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Jerry-GeJerry-Ge
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Revert "[mlir][tosa] Change MatMul zero-point to inputs (#129785)"
This reverts commit 106c964.
1 parent 50cfdf5 commit 10334f9

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15 files changed

+98
-232
lines changed

15 files changed

+98
-232
lines changed

‎mlir/include/mlir/Dialect/Tosa/IR/TosaComplianceData.h.inc

Lines changed: 6 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -35,11 +35,9 @@ profileComplianceMap = {
3535
{fp16T, fp16T, fp32T, fp32T},
3636
{fp32T, fp32T, fp32T, fp32T}}}}},
3737
{"tosa.matmul",
38-
{{{Profile::pro_int}, {{i8T, i8T, i8T, i8T, i32T}}},
38+
{{{Profile::pro_int}, {{i8T, i8T, i32T}}},
3939
{{Profile::pro_fp},
40-
{{fp16T, fp16T, fp16T, fp16T, fp16T},
41-
{fp16T, fp16T, fp16T, fp16T, fp32T},
42-
{fp32T, fp32T, fp32T, fp32T, fp32T}}}}},
40+
{{fp16T, fp16T, fp16T}, {fp16T, fp16T, fp32T}, {fp32T, fp32T, fp32T}}}}},
4341
{"tosa.max_pool2d",
4442
{{{Profile::pro_int}, {{i8T, i8T}}},
4543
{{Profile::pro_fp}, {{fp16T, fp16T}, {fp32T, fp32T}}}}},
@@ -275,10 +273,10 @@ extensionComplianceMap = {
275273
{{Extension::int16}, {{i16T, i8T, i48T, i48T}}},
276274
{{Extension::bf16}, {{bf16T, bf16T, fp32T, fp32T}}}}},
277275
{"tosa.matmul",
278-
{{{Extension::int16}, {{i16T, i16T, i16T, i16T, i48T}}},
279-
{{Extension::fp8e4m3}, {{fp8e4m3T, fp8e4m3T, fp8e4m3T, fp8e4m3T, fp16T}}},
280-
{{Extension::fp8e5m2}, {{fp8e5m2T, fp8e5m2T, fp8e5m2T, fp8e5m2T, fp16T}}},
281-
{{Extension::bf16}, {{bf16T, bf16T, bf16T, bf16T, fp32T}}}}},
276+
{{{Extension::int16}, {{i16T, i16T, i48T}}},
277+
{{Extension::fp8e4m3}, {{fp8e4m3T, fp8e4m3T, fp16T}}},
278+
{{Extension::fp8e5m2}, {{fp8e5m2T, fp8e5m2T, fp16T}}},
279+
{{Extension::bf16}, {{bf16T, bf16T, fp32T}}}}},
282280
{"tosa.max_pool2d",
283281
{{{Extension::int16}, {{i16T, i16T}}},
284282
{{Extension::fp8e4m3}, {{fp8e4m3T, fp8e4m3T}}},

‎mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td

Lines changed: 2 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -311,8 +311,8 @@ def Tosa_MatMulOp : Tosa_InferShapedTypeOp<"matmul"> {
311311
let arguments = (ins
312312
Tosa_Tensor3D:$a,
313313
Tosa_Tensor3D:$b,
314-
Tosa_ScalarIntOrFloatTensor:$a_zp,
315-
Tosa_ScalarIntOrFloatTensor:$b_zp
314+
OptionalAttr<I32Attr>:$a_zp,
315+
OptionalAttr<I32Attr>:$b_zp
316316
);
317317

318318
let results = (outs
@@ -324,13 +324,6 @@ def Tosa_MatMulOp : Tosa_InferShapedTypeOp<"matmul"> {
324324
Extension<[Tosa_EXT_INT16, Tosa_EXT_FP8E4M3, Tosa_EXT_FP8E5M2, Tosa_EXT_BF16]>,
325325
];
326326

327-
let extraClassDeclaration = [{
328-
FailureOr<int64_t> getAZeroPoint();
329-
FailureOr<int64_t> getBZeroPoint();
330-
LogicalResult verifyAZeroPoint(int64_t zp);
331-
LogicalResult verifyBZeroPoint(int64_t zp);
332-
}];
333-
334327
let builders = [Tosa_MatMulOpQuantInfoBuilder];
335328
let hasVerifier = 1;
336329
}

‎mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp

Lines changed: 9 additions & 32 deletions
Original file line numberDiff line numberDiff line change
@@ -270,8 +270,8 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
270270
return rewriter.notifyMatchFailure(
271271
op, "weight zero point cannot be statically determined");
272272

273-
const int64_t inputZpVal = *maybeIZp;
274-
const int64_t weightZpVal = *maybeWZp;
273+
int64_t inputZpVal = *maybeIZp;
274+
int64_t weightZpVal = *maybeWZp;
275275

276276
if (op.verifyInputZeroPoint(inputZpVal).failed())
277277
return rewriter.notifyMatchFailure(
@@ -466,8 +466,8 @@ class DepthwiseConvConverter
466466
return rewriter.notifyMatchFailure(
467467
op, "weight zero point cannot be statically determined");
468468

469-
const int64_t inputZpVal = *maybeIZp;
470-
const int64_t weightZpVal = *maybeWZp;
469+
int64_t inputZpVal = *maybeIZp;
470+
int64_t weightZpVal = *maybeWZp;
471471

472472
if (op.verifyInputZeroPoint(inputZpVal).failed())
473473
return rewriter.notifyMatchFailure(
@@ -621,38 +621,15 @@ class MatMulConverter : public OpConversionPattern<tosa::MatMulOp> {
621621
.create<linalg::FillOp>(loc, ValueRange{zero},
622622
ValueRange{emptyTensor})
623623
.result();
624-
625-
FailureOr<int64_t> maybeAZp = op.getAZeroPoint();
626-
FailureOr<int64_t> maybeBZp = op.getBZeroPoint();
627-
if (failed(maybeAZp))
628-
return rewriter.notifyMatchFailure(
629-
op, "input a zero point cannot be statically determined");
630-
if (failed(maybeBZp))
631-
return rewriter.notifyMatchFailure(
632-
op, "input b zero point cannot be statically determined");
633-
634-
const int64_t aZpVal = *maybeAZp;
635-
const int64_t bZpVal = *maybeBZp;
636-
637-
if (op.verifyAZeroPoint(aZpVal).failed())
638-
return rewriter.notifyMatchFailure(
639-
op, "input a zero point must be zero for non-int8 integer types");
640-
641-
if (op.verifyBZeroPoint(bZpVal).failed())
642-
return rewriter.notifyMatchFailure(
643-
op, "input b zero point must be zero for non-int8 integer types");
644-
645-
if (aZpVal == 0 && bZpVal == 0) {
624+
if (!op.getAZp() && !op.getBZp()) {
646625
rewriter.replaceOpWithNewOp<linalg::BatchMatmulOp>(
647626
op, TypeRange{op.getType()},
648627
ValueRange{adaptor.getA(), adaptor.getB()}, ValueRange{zeroTensor});
649628
return success();
650629
}
651630

652-
auto aZp = rewriter.create<arith::ConstantOp>(
653-
loc, rewriter.getI32IntegerAttr(aZpVal));
654-
auto bZp = rewriter.create<arith::ConstantOp>(
655-
loc, rewriter.getI32IntegerAttr(bZpVal));
631+
auto aZp = rewriter.create<arith::ConstantOp>(loc, op.getAZpAttr());
632+
auto bZp = rewriter.create<arith::ConstantOp>(loc, op.getBZpAttr());
656633
rewriter.replaceOpWithNewOp<linalg::QuantizedBatchMatmulOp>(
657634
op, TypeRange{op.getType()},
658635
ValueRange{adaptor.getA(), adaptor.getB(), aZp, bZp}, zeroTensor);
@@ -857,8 +834,8 @@ class AvgPool2dConverter : public OpRewritePattern<tosa::AvgPool2dOp> {
857834
return rewriter.notifyMatchFailure(
858835
op, "output zero point could not be statically determined");
859836

860-
const int64_t inputZpVal = *maybeIZp;
861-
const int64_t outputZpVal = *maybeOZp;
837+
int64_t inputZpVal = *maybeIZp;
838+
int64_t outputZpVal = *maybeOZp;
862839

863840
// Apply padding as necessary.
864841
llvm::SmallVector<int64_t> pad;

‎mlir/lib/Dialect/Tosa/IR/ShardingInterfaceImpl.cpp

Lines changed: 0 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -55,8 +55,6 @@ struct MatMulOpSharding
5555
SmallVector<AffineMap> maps;
5656
maps.push_back(AffineMap::getMultiDimMapWithTargets(4, {0, 1, 3}, ctx));
5757
maps.push_back(AffineMap::getMultiDimMapWithTargets(4, {0, 3, 2}, ctx));
58-
maps.push_back(AffineMap::get(0, 0, {}, ctx));
59-
maps.push_back(AffineMap::get(0, 0, {}, ctx));
6058
maps.push_back(AffineMap::getMultiDimMapWithTargets(4, {0, 1, 2}, ctx));
6159
return maps;
6260
}

‎mlir/lib/Dialect/Tosa/IR/TosaOps.cpp

Lines changed: 25 additions & 39 deletions
Original file line numberDiff line numberDiff line change
@@ -629,13 +629,23 @@ buildTransConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
629629
static void buildMatMulOpWithQuantInfo(OpBuilder &builder,
630630
OperationState &result, Type outputType,
631631
Value a, Value b) {
632-
auto zps = createZPsAsConst(builder, a, b);
633-
result.addOperands({a, b, zps.first, zps.second});
632+
result.addOperands({a, b});
633+
auto quantAttr = ::buildMatMulOpQuantizationAttr(builder, a, b);
634634

635-
Type finalOutputType{outputType};
636-
if (auto quantAttr = buildMatMulOpQuantizationAttr(builder, a, b)) {
637-
auto eType = getStorageElementTypeOrSelf(a.getType());
638-
auto inputBits = eType.getIntOrFloatBitWidth();
635+
if (quantAttr) {
636+
result.addAttribute("a_zp", builder.getI32IntegerAttr(
637+
static_cast<int32_t>(quantAttr.getAZp())));
638+
result.addAttribute("b_zp", builder.getI32IntegerAttr(
639+
static_cast<int32_t>(quantAttr.getBZp())));
640+
641+
auto inputType = llvm::dyn_cast<ShapedType>(a.getType());
642+
assert(inputType && "Input must be a shaped tensor type!");
643+
644+
auto inputQType = llvm::dyn_cast<mlir::quant::UniformQuantizedType>(
645+
inputType.getElementType());
646+
assert(inputQType && "Tensor must have quantized datatype!");
647+
648+
unsigned inputBits = inputQType.getStorageTypeIntegralWidth();
639649

640650
auto outputShapedType = llvm::dyn_cast<ShapedType>(outputType);
641651
assert(outputShapedType && "Output must be a shaped type");
@@ -645,10 +655,11 @@ static void buildMatMulOpWithQuantInfo(OpBuilder &builder,
645655
accElementType = builder.getIntegerType(48);
646656
else
647657
accElementType = builder.getI32Type();
648-
649-
finalOutputType = outputShapedType.clone(accElementType);
658+
auto accType = outputShapedType.clone(accElementType);
659+
result.addTypes(accType);
660+
} else {
661+
result.addTypes(outputType);
650662
}
651-
result.addTypes(finalOutputType);
652663
}
653664

654665
/// Both the tosa.avg_pool2d and unary ops use the same
@@ -1129,39 +1140,16 @@ LogicalResult MatMulOp::verify() {
11291140
return emitOpError("expect quantized operands to have same widths, got ")
11301141
<< aQuantWidth << " and " << bQuantWidth;
11311142
}
1132-
} else {
1133-
// non-quantized element types
1134-
if (aElementType != bElementType) {
1135-
return emitOpError("expect same element type for inputs a and b, got ")
1136-
<< aElementType << " and " << bElementType;
1137-
}
1138-
}
11391143

1140-
// check a_zp and b_zp
1141-
auto aEType = getStorageElementTypeOrSelf(aType);
1142-
auto aZpEType = getStorageElementTypeOrSelf(getAZp().getType());
1143-
if (aEType != aZpEType) {
1144-
return emitOpError("expect input a and a_zp have the same "
1145-
"element type, got ")
1146-
<< aEType << " and " << aZpEType;
1144+
return success();
11471145
}
11481146

1149-
auto bEType = getStorageElementTypeOrSelf(bType);
1150-
auto bZpEType = getStorageElementTypeOrSelf(getBZp().getType());
1151-
if (bEType != bZpEType) {
1152-
return emitOpError("expect input b and b_zp have the same "
1153-
"element type, got ")
1154-
<< bEType << " and " << bZpEType;
1147+
// non-quantized element types
1148+
if (aElementType != bElementType) {
1149+
return emitOpError("expect same element type for inputs a and b, got ")
1150+
<< aElementType << " and " << bElementType;
11551151
}
11561152

1157-
FailureOr<int64_t> maybeAZp = getAZeroPoint();
1158-
if (succeeded(maybeAZp) && verifyAZeroPoint(*maybeAZp).failed())
1159-
return failure();
1160-
1161-
FailureOr<int64_t> maybeBZp = getBZeroPoint();
1162-
if (succeeded(maybeBZp) && verifyBZeroPoint(*maybeBZp).failed())
1163-
return failure();
1164-
11651153
return success();
11661154
}
11671155

@@ -1726,8 +1714,6 @@ ZERO_POINT_HELPER(TransposeConv2DOp, Input)
17261714
ZERO_POINT_HELPER(TransposeConv2DOp, Weight)
17271715
ZERO_POINT_HELPER(AvgPool2dOp, Input)
17281716
ZERO_POINT_HELPER(AvgPool2dOp, Output)
1729-
ZERO_POINT_HELPER(MatMulOp, A)
1730-
ZERO_POINT_HELPER(MatMulOp, B)
17311717
#undef ZERO_POINT_HELPER
17321718

17331719
LogicalResult tosa::TransposeOp::inferReturnTypeComponents(

‎mlir/lib/Dialect/Tosa/Transforms/TosaProfileCompliance.cpp

Lines changed: 1 addition & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -178,15 +178,6 @@ void ProfileInfoDepot::populateProfileInfo(tosa::RescaleOp op) {
178178
addValue(op.getOutput());
179179
}
180180

181-
template <>
182-
void ProfileInfoDepot::populateProfileInfo(tosa::MatMulOp op) {
183-
addValue(op.getA());
184-
addValue(op.getB());
185-
addValue(op.getAZp());
186-
addValue(op.getBZp());
187-
addValue(op.getOutput());
188-
}
189-
190181
LogicalResult ProfileInfoDepot::populatationDispatch(Operation *op) {
191182
// This helper function only populates the info for the customised operands.
192183
#define POPULATE_PROFILE_INFO_CUSTOM(tosaOp) \
@@ -227,7 +218,6 @@ LogicalResult ProfileInfoDepot::populatationDispatch(Operation *op) {
227218
POPULATE_PROFILE_INFO_CUSTOM(Resize)
228219
POPULATE_PROFILE_INFO_CUSTOM(Select)
229220
POPULATE_PROFILE_INFO_CUSTOM(Rescale)
230-
POPULATE_PROFILE_INFO_CUSTOM(MatMul)
231221

232222
// Type Invariant Extension, a capability extension that is independent
233223
// of the data type, meaning any compatible type can be used. No type
@@ -245,6 +235,7 @@ LogicalResult ProfileInfoDepot::populatationDispatch(Operation *op) {
245235
POPULATE_PROFILE_INFO_COMMON(Cast)
246236
POPULATE_PROFILE_INFO_COMMON(Const)
247237
POPULATE_PROFILE_INFO_COMMON(ArgMax)
238+
POPULATE_PROFILE_INFO_COMMON(MatMul)
248239
POPULATE_PROFILE_INFO_COMMON(Sub)
249240
POPULATE_PROFILE_INFO_COMMON(Maximum)
250241
POPULATE_PROFILE_INFO_COMMON(Minimum)

‎mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir

Lines changed: 6 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -8,9 +8,7 @@ func.func @matmul(%arg0: tensor<1x5x3xf32>, %arg1: tensor<1x3x6xf32>) -> (tensor
88
// CHECK: [[INIT:%.+]] = tensor.empty()
99
// CHECK: [[FILLED:%.+]] = linalg.fill ins([[C0]] : f32) outs([[INIT]] : tensor<1x5x6xf32>) -> tensor<1x5x6xf32>
1010
// CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x5x3xf32>, tensor<1x3x6xf32>) outs([[FILLED]] : tensor<1x5x6xf32>) -> tensor<1x5x6xf32>
11-
%a_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
12-
%b_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
13-
%0 = tosa.matmul %arg0, %arg1, %a_zp, %b_zp : (tensor<1x5x3xf32>, tensor<1x3x6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x6xf32>
11+
%0 = tosa.matmul %arg0, %arg1 : (tensor<1x5x3xf32>, tensor<1x3x6xf32>) -> tensor<1x5x6xf32>
1412
return %0 : tensor<1x5x6xf32>
1513
}
1614

@@ -25,9 +23,7 @@ func.func @matmul_quantized(%arg0: tensor<1x5x3xi8>, %arg1: tensor<1x3x6xi8>) ->
2523
// CHECK: [[ONE:%.+]] = arith.constant 1
2624
// CHECK: [[TWO:%.+]] = arith.constant 2
2725
// CHECK: linalg.quantized_batch_matmul ins(%arg0, %arg1, [[ONE]], [[TWO]] : tensor<1x5x3xi8>, tensor<1x3x6xi8>, i32, i32) outs([[FILLED]] : tensor<1x5x6xi32>) -> tensor<1x5x6xi32>
28-
%a_zp = "tosa.const"() <{values = dense<1> : tensor<1xi8>}> : () -> tensor<1xi8>
29-
%b_zp = "tosa.const"() <{values = dense<2> : tensor<1xi8>}> : () -> tensor<1xi8>
30-
%0 = tosa.matmul %arg0, %arg1, %a_zp, %b_zp : (tensor<1x5x3xi8>, tensor<1x3x6xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x5x6xi32>
26+
%0 = tosa.matmul %arg0, %arg1 {a_zp = 1 : i32, b_zp = 2 : i32} : (tensor<1x5x3xi8>, tensor<1x3x6xi8>) -> tensor<1x5x6xi32>
3127
return %0 : tensor<1x5x6xi32>
3228
}
3329

@@ -41,9 +37,7 @@ func.func @matmul_dyn_batch(%arg0: tensor<?x5x3xf32>, %arg1: tensor<?x3x6xf32>)
4137
// CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM]])
4238
// CHECK: %[[FILLED:.+]] = linalg.fill ins(%[[C0_0]] : f32) outs(%[[INIT]] : tensor<?x5x6xf32>) -> tensor<?x5x6xf32>
4339
// CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<?x5x3xf32>, tensor<?x3x6xf32>) outs(%[[FILLED]] : tensor<?x5x6xf32>) -> tensor<?x5x6xf32>
44-
%a_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
45-
%b_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
46-
%0 = tosa.matmul %arg0, %arg1, %a_zp, %b_zp : (tensor<?x5x3xf32>, tensor<?x3x6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x5x6xf32>
40+
%0 = tosa.matmul %arg0, %arg1 : (tensor<?x5x3xf32>, tensor<?x3x6xf32>) -> tensor<?x5x6xf32>
4741
return %0 : tensor<?x5x6xf32>
4842
}
4943

@@ -57,9 +51,7 @@ func.func @matmul_dyn_independent_dim(%arg0: tensor<1x5x3xf32>, %arg1: tensor<1x
5751
// CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM]])
5852
// CHECK: %[[FILLED:.+]] = linalg.fill ins(%[[C0]] : f32) outs(%[[INIT]] : tensor<1x5x?xf32>) -> tensor<1x5x?xf32>
5953
// CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x5x3xf32>, tensor<1x3x?xf32>) outs(%[[FILLED]] : tensor<1x5x?xf32>) -> tensor<1x5x?xf32>
60-
%a_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
61-
%b_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
62-
%0 = tosa.matmul %arg0, %arg1, %a_zp, %b_zp : (tensor<1x5x3xf32>, tensor<1x3x?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x?xf32>
54+
%0 = tosa.matmul %arg0, %arg1 : (tensor<1x5x3xf32>, tensor<1x3x?xf32>) -> tensor<1x5x?xf32>
6355
return %0 : tensor<1x5x?xf32>
6456
}
6557

@@ -71,9 +63,7 @@ func.func @matmul_dyn_independent_dim(%arg0: tensor<1x5x?xf32>, %arg1: tensor<1x
7163
// CHECK: %[[INIT:.+]] = tensor.empty()
7264
// CHECK: %[[FILLED:.+]] = linalg.fill ins(%[[C0]] : f32) outs(%[[INIT]] : tensor<1x5x6xf32>) -> tensor<1x5x6xf32>
7365
// CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x5x?xf32>, tensor<1x?x6xf32>) outs(%[[FILLED]] : tensor<1x5x6xf32>) -> tensor<1x5x6xf32>
74-
%a_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
75-
%b_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
76-
%0 = tosa.matmul %arg0, %arg1, %a_zp, %b_zp : (tensor<1x5x?xf32>, tensor<1x?x6xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x5x6xf32>
66+
%0 = tosa.matmul %arg0, %arg1 : (tensor<1x5x?xf32>, tensor<1x?x6xf32>) -> tensor<1x5x6xf32>
7767
return %0 : tensor<1x5x6xf32>
7868
}
7969

@@ -87,9 +77,7 @@ func.func @matmul_dyn_output(%arg0: tensor<1x1x8xf32>, %arg1: tensor<1x8x1xf32>)
8777
// CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM0]]) : tensor<?x1x1xf32>
8878
// CHECK: %[[FILLED:.+]] = linalg.fill ins(%[[CST]] : f32) outs(%[[INIT]] : tensor<?x1x1xf32>) -> tensor<?x1x1xf32>
8979
// CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x1x8xf32>, tensor<1x8x1xf32>) outs(%[[FILLED]] : tensor<?x1x1xf32>) -> tensor<?x1x1xf32>
90-
%a_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
91-
%b_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>
92-
%0 = tosa.matmul %arg0, %arg1, %a_zp, %b_zp : (tensor<1x1x8xf32>, tensor<1x8x1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x1x1xf32>
80+
%0 = tosa.matmul %arg0, %arg1 : (tensor<1x1x8xf32>, tensor<1x8x1xf32>) -> tensor<?x1x1xf32>
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return %0 : tensor<?x1x1xf32>
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}
9583

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