[Mlir-commits] [mlir] [mlir][linalg] Fix weight dimension ordering in 2D grouped conv (PR #73855)
Felix Schneider
llvmlistbot at llvm.org
Wed Nov 29 13:24:50 PST 2023
https://github.com/ubfx created https://github.com/llvm/llvm-project/pull/73855
The `conv_2d_ngchw_fgchw` Op implements 2d grouped convolution with dimensions ordered as given in the name. However, the current implementation orders weights as `gfchw` instead of `fgchw`. This was already pointed out in an old phabricator revision which never landed: https://reviews.llvm.org/D150064
This patch
1) Adds a new op `conv_2d_ngchw_gfchw`
2) Fixes the dimension ordering of the old op `conv_2d_ngchw_fgchw`
3) Adds tests with non-dynamic dimensions so that it's easier to
understand.
>From f99c556675aa8462789f74b174d0f0fcb91d03ff Mon Sep 17 00:00:00 2001
From: Felix Schneider <fx.schn at gmail.com>
Date: Tue, 28 Nov 2023 20:54:35 +0100
Subject: [PATCH] [mlir][linalg] Fix weight dimension ordering in 2D grouped
conv
The `conv_2d_ngchw_fgchw` Op implements 2d grouped convolution
with dimensions ordered as given in the name. However, the current
implementation orders weights as `gfchw` instead of `fgchw`.
This was already pointed out in an old phabricator revision which
never landed: https://reviews.llvm.org/D150064
This patch
1) Adds a new op `conv_2d_ngchw_gfchw`
2) Fixes the dimension ordering of the old op `conv_2d_ngchw_fgchw`
3) Adds tests with non-dynamic dimensions so that it's easier to
understand.
---
.../Linalg/IR/LinalgNamedStructuredOps.yaml | 101 +++++++++++++++++-
.../linalg/opdsl/ops/core_named_ops.py | 28 ++++-
mlir/test/Dialect/Linalg/named-ops.mlir | 32 ++++++
3 files changed, 159 insertions(+), 2 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index 12d520cd382413a..1ff6c4086cf3576 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -2911,7 +2911,106 @@ structured_op: !LinalgStructuredOpConfig
kind: output_tensor
type_var: U
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] ->
- (s0, s11, s1, s3, s7)>
+ (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] -> (d2, d1, 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_ngchw_gfchw
+ cpp_class_name: Conv2DNgchwGfchwOp
+ doc: |-
+ Performs 2-D grouped convolution.
+
+ 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.
+ 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: 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
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 62b7da2ae2b5337..5b05364f6d35f3b 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
@@ -780,7 +780,7 @@ def conv_2d_ngchw_fgchw(
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.FG, S.G, S.C, S.KH, S.KW),
- O=TensorDef(U, S.N, S.FG, S.G, S.OH, S.OW, output=True),
+ 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]),
):
@@ -790,6 +790,32 @@ def conv_2d_ngchw_fgchw(
* Input: NGCHW.
* Kernel: FGCHW.
+ 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.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, K[D.fg, D.g, D.c, D.kh, D.kw])
+
+
+ at linalg_structured_op
+def conv_2d_ngchw_gfchw(
+ 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),
+ 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.
+
+ 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.
"""
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
index 5ca35155854d332..29977a71dbb8644 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_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
+ // CHECK-SAME: dilations = dense<1> : tensor<2xi64>
+ // CHECK-SAME: strides = dense<1> : tensor<2xi64>
+ // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x5x3x32x32xf32>, tensor<2x5x3x3x3xf32>)
+ // CHECK-SAME: outs(%{{.+}} : tensor<1x5x2x30x30xf32>) -> tensor<1x5x2x30x30xf32>
+ %0 = linalg.conv_2d_ngchw_fgchw {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x5x3x32x32xf32>, tensor<2x5x3x3x3xf32>)
+ outs (%init: tensor<1x5x2x30x30xf32>) -> tensor<1x5x2x30x30xf32>
+ return %0 : tensor<1x5x2x30x30xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @conv_2d_ngchw_gfchw
+func.func @conv_2d_ngchw_gfchw(%input: tensor<1x5x3x32x32xf32>, %filter: tensor<5x2x3x3x3xf32>, %init: tensor<1x5x2x30x30xf32>) -> tensor<1x5x2x30x30xf32> {
+ // CHECK: linalg.conv_2d_ngchw_gfchw
+ // CHECK-SAME: dilations = dense<1> : tensor<2xi64>
+ // CHECK-SAME: strides = dense<1> : tensor<2xi64>
+ // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x5x3x32x32xf32>, tensor<5x2x3x3x3xf32>)
+ // CHECK-SAME: outs(%{{.+}} : tensor<1x5x2x30x30xf32>) -> tensor<1x5x2x30x30xf32>
+ %0 = linalg.conv_2d_ngchw_gfchw {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x5x3x32x32xf32>, tensor<5x2x3x3x3xf32>)
+ outs (%init: tensor<1x5x2x30x30xf32>) -> tensor<1x5x2x30x30xf32>
+ return %0 : tensor<1x5x2x30x30xf32>
+}
+
+// -----
+
// 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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