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Add converter for index.Tensor_hacked_twin #98

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merged 12 commits into from
Sep 20, 2024
Merged

Add converter for index.Tensor_hacked_twin #98

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cd6f99d
add converter for index.Tensor_hacked_twin
matthewfl Sep 18, 2024
db2ba3f
clang format
matthewfl Sep 18, 2024
936d691
make it use the same build as everything else when running tcp-opt so…
matthewfl Sep 19, 2024
543f8ee
add a verifier for gather
matthewfl Sep 19, 2024
0e8e84a
cp
matthewfl Sep 19, 2024
60fdf0d
add casting of int types for resources
matthewfl Sep 20, 2024
b7fc082
tests pass
matthewfl Sep 20, 2024
9d4a032
formatting
matthewfl Sep 20, 2024
ca634c7
formatting
matthewfl Sep 20, 2024
fe6f405
gather allows for non-gather dims to be smaller than input tensor. A…
matthewfl Sep 20, 2024
ea0e5d8
update error message
matthewfl Sep 20, 2024
5439258
Merge remote-tracking branch 'gh-public/main' into mfl/index-tensor-h…
matthewfl Sep 20, 2024
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60 changes: 60 additions & 0 deletions lib/Conversion/TorchToTcp/DataMovement.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -278,6 +278,63 @@ class ConvertAtenIndexSelectOp : public OpConversionPattern<AtenIndexSelectOp> {
}
};

class ConvertAtenIndexTensorHackedTwin
: public OpConversionPattern<AtenIndexTensorHackedTwinOp> {
using OpConversionPattern::OpConversionPattern;

LogicalResult
matchAndRewrite(AtenIndexTensorHackedTwinOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto self = adaptor.getSelf();
auto indicesList = op.getIndices();
SmallVector<Value> indices;
if (!getListConstructElements(indicesList, indices))
return op.emitError("Failed to match list of indices");
indices = getTypeConvertedValues(rewriter, op.getLoc(), getTypeConverter(),
indices);

// possible that this should ignore the first batch dim?
if (indices.size() != cast<RankedTensorType>(self.getType()).getRank())
return op.emitError(
"Expected the number of indicies to equal rank of self");

for (unsigned int i = 0; i < indices.size(); i++) {
auto idx = indices[i];
int numNonOneAxis = 0;
auto ttype = cast<RankedTensorType>(idx.getType());
for (int j = 0; j < ttype.getRank(); j++)
if (ttype.getShape()[j] != 1)
numNonOneAxis++;
if (numNonOneAxis > 1)
return op.emitError(
"Expected the input shape to have a single non-one axis");
// convert it to a 1-dim vector
if (ttype.getRank() != 1) {
ReassociationIndices reassocIndices;
for (int j = 0; j < ttype.getRank(); j++)
reassocIndices.push_back(j);
SmallVector<ReassociationIndices> ri = {reassocIndices};
auto reshape =
rewriter.create<tensor::CollapseShapeOp>(op.getLoc(), idx, ri);
idx = reshape.getResult();
}

SmallVector<int64_t> outShape(
cast<RankedTensorType>(self.getType()).getShape());
outShape[i] = ttype.getNumElements();
auto outType = RankedTensorType::get(
outShape, cast<RankedTensorType>(self.getType()).getElementType());

auto gather = rewriter.create<tcp::GatherOp>(
op.getLoc(), outType, self, idx, rewriter.getIndexAttr(i));
self = gather.getResult();
}

rewriter.replaceOp(op, self);
return success();
}
};

} // namespace

void torch_to_tcp::populateDataMovementPatternsAndLegality(
Expand All @@ -294,4 +351,7 @@ void torch_to_tcp::populateDataMovementPatternsAndLegality(
torch_to_tcp::addPatternIfOpInConvertTorchOpsSet<ConvertAtenIndexSelectOp,
AtenIndexSelectOp>(
typeConverter, patterns, target, convertTorchOpsSet);
torch_to_tcp::addPatternIfOpInConvertTorchOpsSet<
ConvertAtenIndexTensorHackedTwin, AtenIndexTensorHackedTwinOp>(
typeConverter, patterns, target, convertTorchOpsSet);
}
45 changes: 0 additions & 45 deletions lib/Conversion/TorchToTcp/TcpCustomOp.cpp
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Expand Up @@ -29,49 +29,6 @@ using namespace mlir::torch;
using namespace mlir::torch::Torch;

namespace {
class ConvertAtenGatherOp : public OpConversionPattern<AtenGatherOp> {
public:
using OpConversionPattern::OpConversionPattern;

LogicalResult
matchAndRewrite(AtenGatherOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
torch_to_tcp::TorchToTcpCustomOpConversionHelper helper{op, rewriter,
getTypeConverter()};

helper.addOperand("self", adaptor.getSelf());
helper.addOperand("index", adaptor.getIndex());
helper.addIntAttr("axis", op.getDim());

return helper.replace();
}
};

class ConvertAtenIndexTensorHackedTwinOp
: public OpConversionPattern<AtenIndexTensorHackedTwinOp> {
public:
using OpConversionPattern::OpConversionPattern;

LogicalResult
matchAndRewrite(AtenIndexTensorHackedTwinOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {

torch_to_tcp::TorchToTcpCustomOpConversionHelper helper{op, rewriter,
getTypeConverter()};

Value input = adaptor.getSelf();
auto inputTensorType = input.getType().dyn_cast<RankedTensorType>();
// Check input is a tensor type.
if (!inputTensorType)
return rewriter.notifyMatchFailure(
op, "Only tensor types input are currently supported");

helper.addOperand("self", input);
helper.addAsMultipleTensorOperands("index_", op.getIndices());

return helper.replace();
}
};

class ConvertAten_IndexPutImplOp
: public OpConversionPattern<Aten_IndexPutImplOp> {
Expand Down Expand Up @@ -380,8 +337,6 @@ void torch_to_tcp::populateTcpCustomOpPatternsAndLegality(
#define INSERT_ATEN_TO_TCP_CUSTOM_OP_PATTERN(AtenOp) \
torch_to_tcp::addPatternIfOpInConvertTorchOpsSet<Convert##AtenOp, AtenOp>( \
typeConverter, patterns, target, convertTorchOpsSet)
INSERT_ATEN_TO_TCP_CUSTOM_OP_PATTERN(AtenGatherOp);
INSERT_ATEN_TO_TCP_CUSTOM_OP_PATTERN(AtenIndexTensorHackedTwinOp);
INSERT_ATEN_TO_TCP_CUSTOM_OP_PATTERN(Aten_IndexPutImplOp);
INSERT_ATEN_TO_TCP_CUSTOM_OP_PATTERN(AtenFakeQuantizePerTensorAffineOp);
INSERT_ATEN_TO_TCP_CUSTOM_OP_PATTERN(
Expand Down
26 changes: 26 additions & 0 deletions test/Conversion/TorchToTcp/data_movement.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -64,3 +64,29 @@ func.func @torch.aten.index_select(%arg0: !torch.vtensor<[4,3],f32>, %arg1: !tor
%0 = torch.aten.index_select %arg0, %int-1, %arg1: !torch.vtensor<[4,3],f32>, !torch.int, !torch.vtensor<[2],si64> -> !torch.vtensor<[4,2],f32>
return %0 : !torch.vtensor<[4,2],f32>
}

// -----

// CHECK-label: @torch.aten.index.tensor_hacked_twin
// CHECK-DAG: %[[CAST0:.+]] = torch_c.to_builtin_tensor %arg0
// CHECK-DAG: %[[GATHER0:.+]] = tcp.gather %[[CAST0]], %[[SELECT0:.+]] {dim = 0 : index} : tensor<1x20x30xf32>, tensor<1xi64> -> tensor<1x20x30xf32>
// CHECK-DAG: %[[GATHER1:.+]] = tcp.gather %[[GATHER0]], %[[SELECT1:.+]] {dim = 1 : index} : tensor<1x20x30xf32>, tensor<5xi64> -> tensor<1x5x30xf32>
// CHECK-DAG: %[[GATHER2:.+]] = tcp.gather %[[GATHER1]], %[[SELECT2:.+]] {dim = 2 : index} : tensor<1x5x30xf32>, tensor<20xi64> -> tensor<1x5x20xf32>
// CHECK-DAG: %[[RET:.+]] = torch_c.from_builtin_tensor %[[GATHER2]]
// CHECK: return %[[RET]]
func.func @torch.aten.index.tensor_hacked_twin(%arg0: !torch.vtensor<[1,20,30],f32>, %select1: !torch.vtensor<[5,1],si64>, %select2: !torch.vtensor<[20],si64>) -> !torch.vtensor<[1,5,20],f32> {
// there is a strange pattern that is being generated when selecting one axis. It seems that it uses the Tensor_hacked_twin to select along all axis, but uses
// arange to select all of the
%none = torch.constant.none
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int4 = torch.constant.int 4 // this is a dtype on arange....
%int-1 = torch.constant.int -1
%arange = torch.aten.arange.start_step %int0, %int1, %int1, %int4, %none, %none, %none : !torch.int, !torch.int, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[1],si64>
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torch.aten.arange.start_step will remain unchanged right? As we don't handle it in tcp.

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Currently this remains unchanged. I was thinking that we should add an optimization pass that removes this when it selects everything, but I have yet to do that

%arange1 = torch.aten.unsqueeze %arange, %int-1 : !torch.vtensor<[1],si64>, !torch.int -> !torch.vtensor<[1,1],si64>
%arange2 = torch.aten.unsqueeze %arange1, %int-1 : !torch.vtensor<[1,1],si64>, !torch.int -> !torch.vtensor<[1,1,1],si64>

%l = torch.prim.ListConstruct %arange2, %select1, %select2 : (!torch.vtensor<[1,1,1],si64>, !torch.vtensor<[5,1],si64>, !torch.vtensor<[20],si64>) -> !torch.list<vtensor>
%ret = torch.aten.index.Tensor_hacked_twin %arg0, %l : !torch.vtensor<[1,20,30],f32>, !torch.list<vtensor> -> !torch.vtensor<[1,5,20],f32>
return %ret : !torch.vtensor<[1,5,20],f32>
}
38 changes: 0 additions & 38 deletions test/Conversion/TorchToTcp/tcp_custom_ops.mlir
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@@ -1,43 +1,5 @@
// RUN: tcp-opt <%s -convert-torch-to-tcp-custom-op -canonicalize -split-input-file | FileCheck %s

// CHECK-LABEL: func.func @torch.aten.gather_op(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[2,2],si64>
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[2,2],f32>) -> !torch.vtensor<[2,2],f32> {
// CHECK-DAG: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[2,2],f32> -> tensor<2x2xf32>
// CHECK-DAG: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[2,2],si64> -> tensor<2x2xi64>
// CHECK: %[[T2:.*]] = tcp.custom_op("torch.aten.gather") %[[T0]], %[[T1]] {axis = 1 : i64, torch_operand_names = ["self", "index"]} : tensor<2x2xf32>, tensor<2x2xi64> -> tensor<2x2xf32>
// CHECK: %[[T3:.*]] = torch_c.from_builtin_tensor %[[T2]] : tensor<2x2xf32> -> !torch.vtensor<[2,2],f32>
// CHECK: return %[[T3]] : !torch.vtensor<[2,2],f32>
func.func @torch.aten.gather_op(%arg0: !torch.vtensor<[2,2],si64>, %arg1: !torch.vtensor<[2,2],f32>) -> !torch.vtensor<[2,2],f32> {
%false = torch.constant.bool false
%int1 = torch.constant.int 1
%0 = torch.aten.gather %arg1, %int1, %arg0, %false : !torch.vtensor<[2,2],f32>, !torch.int, !torch.vtensor<[2,2],si64>, !torch.bool -> !torch.vtensor<[2,2],f32>
return %0 : !torch.vtensor<[2,2],f32>
}

// -----

// CHECK-LABEL: func.func @torch.aten.index_hacked_twin_op(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[1,30,19,41],f32>
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[1,1,1,1],si64>
// CHECK-SAME: %[[ARG2:.*]]: !torch.vtensor<[30,1,1],si64>
// CHECK-SAME: %[[ARG3:.*]]: !torch.vtensor<[19,1],si64>
// CHECK-SAME: %[[ARG4:.*]]: !torch.vtensor<[3],si64>) -> !torch.vtensor<[1,30,19,3],f32> {
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[1,30,19,41],f32> -> tensor<1x30x19x41xf32>
// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[1,1,1,1],si64> -> tensor<1x1x1x1xi64>
// CHECK: %[[T2:.*]] = torch_c.to_builtin_tensor %[[ARG2]] : !torch.vtensor<[30,1,1],si64> -> tensor<30x1x1xi64>
// CHECK: %[[T3:.*]] = torch_c.to_builtin_tensor %[[ARG3]] : !torch.vtensor<[19,1],si64> -> tensor<19x1xi64>
// CHECK: %[[T4:.*]] = torch_c.to_builtin_tensor %[[ARG4]] : !torch.vtensor<[3],si64> -> tensor<3xi64>
// CHECK: %[[T5:.*]] = tcp.custom_op("torch.aten.index.Tensor_hacked_twin") %[[T0]], %[[T1]], %[[T2]], %[[T3]], %[[T4]] {torch_operand_names = ["self", "index_0", "index_1", "index_2", "index_3"]} : tensor<1x30x19x41xf32>, tensor<1x1x1x1xi64>, tensor<30x1x1xi64>, tensor<19x1xi64>, tensor<3xi64> -> tensor<1x30x19x3xf32>
// CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<1x30x19x3xf32> -> !torch.vtensor<[1,30,19,3],f32>
// CHECK: return %[[T6]] : !torch.vtensor<[1,30,19,3],f32>
func.func @torch.aten.index_hacked_twin_op(%arg0: !torch.vtensor<[1,30,19,41],f32>, %arg1: !torch.vtensor<[1,1,1,1],si64>, %arg2: !torch.vtensor<[30,1,1],si64>, %arg3: !torch.vtensor<[19,1],si64>, %arg4: !torch.vtensor<[3],si64>) -> !torch.vtensor<[1,30,19,3],f32> {
%0 = torch.prim.ListConstruct %arg1, %arg2, %arg3, %arg4 : (!torch.vtensor<[1,1,1,1],si64>, !torch.vtensor<[30,1,1],si64>, !torch.vtensor<[19,1],si64>, !torch.vtensor<[3],si64>) -> !torch.list<vtensor>
%1 = torch.aten.index.Tensor_hacked_twin %arg0, %0 : !torch.vtensor<[1,30,19,41],f32>, !torch.list<vtensor> -> !torch.vtensor<[1,30,19,3],f32>
return %1 : !torch.vtensor<[1,30,19,3],f32>
}

// -----

// CHECK-LABEL: func.func @torch.aten.index_put_impl_op(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[25],f32>
Expand Down
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