[Mlir-commits] [mlir] 705f858 - [Tosa] Disable tosa folder for non-int/float/index types (#71757)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Thu Jun 13 02:01:41 PDT 2024
Author: Tai Ly
Date: 2024-06-13T12:01:37+03:00
New Revision: 705f858157305865d9c14594a6c4c9fd6e46d835
URL: https://github.com/llvm/llvm-project/commit/705f858157305865d9c14594a6c4c9fd6e46d835
DIFF: https://github.com/llvm/llvm-project/commit/705f858157305865d9c14594a6c4c9fd6e46d835.diff
LOG: [Tosa] Disable tosa folder for non-int/float/index types (#71757)
In order to fold, we need to create DenseElementsAttr, which does not
support quantized element types. This patch adds tests for folding
quntized element types and disable tosa folders where appropriate.
refactored canonicalize.mlir test to use --split-input-file
also fixed verifier for trait MulOperandsAndResultElementType for
quantized element types
Signed-off-by: Tai Ly <tai.ly at arm.com>
Added:
Modified:
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
mlir/test/Dialect/Tosa/canonicalize.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
index ec3c2cb011c35..7ed89bff474a2 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
@@ -78,7 +78,9 @@ class MulOperandsAndResultElementType
return success();
}
- return failure();
+ // In cases of all other types, op requires the same element
+ // type for all operands and result.
+ return impl::verifySameOperandsAndResultElementType(op);
}
};
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
index e4e5fe3d1db30..8687be075ea67 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
@@ -491,6 +491,11 @@ OpFoldResult AddOp::fold(FoldAdaptor adaptor) {
if (!lhsTy || !rhsTy || !resultTy)
return {};
+ // Cannot create an ElementsAttr from non-int/float/index types
+ if (!lhsTy.getElementType().isIntOrIndexOrFloat() ||
+ !rhsTy.getElementType().isIntOrIndexOrFloat())
+ return {};
+
auto resultETy = resultTy.getElementType();
auto lhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1());
auto rhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput2());
@@ -529,6 +534,7 @@ OpFoldResult IntDivOp::fold(FoldAdaptor adaptor) {
if (lhsTy != rhsTy)
return {};
+ // IntDivOp inputs must be integer type, no need to check for quantized type
auto resultETy = resultTy.getElementType();
auto lhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1());
auto rhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput2());
@@ -626,6 +632,11 @@ OpFoldResult SubOp::fold(FoldAdaptor adaptor) {
if (!lhsTy || !rhsTy || !resultTy)
return {};
+ // Cannot create an ElementsAttr from non-int/float/index types
+ if (!lhsTy.getElementType().isIntOrIndexOrFloat() ||
+ !rhsTy.getElementType().isIntOrIndexOrFloat())
+ return {};
+
auto resultETy = resultTy.getElementType();
auto lhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1());
auto rhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput2());
@@ -821,6 +832,10 @@ OpFoldResult ReshapeOp::fold(FoldAdaptor adaptor) {
return getResult();
}
+ // Cannot create an ElementsAttr from non-int/float/index types
+ if (!inputTy.getElementType().isIntOrIndexOrFloat())
+ return {};
+
// reshape(const(x)) -> const(reshape-attr(x))
if (auto operand = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1())) {
// Constants must have static shape.
@@ -956,13 +971,12 @@ OpFoldResult TileOp::fold(FoldAdaptor adaptor) {
}
OpFoldResult TransposeOp::fold(FoldAdaptor adaptor) {
- auto inputTy = llvm::cast<ShapedType>(getInput1().getType());
auto resultTy = llvm::cast<ShapedType>(getType());
// Transposing splat values just means reshaping.
if (auto input = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1())) {
if (input.isSplat() && resultTy.hasStaticShape() &&
- inputTy.getElementType() == resultTy.getElementType())
+ input.getType().getElementType() == resultTy.getElementType())
return input.reshape(resultTy);
}
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index 6eac759a08364..077d79ad320c8 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -1,4 +1,4 @@
-// RUN: mlir-opt -canonicalize="test-convergence" %s | FileCheck %s
+// RUN: mlir-opt --split-input-file -canonicalize="test-convergence" %s | FileCheck %s
// CHECK-LABEL: @argmax_nofold
func.func @argmax_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xi32> {
@@ -7,6 +7,8 @@ func.func @argmax_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xi32> {
return %0 : tensor<?x1xi32>
}
+// -----
+
// CHECK-LABEL: @add_bcast_zero_int
func.func @add_bcast_zero_int(%arg0: tensor<4x2x3xi32>) -> tensor<4x2x3xi32> {
// CHECK-NOT: tosa.add
@@ -16,6 +18,8 @@ func.func @add_bcast_zero_int(%arg0: tensor<4x2x3xi32>) -> tensor<4x2x3xi32> {
return %1 : tensor<4x2x3xi32>
}
+// -----
+
// CHECK-LABEL: @add_zero_int
func.func @add_zero_int(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
// CHECK: return %arg0
@@ -25,6 +29,8 @@ func.func @add_zero_int(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
return %1 : tensor<2x3xi32>
}
+// -----
+
// CHECK-LABEL: @cast_fold
func.func @cast_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
@@ -32,6 +38,8 @@ func.func @cast_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @cast_nofold
func.func @cast_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xi32> {
// CHECK: tosa.cast
@@ -39,6 +47,8 @@ func.func @cast_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xi32> {
return %0 : tensor<?x1xi32>
}
+// -----
+
// CHECK-LABEL: @clamp_i32_not_noop
func.func @clamp_i32_not_noop(%arg0: tensor<4xi32>) -> tensor<4xi32> {
// CHECK: tosa.clamp
@@ -46,6 +56,8 @@ func.func @clamp_i32_not_noop(%arg0: tensor<4xi32>) -> tensor<4xi32> {
return %0 : tensor<4xi32>
}
+// -----
+
// CHECK-LABEL: @clamp_f16_not_noop
func.func @clamp_f16_not_noop(%arg0: tensor<4xf16>) -> tensor<4xf16> {
// CHECK: tosa.clamp
@@ -53,6 +65,8 @@ func.func @clamp_f16_not_noop(%arg0: tensor<4xf16>) -> tensor<4xf16> {
return %0 : tensor<4xf16>
}
+// -----
+
// CHECK-LABEL: @clamp_f32_not_noop
func.func @clamp_f32_not_noop(%arg0: tensor<4xf32>) -> tensor<4xf32> {
// CHECK: tosa.clamp
@@ -60,6 +74,8 @@ func.func @clamp_f32_not_noop(%arg0: tensor<4xf32>) -> tensor<4xf32> {
return %0 : tensor<4xf32>
}
+// -----
+
// CHECK-LABEL: @clamp_f16_is_noop
func.func @clamp_f16_is_noop(%arg0: tensor<4xf16>) -> tensor<4xf16> {
// CHECK: return %arg0
@@ -69,6 +85,8 @@ func.func @clamp_f16_is_noop(%arg0: tensor<4xf16>) -> tensor<4xf16> {
return %0 : tensor<4xf16>
}
+// -----
+
// CHECK-LABEL: @clamp_f32_is_noop
func.func @clamp_f32_is_noop(%arg0: tensor<4xf32>) -> tensor<4xf32> {
// CHECK: return %arg0
@@ -78,6 +96,8 @@ func.func @clamp_f32_is_noop(%arg0: tensor<4xf32>) -> tensor<4xf32> {
return %0 : tensor<4xf32>
}
+// -----
+
// CHECK-LABEL: @clamp_int8_is_noop
func.func @clamp_int8_is_noop(%arg0: tensor<4xi8>) -> tensor<4xi8> {
// CHECK: return %arg0
@@ -86,6 +106,8 @@ func.func @clamp_int8_is_noop(%arg0: tensor<4xi8>) -> tensor<4xi8> {
return %0 : tensor<4xi8>
}
+// -----
+
// CHECK-LABEL: @clamp_int16_is_noop
func.func @clamp_int16_is_noop(%arg0: tensor<4xi16>) -> tensor<4xi16> {
// CHECK: return %arg0
@@ -94,6 +116,8 @@ func.func @clamp_int16_is_noop(%arg0: tensor<4xi16>) -> tensor<4xi16> {
return %0 : tensor<4xi16>
}
+// -----
+
// CHECK-LABEL: @clamp_uint8_is_noop
func.func @clamp_uint8_is_noop(%arg0: tensor<4xui8>) -> tensor<4xui8> {
// CHECK: return %arg0
@@ -102,6 +126,8 @@ func.func @clamp_uint8_is_noop(%arg0: tensor<4xui8>) -> tensor<4xui8> {
return %0 : tensor<4xui8>
}
+// -----
+
// CHECK-LABEL: @clamp_twice_is_single_clamp
func.func @clamp_twice_is_single_clamp(%arg0: tensor<4xi8>) -> tensor<4xi8> {
// CHECK: tosa.clamp %arg0 {max_fp = 3.000000e+00 : f32, max_int = 2 : i64, min_fp = -3.000000e+00 : f32, min_int = -2 : i64}
@@ -110,6 +136,8 @@ func.func @clamp_twice_is_single_clamp(%arg0: tensor<4xi8>) -> tensor<4xi8> {
return %1 : tensor<4xi8>
}
+// -----
+
// CHECK-LABEL: @concat_fold
func.func @concat_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
@@ -117,6 +145,8 @@ func.func @concat_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @concat_fold_cast
func.func @concat_fold_cast(%arg0: tensor<?x1xf32>) -> tensor<?x?xf32> {
// CHECK: %[[VAR0:.*]] = tensor.cast %arg0
@@ -125,6 +155,8 @@ func.func @concat_fold_cast(%arg0: tensor<?x1xf32>) -> tensor<?x?xf32> {
return %0 : tensor<?x?xf32>
}
+// -----
+
// CHECK-LABEL: @conv2d_stride_2
func.func @conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>) -> tensor<4x10x10x3xf32> {
// CHECK: tosa.conv2d
@@ -134,6 +166,8 @@ func.func @conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>) -> tensor<4x10x10x3xf32
return %0 : tensor<4x10x10x3xf32>
}
+// -----
+
// CHECK-LABEL: @conv2d_weight_2x2
func.func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x10x10x1xf32> {
// CHECK: tosa.conv2d
@@ -143,6 +177,8 @@ func.func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x10x10x1xf
return %0 : tensor<4x10x10x1xf32>
}
+// -----
+
// CHECK-LABEL: @depthwise_conv2d_stride_2
func.func @depthwise_conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x10x10x6xf32> {
// CHECK: tosa.depthwise_conv2d
@@ -150,6 +186,8 @@ func.func @depthwise_conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor
return %0 : tensor<4x10x10x6xf32>
}
+// -----
+
// CHECK-LABEL: @depthwise_conv2d_weight_2x2
func.func @depthwise_conv2d_weight_2x2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<2x2x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x10x10x6xf32> {
// CHECK: tosa.depthwise_conv2d
@@ -157,6 +195,8 @@ func.func @depthwise_conv2d_weight_2x2(%arg0: tensor<4x10x10x2xf32>, %arg1: tens
return %0 : tensor<4x10x10x6xf32>
}
+// -----
+
// CHECK-LABEL: @max_pool2d_is_noop
func.func @max_pool2d_is_noop(%arg0: tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf32> {
// CHECK-NOT: tosa.max_pool2d
@@ -165,6 +205,8 @@ func.func @max_pool2d_is_noop(%arg0: tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf3
return %0 : tensor<10x1x1x3xf32>
}
+// -----
+
// CHECK-LABEL: @pad_noop
func.func @pad_noop(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: return %arg0
@@ -173,6 +215,8 @@ func.func @pad_noop(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
return %1 : tensor<?x?xf32>
}
+// -----
+
// CHECK-LABEL: @pad_determine_val_i32
func.func @pad_determine_val_i32(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi32>) -> tensor<?x?xi32> {
// CHECK: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0> : tensor<i32>}
@@ -182,6 +226,8 @@ func.func @pad_determine_val_i32(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi32>
return %1 : tensor<?x?xi32>
}
+// -----
+
// CHECK-LABEL: @pad_determine_val_f32
func.func @pad_determine_val_f32(%arg0: tensor<?x?xf32>, %arg1 : tensor<2x2xi32>) -> tensor<?x?xf32> {
// CHECK: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<f32>}
@@ -191,6 +237,8 @@ func.func @pad_determine_val_f32(%arg0: tensor<?x?xf32>, %arg1 : tensor<2x2xi32>
return %1 : tensor<?x?xf32>
}
+// -----
+
// CHECK-LABEL: @pad_determine_val_quant
func.func @pad_determine_val_quant(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi32>) -> tensor<?x?xi32> {
// CHECK: %[[ZERO:.+]] = "tosa.const"() <{value = dense<42> : tensor<i32>}
@@ -200,6 +248,8 @@ func.func @pad_determine_val_quant(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi3
return %1 : tensor<?x?xi32>
}
+// -----
+
// CHECK-LABEL: @mul_one_float
func.func @mul_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
// CHECK: return %arg0
@@ -209,6 +259,8 @@ func.func @mul_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
return %1 : tensor<2x3xf32>
}
+// -----
+
// CHECK-LABEL: @mul_bcast_one_float
func.func @mul_bcast_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
// CHECK: return %arg0
@@ -218,6 +270,8 @@ func.func @mul_bcast_one_float(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
return %1 : tensor<2x3xf32>
}
+// -----
+
// CHECK-LABEL: @mul_one_int
func.func @mul_one_int(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
// CHECK: return %arg0
@@ -227,6 +281,8 @@ func.func @mul_one_int(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> {
return %1 : tensor<2x3xi32>
}
+// -----
+
// CHECK-LABEL: @mul_zero_broadcast
func.func @mul_zero_broadcast(%arg0: tensor<2x3xf32>) -> (tensor<2x3xf32>, tensor<2x3xf32>) {
// CHECK: %[[ZERO:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<2x3xf32>}
@@ -240,6 +296,8 @@ func.func @mul_zero_broadcast(%arg0: tensor<2x3xf32>) -> (tensor<2x3xf32>, tenso
return %1, %2 : tensor<2x3xf32>, tensor<2x3xf32>
}
+// -----
+
// CHECK-LABEL: @select_same_value
func.func @select_same_value(%arg0: tensor<2x3xi1>, %arg1: tensor<2x3xi32>) -> tensor<2x3xi32> {
%0 = tosa.select %arg0, %arg1, %arg1 : (tensor<2x3xi1>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
@@ -248,6 +306,8 @@ func.func @select_same_value(%arg0: tensor<2x3xi1>, %arg1: tensor<2x3xi32>) -> t
return %0 : tensor<2x3xi32>
}
+// -----
+
// CHECK-LABEL: @select_true_value
func.func @select_true_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>) -> tensor<2x3xi32> {
%c1 = "tosa.const"() {value = dense<1> : tensor<2x3xi1>} : () -> tensor<2x3xi1>
@@ -257,6 +317,8 @@ func.func @select_true_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>) ->
return %0 : tensor<2x3xi32>
}
+// -----
+
// CHECK-LABEL: @select_false_value
func.func @select_false_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>) -> tensor<2x3xi32> {
%c0 = "tosa.const"() {value = dense<0> : tensor<2x3xi1>} : () -> tensor<2x3xi1>
@@ -266,6 +328,8 @@ func.func @select_false_value(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>) ->
return %0 : tensor<2x3xi32>
}
+// -----
+
// CHECK-LABEL: @select_not_pred
func.func @select_not_pred(%arg0: tensor<2x3xi1>, %arg1: tensor<2x3xi32>, %arg2: tensor<2x3xi32>) -> tensor<2x3xi32> {
%0 = tosa.logical_not %arg0 : (tensor<2x3xi1>) -> tensor<2x3xi1>
@@ -274,6 +338,8 @@ func.func @select_not_pred(%arg0: tensor<2x3xi1>, %arg1: tensor<2x3xi32>, %arg2:
return %1 : tensor<2x3xi32>
}
+// -----
+
// CHECK-LABEL: @reduce_all_fold
func.func @reduce_all_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
@@ -281,6 +347,8 @@ func.func @reduce_all_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_all_nofold
func.func @reduce_all_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_all
@@ -288,6 +356,8 @@ func.func @reduce_all_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_any_fold
func.func @reduce_any_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
@@ -295,6 +365,8 @@ func.func @reduce_any_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_any_nofold
func.func @reduce_any_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_any
@@ -302,6 +374,8 @@ func.func @reduce_any_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_max_fold
func.func @reduce_max_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
@@ -309,6 +383,8 @@ func.func @reduce_max_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_max_nofold
func.func @reduce_max_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_max
@@ -316,6 +392,8 @@ func.func @reduce_max_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_min_fold
func.func @reduce_min_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
@@ -323,6 +401,8 @@ func.func @reduce_min_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_min_nofold
func.func @reduce_min_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_min
@@ -330,6 +410,8 @@ func.func @reduce_min_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_prod_fold
func.func @reduce_prod_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
@@ -337,6 +419,8 @@ func.func @reduce_prod_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_prod_nofold
func.func @reduce_prod_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_prod
@@ -344,6 +428,8 @@ func.func @reduce_prod_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_sum_fold
func.func @reduce_sum_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: return %arg0
@@ -351,6 +437,8 @@ func.func @reduce_sum_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reduce_sum_nofold
func.func @reduce_sum_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// CHECK: tosa.reduce_sum
@@ -358,6 +446,8 @@ func.func @reduce_sum_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
return %0 : tensor<?x1xf32>
}
+// -----
+
// CHECK-LABEL: @reshape_canonicalize
func.func @reshape_canonicalize(%arg0: tensor<?x10xf32>) -> tensor<?x10xf32> {
// CHECK: return %arg0
@@ -365,6 +455,8 @@ func.func @reshape_canonicalize(%arg0: tensor<?x10xf32>) -> tensor<?x10xf32> {
return %0 : tensor<?x10xf32>
}
+// -----
+
// CHECK-LABEL: @reshape_canonicalize_dyn_nofold
func.func @reshape_canonicalize_dyn_nofold(%arg0: tensor<?x?x10xf32>) -> tensor<?x?x10xf32> {
// CHECK: %[[VAR0:.+]] = tosa.reshape %arg0 {new_shape = array<i64: -1, 2, 10>} : (tensor<?x?x10xf32>) -> tensor<?x?x10xf32>
@@ -373,6 +465,8 @@ func.func @reshape_canonicalize_dyn_nofold(%arg0: tensor<?x?x10xf32>) -> tensor<
return %0 : tensor<?x?x10xf32>
}
+// -----
+
// CHECK-LABEL: @reshape_canonicalize_double
func.func @reshape_canonicalize_double(%arg0: tensor<?x10xf32>) -> tensor<?x5xf32> {
// CHECK: %[[VAL_1:.*]] = tosa.reshape %arg0 {new_shape = array<i64: -1, 5>}
@@ -382,6 +476,8 @@ func.func @reshape_canonicalize_double(%arg0: tensor<?x10xf32>) -> tensor<?x5xf3
return %1 : tensor<?x5xf32>
}
+// -----
+
// CHECK-LABEL: @reshape_canonicalize_const
func.func @reshape_canonicalize_const() -> tensor<1x5xi32> {
// CHECK: %[[VAR0:.+]] = "tosa.const"() <{value = dense<{{\[\[}}0, 1, 2, 3, 4]]> : tensor<1x5xi32>}
@@ -391,6 +487,8 @@ func.func @reshape_canonicalize_const() -> tensor<1x5xi32> {
return %1 : tensor<1x5xi32>
}
+// -----
+
// CHECK-LABEL: @reshape_canonicalize_const_dynamic
func.func @reshape_canonicalize_const_dynamic() -> tensor<1x?xi32> {
// CHECK: tosa.reshape
@@ -399,6 +497,8 @@ func.func @reshape_canonicalize_const_dynamic() -> tensor<1x?xi32> {
return %1 : tensor<1x?xi32>
}
+// -----
+
// CHECK-LABEL: @reshape_canonicalize_const_splat
func.func @reshape_canonicalize_const_splat() -> (tensor<10xi32>, tensor<1x10xi32>) {
// CHECK-DAG: %[[VAR0:.+]] = "tosa.const"() <{value = dense<0> : tensor<10xi32>}
@@ -409,6 +509,8 @@ func.func @reshape_canonicalize_const_splat() -> (tensor<10xi32>, tensor<1x10xi3
return %0 , %1 : tensor<10xi32>, tensor<1x10xi32>
}
+// -----
+
// CHECK-LABEL: @reshape_canonicalize_const_sparse
func.func @reshape_canonicalize_const_sparse() -> (tensor<3xi32>, tensor<1x3xi32>) {
// CHECK: tosa.reshape
@@ -417,23 +519,32 @@ func.func @reshape_canonicalize_const_sparse() -> (tensor<3xi32>, tensor<1x3xi32
return %0 , %1 : tensor<3xi32>, tensor<1x3xi32>
}
-// CHECK-LABEL: @reshape_canonicalize_quant
-func.func @reshape_canonicalize_quant() -> (tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>) {
- // CHECK{LITERAL}: "tosa.const"() <{value = dense<[[1, 2, 3]]> : tensor<1x3xi8>}> : () -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>
+// -----
+
+// CHECK-LABEL: @reshape_canonicalize_quant_nofold
+func.func @reshape_canonicalize_quant_nofold() -> (tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>) {
+ // disabled folding for quantized element types
+ // CHECK{LITERAL}: "tosa.const"() <{value = dense<[1, 2, 3]> : tensor<3xi8>}> : () -> tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>
+ // CHECK{LITERAL}: tosa.reshape %0 {new_shape = array<i64: 1, 3>} : (tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>) -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>
%0 = "tosa.const"() {value = dense<[1, 2, 3]> : tensor<3xi8>} : ()-> tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>
%1 = tosa.reshape %0 {new_shape = array<i64: 1, 3>} : (tensor<3x!quant.uniform<i8:f32, 1.000000e+00>>) -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>
return %1 : tensor<1x3x!quant.uniform<i8:f32, 1.000000e+00>>
}
+// -----
+
// CHECK-LABEL: @transpose_canonicalize_strip_quant
-func.func @transpose_canonicalize_strip_quant() -> (tensor<2x1x3xi8>) {
- // CHECK: "tosa.const"() <{value = dense<0> : tensor<2x1x3xi8>}> : () -> tensor<2x1x3xi8>
+func.func @transpose_canonicalize_strip_quant() -> (tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>) {
+ // CHECK: "tosa.const"() <{value = dense<0> : tensor<1x2x3xi8>}> : () -> tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>
+ // CHECK: tosa.reshape %0 {new_shape = array<i64: 2, 1, 3>} : (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>) -> tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
%perms = "tosa.const"() {value = dense<[1, 0, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
%0 = "tosa.const"() {value = dense<0> : tensor<1x2x3xi8>} : ()-> tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>
- %1 = tosa.transpose %0, %perms : (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>, tensor<3xi32>) -> tensor<2x1x3xi8>
- return %1 : tensor<2x1x3xi8>
+ %1 = tosa.transpose %0, %perms : (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>, tensor<3xi32>) -> tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
+ return %1 : tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
}
+// -----
+
// CHECK-LABEL: @slice_fold
func.func @slice_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
// CHECK: return %arg0
@@ -441,6 +552,8 @@ func.func @slice_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
return %0 : tensor<3x4xf32>
}
+// -----
+
// CHECK-LABEL: @slice_nofold
func.func @slice_nofold(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32> {
// CHECK: tosa.slice
@@ -448,6 +561,8 @@ func.func @slice_nofold(%arg0: tensor<?x4xf32>) -> tensor<?x4xf32> {
return %0 : tensor<?x4xf32>
}
+// -----
+
// CHECK-LABEL: @tile_fold
func.func @tile_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
// CHECK: return %arg0
@@ -455,6 +570,8 @@ func.func @tile_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
return %0 : tensor<3x4xf32>
}
+// -----
+
// CHECK-LABEL: @tile_nofold
func.func @tile_nofold(%arg0: tensor<3x4xf32>) -> tensor<3x8xf32> {
// CHECK: tosa.tile
@@ -462,6 +579,8 @@ func.func @tile_nofold(%arg0: tensor<3x4xf32>) -> tensor<3x8xf32> {
return %0 : tensor<3x8xf32>
}
+// -----
+
// CHECK-LABEL: @transpose_no_op
func.func @transpose_no_op(%arg0: tensor<3x4x5x6xf32>) -> tensor<3x4x5x6xf32> {
// CHECK: return %arg0
@@ -471,6 +590,8 @@ func.func @transpose_no_op(%arg0: tensor<3x4x5x6xf32>) -> tensor<3x4x5x6xf32> {
return %1 : tensor<3x4x5x6xf32>
}
+// -----
+
// CHECK-LABEL: @transpose_is_reshape
func.func @transpose_is_reshape(%arg0: tensor<1x4x5x1xf32>) -> tensor<1x4x1x5xf32> {
// CHECK: tosa.reshape %arg0 {new_shape = array<i64: 1, 4, 1, 5>} : (tensor<1x4x5x1xf32>) -> tensor<1x4x1x5xf32>
@@ -479,6 +600,8 @@ func.func @transpose_is_reshape(%arg0: tensor<1x4x5x1xf32>) -> tensor<1x4x1x5xf3
return %0 : tensor<1x4x1x5xf32>
}
+// -----
+
// CHECK-LABEL: @single_bit_reshape
// https://github.com/llvm/llvm-project/issues/55440
func.func @single_bit_reshape() -> tensor<1xi1> {
@@ -603,7 +726,7 @@ func.func @fold_abs_abs(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
// -----
// CHECK-LABEL: @fold_reduce_rank_zero
-func.func nested @fold_reduce_rank_zero() {
+func.func @fold_reduce_rank_zero() {
// CHECK-NOT: tosa.reduce_min
// CHECK-NOT: tosa.reverse
%0 = tensor.empty() : tensor<i32>
@@ -622,6 +745,113 @@ func.func nested @fold_tile_rank_zero() -> tensor<i32> {
return %1 : tensor<i32>
}
+// -----
+
+// CHECK-LABEL: @reshape_quant_nofold
+// check that segfault is fixed
+func.func @reshape_quant_nofold() -> tensor<1x1x1x1xi32> {
+ %0 = "tosa.const"() {value = dense<127> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %1 = tosa.reshape %0 {new_shape = array<i64: 1, 1, 1, 1>} : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<1x1x1x1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %2 = tosa.rescale %1 {double_round = true, input_zp = -128 : i32, multiplier = array<i32: 1073741824>, output_zp = 0 : i32, per_channel = false, scale32 = true, shift = array<i8: 30>} : (tensor<1x1x1x1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<1x1x1x1xi32>
+ return %2 : tensor<1x1x1x1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @add_quant_nofold
+// check that segfault is fixed
+func.func @add_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
+ %0 = "tosa.const"() {value = dense<127> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %1 = tosa.add %0, %0 : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ return %1 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+}
+
+// -----
+
+// CHECK-LABEL: @sub_quant_nofold
+// check that segfault is fixed
+func.func @sub_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
+ %0 = "tosa.const"() {value = dense<127> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %1 = tosa.sub %0, %0 : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ return %1 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+}
+
+// -----
+
+// CHECK-LABEL: @greater_quant_fold
+func.func @greater_quant_fold() -> tensor<i1> {
+ %0 = "tosa.const"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ // CHECK: "tosa.const"() <{value = dense<false>
+ %2 = "tosa.greater"(%0, %0) : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<i1>
+ return %2 : tensor<i1>
+}
+
+// -----
+
+// CHECK-LABEL: @greater_equal_quant_fold
+func.func @greater_equal_quant_fold() -> tensor<i1> {
+ %0 = "tosa.const"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ // CHECK: "tosa.const"() <{value = dense<true>
+ %2 = "tosa.greater_equal"(%0, %0) : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<i1>
+ return %2 : tensor<i1>
+}
+
+// -----
+
+// CHECK-LABEL: @equal_quant_fold
+func.func @equal_quant_fold() -> tensor<i1> {
+ %0 = "tosa.const"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ // CHECK: "tosa.const"() <{value = dense<true>
+ %2 = "tosa.equal"(%0, %0) : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<i1>
+ return %2 : tensor<i1>
+}
+
+// -----
+
+// CHECK-LABEL: @cast_quant_nofold
+func.func @cast_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:3>> {
+ // CHECK: tosa.cast
+ %0 = "tosa.const"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %1 = "tosa.cast"(%0) : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:3>>
+ return %1 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:3>>
+}
+
+// -----
+
+// CHECK-LABEL: @reverse_quant_fold
+func.func @reverse_quant_fold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
+ // CHECK: %[[CST:.*]] = "tosa.const"() <{value = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ // CHECK: return %[[CST]]
+ %0 = "tosa.const"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %1 = "tosa.reverse"(%0) { axis = 0 : i32 } : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ return %1 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+}
+
+// -----
+
+// CHECK-LABEL: @select_quant_fold
+func.func @select_quant_fold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
+ // CHECK: %[[CONST_0:.*]] = "tosa.const"() <{value = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ // CHECK: return %[[CONST_0]]
+ %0 = "tosa.const"() {value = dense<true> : tensor<i1>} : () -> tensor<i1>
+ %1 = "tosa.const"() {value = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %2 = "tosa.const"() {value = dense<127> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %3 = "tosa.select"(%0, %1, %2) : (tensor<i1>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ return %3 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+}
+
+// -----
+
+// CHECK-LABEL: @mul_quant_nofold
+func.func @mul_quant_nofold() -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
+ // CHECK: tosa.mul
+ %0 = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %1 = "tosa.const"() {value = dense<1> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ %2 = tosa.mul %0, %1 { shift = 0 : i8} : (tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+ return %2 : tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
+}
+
+
// -----
// CHECK-LABEL: @fold_reciprocal
More information about the Mlir-commits
mailing list