) == set(
             self.target_shape
         ), f"Target shape {self.target_shape} must be a permutation of source shape {src_type.symbolic_shape}"
-        self.type = Register[*self.target_shape, src_type.dtype]
+        self.type = Register[(*self.target_shape, src_type.dtype)]
 
     def transform_index(
         self, index: dict[IndexSymbol, IndexSequence]

iree/turbine/kernel/wave/scheduling/schedule.py

@@ -105,15 +105,15 @@ def schedule_reduction(
         # is not dynamic.
         max_induction_variable = int(max_induction_variable)
         if max_induction_variable <= scheduler.num_stages - 1:
-            logger.warn(
+            logger.warning(
                 "Not enough iterations to pipeline the loop. Skipping pipelining."
             )
             return {}
     else:
         # Otherwise, we need to rely on assumptions provided by the author.
         assumptions = get_assumptions(constraints)
         if not assumptions:
-            logger.warn(
+            logger.warning(
                 "No assumptions provided to determine if the loop can be pipelined. Skipping pipelining."
             )
             return {}
@@ -122,7 +122,7 @@ def schedule_reduction(
             constraints, max_induction_variable > scheduler.num_stages - 1
         )
         if not result:
-            logger.warn(
+            logger.warning(
                 "Not enough iterations to pipeline the loop. Skipping pipelining."
             )
             return {}

iree/turbine/support/debugging.py

@@ -11,6 +11,7 @@
 import logging
 import re
 import os
+import sys
 import torch
 import numpy as np
 
@@ -54,7 +55,7 @@ class DebugFlags:
     def set(self, part: str):
         m = re.match(SETTING_PART_PATTERN, part)
         if not m:
-            logger.warn("Syntax error in %s flag: '%s'", FLAGS_ENV_NAME, part)
+            logger.warning("Syntax error in %s flag: '%s'", FLAGS_ENV_NAME, part)
             return
         name = m.group(2)
         value = m.group(4)
@@ -64,19 +65,22 @@ def set(self, part: str):
             logical_sense = m.group(1) != "-"
 
         if name == "log_level":
-            log_level_mapping = logging.getLevelNamesMapping()
-            try:
-                self.log_level = log_level_mapping[value.upper()]
-            except KeyError:
-                logger.warn("Log level '%s' unknown (ignored)", value)
+            if sys.version_info >= (3, 11):
+                log_level_mapping = logging.getLevelNamesMapping()  # Added in 3.11
+                try:
+                    self.log_level = log_level_mapping[value.upper()]
+                except KeyError:
+                    logger.warning("Log level '%s' unknown (ignored)", value)
+            else:
+                logger.warning("'log_level' flag requires Python >= 3.11")
         elif name == "asserts":
             self.asserts = logical_sense
             global NDEBUG
             NDEBUG = not logical_sense
         elif name == "runtime_trace_dir":
             self.runtime_trace_dir = value
         else:
-            logger.warn("Unrecognized %s flag: '%s'", FLAGS_ENV_NAME, name)
+            logger.warning("Unrecognized %s flag: '%s'", FLAGS_ENV_NAME, name)
 
     @staticmethod
     def parse(settings: str) -> "DebugFlags":

iree/turbine/tools/interpreter.py

@@ -151,7 +151,7 @@ def callback(self, op: Operation) -> None:
                     offset = [0 for _ in range(len(load_indices))]
                     for i in range(*result_shape):
                         ind = [int(x) + y for x, y in zip(load_indices, offset)]
-                        value[i] = memref[*ind]
+                        value[i] = memref[(*ind,)]
                         offset[-1] += 1
                 case vector_d.ExtractStridedSliceOp:
                     vector = self.symbol_table[op.vector]
@@ -168,7 +168,7 @@ def callback(self, op: Operation) -> None:
                     offset = [0 for _ in range(len(store_indices))]
                     for i in range(*result_shape):
                         memref[
-                            *[int(x) + y for x, y in zip(store_indices, offset)]
+                            (*[int(x) + y for x, y in zip(store_indices, offset)],)
                         ] = vector[i]
                         offset[-1] += 1
                 case vector_d.MaskedStoreOp:
@@ -185,7 +185,7 @@ def callback(self, op: Operation) -> None:
                     for i in range(*result_shape):
                         if mask[i]:
                             ind = [int(x) + y for x, y in zip(store_indices, offset)]
-                            memref[*ind] = vector[i]
+                            memref[(*ind,)] = vector[i]
 
                         offset[-1] += 1
                 case vector_d.ConstantMaskOp:
@@ -313,7 +313,7 @@ def interpret_ndrange(
     ):
         for wg in np.ndindex(*workgroup_count):
             for t in np.ndindex(*workgroup_size):
-                Interpreter([*wg], [*t]).interpret(asm)
+                Interpreter([(*wg,)], [(*t,)]).interpret(asm)
 
 
 if __name__ == "__main__":

lit_tests/kernel/wave/barriers.py

@@ -1,8 +1,6 @@
 # RUN: python %s | FileCheck %s
 
 import logging
-from typing import Callable
-import unittest
 import iree.turbine.kernel as tk
 import iree.turbine.kernel.lang as tkl
 import iree.turbine.kernel.wave as tkw
@@ -262,4 +260,3 @@ def test_gemm():
 
 if __name__ == "__main__":
     logging.basicConfig(level=logging.DEBUG)
-    unittest.main()

lit_tests/kernel/wave/expansion.py

@@ -1,7 +1,6 @@
 # RUN: python %s | FileCheck %s
 
 import logging
-import unittest
 import iree.turbine.kernel as tk
 import iree.turbine.kernel.lang as tkl
 import iree.turbine.kernel.wave as tkw
@@ -1109,4 +1108,3 @@ def test_chained_gemm_32x32x8():
 
 if __name__ == "__main__":
     logging.basicConfig(level=logging.DEBUG)
-    unittest.main()

lit_tests/kernel/wave/index_sequence_analysis.py

@@ -1,7 +1,6 @@
 # RUN: python %s | FileCheck %s
 
 import logging
-import unittest
 import iree.turbine.kernel as tk
 import iree.turbine.kernel.lang as tkl
 import iree.turbine.kernel.wave as tkw
@@ -350,4 +349,3 @@ def test_gemm():
 
 if __name__ == "__main__":
     logging.basicConfig(level=logging.DEBUG)
-    unittest.main()

lit_tests/kernel/wave/minimize_global_loads.py

@@ -1,8 +1,6 @@
 # RUN: python %s | FileCheck %s
 
 import logging
-from typing import Callable
-import unittest
 import iree.turbine.kernel as tk
 import iree.turbine.kernel.lang as tkl
 import iree.turbine.kernel.wave as tkw
@@ -267,4 +265,3 @@ def test_gemm():
 
 if __name__ == "__main__":
     logging.basicConfig(level=logging.DEBUG)
-    unittest.main()

lit_tests/kernel/wave/promotion.py

@@ -1,7 +1,6 @@
 # RUN: python %s | FileCheck %s
 
 import logging
-import unittest
 import iree.turbine.kernel as tk
 import iree.turbine.kernel.lang as tkl
 import iree.turbine.kernel.wave as tkw
@@ -216,4 +215,3 @@ def test_gemm():
 
 if __name__ == "__main__":
     logging.basicConfig(level=logging.DEBUG)
-    unittest.main()

lit_tests/kernel/wave/scheduling.py

@@ -1,7 +1,6 @@
 # RUN: python %s | FileCheck %s
 
 import logging
-import unittest
 import iree.turbine.kernel as tk
 import iree.turbine.kernel.lang as tkl
 import iree.turbine.kernel.wave as tkw
@@ -246,4 +245,3 @@ def test_gemm_pipelined():
 
 if __name__ == "__main__":
     logging.basicConfig(level=logging.DEBUG)
-    unittest.main()

requirements.txt

@@ -9,6 +9,7 @@ lit==18.1.7
 mypy==1.8.0
 ml_dtypes==0.5.0
 setuptools
+typing_extensions
 wheel
 
 # It is expected that you have installed a PyTorch version/variant specific

setup.py

@@ -110,6 +110,7 @@ def initialize_options(self):
         "torch>=2.3.0",
         f"Jinja2{get_version_spec('Jinja2')}",
         f"ml_dtypes{get_version_spec('ml_dtypes')}",
+        f"typing_extensions{get_version_spec('typing_extensions')}",
     ],
     extras_require={
         "testing": [