[Mlir-commits] [mlir] [mlir][tosa] Change zero points of convolution ops to required inputs (PR #127679)
Tai Ly
llvmlistbot at llvm.org
Wed Feb 19 08:46:21 PST 2025
https://github.com/Tai78641 updated https://github.com/llvm/llvm-project/pull/127679
>From 129a1c6e9a5fe377deff3a8c705b27cb6560672f Mon Sep 17 00:00:00 2001
From: Tai Ly <tai.ly at arm.com>
Date: Fri, 14 Feb 2025 03:01:20 +0000
Subject: [PATCH] [mlir][tosa] Change zero points of convolution ops to
required inputs
This patch changes the input_zp and weight_zp for convolution
operators to be required inputs.
Convolution operators affected are:
CONV2D, CONV3D, DEPTHWISE_CONV2D, and TRANSPOSE_CONV2D.
Signed-off-by: Tai Ly <tai.ly at arm.com>
Change-Id: I7aa6e05580c83c617394c3f7fd18fba8c3f3b0ec
---
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h | 106 ---------
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td | 48 +++-
.../TosaToLinalg/TosaToLinalgNamed.cpp | 63 +++--
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp | 160 +++++++++----
.../Transforms/TosaDecomposeDepthwise.cpp | 24 +-
.../Transforms/TosaDecomposeTransposeConv.cpp | 54 ++---
.../TosaToLinalg/tosa-to-linalg-named.mlir | 93 +++++---
mlir/test/Dialect/Tosa/canonicalize.mlir | 16 +-
mlir/test/Dialect/Tosa/invalid.mlir | 69 ++++--
mlir/test/Dialect/Tosa/level_check.mlir | 225 +++++++++---------
mlir/test/Dialect/Tosa/ops.mlir | 33 +--
.../Tosa/tosa-decompose-depthwise.mlir | 8 +-
.../Tosa/tosa-decompose-transpose-conv.mlir | 12 +-
mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir | 122 +++++-----
.../Dialect/Tosa/tosa-reduce-transposes.mlir | 8 +-
15 files changed, 560 insertions(+), 481 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
index 069073bc2d164..c18b46c9474fc 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
@@ -161,112 +161,6 @@ namespace tosa {
std::optional<Value> createZeroPointTensor(OpBuilder &builder, Location loc,
Type srcElemType, int64_t zp = 0);
-// Get zero point value from the attribute argument.
-LogicalResult getZeroPoint(ElementsAttr zpAttr, int64_t &zp);
-
-// Verify if zero point falls into valid range.
-template <typename T>
-LogicalResult verifyZeroPoint(Type zpElemType, int64_t zp) {
- if constexpr (!std::is_same_v<T, Conv2DOp> && !std::is_same_v<T, Conv3DOp> &&
- !std::is_same_v<T, DepthwiseConv2DOp> &&
- !std::is_same_v<T, TransposeConv2DOp>) {
- return failure();
- }
-
- if (!zpElemType.isIntOrFloat())
- return failure();
-
- if (!zpElemType.isInteger(8) && zp != 0)
- return failure();
-
- if (zpElemType.isSignedInteger(8) && (zp < -128 || zp > 127))
- return failure();
-
- if (zpElemType.isUnsignedInteger(8) && (zp < 0 || zp > 255))
- return failure();
-
- return success();
-}
-
-// Helper type trait to determine if an operation is a tosa convolution.
-template <typename Op>
-struct IsTosaConv : std::false_type {};
-
-template <>
-struct IsTosaConv<tosa::Conv2DOp> : std::true_type {};
-template <>
-struct IsTosaConv<tosa::DepthwiseConv2DOp> : std::true_type {};
-template <>
-struct IsTosaConv<tosa::TransposeConv2DOp> : std::true_type {};
-template <>
-struct IsTosaConv<tosa::Conv3DOp> : std::true_type {};
-
-template <typename Op>
-constexpr bool is_tosa_conv_v = IsTosaConv<Op>::value;
-
-// Helper struct to hold the zero points of a TOSA convolution operation as
-// named 64-bit integer fields.
-struct ConvZpPair {
- ConvZpPair(std::int64_t inputZp, std::int64_t weightZp)
- : inputZp(inputZp), weightZp(weightZp) {}
- std::int64_t inputZp;
- std::int64_t weightZp;
-};
-
-// Helper function which attempts to extract the zero points from a TOSA
-// convolution by matching them against defining ops which should be tosa.const
-// operations.
-//
-// There are three possible results:
-// 1. Failed to extract the zero-points i.e. they should exist and don't or they
-// do exist but are invalid.
-// 2. Succeeded in extracting zero-points.
-// 3. Zero points are "empty" and meaningless for this op i.e. non-quantized
-// convolution.
-using FailOrMaybeZP = llvm::FailureOr<std::optional<ConvZpPair>>;
-template <typename TosaConvOp>
-std::enable_if_t<is_tosa_conv_v<TosaConvOp>, FailOrMaybeZP>
-extractConvZpPair(TosaConvOp op, PatternRewriter &rewriter) {
- // Strictly speaking the base TOSA spec requires that for non int8 types
- // zero points must be zero. However, in the dialect these operands are
- // optional and only required for int8. They have no semantic meaning for
- // non-quantized types and can therefore be safely ignored. This is case 3.
- if (auto opElementTY =
- cast<ShapedType>(op->getOperand(0).getType()).getElementType();
- !opElementTY.isInteger(8))
- return FailOrMaybeZP(std::nullopt);
-
- // Now we know we should have a zero point check it is valid.
- if (!op.getInputZp())
- return rewriter.notifyMatchFailure(op, "missing input zero point");
-
- // Helper to extract the zero point by matching its definition against a
- // constant.
- auto extractZeroPoint = [](Value zpValue) -> std::optional<int64_t> {
- ElementsAttr zpAttr;
- if (!matchPattern(zpValue, m_Constant(&zpAttr)))
- return std::nullopt;
-
- int64_t zp;
- if (tosa::getZeroPoint(zpAttr, zp).failed())
- return std::nullopt;
-
- return std::make_optional(zp);
- };
-
- auto maybeInputZp = extractZeroPoint(op.getInputZp());
- if (!maybeInputZp)
- return rewriter.notifyMatchFailure(op, "unable to extract input zp");
-
- if (!op.getWeightZp())
- return rewriter.notifyMatchFailure(op, "missing weight zero point");
-
- auto maybeWeightZp = extractZeroPoint(op.getWeightZp());
- if (!maybeWeightZp)
- return rewriter.notifyMatchFailure(op, "unable to extract weight zp");
-
- return std::make_optional<ConvZpPair>(*maybeInputZp, *maybeWeightZp);
-}
} // namespace tosa
} // namespace mlir
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index 4d5837ca26c91..5c8be02cb44b6 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -105,8 +105,9 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
Tosa_Tensor4D:$input,
TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
@@ -118,6 +119,13 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
Tosa_Tensor4D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
@@ -136,8 +144,9 @@ def Tosa_Conv3DOp : Tosa_ConvOp<"conv3d"> {
Tosa_Tensor5D:$input,
TosaTensorRankOf<[Tosa_Weight], [5]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr6:$pad,
Tosa_IntArrayAttr3:$stride,
Tosa_IntArrayAttr3:$dilation,
@@ -149,6 +158,13 @@ def Tosa_Conv3DOp : Tosa_ConvOp<"conv3d"> {
Tosa_Tensor5D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
@@ -168,8 +184,9 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
Tosa_Tensor4D:$input,
TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
@@ -181,6 +198,13 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
Tosa_Tensor4D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
@@ -330,8 +354,9 @@ def Tosa_TransposeConv2DOp : Tosa_ConvOp<"transpose_conv2d"> {
Tosa_Tensor4D:$input,
TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr4:$out_pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr4:$out_shape,
@@ -343,6 +368,13 @@ def Tosa_TransposeConv2DOp : Tosa_ConvOp<"transpose_conv2d"> {
Tosa_Tensor4D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_TransConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index a8fd536dd2548..bb7d5a23d9365 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -259,11 +259,21 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
DenseI64ArrayAttr strideTosaAttr = op.getStrideAttr();
DenseI64ArrayAttr dilationTosaAttr = op.getDilationAttr();
- auto failureOrMaybeZps = extractConvZpPair(op, rewriter);
- if (llvm::failed(failureOrMaybeZps))
- return failure();
+ // Get and verify zero points.
+ int64_t inputZpVal;
+ int64_t weightZpVal;
+
+ if (op.getInputZeroPoint(inputZpVal).failed() ||
+ op.getWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "bail out if zero points cannot statically be determined");
+
+ if (op.verifyInputZeroPoint(inputZpVal).failed() ||
+ op.verifyWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "zero point must be zero for non-int8 integer types");
- auto maybeZps = failureOrMaybeZps.value();
+ bool hasZp = (inputZpVal != 0) || (weightZpVal != 0);
if (!weightTy.hasStaticShape() || !biasTy.hasStaticShape())
return rewriter.notifyMatchFailure(
@@ -289,7 +299,7 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
// Apply padding as necessary.
TypedAttr zeroAttr = rewriter.getZeroAttr(inputETy);
- if (maybeZps) {
+ if (hasZp) {
int64_t intMin =
APInt::getSignedMinValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
@@ -297,11 +307,11 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
APInt::getSignedMaxValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
- if (maybeZps->inputZp < intMin || maybeZps->inputZp > intMax)
+ if (inputZpVal < intMin || inputZpVal > intMax)
return rewriter.notifyMatchFailure(
op, "tosa.conv op quantization has zp outside of input range");
- zeroAttr = rewriter.getIntegerAttr(inputETy, maybeZps->inputZp);
+ zeroAttr = rewriter.getIntegerAttr(inputETy, inputZpVal);
}
llvm::SmallVector<int64_t> pad;
@@ -314,8 +324,8 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
// For 2D convolutions, we need to check if the target convolution op
// wants a HWCF kernel layout.
bool wantHwcf =
- maybeZps ? std::is_same_v<LinalgConvQOp, linalg::Conv2DNhwcHwcfQOp>
- : std::is_same_v<LinalgConvOp, linalg::Conv2DNhwcHwcfOp>;
+ hasZp ? std::is_same_v<LinalgConvQOp, linalg::Conv2DNhwcHwcfQOp>
+ : std::is_same_v<LinalgConvOp, linalg::Conv2DNhwcHwcfOp>;
if (wantHwcf) {
// Transpose the kernel to match dimension ordering of the linalg
// convolution operation.
@@ -376,9 +386,9 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
Value broadcastBias =
linalgBroadcastAndMaybeExtSI(rewriter, loc, bias, biasEmptyTensor);
- if (maybeZps) {
- auto iZp = rewriter.getI32IntegerAttr(maybeZps->inputZp);
- auto kZp = rewriter.getI32IntegerAttr(maybeZps->weightZp);
+ if (hasZp) {
+ auto iZp = rewriter.getI32IntegerAttr(inputZpVal);
+ auto kZp = rewriter.getI32IntegerAttr(weightZpVal);
auto iZpVal = rewriter.create<arith::ConstantOp>(loc, iZp);
auto kZpVal = rewriter.create<arith::ConstantOp>(loc, kZp);
@@ -441,18 +451,27 @@ class DepthwiseConvConverter
/*inputSizeDims=*/{1, 2},
/*kernelSizeDims=*/{0, 1}, rewriter);
- auto failureOrMaybeZps = extractConvZpPair(op, rewriter);
- if (llvm::failed(failureOrMaybeZps))
- return failure();
+ // Get and verify zero points.
+ int64_t inputZpVal;
+ int64_t weightZpVal;
+
+ if (op.getInputZeroPoint(inputZpVal).failed() ||
+ op.getWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "bail out if zero points cannot statically be determined");
- auto maybeZps = failureOrMaybeZps.value();
+ if (op.verifyInputZeroPoint(inputZpVal).failed() ||
+ op.verifyWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "zero point must be zero for non-int8 integer types");
+ bool hasZp = (inputZpVal != 0) || (weightZpVal != 0);
auto weightShape = weightTy.getShape();
auto resultShape = resultTy.getShape();
// Apply padding as necessary.
TypedAttr zeroAttr = rewriter.getZeroAttr(inputETy);
- if (maybeZps) {
+ if (hasZp) {
int64_t intMin =
APInt::getSignedMinValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
@@ -460,12 +479,12 @@ class DepthwiseConvConverter
APInt::getSignedMaxValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
- if (maybeZps->inputZp < intMin || maybeZps->inputZp > intMax)
+ if (inputZpVal < intMin || inputZpVal > intMax)
return rewriter.notifyMatchFailure(
op, "tosa.depthwise_conv op quantization has zp outside of input "
"range");
- zeroAttr = rewriter.getIntegerAttr(inputETy, maybeZps->inputZp);
+ zeroAttr = rewriter.getIntegerAttr(inputETy, inputZpVal);
}
llvm::SmallVector<int64_t> pad;
@@ -505,7 +524,7 @@ class DepthwiseConvConverter
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
- if (!maybeZps) {
+ if (!hasZp) {
Value conv = rewriter
.create<linalg::DepthwiseConv2DNhwcHwcmOp>(
loc, linalgConvTy, ValueRange{input, weight},
@@ -532,8 +551,8 @@ class DepthwiseConvConverter
.getResult(0);
rewriter.replaceOp(op, result);
} else {
- IntegerAttr iZp = rewriter.getI32IntegerAttr(maybeZps->inputZp);
- IntegerAttr wZp = rewriter.getI32IntegerAttr(maybeZps->weightZp);
+ IntegerAttr iZp = rewriter.getI32IntegerAttr(inputZpVal);
+ IntegerAttr wZp = rewriter.getI32IntegerAttr(weightZpVal);
auto iZpVal = rewriter.create<arith::ConstantOp>(loc, iZp);
auto kZpVal = rewriter.create<arith::ConstantOp>(loc, wZp);
Value conv =
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index d21e218308df7..1d82a71f38220 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -211,6 +211,18 @@ void mlir::tosa::printTypeOrAttr(OpAsmPrinter &p, Operation *op, TypeAttr type,
}
}
+//===----------------------------------------------------------------------===//
+// Tosa utilities.
+//===----------------------------------------------------------------------===//
+
+static Type getStorageElementTypeOrSelf(Type type) {
+ auto elementType = getElementTypeOrSelf(type);
+ if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(elementType))
+ elementType = quantType.getStorageType();
+
+ return elementType;
+}
+
//===----------------------------------------------------------------------===//
// TOSA Operator Verifiers.
//===----------------------------------------------------------------------===//
@@ -243,6 +255,9 @@ static LogicalResult verifyConvOp(T op) {
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(inputEType))
inputEType = quantType.getStorageType();
+ if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(weightEType))
+ weightEType = quantType.getStorageType();
+
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(biasEType))
biasEType = quantType.getStorageType();
@@ -279,37 +294,31 @@ static LogicalResult verifyConvOp(T op) {
return failure();
}
- // We require an explicit input zero point and weight zero point for i8
- // convolution.
- if (!op.getInputZp() && !op.getWeightZp())
- return inputEType.isInteger(8) ? failure() : success();
+ auto inputZpEType = getStorageElementTypeOrSelf(op.getInputZp().getType());
+ if (inputEType != inputZpEType) {
+ return op.emitOpError("expect both input and its zero point are the same "
+ "element type, got ")
+ << inputEType << " and " << inputZpEType;
+ }
- ElementsAttr inputZpAttr;
- ElementsAttr weightZpAttr;
- if (!matchPattern(op.getInputZp(), m_Constant(&inputZpAttr)) ||
- !matchPattern(op.getWeightZp(), m_Constant(&weightZpAttr))) {
- op.emitOpError(
- "bail out if the actual value of zero points cannot be determined");
- return failure();
+ auto weightZpEType = getStorageElementTypeOrSelf(op.getWeightZp().getType());
+ if (weightEType != weightZpEType) {
+ return op.emitOpError("expect both weight and its zero point are the same "
+ "element type, got ")
+ << weightEType << " and " << weightZpEType;
}
- // Get and verify explicit zero points.
int64_t inputZpVal;
- int64_t weightZpVal;
+ if (op.getInputZeroPoint(inputZpVal).succeeded() &&
+ op.verifyInputZeroPoint(inputZpVal).failed())
+ return op.emitOpError(
+ "input zero point must be zero for non-int8 integer types");
- if (tosa::getZeroPoint(inputZpAttr, inputZpVal).failed() ||
- tosa::verifyZeroPoint<T>(getElementTypeOrSelf(inputZpAttr), inputZpVal)
- .failed()) {
- op.emitOpError("input zero point must be zero for non-int8 integer types");
- return failure();
- }
-
- if (tosa::getZeroPoint(weightZpAttr, weightZpVal).failed() ||
- tosa::verifyZeroPoint<T>(getElementTypeOrSelf(weightZpAttr), weightZpVal)
- .failed()) {
- op.emitOpError("weight zero point must be zero for non-int8 integer types");
- return failure();
- }
+ int64_t weightZpVal;
+ if (op.getWeightZeroPoint(weightZpVal).succeeded() &&
+ op.verifyWeightZeroPoint(weightZpVal).failed())
+ return op.emitOpError(
+ "weight zero point must be zero for non-int8 integer types");
return success();
}
@@ -1371,6 +1380,79 @@ llvm::LogicalResult tosa::ReshapeOp::verify() {
return mlir::success();
}
+template <typename T>
+static LogicalResult getZeroPoint(T op, Value val, int64_t &zp) {
+ ElementsAttr zpAttr;
+ if (!matchPattern(val, m_Constant(&zpAttr))) {
+ return failure();
+ }
+
+ Type zpElemType = zpAttr.getElementType();
+ if (auto quantType =
+ llvm::dyn_cast<mlir::quant::UniformQuantizedType>(zpElemType)) {
+ zp = quantType.getZeroPoint();
+ return success();
+ }
+
+ if (llvm::isa<FloatType>(zpElemType)) {
+ if (!zpAttr.getValues<APFloat>()[0].isZero())
+ return op.emitOpError(
+ "non-zero zero point is not allowed for float types");
+ zp = 0;
+ return success();
+ }
+
+ if (llvm::isa<IntegerType>(zpElemType)) {
+ zp = zpAttr.getValues<APInt>()[0].getSExtValue();
+ return success();
+ }
+
+ return op.emitOpError("zero point is not allowed for unsupported types");
+}
+
+template <typename T>
+static LogicalResult verifyZeroPoint(T op, Value val, int64_t &zp) {
+ // TODO clean it up when the entire zero point (attribute -> input tensor
+ // type) change is done. Remaining Matmul, Rescale, Negate, and AvgPool2D.
+ if constexpr (!std::is_same_v<T, Conv2DOp> && !std::is_same_v<T, Conv3DOp> &&
+ !std::is_same_v<T, DepthwiseConv2DOp> &&
+ !std::is_same_v<T, TransposeConv2DOp>)
+ return failure();
+
+ Type zpElemType = getElementTypeOrSelf(val);
+
+ if (!zpElemType.isIntOrFloat())
+ return op.emitOpError("zero point is not integer or float typss");
+
+ if (!zpElemType.isInteger(8) && zp != 0)
+ return op.emitOpError("zero point must be zero for non-int8 integer types");
+
+ if (zp < -128 || zp > 127)
+ return failure();
+
+ return success();
+}
+
+#define ZERO_POINT_HELPER(OP) \
+ LogicalResult tosa::OP::getInputZeroPoint(int64_t &zp) { \
+ return getZeroPoint(*this, getInputZp(), zp); \
+ } \
+ LogicalResult tosa::OP::getWeightZeroPoint(int64_t &zp) { \
+ return getZeroPoint(*this, getWeightZp(), zp); \
+ } \
+ LogicalResult tosa::OP::verifyInputZeroPoint(int64_t zp) { \
+ return verifyZeroPoint(*this, getInputZp(), zp); \
+ } \
+ LogicalResult tosa::OP::verifyWeightZeroPoint(int64_t zp) { \
+ return verifyZeroPoint(*this, getWeightZp(), zp); \
+ }
+
+ZERO_POINT_HELPER(Conv2DOp)
+ZERO_POINT_HELPER(Conv3DOp)
+ZERO_POINT_HELPER(DepthwiseConv2DOp)
+ZERO_POINT_HELPER(TransposeConv2DOp)
+#undef ZERO_POINT_HELPER
+
LogicalResult tosa::TransposeOp::getConstantPerms(SmallVector<int32_t> &perms) {
// Perms must be constants.
DenseIntElementsAttr permsAttr;
@@ -2509,36 +2591,12 @@ void WhileOp::print(OpAsmPrinter &parser) {
parser.printOptionalAttrDictWithKeyword((*this)->getAttrs());
}
-LogicalResult mlir::tosa::getZeroPoint(ElementsAttr zpAttr, int64_t &zp) {
- Type zpElemType = zpAttr.getElementType();
- if (auto quantType =
- llvm::dyn_cast<mlir::quant::UniformQuantizedType>(zpElemType)) {
- zp = quantType.getZeroPoint();
- return success();
- }
- if (llvm::isa<FloatType>(zpElemType)) {
- // non-zero zero point is not allowed for float types.
- if (!zpAttr.getValues<APFloat>()[0].isZero())
- return failure();
- zp = 0;
- return success();
- }
- if (llvm::isa<IntegerType>(zpElemType)) {
- zp = zpAttr.getValues<APInt>()[0].getSExtValue();
- return success();
- }
- // zero point is not allowed for unsupported types.
- return failure();
-}
-
// Create a rank-1 const tensor for zero point of the source tensor.
std::optional<Value> mlir::tosa::createZeroPointTensor(OpBuilder &builder,
Location loc,
Type srcElemType,
int64_t zp) {
- srcElemType = getElementTypeOrSelf(srcElemType);
- if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(srcElemType))
- srcElemType = quantType.getStorageType();
+ srcElemType = getStorageElementTypeOrSelf(srcElemType);
auto zpType = mlir::RankedTensorType::get({1}, srcElemType);
if (llvm::isa<FloatType>(srcElemType)) {
auto zpAttr = DenseElementsAttr::get(
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
index b26397d0e3ed7..fc945928e4908 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
@@ -76,12 +76,22 @@ struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
weight = rewriter.create<tosa::CastOp>(op.getLoc(), weightType, weight);
}
- auto failureOrMaybeZps = extractConvZpPair(op, rewriter);
- if (failed(failureOrMaybeZps))
- return failure();
+ // Get and verify zero points.
+ int64_t iZp;
+ int64_t wZp;
+
+ if (op.getInputZeroPoint(iZp).failed() ||
+ op.getWeightZeroPoint(wZp).failed())
+ return rewriter.notifyMatchFailure(
+ op, "bail out if zero points cannot statically be determined");
+
+ if (op.verifyInputZeroPoint(iZp).failed() ||
+ op.verifyWeightZeroPoint(wZp).failed())
+ return rewriter.notifyMatchFailure(
+ op, "zero point must be zero for non-int8 integer types");
+
+ if (iZp != 0 || wZp != 0) {
- auto maybeZps = failureOrMaybeZps.value();
- if (maybeZps) {
auto applyZp = [&](Value val, int64_t zp) -> Value {
if (zp == 0)
return val;
@@ -96,8 +106,8 @@ struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
zpVal);
};
- input = applyZp(input, maybeZps->inputZp);
- weight = applyZp(weight, maybeZps->weightZp);
+ input = applyZp(input, iZp);
+ weight = applyZp(weight, wZp);
}
ArrayRef<int64_t> padAttr = op.getPad();
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
index 26baddcf1dd15..d34b17e6be8c8 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
@@ -134,16 +134,25 @@ class TransposeConvStridedConverter
Value weightPaddingVal =
getTosaConstShape(rewriter, op->getLoc(), weightPadding);
- auto failureOrMaybeZps = extractConvZpPair(op, rewriter);
- if (failed(failureOrMaybeZps))
- return failure();
+ // Get and verify zero points.
+ int64_t inputZpVal;
+ int64_t weightZpVal;
+
+ if (op.getInputZeroPoint(inputZpVal).failed() ||
+ op.getWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "bail out if zero points cannot statically be determined");
+
+ if (op.verifyInputZeroPoint(inputZpVal).failed() ||
+ op.verifyWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "zero point must be zero for non-int8 integer types");
- auto maybeZps = failureOrMaybeZps.value();
- if (maybeZps) {
+ if (weightZpVal != 0) {
weight = CreateOpAndInferShape<tosa::PadOp>(
rewriter, loc, UnrankedTensorType::get(weightETy), weight,
- weightPaddingVal, nullptr,
- rewriter.getI32IntegerAttr(maybeZps->weightZp));
+ weightPaddingVal, nullptr, rewriter.getI32IntegerAttr(weightZpVal));
+
} else {
weight = CreateOpAndInferShape<tosa::PadOp>(
rewriter, loc, UnrankedTensorType::get(weightETy), weight,
@@ -201,11 +210,10 @@ class TransposeConvStridedConverter
Value inputPaddingVal =
getTosaConstShape(rewriter, op->getLoc(), inputPadding);
- if (maybeZps) {
+ if (inputZpVal != 0) {
input = CreateOpAndInferShape<tosa::PadOp>(
rewriter, loc, UnrankedTensorType::get(inputETy), input,
- inputPaddingVal, nullptr,
- rewriter.getI32IntegerAttr(maybeZps->inputZp));
+ inputPaddingVal, nullptr, rewriter.getI32IntegerAttr(inputZpVal));
} else {
input = CreateOpAndInferShape<tosa::PadOp>(
rewriter, loc, UnrankedTensorType::get(inputETy), input,
@@ -222,28 +230,20 @@ class TransposeConvStridedConverter
biasETy),
rewriter.getZeroAttr(biasETy)));
- Value inputZp, weightZp;
- if (maybeZps) {
- auto maybeInputZp = createZeroPointTensor(
- rewriter, loc, getElementTypeOrSelf(input.getType()),
- maybeZps->inputZp);
- auto maybeWeightZp = createZeroPointTensor(
- rewriter, loc, getElementTypeOrSelf(weight.getType()),
- maybeZps->weightZp);
-
- if (!maybeInputZp.has_value() || !maybeWeightZp.has_value()) {
- return rewriter.notifyMatchFailure(
- op, "fail to create a const zero point tensor");
- }
-
- inputZp = *maybeInputZp;
- weightZp = *maybeWeightZp;
+ auto inputZp =
+ createZeroPointTensor(rewriter, loc, input.getType(), inputZpVal);
+ auto weightZp =
+ createZeroPointTensor(rewriter, loc, weight.getType(), weightZpVal);
+
+ if (!inputZp.has_value() || !weightZp.has_value()) {
+ return rewriter.notifyMatchFailure(
+ op, "fail to create a const zero point tensor");
}
// Perform the convolution using the zero bias.
Value conv2d = CreateOpAndInferShape<tosa::Conv2DOp>(
rewriter, loc, UnrankedTensorType::get(resultETy), input,
- weight, zeroBias, inputZp, weightZp,
+ weight, zeroBias, inputZp.value(), weightZp.value(),
/*pad=*/rewriter.getDenseI64ArrayAttr({0, 0, 0, 0}),
/*stride=*/rewriter.getDenseI64ArrayAttr({1, 1}),
/*dilation=*/rewriter.getDenseI64ArrayAttr({1, 1}),
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index a524359b49759..795acf403ca5a 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -452,7 +452,9 @@ func.func @avg_pool_dyn(%arg0: tensor<?x6x34x62xf32>) -> (tensor<?x5x33x62xf32>)
func.func @conv2d_scalar_bias_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27xf32>, %bias: tensor<1xf32>) -> () {
// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x45x40x28xf32>
// CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<1xf32>) outs(%[[INIT]] : tensor<1x45x40x28xf32>) {
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
return
}
@@ -470,11 +472,12 @@ func.func @conv2d_i8(%input: tensor<1x49x42x27xi8>, %weights: tensor<28x1x1x27xi
// 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: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>) outs(%[[BROADCAST]] : tensor<1x45x40x28xi32>) -> tensor<1x45x40x28xi32>
+ // HWCF: linalg.conv_2d_nhwc_hwcf {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x42x27xi8>, tensor<1x1x27x28xi8>) outs(%{{[a-zA-Z0-9_]*}} : tensor<1x45x40x28xi32>) -> tensor<1x45x40x28xi32>
- %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>
+ %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_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>
return
}
@@ -492,10 +495,12 @@ func.func @conv2d_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27
// CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xf32>) outs(%[[INIT]] : tensor<1x45x40x28xf32>) {
// CHECK: linalg.yield
// CHECK: } -> tensor<1x45x40x28xf32>
- // CHECK: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%1 : tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf32>
+ // CHECK: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%[[BROADCAST]] : tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf32>
// HWCF: linalg.conv_2d_nhwc_hwcf {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x42x27xf32>, tensor<3x3x27x28xf32>) outs(%{{[a-zA-Z0-9_]*}} : tensor<1x45x40x28xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>) -> tensor<1x45x40x28xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
return
}
@@ -513,8 +518,10 @@ func.func @conv2d_dyn(%input: tensor<?x49x42x27xf32>, %weights: tensor<28x3x3x27
// CHECK: ^bb0(%[[IN:.+]]: f32, %{{.+}}: f32):
// CHECK: linalg.yield %[[IN]] : f32
// CHECK: } -> tensor<?x45x40x28xf32>
- // CHECK: %2 = linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<?x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%[[BROADCAST]] : tensor<?x45x40x28xf32>) -> tensor<?x45x40x28xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<?x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>) -> tensor<?x45x40x28xf32>
+ // CHECK: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<?x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%[[BROADCAST]] : tensor<?x45x40x28xf32>) -> tensor<?x45x40x28xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<?x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x45x40x28xf32>
return
}
@@ -568,9 +575,11 @@ func.func @conv2d_dyn_w_h(%input: tensor<1x?x?x27xf32>, %weights: tensor<28x3x3x
// CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xf32>) outs(%[[INIT]] : tensor<1x?x?x28xf32>) {
// CHECK: linalg.yield
// CHECK: } -> tensor<1x?x?x28xf32>
- // CHECK: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x?x?x27xf32>, tensor<28x3x3x27xf32>) outs(%17 : tensor<1x?x?x28xf32>) -> tensor<1x?x?x28xf32>
+ // CHECK: linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x?x?x27xf32>, tensor<28x3x3x27xf32>) outs(%[[BROADCAST]] : tensor<1x?x?x28xf32>) -> tensor<1x?x?x28xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x?x?x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>) -> tensor<1x?x?x28xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x?x?x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x?x?x28xf32>
return
}
@@ -593,7 +602,9 @@ func.func @conv2d_dyn_output(%input: tensor<2x6x5x4xf32>, %weights: tensor<4x3x3
// linalg.yield %[[ADD]] : f32
// } -> tensor<?x4x3x4xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x6x5x4xf32 >, tensor<4x3x3x4xf32>, tensor<4xf32>) -> tensor<?x4x3x4xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x6x5x4xf32>, tensor<4x3x3x4xf32>, tensor<4xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x4x3x4xf32>
return
}
@@ -605,7 +616,10 @@ func.func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28
// CHECK: tensor.pad %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
// CHECK: tensor.yield %[[C0]]
// CHECK: linalg.conv_2d_nhwc_fhwc
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>) -> tensor<1x45x40x28xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>}
+ : (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x45x40x28xf32>
return
}
@@ -617,9 +631,10 @@ func.func @conv2d_quant(%arg0 : tensor<1x12x12x1xi8>, %arg1 : tensor<1024x3x3x1x
// CHECK: tensor.pad %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
// CHECK: tensor.yield %[[C22]]
// CHECK: linalg.conv_2d_nhwc_fhwc_q
- %input_zp = "tosa.const"() {value = dense<-22> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {value = dense<42> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.conv2d %arg0, %arg1, %arg2 , %input_zp, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>} : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x12x12x1024xi32>
+ %input_zp = "tosa.const"() <{value = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>}
+ : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x12x12x1024xi32>
return
}
@@ -641,7 +656,9 @@ func.func @depthwise_conv(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf
// CHECK: [[ADD:%.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
// CHECK: linalg.yield [[ADD]] : f32
// CHECK: } -> tensor<1x5x5x33xf32>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> tensor<1x5x5x33xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
return
}
@@ -657,7 +674,9 @@ func.func @depthwise_conv_scalar_bias(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tenso
// CHECK: [[ADD:%.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
// CHECK: linalg.yield [[ADD]] : f32
// CHECK: } -> tensor<1x5x5x33xf32>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
return
}
@@ -681,7 +700,9 @@ func.func @depthwise_conv_dyn(%arg0 : tensor<?x7x5x3xf32>, %arg1 : tensor<3x1x3x
// CHECK: %[[ADD:.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
// CHECK: linalg.yield %[[ADD]] : f32
// CHECK: } -> tensor<?x5x5x33xf32>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<?x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> tensor<?x5x5x33xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<?x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x5x5x33xf32>
return
}
@@ -703,7 +724,9 @@ func.func @depthwise_conv_strides(%arg0 : tensor<1x11x9x3xf32>, %arg1 : tensor<3
// CHECK: [[ADD:%.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
// CHECK: linalg.yield [[ADD]] : f32
// CHECK: } -> tensor<1x5x5x33xf32>
- %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1> } : (tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> tensor<1x5x5x33xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1> } : (tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
return
}
@@ -731,8 +754,8 @@ func.func @depthwise_conv_quant(%arg0 : tensor<1x12x12x4xi8>, %arg1 : tensor<3x3
// CHECK: [[ADD:%.+]] = arith.addi %[[ARG3]], %[[ARG4]] : i32
// CHECK: linalg.yield [[ADD]] : i32
// CHECK: } -> tensor<1x12x12x512xi32>
- %input_zp = "tosa.const"() {value = dense<-128> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {value = dense<42> : tensor<1xi8>} : () -> tensor<1xi8>
+ %input_zp = "tosa.const"() <{value = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
%0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x12x12x512xi32>
return
}
@@ -757,9 +780,9 @@ func.func @depthwise_conv_quant_dilations(%arg0 : tensor<1x14x14x4xi8>, %arg1 :
// CHECK: [[ADD:%.+]] = arith.addi %[[ARG3]], %[[ARG4]] : i32
// CHECK: linalg.yield [[ADD]] : i32
// CHECK: } -> tensor<1x10x10x512xi32>
- %input_zp = "tosa.const"() {value = dense<-128> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {value = dense<42> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 , %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 2> } : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x10x10x512xi32>
+ %input_zp = "tosa.const"() <{value = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{value = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 2> } : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x10x10x512xi32>
return
}
@@ -775,7 +798,9 @@ func.func @depthwise_conv2d_dyn_w_h(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<3x
// CHECK: } : tensor<2x?x?x3xf32> to tensor<2x?x?x3xf32>
// CHECK: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<[1, 2]> : tensor<2xi64>} ins(%[[PADDED]], %arg1 : tensor<2x?x?x3xf32>, tensor<3x6x3x5xf32>) outs(%{{.*}} : tensor<2x?x?x3x5xf32>) -> tensor<2x?x?x3x5xf32>
// CHECK: %[[COLLAPSED:.+]] = tensor.collapse_shape %[[CONV]] {{\[}}[0], [1], [2], [3, 4]]
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, pad = array<i64: 1, 2, 3, 4>, dilation = array<i64: 2, 1>, stride = array<i64: 1, 2>} : (tensor<2x?x?x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x?x?x15xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 2, 3, 4>, dilation = array<i64: 2, 1>, stride = array<i64: 1, 2>} : (tensor<2x?x?x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x15xf32>
return
}
@@ -791,7 +816,7 @@ func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4
// CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xf32>
// CHECK: %[[BROADCAST:.+]] = linalg.generic
// CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
- // CHECK-SAME: ins(%arg2 : tensor<28xf32>) outs(%1 : tensor<1x47x45x43x28xf32>) {
+ // CHECK-SAME: ins(%arg2 : tensor<28xf32>) outs(%[[INIT]] : tensor<1x47x45x43x28xf32>) {
// CHECK: ^bb0(%[[IN:.+]]: f32, %[[OUT:.+]]: f32):
// CHECK: linalg.yield %[[IN]] : f32
// CHECK: } -> tensor<1x47x45x43x28xf32>
@@ -799,7 +824,9 @@ func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4
// 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>
- %0 = tosa.conv3d %input, %weights, %bias {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<1x47x45x43x28xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = 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>
return
}
@@ -813,7 +840,9 @@ func.func @conv3d_scalar_bias_f32(%input: tensor<1x49x48x47x27xf32>, %weights: t
// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xf32>
// CHECK: %[[BROADCAST:.+]] = linalg.generic
// CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
- %0 = tosa.conv3d %input, %weights, %bias {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<1xf32>) -> tensor<1x47x45x43x28xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = 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<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x47x45x43x28xf32>
return
}
@@ -841,9 +870,9 @@ func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<28x3x4x5
// CHECK-SAME: ins(%arg0, %[[TRANSPOSE]], %[[IZP]], %[[FZP]] : tensor<1x49x48x47x27xi8>, tensor<3x4x5x27x28xi8>, i32, i32)
// CHECK-SAME: outs(%[[BROADCAST]] : tensor<1x47x45x43x28xi32>) -> tensor<1x47x45x43x28xi32>
- %input_zp = "tosa.const"() {value = dense<-128> : tensor<1xi8>} : () -> tensor<1xi8>
- %weight_zp = "tosa.const"() {value = 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>
+ %input_zp = "tosa.const"() <{value = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{value = 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>
return
}
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index 0e177a076ee7a..9fc11ae593480 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -205,7 +205,9 @@ func.func @conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>) -> tensor<4x10x10x3xf32
// 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>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
+ %0 = tosa.conv2d %arg0, %weight, %bias, %input_zp, %weight_zp {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<1xf32>, tensor<1xf32>) -> tensor<4x10x10x3xf32>
return %0 : tensor<4x10x10x3xf32>
}
@@ -216,25 +218,27 @@ func.func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x10x10x1xf
// 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>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : ()-> tensor<1xf32>
+ %0 = tosa.conv2d %arg0, %weight, %bias, %input_zp, %weight_zp {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<1xf32>, tensor<1xf32>) -> tensor<4x10x10x1xf32>
return %0 : tensor<4x10x10x1xf32>
}
// -----
// CHECK-LABEL: @depthwise_conv2d_stride_2
-func.func @depthwise_conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x10x10x6xf32> {
+func.func @depthwise_conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<6xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<4x10x10x6xf32> {
// CHECK: tosa.depthwise_conv2d
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x10x10x6xf32>
return %0 : tensor<4x10x10x6xf32>
}
// -----
// CHECK-LABEL: @depthwise_conv2d_weight_2x2
-func.func @depthwise_conv2d_weight_2x2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<2x2x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x10x10x6xf32> {
+func.func @depthwise_conv2d_weight_2x2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<2x2x2x3xf32>, %arg2: tensor<6xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<4x10x10x6xf32> {
// CHECK: tosa.depthwise_conv2d
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<2x2x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<2x2x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x10x10x6xf32>
return %0 : tensor<4x10x10x6xf32>
}
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index 1307da88d1e64..9515b083996ac 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -24,9 +24,11 @@ func.func @test_const_non_tensor_attr() {
// -----
func.func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
- // expected-error at +1 {{expect both input and weight to be float or not together, got 'f32' and 'i8'}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<1x29x29x4xf32>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
+ %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %weight_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.conv2d' op expect both input and weight to be float or not together, got 'f32' and 'i8'}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x29x29x4xf32>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
@@ -34,7 +36,7 @@ func.func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>,
func.func @test_conv2d(%arg0: tensor<*xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
%zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- // expected-error at +1 {{expect a ranked tensor for input, got <block argument> of type 'tensor<*xi8>' at index: 0}}
+ // expected-error at +1 {{'tosa.conv2d' op expect a ranked tensor for input, got <block argument> of type 'tensor<*xi8>' at index: 0}}
%0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<*xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
@@ -52,6 +54,28 @@ func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2:
// -----
+func.func @test_conv2d_input_zp(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x3x3x4xf16>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
+ %input_zp = "tosa.const"() <{value = dense<-1.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ // expected-error at +1 {{'tosa.conv2d' op non-zero zero point is not allowed for float types}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x29x29x4xf16>, tensor<16x3x3x4xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x27x27x16xf16>
+ return %0 : tensor<1x27x27x16xf16>
+}
+
+// -----
+
+func.func @test_conv2d_weight_zp(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x3x3x4xf16>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %weight_zp = "tosa.const"() <{value = dense<-1.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ // expected-error at +1 {{'tosa.conv2d' op non-zero zero point is not allowed for float types}}
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f16, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x29x29x4xf16>, tensor<16x3x3x4xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x27x27x16xf16>
+ return %0 : tensor<1x27x27x16xf16>
+}
+
+// -----
+
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
%zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for i8 tensor is not i32}}
@@ -74,45 +98,50 @@ func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xi16>, %arg1: tensor<16x3
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf8E5M2>, %arg1: tensor<16x3x3x4xf8E5M2>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
+ %zp = "tosa.const"() {value = dense<0.0> : tensor<1xf8E5M2>} : () -> tensor<1xf8E5M2>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for f8 tensor is not f16}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<1x29x29x4xf8E5M2>, tensor<16x3x3x4xf8E5M2>, tensor<16xf16>) -> tensor<1x27x27x16xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x29x29x4xf8E5M2>, tensor<16x3x3x4xf8E5M2>, tensor<16xf16>, tensor<1xf8E5M2>, tensor<1xf8E5M2>) -> tensor<1x27x27x16xf16>
return %0 : tensor<1x27x27x16xf16>
}
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf8E4M3>, %arg1: tensor<16x3x3x4xf8E4M3>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
+ %zp = "tosa.const"() {value = dense<0.0> : tensor<1xf8E4M3>} : () -> tensor<1xf8E4M3>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for f8 tensor is not f16}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<1x29x29x4xf8E4M3>, tensor<16x3x3x4xf8E4M3>, tensor<16xf16>) -> tensor<1x27x27x16xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x29x29x4xf8E4M3>, tensor<16x3x3x4xf8E4M3>, tensor<16xf16>, tensor<1xf8E4M3>, tensor<1xf8E4M3>) -> tensor<1x27x27x16xf16>
return %0 : tensor<1x27x27x16xf16>
}
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf16>, %arg1: tensor<16x3x3x4xf16>, %arg2: tensor<16xf16>) -> tensor<1x27x27x16xf16> {
+ %zp = "tosa.const"() {value = dense<0.0> : tensor<1xf16>} : () -> tensor<1xf16>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for f16 tensor is not f16/f32}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<1x29x29x4xf16>, tensor<16x3x3x4xf16>, tensor<16xf16>) -> tensor<1x27x27x16xf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x29x29x4xf16>, tensor<16x3x3x4xf16>, tensor<16xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x27x27x16xf16>
return %0 : tensor<1x27x27x16xf16>
}
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xbf16>, %arg1: tensor<16x3x3x4xbf16>, %arg2: tensor<16xbf16>) -> tensor<1x27x27x16xbf16> {
+ %zp = "tosa.const"() {value = dense<0.0> : tensor<1xbf16>} : () -> tensor<1xbf16>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for bf16 tensor is not f32}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<1x29x29x4xbf16>, tensor<16x3x3x4xbf16>, tensor<16xbf16>) -> tensor<1x27x27x16xbf16>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x29x29x4xbf16>, tensor<16x3x3x4xbf16>, tensor<16xbf16>, tensor<1xbf16>, tensor<1xbf16>) -> tensor<1x27x27x16xbf16>
return %0 : tensor<1x27x27x16xbf16>
}
// -----
func.func @test_conv2d_acc_type(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
+ %zp = "tosa.const"() {value = dense<0.0> : tensor<1xf32>} : () -> tensor<1xf32>
// expected-error at +1 {{'tosa.conv2d' op accumulator type for f32 tensor is not f32}}
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<1x29x29x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>) -> tensor<1x27x27x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x29x29x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
}
@@ -540,18 +569,22 @@ func.func @test_const_attribute_type_mismatch() -> tensor<100x100xf32> {
// -----
func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
+ %input_zp = "tosa.const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
+ %weight_zp = "tosa.const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.conv2d' op operand #0 must be 4-d tosa-conformant tensor, but got 'tensor<1x29x0x4xf32>'}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<1x29x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>) -> tensor<1x27x27x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x29x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<i32>, tensor<i32>) -> tensor<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
}
// -----
func.func @test_conv2d_zero_dim_input(%arg0: tensor<1x?x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
+ %input_zp = "tosa.const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
+ %weight_zp = "tosa.const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
// expected-error at +1 {{'tosa.conv2d' op operand #0 must be 4-d tosa-conformant tensor, but got 'tensor<1x?x0x4xf32>'}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
- : (tensor<1x?x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>) -> tensor<1x27x27x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ : (tensor<1x?x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<i32>, tensor<i32>) -> tensor<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
}
diff --git a/mlir/test/Dialect/Tosa/level_check.mlir b/mlir/test/Dialect/Tosa/level_check.mlir
index 6f49195d30e97..02b9736c06468 100644
--- a/mlir/test/Dialect/Tosa/level_check.mlir
+++ b/mlir/test/Dialect/Tosa/level_check.mlir
@@ -227,253 +227,253 @@ 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>, %arg3: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// 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>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {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<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
-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>, %arg3: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// 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>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {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<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
-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>, %arg3: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// 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>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {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<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
-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>, %arg3: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// 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>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>, stride = array<i64: 1, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
-func.func @test_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// 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, 1, 8193, 1>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 8193, 1>, stride = array<i64: 1, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
-func.func @test_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// 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, 1, 0, 8193>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 8193>, stride = array<i64: 1, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
-func.func @test_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
- (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
-func.func @test_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
+func.func @test_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
- (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg3 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
+ (tensor<1x32x32x8xf32>, tensor<16x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_dilation_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_dilation_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: dilation_d * KD <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 4097, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 4097, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_dilation_y(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_dilation_y(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: dilation_y * KH <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 4097, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 4097, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_dilation_x(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_dilation_x(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: dilation_x * KW <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 4097>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 4097>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_pad_d0(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_pad_d0(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 8193, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 8193, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_pad_d1(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_pad_d1(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 8193, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 8193, 0, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_pad_top(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_pad_top(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 8193, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 8193, 1, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_pad_bottom(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_pad_bottom(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 8193, 0, 1>, stride = array<i64: 1, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 8193, 0, 1>, stride = array<i64: 1, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_pad_left(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_pad_left(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 8193, 1>, stride = array<i64: 1, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 8193, 1>, stride = array<i64: 1, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_pad_right(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_pad_right(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 8193>, stride = array<i64: 1, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 8193>, stride = array<i64: 1, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_stride_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_stride_d(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 8193, 1, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 8193, 1, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_stride_y(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_stride_y(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 8193, 1>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 8193, 1>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_conv3d_stride_x(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x1x32x32x16xf32> {
+func.func @test_conv3d_stride_x(%arg0: tensor<1x1x32x32x8xf32>, %arg1: tensor<16x2x2x2x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x1x32x32x16xf32> {
// expected-error at +1 {{'tosa.conv3d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 8193>} :
- (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>) -> tensor<1x1x32x32x16xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 1, 8193>} :
+ (tensor<1x1x32x32x8xf32>, tensor<16x2x2x2x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x1x32x32x16xf32>
return %0 : tensor<1x1x32x32x16xf32>
}
// -----
-func.func @test_depthwise_conv2d_dilation_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
+func.func @test_depthwise_conv2d_dilation_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: dilation_y * KH <= MAX_KERNEL}}
- %0 = "tosa.depthwise_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<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 4097, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
// -----
-func.func @test_depthwise_conv2d_dilation_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
+func.func @test_depthwise_conv2d_dilation_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: dilation_x * KW <= MAX_KERNEL}}
- %0 = "tosa.depthwise_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<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 4097>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
// -----
-func.func @test_depthwise_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
+func.func @test_depthwise_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.depthwise_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<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 8193, 1, 0, 1>, stride = array<i64: 1, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
// -----
-func.func @test_depthwise_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
+func.func @test_depthwise_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 8193, 0, 1>, stride = array<i64: 1, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
// -----
-func.func @test_depthwise_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
+func.func @test_depthwise_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 8193, 1>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 8193, 1>, stride = array<i64: 1, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
// -----
-func.func @test_depthwise_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
+func.func @test_depthwise_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 8193>, stride = array<i64: 1, 1>} :
- (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 8193>, stride = array<i64: 1, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
// -----
-func.func @test_depthwise_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
+func.func @test_depthwise_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
- (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 8193, 1>} :
+ (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
// -----
-func.func @test_depthwise_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>) -> tensor<1x32x32x64xf32> {
+func.func @test_depthwise_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x8xf32>, %arg2: tensor<64xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x64xf32> {
// expected-error at +1 {{'tosa.depthwise_conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
- (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>) -> tensor<1x32x32x64xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 8193>} :
+ (tensor<1x32x32x8xf32>, tensor<2x2x8x8xf32>, tensor<64xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x64xf32>
return %0 : tensor<1x32x32x64xf32>
}
@@ -533,15 +533,6 @@ func.func @test_maxpool2d_kernel_x(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x
// -----
-func.func @test_maxpool2d_stride_y(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
- // expected-error at +1 {{'tosa.max_pool2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.max_pool2d"(%arg0) {kernel = array<i64: 1, 1>, pad = array<i64: 4, 4, 4, 4>, stride = array<i64: 8193, 1>} :
- (tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
- return %0 : tensor<1x32x32x8xf32>
-}
-
-// -----
-
func.func @test_maxpool2d_stride_x(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
// expected-error at +1 {{'tosa.max_pool2d' op failed level check: stride <= MAX_STRIDE}}
%0 = "tosa.max_pool2d"(%arg0) {kernel = array<i64: 1, 1>, pad = array<i64: 4, 4, 4, 4>, stride = array<i64: 1, 8193>} :
@@ -604,73 +595,73 @@ func.func @test_rfft2d_input_w(%arg0: tensor<13x8x8193xf32>) -> (tensor<13x8x9xf
// -----
-func.func @test_transpose_conv2d_weight_h(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x8193x1x8xf32>, %arg2: tensor<16xf32>) -> 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<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: KH <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<1x32x32x16xf32>
+ %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>
}
// -----
-func.func @test_transpose_conv2d_weight_w(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x8193x8xf32>, %arg2: tensor<16xf32>) -> 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<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: KW <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<1x32x32x16xf32>
+ %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>
}
// -----
-func.func @test_transpose_conv2d_pad_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> 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<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<1x32x32x16xf32>
+ %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>
}
// -----
-func.func @test_transpose_conv2d_pad_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> 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<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<1x32x32x16xf32>
+ %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>
}
// -----
-func.func @test_transpose_conv2d_pad_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> 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<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<1x32x32x16xf32>
+ %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>
}
// -----
-func.func @test_transpose_conv2d_pad_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> 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<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: pad <= MAX_KERNEL}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<1x32x32x16xf32>
+ %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>
}
// -----
-func.func @test_transpose_conv2d_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> 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<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<1x32x32x16xf32>
+ %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>
}
// -----
-func.func @test_transpose_conv2d_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> 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<1x32x32x16xf32> {
// expected-error at +1 {{'tosa.transpose_conv2d' op failed level check: stride <= MAX_STRIDE}}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {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<1x32x32x16xf32>
+ %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>
}
diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
index 4a7e48ea702bf..e3e920212a284 100644
--- a/mlir/test/Dialect/Tosa/ops.mlir
+++ b/mlir/test/Dialect/Tosa/ops.mlir
@@ -53,8 +53,8 @@ func.func @test_avg_pool2d_q8(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>
// -----
// CHECK-LABEL: conv2d
-func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {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<1x4x4x8xf32>
+func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ %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<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -79,37 +79,38 @@ func.func @test_conv2d_quant_any(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %a
func.func @test_conv2d_q8xi4(%arg0: tensor<1x11x11x3xi8>) -> tensor<1x1x1x3xi8> {
%0 = "tosa.const"() {value = dense<0> : tensor<3x11x11x3xi4>} : () -> tensor<3x11x11x3xi4>
%1 = "tosa.const"() {value = dense<[12, 23, 55]> : tensor<3xi32>} : () -> tensor<3xi32>
- %zp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
- %2 = "tosa.conv2d"(%arg0, %0, %1, %zp, %zp) {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x11x11x3xi8>, tensor<3x11x11x3xi4>, tensor<3xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x1x1x3xi32>
+ %izp = "tosa.const"() {value = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
+ %wzp = "tosa.const"() {value = dense<0> : tensor<1xi4>} : () -> tensor<1xi4>
+ %2 = "tosa.conv2d"(%arg0, %0, %1, %izp, %wzp) {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x11x11x3xi8>, tensor<3x11x11x3xi4>, tensor<3xi32>, tensor<1xi8>, tensor<1xi4>) -> tensor<1x1x1x3xi32>
%3 = "tosa.rescale"(%2) {double_round = true, input_zp = 0 : i32, multiplier = array<i32: 2026291432, 1079222024, 1693132724>, output_zp = 27 : i32, per_channel = true, scale32 = true, shift = array<i8: 37, 36, 37>} : (tensor<1x1x1x3xi32>) -> tensor<1x1x1x3xi8>
return %3 : tensor<1x1x1x3xi8>
}
// -----
// CHECK-LABEL: conv3d
-func.func @test_conv3d(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<34xf32>) -> tensor<1x4x8x21x34xf32> {
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>) -> tensor<1x4x8x21x34xf32>
+func.func @test_conv3d(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<34xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
// -----
// CHECK-LABEL: conv3d_with_local_bound
-func.func @test_conv3d_with_local_bound(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<34xf32>) -> tensor<1x4x8x21x34xf32> {
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, local_bound = true} : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>) -> tensor<1x4x8x21x34xf32>
+func.func @test_conv3d_with_local_bound(%arg0: tensor<1x4x8x21x17xf32>, %arg1: tensor<34x1x1x1x17xf32>, %arg2: tensor<34xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x8x21x34xf32> {
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, local_bound = true} : (tensor<1x4x8x21x17xf32>, tensor<34x1x1x1x17xf32>, tensor<34xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x8x21x34xf32>
return %0 : tensor<1x4x8x21x34xf32>
}
// -----
// CHECK-LABEL: depthwise_conv2d
-func.func @test_depthwise_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>) -> tensor<1x4x4x8xf32>
+func.func @test_depthwise_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ %0 = tosa.depthwise_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>} : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
// -----
// CHECK-LABEL: depthwise_conv2d_with_local_bound
-func.func @test_depthwise_conv2d_with_local_bound(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {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<1x1x4x2xf32>, tensor<8xf32>) -> tensor<1x4x4x8xf32>
+func.func @test_depthwise_conv2d_with_local_bound(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+ %0 = tosa.depthwise_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<1x1x4x2xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
return %0 : tensor<1x4x4x8xf32>
}
@@ -171,15 +172,15 @@ func.func @test_rfft2d_with_local_bound(%arg0: tensor<13x8x16xf32>) -> (tensor<1
// -----
// CHECK-LABEL: transpose_conv2d
-func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {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<16xf32>) -> tensor<1x32x32x16xf32>
+func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ %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<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
// -----
// CHECK-LABEL: transpose_conv2d_with_local_bound
-func.func @test_transpose_conv2d_with_local_bound(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>, local_bound = false} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
+func.func @test_transpose_conv2d_with_local_bound(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+ %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>, local_bound = false} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
return %0 : tensor<1x32x32x16xf32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
index 6562a7c2ab55c..bbbbe12aeb5f2 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
@@ -22,7 +22,8 @@ func.func @depthwise_conv2d_as_mul(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1
// CHECK: %[[VAR5:.*]] = tosa.add %[[VAR3]], %[[VAR4]]
// CHECK-SAME: -> tensor<4x10x10x6xf32>
// CHECK: return %[[VAR5]]
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
+ %zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x10x10x6xf32>
return %0 : tensor<4x10x10x6xf32>
}
@@ -49,7 +50,7 @@ func.func @depthwise_conv2d_as_mul_q(%arg0: tensor<4x10x10x2xi8>, %arg1: tensor<
// CHECK: %[[add:.+]] = tosa.add %[[reO]], %[[reArg2]]
%input_zp = "tosa.const"() {value = dense<7> : tensor<1xi8>} : () -> tensor<1xi8>
%weight_zp = "tosa.const"() {value = dense<11> : tensor<1xi8>} : () -> tensor<1xi8>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<4x10x10x6xi32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<4x10x10x6xi32>
return %0 : tensor<4x10x10x6xi32>
}
@@ -71,6 +72,7 @@ func.func @depthwise_conv2d_as_mul_padded(%arg0: tensor<4x10x10x2xf32>, %arg1: t
// CHECK: %[[reOut:.+]] = tosa.reshape %[[mul]], %[[CONST4]]
// CHECK: %[[reArg2:.+]] = tosa.reshape %arg2, %[[CONST5]]
// CHECK: %[[add:.+]] = tosa.add %[[reOut]], %[[reArg2]]
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x12x12x6xf32>
+ %zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<4x12x12x6xf32>
return %0 : tensor<4x12x12x6xf32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
index bd18b7ea0fdff..dce7efdaf3526 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
@@ -2,11 +2,13 @@
// CHECK-LABEL: @transpose_conv2d
func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x18x19x5xf32> {
+ // CHECK-DAG: %[[ZP:.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: %[[REV1:.+]] = tosa.reverse %arg1 {axis = 1 : i32}
// CHECK: %[[REV2:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}
- // CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2
+ // CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2, %[[ZP]], %[[ZP]]
// CHECK-SAME: dilation = array<i64: 1, 1>, pad = array<i64: 2, 2, 5, 5>, stride = array<i64: 1, 1>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2{acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x18x19x5xf32>
+ %zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x18x19x5xf32>
return %0 : tensor<2x18x19x5xf32>
}
@@ -72,8 +74,9 @@ func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<
// CHECK-DAG: %[[NEWINPUT:.+]] = tosa.pad %arg0, %[[PAD]]
// Manipulate the final shape.
+ // CHECK-DAG: %[[ZP:.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK-DAG: %[[BIAS:.+]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<30xf32>}
- // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+ // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]], %[[ZP]], %[[ZP]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
// CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {value = dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>}
// CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]
// CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]], %[[TRANS2]]
@@ -83,7 +86,8 @@ func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<
// CHECK-DAG: %[[CONST9:.+]] = tosa.const_shape {value = dense<[1, 1, 1, 5]> : tensor<4xindex>}
// CHECK-DAG: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST9]]
// CHECK: %[[ADD:.+]] = tosa.add %[[SLICE]], %[[RESHAPE_ARG2]]
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2{acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>) -> tensor<2x35x47x5xf32>
+ %zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x35x47x5xf32>
%1 = tensor.cast %0 : tensor<2x35x47x5xf32> to tensor<2x?x?x5xf32>
return %1 : tensor<2x?x?x5xf32>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
index fa590ab495ada..ed2a8d8fe3cb9 100644
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -699,18 +699,18 @@ func.func @test_pool_static(%arg0: tensor<3x5x6x7xf32>) {
// -----
// CHECK-LABEL: @conv2d_static
-func.func @conv2d_static(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
+func.func @conv2d_static(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv2d_dynamic_input
-func.func @conv2d_dynamic_input(%input: tensor<?x?x?x?xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
+func.func @conv2d_dynamic_input(%input: tensor<?x?x?x?xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<?x?x?x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -741,18 +741,18 @@ func.func @test_pool_padded(%arg0: tensor<3x5x6x7xf32>) {
// -----
// CHECK-LABEL: @conv2d_dynamic_weight
-func.func @conv2d_dynamic_weight(%input: tensor<2x8x9x3xf32>, %weights: tensor<?x?x?x?xf32>, %bias: tensor<5xf32>) -> () {
+func.func @conv2d_dynamic_weight(%input: tensor<2x8x9x3xf32>, %weights: tensor<?x?x?x?xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x?x?x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv2d_dynamic_bias
-func.func @conv2d_dynamic_bias(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<?xf32>) -> () {
+func.func @conv2d_dynamic_bias(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<?xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
@@ -771,223 +771,223 @@ func.func @test_pool_stride(%arg0: tensor<3x11x12x7xf32>) {
// -----
// CHECK-LABEL: @conv2d_padded
-func.func @conv2d_padded(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
+func.func @conv2d_padded(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x9x11x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv2d_dilated
-func.func @conv2d_dilated(%input: tensor<2x12x14x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
+func.func @conv2d_dilated(%input: tensor<2x12x14x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x5xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 3, 2>} : (tensor<2x12x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 3, 2>} : (tensor<2x12x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv2d_strided
-func.func @conv2d_strided(%input: tensor<1x13x14x1xf32>, %weights: tensor<1x1x1x1xf32>, %bias: tensor<1xf32>) -> () {
+func.func @conv2d_strided(%input: tensor<1x13x14x1xf32>, %weights: tensor<1x1x1x1xf32>, %bias: tensor<1xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<1x5x7x1xf32>
- %0 = tosa.conv2d %input, %weights, %bias {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>, dilation = array<i64: 1, 1>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>, dilation = array<i64: 1, 1>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv3d_static
-func.func @conv3d_static(%input: tensor<2x8x9x10x3xf32>, %weights: tensor<5x3x6x4x3xf32>, %bias: tensor<5xf32>) -> () {
+func.func @conv3d_static(%input: tensor<2x8x9x10x3xf32>, %weights: tensor<5x3x6x4x3xf32>, %bias: tensor<5xf32>, %input_zp: tensor<1xf32>, %weight_zp: tensor<1xf32>) -> () {
// CHECK: -> tensor<2x6x4x7x5xf32>
- %0 = tosa.conv3d %input, %weights, %bias {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv3d_dynamic_input
-func.func @conv3d_dynamic_input(%arg0: tensor<?x?x?x?x?xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>) {
+func.func @conv3d_dynamic_input(%arg0: tensor<?x?x?x?x?xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<?x?x?x?x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<?x?x?x?x?xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<?x?x?x?x?xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv3d_dynamic_weight
-func.func @conv3d_dynamic_weight(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<?x?x?x?x?xf32>, %arg2: tensor<5xf32>) {
+func.func @conv3d_dynamic_weight(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<?x?x?x?x?xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x?x?x?x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<?x?x?x?x?xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<?x?x?x?x?xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv3d_dynamic_bias
-func.func @conv3d_dynamic_bias(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<?xf32>) {
+func.func @conv3d_dynamic_bias(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<?xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x7x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<?xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv3d_padded
-func.func @conv3d_padded(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>) {
+func.func @conv3d_padded(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x9x11x18x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 2, 3, 4, 5, 6>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 1, 2, 3, 4, 5, 6>, stride = array<i64: 1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv3d_dilated
-func.func @conv3d_dilated(%arg0: tensor<2x12x14x16x3xf32>, %arg1: tensor<5x3x6x2x3xf32>, %arg2: tensor<5xf32>) {
+func.func @conv3d_dilated(%arg0: tensor<2x12x14x16x3xf32>, %arg1: tensor<5x3x6x2x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x12x5xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 3, 2, 4>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x12x14x16x3xf32>, tensor<5x3x6x2x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 3, 2, 4>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>} : (tensor<2x12x14x16x3xf32>, tensor<5x3x6x2x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @conv3d_strided
-func.func @conv3d_strided(%arg0: tensor<1x13x14x15x1xf32>, %arg1: tensor<1x1x1x1x1xf32>, %arg2: tensor<1xf32>) {
+func.func @conv3d_strided(%arg0: tensor<1x13x14x15x1xf32>, %arg1: tensor<1x1x1x1x1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<1x5x7x4x1xf32>
- %0 = tosa.conv3d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 3, 2, 4>} : (tensor<1x13x14x15x1xf32>, tensor<1x1x1x1x1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+ %0 = tosa.conv3d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 3, 2, 4>} : (tensor<1x13x14x15x1xf32>, tensor<1x1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
return
}
// -----
// CHECK-LABEL: @depthwise_conv2d_static
-func.func @depthwise_conv2d_static(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
+func.func @depthwise_conv2d_static(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
+ %0 = tosa.depthwise_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>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x6x4x15xf32>
return
}
// -----
// CHECK-LABEL: @depthwise_conv2d_dynamic_input
-func.func @depthwise_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
+func.func @depthwise_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<?x?x?x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<?x?x?x15xf32>
+ %0 = tosa.depthwise_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>} : (tensor<?x?x?x?xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x15xf32>
return
}
// -----
// CHECK-LABEL: @depthwise_conv2d_dynamic_weight
-func.func @depthwise_conv2d_dynamic_weight(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<15xf32>) {
+func.func @depthwise_conv2d_dynamic_weight(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x?x?x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<15xf32>) -> tensor<2x?x?x15xf32>
+ %0 = tosa.depthwise_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>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x15xf32>
return
}
// -----
// CHECK-LABEL: @depthwise_conv2d_dynamic_bias
-func.func @depthwise_conv2d_dynamic_bias(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<?xf32>) {
+func.func @depthwise_conv2d_dynamic_bias(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<?xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<?xf32>) -> tensor<2x6x4x15xf32>
+ %0 = tosa.depthwise_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>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x6x4x15xf32>
return
}
// -----
// CHECK-LABEL: @depthwise_conv2d_padded
-func.func @depthwise_conv2d_padded(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
+func.func @depthwise_conv2d_padded(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x9x11x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x9x11x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 2, 3, 4>, stride = array<i64: 1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x9x11x15xf32>
return
}
// -----
// CHECK-LABEL: @depthwise_conv2d_dilated
-func.func @depthwise_conv2d_dilated(%arg0: tensor<2x12x14x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
+func.func @depthwise_conv2d_dilated(%arg0: tensor<2x12x14x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x6x4x15xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 3, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x12x14x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 3, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x12x14x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x6x4x15xf32>
return
}
// -----
// CHECK-LABEL: @depthwise_conv2d_strided
-func.func @depthwise_conv2d_strided(%arg0: tensor<1x13x14x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>) {
+func.func @depthwise_conv2d_strided(%arg0: tensor<1x13x14x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<1x5x7x1xf32>
- %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x5x7x1xf32>
+ %0 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 3, 2>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x7x1xf32>
return
}
// -----
// CHECK-LABEL: @transpose_conv2d_out_shape
-func.func @transpose_conv2d_out_shape(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
+func.func @transpose_conv2d_out_shape(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x8x9x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, 8, 9, -1>, stride = array<i64: 1, 1>} : (tensor<2x?x?x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x8x9x5xf32>
+ %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, 8, 9, -1>, stride = array<i64: 1, 1>} : (tensor<2x?x?x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x8x9x5xf32>
return
}
// -----
// CHECK-LABEL: @transpose_conv2d_static
-func.func @transpose_conv2d_static(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
+func.func @transpose_conv2d_static(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x18x19x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
+ %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, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x5xf32>
return
}
// -----
// CHECK-LABEL: @transpose_conv2d_static_strided
-func.func @transpose_conv2d_static_strided(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
+func.func @transpose_conv2d_static_strided(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x33x45x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 2, 3>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
+ %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, -1, -1, -1>, stride = array<i64: 2, 3>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x5xf32>
return
}
// -----
// CHECK-LABEL: @transpose_conv2d_dynamic_input
-func.func @transpose_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
+func.func @transpose_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<?x?x?x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x5xf32>
+ %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, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?x?x5xf32>
return
}
// -----
// CHECK-LABEL: @transpose_conv2d_dynamic_weights
-func.func @transpose_conv2d_dynamic_weights(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<5xf32>) {
+func.func @transpose_conv2d_dynamic_weights(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x?x?x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x6x4x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
+ %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, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x6x4x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x?x?x5xf32>
return
}
// -----
// CHECK-LABEL: @transpose_conv2d_dynamic_bias
-func.func @transpose_conv2d_dynamic_bias(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<?xf32>) {
+func.func @transpose_conv2d_dynamic_bias(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<?xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<2x8x9x5xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>) -> tensor<2x8x9x5xf32>
+ %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, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x8x9x5xf32>
return
}
// -----
// CHECK-LABEL: @transpose_conv2d_padded
-func.func @transpose_conv2d_padded(%arg0: tensor<2x9x11x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
+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 {acc_type = f32, out_pad = array<i64: 1, 0, 3, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x10x13x5xf32>
+ %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 1, 0, 3, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 1, 1>} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x10x13x5xf32>
return
}
// CHECK-LABEL: @transpose_conv2d_strided
-func.func @transpose_conv2d_strided(%arg0: tensor<1x5x7x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>) {
+func.func @transpose_conv2d_strided(%arg0: tensor<1x5x7x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) {
// CHECK: -> tensor<1x13x13x1xf32>
- %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: -1, -1, -1, -1>, stride = array<i64: 3, 2>} : (tensor<1x5x7x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x13x13x1xf32>
+ %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, -1, -1, -1>, stride = array<i64: 3, 2>} : (tensor<1x5x7x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x13x13x1xf32>
return
}
@@ -1416,10 +1416,10 @@ func.func @test_non_tosa_consumer_still_propagates(%arg0: tensor<1x1x8xf32>, %ar
// -----
// CHECK-LABEL: test_tosa_use_def_chain
-func.func @test_tosa_use_def_chain(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>) -> tensor<?x16x16x16xf32> {
+func.func @test_tosa_use_def_chain(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<?x16x16x16xf32> {
// CHECK: [[CONV:%.+]] = tosa.conv2d %arg0, %arg1, %arg2
- // CHECK: (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
- %0 = tosa.conv2d %arg0, %arg1, %arg2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>} : (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<?x32x32x16xf32>
+ // CHECK: (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+ %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>} : (tensor<1x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x32x32x16xf32>
// CHECK: tosa.max_pool2d [[CONV]]
// CHECK: (tensor<1x32x32x16xf32>) -> tensor<1x16x16x16xf32>
%1 = tosa.max_pool2d %0 {kernel = array<i64: 2, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>} : (tensor<?x32x32x16xf32>) -> tensor<?x16x16x16xf32>
diff --git a/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir b/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
index 3a293009a5455..f890aef9739db 100644
--- a/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
@@ -94,6 +94,8 @@ func.func @test_transpose_tracks_to_nullifying__converging_binary(%arg0: tensor<
// CHECK: %[[RES:.*]] = tosa.transpose %[[CONV3]]
// CHECK: return %[[RES]]
func.func @test_torch_conv2d_with_elementwise_in_between(%arg0: tensor<3x3x10x10xf32>) -> tensor<3x3x7x7xf32> {
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %weight_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
%0 = "tosa.const"() <{value = dense_resource<torch_tensor_3_torch.float32_2> : tensor<3xf32>}> : () -> tensor<3xf32>
%1 = "tosa.const"() <{value = dense_resource<torch_tensor_3_3_2_2_torch.float32_2> : tensor<3x3x2x2xf32>}> : () -> tensor<3x3x2x2xf32>
%2 = "tosa.const"() <{value = dense_resource<torch_tensor_3_torch.float32_1> : tensor<3xf32>}> : () -> tensor<3xf32>
@@ -104,17 +106,17 @@ func.func @test_torch_conv2d_with_elementwise_in_between(%arg0: tensor<3x3x10x10
%7 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
%8 = tosa.transpose %arg0, %6 : (tensor<3x3x10x10xf32>, tensor<4xi32>) -> tensor<3x10x10x3xf32>
%9 = tosa.transpose %4, %6 : (tensor<3x3x2x2xf32>, tensor<4xi32>) -> tensor<3x2x2x3xf32>
- %10 = tosa.conv2d %8, %9, %5 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x10x10x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>) -> tensor<3x9x9x3xf32>
+ %10 = tosa.conv2d %8, %9, %5, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x10x10x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x9x9x3xf32>
%11 = tosa.transpose %10, %7 : (tensor<3x9x9x3xf32>, tensor<4xi32>) -> tensor<3x3x9x9xf32>
%12 = tosa.ceil %11 : (tensor<3x3x9x9xf32>) -> tensor<3x3x9x9xf32>
%13 = tosa.transpose %12, %6 : (tensor<3x3x9x9xf32>, tensor<4xi32>) -> tensor<3x9x9x3xf32>
%14 = tosa.transpose %3, %6 : (tensor<3x3x2x2xf32>, tensor<4xi32>) -> tensor<3x2x2x3xf32>
- %15 = tosa.conv2d %13, %14, %2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x9x9x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>) -> tensor<3x8x8x3xf32>
+ %15 = tosa.conv2d %13, %14, %2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x9x9x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x8x8x3xf32>
%16 = tosa.transpose %15, %7 : (tensor<3x8x8x3xf32>, tensor<4xi32>) -> tensor<3x3x8x8xf32>
%17 = tosa.floor %16 : (tensor<3x3x8x8xf32>) -> tensor<3x3x8x8xf32>
%18 = tosa.transpose %17, %6 : (tensor<3x3x8x8xf32>, tensor<4xi32>) -> tensor<3x8x8x3xf32>
%19 = tosa.transpose %1, %6 : (tensor<3x3x2x2xf32>, tensor<4xi32>) -> tensor<3x2x2x3xf32>
- %20 = tosa.conv2d %18, %19, %0 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x8x8x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>) -> tensor<3x7x7x3xf32>
+ %20 = tosa.conv2d %18, %19, %0, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x8x8x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<3x7x7x3xf32>
%21 = tosa.transpose %20, %7 : (tensor<3x7x7x3xf32>, tensor<4xi32>) -> tensor<3x3x7x7xf32>
return %21 : tensor<3x3x7x7xf32>
}
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