[Mlir-commits] [mlir] [mlir][linalg] Add Grouped Convolution Ops: conv_2d_nhwgc_gfhwc and conv_2d_nhwgc_gfhwc_q (PR #108192)
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Fri Nov 8 06:25:59 PST 2024
https://github.com/stefankoncarevic updated https://github.com/llvm/llvm-project/pull/108192
>From a1e6dfc79667818d3562340b8e7ae8790a00a15b Mon Sep 17 00:00:00 2001
From: Stefan Koncarevic <skoncare at amd.com>
Date: Thu, 19 Sep 2024 12:51:40 +0000
Subject: [PATCH] This commit introduces two new Linalg operations:
`conv_2d_nhwgc_gfhwc` and `conv_2d_nhwgc_gfhwc_q`. These operations perform
2-D grouped convolutions with and without zero point offsets, respectively.
The input layout is NHWGC, and the kernel layout is GFHWC. These additions
enhance support for grouped convolution operations in MLIR.
---
.../Linalg/IR/LinalgNamedStructuredOps.yaml | 237 ++++++++++++++++++
.../linalg/opdsl/ops/core_named_ops.py | 61 +++++
mlir/test/Dialect/Linalg/named-ops.mlir | 32 +++
3 files changed, 330 insertions(+)
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index ee88ca516de6ff..b0ea1f76955816 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -3475,6 +3475,243 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: K
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_2d_nhwgc_gfhwc
+ cpp_class_name: Conv2DNhwgcGfhwcOp
+ doc: |-
+ Performs 2-D grouped convolution.
+
+ Layout:
+ * Input: NHWGC.
+ * Kernel: GFHWC.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ implements:
+ - LinalgConvolutionOpInterface
+structured_op: !LinalgStructuredOpConfig
+ args:
+ - !LinalgOperandDefConfig
+ name: I
+ kind: input_tensor
+ type_var: T1
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, 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] ->
+ (s9, s11, s3, s7, s10)>
+ - !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, s5, s9, s11)>
+ - !LinalgOperandDefConfig
+ name: strides
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11]
+ -> (s2, s6)>
+ 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]
+ -> (s4, s8)>
+ 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 * s2 + d5 * s4, d2 * s6 + d6 * s8, d3, d7)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
+ s8, s9, s10, s11] -> (d3, d4, 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] -> (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: type
+ fn_name: cast_signed
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ scalar_fn:
+ kind: type
+ fn_name: cast_signed
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_2d_nhwgc_gfhwc_q
+ cpp_class_name: Conv2DNhwgcGfhwcQOp
+ doc: |-
+ Performs 2-D grouped convolution with zero point offsets.
+
+ Layout:
+ * Input: NHWGC.
+ * Kernel: GFHWC.
+
+ 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] ->
+ (s9, s11, s3, s7, s10)>
+ - !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, s5, s9, s11)>
+ - !LinalgOperandDefConfig
+ name: strides
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11]
+ -> (s2, s6)>
+ 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]
+ -> (s4, s8)>
+ 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 * s2 + d5 * s4, d2 * s6 + d6 * s8, d3, d7)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
+ s8, s9, s10, s11] -> (d3, d4, 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_2d_ngchw_gfchw_q
cpp_class_name: Conv2DNgchwGfchwQOp
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 5c1c984b136058..c95cd5eecfffca 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
@@ -964,6 +964,67 @@ 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_nhwgc_gfhwc(
+ I=TensorDef(
+ T1, S.N, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW, S.G, S.C
+ ),
+ K=TensorDef(T2, S.G, S.FG, S.KH, S.KW, S.C),
+ O=TensorDef(U, S.N, S.OH, S.OW, S.G, S.FG, 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.
+
+ Layout:
+ * Input: NHWGC.
+ * Kernel: GFHWC.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ """
+ implements(ConvolutionOpInterface)
+ domain(D.n, D.oh, D.ow, D.g, D.fg, D.kh, D.kw, D.c)
+ O[D.n, D.oh, D.ow, D.g, D.fg] += TypeFn.cast_signed(
+ U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.g, D.c]
+ ) * TypeFn.cast_signed(U, K[D.g, D.fg, D.kh, D.kw, D.c])
+
+
+ at linalg_structured_op
+def conv_2d_nhwgc_gfhwc_q(
+ I=TensorDef(
+ T1, S.N, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW, S.G, S.C
+ ),
+ K=TensorDef(T2, S.G, S.FG, S.KH, S.KW, S.C),
+ IZp=ScalarDef(I32),
+ KZp=ScalarDef(I32),
+ O=TensorDef(U, S.N, S.OH, S.OW, S.G, S.FG, 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: NHWGC.
+ * Kernel: GFHWC.
+
+ 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.oh, D.ow, D.g, D.fg, D.kh, D.kw, D.c)
+ O[D.n, D.oh, D.ow, D.g, D.fg] += (
+ TypeFn.cast_signed(
+ U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.g, D.c]
+ )
+ - TypeFn.cast_signed(U, IZp)
+ ) * (
+ TypeFn.cast_signed(U, K[D.g, D.fg, D.kh, D.kw, D.c])
+ - TypeFn.cast_signed(U, KZp)
+ )
+
+
@linalg_structured_op
def conv_2d_ngchw_gfchw_q(
I=TensorDef(
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
index 65c18de8424771..68aa5a85b5e0e6 100644
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -409,6 +409,38 @@ func.func @conv_2d_ngchw_fgchw(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x
// -----
+// CHECK-LABEL: func @conv_2d_nhwgc_gfhwc
+func.func @conv_2d_nhwgc_gfhwc(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
+ // CHECK: linalg.conv_2d_nhwgc_gfhwc
+ // CHECK-SAME: dilations = dense<1> : tensor<2xi64>
+ // CHECK-SAME: strides = dense<1> : tensor<2xi64>
+ // 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_2d_nhwgc_gfhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
+ outs (%output: memref<?x?x?x?x?xf32>)
+ return
+}
+
+// -----
+
+// CHECK-LABEL: func @conv_2d_nhwgc_gfhwc_tensor
+func.func @conv_2d_nhwgc_gfhwc_tensor(%input: tensor<1x28x28x2x3xf32>, %filter: tensor<2x8x3x3x3xf32>, %output: tensor<1x26x26x2x8xf32>) -> tensor<1x26x26x2x8xf32> {
+ // CHECK: linalg.conv_2d_nhwgc_gfhwc
+ // CHECK-SAME: dilations = dense<1> : tensor<2xi64>
+ // CHECK-SAME: strides = dense<1> : tensor<2xi64>
+ // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x28x28x2x3xf32>, tensor<2x8x3x3x3xf32>)
+ // CHECK-SAME: outs(%{{.+}} : tensor<1x26x26x2x8xf32>) -> tensor<1x26x26x2x8xf32>
+ %0 = linalg.conv_2d_nhwgc_gfhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x28x28x2x3xf32>, tensor<2x8x3x3x3xf32>)
+ outs (%output: tensor<1x26x26x2x8xf32>) -> tensor<1x26x26x2x8xf32>
+ return %0 : tensor<1x26x26x2x8xf32>
+}
+
+// -----
+
// CHECK-LABEL: func @conv_2d_ngchw_fgchw_dimensions
func.func @conv_2d_ngchw_fgchw_dimensions(%input: tensor<1x5x3x32x32xf32>, %filter: tensor<2x5x3x3x3xf32>, %init: tensor<1x5x2x30x30xf32>) -> tensor<1x5x2x30x30xf32> {
// CHECK: linalg.conv_2d_ngchw_fgchw
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