[mlir][tosa] Enhance CONV3D & DEPTHWISE_CONV2D verifier #135738
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@llvm/pr-subscribers-mlir @llvm/pr-subscribers-mlir-linalg Author: TatWai Chong (tatwaichong) ChangesVerify the correctness of pad, stride, dilation, and dimension of input/weight/bias/output. Patch is 74.01 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/135738.diff 11 Files Affected:
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 9579d71a2afe9..194c577edd368 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -428,6 +428,152 @@ static LogicalResult verifyConvOpModes(T op) {
return success();
}
+//===----------------------------------------------------------------------===//
+// ERROR_IF functions.
+// ERROR_IF is a predicate that must set an error if the condition holds.
+//===----------------------------------------------------------------------===//
+
+template <typename T>
+static LogicalResult verifyConvOpErrorIf(T op) {
+ llvm::ArrayRef<int64_t> padding = op.getPad();
+ if (llvm::any_of(padding, [](int64_t p) { return p < 0; }))
+ return op.emitOpError("expect all padding values to be >= 0, got ")
+ << padding;
+
+ llvm::ArrayRef<int64_t> strides = op.getStride();
+ if (llvm::any_of(strides, [](int64_t s) { return s < 1; }))
+ return op.emitOpError("expect all stride values to be >= 1, got ")
+ << strides;
+
+ llvm::ArrayRef<int64_t> dilations = op.getDilation();
+ if (llvm::any_of(dilations, [](int64_t d) { return d < 1; }))
+ return op.emitOpError("expect all dilation values to be >= 1, got ")
+ << dilations;
+
+ const RankedTensorType outputType =
+ llvm::dyn_cast<RankedTensorType>(op.getOutput().getType());
+ if (!outputType)
+ // Skip following checks if output is not ranked
+ return success();
+
+ const RankedTensorType inputType =
+ llvm::dyn_cast<RankedTensorType>(op.getInput().getType());
+ const RankedTensorType weightType =
+ llvm::dyn_cast<RankedTensorType>(op.getWeight().getType());
+
+ if (inputType && weightType) {
+ const auto verifyOutputSize =
+ [&op](const int64_t inputSize, const int64_t kernelSize,
+ const int64_t outputSize, const int64_t padBefore,
+ const int64_t padAfter, const int64_t stride,
+ const int64_t dilation, const llvm::StringRef dimName,
+ const llvm::StringRef dimAxis,
+ const llvm::StringRef padBeforeName,
+ const llvm::StringRef padAfterName) -> LogicalResult {
+ if (inputSize == ShapedType::kDynamic ||
+ kernelSize == ShapedType::kDynamic)
+ return success();
+
+ // ERROR_IF: O != idiv_check(I - 1 + pa + pb - (K - 1) * d, s) + 1
+
+ const std::optional<int64_t> calculatedOutSizeMinusOne = idivCheck(
+ inputSize - 1 + padBefore + padAfter - (kernelSize - 1) * dilation,
+ stride);
+ if (!calculatedOutSizeMinusOne.has_value())
+ return op.emitOpError("expected input_")
+ << dimName << " - 1 + pad_" << padBeforeName << " + pad_"
+ << padAfterName << " - (kernel_" << dimName
+ << " - 1) * dilation_" << dimAxis
+ << " to be wholly divisible by stride_" << dimAxis << ", got ("
+ << inputSize << " - 1 + " << padBefore << " + " << padAfter
+ << " - (" << kernelSize << " - 1) * " << dilation << ") / "
+ << stride;
+
+ const int64_t calculatedOutSize = calculatedOutSizeMinusOne.value() + 1;
+ if (outputSize != ShapedType::kDynamic && calculatedOutSize != outputSize)
+ return op.emitOpError("calculated output ")
+ << dimName << " did not match expected: "
+ << "calculated=" << calculatedOutSize
+ << ", expected=" << outputSize;
+
+ return success();
+ };
+
+ /// ERROR_IF: O != idiv_check(I - 1 + p_a + p_b - (K - 1) * d, s) + 1
+
+ // input = [_,IH,IW,_], weight = [_,KH,KW,_], output = [_,OH,OW,_]
+ if constexpr (std::is_same<T, tosa::Conv2DOp>::value) {
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(1), weightType.getDimSize(1),
+ outputType.getDimSize(1), padding[0], padding[1], strides[0],
+ dilations[0], "height", "y", "top", "bottom")))
+ return failure();
+
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(2), weightType.getDimSize(2),
+ outputType.getDimSize(2), padding[2], padding[3], strides[1],
+ dilations[1], "width", "x", "left", "right")))
+ return failure();
+ }
+
+ // input = [_,IH,IW,_], weight = [KH,KW,_,_], output = [_,OH,OW,_]
+ if constexpr (std::is_same<T, tosa::DepthwiseConv2DOp>::value) {
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(1), weightType.getDimSize(0),
+ outputType.getDimSize(1), padding[0], padding[1], strides[0],
+ dilations[0], "height", "y", "top", "bottom")))
+ return failure();
+
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(2), weightType.getDimSize(1),
+ outputType.getDimSize(2), padding[2], padding[3], strides[1],
+ dilations[1], "width", "x", "left", "right")))
+ return failure();
+ }
+
+ // input = [_,ID,IH,IW,_], weight = [_,KD,KH,KW,_], output = [_,OD,OH,OW,_]
+ if constexpr (std::is_same<T, tosa::Conv3DOp>::value) {
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(1), weightType.getDimSize(1),
+ outputType.getDimSize(1), padding[0], padding[1], strides[0],
+ dilations[0], "depth", "d", "front", "back")))
+ return failure();
+
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(2), weightType.getDimSize(2),
+ outputType.getDimSize(2), padding[2], padding[3], strides[1],
+ dilations[1], "height", "y", "top", "bottom")))
+ return failure();
+
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(3), weightType.getDimSize(3),
+ outputType.getDimSize(3), padding[4], padding[5], strides[2],
+ dilations[2], "width", "x", "left", "right")))
+ return failure();
+ }
+ }
+
+ const RankedTensorType biasType =
+ llvm::dyn_cast<RankedTensorType>(op.getBias().getType());
+ if (!biasType)
+ // Skip following checks if bias is not ranked
+ return success();
+
+ const int64_t biasChannels = biasType.getDimSize(0);
+ const int64_t outputChannels = outputType.getDimSize(3);
+ if (biasChannels == ShapedType::kDynamic ||
+ outputChannels == ShapedType::kDynamic)
+ // Skip following checks if biasChannels or outputChannels is dynamic dim
+ return success();
+
+ if (biasChannels != outputChannels && biasChannels != 1)
+ return op.emitOpError(
+ "bias channels expected to be equal to output channels (")
+ << outputChannels << ") or 1, got " << biasChannels;
+
+ return success();
+}
+
// verify that inType and outType have same element types
template <typename T>
static LogicalResult verifySameElementTypes(T op, Type inType, Type outType) {
@@ -2570,99 +2716,9 @@ LogicalResult Conv2DOp::inferReturnTypeComponents(
}
LogicalResult Conv2DOp::verify() {
- if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
+ if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed() ||
+ verifyConvOpErrorIf(*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 auto verifyOutputSize =
- [this](const int64_t inputSize, const int64_t kernelSize,
- const int64_t outputSize, const int64_t padBefore,
- const int64_t padAfter, const int64_t stride,
- const int64_t dilation, const llvm::StringRef dimName,
- const llvm::StringRef dimAxis,
- const llvm::StringRef padBeforeName,
- const llvm::StringRef padAfterName) -> LogicalResult {
- if (inputSize == ShapedType::kDynamic ||
- kernelSize == ShapedType::kDynamic)
- return success();
-
- const std::optional<int64_t> calculatedOutSizeMinusOne = idivCheck(
- inputSize - 1 + padBefore + padAfter - (kernelSize - 1) * dilation,
- stride);
- if (!calculatedOutSizeMinusOne.has_value())
- return emitOpError("expected input_")
- << dimName << " - 1 + pad_" << padBeforeName << " + pad_"
- << padAfterName << " - (kernel_" << dimName
- << " - 1) * dilation_" << dimAxis
- << " to be wholly divisible by stride_" << dimAxis << ", got ("
- << inputSize << " - 1 + " << padBefore << " + " << padAfter
- << " - (" << kernelSize << " - 1) * " << dilation << ") / "
- << stride;
-
- const int64_t calculatedOutSize = calculatedOutSizeMinusOne.value() + 1;
- if (outputSize != ShapedType::kDynamic && calculatedOutSize != outputSize)
- return emitOpError("calculated output ")
- << dimName << " did not match expected: "
- << "calculated=" << calculatedOutSize
- << ", expected=" << outputSize;
-
- return success();
- };
-
- if (failed(verifyOutputSize(
- inputType.getDimSize(1), weightType.getDimSize(1),
- outputType.getDimSize(1), padding[0], padding[1], strides[0],
- dilations[0], "height", "y", "top", "bottom")))
- return failure();
-
- if (failed(verifyOutputSize(
- inputType.getDimSize(2), weightType.getDimSize(2),
- outputType.getDimSize(2), padding[2], padding[3], strides[1],
- dilations[1], "width", "x", "left", "right")))
- return failure();
- }
-
- const RankedTensorType biasType =
- llvm::dyn_cast<RankedTensorType>(getBias().getType());
- if (!biasType)
- // Skip following checks if bias is not ranked
- return success();
-
- const int64_t biasChannels = biasType.getDimSize(0);
- const int64_t outputChannels = outputType.getDimSize(3);
- if (biasChannels == ShapedType::kDynamic ||
- outputChannels == ShapedType::kDynamic)
- // Skip following checks if biasChannels or outputChannels is dynamic dim
- return success();
-
- if (biasChannels != outputChannels && biasChannels != 1)
- return emitOpError(
- "bias channels expected to be equal to output channels (")
- << outputChannels << ") or 1, got " << biasChannels;
return success();
}
@@ -2737,7 +2793,8 @@ LogicalResult Conv3DOp::inferReturnTypeComponents(
}
LogicalResult Conv3DOp::verify() {
- if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
+ if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed() ||
+ verifyConvOpErrorIf(*this).failed())
return failure();
return success();
}
@@ -2847,7 +2904,8 @@ LogicalResult DepthwiseConv2DOp::inferReturnTypeComponents(
}
LogicalResult DepthwiseConv2DOp::verify() {
- if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
+ if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed() ||
+ verifyConvOpErrorIf(*this).failed())
return failure();
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 242772fe5cdcf..a737a8a05bae6 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -878,22 +878,22 @@ func.func @depthwise_conv2d_f16_f32_acc(%arg0 : tensor<1x7x5x3xf16>, %arg1 : ten
// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
// CHECK-LABEL: @conv3d_f32
-func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4x5x27xf32>, %bias: tensor<28xf32>) -> () {
- // CHECK-DAG: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<28x3x4x5x27xf32>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x28xf32>) permutation = [1, 2, 3, 4, 0]
- // CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xf32>
+func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<43x3x4x5x27xf32>, %bias: tensor<43xf32>) -> () {
+ // CHECK-DAG: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<43x3x4x5x27xf32>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x43xf32>) permutation = [1, 2, 3, 4, 0]
+ // CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x43xf32>
// CHECK: %[[BROADCAST:.+]] = linalg.generic
// CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
- // CHECK-SAME: ins(%arg2 : tensor<28xf32>) outs(%[[INIT]] : tensor<1x47x45x43x28xf32>) {
+ // CHECK-SAME: ins(%arg2 : tensor<43xf32>) outs(%[[INIT]] : tensor<1x47x45x43x43xf32>) {
// CHECK: ^bb0(%[[IN:.+]]: f32, %[[OUT:.+]]: f32):
// CHECK: linalg.yield %[[IN]] : f32
- // CHECK: } -> tensor<1x47x45x43x28xf32>
+ // CHECK: } -> tensor<1x47x45x43x43xf32>
// CHECK: linalg.conv_3d_ndhwc_dhwcf
// CHECK-SAME: {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
- // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x48x47x27xf32>, tensor<3x4x5x27x28xf32>)
- // CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf32>
+ // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x48x47x27xf32>, tensor<3x4x5x27x43xf32>)
+ // CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf32>, tensor<28x3x4x5x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x28xf32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf32>, tensor<43x3x4x5x27xf32>, tensor<43xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x43xf32>
return
}
@@ -919,40 +919,40 @@ func.func @conv3d_scalar_bias_f32(%input: tensor<1x49x48x47x27xf32>, %weights: t
// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
// CHECK-LABEL: @conv3d_i8
-func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<28x3x4x5x27xi8>, %bias: tensor<28xi32>) -> () {
- // CHECK-DAG: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<28x3x4x5x27xi8>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x28xi8>) permutation = [1, 2, 3, 4, 0]
- // CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xi32>
+func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<43x3x4x5x27xi8>, %bias: tensor<43xi32>) -> () {
+ // CHECK-DAG: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<43x3x4x5x27xi8>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x43xi8>) permutation = [1, 2, 3, 4, 0]
+ // CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x43xi32>
// CHECK: %[[BROADCAST:.+]] = linalg.generic
// CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
- // CHECK-SAME: ins(%arg2 : tensor<28xi32>)
- // CHECK-SAME: outs(%[[INIT]] : tensor<1x47x45x43x28xi32>) {
+ // CHECK-SAME: ins(%arg2 : tensor<43xi32>)
+ // CHECK-SAME: outs(%[[INIT]] : tensor<1x47x45x43x43xi32>) {
// CHECK: ^bb0(%[[IN:.+]]: i32, %[[OUT:.+]]: i32):
// CHECK: linalg.yield %[[IN]] : i32
- // CHECK: } -> tensor<1x47x45x43x28xi32>
+ // CHECK: } -> tensor<1x47x45x43x43xi32>
// CHECK: %[[IZP:.+]] = arith.constant -128 : i32
// CHECK: %[[FZP:.+]] = arith.constant 42 : i32
// CHECK: linalg.conv_3d_ndhwc_dhwcf_q
// CHECK-SAME: {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
- // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]], %[[IZP]], %[[FZP]] : tensor<1x49x48x47x27xi8>, tensor<3x4x5x27x28xi8>, i32, i32)
- // CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x28xi32>) -> tensor<1x47x45x43x28xi32>
+ // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]], %[[IZP]], %[[FZP]] : tensor<1x49x48x47x27xi8>, tensor<3x4x5x27x43xi8>, i32, i32)
+ // CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x43xi32>) -> tensor<1x47x45x43x43xi32>
%input_zp = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xi8>, tensor<28x3x4x5x27xi8>, tensor<28xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x47x45x43x28xi32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xi8>, tensor<43x3x4x5x27xi8>, tensor<43xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x47x45x43x43xi32>
return
}
// -----
// CHECK-LABEL: @conv3d_f16_f32_acc
-func.func @conv3d_f16_f32_acc(%input: tensor<1x49x48x47x27xf16>, %weights: tensor<28x3x4x5x27xf16>, %bias: tensor<28xf16>) -> () {
+func.func @conv3d_f16_f32_acc(%input: tensor<1x49x48x47x27xf16>, %weights: tensor<43x3x4x5x27xf16>, %bias: tensor<43xf16>) -> () {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- // CHECK: linalg.generic {{{.*}}} ins(%{{.*}} : tensor<28xf16>) outs(%{{.*}} : tensor<1x47x45x43x28xf32>)
+ // CHECK: linalg.generic {{{.*}}} ins(%{{.*}} : tensor<43xf16>) outs(%{{.*}} : tensor<1x47x45x43x43xf32>)
// CHECK: arith.extf %{{.*}} : f16 to f32
- // CHECK: %[[CONV:.*]] = linalg.conv_3d_ndhwc_dhwcf {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x48x47x27xf16>, tensor<3x4x5x27x28xf16>) outs(%{{.*}} : tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf32>
- // CHECK: tosa.cast %[[CONV]] : (tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf16>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf16>, tensor<28x3x4x5x27xf16>, tensor<28xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x47x45x43x28xf16>
+ // CHECK: %[[CONV:.*]] = linalg.conv_3d_ndhwc_dhwcf {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x48x47x27xf16>, tensor<3x4x5x27x43xf16>) outs(%{{.*}} : tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf32>
+ // CHECK: tosa.cast %[[CONV]] : (tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf16>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf16>, tensor<43x3x4x5x27xf16>, tensor<43xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x47x45x43x43xf16>
return
}
diff --git a/mlir/test/Dialect/Tosa/availability.mlir b/mlir/test/Dialect/Tosa/availability.mlir
index 75126a11ac504..7374cfd1145b9 100644
--- a/mlir/test/Dialect/...
[truncated]
|
|
@llvm/pr-subscribers-mlir-tosa Author: TatWai Chong (tatwaichong) ChangesVerify the correctness of pad, stride, dilation, and dimension of input/weight/bias/output. Patch is 74.01 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/135738.diff 11 Files Affected:
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 9579d71a2afe9..194c577edd368 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -428,6 +428,152 @@ static LogicalResult verifyConvOpModes(T op) {
return success();
}
+//===----------------------------------------------------------------------===//
+// ERROR_IF functions.
+// ERROR_IF is a predicate that must set an error if the condition holds.
+//===----------------------------------------------------------------------===//
+
+template <typename T>
+static LogicalResult verifyConvOpErrorIf(T op) {
+ llvm::ArrayRef<int64_t> padding = op.getPad();
+ if (llvm::any_of(padding, [](int64_t p) { return p < 0; }))
+ return op.emitOpError("expect all padding values to be >= 0, got ")
+ << padding;
+
+ llvm::ArrayRef<int64_t> strides = op.getStride();
+ if (llvm::any_of(strides, [](int64_t s) { return s < 1; }))
+ return op.emitOpError("expect all stride values to be >= 1, got ")
+ << strides;
+
+ llvm::ArrayRef<int64_t> dilations = op.getDilation();
+ if (llvm::any_of(dilations, [](int64_t d) { return d < 1; }))
+ return op.emitOpError("expect all dilation values to be >= 1, got ")
+ << dilations;
+
+ const RankedTensorType outputType =
+ llvm::dyn_cast<RankedTensorType>(op.getOutput().getType());
+ if (!outputType)
+ // Skip following checks if output is not ranked
+ return success();
+
+ const RankedTensorType inputType =
+ llvm::dyn_cast<RankedTensorType>(op.getInput().getType());
+ const RankedTensorType weightType =
+ llvm::dyn_cast<RankedTensorType>(op.getWeight().getType());
+
+ if (inputType && weightType) {
+ const auto verifyOutputSize =
+ [&op](const int64_t inputSize, const int64_t kernelSize,
+ const int64_t outputSize, const int64_t padBefore,
+ const int64_t padAfter, const int64_t stride,
+ const int64_t dilation, const llvm::StringRef dimName,
+ const llvm::StringRef dimAxis,
+ const llvm::StringRef padBeforeName,
+ const llvm::StringRef padAfterName) -> LogicalResult {
+ if (inputSize == ShapedType::kDynamic ||
+ kernelSize == ShapedType::kDynamic)
+ return success();
+
+ // ERROR_IF: O != idiv_check(I - 1 + pa + pb - (K - 1) * d, s) + 1
+
+ const std::optional<int64_t> calculatedOutSizeMinusOne = idivCheck(
+ inputSize - 1 + padBefore + padAfter - (kernelSize - 1) * dilation,
+ stride);
+ if (!calculatedOutSizeMinusOne.has_value())
+ return op.emitOpError("expected input_")
+ << dimName << " - 1 + pad_" << padBeforeName << " + pad_"
+ << padAfterName << " - (kernel_" << dimName
+ << " - 1) * dilation_" << dimAxis
+ << " to be wholly divisible by stride_" << dimAxis << ", got ("
+ << inputSize << " - 1 + " << padBefore << " + " << padAfter
+ << " - (" << kernelSize << " - 1) * " << dilation << ") / "
+ << stride;
+
+ const int64_t calculatedOutSize = calculatedOutSizeMinusOne.value() + 1;
+ if (outputSize != ShapedType::kDynamic && calculatedOutSize != outputSize)
+ return op.emitOpError("calculated output ")
+ << dimName << " did not match expected: "
+ << "calculated=" << calculatedOutSize
+ << ", expected=" << outputSize;
+
+ return success();
+ };
+
+ /// ERROR_IF: O != idiv_check(I - 1 + p_a + p_b - (K - 1) * d, s) + 1
+
+ // input = [_,IH,IW,_], weight = [_,KH,KW,_], output = [_,OH,OW,_]
+ if constexpr (std::is_same<T, tosa::Conv2DOp>::value) {
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(1), weightType.getDimSize(1),
+ outputType.getDimSize(1), padding[0], padding[1], strides[0],
+ dilations[0], "height", "y", "top", "bottom")))
+ return failure();
+
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(2), weightType.getDimSize(2),
+ outputType.getDimSize(2), padding[2], padding[3], strides[1],
+ dilations[1], "width", "x", "left", "right")))
+ return failure();
+ }
+
+ // input = [_,IH,IW,_], weight = [KH,KW,_,_], output = [_,OH,OW,_]
+ if constexpr (std::is_same<T, tosa::DepthwiseConv2DOp>::value) {
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(1), weightType.getDimSize(0),
+ outputType.getDimSize(1), padding[0], padding[1], strides[0],
+ dilations[0], "height", "y", "top", "bottom")))
+ return failure();
+
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(2), weightType.getDimSize(1),
+ outputType.getDimSize(2), padding[2], padding[3], strides[1],
+ dilations[1], "width", "x", "left", "right")))
+ return failure();
+ }
+
+ // input = [_,ID,IH,IW,_], weight = [_,KD,KH,KW,_], output = [_,OD,OH,OW,_]
+ if constexpr (std::is_same<T, tosa::Conv3DOp>::value) {
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(1), weightType.getDimSize(1),
+ outputType.getDimSize(1), padding[0], padding[1], strides[0],
+ dilations[0], "depth", "d", "front", "back")))
+ return failure();
+
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(2), weightType.getDimSize(2),
+ outputType.getDimSize(2), padding[2], padding[3], strides[1],
+ dilations[1], "height", "y", "top", "bottom")))
+ return failure();
+
+ if (failed(verifyOutputSize(
+ inputType.getDimSize(3), weightType.getDimSize(3),
+ outputType.getDimSize(3), padding[4], padding[5], strides[2],
+ dilations[2], "width", "x", "left", "right")))
+ return failure();
+ }
+ }
+
+ const RankedTensorType biasType =
+ llvm::dyn_cast<RankedTensorType>(op.getBias().getType());
+ if (!biasType)
+ // Skip following checks if bias is not ranked
+ return success();
+
+ const int64_t biasChannels = biasType.getDimSize(0);
+ const int64_t outputChannels = outputType.getDimSize(3);
+ if (biasChannels == ShapedType::kDynamic ||
+ outputChannels == ShapedType::kDynamic)
+ // Skip following checks if biasChannels or outputChannels is dynamic dim
+ return success();
+
+ if (biasChannels != outputChannels && biasChannels != 1)
+ return op.emitOpError(
+ "bias channels expected to be equal to output channels (")
+ << outputChannels << ") or 1, got " << biasChannels;
+
+ return success();
+}
+
// verify that inType and outType have same element types
template <typename T>
static LogicalResult verifySameElementTypes(T op, Type inType, Type outType) {
@@ -2570,99 +2716,9 @@ LogicalResult Conv2DOp::inferReturnTypeComponents(
}
LogicalResult Conv2DOp::verify() {
- if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
+ if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed() ||
+ verifyConvOpErrorIf(*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 auto verifyOutputSize =
- [this](const int64_t inputSize, const int64_t kernelSize,
- const int64_t outputSize, const int64_t padBefore,
- const int64_t padAfter, const int64_t stride,
- const int64_t dilation, const llvm::StringRef dimName,
- const llvm::StringRef dimAxis,
- const llvm::StringRef padBeforeName,
- const llvm::StringRef padAfterName) -> LogicalResult {
- if (inputSize == ShapedType::kDynamic ||
- kernelSize == ShapedType::kDynamic)
- return success();
-
- const std::optional<int64_t> calculatedOutSizeMinusOne = idivCheck(
- inputSize - 1 + padBefore + padAfter - (kernelSize - 1) * dilation,
- stride);
- if (!calculatedOutSizeMinusOne.has_value())
- return emitOpError("expected input_")
- << dimName << " - 1 + pad_" << padBeforeName << " + pad_"
- << padAfterName << " - (kernel_" << dimName
- << " - 1) * dilation_" << dimAxis
- << " to be wholly divisible by stride_" << dimAxis << ", got ("
- << inputSize << " - 1 + " << padBefore << " + " << padAfter
- << " - (" << kernelSize << " - 1) * " << dilation << ") / "
- << stride;
-
- const int64_t calculatedOutSize = calculatedOutSizeMinusOne.value() + 1;
- if (outputSize != ShapedType::kDynamic && calculatedOutSize != outputSize)
- return emitOpError("calculated output ")
- << dimName << " did not match expected: "
- << "calculated=" << calculatedOutSize
- << ", expected=" << outputSize;
-
- return success();
- };
-
- if (failed(verifyOutputSize(
- inputType.getDimSize(1), weightType.getDimSize(1),
- outputType.getDimSize(1), padding[0], padding[1], strides[0],
- dilations[0], "height", "y", "top", "bottom")))
- return failure();
-
- if (failed(verifyOutputSize(
- inputType.getDimSize(2), weightType.getDimSize(2),
- outputType.getDimSize(2), padding[2], padding[3], strides[1],
- dilations[1], "width", "x", "left", "right")))
- return failure();
- }
-
- const RankedTensorType biasType =
- llvm::dyn_cast<RankedTensorType>(getBias().getType());
- if (!biasType)
- // Skip following checks if bias is not ranked
- return success();
-
- const int64_t biasChannels = biasType.getDimSize(0);
- const int64_t outputChannels = outputType.getDimSize(3);
- if (biasChannels == ShapedType::kDynamic ||
- outputChannels == ShapedType::kDynamic)
- // Skip following checks if biasChannels or outputChannels is dynamic dim
- return success();
-
- if (biasChannels != outputChannels && biasChannels != 1)
- return emitOpError(
- "bias channels expected to be equal to output channels (")
- << outputChannels << ") or 1, got " << biasChannels;
return success();
}
@@ -2737,7 +2793,8 @@ LogicalResult Conv3DOp::inferReturnTypeComponents(
}
LogicalResult Conv3DOp::verify() {
- if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
+ if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed() ||
+ verifyConvOpErrorIf(*this).failed())
return failure();
return success();
}
@@ -2847,7 +2904,8 @@ LogicalResult DepthwiseConv2DOp::inferReturnTypeComponents(
}
LogicalResult DepthwiseConv2DOp::verify() {
- if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
+ if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed() ||
+ verifyConvOpErrorIf(*this).failed())
return failure();
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 242772fe5cdcf..a737a8a05bae6 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -878,22 +878,22 @@ func.func @depthwise_conv2d_f16_f32_acc(%arg0 : tensor<1x7x5x3xf16>, %arg1 : ten
// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
// CHECK-LABEL: @conv3d_f32
-func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4x5x27xf32>, %bias: tensor<28xf32>) -> () {
- // CHECK-DAG: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<28x3x4x5x27xf32>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x28xf32>) permutation = [1, 2, 3, 4, 0]
- // CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xf32>
+func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<43x3x4x5x27xf32>, %bias: tensor<43xf32>) -> () {
+ // CHECK-DAG: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<43x3x4x5x27xf32>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x43xf32>) permutation = [1, 2, 3, 4, 0]
+ // CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x43xf32>
// CHECK: %[[BROADCAST:.+]] = linalg.generic
// CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
- // CHECK-SAME: ins(%arg2 : tensor<28xf32>) outs(%[[INIT]] : tensor<1x47x45x43x28xf32>) {
+ // CHECK-SAME: ins(%arg2 : tensor<43xf32>) outs(%[[INIT]] : tensor<1x47x45x43x43xf32>) {
// CHECK: ^bb0(%[[IN:.+]]: f32, %[[OUT:.+]]: f32):
// CHECK: linalg.yield %[[IN]] : f32
- // CHECK: } -> tensor<1x47x45x43x28xf32>
+ // CHECK: } -> tensor<1x47x45x43x43xf32>
// CHECK: linalg.conv_3d_ndhwc_dhwcf
// CHECK-SAME: {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
- // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x48x47x27xf32>, tensor<3x4x5x27x28xf32>)
- // CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf32>
+ // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x48x47x27xf32>, tensor<3x4x5x27x43xf32>)
+ // CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf32>
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf32>, tensor<28x3x4x5x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x28xf32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf32>, tensor<43x3x4x5x27xf32>, tensor<43xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x43xf32>
return
}
@@ -919,40 +919,40 @@ func.func @conv3d_scalar_bias_f32(%input: tensor<1x49x48x47x27xf32>, %weights: t
// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
// CHECK-LABEL: @conv3d_i8
-func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<28x3x4x5x27xi8>, %bias: tensor<28xi32>) -> () {
- // CHECK-DAG: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<28x3x4x5x27xi8>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x28xi8>) permutation = [1, 2, 3, 4, 0]
- // CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xi32>
+func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<43x3x4x5x27xi8>, %bias: tensor<43xi32>) -> () {
+ // CHECK-DAG: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<43x3x4x5x27xi8>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x43xi8>) permutation = [1, 2, 3, 4, 0]
+ // CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x43xi32>
// CHECK: %[[BROADCAST:.+]] = linalg.generic
// CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
- // CHECK-SAME: ins(%arg2 : tensor<28xi32>)
- // CHECK-SAME: outs(%[[INIT]] : tensor<1x47x45x43x28xi32>) {
+ // CHECK-SAME: ins(%arg2 : tensor<43xi32>)
+ // CHECK-SAME: outs(%[[INIT]] : tensor<1x47x45x43x43xi32>) {
// CHECK: ^bb0(%[[IN:.+]]: i32, %[[OUT:.+]]: i32):
// CHECK: linalg.yield %[[IN]] : i32
- // CHECK: } -> tensor<1x47x45x43x28xi32>
+ // CHECK: } -> tensor<1x47x45x43x43xi32>
// CHECK: %[[IZP:.+]] = arith.constant -128 : i32
// CHECK: %[[FZP:.+]] = arith.constant 42 : i32
// CHECK: linalg.conv_3d_ndhwc_dhwcf_q
// CHECK-SAME: {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
- // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]], %[[IZP]], %[[FZP]] : tensor<1x49x48x47x27xi8>, tensor<3x4x5x27x28xi8>, i32, i32)
- // CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x28xi32>) -> tensor<1x47x45x43x28xi32>
+ // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]], %[[IZP]], %[[FZP]] : tensor<1x49x48x47x27xi8>, tensor<3x4x5x27x43xi8>, i32, i32)
+ // CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x43xi32>) -> tensor<1x47x45x43x43xi32>
%input_zp = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
%weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xi8>, tensor<28x3x4x5x27xi8>, tensor<28xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x47x45x43x28xi32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xi8>, tensor<43x3x4x5x27xi8>, tensor<43xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x47x45x43x43xi32>
return
}
// -----
// CHECK-LABEL: @conv3d_f16_f32_acc
-func.func @conv3d_f16_f32_acc(%input: tensor<1x49x48x47x27xf16>, %weights: tensor<28x3x4x5x27xf16>, %bias: tensor<28xf16>) -> () {
+func.func @conv3d_f16_f32_acc(%input: tensor<1x49x48x47x27xf16>, %weights: tensor<43x3x4x5x27xf16>, %bias: tensor<43xf16>) -> () {
%input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
%weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
- // CHECK: linalg.generic {{{.*}}} ins(%{{.*}} : tensor<28xf16>) outs(%{{.*}} : tensor<1x47x45x43x28xf32>)
+ // CHECK: linalg.generic {{{.*}}} ins(%{{.*}} : tensor<43xf16>) outs(%{{.*}} : tensor<1x47x45x43x43xf32>)
// CHECK: arith.extf %{{.*}} : f16 to f32
- // CHECK: %[[CONV:.*]] = linalg.conv_3d_ndhwc_dhwcf {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x48x47x27xf16>, tensor<3x4x5x27x28xf16>) outs(%{{.*}} : tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf32>
- // CHECK: tosa.cast %[[CONV]] : (tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf16>
- %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf16>, tensor<28x3x4x5x27xf16>, tensor<28xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x47x45x43x28xf16>
+ // CHECK: %[[CONV:.*]] = linalg.conv_3d_ndhwc_dhwcf {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x48x47x27xf16>, tensor<3x4x5x27x43xf16>) outs(%{{.*}} : tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf32>
+ // CHECK: tosa.cast %[[CONV]] : (tensor<1x47x45x43x43xf32>) -> tensor<1x47x45x43x43xf16>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf16>, tensor<43x3x4x5x27xf16>, tensor<43xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x47x45x43x43xf16>
return
}
diff --git a/mlir/test/Dialect/Tosa/availability.mlir b/mlir/test/Dialect/Tosa/availability.mlir
index 75126a11ac504..7374cfd1145b9 100644
--- a/mlir/test/Dialect/...
[truncated]
|
|
Wait for #135219 to be merged. Then move the tests in this PR to the new |
|
Hi @tatwaichong, can you resolve the merge conflicts? |
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Thanks for the rebase! Overall looks good after the 1st pass. Could you also mention the code refactoring for Conv2d checks in the commit message and PR title? |
|
Need another rebase to resolve merge conflicts. |
Verify the pad, stride, dilation, and dimension of input/output.
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lhutton1
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Verify the correctness of pad, stride, dilation, and dimension of input/weight/bias/output. Adapt and extend the existing conv2d error_if function to support additional convolution variants.
IanWood1
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May 6, 2025
Verify the correctness of pad, stride, dilation, and dimension of input/weight/bias/output. Adapt and extend the existing conv2d error_if function to support additional convolution variants.
IanWood1
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May 6, 2025
Verify the correctness of pad, stride, dilation, and dimension of input/weight/bias/output. Adapt and extend the existing conv2d error_if function to support additional convolution variants.
lhutton1
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Jun 11, 2025
Verify the correctness of pad, stride, dilation, and dimension of input/weight/bias/output. Adapt and extend the existing conv2d error_if function to support additional convolution variants. (cherry-picked from commit e2a9902) Change-Id: Ic3bf041c3da8f8abbe8798b49948a811d63e187e
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Verify the correctness of pad, stride, dilation, and dimension of input/weight/bias/output.
Adapt and extend the existing conv2d error_if function to support additional convolution variants.