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tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.cc
auto input = splitv_op.getValue(); auto input_type = mlir::dyn_cast_or_null<RankedTensorType>(input.getType()); if (!input_type || !input_type.hasRank()) return failure(); for (auto result : splitv_op.getResults()) { auto result_type = mlir::dyn_cast<RankedTensorType>(result.getType()); if (result_type == nullptr) return failure(); } const int64_t rank = input_type.getRank();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 25.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/unfuse_batch_norm_pass.cc
auto inputs = bn_op.getOperand(); auto input_type = mlir::dyn_cast<RankedTensorType>(inputs.getType()); if (!input_type) { return failure(); } auto feature_index = bn_op.getFeatureIndex(); // Compute mean int64_t input_last_dim = input_type.getRank() - 1; auto dims_type = RankedTensorType::get(/*shape=*/{input_last_dim},
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/tftext_utils.cc
// * 3rd output is the outer offset. auto input_type = GetInputType(func, 0); if (!input_type || !mlir::isa<StringType>(input_type.getElementType()) || !input_type.hasRank()) { return func.emitError() << "Input should be a string tensor"; } const std::vector<int> kValidNumOfOutput = {1, 2, 3}; if (input_type.getRank() >= kValidNumOfOutput.size()) { return func.emitError()
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 14.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/utils.h
auto input_type = input.getType().cast<ShapedType>(); if (permutation_array.size() != input_type.getRank()) { return nullptr; } llvm::SmallVector<int64_t> transposed_shape(permutation_array.size()); for (int64_t i = 0; i < permutation_array.size(); ++i) { transposed_shape[i] = input_type.getDimSize(permutation_array[i]); } auto transposed_type =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions/util.cc
llvm::ArrayRef<int64_t> permutation_array, ShapedType input_type, ConversionPatternRewriter& rewriter) { assert(permutation_array.size() == input_type.getRank()); llvm::SmallVector<int64_t> transposed_shape(permutation_array.size()); for (int64_t i = 0; i < permutation_array.size(); ++i) { transposed_shape[i] = input_type.getDimSize(permutation_array[i]); } auto transposed_type =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/xla_sharding_util.cc
// known. mlir::Type output_type; auto input_type = mlir::cast<mlir::TensorType>(src_input.getType()); if (input_type.hasRank()) { if (input_type.getShape()[split_dimension] == mlir::ShapedType::kDynamic) { output_type = input_type; } else { auto shape = llvm::to_vector<4>(input_type.getShape()); if (shape[split_dimension] % num_split != 0) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 21:28:13 UTC 2024 - 34K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_composite_functions_tf.cc
LogicalResult CheckFusableLayerNormalizedLstmCellSimple( func::FuncOp lstm_func) { for (int i = 0; i < 5; ++i) { auto input = lstm_func.getArgument(i); auto input_type = mlir::dyn_cast_or_null<RankedTensorType>(input.getType()); if (!input_type) { lstm_func.emitWarning( "we cannot fuse this lstm func because all the inputs have not " "ranked tensor type."); return failure(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/lstm_utils.cc
Operation** result) { auto input_type = mlir::cast<RankedTensorType>(input.getType()); SmallVector<int64_t, 4> output_shape; int size_of_splits; if (input_type.getRank() < axis || axis < 0) return failure(); for (int i = 0; i < input_type.getRank(); ++i) { int64_t dim = input_type.getDimSize(i); if (i == axis) { if (dim % splits != 0) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 36.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/ir/UniformSupport.h
// process. struct ExpressedToQuantizedConverter { // Creates a converter for the given input type. static ExpressedToQuantizedConverter forInputType(Type input_type); // Converts the inputType to be based on the given elemental type, // returning the new type (or nullptr and emit an error on failure). Type convert(quant::QuantizedType elemental_type) const; // Whether the conversion is legal.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_helper.h
if (dyn_cast_or_null<quantfork::QuantizeCastOp>(next_op)) return failure(); auto input_type = mlir::cast<ShapedType>(transpose_op.getInput().getType()); auto perm_type = mlir::cast<ShapedType>(transpose_op.getPerm().getType()); if (input_type.hasStaticShape() && perm_type.hasStaticShape()) { if (perm_type.getNumElements() != input_type.getRank()) { return transpose_op.emitOpError(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 28K bytes - Viewed (0)