Skip to content

[AutoBump] Merge with fixes of 2b01f8b7 (Oct 26) (95) #480

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jan 29, 2025
Merged

[AutoBump] Merge with fixes of 2b01f8b7 (Oct 26) (95) #480

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
2b01f8b
[Tosa] : Add support for negative indices in index.tensor and index.T…
sahas3 Oct 25, 2024
5cbaae6
[AutoBump] Merge with fixes of 2b01f8b7 (Oct 26)
mgehre-amd Jan 28, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
83 changes: 48 additions & 35 deletions lib/Conversion/TorchToTosa/TorchToTosa.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4639,6 +4639,25 @@ class ConvertAtenIndexTensorOpNone
}
};

Value wrapNegativeIndices(Value index, int maxIndex, Operation *op,
ConversionPatternRewriter &rewriter) {

auto zeroValue = tosa::getConstTensor<int32_t>(rewriter, op, 0, {}).value();
auto maxIndexValue =
tosa::getConstTensor<int32_t>(rewriter, op, maxIndex, {}).value();

auto indexType = dyn_cast<RankedTensorType>(index.getType());

auto wrappedIndicesOp = tosa::CreateOpAndInfer<tosa::AddOp>(
rewriter, op->getLoc(), indexType, maxIndexValue, index);
auto boolType = indexType.clone(rewriter.getIntegerType(1));
auto isNegativeIndices = tosa::CreateOpAndInfer<tosa::GreaterOp>(
rewriter, op->getLoc(), boolType, zeroValue, index);
return tosa::CreateOpAndInfer<tosa::SelectOp>(rewriter, op->getLoc(),
indexType, isNegativeIndices,
wrappedIndicesOp, index);
}

template <>
LogicalResult ConvertAtenOp<AtenIndexTensorHackedTwinOp>::matchAndRewrite(
AtenIndexTensorHackedTwinOp op, OpAdaptor adaptor,
Expand Down Expand Up @@ -4677,6 +4696,8 @@ LogicalResult ConvertAtenOp<AtenIndexTensorHackedTwinOp>::matchAndRewrite(

auto outType = getTypeConverter()->convertType(op.getType());

Operation *indicesTf;

// Support for multiple indexes
if (indexTensors.size() > 1) {
// t[i, i]
Expand Down Expand Up @@ -4710,6 +4731,8 @@ LogicalResult ConvertAtenOp<AtenIndexTensorHackedTwinOp>::matchAndRewrite(
index);
}

index = wrapNegativeIndices(index, inputTensorType.getShape()[i], op,
rewriter);
// Expand last dim of index to tf indices [2,3] -> [2,3,1]
SmallVector<int64_t> indiceShapeOneDim;
for (auto shape : indexShape) {
Expand Down Expand Up @@ -4852,57 +4875,47 @@ LogicalResult ConvertAtenOp<AtenIndexTensorHackedTwinOp>::matchAndRewrite(
auto indicesShapeConcat = indexesShape[0];
uint64_t lastDim = indexesRank[0];
indicesShapeConcat.push_back(indicesTfConcatTensors.size());
auto indicesTf = tosa::CreateOpAndInfer<tosa::ConcatOp>(
indicesTf = tosa::CreateOpAndInfer<tosa::ConcatOp>(
rewriter, op->getLoc(),
GetTypeFromTensorShape(indicesShapeConcat, rewriter.getIntegerType(32)),
indicesTfConcatTensors, lastDim);

if (!indicesTf) {
return rewriter.notifyMatchFailure(
op, "Convert TorchIndex To TfIndices fail.");
}
// do the tf gathernp algorithm with tf style indices as input.
auto result = tosa::convertGatherNdOp(rewriter, op, outType, input,
indicesTf.getResult());
} else {

if (!result) {
return rewriter.notifyMatchFailure(
op, "Convert GatherNdOp fail for index tensor.");
// Single index
auto index = indexTensors[0];
auto indexType = dyn_cast<RankedTensorType>(index.getType());
auto indexShape = indexType.getShape();
// index i64 to i32 for tosa compatible
if (indexType.getElementType() != rewriter.getIntegerType(32)) {
index = rewriter.create<tosa::CastOp>(
op->getLoc(),
RankedTensorType::get(indexShape, rewriter.getIntegerType(32)),
index);
}
rewriter.replaceOp(op, {result.value()});

return success();
}
index =
wrapNegativeIndices(index, inputTensorType.getShape()[0], op, rewriter);

// Support for multiple index
auto index = indexTensors[0];
auto indexType = dyn_cast<RankedTensorType>(index.getType());
auto indexShape = indexType.getShape();
// index i64 to i32 for tosa compatible
if (indexType.getElementType() != rewriter.getIntegerType(32)) {
index = rewriter.create<tosa::CastOp>(
op->getLoc(),
RankedTensorType::get(indexShape, rewriter.getIntegerType(32)), index);
}

// Expand last dim of index to tf indices [2,3] -> [2,3,1]
SmallVector<int64_t> indicesShape;
for (auto shape : indexShape) {
indicesShape.push_back(shape);
// Expand last dim of index to tf indices [2,3] -> [2,3,1]
SmallVector<int64_t> indicesShape;
for (auto shape : indexShape) {
indicesShape.push_back(shape);
}
indicesShape.push_back(1);
indicesTf = tosa::CreateOpAndInfer<tosa::ReshapeOp>(
rewriter, op->getLoc(),
RankedTensorType::get(indicesShape, rewriter.getIntegerType(32)), index,
rewriter.getDenseI64ArrayAttr(indicesShape));
}
indicesShape.push_back(1);
auto indicesTf = tosa::CreateOpAndInfer<tosa::ReshapeOp>(
rewriter, op->getLoc(),
RankedTensorType::get(indicesShape, rewriter.getIntegerType(32)), index,
rewriter.getDenseI64ArrayAttr(indicesShape));

if (!indicesTf) {
return rewriter.notifyMatchFailure(op,
"Convert TorchIndex To TfIndices fail.");
}
// do the tf gathernp algorithm with tf style indices as input.
auto result = tosa::convertGatherNdOp(rewriter, op, outType, input,
indicesTf.getResult());
indicesTf->getResult(0));

if (!result) {
return rewriter.notifyMatchFailure(
Expand Down
6 changes: 3 additions & 3 deletions projects/pt1/e2e_testing/xfail_sets.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1747,15 +1747,13 @@
"ArangeStartOutModule_basic",
"ScatterSrcStaticModule_basic",
# Runtime op verification: Out of bounds access
"IndexTensorNegativeIndexModule_basic",
"ReduceAllDimEmpty_basic",
}

FX_IMPORTER_TOSA_CRASHING_SET = {
"ScatterSrcModule_basic",
"ScatterSrcStaticModule_basic",
"HBC_basic",
"IndexTensorNegativeIndexModule_basic",
"InterpolateDynamicModule_scales_recompute_bilinear",
"InterpolateDynamicModule_sizes_bilinear",
"InterpolateDynamicModule_sizes_nearest",
Expand Down Expand Up @@ -2217,6 +2215,7 @@
"HardswishRandomModule_basic",
"HardtanhBackward_basic",
"IndexTensorMultiIndexStaticModule_basic",
"IndexTensorNegativeIndexModule_basic",
"IndexTensorStaticModule_basic",
"IscloseStaticModuleTrue_basic",
"IscloseStaticModule_basic",
Expand Down Expand Up @@ -3670,7 +3669,7 @@
"IndexPutImpl3DFloatAccumulateModule_basic",
"IndexPutImpl3DFloatNonAccumulateModule_basic",
"IndexPutImplIndexWithNoneModule_basic",
"IndexTensorNegativeIndexModule_basic",
"IndexSelectRank0IdxModule_basic",
"InterpolateDynamicModule_sizes_bilinear",
"InterpolateDynamicModule_sizes_nearest",
"InterpolateStaticModule_scales_bilinear_align_corners",
Expand Down Expand Up @@ -4000,6 +3999,7 @@
"GridSamplerBasic2_basic",
"GridSamplerBasic3_basic",
"GridSamplerBasic4_basic",
"IndexSelectRank0IdxModule_basic",
"IouOfModule_basic",
"MaxPool1dEmptyStrideStaticModule_basic",
"MaxPool1dStaticCeilModeTrueModule_basic",
Expand Down
32 changes: 32 additions & 0 deletions test/Conversion/TorchToTosa/basic.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2373,3 +2373,35 @@ func.func @torch.aten.diag_embed$basic(%arg0: !torch.vtensor<[2,3,4],f32>) -> !t
%0 = torch.aten.diag_embed %arg0, %int0, %int-2, %int-1 : !torch.vtensor<[2,3,4],f32>, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[2,3,4,4],f32>
return %0 : !torch.vtensor<[2,3,4,4],f32>
}

// -----

// CHECK-LABEL: func.func @torch.aten.index.Tensor_hacked_twin(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[2,4,2],si64>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[],si64>) -> !torch.vtensor<[4,2],si64> {
// CHECK: %[[VAL_0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[2,4,2],si64> -> tensor<2x4x2xi64>
// CHECK: %[[VAL_1:.*]] = torch.prim.ListConstruct %[[ARG1]] : (!torch.vtensor<[],si64>) -> !torch.list<vtensor>
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[],si64> -> tensor<i64>
// CHECK: %[[VAL_3:.*]] = tosa.cast %[[VAL_2]] : (tensor<i64>) -> tensor<i32>
// CHECK: %[[VAL_4:.*]] = "tosa.const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
// CHECK: %[[VAL_5:.*]] = "tosa.const"() <{value = dense<2> : tensor<i32>}> : () -> tensor<i32>
// CHECK: %[[VAL_6:.*]] = tosa.add %[[VAL_5]], %[[VAL_3]] : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: %[[VAL_7:.*]] = tosa.greater %[[VAL_4]], %[[VAL_3]] : (tensor<i32>, tensor<i32>) -> tensor<i1>
// CHECK: %[[VAL_8:.*]] = tosa.select %[[VAL_7]], %[[VAL_6]], %[[VAL_3]] : (tensor<i1>, tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: %[[VAL_9:.*]] = tosa.reshape %[[VAL_8]] {new_shape = array<i64: 1>} : (tensor<i32>) -> tensor<1xi32>
// CHECK: %[[VAL_10:.*]] = tosa.reshape %[[VAL_0]] {new_shape = array<i64: 1, 2, 8>} : (tensor<2x4x2xi64>) -> tensor<1x2x8xi64>
// CHECK: %[[VAL_11:.*]] = tosa.reshape %[[VAL_9]] {new_shape = array<i64: 1, 1>} : (tensor<1xi32>) -> tensor<1x1xi32>
// CHECK: %[[VAL_12:.*]] = "tosa.const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
// CHECK: %[[VAL_13:.*]] = tosa.mul %[[VAL_11]], %[[VAL_12]] {shift = 0 : i8} : (tensor<1x1xi32>, tensor<1xi32>) -> tensor<1x1xi32>
// CHECK: %[[VAL_14:.*]] = tosa.reduce_sum %[[VAL_13]] {axis = 1 : i32} : (tensor<1x1xi32>) -> tensor<1x1xi32>
// CHECK: %[[VAL_15:.*]] = tosa.reshape %[[VAL_14]] {new_shape = array<i64: 1, 1>} : (tensor<1x1xi32>) -> tensor<1x1xi32>
// CHECK: %[[VAL_16:.*]] = tosa.gather %[[VAL_10]], %[[VAL_15]] : (tensor<1x2x8xi64>, tensor<1x1xi32>) -> tensor<1x1x8xi64>
// CHECK: %[[VAL_17:.*]] = tosa.reshape %[[VAL_16]] {new_shape = array<i64: 4, 2>} : (tensor<1x1x8xi64>) -> tensor<4x2xi64>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[VAL_17]] : tensor<4x2xi64> -> !torch.vtensor<[4,2],si64>
// CHECK: return %[[RESULT]] : !torch.vtensor<[4,2],si64>

func.func @torch.aten.index.Tensor_hacked_twin(%arg0: !torch.vtensor<[2,4,2],si64>, %arg1: !torch.vtensor<[],si64>) -> !torch.vtensor<[4,2],si64> {
%0 = torch.prim.ListConstruct %arg1 : (!torch.vtensor<[],si64>) -> !torch.list<vtensor>
%1 = torch.aten.index.Tensor_hacked_twin %arg0, %0 : !torch.vtensor<[2,4,2],si64>, !torch.list<vtensor> -> !torch.vtensor<[4,2],si64>
return %1 : !torch.vtensor<[4,2],si64>
}
Loading