[Mlir-commits] [mlir] 8ef94dd - [mlir][linalg] add conv_1d_ncw_fcw
Lei Zhang
llvmlistbot at llvm.org
Thu Sep 8 16:49:10 PDT 2022
Author: Stanley Winata
Date: 2022-09-08T19:48:45-04:00
New Revision: 8ef94dde560e9525e2ac4f7e38048ff8ae079c03
URL: https://github.com/llvm/llvm-project/commit/8ef94dde560e9525e2ac4f7e38048ff8ae079c03
DIFF: https://github.com/llvm/llvm-project/commit/8ef94dde560e9525e2ac4f7e38048ff8ae079c03.diff
LOG: [mlir][linalg] add conv_1d_ncw_fcw
Reviewed By: hanchung, antiagainst
Differential Revision: https://reviews.llvm.org/D133465
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 33583d5b1f01c..cd943e2183252 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -1213,6 +1213,94 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: K
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_1d_ncw_fcw
+ cpp_class_name: Conv1DNcwFcwOp
+ doc: |-
+ Performs 1-D convolution.
+
+ Layout:
+ * Input: NCW.
+ * Kernel: FCW.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ implements:
+ - LinalgConvolutionOpInterface
+structured_op: !LinalgStructuredOpConfig
+ args:
+ - !LinalgOperandDefConfig
+ name: I
+ kind: input_tensor
+ type_var: T1
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s0, s1, s2 * s3 + s4
+ * s5)>
+ - !LinalgOperandDefConfig
+ name: K
+ kind: input_tensor
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s6, s1, s4)>
+ - !LinalgOperandDefConfig
+ name: O
+ kind: output_tensor
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s0, s6, s2)>
+ - !LinalgOperandDefConfig
+ name: strides
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s3)>
+ default_indices:
+ - 1
+ - !LinalgOperandDefConfig
+ name: dilations
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s5)>
+ default_indices:
+ - 1
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d0, d3, d2 *
+ s3 + d4 * s5)>
+ - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d1, d3, d4)>
+ - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d0, d1, d2)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - reduction
+ - reduction
+ assignments:
+ - !ScalarAssign
+ arg: O
+ value: !ScalarExpression
+ scalar_fn:
+ kind: binary
+ fn_name: add
+ operands:
+ - !ScalarExpression
+ scalar_arg: O
+ - !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: I
+ - !ScalarExpression
+ scalar_fn:
+ kind: type
+ fn_name: cast_signed
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: conv_2d_nhwc_hwcf
cpp_class_name: Conv2DNhwcHwcfOp
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 b22e6c3b1e415..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
@@ -269,6 +269,26 @@ def conv_1d_nwc_wcf(I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW, S.C),
U, I[D.n, D.ow * S.SW + D.kw * S.DW, D.c]) * TypeFn.cast_signed(
U, K[D.kw, D.c, D.f])
+ at linalg_structured_op
+def conv_1d_ncw_fcw(I=TensorDef(T1, S.N, S.C, S.OW * S.SW + S.KW * S.DW),
+ K=TensorDef(T2, S.F, S.C, S.KW),
+ O=TensorDef(U, S.N, S.F, S.OW, output=True),
+ strides=IndexAttrDef(S.SW, default=[1]),
+ dilations=IndexAttrDef(S.DW, default=[1])):
+ """Performs 1-D convolution.
+
+ Layout:
+ * Input: NCW.
+ * Kernel: FCW.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ """
+ implements(ConvolutionOpInterface)
+ domain(D.n, D.f, D.ow, D.c, D.kw)
+ O[D.n, D.f, D.ow] += TypeFn.cast_signed(
+ U, I[D.n, D.c, D.ow * S.SW + D.kw * S.DW]) * TypeFn.cast_signed(
+ U, K[D.f, D.c, D.kw])
@linalg_structured_op
def conv_2d_nhwc_hwcf(I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH,
diff --git a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
index 86bd070c8835d..7fdabbae1c159 100644
--- a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
@@ -178,6 +178,32 @@ func.func @conv_1d_nwc_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>
// -----
+func.func @conv_1d_ncw_fcw(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
+ linalg.conv_1d_ncw_fcw {dilations = dense<1> : tensor<1xi64>,
+ strides = dense<1> : tensor<1xi64>}
+ ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>)
+ outs (%output: memref<?x?x?xf32>)
+ return
+}
+// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d3, d2 + d4)>
+// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d1, d3, d4)>
+// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2)>
+
+// CHECK: func @conv_1d_ncw_fcw
+
+// CHECK: linalg.generic
+// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
+// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction"]}
+// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>)
+// CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
+
+// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
+// CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[BBARG0]], %[[BBARG1]] : f32
+// CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[BBARG2]], %[[MUL]] : f32
+// CHECK-NEXT: linalg.yield %[[ADD]] : f32
+
+// -----
+
func.func @generalize_fill(%output: memref<?x?xf32>, %value : f32) {
linalg.fill ins(%value : f32) outs(%output : memref<?x?xf32>)
return
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
index 6512847c8530d..f4126b4cf9ea9 100644
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -243,6 +243,38 @@ func.func @conv_1d_nwc_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>
// -----
+// CHECK-LABEL: func @conv_1d_ncw_fcw
+func.func @conv_1d_ncw_fcw(%input: tensor<?x?x?xf32>, %filter: tensor<?x?x?xf32>, %init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
+ // CHECK: %{{.+}} = linalg.conv_1d_ncw_fcw
+ // CHECK-SAME: dilations = dense<1> : tensor<1xi64>
+ // CHECK-SAME: strides = dense<1> : tensor<1xi64>
+ // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<?x?x?xf32>, tensor<?x?x?xf32>)
+ // CHECK-SAME: outs(%{{.+}} : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
+ %0 = linalg.conv_1d_ncw_fcw {dilations = dense<1> : tensor<1xi64>,
+ strides = dense<1> : tensor<1xi64>}
+ ins (%input, %filter: tensor<?x?x?xf32>, tensor<?x?x?xf32>)
+ outs (%init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
+ return %0 : tensor<?x?x?xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @conv_1d_ncw_fcw
+func.func @conv_1d_ncw_fcw(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
+ // CHECK: linalg.conv_1d_ncw_fcw
+ // CHECK-SAME: dilations = dense<1> : tensor<1xi64>
+ // CHECK-SAME: strides = dense<1> : tensor<1xi64>
+ // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>)
+ // CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
+ linalg.conv_1d_ncw_fcw {dilations = dense<1> : tensor<1xi64>,
+ strides = dense<1> : tensor<1xi64>}
+ ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>)
+ outs (%output: memref<?x?x?xf32>)
+ return
+}
+
+// -----
+
// CHECK-LABEL: func @conv_2d_nhwc_hwcf
func.func @conv_2d_nhwc_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
// CHECK: %{{.+}} = linalg.conv_2d_nhwc_hwcf
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