[mlir[tosa] Switch zero point of avgpool2d to input variable type #128983
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@llvm/pr-subscribers-mlir-tosa @llvm/pr-subscribers-mlir-linalg Author: Tai Ly (Tai78641) ChangesThis commit changes the TOSA operator AvgPool2d's zero point attributes to inputs to align with TOSA 1.0 spec. Change-Id: Ieee6ba824327913bc8462cbcb7a74c0b6dd53d21 Patch is 44.40 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/128983.diff 15 Files Affected:
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaComplianceData.h.inc b/mlir/include/mlir/Dialect/Tosa/IR/TosaComplianceData.h.inc
index 2617a902c3a0d..941a7fe2b23ad 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaComplianceData.h.inc
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaComplianceData.h.inc
@@ -5,9 +5,9 @@ profileComplianceMap = {
{{{Profile::pro_int}, {{i8T, i32T}}},
{{Profile::pro_fp}, {{fp16T, i32T}, {fp32T, i32T}}}}},
{"tosa.avg_pool2d",
- {{{Profile::pro_int}, {{i8T, i32T, i8T}}},
+ {{{Profile::pro_int}, {{i8T, i8T, i8T, i32T, i8T}}},
{{Profile::pro_fp},
- {{fp16T, fp16T, fp16T}, {fp16T, fp32T, fp16T}, {fp32T, fp32T, fp32T}}}}},
+ {{fp16T, fp16T, fp16T, fp16T, fp16T}, {fp16T, fp16T, fp16T, fp32T, fp16T}, {fp32T, fp32T, fp32T, fp32T, fp32T}}}}},
{"tosa.conv2d",
{{{Profile::pro_int}, {{i8T, i8T, i32T, i32T, i32T}}},
{{Profile::pro_fp},
@@ -243,10 +243,10 @@ extensionComplianceMap = {
{{Extension::fp8e5m2}, {{fp8e5m2T, i32T}}},
{{Extension::bf16}, {{bf16T, i32T}}}}},
{"tosa.avg_pool2d",
- {{{Extension::int16}, {{i16T, i32T, i16T}}},
- {{Extension::fp8e4m3}, {{fp8e4m3T, fp16T, fp8e4m3T}}},
- {{Extension::fp8e5m2}, {{fp8e5m2T, fp16T, fp8e5m2T}}},
- {{Extension::bf16}, {{bf16T, fp32T, bf16T}}}}},
+ {{{Extension::int16}, {{i16T, i16T, i16T, i32T, i16T}}},
+ {{Extension::fp8e4m3}, {{fp8e4m3T, fp8e4m3T, fp8e4m3T, fp16T, fp8e4m3T}}},
+ {{Extension::fp8e5m2}, {{fp8e5m2T, fp8e5m2T, fp8e5m2T, fp16T, fp8e5m2T}}},
+ {{Extension::bf16}, {{bf16T, bf16T, bf16T, fp32T, bf16T}}}}},
{"tosa.conv2d",
{{{Extension::int4}, {{i8T, i4T, i32T, i32T, i32T}}},
{{Extension::int16}, {{i16T, i8T, i48T, i48T, i48T}}},
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index ddfec2c9bfcd3..27a4bc57d1964 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -79,12 +79,12 @@ def Tosa_AvgPool2dOp : Tosa_InferShapedTypeOp<"avg_pool2d"> {
let arguments = (ins
Tosa_Tensor4D:$input,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$output_zp,
Tosa_IntArrayAttr2:$kernel,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr4:$pad,
- TypeAttrOf<Tosa_AccType>:$acc_type,
- OptionalAttr<I32Attr>:$input_zp,
- OptionalAttr<I32Attr>:$output_zp
+ TypeAttrOf<Tosa_AccType>:$acc_type
);
let results = (outs
@@ -97,6 +97,14 @@ def Tosa_AvgPool2dOp : Tosa_InferShapedTypeOp<"avg_pool2d"> {
];
let builders = [Tosa_AvgPool2dOpQuantInfoBuilder];
+
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getOutputZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyOutputZeroPoint(int64_t zp);
+ }];
+
let hasVerifier = 1;
}
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index 006e35806d64f..9c8d4f75c6e37 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -804,6 +804,15 @@ class AvgPool2dConverter : public OpRewritePattern<tosa::AvgPool2dOp> {
return failure();
SmallVector<Value> dynamicDims = *dynamicDimsOr;
+ int64_t inputZpVal;
+ int64_t outputZpVal;
+ if (op.getInputZeroPoint(inputZpVal).failed() ||
+ op.getOutputZeroPoint(outputZpVal).failed()) {
+ (void)rewriter.notifyMatchFailure(
+ op, "zero points could not be statically determined");
+ return failure();
+ }
+
// Apply padding as necessary.
llvm::SmallVector<int64_t> pad;
pad.resize(2, 0);
@@ -923,9 +932,9 @@ class AvgPool2dConverter : public OpRewritePattern<tosa::AvgPool2dOp> {
// If we have quantization information we need to apply an offset
// for the input zp value.
- if (op.getInputZp()) {
- auto inputZp =
- rewriter.create<arith::ConstantOp>(loc, op.getInputZpAttr());
+ if (inputZpVal != 0) {
+ auto inputZp = rewriter.create<arith::ConstantOp>(
+ loc, b.getIntegerAttr(accETy, inputZpVal));
Value offset =
rewriter.create<arith::MulIOp>(loc, accETy, count, inputZp);
poolVal =
@@ -977,9 +986,9 @@ class AvgPool2dConverter : public OpRewritePattern<tosa::AvgPool2dOp> {
// If we have quantization information we need to apply output
// zeropoint.
- if (op.getOutputZp()) {
- auto outputZp =
- rewriter.create<arith::ConstantOp>(loc, op.getOutputZpAttr());
+ if (outputZpVal != 0) {
+ auto outputZp = rewriter.create<arith::ConstantOp>(
+ loc, b.getIntegerAttr(scaled.getType(), outputZpVal));
scaled = rewriter.create<arith::AddIOp>(loc, scaled, outputZp)
.getResult();
}
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 7b50eceb081dd..63e183538c257 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -455,18 +455,10 @@ LogicalResult tosa::ArgMaxOp::verify() {
}
LogicalResult tosa::AvgPool2dOp::verify() {
- auto inputType = llvm::cast<ShapedType>(getInput().getType());
-
- auto inputETy = inputType.getElementType();
- auto resultETy = llvm::cast<ShapedType>(getType()).getElementType();
-
- if (auto quantType =
- llvm::dyn_cast<mlir::quant::UniformQuantizedType>(inputETy))
- inputETy = quantType.getStorageType();
-
- if (auto quantType =
- llvm::dyn_cast<mlir::quant::UniformQuantizedType>(resultETy))
- resultETy = quantType.getStorageType();
+ const Type inputETy = getStorageElementTypeOrSelf(getInput().getType());
+ const Type resultETy = getStorageElementTypeOrSelf(getOutput().getType());
+ const Type inputZpETy = getStorageElementTypeOrSelf(getInputZp().getType());
+ const Type outputZpETy = getStorageElementTypeOrSelf(getOutputZp().getType());
auto accType = getAccType();
if (llvm::isa<IntegerType>(inputETy) && !accType.isInteger(32))
@@ -481,6 +473,28 @@ LogicalResult tosa::AvgPool2dOp::verify() {
if (inputETy.isF32() && !accType.isF32())
return emitOpError("accumulator type for f32 tensor is not f32");
+ if (inputETy != inputZpETy)
+ return emitOpError("expect both input and its zero point are the same "
+ "element type, got ")
+ << inputETy << " and " << inputZpETy;
+
+ if (resultETy != outputZpETy)
+ return emitOpError("expect both output and its zero point are the same "
+ "element type, got ")
+ << resultETy << " and " << outputZpETy;
+
+ int64_t inputZpVal;
+ if (getInputZeroPoint(inputZpVal).succeeded() &&
+ verifyInputZeroPoint(inputZpVal).failed())
+ return emitOpError(
+ "input zero point must be zero for non-int8 integer types");
+
+ int64_t outputZpVal;
+ if (getOutputZeroPoint(outputZpVal).succeeded() &&
+ verifyOutputZeroPoint(outputZpVal).failed())
+ return emitOpError(
+ "output zero point must be zero for non-int8 integer types");
+
if ((inputETy.isF32() && resultETy.isF32()) ||
(inputETy.isF16() && resultETy.isF16()) ||
(inputETy.isBF16() && resultETy.isBF16()) ||
@@ -629,27 +643,37 @@ static void buildMatMulOpWithQuantInfo(OpBuilder &builder,
}
/// Both the tosa.avg_pool2d and unary ops use the same
-/// UnaruOpQuantizationAttr but avg_pool operator has its own builder as it
+/// UnaryOpQuantizationAttr but avg_pool operator has its own builder as it
/// has additional parameters not part of the unary ops.
static void
buildAvgPool2dOpWithQuantInfo(OpBuilder &builder, OperationState &result,
Type outputType, Value input,
DenseArrayAttr kernel, DenseArrayAttr stride,
DenseArrayAttr pad, TypeAttr accType) {
- result.addOperands(input);
+ const Location loc{result.location};
+ int64_t inputZp{0};
+ int64_t outputZp{0};
+
+ auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType);
+ if (quantAttr) {
+ inputZp = quantAttr.getInputZp();
+ outputZp = quantAttr.getOutputZp();
+ }
+ const std::optional<Value> inputZpOp =
+ createZeroPointTensor(builder, loc, input.getType(), inputZp);
+ assert(
+ inputZpOp.has_value() &&
+ "Failed to create input zero point tensor for quantized AVG_POOL2D op");
+ const std::optional<Value> outputZpOp =
+ createZeroPointTensor(builder, loc, outputType, outputZp);
+ assert(
+ outputZpOp.has_value() &&
+ "Failed to create output zero point tensor for quantized AVG_POOL2D op");
+ result.addOperands({input, inputZpOp.value(), outputZpOp.value()});
result.addAttribute("kernel", kernel);
result.addAttribute("stride", stride);
result.addAttribute("pad", pad);
result.addAttribute("acc_type", accType);
- auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType);
- if (quantAttr) {
- result.addAttribute("input_zp",
- builder.getI32IntegerAttr(
- static_cast<int32_t>(quantAttr.getInputZp())));
- result.addAttribute("output_zp",
- builder.getI32IntegerAttr(
- static_cast<int32_t>(quantAttr.getOutputZp())));
- }
result.types.push_back(outputType);
}
@@ -1425,13 +1449,6 @@ static LogicalResult getZeroPoint(T op, Value val, int64_t &zp) {
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())
@@ -1446,24 +1463,24 @@ static LogicalResult verifyZeroPoint(T op, Value val, int64_t &zp) {
return success();
}
-#define ZERO_POINT_HELPER(OP) \
- LogicalResult tosa::OP::getInputZeroPoint(int64_t &zp) { \
- return getZeroPoint(*this, getInputZp(), zp); \
+#define ZERO_POINT_HELPER(OP, OPERAND_NAME) \
+ LogicalResult tosa::OP::get##OPERAND_NAME##ZeroPoint(int64_t &zp) { \
+ return getZeroPoint(*this, get##OPERAND_NAME##Zp(), 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)
+ LogicalResult tosa::OP::verify##OPERAND_NAME##ZeroPoint(int64_t zp) { \
+ return verifyZeroPoint(*this, get##OPERAND_NAME##Zp(), zp); \
+ }
+
+ZERO_POINT_HELPER(Conv2DOp, Input)
+ZERO_POINT_HELPER(Conv2DOp, Weight)
+ZERO_POINT_HELPER(Conv3DOp, Input)
+ZERO_POINT_HELPER(Conv3DOp, Weight)
+ZERO_POINT_HELPER(DepthwiseConv2DOp, Input)
+ZERO_POINT_HELPER(DepthwiseConv2DOp, Weight)
+ZERO_POINT_HELPER(TransposeConv2DOp, Input)
+ZERO_POINT_HELPER(TransposeConv2DOp, Weight)
+ZERO_POINT_HELPER(AvgPool2dOp, Input)
+ZERO_POINT_HELPER(AvgPool2dOp, Output)
#undef ZERO_POINT_HELPER
LogicalResult tosa::TransposeOp::inferReturnTypeComponents(
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaProfileCompliance.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaProfileCompliance.cpp
index 1d8aaa65c2976..345616c9563b5 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaProfileCompliance.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaProfileCompliance.cpp
@@ -58,6 +58,8 @@ void ProfileInfoDepot::populateProfileInfo(tosa::ConcatOp op) {
template <>
void ProfileInfoDepot::populateProfileInfo(tosa::AvgPool2dOp op) {
addValue(op.getInput());
+ addValue(op.getInputZp());
+ addValue(op.getOutputZp());
addType(op.getAccType());
addValue(op.getOutput());
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
index 5db3f56cf459e..3bd1049f1a0ce 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
@@ -1,9 +1,9 @@
// RUN: mlir-opt --split-input-file -pass-pipeline="builtin.module(func.func(tosa-to-linalg))" %s -verify-diagnostics
// CHECK-LABEL: @avg_pool2d_with_unsupported_quant_type
-func.func @avg_pool2d_with_unsupported_quant_type(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
+func.func @avg_pool2d_with_unsupported_quant_type(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, %arg1: tensor<1xi8>, %arg2: tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
// expected-error@+1 {{failed to legalize operation 'tosa.avg_pool2d'}}
- %0 = "tosa.avg_pool2d"(%arg0) {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
return %0 : tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index 332b706871547..386855420f48e 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -290,7 +290,9 @@ func.func @avg_pool_f32(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x5x33x62xf32>)
// CHECK: %[[FLT:.+]] = arith.sitofp %[[CAST]]
// CHECK: %[[DIV:.+]] = arith.divf %[[IN]], %[[FLT]]
// CHECK: linalg.yield %[[DIV]]
- %0 = tosa.avg_pool2d %arg0 {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf32>) -> tensor<1x5x33x62xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %output_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x33x62xf32>
return %0 : tensor<1x5x33x62xf32>
}
@@ -375,7 +377,9 @@ func.func @avg_pool_f16_f32acc(%arg0: tensor<1x6x34x62xf16>) -> (tensor<1x5x33x6
// CHECK: %[[DIV:.+]] = arith.divf %[[IN]], %[[FLT]]
// CHECK: %[[TRUNC:.+]] = arith.truncf %[[DIV]]
// CHECK: linalg.yield %[[TRUNC]]
- %0 = tosa.avg_pool2d %arg0 {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf16>) -> tensor<1x5x33x62xf16>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %output_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x5x33x62xf16>
return %0 : tensor<1x5x33x62xf16>
}
@@ -416,7 +420,9 @@ func.func @avg_pool_i8(%arg0: tensor<1x6x34x62xi8>) -> (tensor<1x5x33x62xi8>) {
// CHECK: %[[CLAMP:.+]] = arith.minsi %[[CMAX]], %[[LOW]]
// CHECK: %[[TRUNC:.+]] = arith.trunci %[[CLAMP]]
// CHECK: linalg.yield %[[TRUNC]]
- %0 = tosa.avg_pool2d %arg0 {acc_type = i32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xi8>) -> tensor<1x5x33x62xi8>
+ %input_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %output_zp = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = i32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x5x33x62xi8>
return %0 : tensor<1x5x33x62xi8>
}
@@ -439,7 +445,9 @@ func.func @avg_pool_dyn(%arg0: tensor<?x6x34x62xf32>) -> (tensor<?x5x33x62xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<?x5x33x62xf32>) -> tensor<?x5x33x62xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x5x33x62xf32>
// CHECK: %[[GENERIC:.+]] = linalg.generic
- %0 = tosa.avg_pool2d %arg0 {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<?x6x34x62xf32>) -> tensor<?x5x33x62xf32>
+ %input_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %output_zp = "tosa.const"() <{value = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
+ %0 = tosa.avg_pool2d %arg0, %input_zp, %output_zp {acc_type = f32, pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<?x6x34x62xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x5x33x62xf32>
return %0 : tensor<?x5x33x62xf32>
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir
index 73da2810abe04..ecd5c792e08b6 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir
@@ -23,18 +23,18 @@ func.func @tensor_with_unknown_rank(%arg0: tensor<*xi8>) -> tensor<*xi8> {
// -----
// check that tosa verify kick in
-func.func @test_avg_pool2d_zero_dim_input(%arg0: tensor<1x0x?x9xf32>) -> tensor<1x7x7x9xf32> {
+func.func @test_avg_pool2d_zero_dim_input(%arg0: tensor<1x0x?x9xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x7x7x9xf32> {
// expected-error@+1 {{'tosa.avg_pool2d' op operand #0 must be 4-d tosa-conformant tensor, but got 'tensor<1x0x?x9xf32>'}}
- %0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
- : (tensor<1x0x?x9xf32>) -> tensor<1x7x7x9xf32>
+ %0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
+ : (tensor<1x0x?x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
}
// -----
// check that --tosa-to-linalg kick in
-func.func @avg_pool2d_with_unsupported_quant_type(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
+func.func @avg_pool2d_with_unsupported_quant_type(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>, %arg1: tensor<1xi8>, %arg2: tensor<1xi8>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
// expected-error@+1 {{failed to legalize operation 'tosa.avg_pool2d'}}
- %0 = "tosa.avg_pool2d"(%arg0) {acc_type = i32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor...
[truncated]
|
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✅ With the latest revision this PR passed the C/C++ code formatter. |
This commit changes the zero point attribute to an input to align with the 1.0 spec. Change-Id: Ieee6ba824327913bc8462cbcb7a74c0b6dd53d21 Signed-off-by: Luke Hutton <[email protected]>
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This commit changes the TOSA operator AvgPool2d's zero point attributes to inputs to align with TOSA 1.0 spec.
Change-Id: Ieee6ba824327913bc8462cbcb7a74c0b6dd53d21