forked from OSchip/llvm-project
[mlir][linalg] Add named ops for depthwise 3d convolution
Also complete the set by adding a variant of depthwise 1d convolution with the multiplier != 1. Differential Revision: https://reviews.llvm.org/D127687
This commit is contained in:
Benjamin Kramer
parent
671eb7dc1e
commit
ba0222cdc6
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@ -1827,6 +1827,91 @@ structured_op: !LinalgStructuredOpConfig
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- !ScalarExpression
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scalar_arg: K
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--- !LinalgOpConfig
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metadata: !LinalgOpMetadata
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name: depthwise_conv_1d_nwc_wcm
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cpp_class_name: DepthwiseConv1DNwcWcmOp
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doc: |-
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Performs depth-wise 1-D convolution.
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Numeric casting is performed on the operands to the inner multiply, promoting
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them to the same data type as the accumulator/output.
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implements:
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- LinalgConvolutionOpInterface
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structured_op: !LinalgStructuredOpConfig
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args:
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- !LinalgOperandDefConfig
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name: I
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kind: input_tensor
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type_var: T1
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shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s0, s1 * s2 + s3 * s4,
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s5)>
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- !LinalgOperandDefConfig
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name: K
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kind: input_tensor
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type_var: T2
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shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s3, s5, s6)>
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- !LinalgOperandDefConfig
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name: O
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kind: output_tensor
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type_var: U
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shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s0, s1, s5, s6)>
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- !LinalgOperandDefConfig
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name: strides
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kind: index_attr
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index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s2)>
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default_indices:
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- 1
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- !LinalgOperandDefConfig
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name: dilations
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kind: index_attr
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index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s4)>
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default_indices:
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- 1
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indexing_maps: !LinalgIndexingMapsConfig
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static_indexing_maps:
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- affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d0, d1 * s2
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+ d4 * s4, d2)>
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- affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d4, d2, d3)>
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- affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d0, d1, d2,
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d3)>
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iterator_types:
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- parallel
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- parallel
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- parallel
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- parallel
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- reduction
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assignments:
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- !ScalarAssign
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arg: O
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value: !ScalarExpression
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scalar_fn:
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kind: binary
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fn_name: add
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operands:
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- !ScalarExpression
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scalar_arg: O
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- !ScalarExpression
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scalar_fn:
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kind: binary
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fn_name: mul
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operands:
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- !ScalarExpression
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scalar_fn:
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kind: type
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fn_name: cast_signed
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type_var: U
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operands:
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- !ScalarExpression
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scalar_arg: I
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- !ScalarExpression
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scalar_fn:
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kind: type
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fn_name: cast_signed
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type_var: U
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operands:
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- !ScalarExpression
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scalar_arg: K
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--- !LinalgOpConfig
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metadata: !LinalgOpMetadata
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name: depthwise_conv_2d_nhwc_hwc
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cpp_class_name: DepthwiseConv2DNhwcHwcOp
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@ -2276,6 +2361,205 @@ structured_op: !LinalgStructuredOpConfig
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- !ScalarExpression
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scalar_arg: KZp
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--- !LinalgOpConfig
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metadata: !LinalgOpMetadata
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name: depthwise_conv_3d_ndhwc_dhwc
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cpp_class_name: DepthwiseConv3DNdhwcDhwcOp
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doc: |-
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Performs depth-wise 3-D convolution.
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Numeric casting is performed on the operands to the inner multiply, promoting
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them to the same data type as the accumulator/output. Multiplier is set to 1
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which is a special case for most depthwise convolutions.
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implements:
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- LinalgConvolutionOpInterface
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structured_op: !LinalgStructuredOpConfig
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args:
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- !LinalgOperandDefConfig
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name: I
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kind: input_tensor
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type_var: T1
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shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
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s13] -> (s0, s1 * s2 + s3 * s4, s5 * s6 + s7 * s8, s9 * s10 + s11 * s12, s13)>
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- !LinalgOperandDefConfig
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name: K
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kind: input_tensor
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type_var: T2
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shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
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s13] -> (s3, s7, s11, s13)>
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- !LinalgOperandDefConfig
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name: O
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kind: output_tensor
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type_var: U
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shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
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s13] -> (s0, s1, s5, s9)>
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- !LinalgOperandDefConfig
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name: strides
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kind: index_attr
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index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
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s12, s13] -> (s2, s6, s10)>
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default_indices:
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- 1
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- 1
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- 1
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- !LinalgOperandDefConfig
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name: dilations
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kind: index_attr
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index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
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s12, s13] -> (s4, s8, s12)>
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default_indices:
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- 1
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- 1
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- 1
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indexing_maps: !LinalgIndexingMapsConfig
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static_indexing_maps:
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- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
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s8, s9, s10, s11, s12, s13] -> (d0, d1 * s2 + d4 * s4, d2 * s6 + d5 * s8, d3
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* s10 + d6 * s12, d7)>
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- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
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s8, s9, s10, s11, s12, s13] -> (d4, d5, d6, d7)>
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- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
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s8, s9, s10, s11, s12, s13] -> (d0, d1, d2, d3, d7)>
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iterator_types:
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- parallel
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- parallel
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- parallel
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- parallel
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- reduction
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- reduction
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- reduction
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- parallel
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assignments:
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- !ScalarAssign
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arg: O
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value: !ScalarExpression
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scalar_fn:
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kind: binary
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fn_name: add
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operands:
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- !ScalarExpression
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scalar_arg: O
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- !ScalarExpression
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scalar_fn:
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kind: binary
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fn_name: mul
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operands:
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- !ScalarExpression
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scalar_fn:
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kind: type
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fn_name: cast_signed
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type_var: U
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operands:
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- !ScalarExpression
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scalar_arg: I
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- !ScalarExpression
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scalar_fn:
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kind: type
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fn_name: cast_signed
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type_var: U
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operands:
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- !ScalarExpression
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scalar_arg: K
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--- !LinalgOpConfig
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metadata: !LinalgOpMetadata
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name: depthwise_conv_3d_ndhwc_dhwcm
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cpp_class_name: DepthwiseConv3DNdhwcDhwcmOp
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doc: |-
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Performs depth-wise 3-D convolution.
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Numeric casting is performed on the operands to the inner multiply, promoting
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them to the same data type as the accumulator/output.
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implements:
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- LinalgConvolutionOpInterface
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structured_op: !LinalgStructuredOpConfig
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args:
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- !LinalgOperandDefConfig
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name: I
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kind: input_tensor
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type_var: T1
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shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
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s13, s14] -> (s0, s1 * s2 + s3 * s4, s5 * s6 + s7 * s8, s9 * s10 + s11 * s12,
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s13)>
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- !LinalgOperandDefConfig
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name: K
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kind: input_tensor
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type_var: T2
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shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
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s13, s14] -> (s3, s7, s11, s13, s14)>
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- !LinalgOperandDefConfig
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name: O
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kind: output_tensor
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type_var: U
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shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
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s13, s14] -> (s0, s1, s5, s9, s14)>
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- !LinalgOperandDefConfig
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name: strides
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kind: index_attr
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index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
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s12, s13, s14] -> (s2, s6, s10)>
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default_indices:
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- 1
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- 1
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- 1
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- !LinalgOperandDefConfig
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name: dilations
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kind: index_attr
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index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
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s12, s13, s14] -> (s4, s8, s12)>
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default_indices:
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- 1
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- 1
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- 1
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indexing_maps: !LinalgIndexingMapsConfig
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static_indexing_maps:
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- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
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s7, s8, s9, s10, s11, s12, s13, s14] -> (d0, d1 * s2 + d5 * s4, d2 * s6 + d6
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* s8, d3 * s10 + d7 * s12, d8)>
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- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
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s7, s8, s9, s10, s11, s12, s13, s14] -> (d5, d6, d7, d8, d4)>
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- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
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s7, s8, s9, s10, s11, s12, s13, s14] -> (d0, d1, d2, d3, d8, d4)>
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iterator_types:
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- parallel
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- parallel
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- parallel
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- parallel
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- parallel
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- reduction
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- reduction
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- reduction
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- parallel
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assignments:
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- !ScalarAssign
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arg: O
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value: !ScalarExpression
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scalar_fn:
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kind: binary
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fn_name: add
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operands:
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- !ScalarExpression
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scalar_arg: O
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- !ScalarExpression
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scalar_fn:
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kind: binary
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fn_name: mul
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operands:
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- !ScalarExpression
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scalar_fn:
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kind: type
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fn_name: cast_signed
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type_var: U
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operands:
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- !ScalarExpression
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scalar_arg: I
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- !ScalarExpression
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scalar_fn:
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kind: type
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fn_name: cast_signed
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type_var: U
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operands:
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- !ScalarExpression
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scalar_arg: K
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--- !LinalgOpConfig
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metadata: !LinalgOpMetadata
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name: pooling_nhwc_sum
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cpp_class_name: PoolingNhwcSumOp
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@ -414,6 +414,26 @@ def depthwise_conv_1d_nwc_wc(I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW,
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TypeFn.cast_signed(U, K[D.kw, D.ic])
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@linalg_structured_op
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def depthwise_conv_1d_nwc_wcm(I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW,
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S.IC),
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K=TensorDef(T2, S.KW, S.IC, S.CM),
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O=TensorDef(U, S.N, S.OW, S.IC, S.CM,
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output=True),
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strides=IndexAttrDef(S.SW, default=[1]),
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dilations=IndexAttrDef(S.DW, default=[1])):
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"""Performs depth-wise 1-D convolution.
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Numeric casting is performed on the operands to the inner multiply, promoting
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them to the same data type as the accumulator/output.
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"""
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implements(ConvolutionOpInterface)
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domain(D.n, D.ow, D.ic, D.cm, D.kw)
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O[D.n, D.ow, D.ic, D.cm] += \
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TypeFn.cast_signed(U, I[D.n, D.ow * S.SW + D.kw * S.DW, D.ic]) * \
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TypeFn.cast_signed(U, K[D.kw, D.ic, D.cm])
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@linalg_structured_op
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def depthwise_conv_2d_nhwc_hwc(I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH,
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S.OW * S.SW + S.KW * S.DW, S.IC),
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@ -536,6 +556,64 @@ def depthwise_conv_2d_nhwc_hwcm_q(I=TensorDef(T1, S.N,
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TypeFn.cast_signed(U, KZp)))
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@linalg_structured_op
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def depthwise_conv_3d_ndhwc_dhwc(I=TensorDef(T1, S.N, S.OD * S.SD + S.KD * S.DD,
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S.OH * S.SH + S.KH * S.DH,
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S.OW * S.SW + S.KW * S.DW, S.IC),
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K=TensorDef(T2, S.KD, S.KH, S.KW, S.IC),
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O=TensorDef(U, S.N, S.OD, S.OH, S.OW,
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output=True),
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strides=IndexAttrDef(S.SD,
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S.SH,
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S.SW,
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default=[1, 1, 1]),
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dilations=IndexAttrDef(S.DD,
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S.DH,
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S.DW,
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default=[1, 1, 1])):
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"""Performs depth-wise 3-D convolution.
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Numeric casting is performed on the operands to the inner multiply, promoting
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them to the same data type as the accumulator/output. Multiplier is set to 1
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which is a special case for most depthwise convolutions.
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"""
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implements(ConvolutionOpInterface)
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domain(D.n, D.od, D.oh, D.ow, D.kd, D.kh, D.kw, D.ic)
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O[D.n, D.od, D.oh, D.ow, D.ic] += TypeFn.cast_signed(
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U, I[D.n, D.od * S.SD + D.kd * S.DD, D.oh * S.SH + D.kh * S.DH,
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D.ow * S.SW + D.kw * S.DW, D.ic]) * TypeFn.cast_signed(
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U, K[D.kd, D.kh, D.kw, D.ic])
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@linalg_structured_op
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def depthwise_conv_3d_ndhwc_dhwcm(I=TensorDef(T1,
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S.N, S.OD * S.SD + S.KD * S.DD,
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S.OH * S.SH + S.KH * S.DH,
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S.OW * S.SW + S.KW * S.DW, S.IC),
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K=TensorDef(T2, S.KD, S.KH, S.KW, S.IC, S.CM),
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O=TensorDef(U, S.N, S.OD, S.OH, S.OW, S.CM,
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output=True),
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strides=IndexAttrDef(S.SD,
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S.SH,
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S.SW,
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default=[1, 1, 1]),
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dilations=IndexAttrDef(S.DD,
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S.DH,
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S.DW,
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default=[1, 1, 1])):
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"""Performs depth-wise 3-D convolution.
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Numeric casting is performed on the operands to the inner multiply, promoting
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them to the same data type as the accumulator/output.
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"""
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implements(ConvolutionOpInterface)
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domain(D.n, D.od, D.oh, D.ow, D.cm, D.kd, D.kh, D.kw, D.ic)
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O[D.n, D.od, D.oh, D.ow, D.ic, D.cm] += TypeFn.cast_signed(
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U, I[D.n, D.od * S.SD + D.kd * S.DD, D.oh * S.SH + D.kh * S.DH,
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D.ow * S.SW + D.kw * S.DW, D.ic]) * TypeFn.cast_signed(
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U, K[D.kd, D.kh, D.kw, D.ic, D.cm])
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@linalg_structured_op
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def pooling_nhwc_sum(I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH,
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S.OW * S.SW + S.KW * S.DW, S.C),
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@ -1,5 +1,33 @@
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// RUN: mlir-opt -split-input-file -verify-diagnostics %s | FileCheck %s
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// CHECK-LABEL: func @depthwise_conv_1d_nwc_wcm
|
||||
func.func @depthwise_conv_1d_nwc_wcm(%input: tensor<1x12x8xf32>, %filter: tensor<3x8x8xf32>) -> tensor<1x10x8x8xf32> {
|
||||
%zero = arith.constant 0.000000e+00 : f32
|
||||
%init = linalg.init_tensor [1, 10, 8, 8] : tensor<1x10x8x8xf32>
|
||||
%fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x10x8x8xf32>) -> tensor<1x10x8x8xf32>
|
||||
// CHECK: depthwise_conv_1d_nwc_wcm
|
||||
%0 = linalg.depthwise_conv_1d_nwc_wcm {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
|
||||
ins(%input, %filter : tensor<1x12x8xf32>, tensor<3x8x8xf32>)
|
||||
outs(%fill : tensor<1x10x8x8xf32>) -> tensor<1x10x8x8xf32>
|
||||
return %0 : tensor<1x10x8x8xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @depthwise_conv_1d_nwc_wc
|
||||
func.func @depthwise_conv_1d_nwc_wc(%input: tensor<1x12x8xf32>, %filter: tensor<3x8xf32>) -> tensor<1x10x8xf32> {
|
||||
%zero = arith.constant 0.000000e+00 : f32
|
||||
%init = linalg.init_tensor [1, 10, 8] : tensor<1x10x8xf32>
|
||||
%fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x10x8xf32>) -> tensor<1x10x8xf32>
|
||||
// CHECK: depthwise_conv_1d_nwc_wc
|
||||
%0 = linalg.depthwise_conv_1d_nwc_wc {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
|
||||
ins(%input, %filter : tensor<1x12x8xf32>, tensor<3x8xf32>)
|
||||
outs(%fill : tensor<1x10x8xf32>) -> tensor<1x10x8xf32>
|
||||
return %0 : tensor<1x10x8xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @depthwise_conv_2d_nhwc_hwcm_tensor
|
||||
func.func @depthwise_conv_2d_nhwc_hwcm_tensor(%input: tensor<2x4x5x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x3x4x2x3xf32> {
|
||||
%zero = arith.constant 0.000000e+00 : f32
|
||||
|
|
@ -130,6 +158,34 @@ func.func @depthwise_conv_2d_input_nhwc_filter_wrong_stride_size(%input: memref<
|
|||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @depthwise_conv_3d_ndhwc_dhwcm
|
||||
func.func @depthwise_conv_3d_ndhwc_dhwcm(%input: tensor<2x6x13x12x6xf32>, %filter: tensor<2x1x3x6x6xf32>) -> tensor<2x3x13x4x6x6xf32> {
|
||||
%zero = arith.constant 0.000000e+00 : f32
|
||||
%init = linalg.init_tensor [2, 3, 13, 4, 6, 6] : tensor<2x3x13x4x6x6xf32>
|
||||
%fill = linalg.fill ins(%zero : f32) outs(%init : tensor<2x3x13x4x6x6xf32>) -> tensor<2x3x13x4x6x6xf32>
|
||||
// CHECK: depthwise_conv_3d_ndhwc_dhwcm
|
||||
%0 = linalg.depthwise_conv_3d_ndhwc_dhwcm {dilations = dense<1> : tensor<3xi64>, strides = dense<[2, 1, 3]> : tensor<3xi64>}
|
||||
ins(%input, %filter : tensor<2x6x13x12x6xf32>, tensor<2x1x3x6x6xf32>)
|
||||
outs(%fill : tensor<2x3x13x4x6x6xf32>) -> tensor<2x3x13x4x6x6xf32>
|
||||
return %0 : tensor<2x3x13x4x6x6xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @depthwise_conv_3d_ndhwc_dhwc
|
||||
func.func @depthwise_conv_3d_ndhwc_dhwc(%input: tensor<2x6x13x12x6xf32>, %filter: tensor<2x1x3x6xf32>) -> tensor<2x3x13x4x6xf32> {
|
||||
%zero = arith.constant 0.000000e+00 : f32
|
||||
%init = linalg.init_tensor [2, 3, 13, 4, 6] : tensor<2x3x13x4x6xf32>
|
||||
%fill = linalg.fill ins(%zero : f32) outs(%init : tensor<2x3x13x4x6xf32>) -> tensor<2x3x13x4x6xf32>
|
||||
// CHECK: depthwise_conv_3d_ndhwc_dhwc
|
||||
%0 = linalg.depthwise_conv_3d_ndhwc_dhwc {dilations = dense<1> : tensor<3xi64>, strides = dense<[2, 1, 3]> : tensor<3xi64>}
|
||||
ins(%input, %filter : tensor<2x6x13x12x6xf32>, tensor<2x1x3x6xf32>)
|
||||
outs(%fill : tensor<2x3x13x4x6xf32>) -> tensor<2x3x13x4x6xf32>
|
||||
return %0 : tensor<2x3x13x4x6xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_1d_nwc_wcf
|
||||
func.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
|
||||
|
|
|
|||
Loading…
Reference in New Issue