| /external/tensorflow/tensorflow/compiler/mlir/tensorflow/transforms/ |
| D | lower_tf.cc | 341 auto input_ty = input.getType().cast<ShapedType>(); in matchAndRewrite() local
342 auto element_ty = input_ty.getElementType(); in matchAndRewrite()
403 op.getLoc(), input_ty, input, nudged_float_min, nudged_float_max); in matchAndRewrite()
405 quantized_input = rewriter.create<SubOp>(op.getLoc(), input_ty, in matchAndRewrite()
408 quantized_input = rewriter.create<MulOp>(op.getLoc(), input_ty, in matchAndRewrite()
416 quantized_input = rewriter.create<AddV2Op>(op.getLoc(), input_ty, in matchAndRewrite()
422 Value output = rewriter.create<MulOp>(op.getLoc(), input_ty, in matchAndRewrite()
425 output = rewriter.create<AddV2Op>(op.getLoc(), input_ty, output, in matchAndRewrite()
760 Type input_ty = input.getType(); in matchAndRewrite() local
761 if (input_ty != prev_input_ty) { in matchAndRewrite()
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| D | shape_inference.cc | 1612 if (auto input_ty = func_type.getInput(i).dyn_cast<RankedTensorType>()) { in InferShapeForFunction() local
1613 if (input_ty.getRank() != shape.size()) { in InferShapeForFunction()
1616 element_type = input_ty.getElementType(); in InferShapeForFunction()
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| /external/tensorflow/tensorflow/compiler/mlir/tensorflow/ir/ |
| D | tf_ops_a_m.cc | 1292 auto input_ty = op.x().getType().template dyn_cast<RankedTensorType>(); in Verify() local
1293 if (input_ty) { in Verify()
1294 int64_t rank = input_ty.getRank(); in Verify()
1575 const TensorType input_ty = input.getType().template cast<TensorType>(); in inferConvReturnTypes() local
1636 if (input_ty.hasRank()) { in inferConvReturnTypes()
1638 input_ty.getDimSize(GetTensorBatchDimIndex(num_dims, format)); in inferConvReturnTypes()
1647 if (input_ty.hasRank() && filter_ty.hasRank()) { in inferConvReturnTypes()
1661 if (input_ty.isDynamicDim(dim) || filter_ty.isDynamicDim(i)) continue; in inferConvReturnTypes()
1664 input_ty.getDimSize(dim), filter_ty.getDimSize(i), in inferConvReturnTypes()
1674 {RankedTensorType::get(return_shape, input_ty.getElementType())}); in inferConvReturnTypes()
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| D | tf_ops_n_z.cc | 889 static Attribute ConvertShapeToAttr(Type input_ty, int out_width) { in ConvertShapeToAttr() argument
890 auto ranked_ty = input_ty.dyn_cast<RankedTensorType>(); in ConvertShapeToAttr()
902 {rank}, IntegerType::get(input_ty.getContext(), out_width)); in ConvertShapeToAttr()
1084 auto input_ty = op.input().getType().dyn_cast<RankedTensorType>(); in Verify() local
1085 if (input_ty && begin_ty.getNumElements() != input_ty.getRank()) { in Verify()
1091 if (output_ty && input_ty && output_ty.getRank() != input_ty.getRank()) { in Verify()
1095 << input_ty.getRank() << " and output rank " << output_ty.getRank(); in Verify()
1108 int64_t input_size = input_ty ? input_ty.getShape()[dim] : -1; in Verify()
1131 } else if (input_ty) { in Verify()
1135 auto input_shape = input_ty.getShape(); in Verify()
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| D | tf_ops_helpers.inc | 101 Type input_ty = input.getType();
102 Type element_ty = getElementTypeOrSelf(input_ty);
105 auto ranked_ty = input_ty.dyn_cast<RankedTensorType>();
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| /external/tensorflow/tensorflow/compiler/mlir/xla/transforms/ |
| D | legalize_tf.cc | 986 auto input_ty = input.getType().dyn_cast<RankedTensorType>(); in CanBeTranslatedToDynamicSlice() local
987 if (!input_ty) return false; in CanBeTranslatedToDynamicSlice()
991 int64_t input_rank = input_ty.getRank(); in CanBeTranslatedToDynamicSlice()
992 ArrayRef<int64_t> input_shape = input_ty.getShape(); in CanBeTranslatedToDynamicSlice()
1020 auto input_ty = input.getType().dyn_cast<RankedTensorType>(); in TFSliceSizes2HLOSliceSizes() local
1021 int64_t input_rank = input_ty.getRank(); in TFSliceSizes2HLOSliceSizes()
1022 ArrayRef<int64_t> input_shape = input_ty.getShape(); in TFSliceSizes2HLOSliceSizes()
1171 auto input_ty = op.input().getType().template dyn_cast<RankedTensorType>(); in matchAndRewrite() local
1178 for (RankedTensorType ty : {input_ty, filter_ty, result_ty}) in matchAndRewrite()
1220 input_ty.getDimSize(dim), filter_ty.getDimSize(i), dilation, stride, in matchAndRewrite()
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| /external/tensorflow/tensorflow/lite/python/ |
| D | lite.py | 322 def quantizer_flags(self, input_ty=None, output_ty=None): argument
325 inference_input_type = input_ty if input_ty else _dtypes.float32
347 def flags_modify_model_io_type(self, input_ty=None, output_ty=None): argument
352 "inference_input_type": input_ty if input_ty else _dtypes.float32,
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| /external/tensorflow/tensorflow/compiler/mlir/lite/ir/ |
| D | tfl_ops.cc | 1352 auto input_ty = input.getType().cast<TensorType>(); in GetReshapeOutputType() local
1353 auto element_ty = input_ty.getElementType(); in GetReshapeOutputType()
1407 if (!input_ty.hasStaticShape()) { in GetReshapeOutputType()
1417 for (const auto &dim : input_ty.getShape()) { in GetReshapeOutputType()
1453 auto input_ty = op.input().getType().cast<TensorType>(); in Verify() local
1454 if (output_ty.hasStaticShape() && input_ty.hasStaticShape()) { in Verify()
1456 const int64_t input_ty_size = input_ty.getNumElements(); in Verify()
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| /external/tensorflow/tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/IR/ |
| D | hlo_ops.cc | 2550 ShapedType input_ty = input.getType().cast<ShapedType>(); in matchAndRewrite() local
2551 if (input_ty.isDynamicDim(dimension)) { in matchAndRewrite()
2554 auto dim_size = input_ty.getShape()[dimension]; in matchAndRewrite()
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