[Mlir-commits] [mlir] [mlir][tosa] Enhance the conv2d verifier (PR #128693)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Tue Feb 25 02:54:06 PST 2025
llvmbot wrote:
<!--LLVM PR SUMMARY COMMENT-->
@llvm/pr-subscribers-mlir-tosa
@llvm/pr-subscribers-mlir
Author: Luke Hutton (lhutton1)
<details>
<summary>Changes</summary>
This commit adds additional checks to the conv2d verifier that check error_if conditions from the tosa specification. Notably, it adds padding, stride and dilation invalid value checking, output height and width checking and bias size checking.
---
Patch is 26.73 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/128693.diff
7 Files Affected:
- (modified) mlir/lib/Dialect/Tosa/IR/TosaOps.cpp (+102-7)
- (modified) mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir (+6-6)
- (modified) mlir/test/Dialect/Tosa/canonicalize.mlir (+6-6)
- (modified) mlir/test/Dialect/Tosa/invalid.mlir (+72)
- (modified) mlir/test/Dialect/Tosa/level_check.mlir (+24-24)
- (modified) mlir/test/Dialect/Tosa/quant-test.mlir (+3-3)
- (modified) mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir (+3-3)
``````````diff
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 295b010da0ee0..5920079118ab5 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -214,6 +214,16 @@ void mlir::tosa::printTypeOrAttr(OpAsmPrinter &p, Operation *op, TypeAttr type,
}
}
+//===----------------------------------------------------------------------===//
+// Tosa utilities.
+//===----------------------------------------------------------------------===//
+
+std::optional<int64_t> idivCheck(const int64_t lhs, const int64_t rhs) {
+ if (lhs % rhs != 0)
+ return std::nullopt;
+ return lhs / rhs;
+}
+
//===----------------------------------------------------------------------===//
// TOSA Operator Verifiers.
//===----------------------------------------------------------------------===//
@@ -1666,13 +1676,6 @@ LogicalResult tosa::ResizeOp::verify() {
const int64_t borderY = borderValues[0];
const int64_t borderX = borderValues[1];
- auto idivCheck = [](const int64_t lhs,
- const int64_t rhs) -> std::optional<int64_t> {
- if (lhs % rhs != 0)
- return std::nullopt;
- return lhs / rhs;
- };
-
// Don't check with input height that could be broadcast (ih != 1)
// since Linalg, a consumer of TOSA, expects broadcasting support
// in resize to be available. Taking the cautious approach for now,
@@ -2012,6 +2015,98 @@ LogicalResult Conv2DOp::inferReturnTypeComponents(
LogicalResult Conv2DOp::verify() {
if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
return failure();
+
+ llvm::ArrayRef<int64_t> padding = getPad();
+ if (llvm::any_of(padding, [](int64_t p) { return p < 0; }))
+ return emitOpError("expect all padding values to be >= 0, got ") << padding;
+
+ llvm::ArrayRef<int64_t> strides = getStride();
+ if (llvm::any_of(strides, [](int64_t s) { return s < 1; }))
+ return emitOpError("expect all stride values to be >= 1, got ") << strides;
+
+ llvm::ArrayRef<int64_t> dilations = getDilation();
+ if (llvm::any_of(dilations, [](int64_t d) { return d < 1; }))
+ return emitOpError("expect all dilation values to be >= 1, got ")
+ << dilations;
+
+ const RankedTensorType outputType =
+ llvm::dyn_cast<RankedTensorType>(getOutput().getType());
+ if (!outputType)
+ // Skip following checks if output is not ranked
+ return success();
+
+ const RankedTensorType inputType =
+ llvm::dyn_cast<RankedTensorType>(getInput().getType());
+ const RankedTensorType weightType =
+ llvm::dyn_cast<RankedTensorType>(getWeight().getType());
+
+ if (inputType && weightType) {
+ const int64_t ih = inputType.getDimSize(1);
+ const int64_t kh = weightType.getDimSize(1);
+ const int64_t oh = outputType.getDimSize(1);
+
+ const int64_t pad_top = padding[0];
+ const int64_t pad_bottom = padding[1];
+ const int64_t stride_y = strides[0];
+ const int64_t dilation_y = dilations[0];
+
+ if (ih != ShapedType::kDynamic) {
+ const std::optional<int64_t> calculatedOutHeightMinusOne = idivCheck(
+ (ih - 1) + pad_top + pad_bottom - (kh - 1) * dilation_y, stride_y);
+ if (!calculatedOutHeightMinusOne.has_value())
+ return emitOpError("expected (input_height - 1) + pad_top + pad_bottom "
+ "- (kernel_height - 1) * dilation_y ")
+ << "to be wholly divisible by stride_y, got ((" << ih
+ << " - 1) + " << pad_top << " + " << pad_bottom << " - (" << kh
+ << " - 1) * " << dilation_y << ") / " << stride_y;
+ const int64_t calculatedOutHeight =
+ calculatedOutHeightMinusOne.value() + 1;
+ if (oh != ShapedType::kDynamic && calculatedOutHeight != oh)
+ return emitOpError("calculated output height did not match expected: ")
+ << "calculated=" << calculatedOutHeight << ", expected=" << oh;
+ }
+
+ const int64_t iw = inputType.getDimSize(2);
+ const int64_t kw = weightType.getDimSize(2);
+ const int64_t ow = outputType.getDimSize(2);
+
+ const int64_t pad_left = padding[2];
+ const int64_t pad_right = padding[3];
+ const int64_t stride_x = strides[1];
+ const int64_t dilation_x = dilations[1];
+
+ if (iw != ShapedType::kDynamic) {
+ const std::optional<int64_t> calculatedOutWidthMinusOne = idivCheck(
+ (iw - 1) + pad_left + pad_right - (kw - 1) * dilation_x, stride_x);
+ if (!calculatedOutWidthMinusOne.has_value())
+ return emitOpError("expected (input_width - 1) + pad_left + pad_right "
+ "- (kernel_width - 1) * dilation_x ")
+ << "to be wholly divisible by stride_x, got ((" << iw
+ << " - 1) + " << pad_left << " + " << pad_right << " - (" << kw
+ << " - 1) * " << dilation_x << ") / " << stride_x;
+ const int64_t calculatedOutWidth = calculatedOutWidthMinusOne.value() + 1;
+ if (ow != ShapedType::kDynamic && calculatedOutWidth != ow)
+ return emitOpError("calculated output width did not match expected: ")
+ << "calculated=" << calculatedOutWidth << ", expected=" << ow;
+ }
+ }
+
+ const RankedTensorType biasType =
+ llvm::dyn_cast<RankedTensorType>(getBias().getType());
+ if (!biasType)
+ // Skip following checks if bias is not ranked
+ return success();
+
+ const int64_t bc = biasType.getDimSize(0);
+ const int64_t oc = outputType.getDimSize(3);
+ if (bc == ShapedType::kDynamic || oc == ShapedType::kDynamic)
+ // Skip following checks if bc or oc is dynamic dim
+ return success();
+
+ if (bc != oc && bc != 1)
+ return emitOpError(
+ "bias channels expected to be equal to output channels (")
+ << oc << ") or 1, got " << bc;
return success();
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index a524359b49759..0e7ac8655689c 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -465,16 +465,16 @@ func.func @conv2d_scalar_bias_f32(%input: tensor<1x49x42x27xf32>, %weights: tens
func.func @conv2d_i8(%input: tensor<1x49x42x27xi8>, %weights: tensor<28x1x1x27xi8>, %bias: tensor<28xi8>) -> () {
// HWCF: %[[TRANSPOSE_DIMS:.+]] = arith.constant dense<[1, 2, 3, 0]> : tensor<4xi32>
// HWCF: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<28x1x1x27xi8>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<1x1x27x28xi8>) permutation = [1, 2, 3, 0]
- // CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x45x40x28xi32>
- // CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xi8>) outs(%[[INIT]] : tensor<1x45x40x28xi32>) {
+ // CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x49x42x28xi32>
+ // CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xi8>) outs(%[[INIT]] : tensor<1x49x42x28xi32>) {
// CHECK: arith.extsi
// CHECK: linalg.yield
- // CHECK: } -> tensor<1x45x40x28xi32>
- // CHECK: linalg.conv_2d_nhwc_fhwc_q {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %c0_i32, %c0_i32_0 : tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, i32, i32) outs(%[[BROADCAST]] : tensor<1x45x40x28xi32>) -> tensor<1x45x40x28xi32>
- // HWCF: linalg.conv_2d_nhwc_hwcf_q {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[TRANSPOSE]], %c0_i32, %c0_i32_0 : tensor<1x49x42x27xi8>, tensor<1x1x27x28xi8>, i32, i32) outs(%{{[a-zA-Z0-9_]*}} : tensor<1x45x40x28xi32>) -> tensor<1x45x40x28xi32>
+ // CHECK: } -> tensor<1x49x42x28xi32>
+ // CHECK: linalg.conv_2d_nhwc_fhwc_q {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %c0_i32, %c0_i32_0 : tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, i32, i32) outs(%[[BROADCAST]] : tensor<1x49x42x28xi32>) -> tensor<1x49x42x28xi32>
+ // HWCF: linalg.conv_2d_nhwc_hwcf_q {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[TRANSPOSE]], %c0_i32, %c0_i32_0 : tensor<1x49x42x27xi8>, tensor<1x1x27x28xi8>, i32, i32) outs(%{{[a-zA-Z0-9_]*}} : tensor<1x49x42x28xi32>) -> tensor<1x49x42x28xi32>
%zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.conv2d %input, %weights, %bias, %zp, %zp {acc_type = i32, dilation = array<i64: 2, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, tensor<28xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x45x40x28xi32>
+ %0 = tosa.conv2d %input, %weights, %bias, %zp, %zp {acc_type = i32, dilation = array<i64: 2, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, tensor<28xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x49x42x28xi32>
return
}
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index 0e177a076ee7a..3f37c2e9cc6ff 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -201,23 +201,23 @@ func.func @concat_fold_cast(%arg0: tensor<?x1xf32>) -> tensor<?x?xf32> {
// -----
// CHECK-LABEL: @conv2d_stride_2
-func.func @conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>) -> tensor<4x10x10x3xf32> {
+func.func @conv2d_stride_2(%arg0: tensor<4x11x11x2xf32>) -> tensor<4x6x6x3xf32> {
// CHECK: tosa.conv2d
%weight = "tosa.const"() {value = dense<[[[[1.0, 1.0]]], [[[1.0, 1.0]]], [[[1.0, 1.0]]]]> : tensor<3x1x1x2xf32>} : ()-> tensor<3x1x1x2xf32>
%bias = "tosa.const"() {value = dense<0.0> : tensor<3xf32>} : ()-> tensor<3xf32>
- %0 = tosa.conv2d %arg0, %weight, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
- return %0 : tensor<4x10x10x3xf32>
+ %0 = tosa.conv2d %arg0, %weight, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x11x11x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x6x6x3xf32>
+ return %0 : tensor<4x6x6x3xf32>
}
// -----
// CHECK-LABEL: @conv2d_weight_2x2
-func.func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x10x10x1xf32> {
+func.func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x9x9x1xf32> {
// CHECK: tosa.conv2d
%weight = "tosa.const"() {value = dense<[[[[1.0], [1.0]], [[1.0], [1.0]]]]> : tensor<1x2x2x1xf32>} : ()-> tensor<1x2x2x1xf32>
%bias = "tosa.const"() {value = dense<0.0> : tensor<1xf32>} : ()-> tensor<1xf32>
- %0 = tosa.conv2d %arg0, %weight, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x1xf32>, tensor<1x2x2x1xf32>, tensor<1xf32>) -> tensor<4x10x10x1xf32>
- return %0 : tensor<4x10x10x1xf32>
+ %0 = tosa.conv2d %arg0, %weight, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x1xf32>, tensor<1x2x2x1xf32>, tensor<1xf32>) -> tensor<4x9x9x1xf32>
+ return %0 : tensor<4x9x9x1xf32>
}
// -----
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index 1aa8547cb2fdb..e63cbdc08ff10 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -1197,3 +1197,75 @@ func.func @broadcast_resize_bilinear_i8(%arg0 : tensor<3x1x1x7xi8>) -> tensor<3x
return %resize : tensor<3x4x5x7xi32>
}
+
+// -----
+
+func.func @test_conv2d_invalid_padding(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ // expected-error at +1 {{'tosa.conv2d' op expect all padding values to be >= 0, got 0, 0, -1, 0}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, -1, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ return %0 : tensor<1x4x4x8xf32>
+}
+
+// -----
+
+func.func @test_conv2d_invalid_stride(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ // expected-error at +1 {{'tosa.conv2d' op expect all stride values to be >= 1, got 0, 1}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 0, 1>, local_bound = true}
+ : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ return %0 : tensor<1x4x4x8xf32>
+}
+
+// -----
+
+func.func @test_conv2d_invalid_dilation(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ // expected-error at +1 {{'tosa.conv2d' op expect all dilation values to be >= 1, got 1, 0}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 0>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ return %0 : tensor<1x4x4x8xf32>
+}
+
+// -----
+
+func.func @test_conv2d_wholly_divisible_height(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ // expected-error at +1 {{'tosa.conv2d' op expected (input_height - 1) + pad_top + pad_bottom - (kernel_height - 1) * dilation_y to be wholly divisible by stride_y, got ((4 - 1) + 0 + 0 - (1 - 1) * 1) / 2}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 1>, local_bound = true}
+ : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ return %0 : tensor<1x4x4x8xf32>
+}
+
+// -----
+
+func.func @test_conv2d_wholly_divisible_width(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ // expected-error at +1 {{'tosa.conv2d' op expected (input_width - 1) + pad_left + pad_right - (kernel_width - 1) * dilation_x to be wholly divisible by stride_x, got ((4 - 1) + 0 + 0 - (1 - 1) * 1) / 2}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 2>, local_bound = true}
+ : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ return %0 : tensor<1x4x4x8xf32>
+}
+
+// -----
+
+func.func @test_conv2d_unexpected_output_height(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x6x4x8xf32> {
+ // expected-error at +1 {{'tosa.conv2d' op calculated output height did not match expected: calculated=4, expected=6}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x6x4x8xf32>
+ return %0 : tensor<1x6x4x8xf32>
+}
+
+// -----
+
+func.func @test_conv2d_unexpected_output_width(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x6x8xf32> {
+ // expected-error at +1 {{'tosa.conv2d' op calculated output width did not match expected: calculated=4, expected=6}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x6x8xf32>
+ return %0 : tensor<1x4x6x8xf32>
+}
+
+// -----
+
+func.func @test_conv2d_invalid_bias_size(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<7xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ // expected-error at +1 {{'tosa.conv2d' op bias channels expected to be equal to output channels (8) or 1, got 7}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true}
+ : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<7xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+ return %0 : tensor<1x4x4x8xf32>
+}
diff --git a/mlir/test/Dialect/Tosa/level_check.mlir b/mlir/test/Dialect/Tosa/level_check.mlir
index 90c4551564d1e..ccc8119271f59 100644
--- a/mlir/test/Dialect/Tosa/level_check.mlir
+++ b/mlir/test/Dialect/Tosa/level_check.mlir
@@ -226,74 +226,74 @@ func.func @test_avgpool2d_pad_right(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32
// -----
-func.func @test_conv2d_dilation_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_conv2d_dilation_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<*xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: dilation_y * KH <= MAX_KERNEL}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 4097, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<*xf32>
+ return %0 : tensor<*xf32>
}
// -----
-func.func @test_conv2d_dilation_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_conv2d_dilation_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<*xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: dilation_x * KW <= MAX_KERNEL}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 4097>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<*xf32>
+ return %0 : tensor<*xf32>
}
// -----
-func.func @test_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x8225x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 8193, 1, 0, 1>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x8225x32x16xf32>
+ return %0 : tensor<1x8225x32x16xf32>
}
// -----
-func.func @test_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x8224x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: pad <= MAX_KERNEL}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>...
[truncated]
``````````
</details>
https://github.com/llvm/llvm-project/pull/128693
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