mirror of https://github.com/llvm/torch-mlir
[onnx][torch][linalg] Implementing align-corner modes for gridsampler (#3171)
Align corner modes which select what the corners mean. Either the center of the corner points or the edges of the edge points. --------- Co-authored-by: Rob Suderman <rob.suderman@gmail.com>pull/3183/head
Andreas Falkenberg
GitHub
parent
3aa81f78d8
commit
b66eabd492
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@ -140,9 +140,6 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
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if (binder.s64IntegerAttr(align, "align_corners", 0))
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return rewriter.notifyMatchFailure(binder.op,
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"align_corners bind failure");
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if (align != 1)
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return rewriter.notifyMatchFailure(
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binder.op, "currently only align_corners = 1 supported");
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Value interpolationMode = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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@ -150,9 +147,11 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
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Value paddingMode = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
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bool alignMode = align;
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Value alignCorners = rewriter.create<Torch::ConstantBoolOp>(
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binder.getLoc(), rewriter.getType<Torch::BoolType>(),
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rewriter.getBoolAttr(false));
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rewriter.getBoolAttr(alignMode));
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rewriter.replaceOpWithNewOp<Torch::AtenGridSamplerOp>(
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binder.op, resultType, input, grid, interpolationMode, paddingMode,
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@ -2565,6 +2565,7 @@ public:
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resultSize.push_back(rewriter.create<tensor::DimOp>(loc, grid, 1));
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if (resultType.isDynamicDim(3))
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resultSize.push_back(rewriter.create<tensor::DimOp>(loc, grid, 2));
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Value alignCorners = adaptor.getAlignCorners();
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Value resultFinal =
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rewriter.create<tensor::EmptyOp>(loc, resultType, resultSize);
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auto sGrid = rewriter.create<linalg::GenericOp>(
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@ -2573,30 +2574,56 @@ public:
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[&](OpBuilder &b, Location loc, ValueRange args) {
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Value gr0 = args[1];
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Value gr1 = args[0];
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Value gr0Half = b.create<arith::DivFOp>(loc, gr0, twoFloat);
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Value gr1Half = b.create<arith::DivFOp>(loc, gr1, twoFloat);
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Value gr0HalfSelect =
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b.create<arith::SelectOp>(loc, alignCorners, zeroFloat, gr0Half);
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Value gr1HalfSelect =
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b.create<arith::SelectOp>(loc, alignCorners, zeroFloat, gr1Half);
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Value gplus0 = b.create<arith::AddFOp>(loc, gr0, oneFloat);
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Value gplus1 = b.create<arith::AddFOp>(loc, gr1, oneFloat);
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Value result0 = b.create<arith::MulFOp>(loc, gplus0, innerDim0e);
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Value result1 = b.create<arith::MulFOp>(loc, gplus1, innerDim1e);
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Value lower0 = b.create<arith::FPToSIOp>(loc, int64type, result0);
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Value lower1 = b.create<arith::FPToSIOp>(loc, int64type, result1);
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Value gPlusMul0 = b.create<arith::MulFOp>(loc, gplus0, innerDim0e);
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Value gPlusMul1 = b.create<arith::MulFOp>(loc, gplus1, innerDim1e);
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Value result0 =
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b.create<arith::AddFOp>(loc, gPlusMul0, gr0HalfSelect);
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Value result1 =
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b.create<arith::AddFOp>(loc, gPlusMul1, gr1HalfSelect);
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Value checkLowerBound0 = b.create<arith::CmpFOp>(
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loc, arith::CmpFPredicate::OLT, result0, zeroFloat);
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Value checkLowerBound1 = b.create<arith::CmpFOp>(
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loc, arith::CmpFPredicate::OLT, result1, zeroFloat);
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Value lowerOrig0 = b.create<arith::FPToSIOp>(loc, int64type, result0);
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Value lowerOrig1 = b.create<arith::FPToSIOp>(loc, int64type, result1);
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Value zeroInt =
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b.create<arith::ConstantOp>(loc, b.getIntegerAttr(int64type, 0));
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Value oneInt =
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b.create<arith::ConstantOp>(loc, b.getIntegerAttr(int64type, 1));
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Value lowerSub0 = b.create<arith::SubIOp>(loc, lowerOrig0, oneInt);
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Value lowerSub1 = b.create<arith::SubIOp>(loc, lowerOrig1, oneInt);
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Value lower0 = b.create<arith::SelectOp>(loc, checkLowerBound0,
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lowerSub0, lowerOrig0);
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Value lower1 = b.create<arith::SelectOp>(loc, checkLowerBound1,
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lowerSub1, lowerOrig1);
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Value lowerValid0 =
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b.create<arith::SelectOp>(loc, checkLowerBound0, zeroInt, lower0);
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Value lowerValid1 =
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b.create<arith::SelectOp>(loc, checkLowerBound1, zeroInt, lower1);
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Value upper0 =
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b.create<arith::AddIOp>(loc, int64type, lower0, oneInt);
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Value upper1 =
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b.create<arith::AddIOp>(loc, int64type, lower1, oneInt);
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Value notValid0 = rewriter.create<arith::CmpIOp>(
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Value notValidUpper0 = rewriter.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::sgt, upper0, innerDim0c);
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Value notValid1 = rewriter.create<arith::CmpIOp>(
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Value notValidUpper1 = rewriter.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::sgt, upper1, innerDim1c);
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Value upperValid0 =
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b.create<arith::SelectOp>(loc, notValid0, lower0, upper0);
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b.create<arith::SelectOp>(loc, notValidUpper0, lower0, upper0);
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Value upperValid1 =
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b.create<arith::SelectOp>(loc, notValid1, lower1, upper1);
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b.create<arith::SelectOp>(loc, notValidUpper1, lower1, upper1);
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Value lw0 =
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b.create<arith::IndexCastOp>(loc, b.getIndexType(), lower0);
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b.create<arith::IndexCastOp>(loc, b.getIndexType(), lowerValid0);
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Value lw1 =
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b.create<arith::IndexCastOp>(loc, b.getIndexType(), lower1);
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b.create<arith::IndexCastOp>(loc, b.getIndexType(), lowerValid1);
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Value up0 =
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b.create<arith::IndexCastOp>(loc, b.getIndexType(), upperValid0);
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Value up1 =
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@ -2604,23 +2631,31 @@ public:
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Value N = b.create<linalg::IndexOp>(loc, 0);
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Value C = b.create<linalg::IndexOp>(loc, 1);
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Value result00 = lambdaExtract(b, loc, input, N, C, lw0, lw1);
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Value result00a = b.create<arith::SelectOp>(loc, checkLowerBound0,
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zeroFloat, result00);
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Value result00b = b.create<arith::SelectOp>(loc, checkLowerBound1,
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zeroFloat, result00a);
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Value result01 = lambdaExtract(b, loc, input, N, C, lw0, up1);
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Value result01a =
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b.create<arith::SelectOp>(loc, notValid1, zeroFloat, result01);
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Value result01a = b.create<arith::SelectOp>(loc, notValidUpper1,
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zeroFloat, result01);
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Value result01b = b.create<arith::SelectOp>(loc, checkLowerBound0,
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zeroFloat, result01a);
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Value result10 = lambdaExtract(b, loc, input, N, C, up0, lw1);
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Value result10a =
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b.create<arith::SelectOp>(loc, notValid0, zeroFloat, result10);
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Value result10a = b.create<arith::SelectOp>(loc, notValidUpper0,
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zeroFloat, result10);
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Value result10b = b.create<arith::SelectOp>(loc, checkLowerBound1,
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zeroFloat, result10a);
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Value result11 = lambdaExtract(b, loc, input, N, C, up0, up1);
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Value result11a =
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b.create<arith::SelectOp>(loc, notValid0, zeroFloat, result11);
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Value result11b =
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b.create<arith::SelectOp>(loc, notValid1, zeroFloat, result11a);
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Value result11a = b.create<arith::SelectOp>(loc, notValidUpper0,
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zeroFloat, result11);
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Value result11b = b.create<arith::SelectOp>(loc, notValidUpper1,
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zeroFloat, result11a);
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Value lw0a = b.create<arith::SIToFPOp>(loc, floatType, lower0);
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Value lw1a = b.create<arith::SIToFPOp>(loc, floatType, lower1);
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Value d1 = b.create<arith::SubFOp>(loc, result0, lw0a);
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Value d0 = b.create<arith::SubFOp>(loc, result1, lw1a);
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Value resultScaled0 = lambdaInter(b, loc, result00, result01a, d0);
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Value resultScaled1 = lambdaInter(b, loc, result10a, result11b, d0);
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Value resultScaled0 = lambdaInter(b, loc, result00b, result01b, d0);
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Value resultScaled1 = lambdaInter(b, loc, result10b, result11b, d0);
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Value resultScaled =
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lambdaInter(b, loc, resultScaled0, resultScaled1, d1);
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b.create<linalg::YieldOp>(loc, resultScaled);
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@ -35,7 +35,7 @@ class GridSamplerBasic1(torch.nn.Module):
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def GridSamplerBasic1_basic(
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module, tu: TestUtils):
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inp = torch.rand(7,8,12,4)
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grd = torch.rand(7,11,13,2)*2-1
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grd = torch.rand(7,11,13,2)*2.0-1.0
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module.forward(inp, grd)
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@ -69,3 +69,34 @@ def GridSamplerBasic2_basic(
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grd = torch.tensor([[[[-0.3498, -0.8196],[-0.2127, 0.2138],[-0.6515, -0.0513]]]]).type(torch.FloatTensor)
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module.forward(inp, grd)
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class GridSamplerBasic3(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([1, 1, 4, 4], torch.float32, True),
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([1, 1, 3, 2], torch.float32, True)
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])
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def forward(self, x, g):
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interpolation_mode=0,
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padding_mode=0,
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align_corners=False,
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tRes = torch.ops.aten.grid_sampler(x, g, interpolation_mode[0],
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padding_mode[0], align_corners[0])
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return tRes
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@register_test_case(
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module_factory=lambda: GridSamplerBasic3())
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def GridSamplerBasic3_basic(
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module, tu: TestUtils):
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inp = torch.tensor([[[[0.4963, 0.7682, 0.0885, 0.1320],
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[0.3074, 0.6341, 0.4901, 0.8964],
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[0.4556, 0.6323, 0.3489, 0.4017],
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[0.0223, 0.1689, 0.2939, 0.5185]]]]).type(torch.FloatTensor)
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grd = torch.tensor([[[[-0.3498, -0.8196],[-0.2127, 0.2138],[-0.6515, -0.0513]]]]).type(torch.FloatTensor)
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module.forward(inp, grd)
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@ -539,14 +539,26 @@ func.func @test_gelu_tanh_2(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtenso
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// -----
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// CHECK-LABEL: @test_grid_sampler
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// CHECK-LABEL: @test_grid_sampler01
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// CHECK: %[[INT0:.*]] = torch.constant.int 0
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// CHECK: %[[INT0_0:.*]] = torch.constant.int 0
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// CHECK: %[[B0:.*]] = torch.constant.bool false
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// CHECK: %[[A0:.*]] = torch.aten.grid_sampler %arg0, %arg1, %[[INT0]], %[[INT0_0]], %[[B0]] : !torch.vtensor<[5,10,10,4],f32>
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func.func @test_grid_sampler(%arg0: !torch.vtensor<[5,10,10,4],f32>, %arg1: !torch.vtensor<[5,7,8,2],f32>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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%4 = torch.operator "onnx.GridSample" (%arg0, %arg1) {torch.onnx.align_corners = 1 : si64, torch.onnx.padding_mode = "zeros"} : (!torch.vtensor<[5,10,10,4],f32>, !torch.vtensor<[5,7,8,2],f32>) -> !torch.vtensor<[?,?,?,?],f32>
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return %4 : !torch.vtensor<[?,?,?,?],f32>
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func.func @test_grid_sampler01(%arg0: !torch.vtensor<[5,10,10,4],f32>, %arg1: !torch.vtensor<[5,7,8,2],f32>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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%0 = torch.operator "onnx.GridSample" (%arg0, %arg1) {torch.onnx.align_corners = 0 : si64, torch.onnx.padding_mode = "zeros"} : (!torch.vtensor<[5,10,10,4],f32>, !torch.vtensor<[5,7,8,2],f32>) -> !torch.vtensor<[?,?,?,?],f32>
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return %0 : !torch.vtensor<[?,?,?,?],f32>
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}
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// -----
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// CHECK-LABEL: @test_grid_sampler02
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// CHECK: %[[INT0:.*]] = torch.constant.int 0
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// CHECK: %[[INT0_0:.*]] = torch.constant.int 0
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// CHECK: %[[B0:.*]] = torch.constant.bool true
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// CHECK: %[[A0:.*]] = torch.aten.grid_sampler %arg0, %arg1, %[[INT0]], %[[INT0_0]], %[[B0]] : !torch.vtensor<[5,10,10,4],f32>
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func.func @test_grid_sampler02(%arg0: !torch.vtensor<[5,10,10,4],f32>, %arg1: !torch.vtensor<[5,7,8,2],f32>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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%0 = torch.operator "onnx.GridSample" (%arg0, %arg1) {torch.onnx.align_corners = 1 : si64, torch.onnx.padding_mode = "zeros"} : (!torch.vtensor<[5,10,10,4],f32>, !torch.vtensor<[5,7,8,2],f32>) -> !torch.vtensor<[?,?,?,?],f32>
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return %0 : !torch.vtensor<[?,?,?,?],f32>
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}
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// -----
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@ -57,4 +57,33 @@ func.func @grid_sampler2(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vte
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%int1 = torch.constant.int 0
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%4 = torch.aten.grid_sampler %arg0, %arg1, %int0, %int1, %true : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.int, !torch.int, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
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return %4 : !torch.vtensor<[?,?,?,?],f32>
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}
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}
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// -----
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// CHECK-LABEL: func @grid_sampler3
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// CHECK: #map
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// CHECK-DAG: %[[X15:.*]] = arith.mulf %[[X13:.*]], %[[X8:.*]] : f32
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// CHECK-DAG: %[[X16:.*]] = arith.mulf %[[X14:.*]], %[[X9:.*]] : f32
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// CHECK-DAG: %[[X40:.*]] = arith.mulf %[[EXTRACTED:.*]], %[[X39:.*]] : f32
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// CHECK-DAG: %[[X41:.*]] = arith.mulf %[[X31:.*]], %[[X37:.*]] : f32
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// CHECK-DAG: %[[X42:.*]] = arith.addf %[[X40:.*]], %[[X41]] : f32
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// CHECK-DAG: %[[X43:.*]] = arith.subf %[[CST_1:.*]], %[[X37]] : f32
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// CHECK-DAG: %[[X45:.*]] = arith.mulf %[[X34:.*]], %[[X37]] : f32
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// CHECK-DAG: %[[X46:.*]] = arith.addf %[[X44:.*]], %[[X45]] : f32
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// CHECK-DAG: %[[X47:.*]] = arith.subf %[[CST_1]], %[[X38:.*]] : f32
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// CHECK-DAG: %[[X48:.*]] = arith.mulf %[[X42]], %[[XX47:.*]] : f32
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// CHECK-DAG: %[[X49:.*]] = arith.mulf %[[X46]], %[[XX38:.*]] : f32
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// CHECK-DAG: %[[X50:.*]] = arith.addf %[[X48]], %[[X49]] : f32
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// CHECK-DAG: linalg.yield %[[X50]] : f32
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// CHECK: } -> tensor<?x?x?x?xf32>
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// CHECK: %[[X12:.*]] = torch_c.from_builtin_tensor %[[X11:.*]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
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// CHECK: return %[[X12]] : !torch.vtensor<[?,?,?,?],f32>
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func.func @grid_sampler3(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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%false = torch.constant.bool 1
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%int0 = torch.constant.int 0
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%int1 = torch.constant.int 0
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%4 = torch.aten.grid_sampler %arg0, %arg1, %int0, %int1, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.int, !torch.int, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
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return %4 : !torch.vtensor<[?,?,?,?],f32>
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}
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