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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/tf2xla/transforms/legalize_tf.cc
// accumulation over the given input type. Type GetSumAccumulationType(Type input_type) { MLIRContext *ctx = input_type.getContext(); if (input_type.isBF16() || input_type.isF16()) return FloatType::getF32(ctx); if (input_type.isSignlessInteger(8) || input_type.isSignlessInteger(16)) return IntegerType::get(ctx, 32); return input_type; } // Returns axis in HLO format from TF elements attr with exactly one element or
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K 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/lite/transforms/optimize_op_order.cc
// can have smaller memory usage. auto input_type = mlir::dyn_cast<RankedTensorType>(dequantize_op.getOutput().getType()); auto output_type = mlir::dyn_cast<RankedTensorType>( passthrough_op->getResult(0).getType()); if (!input_type || !output_type || get_num_elements(input_type) <= get_num_elements(output_type)) { return failure(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_arith_ops_folder.cc
if (!dims_type) return success(); if (dims_type.getRank() > 1) return emitError(loc, "dimensions can only be 0D or 1D tensor"); auto input_type = mlir::dyn_cast<RankedTensorType>(input.getType()); if (!input_type) return success(); int64_t rank = input_type.getRank(); DenseIntElementsAttr dims_attr; if (!matchPattern(dims, m_Constant(&dims_attr))) return success();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 3K 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/optimize_batch_matmul.cc
// Create a tfl.transpose op that performs ZX transpose on `input`. auto create_z_x_transpose_op = [&](Value input) -> Value { RankedTensorType input_type = mlir::cast<RankedTensorType>(input.getType()); const int input_rank = input_type.getRank(); // Create a 1D I32 tensor for representing the dimension permutation. auto permuation_tensor_type =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 9.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/utils/test_metadata_config.cc
for (auto input_type : func_type.getInputs()) { tensorflow::TensorShape tensor_shape; xla::Shape xla_shape = xla::TypeToShape(input_type); TF_RETURN_IF_ERROR(tensorflow::TensorShape::BuildTensorShape( xla_shape.dimensions(), &tensor_shape)); arg_shapes.emplace_back(tensor_shape); DataType dtype; TF_RETURN_IF_ERROR(ConvertToDataType(input_type, &dtype));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 13 23:59:33 UTC 2024 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/utils/fake_quant_utils.h
int quant_dim = -1; auto input_type = mlir::cast<ShapedType>(input.getType()); if (PerAxis) { if (!input_type.hasRank()) { tf_op.emitError("The input should have known rank for per-channel op."); return failure(); } // This is a special case that the quant_dim is the last dimensions. quant_dim = input_type.getRank() - 1; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.3K bytes - Viewed (0)