[Mlir-commits] [mlir] a935a0b - Adding a new variant of DepthwiseConv2D
George Petterson
llvmlistbot at llvm.org
Thu Jul 21 11:37:30 PDT 2022
Author: George Petterson
Date: 2022-07-21T14:36:57-04:00
New Revision: a935a0bf5070f8c06c36eee03893226dd734ba0a
URL: https://github.com/llvm/llvm-project/commit/a935a0bf5070f8c06c36eee03893226dd734ba0a
DIFF: https://github.com/llvm/llvm-project/commit/a935a0bf5070f8c06c36eee03893226dd734ba0a.diff
LOG: Adding a new variant of DepthwiseConv2D
This is the same as the existing multiplier-1 variant of DepthwiseConv2D, but in PyTorch dim order.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D128575
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/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 85b0d641ba915..33583d5b1f01c 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -2104,6 +2104,98 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: K
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: depthwise_conv_2d_nchw_chw
+ cpp_class_name: DepthwiseConv2DNchwChwOp
+ doc: |-
+ Performs depth-wise 2-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output. Multiplier is set to 1
+ which is a special case for most depthwise convolutions.
+ 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] -> (s0, s9, s1 *
+ s2 + s3 * s4, s5 * s6 + s7 * s8)>
+ - !LinalgOperandDefConfig
+ name: K
+ kind: input_tensor
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9] -> (s9, s3, s7)>
+ - !LinalgOperandDefConfig
+ name: O
+ kind: output_tensor
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9] -> (s0, s9, s1, s5)>
+ - !LinalgOperandDefConfig
+ name: strides
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9] -> (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] -> (s4,
+ s8)>
+ default_indices:
+ - 1
+ - 1
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9]
+ -> (d0, d3, d1 * s2 + d4 * s4, d2 * s6 + d5 * s8)>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9]
+ -> (d3, d4, d5)>
+ - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9]
+ -> (d0, d3, d1, d2)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - 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: depthwise_conv_2d_nhwc_hwc_q
cpp_class_name: DepthwiseConv2DNhwcHwcQOp
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 7dd3f9495a796..b22e6c3b1e415 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
@@ -482,6 +482,32 @@ def depthwise_conv_2d_nhwc_hwc(I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH,
D.ic]) * TypeFn.cast_signed(U, K[D.kh, D.kw, D.ic])
+ at linalg_structured_op
+def depthwise_conv_2d_nchw_chw(I=TensorDef(T1, S.N, S.IC, S.OH * S.SH + S.KH * S.DH,
+ S.OW * S.SW + S.KW * S.DW),
+ K=TensorDef(T2, S.IC, S.KH, S.KW),
+ O=TensorDef(U,
+ S.N,
+ S.IC,
+ 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 depth-wise 2-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output. Multiplier is set to 1
+ which is a special case for most depthwise convolutions.
+ """
+ implements(ConvolutionOpInterface)
+ domain(D.n, D.oh, D.ow, D.ic, D.kh, D.kw)
+ O[D.n, D.ic, D.oh, D.ow] += TypeFn.cast_signed(
+ U, I[D.n, D.ic, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW]) * TypeFn.cast_signed(U, K[D.ic, D.kh, D.kw])
+
+
@linalg_structured_op
def depthwise_conv_2d_nhwc_hwc_q(I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH,
S.OW * S.SW + S.KW * S.DW, S.IC),
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
index e1ade46716533..9c60df33cedd3 100644
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -95,6 +95,31 @@ func.func @depthwise_conv_2d_nhwc_hwc_memref(%input: memref<1x113x113x96xf32>, %
return
}
+// CHECK-LABEL: func @depthwise_conv_2d_nchw_chw_tensor
+func.func @depthwise_conv_2d_nchw_chw_tensor(%input: tensor<1x96x113x113xf32>, %filter: tensor<96x3x3xf32>) -> tensor<1x96x56x56xf32> {
+ %init = linalg.init_tensor [1, 96, 56, 56] : tensor<1x96x56x56xf32>
+ // CHECK: %{{.+}} = linalg.depthwise_conv_2d_nchw_chw
+ // CHECK-SAME: {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
+ // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x96x113x113xf32>, tensor<96x3x3xf32>)
+ // CHECK-SAME: outs(%{{.+}} : tensor<1x96x56x56xf32>) -> tensor<1x96x56x56xf32>
+ %0 = linalg.depthwise_conv_2d_nchw_chw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
+ ins(%input, %filter: tensor<1x96x113x113xf32>, tensor<96x3x3xf32>)
+ outs(%init: tensor<1x96x56x56xf32>) -> tensor<1x96x56x56xf32>
+ return %0: tensor<1x96x56x56xf32>
+}
+
+// CHECK-LABEL: func @depthwise_conv_2d_nchw_chw_memref
+func.func @depthwise_conv_2d_nchw_chw_memref(%input: memref<1x96x113x113xf32>, %filter: memref<96x3x3xf32>, %output: memref<1x96x56x56xf32>) {
+ // CHECK: linalg.depthwise_conv_2d_nchw_chw
+ // CHECK-SAME: {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
+ // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x96x113x113xf32>, memref<96x3x3xf32>)
+ // CHECK-SAME: outs(%{{.+}} : memref<1x96x56x56xf32>)
+ linalg.depthwise_conv_2d_nchw_chw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
+ ins(%input, %filter: memref<1x96x113x113xf32>, memref<96x3x3xf32>)
+ outs(%output: memref<1x96x56x56xf32>)
+ return
+}
+
func.func @depthwise_conv_2d_nhwc_hwcm_tensor_dilated(%input: tensor<2x8x9x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x6x7x2x3xf32> {
%zero = arith.constant 0.000000e+00 : f32
%init = linalg.init_tensor [2, 6, 7, 2, 3] : tensor<2x6x7x2x3xf32>
@@ -234,6 +259,22 @@ func.func @conv_2d_nhwc_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x
// -----
+// CHECK-LABEL: func @conv_2d_ngchw_fgchw
+func.func @conv_2d_ngchw_fgchw(%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_2d_ngchw_fgchw
+ // CHECK-SAME: dilations = dense<1> : tensor<2xi64>
+ // CHECK-SAME: strides = dense<1> : tensor<2xi64>
+ // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
+ // CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = linalg.conv_2d_ngchw_fgchw {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
+ outs (%init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+
+// -----
+
// CHECK-LABEL: func @conv_2d_nhwc_fhwc
func.func @conv_2d_nhwc_fhwc(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
// CHECK: %{{.+}} = linalg.conv_2d_nhwc_fhwc
@@ -282,6 +323,22 @@ func.func @conv_2d_nhwc_hwcf(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x
// -----
+// CHECK-LABEL: func @conv_2d_ngchw_fgchw
+func.func @conv_2d_ngchw_fgchw(%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_ngchw_fgchw
+ // 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_ngchw_fgchw {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_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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