[Mlir-commits] [mlir] 80e61e3 - Remove redundant linalg.matmul_signed (#98615)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Sat Jul 13 06:46:48 PDT 2024
Author: Renato Golin
Date: 2024-07-13T14:46:44+01:00
New Revision: 80e61e38428964c9c9abac5b7a59bb513b5b1c3d
URL: https://github.com/llvm/llvm-project/commit/80e61e38428964c9c9abac5b7a59bb513b5b1c3d
DIFF: https://github.com/llvm/llvm-project/commit/80e61e38428964c9c9abac5b7a59bb513b5b1c3d.diff
LOG: Remove redundant linalg.matmul_signed (#98615)
`linalg.matmul` already has an attribute for casts, defaults to signed
but allowed unsigned, so the operation `linalg.matmul_unsigned` is
redundant. The generalization test has an example on how to lower to
unsigned matmul in linalg.
This is the first PR in a list of many that will simplify the linalg
operations by using similar attributes.
Ref:
https://discourse.llvm.org/t/rfc-transpose-attribute-for-linalg-matmul-operations/80092
Added:
Modified:
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
mlir/test/Dialect/Linalg/generalize-named-polymorphic-ops.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index a344add6a4e54..c31b7c4f6c108 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -1137,74 +1137,6 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: B
--- !LinalgOpConfig
-metadata: !LinalgOpMetadata
- name: matmul_unsigned
- cpp_class_name: MatmulUnsignedOp
- doc: |-
- Performs an unsigned matrix multiplication of two 2D inputs.
-
- Numeric casting is performed on the operands to the inner multiply, promoting
- them to the same data type as the accumulator/output.
- implements:
- - LinalgContractionOpInterface
-structured_op: !LinalgStructuredOpConfig
- args:
- - !LinalgOperandDefConfig
- name: A
- kind: input_tensor
- type_var: T1
- shape_map: affine_map<()[s0, s1, s2] -> (s0, s1)>
- - !LinalgOperandDefConfig
- name: B
- kind: input_tensor
- type_var: T2
- shape_map: affine_map<()[s0, s1, s2] -> (s1, s2)>
- - !LinalgOperandDefConfig
- name: C
- kind: output_tensor
- type_var: U
- shape_map: affine_map<()[s0, s1, s2] -> (s0, s2)>
- indexing_maps: !LinalgIndexingMapsConfig
- static_indexing_maps:
- - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d2)>
- - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d2, d1)>
- - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d1)>
- iterator_types:
- - parallel
- - parallel
- - reduction
- assignments:
- - !ScalarAssign
- arg: C
- value: !ScalarExpression
- scalar_fn:
- kind: binary
- fn_name: add
- operands:
- - !ScalarExpression
- scalar_arg: C
- - !ScalarExpression
- scalar_fn:
- kind: binary
- fn_name: mul
- operands:
- - !ScalarExpression
- scalar_fn:
- kind: type
- fn_name: cast_unsigned
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: A
- - !ScalarExpression
- scalar_fn:
- kind: type
- fn_name: cast_unsigned
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: B
---- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: quantized_matmul
cpp_class_name: QuantizedMatmulOp
diff --git a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
index cbb2d1cec103c..3ceee8e370445 100644
--- a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
+++ b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
@@ -388,24 +388,6 @@ def matmul(
C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n])
- at linalg_structured_op
-def matmul_unsigned(
- A=TensorDef(T1, S.M, S.K),
- B=TensorDef(T2, S.K, S.N),
- C=TensorDef(U, S.M, S.N, output=True),
-):
- """Performs an unsigned matrix multiplication of two 2D inputs.
-
- Numeric casting is performed on the operands to the inner multiply, promoting
- them to the same data type as the accumulator/output.
- """
- domain(D.m, D.n, D.k)
- implements(ContractionOpInterface)
- C[D.m, D.n] += TypeFn.cast_unsigned(U, A[D.m, D.k]) * TypeFn.cast_unsigned(
- U, B[D.k, D.n]
- )
-
-
@linalg_structured_op
def quantized_matmul(
A=TensorDef(T1, S.M, S.K),
diff --git a/mlir/test/Dialect/Linalg/generalize-named-polymorphic-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-polymorphic-ops.mlir
index 1940a4a2912cb..c170c5be4abff 100644
--- a/mlir/test/Dialect/Linalg/generalize-named-polymorphic-ops.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-named-polymorphic-ops.mlir
@@ -79,8 +79,9 @@ func.func @generalize_matmul_tensor_f16f64i32(%A : tensor<16x8xf16>, %B: tensor<
// -----
func.func @generalize_matmul_unsigned_tensor_i16i64i32(%A : tensor<16x8xi16>, %B: tensor<8x32xi64>, %C: tensor<16x32xi32>) -> tensor<16x32xi32> {
- %0 = linalg.matmul_unsigned ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
- outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>
+ %0 = linalg.matmul { cast = #linalg.type_fn<cast_unsigned> }
+ ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
+ outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>
return %0: tensor<16x32xi32>
}
@@ -92,8 +93,9 @@ func.func @generalize_matmul_unsigned_tensor_i16i64i32(%A : tensor<16x8xi16>, %B
// -----
func.func @generalize_matmul_unsigned_tensor_i16i64f32(%A : tensor<16x8xi16>, %B: tensor<8x32xi64>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> {
- %0 = linalg.matmul_unsigned ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
- outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>
+ %0 = linalg.matmul { cast = #linalg.type_fn<cast_unsigned> }
+ ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
+ outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>
return %0: tensor<16x32xf32>
}
@@ -105,8 +107,9 @@ func.func @generalize_matmul_unsigned_tensor_i16i64f32(%A : tensor<16x8xi16>, %B
// -----
func.func @generalize_matmul_unsigned_tensor_f16f64i32(%A : tensor<16x8xf16>, %B: tensor<8x32xf64>, %C: tensor<16x32xi32>) -> tensor<16x32xi32> {
- %0 = linalg.matmul_unsigned ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)
- outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>
+ %0 = linalg.matmul { cast = #linalg.type_fn<cast_unsigned> }
+ ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)
+ outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>
return %0: tensor<16x32xi32>
}
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