[Mlir-commits] [mlir] ba0997c - [mlir][tosa] Fix depthwise_conv2D strides/dilation and name
Rob Suderman
llvmlistbot at llvm.org
Thu Aug 12 15:45:00 PDT 2021
Author: natashaknk
Date: 2021-08-12T15:43:41-07:00
New Revision: ba0997ca09d3a61a7b67e3b757136ad0d1fcac26
URL: https://github.com/llvm/llvm-project/commit/ba0997ca09d3a61a7b67e3b757136ad0d1fcac26
DIFF: https://github.com/llvm/llvm-project/commit/ba0997ca09d3a61a7b67e3b757136ad0d1fcac26.diff
LOG: [mlir][tosa] Fix depthwise_conv2D strides/dilation and name
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D107997
Added:
Modified:
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index 3e1fcabc8cb9b..feb8e0c1ea5a4 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -988,8 +988,8 @@ structured_op: !LinalgStructuredOpConfig
scalar_arg: KZp
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
- name: depthwise_conv2D_nchw
- cpp_class_name: DepthwiseConv2DNchwOp
+ name: depthwise_conv2D_nhwc
+ cpp_class_name: DepthwiseConv2DNhwcOp
doc: |-
Performs depth-wise 2-D convolution.
@@ -1070,8 +1070,8 @@ structured_op: !LinalgStructuredOpConfig
scalar_arg: K
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
- name: depthwise_conv2D_nchw_q
- cpp_class_name: DepthwiseConv2DNchwQOp
+ name: depthwise_conv2D_nhwc_q
+ cpp_class_name: DepthwiseConv2DNhwcQOp
doc: |-
Performs depth-wise 2-D convolution.
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index 8e24f03a0dacf..ca64e5de39f0e 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -1066,18 +1066,18 @@ class DepthwiseConvConverter
Value conv;
if (!isQuantized) {
conv = rewriter
- .create<linalg::DepthwiseConv2DNchwOp>(
+ .create<linalg::DepthwiseConv2DNhwcOp>(
loc, linalgConvTy, ValueRange{input, weight},
- ValueRange{biasReshape}, dilationAttr, strideAttr)
+ ValueRange{biasReshape}, strideAttr, dilationAttr)
.getResult(0);
} else {
auto iZpVal = rewriter.create<ConstantOp>(loc, iZp);
auto kZpVal = rewriter.create<ConstantOp>(loc, kZp);
conv =
rewriter
- .create<linalg::DepthwiseConv2DNchwQOp>(
+ .create<linalg::DepthwiseConv2DNhwcQOp>(
loc, linalgConvTy, ValueRange{input, weight, iZpVal, kZpVal},
- ValueRange{biasReshape}, dilationAttr, strideAttr)
+ ValueRange{biasReshape}, strideAttr, dilationAttr)
.getResult(0);
}
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 b9faeeb831dfa..b2bd4ce57f9b5 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
@@ -216,7 +216,7 @@ def conv_2d_nhwc_hwcf_q(
]) - cast(U, IZp)) * (cast(U, K[D.kh, D.kw, D.c, D.f]) - cast(U, KZp))
@linalg_structured_op
-def depthwise_conv2D_nchw(
+def depthwise_conv2D_nhwc(
I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
K=TensorDef(T2, S.KH, S.KW, S.IC, S.CM),
O=TensorDef(U, S.N, S.OH, S.OW, S.IC, S.CM, output=True),
@@ -233,7 +233,7 @@ def depthwise_conv2D_nchw(
D.ic]) * cast(U, K[D.kh, D.kw, D.ic, D.cm])
@linalg_structured_op
-def depthwise_conv2D_nchw_q(
+def depthwise_conv2D_nhwc_q(
I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
K=TensorDef(T2, S.KH, S.KW, S.IC, S.CM),
IZp=ScalarDef(I32),
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
index 3c89de3951878..88906b763d390 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -1225,7 +1225,7 @@ func @depthwise_conv(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf32>,
// CHECK: linalg.yield %arg3 : f32
// CHECK: } -> tensor<1x5x5x33xf32>
// CHECK: [[DBIAS:%.+]] = linalg.tensor_expand_shape [[BIAS]] {{\[}}[0], [1], [2], [3, 4]]
- // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nchw {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>) outs([[DBIAS]] : tensor<1x5x5x3x11xf32>)
+ // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>) outs([[DBIAS]] : tensor<1x5x5x3x11xf32>)
// CHECK: linalg.tensor_collapse_shape %3 {{\[}}[0], [1], [2], [3, 4]]
%2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) { pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1] } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> (tensor<1x5x5x33xf32>)
return
@@ -1236,6 +1236,25 @@ func @depthwise_conv(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf32>,
// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK-LABEL: @depthwise_conv_strides
+func @depthwise_conv_strides(%arg0 : tensor<1x11x9x3xf32>, %arg1 : tensor<3x1x3x11xf32>, %arg2 : tensor<33xf32>) -> () {
+ // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 5, 5, 33]
+ // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<33xf32>) outs([[INIT]] : tensor<1x5x5x33xf32>) {
+ // CHECK: ^bb0(%arg3: f32, %arg4: f32): // no predecessors
+ // CHECK: linalg.yield %arg3 : f32
+ // CHECK: } -> tensor<1x5x5x33xf32>
+ // CHECK: [[DBIAS:%.+]] = linalg.tensor_expand_shape [[BIAS]] {{\[}}[0], [1], [2], [3, 4]]
+ // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>) outs([[DBIAS]] : tensor<1x5x5x3x11xf32>)
+ // CHECK: linalg.tensor_collapse_shape %3 {{\[}}[0], [1], [2], [3, 4]]
+ %2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) { pad = [0, 0, 0, 0], stride = [2, 2], dilation = [1, 1] } : (tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> (tensor<1x5x5x33xf32>)
+ return
+}
+
+// -----
+
+// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
+// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+
// CHECK-LABEL: @depthwise_conv_quant
func @depthwise_conv_quant(%arg0 : tensor<1x12x12x4xi8>, %arg1 : tensor<3x3x4x128xi8>, %arg2 : tensor<512xi32>) -> () {
// CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 10, 10, 512]
@@ -1246,7 +1265,7 @@ func @depthwise_conv_quant(%arg0 : tensor<1x12x12x4xi8>, %arg1 : tensor<3x3x4x12
// CHECK: [[DBIAS:%.+]] = linalg.tensor_expand_shape [[BIAS]] {{\[}}[0], [1], [2], [3, 4]]
// CHECK: %[[C128:.+]] = constant -128
// CHECK: %[[C42:.+]] = constant 42
- // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nchw_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %[[C128]], %[[C42]] : tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[DBIAS]] : tensor<1x10x10x4x128xi32>)
+ // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %[[C128]], %[[C42]] : tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[DBIAS]] : tensor<1x10x10x4x128xi32>)
// CHECK: linalg.tensor_collapse_shape %3 {{\[}}[0], [1], [2], [3, 4]]
%0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = [1, 1], dilation = [1, 1] } : (tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>) -> tensor<1x10x10x512xi32>
return
@@ -1254,6 +1273,27 @@ func @depthwise_conv_quant(%arg0 : tensor<1x12x12x4xi8>, %arg1 : tensor<3x3x4x12
// -----
+// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
+// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+
+// CHECK-LABEL: @depthwise_conv_quant_dilations
+func @depthwise_conv_quant_dilations(%arg0 : tensor<1x14x14x4xi8>, %arg1 : tensor<3x3x4x128xi8>, %arg2 : tensor<512xi32>) -> () {
+ // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 10, 10, 512]
+ // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<512xi32>) outs([[INIT]] : tensor<1x10x10x512xi32>) {
+ // CHECK: ^bb0(%arg3: i32, %arg4: i32): // no predecessors
+ // CHECK: linalg.yield %arg3 : i32
+ // CHECK: } -> tensor<1x10x10x512xi32>
+ // CHECK: [[DBIAS:%.+]] = linalg.tensor_expand_shape [[BIAS]] {{\[}}[0], [1], [2], [3, 4]]
+ // CHECK: %[[C128:.+]] = constant -128
+ // CHECK: %[[C42:.+]] = constant 42
+ // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc_q {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %[[C128]], %[[C42]] : tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[DBIAS]] : tensor<1x10x10x4x128xi32>)
+ // CHECK: linalg.tensor_collapse_shape %3 {{\[}}[0], [1], [2], [3, 4]]
+ %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = [1, 1], dilation = [2, 2] } : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>) -> tensor<1x10x10x512xi32>
+ return
+}
+
+// -----
+
// CHECK-LABEL: @transpose_conv
func @transpose_conv(%arg0 : tensor<1x12x12x2xf32>, %arg1 : tensor<4x3x3x2xf32>, %arg2 : tensor<4xf32>) -> () {
// CHECK: linalg.pad_tensor %arg0 low[0, 2, 2, 0] high[0, 2, 2, 0]
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