[Mlir-commits] [mlir] 42a0fb2 - [mlir][linalg] Add linalg.conv_2d_ngchw_gfchw_q to named ops (#92136)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Wed May 29 03:55:08 PDT 2024
Author: zjgarvey
Date: 2024-05-29T12:55:05+02:00
New Revision: 42a0fb2333344077dc8aafd65b50d0ece886cf4e
URL: https://github.com/llvm/llvm-project/commit/42a0fb2333344077dc8aafd65b50d0ece886cf4e
DIFF: https://github.com/llvm/llvm-project/commit/42a0fb2333344077dc8aafd65b50d0ece886cf4e.diff
LOG: [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.
Added:
Modified:
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
mlir/test/Dialect/Linalg/generalize-named-ops.mlir
mlir/test/Dialect/Linalg/named-ops.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index eb7dd37010a67..fad234a9dcae9 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -3478,6 +3478,144 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
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
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 d73428a0f4df3..43410aaa6af1b 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
@@ -958,6 +958,41 @@ def conv_2d_ngchw_gfchw(
) * TypeFn.cast_signed(U, K[D.g, D.fg, D.c, D.kh, D.kw])
+ at 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(
diff --git a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
index 4f43ec2c9e1ce..31fac9b4b4165 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 051054e67edf0..02ecbed232c8b 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
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