[Mlir-commits] [mlir] [MLIR][TOSA] Add ERROR_IF checks to TRANSPOSE_CONV2D verifer (PR #133234)
Elen Kalda
llvmlistbot at llvm.org
Thu Mar 27 04:12:17 PDT 2025
https://github.com/ekalda created https://github.com/llvm/llvm-project/pull/133234
This patch extends the verifier with following checks:
ERROR_IF(out_pad_top <= -KH || out_pad_bottom <= -KH); ERROR_IF(out_pad_left <= -KW || out_pad_right <= -KW); ERROR_IF(stride_y < 1 || stride_x < 1);
ERROR_IF(OH != (IH - 1) * stride_y + out_pad_top + out_pad_bottom + KH); ERROR_IF(OW != (IW - 1) * stride_x + out_pad_left + out_pad_right + KW); ERROR_IF(BC != OC && BC != 1);
>From 62394d2905a39500d874ec1fe1ed3c91560db236 Mon Sep 17 00:00:00 2001
From: Elen Kalda <elen.kalda at arm.com>
Date: Fri, 21 Mar 2025 16:51:48 +0000
Subject: [PATCH] [TOSA] Add ERROR_IF checks to TRANSPOSE_CONV2D verifer
This patch extends the verifier with following checks:
ERROR_IF(out_pad_top <= -KH || out_pad_bottom <= -KH);
ERROR_IF(out_pad_left <= -KW || out_pad_right <= -KW);
ERROR_IF(stride_y < 1 || stride_x < 1);
ERROR_IF(OH != (IH - 1) * stride_y + out_pad_top + out_pad_bottom + KH);
ERROR_IF(OW != (IW - 1) * stride_x + out_pad_left + out_pad_right + KW);
ERROR_IF(BC != OC && BC != 1);
Signed-off-by: Elen Kalda <elen.kalda at arm.com>
---
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp | 97 +++++++++++++++++++
mlir/test/Dialect/Tosa/invalid.mlir | 72 ++++++++++++++
mlir/test/Dialect/Tosa/invalid_extension.mlir | 6 +-
mlir/test/Dialect/Tosa/level_check.mlir | 48 ++++-----
mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir | 4 +-
5 files changed, 198 insertions(+), 29 deletions(-)
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index cdba332792eb0..e113a0193a05e 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -2896,6 +2896,103 @@ LogicalResult TransposeConv2DOp::inferReturnTypeComponents(
LogicalResult TransposeConv2DOp::verify() {
if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
return failure();
+
+ const RankedTensorType weightType =
+ llvm::dyn_cast<RankedTensorType>(getWeight().getType());
+ if (!weightType)
+ return success();
+
+ const int64_t kernelHeight = weightType.getDimSize(1);
+ const int64_t kernelWidth = weightType.getDimSize(2);
+
+ // Skip further checks if kernel dimensions are dynamic
+ if (kernelHeight == ShapedType::kDynamic ||
+ kernelWidth == ShapedType::kDynamic)
+ return success();
+
+ llvm::ArrayRef<int64_t> padding = getOutPad();
+ const int64_t outPadTop = padding[0];
+ const int64_t outPadBottom = padding[1];
+ const int64_t outPadLeft = padding[2];
+ const int64_t outPadRight = padding[3];
+
+ if (outPadTop <= -kernelHeight)
+ return emitOpError("Expected out_pad_top > -KH, but got: out_pad_top=")
+ << outPadTop << " and KH=" << kernelHeight;
+ if (outPadBottom <= -kernelHeight)
+ return emitOpError(
+ "Expected out_pad_bottom > -KH, but got: out_pad_bottom=")
+ << outPadBottom << " and KH=" << kernelHeight;
+ if (outPadLeft <= -kernelWidth)
+ return emitOpError("Expected out_pad_left > -KW, but got: out_pad_left=")
+ << outPadLeft << " and KW=" << kernelWidth;
+ if (outPadRight <= -kernelWidth)
+ return emitOpError("Expected out_pad_right > -KW, but got: out_pad_right=")
+ << outPadRight << " and KW=" << kernelWidth;
+
+ llvm::ArrayRef<int64_t> strides = getStride();
+ const int64_t strideY = strides[0];
+ const int64_t strideX = strides[1];
+
+ if (strideY < 1 || strideX < 1)
+ return emitOpError("expect all stride values to be >= 1, got [")
+ << strides << "]";
+
+ const RankedTensorType inputType =
+ llvm::dyn_cast<RankedTensorType>(getInput().getType());
+
+ const RankedTensorType outputType =
+ llvm::dyn_cast<RankedTensorType>(getOutput().getType());
+
+ if (!inputType || !outputType)
+ return success();
+
+ const int64_t inputHeight = inputType.getDimSize(1);
+ const int64_t inputWidth = inputType.getDimSize(2);
+ const int64_t outputHeight = outputType.getDimSize(1);
+ const int64_t outputWidth = outputType.getDimSize(2);
+
+ // Skip further checks if the input or output dimensions are dynamic
+ if (inputHeight == ShapedType::kDynamic ||
+ inputWidth == ShapedType::kDynamic ||
+ outputHeight == ShapedType::kDynamic ||
+ outputWidth == ShapedType::kDynamic)
+ return success();
+
+ if (outputHeight !=
+ (inputHeight - 1) * strideY + outPadTop + outPadBottom + kernelHeight)
+ return emitOpError("dimension mismatch: expected OH == (IH - 1) * stride_y "
+ "+ out_pad_top + out_pad_bottom + KH, but got ")
+ << outputHeight << " != (" << inputHeight << " - 1) * " << strideY
+ << " + " << outPadTop << " + " << outPadBottom << " + "
+ << kernelHeight;
+
+ if (outputWidth !=
+ (inputWidth - 1) * strideX + outPadLeft + outPadRight + kernelWidth)
+ return emitOpError("dimension mismatch: expected OW == (IW - 1) * stride_x "
+ "+ out_pad_left + out_pad_right + KW, but got ")
+ << outputWidth << " != (" << inputWidth << " - 1) * " << strideX
+ << " + " << outPadLeft << " + " << outPadRight << " + "
+ << kernelWidth;
+
+ const RankedTensorType biasType =
+ llvm::dyn_cast<RankedTensorType>(getBias().getType());
+
+ if (!biasType)
+ return success();
+
+ const int64_t biasChannels = biasType.getDimSize(0);
+
+ // Skip further checks if bias is dynamic
+ if (biasChannels == ShapedType::kDynamic)
+ return success();
+
+ const int64_t outputChannels = outputType.getDimSize(3);
+ if (biasChannels != outputChannels && biasChannels != 1)
+ return emitOpError(
+ "bias channels expected to be equal to output channels (")
+ << outputChannels << ") or 1, got " << biasChannels;
+
return success();
}
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index ac8a247da24a7..de29fe9a7250c 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -172,6 +172,78 @@ func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xi8>, %arg1: tensor<16x1
return %0 : tensor<1x32x32x16xi8>
}
+// -----
+
+func.func @test_transpose_conv2d_invalid_padding_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ // expected-error at +1 {{'tosa.transpose_conv2d' op Expected out_pad_top > -KH, but got: out_pad_top=-3 and KH=1}}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: -3, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_padding_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ // expected-error at +1 {{'tosa.transpose_conv2d' op Expected out_pad_bottom > -KH, but got: out_pad_bottom=-1 and KH=1}}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, -1, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_padding_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ // expected-error at +1 {{'tosa.transpose_conv2d' op Expected out_pad_left > -KW, but got: out_pad_left=-8 and KW=1}}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, -8, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_padding_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ // expected-error at +1 {{'tosa.transpose_conv2d' op Expected out_pad_right > -KW, but got: out_pad_right=-9 and KW=1}}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, -9>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ // expected-error at +1 {{'tosa.transpose_conv2d' op expect all stride values to be >= 1, got [0, 1]}}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 0, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ // expected-error at +1 {{'tosa.transpose_conv2d' op expect all stride values to be >= 1, got [1, 0]}}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 0>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_output_height(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x33x32x16xf32> {
+ // expected-error at +1 {{'tosa.transpose_conv2d' op dimension mismatch: expected OH == (IH - 1) * stride_y + out_pad_top + out_pad_bottom + KH, but got 33 != (32 - 1) * 1 + 0 + 0 + 1}}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 33, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x33x32x16xf32>
+ return %0 : tensor<1x33x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_output_width(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x40x16xf32> {
+ // expected-error at +1 {{'tosa.transpose_conv2d' op dimension mismatch: expected OW == (IW - 1) * stride_x + out_pad_left + out_pad_right + KW, but got 40 != (32 - 1) * 1 + 0 + 0 + 1}}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 40, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x40x16xf32>
+ return %0 : tensor<1x32x40x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_bias(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ // expected-error at +1 {{'tosa.transpose_conv2d' op bias channels expected to be equal to output channels (16) or 1, got 5}}
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ return %0 : tensor<1x32x32x16xf32>
+}
+
// -----
// CHECK-LABEL: conv2d_quant_any_acc
func.func @test_conv2d_quant_any_acc(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %arg1: tensor<8x1x1x4x!quant.any<i8<-8:7>>>, %arg2: tensor<8x!quant.any<i8<-8:7>>>) -> tensor<1x4x4x8x!quant.any<i8<-8:7>>> {
diff --git a/mlir/test/Dialect/Tosa/invalid_extension.mlir b/mlir/test/Dialect/Tosa/invalid_extension.mlir
index d1594232e4e1d..dd3d114218309 100644
--- a/mlir/test/Dialect/Tosa/invalid_extension.mlir
+++ b/mlir/test/Dialect/Tosa/invalid_extension.mlir
@@ -165,11 +165,11 @@ func.func @test_depthwise_conv2d_non_const_input_zp(%arg0: tensor<1x4x4x4xi8>, %
// -----
-func.func @test_transpose_conv2d_non_const_weight_zp(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<1x1x4x2xi8>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>) -> tensor<1x4x4x8xi32> {
+func.func @test_transpose_conv2d_non_const_weight_zp(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<1x1x4x2xi8>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>) -> tensor<1x4x7x8xi32> {
%input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.transpose_conv2d' op expected compile time resolvable constant, but got variable value for operand #4}}
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %arg3 {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
- return %0 : tensor<1x4x4x8xi32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %arg3 {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x7x8xi32>
+ return %0 : tensor<1x4x7x8xi32>
}
// -----
diff --git a/mlir/test/Dialect/Tosa/level_check.mlir b/mlir/test/Dialect/Tosa/level_check.mlir
index 0f469761d89e3..12addcd315449 100644
--- a/mlir/test/Dialect/Tosa/level_check.mlir
+++ b/mlir/test/Dialect/Tosa/level_check.mlir
@@ -887,74 +887,74 @@ func.func @test_rfft2d_input_w(%arg0: tensor<13x8x16384xf32>) -> (tensor<13x8x81
// -----
-func.func @test_transpose_conv2d_weight_h(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x8193x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_transpose_conv2d_weight_h(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x8193x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8224x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: KH <= MAX_KERNEL}}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x8193x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x8193x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8224x32x16xf32>
+ return %0 : tensor<1x8224x32x16xf32>
}
// -----
-func.func @test_transpose_conv2d_weight_w(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x8193x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_transpose_conv2d_weight_w(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x8193x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x8224x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: KW <= MAX_KERNEL}}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x1x8193x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x1x8193x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8224x16xf32>
+ return %0 : tensor<1x32x8224x16xf32>
}
// -----
-func.func @test_transpose_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_transpose_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8225x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 8193, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8225x32x16xf32>
+ return %0 : tensor<1x8225x32x16xf32>
}
// -----
-func.func @test_transpose_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_transpose_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x8225x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 8193, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x8225x32x16xf32>
+ return %0 : tensor<1x8225x32x16xf32>
}
// -----
-func.func @test_transpose_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_transpose_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x8225x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 8193, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8225x16xf32>
+ return %0 : tensor<1x32x8225x16xf32>
}
// -----
-func.func @test_transpose_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_transpose_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x8225x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 8193>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x8225x16xf32>
+ return %0 : tensor<1x32x8225x16xf32>
}
// -----
-func.func @test_transpose_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_transpose_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x253984x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: stride <= MAX_STRIDE}}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 8193, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x253984x32x16xf32>
+ return %0 : tensor<1x253984x32x16xf32>
}
// -----
-func.func @test_transpose_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_transpose_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x253984x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: stride <= MAX_STRIDE}}
%0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 8193>} :
- (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
- return %0 : tensor<1x32x32x16xf32>
+ (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x253984x16xf32>
+ return %0 : tensor<1x32x253984x16xf32>
}
// -----
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
index 037d51dccd1cd..761e489bdeae5 100644
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -994,8 +994,8 @@ func.func @transpose_conv2d_dynamic_bias(%arg0: tensor<2x6x4x3xf32>, %arg1: tens
// CHECK-LABEL: @transpose_conv2d_padded
func.func @transpose_conv2d_padded(%arg0: tensor<2x9x11x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
- // CHECK: -> tensor<2x10x13x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 1, 0, 3, 0>, stride = array<i64: 1, 1>} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x10x13x5xf32>
+ // CHECK: -> tensor<2x12x19x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 1, 0, 3, 0>, stride = array<i64: 1, 1>} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x12x19x5xf32>
return
}
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