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[circle-mlir/pass] Revise ConvTransposeOp pass #16457

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Merged
mhs4670go merged 1 commit into from
Mar 31, 2026
Merged

[circle-mlir/pass] Revise ConvTransposeOp pass #16457

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45 changes: 28 additions & 17 deletions circle-mlir/circle-mlir/lib/pass/src/ops/ConvTransposeOp.h
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Original file line number Diff line number Diff line change
Expand Up @@ -128,27 +128,38 @@ class ConvConvTranspose : public mlir::OpConversionPattern<mlir::ONNXConvTranspo
// create output_shape constant
mlir::Value output_shape;
mlir::SmallVector<int32_t, 4> os_i32;
mlir::SmallVector<int64_t, 4> os_i64;
{
int32_t hin = static_cast<int32_t>(inshape[2]);
int32_t win = static_cast<int32_t>(inshape[3]);
int32_t hfs = static_cast<int32_t>(filtershape[2]);
int32_t wfs = static_cast<int32_t>(filtershape[3]);
int32_t hout = (hin - 1) * stride_h + dilation_h * (hfs - 1) + output_padding_h + 1;
int32_t wout = (win - 1) * stride_w + dilation_w * (wfs - 1) + output_padding_w + 1;
int32_t nin = static_cast<int32_t>(inshape[0]);
int32_t ofs = static_cast<int32_t>(filtershape[1]);
os_i32.push_back(nin);
os_i32.push_back(hout);
os_i32.push_back(wout);
os_i32.push_back(ofs); // from IOHW
int64_t dyn = mlir::ShapedType::kDynamic;
int64_t hin = inshape[2];
int64_t win = inshape[3];
int64_t hfs = filtershape[2];
int64_t wfs = filtershape[3];
int64_t hout = dyn;
int64_t wout = dyn;
int64_t nin = dyn;
int64_t ofs = filtershape[1];

if (!mlir::ShapedType::isDynamic(inshape[0]))
nin = inshape[0];
if (!mlir::ShapedType::isDynamic(inshape[2]))
hout = (hin - 1) * stride_h + dilation_h * (hfs - 1) + output_padding_h + 1;
if (!mlir::ShapedType::isDynamic(inshape[3]))
wout = (win - 1) * stride_w + dilation_w * (wfs - 1) + output_padding_w + 1;

os_i64 = {nin, hout, wout, ofs};
os_i32.push_back(static_cast<int32_t>(nin));
os_i32.push_back(static_cast<int32_t>(hout));
os_i32.push_back(static_cast<int32_t>(wout));
os_i32.push_back(static_cast<int32_t>(ofs)); // from IOHW

mlir::Location shape_loc = mlir::NameLoc::get(rewriter.getStringAttr(op_name + "/shape"));
mlir::Type i32 = rewriter.getI32Type();
mlir::RankedTensorType ostype = RankedTensorType::get({4}, i32);
output_shape = rewriter.create<ConstOp>(shape_loc, DenseIntElementsAttr::get(ostype, os_i32));
}

mlir::SmallVector<int64_t> trconv2d_shape({os_i32[0], os_i32[1], os_i32[2], os_i32[3]});
mlir::SmallVector<int64_t> trconv2d_shape({os_i64[0], os_i64[1], os_i64[2], os_i64[3]});
auto trconv_output_type = mlir::RankedTensorType::get(trconv2d_shape, outtype.getElementType());
mlir::Value trconv2d = rewriter.create<TransposeConvOp>(
opLoc, trconv_output_type, output_shape, filter_tran, pre_tran, bias,
Expand All @@ -173,10 +184,10 @@ class ConvConvTranspose : public mlir::OpConversionPattern<mlir::ONNXConvTranspo

mlir::Location ss_loc = mlir::NameLoc::get(rewriter.getStringAttr(op_name + "/slice/size"));
mlir::SmallVector<int32_t, 4> size_i32;
size_i32.push_back(os_i32[0]);
size_i32.push_back(os_i32[1] - 2 * padsValue[0]);
size_i32.push_back(os_i32[2] - 2 * padsValue[1]);
size_i32.push_back(os_i32[3]);
size_i32.push_back(static_cast<int32_t>(os_i64[0]));
size_i32.push_back(static_cast<int32_t>(os_i64[1]) - 2 * padsValue[0]);
size_i32.push_back(static_cast<int32_t>(os_i64[2]) - 2 * padsValue[1]);
size_i32.push_back(static_cast<int32_t>(os_i64[3]));
auto sizeConst =
rewriter.create<ConstOp>(ss_loc, DenseIntElementsAttr::get(bstype, size_i32));

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