[Mlir-commits] [mlir] 871c812 - [mlir][linalg] Finish refactor of TC ops to YAML
Rob Suderman
llvmlistbot at llvm.org
Fri Aug 20 12:40:03 PDT 2021
Author: Rob Suderman
Date: 2021-08-20T12:35:04-07:00
New Revision: 871c8124834de14693536106f7e1bc2d10be4a5c
URL: https://github.com/llvm/llvm-project/commit/871c8124834de14693536106f7e1bc2d10be4a5c
DIFF: https://github.com/llvm/llvm-project/commit/871c8124834de14693536106f7e1bc2d10be4a5c.diff
LOG: [mlir][linalg] Finish refactor of TC ops to YAML
Multiple operations were still defined as TC ops that had equivalent versions
as YAML operations. Reducing to a single compilation path guarantees that
frontends can lower to their equivalent operations without missing the
optimized fastpath.
Some operations are maintained purely for testing purposes (mainly conv{1,2,3}D
as they are included as sole tests in the vectorizaiton transforms.
Differential Revision: https://reviews.llvm.org/D108169
Added:
mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-nwc-wcf-call.mlir
mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-nhwc-hwcf-call.mlir
mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-ndhwc-dhwcf-call.mlir
Modified:
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
mlir/test/Dialect/Linalg/generalize-named-ops.mlir
mlir/test/Dialect/Linalg/invalid.mlir
mlir/test/Dialect/Linalg/named-ops.mlir
mlir/test/Dialect/Linalg/tile-and-fuse-tensors.mlir
Removed:
mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-input-nwc-filter-wcf-call.mlir
mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-input-nhwc-filter-hwcf-call.mlir
mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-input-ndhwc-filter-dhwcf-call.mlir
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index cb5db86bbe65d..a877d866dbb18 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -628,10 +628,10 @@ structured_op: !LinalgStructuredOpConfig
scalar_arg: B
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
- name: conv_2d_nchw
- cpp_class_name: Conv2DNchwOp
+ name: conv_1d
+ cpp_class_name: Conv1DOp
doc: |-
- Performs 2-D convolution.
+ Performs 1-D convolution with no channels.
Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output.
@@ -641,46 +641,225 @@ structured_op: !LinalgStructuredOpConfig
name: I
usage: InputOperand
type_var: T1
- shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s0, s1, s2, s3)>
+ shape_map: affine_map<()[s0, s1, s2] -> (s0)>
- !LinalgOperandDefConfig
name: K
usage: InputOperand
type_var: T2
- shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s4, s1, s5, s6)>
+ shape_map: affine_map<()[s0, s1, s2] -> (s1)>
- !LinalgOperandDefConfig
name: O
usage: OutputOperand
type_var: U
- shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s0, s4, s7, s8, s1)>
+ shape_map: affine_map<()[s0, s1, s2] -> (s2)>
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1)[s0, s1, s2] -> (d0 + d1)>
+ - affine_map<(d0, d1)[s0, s1, s2] -> (d1)>
+ - affine_map<(d0, d1)[s0, s1, s2] -> (d0)>
+ iterator_types:
+ - parallel
+ - reduction
+ assignments:
+ - !ScalarAssign
+ arg: O
+ value: !ScalarExpression
+ scalar_apply:
+ fn_name: add
+ operands:
+ - !ScalarExpression
+ scalar_arg: O
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: mul
+ operands:
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_2d
+ cpp_class_name: Conv2DOp
+ doc: |-
+ Performs 2-D convolution with no channels.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+structured_op: !LinalgStructuredOpConfig
+ args:
+ - !LinalgOperandDefConfig
+ name: I
+ usage: InputOperand
+ type_var: T1
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s0, s1)>
+ - !LinalgOperandDefConfig
+ name: K
+ usage: InputOperand
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s2, s3)>
+ - !LinalgOperandDefConfig
+ name: O
+ usage: OutputOperand
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s4, s5)>
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3, s4, s5] -> (d0 + d2, d1 + d3)>
+ - affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3, s4, s5] -> (d2, d3)>
+ - affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3, s4, s5] -> (d0, d1)>
+ iterator_types:
+ - parallel
+ - parallel
+ - reduction
+ - reduction
+ assignments:
+ - !ScalarAssign
+ arg: O
+ value: !ScalarExpression
+ scalar_apply:
+ fn_name: add
+ operands:
+ - !ScalarExpression
+ scalar_arg: O
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: mul
+ operands:
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_3d
+ cpp_class_name: Conv3DOp
+ doc: |-
+ Performs 3-D convolution with no channels.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+structured_op: !LinalgStructuredOpConfig
+ args:
+ - !LinalgOperandDefConfig
+ name: I
+ usage: InputOperand
+ type_var: T1
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8] -> (s0, s1, s2)>
+ - !LinalgOperandDefConfig
+ name: K
+ usage: InputOperand
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8] -> (s3, s4, s5)>
+ - !LinalgOperandDefConfig
+ name: O
+ usage: OutputOperand
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8] -> (s6, s7, s8)>
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8] -> (d0
+ + d3, d1 + d4, d2 + d5)>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8] -> (d3,
+ d4, d5)>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8] -> (d0,
+ d1, d2)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - reduction
+ - reduction
+ - reduction
+ assignments:
+ - !ScalarAssign
+ arg: O
+ value: !ScalarExpression
+ scalar_apply:
+ fn_name: add
+ operands:
+ - !ScalarExpression
+ scalar_arg: O
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: mul
+ operands:
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_1d_nwc_wcf
+ cpp_class_name: Conv1DNwcWcfOp
+ doc: |-
+ Performs 1-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+structured_op: !LinalgStructuredOpConfig
+ args:
+ - !LinalgOperandDefConfig
+ name: I
+ usage: InputOperand
+ type_var: T1
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7] -> (s0, s1, s2)>
+ - !LinalgOperandDefConfig
+ name: K
+ usage: InputOperand
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7] -> (s3, s2, s4)>
+ - !LinalgOperandDefConfig
+ name: O
+ usage: OutputOperand
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7] -> (s0, s5, s4)>
- !LinalgOperandDefConfig
name: strides
usage: IndexAttribute
type_var: I64
- attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
- s12] -> (s9, s10)>
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7] -> (s6)>
- !LinalgOperandDefConfig
name: dilations
usage: IndexAttribute
type_var: I64
- attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
- s12] -> (s11, s12)>
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7] -> (s7)>
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d4, d2 * s9 + d5 * s11, d3 * s10 + d6 * s12)>
- - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d1, d4, d5, d6)>
- - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d1, d2, d3)>
+ - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6, s7] -> (d0, d1 *
+ s6 + d3 * s7, d4)>
+ - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6, s7] -> (d3, d4,
+ d2)>
+ - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6, s7] -> (d0, d1,
+ d2)>
iterator_types:
- parallel
- parallel
- parallel
- - parallel
- - reduction
- reduction
- reduction
assignments:
@@ -906,6 +1085,286 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: KZp
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_3d_ndhwc_dhwcf
+ cpp_class_name: Conv3DNdhwcDhwcfOp
+ doc: |-
+ Performs 3-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+structured_op: !LinalgStructuredOpConfig
+ args:
+ - !LinalgOperandDefConfig
+ name: I
+ usage: InputOperand
+ type_var: T1
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
+ s13, s14, s15, s16, s17] -> (s0, s1, s2, s3, s4)>
+ - !LinalgOperandDefConfig
+ name: K
+ usage: InputOperand
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
+ s13, s14, s15, s16, s17] -> (s5, s6, s7, s4, s8)>
+ - !LinalgOperandDefConfig
+ name: O
+ usage: OutputOperand
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
+ s13, s14, s15, s16, s17] -> (s0, s9, s10, s11, s8)>
+ - !LinalgOperandDefConfig
+ name: strides
+ usage: IndexAttribute
+ type_var: I64
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12, s13, s14, s15, s16, s17] -> (s12, s13, s14)>
+ - !LinalgOperandDefConfig
+ name: dilations
+ usage: IndexAttribute
+ type_var: I64
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12, s13, s14, s15, s16, s17] -> (s15, s16, s17)>
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14, s15, s16, s17] -> (d0, d1 * s12 + d5 *
+ s15, d2 * s13 + d6 * s16, d3 * s14 + d7 * s17, d8)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14, s15, s16, s17] -> (d5, d6, d7, d8, d4)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14, s15, s16, s17] -> (d0, d1, d2, d3, d4)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - reduction
+ - reduction
+ - reduction
+ - reduction
+ assignments:
+ - !ScalarAssign
+ arg: O
+ value: !ScalarExpression
+ scalar_apply:
+ fn_name: add
+ operands:
+ - !ScalarExpression
+ scalar_arg: O
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: mul
+ operands:
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: depthwise_conv2D_nhw
+ cpp_class_name: DepthwiseConv2DNhwOp
+ doc: |-
+ Performs depth-wise 2-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output. Multiplier is set to 1
+ which is a special case for most dpethwise convolutions.
+structured_op: !LinalgStructuredOpConfig
+ args:
+ - !LinalgOperandDefConfig
+ name: I
+ usage: InputOperand
+ type_var: T1
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] ->
+ (s0, s1, s2, s3)>
+ - !LinalgOperandDefConfig
+ name: K
+ usage: InputOperand
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] ->
+ (s4, s5, s3)>
+ - !LinalgOperandDefConfig
+ name: O
+ usage: OutputOperand
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] ->
+ (s0, s6, s7, s3)>
+ - !LinalgOperandDefConfig
+ name: strides
+ usage: IndexAttribute
+ type_var: I64
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11]
+ -> (s8, s9)>
+ - !LinalgOperandDefConfig
+ name: dilations
+ usage: IndexAttribute
+ type_var: I64
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11]
+ -> (s10, s11)>
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9,
+ s10, s11] -> (d0, d1 * s8 + d4 * s10, d2 * s9 + d5 * s11, d3)>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9,
+ s10, s11] -> (d4, d5, d3)>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9,
+ s10, s11] -> (d0, d1, d2, d3)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - reduction
+ - reduction
+ assignments:
+ - !ScalarAssign
+ arg: O
+ value: !ScalarExpression
+ scalar_apply:
+ fn_name: add
+ operands:
+ - !ScalarExpression
+ scalar_arg: O
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: mul
+ operands:
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: depthwise_conv2D_nhw_q
+ cpp_class_name: DepthwiseConv2DNhwQOp
+ doc: |-
+ Performs depth-wise 2-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+structured_op: !LinalgStructuredOpConfig
+ args:
+ - !LinalgOperandDefConfig
+ name: I
+ usage: InputOperand
+ type_var: T1
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] ->
+ (s0, s1, s2, s3)>
+ - !LinalgOperandDefConfig
+ name: K
+ usage: InputOperand
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] ->
+ (s4, s5, s3)>
+ - !LinalgOperandDefConfig
+ name: IZp
+ usage: InputOperand
+ type_var: I32
+ - !LinalgOperandDefConfig
+ name: KZp
+ usage: InputOperand
+ type_var: I32
+ - !LinalgOperandDefConfig
+ name: O
+ usage: OutputOperand
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] ->
+ (s0, s6, s7, s3)>
+ - !LinalgOperandDefConfig
+ name: strides
+ usage: IndexAttribute
+ type_var: I64
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11]
+ -> (s8, s9)>
+ - !LinalgOperandDefConfig
+ name: dilations
+ usage: IndexAttribute
+ type_var: I64
+ attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11]
+ -> (s10, s11)>
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9,
+ s10, s11] -> (d0, d1 * s8 + d4 * s10, d2 * s9 + d5 * s11, d3)>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9,
+ s10, s11] -> (d4, d5, d3)>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9,
+ s10, s11] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9,
+ s10, s11] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9,
+ s10, s11] -> (d0, d1, d2, d3)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - reduction
+ - reduction
+ assignments:
+ - !ScalarAssign
+ arg: O
+ value: !ScalarExpression
+ scalar_apply:
+ fn_name: add
+ operands:
+ - !ScalarExpression
+ scalar_arg: O
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: mul
+ operands:
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: sub
+ operands:
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: IZp
+ - !ScalarExpression
+ scalar_apply:
+ fn_name: sub
+ operands:
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: KZp
+--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: depthwise_conv2D_nhwc
cpp_class_name: DepthwiseConv2DNhwcOp
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
index 7f0f7deab3e74..0fddacf65f095 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
@@ -5,151 +5,3 @@ def matmul_i8_i8_i32(A: i8(M, K), B: i8(K, N)) -> (C: i32(M, N)) {
// C(m, n) += cast<i32>(A(m, k)) * cast<i32>(B(k, n))
C(m, n) = AddIOp<k>(C(m, n), MulIOp(SignExtendIOp32(A(m, k)), SignExtendIOp32(B(k, n))));
}
-
-ods_def<ConvWOp>:
-def conv_1d(I: f32(W), K: f32(KW)) -> (O: f32(W)) {
- O(w) = AddFOp<kw>(O(w), MulFOp(I(w + kw), K(kw)));
-}
-
-ods_def<ConvHWOp>:
-def conv_2d(I: f32(H, W), K: f32(KH, KW)) -> (O: f32(H, W)) {
- O(h, w) = AddFOp<kh, kw>(O(h, w), MulFOp(I(h + kh, w + kw), K(kh, kw)));
-}
-
-ods_def<ConvDHWOp>:
-def conv_3d(I: f32(D, H, W), K: f32(KD, KH, KW)) -> (O: f32(D, H, W)) {
- O(d, h, w) = AddFOp<kd, kh, kw>(
- O(d, h, w), MulFOp(I(d + kd, h + kh, w + kw), K(kd, kh, kw)));
-}
-
-ods_def<DepthwiseConvInputNHWCFilterHWCFOp>:
-def depthwise_conv_2d_input_nhwc_filter_hwcf
- (I: f32(N, IH, IW, CI), K: f32(KH, KW, CI, CO))
- -> (O: f32(N, OH, OW, CI, CO))
- attr(strides: 2xi64, dilations: 2xi64)
-"""A general depth-wise 2-D convolution operation.
-
-This operation performs depth-wise 2-D convolution over an input `I` and filter
-`F` and generates output `O` using the following computation:
-
-```
- O(n, oh, ow, ci, co) = AddFOp<kh, kw>(
- O(n, oh, ow, ci, co),
- MulFOp(I(n, oh * strides[0] + kh * dilations[0], ow * strides[1] + kw * dilations[1], ci),
- K(kh, kw, ci, co)));
-```
-
-where
-
-* `I` is a 4-D tensor with shape `(N, IH, IW, CI)`.
-* `F` is a 4-D tensor with shape `(KH, KW, CI, CO)`.
-* `O` is a 5-D tensor with shape `(N, OH, OW, CI, CO)`.
-* `strides` is a 2-element vector attribute for window strides along the
- height/width dimension.
-
-The indexing maps for these three tensors contain 7 dimensions, following the
-order of (`N`, `OH`, `OW`, `CI`, `CO`, `KH`, `KW`).
-
-Note: this op only supports any channel multiplier, which is `CO`. To map back
-to 4D result as DepthwiseConvInputNHWCFilterHWCOp, you will have to create a
-Linalg reshape op which collapses `CI` and `CO` into one dimension.
-"""
-{
- O(n, oh, ow, ci, co) = AddFOp<kh, kw>(
- O(n, oh, ow, ci, co),
- MulFOp(I(n, oh * strides[0] + kh * dilations[0], ow * strides[1] + kw * dilations[1], ci),
- K(kh, kw, ci, co)));
-}
-
-ods_def<DepthwiseConvInputNHWCFilterHWCOp>:
-def depthwise_conv_2d_input_nhwc_filter_hwc
- (I: f32(N, IH, IW, C), K: f32(KH, KW, C))
- -> (O: f32(N, OH, OW, C))
- attr(strides: 2xi64, dilations: 2xi64)
-"""A depth-wise 2-D convolution operation.
-
-This operation performs depth-wise 2-D convolution over an input `I` and filter
-`F` and generates output `O` using the following computation:
-
-```
-O(n, oh, ow, c) = AddFOp<kh, kw>(
- O(n, oh, ow, c),
- MulFOp(I(n, oh * strides[0] + kh * dilations[0], ow * strides[1] + kw * dilations[1], c),
- K(kh, kw, c)));
-```
-
-where
-
-* `I` is a 4-D tensor with shape `(N, IH, IW, C)`.
-* `F` is a 3-D tensor with shape `(KH, KW, C)`.
-* `O` is a 4-D tensor with shape `(N, OH, OW, C)`.
-* `strides` is a 2-element vector attribute for window strides along the
- height/width dimension.
-
-The indexing maps for these three tensors contain 6 dimensions, following the
-order of (`N`, `OH`, `OW`, `C`, `KH`, `KW`).
-
-Note: this op only supports channel multiplier == 1.
-"""
-{
- O(n, oh, ow, c) = AddFOp<kh, kw>(
- O(n, oh, ow, c),
- MulFOp(I(n, oh * strides[0] + kh * dilations[0], ow * strides[1] + kw * dilations[1], c),
- K(kh, kw, c)));
-}
-
-ods_def<ConvInputNWCFilterWCFOp>:
-def conv_1d_input_nwc_filter_wcf(I: f32(N, W, C), K: f32(KW, C, F)) -> (O: f32(N, W, F))
- attr(strides: 1xi64, dilations: 1xi64)
-""" A 1-D convolution given NWC layout input and WCF layout filter.
-
-Computes a 1-D convolution given 3-D input and filter. The data layout
-of input is NWC and the data layout of filter is WCF.
-
-The indexing maps for these three tensors contain 5 dimensions, following the
-order of (`N`, `W`, `F`, `KW`, `C`).
-"""
-{
- O(n, w, f) = AddFOp<kw>(
- O(n, w, f),
- MulFOp(I(n, w * strides[0] + kw * dilations[0], c), K(kw, c, f)));
-}
-
-ods_def<ConvInputNHWCFilterHWCFOp>:
-def conv_2d_input_nhwc_filter_hwcf(I: f32(N, H, W, C), K: f32(KH, KW, C, F)) -> (O: f32(N, H, W, F))
- attr(strides: 2xi64, dilations: 2xi64)
-""" A 2-D convolution given NHWC layout input and HWCF layout filter.
-
-Computes a 2-D convolution given 4-D input and filter. The data layout
-of input is NHWC and the data layout of filter is HWCF.
-
-The indexing maps for these three tensors contain 7 dimensions, following the
-order of (`N`, `H`, `W`, `F`, `KH`, `KW`, `C`).
-"""
-{
- O(n, h, w, f) = AddFOp<kh, kw>(
- O(n, h, w, f), MulFOp(I(n, h * strides[0] + kh * dilations[0],
- w * strides[1] + kw * dilations[1], c),
- K(kh, kw, c, f)));
-}
-
-ods_def<ConvInputNDHWCFilterDHWCFOp>:
-def conv_3d_input_ndhwc_filter_dhwcf
- (I: f32(N, D, H, W, C), K: f32(KD, KH, KW, C, F))
- -> (O: f32(N, D, H, W, F))
- attr(strides: 3xi64, dilations: 3xi64)
-""" A 3-D convolution given NDHWC layout input and DHWCF layout filter.
-
-Computes a 3-D convolution given 5-D input and filter. The data layout
-of input is NDHWC and the data layout of filter is DHWCF.
-
-The indexing maps for these three tensors contain 9 dimensions, following the
-order of (`N`, `D`, `H`, `W`, `F`, `KD`, `KH`, `KW`, `C`).
-"""
-{
- O(n, d, h, w, f) = AddFOp<kd, kh, kw>(
- O(n, d, h, w, f), MulFOp(I(n, d * strides[0] + kd * dilations[0],
- h * strides[1] + kh * dilations[1],
- w * strides[2] + kw * dilations[2], c),
- K(kd, kh, kw, c, f)));
-}
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 0a622e8335c90..7007d8b1101ac 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1127,7 +1127,7 @@ LogicalResult ConvOpVectorization<ConvOp, N>::matchAndRewrite(
return success();
}
-using ConvOpConst = ConvOpVectorization<ConvWOp, 1>;
+using ConvOpConst = ConvOpVectorization<Conv1DOp, 1>;
/// Inserts tiling, promotion and vectorization pattern for ConvOp
/// conversion into corresponding pattern lists.
@@ -1165,25 +1165,22 @@ void mlir::linalg::populateConvVectorizationPatterns(
RewritePatternSet tiling(context);
RewritePatternSet promotion(context);
RewritePatternSet vectorization(context);
- populateVectorizationPatterns<ConvWOp, 1>(tiling, promotion, vectorization,
- tileSizes);
-
- populateVectorizationPatterns<ConvInputNWCFilterWCFOp, 3>(
- tiling, promotion, vectorization, tileSizes);
+ populateVectorizationPatterns<Conv1DOp, 1>(tiling, promotion, vectorization,
+ tileSizes);
- populateVectorizationPatterns<ConvHWOp, 2>(tiling, promotion, vectorization,
+ populateVectorizationPatterns<Conv2DOp, 2>(tiling, promotion, vectorization,
tileSizes);
- populateVectorizationPatterns<ConvInputNHWCFilterHWCFOp, 4>(
- tiling, promotion, vectorization, tileSizes);
+ populateVectorizationPatterns<Conv3DOp, 3>(tiling, promotion, vectorization,
+ tileSizes);
- populateVectorizationPatterns<Conv2DNchwOp, 4>(tiling, promotion,
- vectorization, tileSizes);
+ populateVectorizationPatterns<Conv1DNwcWcfOp, 3>(tiling, promotion,
+ vectorization, tileSizes);
- populateVectorizationPatterns<ConvDHWOp, 3>(tiling, promotion, vectorization,
- tileSizes);
+ populateVectorizationPatterns<Conv2DNhwcHwcfOp, 4>(tiling, promotion,
+ vectorization, tileSizes);
- populateVectorizationPatterns<ConvInputNDHWCFilterDHWCFOp, 5>(
+ populateVectorizationPatterns<Conv3DNdhwcDhwcfOp, 5>(
tiling, promotion, vectorization, tileSizes);
patterns.push_back(std::move(tiling));
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 0590e6721f61b..29b1397d1c20a 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
@@ -145,21 +145,63 @@ def dot(
C[None] += cast(U, A[D.m]) * cast(U, B[D.m])
@linalg_structured_op
-def conv_2d_nchw(
- I=TensorDef(T1, S.N, S.C, S.IH, S.IW),
- K=TensorDef(T2, S.F, S.C, S.KH, S.KW),
- O=TensorDef(U, S.N, S.F, S.OH, S.OW, S.C, output=True),
- strides=AttributeDef(S.SH, S.SW),
- dilations=AttributeDef(S.DH, S.DW)):
- """Performs 2-D convolution.
+def conv_1d(
+ I=TensorDef(T1, S.IW),
+ K=TensorDef(T2, S.KW),
+ O=TensorDef(U, S.OW, output=True)):
+ """Performs 1-D convolution with no channels.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ """
+ domain(D.ow, D.kw)
+ O[D.ow] += cast(
+ U, I[D.ow + D.kw]) * cast(U, K[D.kw])
+
+ at linalg_structured_op
+def conv_2d(
+ I=TensorDef(T1, S.IH, S.IW),
+ K=TensorDef(T2, S.KH, S.KW),
+ O=TensorDef(U, S.OH, S.OW, output=True)):
+ """Performs 2-D convolution with no channels.
Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output.
"""
- domain(D.n, D.f, D.oh, D.ow, D.c, D.kh, D.kw)
- O[D.n, D.f, D.oh, D.ow] += cast(
- U, I[D.n, D.c, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
- ]) * cast(U, K[D.f, D.c, D.kh, D.kw])
+ domain(D.oh, D.ow, D.kh, D.kw)
+ O[D.oh, D.ow] += cast(
+ U, I[D.oh + D.kh, D.ow + D.kw]) * cast(U, K[D.kh, D.kw])
+
+ at linalg_structured_op
+def conv_3d(
+ I=TensorDef(T1, S.ID, S.IH, S.IW),
+ K=TensorDef(T2, S.KD, S.KH, S.KW),
+ O=TensorDef(U, S.OD, S.OH, S.OW, output=True)):
+ """Performs 3-D convolution with no channels.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ """
+ domain(D.od, D.oh, D.ow, D.kd, D.kh, D.kw)
+ O[D.od, D.oh, D.ow] += cast(
+ U, I[D.od + D.kd, D.oh + D.kh, D.ow + D.kw]) * cast(U, K[D.kd, D.kh, D.kw])
+
+ at linalg_structured_op
+def conv_1d_nwc_wcf(
+ I=TensorDef(T1, S.N, S.IW, S.C),
+ K=TensorDef(T2, S.KW, S.C, S.F),
+ O=TensorDef(U, S.N, S.OW, S.F, output=True),
+ strides=AttributeDef(S.SW),
+ dilations=AttributeDef(S.DW)):
+ """Performs 1-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ """
+ domain(D.n, D.ow, D.f, D.kw, D.c)
+ O[D.n, D.ow, D.f] += cast(
+ U, I[D.n, D.ow * S.SW + D.kw * S.DW, D.c
+ ]) * cast(U, K[D.kw, D.c, D.f])
@linalg_structured_op
def conv_2d_nhwc_hwcf(
@@ -177,6 +219,7 @@ def conv_2d_nhwc_hwcf(
O[D.n, D.oh, D.ow, D.f] += cast(
U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c
]) * cast(U, K[D.kh, D.kw, D.c, D.f])
+
@linalg_structured_op
def conv_2d_nhwc_hwcf_q(
I=TensorDef(T1, S.N, S.IH, S.IW, S.C),
@@ -197,6 +240,61 @@ def conv_2d_nhwc_hwcf_q(
U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c
]) - cast(U, IZp)) * (cast(U, K[D.kh, D.kw, D.c, D.f]) - cast(U, KZp))
+ at linalg_structured_op
+def conv_3d_ndhwc_dhwcf(
+ I=TensorDef(T1, S.N, S.ID, S.IH, S.IW, S.C),
+ K=TensorDef(T2, S.KD, S.KH, S.KW, S.C, S.F),
+ O=TensorDef(U, S.N, S.OD, S.OH, S.OW, S.F, output=True),
+ strides=AttributeDef(S.SD, S.SH, S.SW),
+ dilations=AttributeDef(S.DD, S.DH, S.DW)):
+ """Performs 3-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ """
+ domain(D.n, D.od, D.oh, D.ow, D.f, D.kd, D.kh, D.kw, D.c)
+ O[D.n, D.od, D.oh, D.ow, D.f] += cast(
+ U, I[D.n, D.od * S.SD + D.kd * S.DD, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c
+ ]) * cast(U, K[D.kd, D.kh, D.kw, D.c, D.f])
+
+ at linalg_structured_op
+def depthwise_conv2D_nhw(
+ I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
+ K=TensorDef(T2, S.KH, S.KW, S.IC),
+ O=TensorDef(U, S.N, S.OH, S.OW, S.IC, output=True),
+ strides=AttributeDef(S.SH, S.SW),
+ dilations=AttributeDef(S.DH, S.DW)):
+ """Performs depth-wise 2-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output. Multiplier is set to 1
+ which is a special case for most dpethwise convolutions.
+ """
+ domain(D.n, D.oh, D.ow, D.ic, D.kh, D.kw)
+ O[D.n, D.oh, D.ow, D.ic] += cast(
+ U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
+ D.ic]) * cast(U, K[D.kh, D.kw, D.ic])
+
+ at linalg_structured_op
+def depthwise_conv2D_nhw_q(
+ I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
+ K=TensorDef(T2, S.KH, S.KW, S.IC),
+ IZp=ScalarDef(I32),
+ KZp=ScalarDef(I32),
+ O=TensorDef(U, S.N, S.OH, S.OW, S.IC, output=True),
+ strides=AttributeDef(S.SH, S.SW),
+ dilations=AttributeDef(S.DH, S.DW)):
+ """Performs depth-wise 2-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ """
+ domain(D.n, D.oh, D.ow, D.ic, D.kh, D.kw)
+ O[D.n, D.oh, D.ow, D.ic] += (
+ (cast(U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
+ D.ic]) - cast(U, IZp)) *
+ (cast(U, K[D.kh, D.kw, D.ic]) - cast(U, KZp)))
+
@linalg_structured_op
def depthwise_conv2D_nhwc(
I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
diff --git a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
index 5260efd28d553..abfeda6b738db 100644
--- a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
@@ -76,8 +76,8 @@ func @generalize_matmul_tensor(%A : tensor<16x8xf32>, %B: tensor<8x32xf32>, %C:
// -----
-func @depthwise_conv_2d_input_nhwc_filter_hwcf(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x3x4x2x3xf32>) {
- linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+func @depthwise_conv2D_nhwc(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x3x4x2x3xf32>) {
+ linalg.depthwise_conv2D_nhwc
{ dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
ins(%input, %filter : memref<2x4x5x2xf32>, memref<2x2x2x3xf32>)
outs(%output : memref<2x3x4x2x3xf32>)
@@ -88,7 +88,7 @@ func @depthwise_conv_2d_input_nhwc_filter_hwcf(%input: memref<2x4x5x2xf32>, %fil
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d5, d6, d3, d4)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3, d4)>
-// CHECK: func @depthwise_conv_2d_input_nhwc_filter_hwcf
+// CHECK: func @depthwise_conv2D_nhwc
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
@@ -103,8 +103,8 @@ func @depthwise_conv_2d_input_nhwc_filter_hwcf(%input: memref<2x4x5x2xf32>, %fil
// -----
-func @depthwise_conv_2d_input_nhwc_filter_hwcf(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x2x3x2x3xf32>) {
- linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+func @depthwise_conv2D_nhwc(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x2x3x2x3xf32>) {
+ linalg.depthwise_conv2D_nhwc
{ dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
ins(%input, %filter : memref<2x4x5x2xf32>, memref<2x2x2x3xf32>)
outs(%output : memref<2x2x3x2x3xf32>)
@@ -115,7 +115,7 @@ func @depthwise_conv_2d_input_nhwc_filter_hwcf(%input: memref<2x4x5x2xf32>, %fil
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d5, d6, d3, d4)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3, d4)>
-// CHECK: func @depthwise_conv_2d_input_nhwc_filter_hwcf
+// CHECK: func @depthwise_conv2D_nhwc
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
@@ -130,8 +130,8 @@ func @depthwise_conv_2d_input_nhwc_filter_hwcf(%input: memref<2x4x5x2xf32>, %fil
// -----
-func @depthwise_conv_2d_input_nhwc_filter_hwc(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
- linalg.depthwise_conv_2d_input_nhwc_filter_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
+func @depthwise_conv2D_nhw(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
+ linalg.depthwise_conv2D_nhw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>)
outs(%output: memref<1x56x56x96xf32>)
return
@@ -141,7 +141,7 @@ func @depthwise_conv_2d_input_nhwc_filter_hwc(%input: memref<1x113x113x96xf32>,
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d4, d5, d3)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d3)>
-// CHECK: func @depthwise_conv_2d_input_nhwc_filter_hwc
+// CHECK: func @depthwise_conv2D_nhw
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
@@ -156,8 +156,8 @@ func @depthwise_conv_2d_input_nhwc_filter_hwc(%input: memref<1x113x113x96xf32>,
// -----
-func @conv_1d_input_nwc_filter_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
- linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>,
+func @conv_1d_nwc_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
+ linalg.conv_1d_nwc_wcf {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>)
@@ -167,11 +167,11 @@ func @conv_1d_input_nwc_filter_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d3, d4, d2)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2)>
-// CHECK: func @conv_1d_input_nwc_filter_wcf
+// CHECK: func @conv_1d_nwc_wcf
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
-// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "parallel"]}
+// 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>)
diff --git a/mlir/test/Dialect/Linalg/invalid.mlir b/mlir/test/Dialect/Linalg/invalid.mlir
index 569b9a1b387db..36860272a80c8 100644
--- a/mlir/test/Dialect/Linalg/invalid.mlir
+++ b/mlir/test/Dialect/Linalg/invalid.mlir
@@ -573,7 +573,7 @@ func @invalid_static_matmul(%arg0: memref<2x4xf32>, %arg1: memref<3x4xf32>, %arg
func @invalid_static_2d_conv(%input : memref<1x3x4x2xf32>, %filter: memref<3x2x2x1xf32>, %output: memref<1x2x3x1xf32>) {
// expected-error @+1 {{inferred input/output operand #0 has shape's dimension #1 to be greater than or equal to 4, but found 3}}
- linalg.conv_2d_input_nhwc_filter_hwcf
+ linalg.conv_2d_nhwc_hwcf
{ dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
ins(%input, %filter : memref<1x3x4x2xf32>, memref<3x2x2x1xf32>)
outs(%output : memref<1x2x3x1xf32>)
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
index 2bcac817d7620..208e4f2baf5f5 100644
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -1,81 +1,81 @@
// RUN: mlir-opt -split-input-file -verify-diagnostics %s | FileCheck %s
-// CHECK-LABEL: func @depthwise_conv_2d_input_nhwc_filter_hwcf_tensor
-func @depthwise_conv_2d_input_nhwc_filter_hwcf_tensor(%input: tensor<2x4x5x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x3x4x2x3xf32> {
+// CHECK-LABEL: func @depthwise_conv2D_nhwc_tensor
+func @depthwise_conv2D_nhwc_tensor(%input: tensor<2x4x5x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x3x4x2x3xf32> {
%zero = constant 0.000000e+00 : f32
%init = linalg.init_tensor [2, 3, 4, 2, 3] : tensor<2x3x4x2x3xf32>
%fill = linalg.fill(%zero, %init) : f32, tensor<2x3x4x2x3xf32> -> tensor<2x3x4x2x3xf32>
- // CHECK: %{{.+}} = linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+ // CHECK: %{{.+}} = linalg.depthwise_conv2D_nhwc
// CHECK-SAME: {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<2x4x5x2xf32>, tensor<2x2x2x3xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<2x3x4x2x3xf32>)
- %0 = linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+ %0 = linalg.depthwise_conv2D_nhwc
{ dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
ins(%input, %filter : tensor<2x4x5x2xf32>, tensor<2x2x2x3xf32>)
outs(%fill : tensor<2x3x4x2x3xf32>) -> tensor<2x3x4x2x3xf32>
return %0 : tensor<2x3x4x2x3xf32>
}
-// CHECK-LABEL: func @depthwise_conv_2d_input_nhwc_filter_hwcf_memref
-func @depthwise_conv_2d_input_nhwc_filter_hwcf_memref(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x3x4x2x3xf32>) {
- // CHECK: linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+// CHECK-LABEL: func @depthwise_conv2D_nhwc_memref
+func @depthwise_conv2D_nhwc_memref(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x3x4x2x3xf32>) {
+ // CHECK: linalg.depthwise_conv2D_nhwc
// CHECK-SAME: {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<2x4x5x2xf32>, memref<2x2x2x3xf32>)
// CHECK-SAME: outs(%{{.+}} : memref<2x3x4x2x3xf32>)
- linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+ linalg.depthwise_conv2D_nhwc
{ dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
ins(%input, %filter : memref<2x4x5x2xf32>, memref<2x2x2x3xf32>)
outs(%output : memref<2x3x4x2x3xf32>)
return
}
-// CHECK-LABEL: func @depthwise_conv_2d_input_nhwc_filter_hwc_tensor
-func @depthwise_conv_2d_input_nhwc_filter_hwc_tensor(%input: tensor<1x113x113x96xf32>, %filter: tensor<3x3x96xf32>) -> tensor<1x56x56x96xf32> {
+// CHECK-LABEL: func @depthwise_conv2D_nhw_tensor
+func @depthwise_conv2D_nhw_tensor(%input: tensor<1x113x113x96xf32>, %filter: tensor<3x3x96xf32>) -> tensor<1x56x56x96xf32> {
%init = linalg.init_tensor [1, 56, 56, 96] : tensor<1x56x56x96xf32>
- // CHECK: %{{.+}} = linalg.depthwise_conv_2d_input_nhwc_filter_hwc
+ // CHECK: %{{.+}} = linalg.depthwise_conv2D_nhw
// CHECK-SAME: {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x113x113x96xf32>, tensor<3x3x96xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<1x56x56x96xf32>) -> tensor<1x56x56x96xf32>
- %0 = linalg.depthwise_conv_2d_input_nhwc_filter_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
+ %0 = linalg.depthwise_conv2D_nhw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
ins(%input, %filter: tensor<1x113x113x96xf32>, tensor<3x3x96xf32>)
outs(%init: tensor<1x56x56x96xf32>) -> tensor<1x56x56x96xf32>
return %0: tensor<1x56x56x96xf32>
}
-// CHECK-LABEL: func @depthwise_conv_2d_input_nhwc_filter_hwc_memref
-func @depthwise_conv_2d_input_nhwc_filter_hwc_memref(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
- // CHECK: linalg.depthwise_conv_2d_input_nhwc_filter_hwc
+// CHECK-LABEL: func @depthwise_conv2D_nhw_memref
+func @depthwise_conv2D_nhw_memref(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
+ // CHECK: linalg.depthwise_conv2D_nhw
// CHECK-SAME: {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x113x113x96xf32>, memref<3x3x96xf32>)
// CHECK-SAME: outs(%{{.+}} : memref<1x56x56x96xf32>)
- linalg.depthwise_conv_2d_input_nhwc_filter_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
+ linalg.depthwise_conv2D_nhw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>)
outs(%output: memref<1x56x56x96xf32>)
return
}
-func @depthwise_conv_2d_input_nhwc_filter_hwcf_tensor_dilated(%input: tensor<2x8x9x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x6x7x2x3xf32> {
+func @depthwise_conv2D_nhwc_tensor_dilated(%input: tensor<2x8x9x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x6x7x2x3xf32> {
%zero = constant 0.000000e+00 : f32
%init = linalg.init_tensor [2, 6, 7, 2, 3] : tensor<2x6x7x2x3xf32>
%fill = linalg.fill(%zero, %init) : f32, tensor<2x6x7x2x3xf32> -> tensor<2x6x7x2x3xf32>
- // CHECK: %{{.+}} = linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+ // CHECK: %{{.+}} = linalg.depthwise_conv2D_nhwc
// CHECK-SAME: {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<2x8x9x2xf32>, tensor<2x2x2x3xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<2x6x7x2x3xf32>)
- %0 = linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+ %0 = linalg.depthwise_conv2D_nhwc
{ dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
ins(%input, %filter : tensor<2x8x9x2xf32>, tensor<2x2x2x3xf32>)
outs(%fill : tensor<2x6x7x2x3xf32>) -> tensor<2x6x7x2x3xf32>
return %0 : tensor<2x6x7x2x3xf32>
}
-// CHECK-LABEL: func @depthwise_conv_2d_input_nhwc_filter_hwcf_memref_dilated
-func @depthwise_conv_2d_input_nhwc_filter_hwcf_memref_dilated(%input: memref<2x8x9x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x6x7x2x3xf32>) {
- // CHECK: linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+// CHECK-LABEL: func @depthwise_conv2D_nhwc_memref_dilated
+func @depthwise_conv2D_nhwc_memref_dilated(%input: memref<2x8x9x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x6x7x2x3xf32>) {
+ // CHECK: linalg.depthwise_conv2D_nhwc
// CHECK-SAME: {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<2x8x9x2xf32>, memref<2x2x2x3xf32>)
// CHECK-SAME: outs(%{{.+}} : memref<2x6x7x2x3xf32>)
- linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
+ linalg.depthwise_conv2D_nhwc
{ dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
ins(%input, %filter : memref<2x8x9x2xf32>, memref<2x2x2x3xf32>)
outs(%output : memref<2x6x7x2x3xf32>)
@@ -86,7 +86,7 @@ func @depthwise_conv_2d_input_nhwc_filter_hwcf_memref_dilated(%input: memref<2x8
func @depthwise_conv_2d_input_nhwc_filter_missing_stride(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
// expected-error @+1 {{missing indexing map required attribute 'strides'}}
- linalg.depthwise_conv_2d_input_nhwc_filter_hwc {dilations = dense<1> : vector<2xi64>}
+ linalg.depthwise_conv2D_nhw {dilations = dense<1> : vector<2xi64>}
ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>)
outs(%output: memref<1x56x56x96xf32>)
return
@@ -96,7 +96,7 @@ func @depthwise_conv_2d_input_nhwc_filter_missing_stride(%input: memref<1x113x11
func @depthwise_conv_2d_input_nhwc_filter_missing_dilations(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
// expected-error @+1 {{missing indexing map required attribute 'dilations'}}
- linalg.depthwise_conv_2d_input_nhwc_filter_hwc {strides = dense<1> : vector<2xi64>}
+ linalg.depthwise_conv2D_nhw {strides = dense<1> : vector<2xi64>}
ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>)
outs(%output: memref<1x56x56x96xf32>)
return
@@ -106,7 +106,7 @@ func @depthwise_conv_2d_input_nhwc_filter_missing_dilations(%input: memref<1x113
func @depthwise_conv_2d_input_nhwc_filter_wrong_stride_element_type(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
// expected-error @+1 {{incorrect element type for indexing map required attribute 'strides'}}
- linalg.depthwise_conv_2d_input_nhwc_filter_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2.0> : vector<2xf32>}
+ linalg.depthwise_conv2D_nhw {dilations = dense<1> : vector<2xi64>, strides = dense<2.0> : vector<2xf32>}
ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>)
outs(%output: memref<1x56x56x96xf32>)
return
@@ -116,7 +116,7 @@ func @depthwise_conv_2d_input_nhwc_filter_wrong_stride_element_type(%input: memr
func @depthwise_conv_2d_input_nhwc_filter_wrong_stride_size(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
// expected-error @+1 {{incorrect shape for indexing map required attribute 'strides'}}
- linalg.depthwise_conv_2d_input_nhwc_filter_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<3xi64> }
+ linalg.depthwise_conv2D_nhw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<3xi64> }
ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>)
outs(%output: memref<1x56x56x96xf32>)
return
@@ -124,14 +124,14 @@ func @depthwise_conv_2d_input_nhwc_filter_wrong_stride_size(%input: memref<1x113
// -----
-// CHECK-LABEL: func @conv_1d_input_nwc_filter_wcf
-func @conv_1d_input_nwc_filter_wcf(%input: tensor<?x?x?xf32>, %filter: tensor<?x?x?xf32>, %init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
- // CHECK: %{{.+}} = linalg.conv_1d_input_nwc_filter_wcf
+// CHECK-LABEL: func @conv_1d_nwc_wcf
+func @conv_1d_nwc_wcf(%input: tensor<?x?x?xf32>, %filter: tensor<?x?x?xf32>, %init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
+ // CHECK: %{{.+}} = linalg.conv_1d_nwc_wcf
// 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_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>,
+ %0 = linalg.conv_1d_nwc_wcf {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>
@@ -140,14 +140,14 @@ func @conv_1d_input_nwc_filter_wcf(%input: tensor<?x?x?xf32>, %filter: tensor<?x
// -----
-// CHECK-LABEL: func @conv_1d_input_nwc_filter_wcf
-func @conv_1d_input_nwc_filter_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
- // CHECK: linalg.conv_1d_input_nwc_filter_wcf
+// CHECK-LABEL: func @conv_1d_nwc_wcf
+func @conv_1d_nwc_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
+ // CHECK: linalg.conv_1d_nwc_wcf
// 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_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>,
+ linalg.conv_1d_nwc_wcf {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>)
@@ -156,14 +156,14 @@ func @conv_1d_input_nwc_filter_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x
// -----
-// CHECK-LABEL: func @conv_2d_input_nhwc_filter_hwcf
-func @conv_2d_input_nhwc_filter_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_input_nhwc_filter_hwcf
+// CHECK-LABEL: func @conv_2d_nhwc_hwcf
+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
// CHECK-SAME: dilations = dense<1> : tensor<2xi64>
// CHECK-SAME: strides = dense<1> : tensor<2xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
- %0 = linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
+ %0 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
outs (%init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
@@ -172,14 +172,14 @@ func @conv_2d_input_nhwc_filter_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tenso
// -----
-// CHECK-LABEL: func @conv_2d_input_nhwc_filter_hwcf
-func @conv_2d_input_nhwc_filter_hwcf(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) {
- // CHECK: linalg.conv_2d_input_nhwc_filter_hwcf
+// CHECK-LABEL: func @conv_2d_nhwc_hwcf
+func @conv_2d_nhwc_hwcf(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) {
+ // CHECK: linalg.conv_2d_nhwc_hwcf
// CHECK-SAME: dilations = dense<1> : tensor<2xi64>
// CHECK-SAME: strides = dense<1> : tensor<2xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?xf32>)
- linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
+ linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
outs (%output: memref<?x?x?x?xf32>)
@@ -188,14 +188,14 @@ func @conv_2d_input_nhwc_filter_hwcf(%input: memref<?x?x?x?xf32>, %filter: memre
// -----
-// CHECK-LABEL: func @conv_3d_input_ndhwc_filter_dhwcf
-func @conv_3d_input_ndhwc_filter_dhwcf(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
- // CHECK: %{{.+}} = linalg.conv_3d_input_ndhwc_filter_dhwcf
+// CHECK-LABEL: func @conv_3d_ndhwc_dhwcf
+func @conv_3d_ndhwc_dhwcf(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+ // CHECK: %{{.+}} = linalg.conv_3d_ndhwc_dhwcf
// CHECK-SAME: dilations = dense<1> : tensor<3xi64>
// CHECK-SAME: strides = dense<1> : tensor<3xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
- %0 = linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
+ %0 = linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>}
ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
outs (%init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
@@ -204,14 +204,14 @@ func @conv_3d_input_ndhwc_filter_dhwcf(%input: tensor<?x?x?x?x?xf32>, %filter: t
// -----
-// CHECK-LABEL: func @conv_3d_input_ndhwc_filter_dhwcf
-func @conv_3d_input_ndhwc_filter_dhwcf(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
- // CHECK: linalg.conv_3d_input_ndhwc_filter_dhwcf
+// CHECK-LABEL: func @conv_3d_ndhwc_dhwcf
+func @conv_3d_ndhwc_dhwcf(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
+ // CHECK: linalg.conv_3d_ndhwc_dhwcf
// CHECK-SAME: dilations = dense<1> : tensor<3xi64>
// CHECK-SAME: strides = dense<1> : tensor<3xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?x?xf32>)
- linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
+ linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>}
ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
outs (%output: memref<?x?x?x?x?xf32>)
diff --git a/mlir/test/Dialect/Linalg/tile-and-fuse-tensors.mlir b/mlir/test/Dialect/Linalg/tile-and-fuse-tensors.mlir
index c905e6f616b3e..2bab8758ee0b6 100644
--- a/mlir/test/Dialect/Linalg/tile-and-fuse-tensors.mlir
+++ b/mlir/test/Dialect/Linalg/tile-and-fuse-tensors.mlir
@@ -79,7 +79,7 @@ func @conv_tensors_static(%input: tensor<1x225x225x3xf32>, %filter: tensor<3x3x3
%init = linalg.init_tensor [1, 112, 112, 32] : tensor<1x112x112x32xf32>
%fill = linalg.fill(%cst, %init) : f32, tensor<1x112x112x32xf32> -> tensor<1x112x112x32xf32>
- %conv = linalg.conv_2d_input_nhwc_filter_hwcf
+ %conv = linalg.conv_2d_nhwc_hwcf
{dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>}
ins(%input, %filter : tensor<1x225x225x3xf32>, tensor<3x3x3x32xf32>)
outs(%fill : tensor<1x112x112x32xf32>) -> tensor<1x112x112x32xf32>
@@ -133,7 +133,7 @@ func @conv_tensors_static(%input: tensor<1x225x225x3xf32>, %filter: tensor<3x3x3
// CHECK-NEXT: %[[ST_ARG2:.+]] = tensor.extract_slice %[[ARG2]][0, %[[IV0]], %[[IV1]], %[[IV2]]] [1, 8, 16, 4] [1, 1, 1, 1] : tensor<1x112x112x32xf32> to tensor<1x8x16x4xf32>
// CHECK-NEXT: %[[ST_FILTER:.+]] = tensor.extract_slice %[[FILTER]][0, 0, 0, %[[IV2]]] [3, 3, 3, 4] [1, 1, 1, 1] : tensor<3x3x3x32xf32> to tensor<3x3x3x4xf32>
// CHECK-NEXT: %[[ST_FILL:.+]] = tensor.extract_slice %[[FILL]][0, %[[IV0]], %[[IV1]], %[[IV2]]] [1, 8, 16, 4] [1, 1, 1, 1] : tensor<1x112x112x32xf32> to tensor<1x8x16x4xf32>
-// CHECK-NEXT: %[[ST_CONV:.+]] = linalg.conv_2d_input_nhwc_filter_hwcf
+// CHECK-NEXT: %[[ST_CONV:.+]] = linalg.conv_2d_nhwc_hwcf
// CHECK-SAME: ins(%[[ST_INPUT]], %[[ST_FILTER]] : tensor<1x17x33x3xf32>, tensor<3x3x3x4xf32>)
// CHECK-SAME: outs(%[[ST_FILL]] : tensor<1x8x16x4xf32>)
// CHECK-NEXT: %[[ADD:.+]] = linalg.generic
@@ -161,7 +161,7 @@ func @conv_tensors_dynamic(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?x
%init = linalg.init_tensor [%n, %oh, %ow, %oc] : tensor<?x?x?x?xf32>
%fill = linalg.fill(%cst, %init) : f32, tensor<?x?x?x?xf32> -> tensor<?x?x?x?xf32>
- %conv = linalg.conv_2d_input_nhwc_filter_hwcf
+ %conv = linalg.conv_2d_nhwc_hwcf
{dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>}
ins(%input, %filter : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
outs(%fill : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
@@ -271,7 +271,7 @@ func @conv_tensors_dynamic(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?x
// CHECK-NEXT: %[[SIZE_ELEM_OC_3:.+]] = affine.min #[[BOUND2_MAP_2]](%[[IV3]], %[[IV2]])[%[[FILL_C]], %[[ELEM_OC]]]
// CHECK-NEXT: %[[ST_FILL:.+]] = tensor.extract_slice %[[FILL]][%[[IV0]], %[[IV1]], %[[IV2]], %[[IV3]]]
// CHECK-SAME: [%[[SIZE_ELEM_N_2]], %[[SIZE_ELEM_OH_2]], %[[SIZE_ELEM_OW_2]], %[[SIZE_ELEM_OC_3]]]
-// CHECK-NEXT: %[[ST_CONV:.+]] = linalg.conv_2d_input_nhwc_filter_hwcf
+// CHECK-NEXT: %[[ST_CONV:.+]] = linalg.conv_2d_nhwc_hwcf
// CHECK-SAME: ins(%[[ST_INPUT]], %[[ST_FILTER]] : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
// CHECK-SAME: outs(%[[ST_FILL]] : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
// CHECK-NEXT: %[[ST_ADD:.+]] = linalg.generic
diff --git a/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-input-nwc-filter-wcf-call.mlir b/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-nwc-wcf-call.mlir
similarity index 91%
rename from mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-input-nwc-filter-wcf-call.mlir
rename to mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-nwc-wcf-call.mlir
index 71d0c8a56e364..022c60d1d02dd 100644
--- a/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-input-nwc-filter-wcf-call.mlir
+++ b/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-nwc-wcf-call.mlir
@@ -29,8 +29,8 @@ func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> me
return %buf : memref<?x?x?xf32>
}
-func @conv_1d_input_nwc_filter_wcf(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
- linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>,
+func @conv_1d_nwc_wcf(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
+ linalg.conv_1d_nwc_wcf {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>)
outs (%arg2: memref<?x?x?xf32>)
@@ -52,7 +52,7 @@ func @main() {
%out1D_nwc = call @alloc_3d_filled_f32(%c3, %c6, %c1, %zero) : (index, index, index, f32) -> (memref<?x?x?xf32>)
memref.store %f10, %in1D_nwc[%c0, %c3, %c0] : memref<?x?x?xf32>
- call @conv_1d_input_nwc_filter_wcf(%in1D_nwc, %filter1D_nwc, %out1D_nwc) : (memref<?x?x?xf32>, memref<?x?x?xf32>, memref<?x?x?xf32>) -> ()
+ call @conv_1d_nwc_wcf(%in1D_nwc, %filter1D_nwc, %out1D_nwc) : (memref<?x?x?xf32>, memref<?x?x?xf32>, memref<?x?x?xf32>) -> ()
%out1D_nwc_ = memref.cast %out1D_nwc : memref<?x?x?xf32> to memref<*xf32>
call @print_memref_f32(%out1D_nwc_): (memref<*xf32>) -> ()
diff --git a/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-input-nhwc-filter-hwcf-call.mlir b/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-nhwc-hwcf-call.mlir
similarity index 92%
rename from mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-input-nhwc-filter-hwcf-call.mlir
rename to mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-nhwc-hwcf-call.mlir
index 50b9861c9dc13..5627c924dadd2 100644
--- a/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-input-nhwc-filter-hwcf-call.mlir
+++ b/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-nhwc-hwcf-call.mlir
@@ -29,8 +29,8 @@ func @alloc_4d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %f
return %buf : memref<?x?x?x?xf32>
}
-func @conv_2d_input_nhwc_filter_hwcf(%arg0: memref<?x?x?x?xf32>, %arg1: memref<?x?x?x?xf32>, %arg2: memref<?x?x?x?xf32>) {
- linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
+func @conv_2d_nhwc_hwcf(%arg0: memref<?x?x?x?xf32>, %arg1: memref<?x?x?x?xf32>, %arg2: memref<?x?x?x?xf32>) {
+ linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%arg0, %arg1: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
outs (%arg2: memref<?x?x?x?xf32>)
@@ -52,7 +52,7 @@ func @main() {
%out2D_nhwc = call @alloc_4d_filled_f32(%c3, %c6, %c6, %c1, %zero) : (index, index, index, index, f32) -> (memref<?x?x?x?xf32>)
memref.store %f10, %in2D_nhwc[%c0, %c0, %c3, %c0] : memref<?x?x?x?xf32>
- call @conv_2d_input_nhwc_filter_hwcf(%in2D_nhwc, %filter2D_nhwc, %out2D_nhwc) : (memref<?x?x?x?xf32>, memref<?x?x?x?xf32>, memref<?x?x?x?xf32>) -> ()
+ call @conv_2d_nhwc_hwcf(%in2D_nhwc, %filter2D_nhwc, %out2D_nhwc) : (memref<?x?x?x?xf32>, memref<?x?x?x?xf32>, memref<?x?x?x?xf32>) -> ()
%out2D_nhwc_ = memref.cast %out2D_nhwc : memref<?x?x?x?xf32> to memref<*xf32>
call @print_memref_f32(%out2D_nhwc_): (memref<*xf32>) -> ()
diff --git a/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-input-ndhwc-filter-dhwcf-call.mlir b/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-ndhwc-dhwcf-call.mlir
similarity index 94%
rename from mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-input-ndhwc-filter-dhwcf-call.mlir
rename to mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-ndhwc-dhwcf-call.mlir
index 490bb62c14dac..a7ef161eaefde 100644
--- a/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-input-ndhwc-filter-dhwcf-call.mlir
+++ b/mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-ndhwc-dhwcf-call.mlir
@@ -29,8 +29,8 @@ func @alloc_5d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %s
return %buf : memref<?x?x?x?x?xf32>
}
-func @conv_3d_input_ndhwc_filter_dhwcf(%arg0: memref<?x?x?x?x?xf32>, %arg1: memref<?x?x?x?x?xf32>, %arg2: memref<?x?x?x?x?xf32>) {
- linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
+func @conv_3d_ndhwc_dhwcf(%arg0: memref<?x?x?x?x?xf32>, %arg1: memref<?x?x?x?x?xf32>, %arg2: memref<?x?x?x?x?xf32>) {
+ linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>}
ins (%arg0, %arg1: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
outs (%arg2: memref<?x?x?x?x?xf32>)
@@ -53,7 +53,7 @@ func @main() {
%out3D_ndhwc = call @alloc_5d_filled_f32(%c1, %c6, %c6, %c6, %c1, %zero) : (index, index, index, index, index, f32) -> (memref<?x?x?x?x?xf32>)
memref.store %f10, %in3D_ndhwc[%c0, %c0, %c0, %c3, %c0] : memref<?x?x?x?x?xf32>
- call @conv_3d_input_ndhwc_filter_dhwcf(%in3D_ndhwc, %filter3D_ndhwc, %out3D_ndhwc) : (memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>) -> ()
+ call @conv_3d_ndhwc_dhwcf(%in3D_ndhwc, %filter3D_ndhwc, %out3D_ndhwc) : (memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>) -> ()
%out3D_ndhwc_ = memref.cast %out3D_ndhwc : memref<?x?x?x?x?xf32> to memref<*xf32>
call @print_memref_f32(%out3D_ndhwc_): (memref<*xf32>) -> ()
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