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[mlir][tosa] Replace UniformQuantizedType by the more generic Quantiz… #126275

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Merged
merged 1 commit into from
Feb 11, 2025
Merged

[mlir][tosa] Replace UniformQuantizedType by the more generic Quantiz… #126275

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30 changes: 21 additions & 9 deletions mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -240,16 +240,13 @@ static LogicalResult verifyConvOp(T op) {
bool biasIsFloat = llvm::isa<FloatType>(biasEType);
bool resultIsFloat = llvm::isa<FloatType>(resultEType);

if (auto quantType =
llvm::dyn_cast<mlir::quant::UniformQuantizedType>(inputEType))
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(inputEType))
inputEType = quantType.getStorageType();

if (auto quantType =
llvm::dyn_cast<mlir::quant::UniformQuantizedType>(biasEType))
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(biasEType))
biasEType = quantType.getStorageType();

if (auto quantType =
llvm::dyn_cast<mlir::quant::UniformQuantizedType>(resultEType))
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(resultEType))
resultEType = quantType.getStorageType();

if (biasIsFloat && resultIsFloat && (biasEType != resultEType)) {
Expand Down Expand Up @@ -346,8 +343,7 @@ static LogicalResult verifyConvOpModes(T op) {
auto inputEType =
llvm::cast<ShapedType>(op.getInput().getType()).getElementType();

if (auto quantType =
llvm::dyn_cast<mlir::quant::UniformQuantizedType>(inputEType))
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(inputEType))
inputEType = quantType.getStorageType();

auto accType = op.getAccType();
Expand All @@ -369,7 +365,23 @@ static LogicalResult verifyConvOpModes(T op) {
if (inputEType.isF32() && !accType.isF32())
return op.emitOpError("accumulator type for f32 tensor is not f32");

return success();
auto resultEType =
llvm::cast<ShapedType>(op.getResult().getType()).getElementType();

if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(resultEType))
resultEType = quantType.getStorageType();

// check allowed input/result element types combinations
if ((inputEType.isInteger(8) && resultEType.isInteger(32)) ||
(inputEType.isInteger(16) && resultEType.isInteger(48)) ||
(isa<Float8E5M2Type>(inputEType) && resultEType.isF16()) ||
(isa<Float8E4M3FNType>(inputEType) && resultEType.isF16()) ||
(inputEType.isF16() && resultEType.isF16()) ||
(inputEType.isBF16() && resultEType.isBF16()) ||
(inputEType.isF32() && resultEType.isF32()))
return success();

return op.emitOpError("input/output element types are incompatible.");
}

// verify that inType and outType have same element types
Expand Down
18 changes: 18 additions & 0 deletions mlir/test/Dialect/Tosa/invalid.mlir
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Expand Up @@ -144,6 +144,24 @@ func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xi8>, %arg1: tensor<16x1
return %0 : tensor<1x32x32x16xi8>
}

// -----
// CHECK-LABEL: conv2d_quant_any_acc
func.func @test_conv2d_quant_any_acc(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %arg1: tensor<8x1x1x4x!quant.any<i8<-8:7>>>, %arg2: tensor<8x!quant.any<i8<-8:7>>>) -> tensor<1x4x4x8x!quant.any<i8<-8:7>>> {
%zp = "tosa.const" () { value = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
// expected-error@+1 {{'tosa.conv2d' op accumulator type for i8 tensor is not i32}}
%0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<1x4x4x4x!quant.any<i8<-8:7>>>, tensor<8x1x1x4x!quant.any<i8<-8:7>>>, tensor<8x!quant.any<i8<-8:7>>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.any<i8<-8:7>>>
return %0 : tensor<1x4x4x8x!quant.any<i8<-8:7>>>
}

// -----
// CHECK-LABEL: conv2d_quant_any_result
func.func @test_conv2d_quant_any_result(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %arg1: tensor<8x1x1x4x!quant.any<i8<-8:7>>>, %arg2: tensor<8x!quant.any<i8<-8:7>>>) -> tensor<1x4x4x8x!quant.any<i8<-8:7>>> {
%zp = "tosa.const" () { value = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
// expected-error@+1 {{'tosa.conv2d' op input/output element types are incompatible}}
%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>, local_bound = true} : (tensor<1x4x4x4x!quant.any<i8<-8:7>>>, tensor<8x1x1x4x!quant.any<i8<-8:7>>>, tensor<8x!quant.any<i8<-8:7>>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.any<i8<-8:7>>>
return %0 : tensor<1x4x4x8x!quant.any<i8<-8:7>>>
}

// -----

func.func @test_concat(%arg0 : tensor<2x1xf32>, %arg1 : tensor<2x2xf32>) -> tensor<?x?xf32> {
Expand Down
16 changes: 16 additions & 0 deletions mlir/test/Dialect/Tosa/ops.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -58,6 +58,22 @@ func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %
return %0 : tensor<1x4x4x8xf32>
}

// -----
// CHECK-LABEL: conv2d_quant_uniform
func.func @test_conv2d_quant_uniform(%arg0: tensor<1x4x4x4x!quant.uniform<i8:f32, 0.01>>, %arg1: tensor<8x1x1x4x!quant.uniform<i8:f32, 0.01>>, %arg2: tensor<8x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>> {
%zp = "tosa.const" () { value = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
%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>, local_bound = true} : (tensor<1x4x4x4x!quant.uniform<i8:f32, 0.01>>, tensor<8x1x1x4x!quant.uniform<i8:f32, 0.01>>, tensor<8x!quant.uniform<i8:f32, 0.01>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>>
return %0 : tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>>
}

// -----
// CHECK-LABEL: conv2d_quant_any
func.func @test_conv2d_quant_any(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %arg1: tensor<8x1x1x4x!quant.any<i8<-8:7>>>, %arg2: tensor<8x!quant.any<i8<-8:7>>>) -> tensor<1x4x4x8x!quant.any<i32<-8:7>>> {
%zp = "tosa.const" () { value = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
%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>, local_bound = true} : (tensor<1x4x4x4x!quant.any<i8<-8:7>>>, tensor<8x1x1x4x!quant.any<i8<-8:7>>>, tensor<8x!quant.any<i8<-8:7>>>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8x!quant.any<i32<-8:7>>>
return %0 : tensor<1x4x4x8x!quant.any<i32<-8:7>>>
}

// -----
// CHECK-LABEL: conv2d_q8xi4
func.func @test_conv2d_q8xi4(%arg0: tensor<1x11x11x3xi8>) -> tensor<1x1x1x3xi8> {
Expand Down