colin
/
onnx-mlir
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Pad value for MaxPool must be negative infinity instead of zero (#20) 

Co-authored-by: Alexandre Eichenberger <alexe@us.ibm.com>
This commit is contained in:
Tung D. Le 2020-03-12 22:30:02 +09:00 committed by GitHub
parent 811b63e031
commit 162ac1bc32
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 8 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
src/pass/onnx_rewrite.td
View File

@ -33,6 +33,11 @@ class StringAttrOfValue<string val>:
class FloatAttrOfValue<int val>: class FloatAttrOfValue<int val>:
NativeCodeCall<"FloatAttr::get($0.getType().cast<TensorType>().getElementType(), " # val # ")">; NativeCodeCall<"FloatAttr::get($0.getType().cast<TensorType>().getElementType(), " # val # ")">;
// Create a FloatAttr for the negative infinity.
def FloatAttrOfNegativeInfinity:
NativeCodeCall<"FloatAttr::get($0.getType().cast<TensorType>().getElementType(), "
"-std::numeric_limits<double>::infinity())">;
// Create an ArrayAttr of IntergerAttr(s) of zero values. // Create an ArrayAttr of IntergerAttr(s) of zero values.
// This function is used for padding attribute in MaxPoolSingleOut. // This function is used for padding attribute in MaxPoolSingleOut.
def createArrayAttrOfZerosFrom: def createArrayAttrOfZerosFrom:
@ -80,7 +85,7 @@ def MaxPoolSingleOutOpPaddingPattern: Pat<
(ONNXMaxPoolSingleOutOp (ONNXMaxPoolSingleOutOp
(ONNXPadConstantValuePadOp $x, (ONNXPadConstantValuePadOp $x,
(insertZerosForNonPaddedDims<2> $pads), (insertZerosForNonPaddedDims<2> $pads),
(FloatAttrOfValue<0> $res), (FloatAttrOfNegativeInfinity $res),
(StringAttrOfValue<"constant">)), (StringAttrOfValue<"constant">)),
$auto_pad, $ceil_mode, $dilation, $kernel_shape, $auto_pad, $ceil_mode, $dilation, $kernel_shape,
(createArrayAttrOfZerosFrom $pads), (createArrayAttrOfZerosFrom $pads),

4
test/mlir/onnx/onnx_canonicalization.mlir
View File

@ -82,7 +82,7 @@ func @test_maxpoolsingleout_split(%arg0: tensor<5x5x32x32xf32>) -> tensor<5x8x32
%0 = "onnx.MaxPoolSingleOut"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0, kernel_shape = [5,3], pads = [1, 2, 3, 4] } : (tensor<5x5x32x32xf32>) -> tensor<5x8x32x39xf32> %0 = "onnx.MaxPoolSingleOut"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0, kernel_shape = [5,3], pads = [1, 2, 3, 4] } : (tensor<5x5x32x32xf32>) -> tensor<5x8x32x39xf32>
"std.return"(%0) : (tensor<5x8x32x39xf32>) -> () "std.return"(%0) : (tensor<5x8x32x39xf32>) -> ()
// CHECK-NEXT: %0 = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0.000000e+00 : f32, mode = "constant", pads = [0, 0, 1, 2, 0, 0, 3, 4]} : (tensor<5x5x32x32xf32>) -> tensor<5x8x32x39xf32> // CHECK-NEXT: %0 = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0xFF800000 : f32, mode = "constant", pads = [0, 0, 1, 2, 0, 0, 3, 4]} : (tensor<5x5x32x32xf32>) -> tensor<5x8x32x39xf32>
// CHECK-NEXT: %1 = "onnx.MaxPoolSingleOut"(%0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [5, 3], pads = [0, 0, 0, 0], storage_order = 0 : i64} : (tensor<5x8x32x39xf32>) -> tensor<5x8x32x39xf32> // CHECK-NEXT: %1 = "onnx.MaxPoolSingleOut"(%0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [5, 3], pads = [0, 0, 0, 0], storage_order = 0 : i64} : (tensor<5x8x32x39xf32>) -> tensor<5x8x32x39xf32>
// CHECK-NEXT: return %1 : tensor<5x8x32x39xf32> // CHECK-NEXT: return %1 : tensor<5x8x32x39xf32>
} }
@ -92,7 +92,7 @@ func @test_maxpoolsingleout_split_unknown_dims(%arg0: tensor<*xf32>) -> tensor<*
%0 = "onnx.MaxPoolSingleOut"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0, kernel_shape = [5,3], pads = [1, 2, 3, 4] } : (tensor<*xf32>) -> tensor<*xf32> %0 = "onnx.MaxPoolSingleOut"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0, kernel_shape = [5,3], pads = [1, 2, 3, 4] } : (tensor<*xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-NEXT: %0 = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0.000000e+00 : f32, mode = "constant", pads = [0, 0, 1, 2, 0, 0, 3, 4]} : (tensor<*xf32>) -> tensor<*xf32> // CHECK-NEXT: %0 = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0xFF800000 : f32, mode = "constant", pads = [0, 0, 1, 2, 0, 0, 3, 4]} : (tensor<*xf32>) -> tensor<*xf32>
// CHECK-NEXT: %1 = "onnx.MaxPoolSingleOut"(%0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [5, 3], pads = [0, 0, 0, 0], storage_order = 0 : i64} : (tensor<*xf32>) -> tensor<*xf32> // CHECK-NEXT: %1 = "onnx.MaxPoolSingleOut"(%0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [5, 3], pads = [0, 0, 0, 0], storage_order = 0 : i64} : (tensor<*xf32>) -> tensor<*xf32>
// CHECK-NEXT: return %1 : tensor<*xf32> // CHECK-NEXT: return %1 : tensor<*xf32>
} }