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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/tensorflow/ir/tf_ops_a_m.cc
llvm::SmallVector<int64_t, 4> input_shape(4, ShapedType::kDynamic); auto input_type = mlir::cast<TensorType>(op.getInput().getType()); if (input_type.hasRank()) { if (input_type.getRank() != 4) return op.emitOpError() << "requires input to be a 4D tensor, but got " << input_type; int64_t input_batch = input_type.getDimSize(0); if (input_batch != ShapedType::kDynamic &&
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 146.7K 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) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/composite_utils.cc
output_shape[1] = composite_result_shape[2]; output_shape[2] = composite_result_shape[3]; output_shape[3] = composite_result_shape[1]; auto input_type = mlir::cast<ShapedType>(old_op->getOperand(0).getType()); return RankedTensorType::get(output_shape, input_type.getElementType()); } } // namespace odml
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 18:33:05 UTC 2024 - 3.4K bytes - Viewed (0)