[mlir][tosa] Add ERROR_IF checks to TRANSPOSE_CONV2D verifier #133234
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@llvm/pr-subscribers-mlir @llvm/pr-subscribers-mlir-tosa Author: Elen Kalda (ekalda) ChangesThis 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); Patch is 22.08 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/133234.diff 5 Files Affected:
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@+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@+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@+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@+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@+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@+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@+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@+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@+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@+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@+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@+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@+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@+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@+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@+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@+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:...
[truncated]
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Jerry-Ge
reviewed
Mar 27, 2025
| return success(); | ||
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| llvm::ArrayRef<int64_t> padding = getOutPad(); | ||
| const int64_t outPadTop = padding[0]; |
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isn't out_pad is of type i32_t? https://www.mlplatform.org/tosa/tosa_spec.html#_transpose_conv2d
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In the dialect these are currently int64 (see https://github.com/llvm/llvm-project/blob/main/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td#L216), though I agree these should probably be changed to conform to the spec at some point
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Other dialects like Linalg (https://mlir.llvm.org/docs/Dialects/Linalg/#linalgconv_2d_nchw_fchw-linalgconv2dnchwfchwop) is using int64_t for dilation and stride, etc . Should we change the TOSA dialect? That will be a lot of code change.
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I kept it as int64_t for consistency with rest of the dialect, if we want to change it, I think it should be a separate patch that makes the change across all operators.
Jerry-Ge
reviewed
Mar 27, 2025
|
you're also checking the bias, should we also include that information in the commit message and the PR description sections? |
Jerry-Ge
reviewed
Mar 27, 2025
lhutton1
reviewed
Mar 28, 2025
| return success(); | ||
|
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||
| llvm::ArrayRef<int64_t> padding = getOutPad(); | ||
| const int64_t outPadTop = padding[0]; |
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In the dialect these are currently int64 (see https://github.com/llvm/llvm-project/blob/main/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td#L216), though I agree these should probably be changed to conform to the spec at some point
lhutton1
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ekalda
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The bias check ( |
lhutton1
reviewed
Apr 1, 2025
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
Outdated
| const RankedTensorType weightType = | ||
| llvm::dyn_cast<RankedTensorType>(getWeight().getType()); | ||
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| llvm::ArrayRef<int64_t> padding = getOutPad(); |
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Annoying nit, but could this be moved closer to its first use and also be made const?
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
Outdated
| if (failed(checkPadAgainstKernelDim(outPadLeft, kernelWidth, | ||
| "out_pad_left", "KW"))) | ||
| return failure(); | ||
| if (failed(checkPadAgainstKernelDim(outPadRight, kernelWidth, |
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Major nit: could put a newline before this one too.
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 <[email protected]>
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lhutton1
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Apr 2, 2025
| return emitOpError("expect all stride values to be >= 1, got [") | ||
| << strides << "]"; | ||
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| const auto inputType = llvm::dyn_cast<RankedTensorType>(getInput().getType()); |
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nit: I think there's a slight bit of readability value in moving these casts to where they're used
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Hi @lhutton1, you didn't respond to my counter argument for checking The So unless I am missing something, if either |
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Ah apologies, I missed this message. In that case this seems okay - likely the checking in |
lhutton1
approved these changes
Apr 3, 2025
|
@ekalda Congratulations on having your first Pull Request (PR) merged into the LLVM Project! Your changes will be combined with recent changes from other authors, then tested by our build bots. If there is a problem with a build, you may receive a report in an email or a comment on this PR. Please check whether problems have been caused by your change specifically, as the builds can include changes from many authors. It is not uncommon for your change to be included in a build that fails due to someone else's changes, or infrastructure issues. How to do this, and the rest of the post-merge process, is covered in detail here. If your change does cause a problem, it may be reverted, or you can revert it yourself. This is a normal part of LLVM development. You can fix your changes and open a new PR to merge them again. If you don't get any reports, no action is required from you. Your changes are working as expected, well done! |
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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);