[Mlir-commits] [mlir] e9dd2b2 - Revert "[MLIR][Linalg] introduce batch-reduce GEMM"
Lorenzo Chelini
llvmlistbot at llvm.org
Mon Sep 19 03:17:50 PDT 2022
Author: Lorenzo Chelini
Date: 2022-09-19T12:17:30+02:00
New Revision: e9dd2b2d4b9ccf8ebbbb72bc8e0249c9296be5e4
URL: https://github.com/llvm/llvm-project/commit/e9dd2b2d4b9ccf8ebbbb72bc8e0249c9296be5e4
DIFF: https://github.com/llvm/llvm-project/commit/e9dd2b2d4b9ccf8ebbbb72bc8e0249c9296be5e4.diff
LOG: Revert "[MLIR][Linalg] introduce batch-reduce GEMM"
This reverts commit f381768a8da6bd6bde8bdff34f080bf12bf20064.
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-ops.mlir
mlir/test/Dialect/Linalg/named-ops.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index d86b82fcbe45a..cd943e2183252 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -548,76 +548,6 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: B
--- !LinalgOpConfig
-metadata: !LinalgOpMetadata
- name: batch_reduce_matmul
- cpp_class_name: BatchReduceMatmulOp
- doc: |-
- Performs a batch-reduce matrix multiplication of two 3D inputs.
- The partial multiplication results are reduced into a 2D output.
-
- 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, s3] -> (s0, s1, s3)>
- - !LinalgOperandDefConfig
- name: B
- kind: input_tensor
- type_var: T2
- shape_map: affine_map<()[s0, s1, s2, s3] -> (s0, s3, s2)>
- - !LinalgOperandDefConfig
- name: C
- kind: output_tensor
- type_var: U
- shape_map: affine_map<()[s0, s1, s2, s3] -> (s0, s1, s2)>
- indexing_maps: !LinalgIndexingMapsConfig
- static_indexing_maps:
- - affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3] -> (d0, d1, d3)>
- - affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3] -> (d0, d3, d2)>
- - affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3] -> (d1, d2)>
- iterator_types:
- - reduction
- - 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_signed
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: A
- - !ScalarExpression
- scalar_fn:
- kind: type
- fn_name: cast_signed
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: B
---- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: quantized_batch_matmul
cpp_class_name: QuantizedBatchMatmulOp
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 b9b292d847cd9..983842cde1325 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
@@ -150,20 +150,6 @@ def quantized_batch_matmul(A=TensorDef(T1, Batch, S.M, S.K),
TypeFn.cast_signed(U, AZp)) * (TypeFn.cast_signed(
U, B[D.b, D.k, D.n]) - TypeFn.cast_signed(U, BZp))
- at linalg_structured_op
-def batch_reduce_matmul(A=TensorDef(T1, Batch, S.M, S.K),
- B=TensorDef(T2, Batch, S.K, S.N),
- C=TensorDef(U, S.M, S.N, output=True)):
- """Performs a batch-reduce matrix multiplication of two 3D inputs.
- The partial multiplication results are reduced into a 2D output.
-
- Numeric casting is performed on the operands to the inner multiply, promoting
- them to the same data type as the accumulator/output.
- """
- domain(D.b, D.m, D.n, D.k)
- implements(ContractionOpInterface)
- C[D.m, D.n] += TypeFn.cast_signed(U, A[D.b, D.m, D.k] * TypeFn.cast_signed(
- U, B[D.b, D.k, D.n])
@linalg_structured_op
def matvec(A=TensorDef(T1, S.M, S.N),
diff --git a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
index e43b13a5e5955..7fdabbae1c159 100644
--- a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
@@ -248,27 +248,3 @@ func.func @generalize_batch_matm_vec(%lhs : memref<?x?x?xi8>, %rhs: memref<?x?xi
// CHECK: %[[MUL:.+]] = arith.mulf %[[BBARG0_F32]], %[[BBARG1_F32]]
// CHECK: %[[ADD:.+]] = arith.addf %[[BBARG2]], %[[MUL]]
// CHECK: linalg.yield %[[ADD]] : f32
-
-// -----
-
-func.func @batch_reduce_gemm(%lhs: memref<7x8x9xf32>, %rhs: memref<7x9x8xf32>, %out: memref<8x8xf32>) {
- linalg.batch_reduce_matmul ins(%lhs, %rhs: memref<7x8x9xf32>, memref<7x9x8xf32>)
- outs(%out: memref<8x8xf32>)
- return
-}
-
-// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>
-// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>
-// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d1, d2)>
-
-// CHECK: @batch_reduce_gemm
-
-// CHECK: linalg.generic
-// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
-// CHECK-SAME: iterator_types = ["reduction", "parallel", "parallel", "reduction"]}
-// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<7x8x9xf32>, memref<7x9x8xf32>)
-// CHECK-SAME: outs(%{{.+}} : memref<8x8xf32>
-// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
-// CHECK: %[[MUL:.+]] = arith.mulf %[[BBARG0]], %[[BBARG1]] : f32
-// CHECK: %[[ADD:.+]] = arith.addf %[[BBARG2]], %[[MUL]] : f32
-// CHECK: linalg.yield %[[ADD]] : f32
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
index 5a7b7ffbb59fd..f4126b4cf9ea9 100644
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -794,23 +794,3 @@ func.func @conv_interface_wrong_num_operands(
}) {dilations = dense<1> : tensor<2xi64>, linalg.memoized_indexing_maps = [#map0, #map1, #map2], operand_segment_sizes = array<i32: 2, 1>, strides = dense<1> : tensor<2xi64>} : (tensor<?x?x?x?xf32>, tensor<?x?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %0 : tensor<?x?x?x?xf32>
}
-
-// -----
-
-func.func @batch_reduce_matmul(%arg0: tensor<8x128x256xf32>, %arg1: tensor<8x256x512xf32>, %arg2: tensor<128x512xf32>) -> tensor<128x512xf32> {
- // CHECK: %{{.+}} = linalg.batch_reduce_matmul
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<8x128x256xf32>, tensor<8x256x512xf32>)
- // CHECK-SAME: outs(%{{.+}} : tensor<128x512xf32>) -> tensor<128x512xf32>
- %0 = linalg.batch_reduce_matmul ins(%arg0, %arg1 : tensor<8x128x256xf32>, tensor<8x256x512xf32>) outs(%arg2: tensor<128x512xf32>) -> tensor<128x512xf32>
- return %0: tensor<128x512xf32>
-}
-
-// -----
-
-func.func @batch_reduce_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) {
- // CHECK: linalg.batch_reduce_matmul
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>)
- // CHECK-SAME: outs(%{{.+}} : memref<?x?xf32>)
- linalg.batch_reduce_matmul ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2: memref<?x?xf32>)
- return
-}
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