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author | zjgarvey <47986913+zjgarvey@users.noreply.github.com> | 2024-05-29 05:55:05 -0500 |
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committer | GitHub <noreply@github.com> | 2024-05-29 12:55:05 +0200 |
commit | 42a0fb2333344077dc8aafd65b50d0ece886cf4e (patch) | |
tree | 658148d4c0f1939065b52b0bbbf10683a1a510c7 | |
parent | 7c917e8268225735bf6fe0f7d8491fc944358e47 (diff) | |
download | llvm-42a0fb2333344077dc8aafd65b50d0ece886cf4e.zip llvm-42a0fb2333344077dc8aafd65b50d0ece886cf4e.tar.gz llvm-42a0fb2333344077dc8aafd65b50d0ece886cf4e.tar.bz2 |
[mlir][linalg] Add linalg.conv_2d_ngchw_gfchw_q to named ops (#92136)
Adds a named op: linalg.conv_2d_ngchw_gfchw_q. This op is similar to
linalg.conv_2d_ngchw_gfchw, but additionally incorporates zero point
offset corrections.
4 files changed, 219 insertions, 0 deletions
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml index eb7dd37..fad234a 100644 --- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml +++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml @@ -3479,6 +3479,144 @@ structured_op: !LinalgStructuredOpConfig scalar_arg: K --- !LinalgOpConfig metadata: !LinalgOpMetadata + name: conv_2d_ngchw_gfchw_q + cpp_class_name: Conv2DNgchwGfchwQOp + doc: |- + Performs 2-D grouped convolution with zero-point offsets. + + Layout: + * Input: NGCHW. + * Kernel: GFCHW. + + Numeric casting is performed on the operands to the inner multiply, promoting + them to the same data type as the accumulator/output. This includes the zero + point offsets common to quantized operations. + implements: + - LinalgConvolutionOpInterface +structured_op: !LinalgStructuredOpConfig + args: + - !LinalgOperandDefConfig + name: I + kind: input_tensor + type_var: T1 + shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] -> + (s0, s1, s2, s3 * s4 + s5 * s6, s7 * s8 + s9 * s10)> + - !LinalgOperandDefConfig + name: K + kind: input_tensor + type_var: T2 + shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] -> + (s1, s11, s2, s5, s9)> + - !LinalgOperandDefConfig + name: IZp + kind: scalar + type_var: I32 + - !LinalgOperandDefConfig + name: KZp + kind: scalar + type_var: I32 + - !LinalgOperandDefConfig + name: O + kind: output_tensor + type_var: U + shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] -> + (s0, s1, s11, s3, s7)> + - !LinalgOperandDefConfig + name: strides + kind: index_attr + index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] + -> (s4, s8)> + default_indices: + - 1 + - 1 + - !LinalgOperandDefConfig + name: dilations + kind: index_attr + index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] + -> (s6, s10)> + default_indices: + - 1 + - 1 + indexing_maps: !LinalgIndexingMapsConfig + static_indexing_maps: + - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7, + s8, s9, s10, s11] -> (d0, d1, d5, d3 * s4 + d6 * s6, d4 * s8 + d7 * s10)> + - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7, + s8, s9, s10, s11] -> (d1, d2, d5, d6, d7)> + - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7, + s8, s9, s10, s11] -> ()> + - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7, + s8, s9, s10, s11] -> ()> + - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7, + s8, s9, s10, s11] -> (d0, d1, d2, d3, d4)> + iterator_types: + - parallel + - parallel + - parallel + - parallel + - parallel + - reduction + - reduction + - reduction + assignments: + - !ScalarAssign + arg: O + value: !ScalarExpression + scalar_fn: + kind: binary + fn_name: add + operands: + - !ScalarExpression + scalar_arg: O + - !ScalarExpression + scalar_fn: + kind: binary + fn_name: mul + operands: + - !ScalarExpression + scalar_fn: + kind: binary + fn_name: sub + operands: + - !ScalarExpression + scalar_fn: + kind: type + fn_name: cast_signed + type_var: U + operands: + - !ScalarExpression + scalar_arg: I + - !ScalarExpression + scalar_fn: + kind: type + fn_name: cast_signed + type_var: U + operands: + - !ScalarExpression + scalar_arg: IZp + - !ScalarExpression + scalar_fn: + kind: binary + fn_name: sub + operands: + - !ScalarExpression + scalar_fn: + kind: type + fn_name: cast_signed + type_var: U + operands: + - !ScalarExpression + scalar_arg: K + - !ScalarExpression + scalar_fn: + kind: type + fn_name: cast_signed + type_var: U + operands: + - !ScalarExpression + scalar_arg: KZp +--- !LinalgOpConfig +metadata: !LinalgOpMetadata name: conv_3d_ndhwc_dhwcf cpp_class_name: Conv3DNdhwcDhwcfOp doc: |- 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 d73428a..43410aa 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 @@ -959,6 +959,41 @@ def conv_2d_ngchw_gfchw( @linalg_structured_op +def conv_2d_ngchw_gfchw_q( + I=TensorDef( + T1, S.N, S.G, S.C, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW + ), + K=TensorDef(T2, S.G, S.FG, S.C, S.KH, S.KW), + IZp=ScalarDef(I32), + KZp=ScalarDef(I32), + O=TensorDef(U, S.N, S.G, S.FG, S.OH, S.OW, output=True), + strides=IndexAttrDef(S.SH, S.SW, default=[1, 1]), + dilations=IndexAttrDef(S.DH, S.DW, default=[1, 1]), +): + """Performs 2-D grouped convolution with zero-point offsets. + + Layout: + * Input: NGCHW. + * Kernel: GFCHW. + + Numeric casting is performed on the operands to the inner multiply, promoting + them to the same data type as the accumulator/output. This includes the zero + point offsets common to quantized operations. + """ + implements(ConvolutionOpInterface) + domain(D.n, D.g, D.fg, D.oh, D.ow, D.c, D.kh, D.kw) + O[D.n, D.g, D.fg, D.oh, D.ow] += ( + TypeFn.cast_signed( + U, I[D.n, D.g, D.c, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW] + ) + - TypeFn.cast_signed(U, IZp) + ) * ( + TypeFn.cast_signed(U, K[D.g, D.fg, D.c, D.kh, D.kw]) + - TypeFn.cast_signed(U, KZp) + ) + + +@linalg_structured_op def conv_3d_ndhwc_dhwcf( I=TensorDef( T1, diff --git a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir index 4f43ec2..31fac9b 100644 --- a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir +++ b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir @@ -204,6 +204,37 @@ func.func @conv_1d_ncw_fcw(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32> // ----- +func.func @conv_2d_ngchw_gfchw_q(%input: memref<?x?x?x?x?xi8>, %filter: memref<?x?x?x?x?xi8>, %inputzp: i32, %filterzp: i32, %output: memref<?x?x?x?x?xi32>) { + linalg.conv_2d_ngchw_gfchw_q {dilations = dense<1> : tensor<2xi64>, + strides = dense<1> : tensor<2xi64>} + ins (%input, %filter, %inputzp, %filterzp: memref<?x?x?x?x?xi8>, memref<?x?x?x?x?xi8>, i32, i32) + outs (%output: memref<?x?x?x?x?xi32>) + return +} +// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d5, d3 + d6, d4 + d7)> +// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d1, d2, d5, d6, d7)> +// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> ()> +// CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d2, d3, d4)> + +// CHECK: func @conv_2d_ngchw_gfchw_q + +// CHECK: linalg.generic +// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]], #[[MAP2]], #[[MAP3]]] +// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction"]} +// CHECK-SAME: ins(%{{.+}}, %{{.+}}, %{{.+}}, %{{.+}} : memref<?x?x?x?x?xi8>, memref<?x?x?x?x?xi8>, i32, i32) +// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?x?xi32>) + +// CHECK: ^{{.+}}(%[[BBARG0:.+]]: i8, %[[BBARG1:.+]]: i8, %[[BBARG2:.+]]: i32, %[[BBARG3:.+]]: i32, %[[BBARG4:.+]]: i32) +// CHECK-NEXT: %[[EXTSI0:.+]] = arith.extsi %[[BBARG0]] : i8 to i32 +// CHECK-NEXT: %[[SUB0:.+]] = arith.subi %[[EXTSI0]], %[[BBARG2]] : i32 +// CHECK-NEXT: %[[EXTSI1:.+]] = arith.extsi %[[BBARG1]] : i8 to i32 +// CHECK-NEXT: %[[SUB1:.+]] = arith.subi %[[EXTSI1]], %[[BBARG3]] : i32 +// CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[SUB0]], %[[SUB1]] : i32 +// CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[BBARG4]], %[[MUL]] : i32 +// CHECK-NEXT: linalg.yield %[[ADD]] : i32 + +// ----- + func.func @generalize_fill(%output: memref<?x?xf32>, %value : f32) { linalg.fill ins(%value : f32) outs(%output : memref<?x?xf32>) return diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir index 051054e67..02ecbed 100644 --- a/mlir/test/Dialect/Linalg/named-ops.mlir +++ b/mlir/test/Dialect/Linalg/named-ops.mlir @@ -441,6 +441,21 @@ func.func @conv_2d_ngchw_gfchw(%input: tensor<1x5x3x32x32xf32>, %filter: tensor< // ----- +// CHECK-LABEL: func @conv_2d_ngchw_gfchw_q +func.func @conv_2d_ngchw_gfchw_q(%input: tensor<1x5x3x32x32xi8>, %filter: tensor<5x2x3x3x3xi8>, %inputzp: i32, %filterzp: i32, %init: tensor<1x5x2x30x30xi32>) -> tensor<1x5x2x30x30xi32> { + // CHECK: linalg.conv_2d_ngchw_gfchw_q + // CHECK-SAME: dilations = dense<1> : tensor<2xi64> + // CHECK-SAME: strides = dense<1> : tensor<2xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x5x3x32x32xi8>, tensor<5x2x3x3x3xi8>, i32, i32) + // CHECK-SAME: outs(%{{.+}} : tensor<1x5x2x30x30xi32>) -> tensor<1x5x2x30x30xi32> + %0 = linalg.conv_2d_ngchw_gfchw_q {dilations = dense<1> : tensor<2xi64>, + strides = dense<1> : tensor<2xi64>} + ins (%input, %filter, %inputzp, %filterzp: tensor<1x5x3x32x32xi8>, tensor<5x2x3x3x3xi8>, i32, i32) + outs (%init: tensor<1x5x2x30x30xi32>) -> tensor<1x5x2x30x30xi32> + return %0 : tensor<1x5x2x30x30xi32> +} +// ----- + // CHECK-LABEL: func @conv_3d_ndhwc_dhwcf func.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 |