[MLIR][Linalg] Remove failing obsolete OpDSL test for linalg.matmul. #115164
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Fixes the GCC build of previous patch related to '[mlir][linalg] Introduce transpose semantic to 'linalg.matmul' ops.
The previous commit (llvm@0348373) was reverted due to a failing OpDSL test for linalg.matmul. Since linalg.matmul is now defined using TableGen ODS instead of Python-based OpDSL, this test is no longer needed. This commit removes the failing obsolete test from opsrun.py and transform.py.
shahidact
requested review from
ftynse,
makslevental,
stellaraccident,
dcaballe,
nicolasvasilache and
rengolin
as code owners
|
@llvm/pr-subscribers-mlir @llvm/pr-subscribers-mlir-linalg Author: Md Asghar Ahmad Shahid (shahidact) ChangesThis PR is related to earlier PR #104783 . The earlier PR commit was reverted due to a failing OpDSL test for linalg.matmul. Since linalg.matmul is now defined using TableGen ODS instead of Python-based OpDSL, this test is failing and needs to be removed. This commit removes the failing obsolete test. Full diff: https://github.com/llvm/llvm-project/pull/115164.diff 3 Files Affected:
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 730c478c2883ef..6a69bc0aef46f3 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -149,6 +149,26 @@ static void fillStructuredOpRegion(OpBuilder &opBuilder, Region ®ion,
// iterator_types is an auto-generated method.
}
+/// Helper to create a typical indexing map for MatmulOp. Returns a list of
+/// AffineMap.
+static SmallVector<AffineMap>
+getDefaultIndexingMapsForMatmul(MLIRContext *context) {
+ AffineExpr d0, d1, d2;
+ SmallVector<AffineMap, 6> indexingMaps;
+ bindDims(context, d0, d1, d2);
+ indexingMaps.push_back(AffineMap::get(3, 0, {d0, d2}, context));
+ indexingMaps.push_back(AffineMap::get(3, 0, {d2, d1}, context));
+ indexingMaps.push_back(AffineMap::get(3, 0, {d0, d1}, context));
+ return indexingMaps;
+}
+
+/// Wrapper to return the typical indexing map array attribute for MatmulOp.
+static SmallVector<Attribute> getDefaultIndexingMapAttr(MLIRContext *context) {
+ return llvm::map_to_vector(
+ getDefaultIndexingMapsForMatmul(context),
+ [](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
+}
+
/// Creates a structured operation given `inputs`, `outputs`, and `attributes`.
/// The result types are derived automatically if `resultTensorTypes` is none.
/// The body of the operation is filled using `regionBuilder`. All ods-gen
diff --git a/mlir/test/python/integration/dialects/linalg/opsrun.py b/mlir/test/python/integration/dialects/linalg/opsrun.py
index f6519fb17a6b98..f77900bc277736 100644
--- a/mlir/test/python/integration/dialects/linalg/opsrun.py
+++ b/mlir/test/python/integration/dialects/linalg/opsrun.py
@@ -50,37 +50,6 @@ def log(*args):
}
"""
-matmul_boiler = """
-func.func @main() -> f32 attributes {llvm.emit_c_interface} {
- %v0 = arith.constant 0.0 : f32
- %v1 = arith.constant -1 : i8
- %v2 = arith.constant 2.0 : f32
-
- %A = memref.alloc() : memref<4x16xi8>
- %B = memref.alloc() : memref<16x8xf32>
- %C0 = memref.alloc() : memref<4x8xf32>
- %C1 = memref.alloc() : memref<4x8xf32>
- linalg.fill ins(%v1 : i8) outs(%A : memref<4x16xi8>)
- linalg.fill ins(%v2 : f32) outs(%B : memref<16x8xf32>)
- linalg.fill ins(%v0 : f32) outs(%C0 : memref<4x8xf32>)
- linalg.fill ins(%v0 : f32) outs(%C1 : memref<4x8xf32>)
-
- call @matmul_signed_on_buffers(%A, %B, %C0) :
- (memref<4x16xi8>, memref<16x8xf32>, memref<4x8xf32>) -> ()
- call @matmul_unsigned_on_buffers(%A, %B, %C1) :
- (memref<4x16xi8>, memref<16x8xf32>, memref<4x8xf32>) -> ()
-
- %c0 = arith.constant 0 : index
- %res0 = memref.load %C0[%c0, %c0] : memref<4x8xf32>
- %res1 = memref.load %C1[%c0, %c0] : memref<4x8xf32>
-
- %0 = arith.addf %res0, %res1 : f32
-
- // TODO: FFI-based solution to allow testing and printing with python code.
- return %0 : f32
-}
-"""
-
fill_boiler = """
func.func @main() -> i32 attributes {llvm.emit_c_interface} {
%O0 = memref.alloc() : memref<i32>
@@ -296,90 +265,6 @@ def elemwise_log_mul_on_buffers(lhs, rhs, out):
test_elemwise_generic()
-def test_matmul_builtin():
- with Context() as ctx, Location.unknown():
- module = Module.create()
- f32 = F32Type.get()
- i8 = IntegerType.get_signless(8)
- with InsertionPoint(module.body):
-
- @func.FuncOp.from_py_func(
- MemRefType.get((4, 16), i8),
- MemRefType.get((16, 8), f32),
- MemRefType.get((4, 8), f32),
- )
- def matmul_signed_on_buffers(lhs, rhs, out):
- linalg.matmul(lhs, rhs, outs=[out])
-
- @func.FuncOp.from_py_func(
- MemRefType.get((4, 16), i8),
- MemRefType.get((16, 8), f32),
- MemRefType.get((4, 8), f32),
- )
- def matmul_unsigned_on_buffers(lhs, rhs, out):
- linalg.matmul(lhs, rhs, outs=[out], cast=TypeFn.cast_unsigned)
-
- execution_engine = ExecutionEngine(transform(module, matmul_boiler))
-
- # TODO: FFI-based solution to allow testing and printing with python code.
- # Prepare arguments: one result f32.
- # Arguments must be passed as pointers.
- c_float_p = ctypes.c_float * 1
- res = c_float_p(-1.0)
- execution_engine.invoke("main", res)
-
- log("RESULT: ", res[0])
- # matmul_signed_on_buffers: -1 * 2.0 * 16 = -32
- # matmul_unsigned_on_buffers: (2^8-1) * 2.0 * 16 = 8160
- # CHECK: RESULT: 8128
-
-
-test_matmul_builtin()
-
-
-def test_matmul_generic():
- with Context() as ctx, Location.unknown():
- module = Module.create()
- f32 = F32Type.get()
- i8 = IntegerType.get_signless(8)
- with InsertionPoint(module.body):
-
- @func.FuncOp.from_py_func(
- MemRefType.get((4, 16), i8),
- MemRefType.get((16, 8), f32),
- MemRefType.get((4, 8), f32),
- )
- def matmul_signed_on_buffers(lhs, rhs, out):
- linalg.matmul(lhs, rhs, outs=[out], emit_generic=True)
-
- @func.FuncOp.from_py_func(
- MemRefType.get((4, 16), i8),
- MemRefType.get((16, 8), f32),
- MemRefType.get((4, 8), f32),
- )
- def matmul_unsigned_on_buffers(lhs, rhs, out):
- linalg.matmul(
- lhs, rhs, outs=[out], cast=TypeFn.cast_unsigned, emit_generic=True
- )
-
- execution_engine = ExecutionEngine(transform(module, matmul_boiler))
-
- # TODO: FFI-based solution to allow testing and printing with python code.
- # Prepare arguments: one result f32.
- # Arguments must be passed as pointers.
- c_float_p = ctypes.c_float * 1
- res = c_float_p(-1.0)
- execution_engine.invoke("main", res)
-
- log("RESULT: ", res[0])
- # matmul_signed_on_buffers = -1 * 2.0 * 16 = -32
- # matmul_unsigned_on_buffers = (2^8-1) * 2.0 * 16 = 8160
- # CHECK: RESULT: 8128
-
-
-test_matmul_generic()
-
-
def test_fill_builtin():
with Context() as ctx, Location.unknown():
module = Module.create()
diff --git a/mlir/test/python/integration/dialects/transform.py b/mlir/test/python/integration/dialects/transform.py
index bc88a61314d0d8..41bec6a762027a 100644
--- a/mlir/test/python/integration/dialects/transform.py
+++ b/mlir/test/python/integration/dialects/transform.py
@@ -94,62 +94,3 @@ def basic(target: any_op_t()):
# CHECK: return
# CHECK: }
print(module_)
-
-
-# CHECK-LABEL: TEST: test_apply_patterns
-@construct_and_print_in_module
-def test_apply_patterns(module_):
- M, N, K = 3, 5, 3
-
- # CHECK-LABEL: func.func @matmul(
- # CHECK-SAME: %[[VAL_0:.*]]: tensor<3x5xf32>, %[[VAL_1:.*]]: tensor<5x3xf32>, %[[VAL_2:.*]]: tensor<3x3xf32>) -> tensor<3x3xf32> {
- # CHECK: %[[VAL_3:.*]] = arith.constant 1 : i32
- # CHECK: %[[VAL_4:.*]] = arith.addi %[[VAL_3]], %[[VAL_3]] : i32
- # CHECK: %[[VAL_5:.*]] = linalg.matmul {cast = #linalg.type_fn<cast_signed>} ins(%[[VAL_0]], %[[VAL_1]] : tensor<3x5xf32>, tensor<5x3xf32>) outs(%[[VAL_2]] : tensor<3x3xf32>) -> tensor<3x3xf32>
- # CHECK: return %[[VAL_5]] : tensor<3x3xf32>
- # CHECK: }
- @func.func(
- T.tensor(M, N, T.f32()), T.tensor(N, K, T.f32()), T.tensor(M, K, T.f32())
- )
- def matmul(A, B, C):
- i = arith.constant(T.i32(), 1)
- v = arith.addi(i, i)
- return linalg.matmul(A, B, outs=[C])
-
- # CHECK-LABEL: module attributes {transform.with_named_sequence} {
- # CHECK: transform.named_sequence @__transform_main(%[[VAL_0:.*]]: !transform.any_op) {
- # CHECK: %[[VAL_1:.*]] = transform.structured.match ops{["linalg.matmul"]} in %[[VAL_0]] : (!transform.any_op) -> !transform.any_op
- # CHECK: %[[VAL_2:.*]] = transform.get_parent_op %[[VAL_1]] {op_name = "func.func"} : (!transform.any_op) -> !pdl.operation
- # CHECK: transform.apply_patterns to %[[VAL_2]] {
- # CHECK: transform.apply_patterns.canonicalization
- # CHECK: } : !pdl.operation
- # CHECK: %[[VAL_3:.*]] = transform.structured.match ops{["func.func"]} in %[[VAL_0]] : (!transform.any_op) -> !transform.any_op
- # CHECK: transform.apply_cse to %[[VAL_3]] : !transform.any_op
- # CHECK: transform.yield
- # CHECK: }
- # CHECK: }
- @module(attrs={"transform.with_named_sequence": UnitAttr.get()})
- def mod():
- @named_sequence("__transform_main", [any_op_t()], [])
- def basic(variant_op: any_op_t()):
- matmul = structured_match(any_op_t(), variant_op, ops=["linalg.matmul"])
- top_func = get_parent_op(pdl.op_t(), matmul, op_name="func.func")
-
- @apply_patterns(top_func)
- def pats():
- apply_patterns_canonicalization()
-
- top_func = structured_match(any_op_t(), variant_op, ops=["func.func"])
- apply_cse(top_func)
-
- print(module_)
-
- pm = PassManager.parse("builtin.module(transform-interpreter)")
- pm.run(module_.operation)
-
- # CHECK-LABEL: func.func @matmul(
- # CHECK-SAME: %[[VAL_0:.*]]: tensor<3x5xf32>, %[[VAL_1:.*]]: tensor<5x3xf32>, %[[VAL_2:.*]]: tensor<3x3xf32>) -> tensor<3x3xf32> {
- # CHECK: %[[VAL_3:.*]] = linalg.matmul {cast = #linalg.type_fn<cast_signed>} ins(%[[VAL_0]], %[[VAL_1]] : tensor<3x5xf32>, tensor<5x3xf32>) outs(%[[VAL_2]] : tensor<3x3xf32>) -> tensor<3x3xf32>
- # CHECK: return %[[VAL_3]] : tensor<3x3xf32>
- # CHECK: }
- print(module_)
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This PR is related to earlier PR #104783 .
The earlier PR commit was reverted due to a failing OpDSL test for linalg.matmul. Since linalg.matmul is now defined using TableGen ODS instead of Python-based OpDSL, this test is failing and needs to be removed.
This commit removes the failing obsolete test.