- Sort Score
- Result 10 results
- Languages All
Results 11 - 20 of 203 for getRank (0.14 sec)
-
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions/util.cc
} PermutationAndShape GetPermutationAndTransposedShape( 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]); }
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/stablehlo/transforms/optimize.cc
if (!lhs_type.hasRank() || !rhs_type.hasRank()) { return rewriter.notifyMatchFailure(op, "unsupported unranked input type"); } if (lhs_type.getRank() < 1 || 2 < lhs_type.getRank() || rhs_type.getRank() < 1 || 2 < rhs_type.getRank()) { return rewriter.notifyMatchFailure( op, "unsupported dot operation type; operands must be vectors or " "matrices"); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 26.9K 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/quantization/common/attrs_and_constraints.cc
const int64_t input_rank = mlir::dyn_cast<ShapedType>(dot_general_op.getOperand(0).getType()) .getRank(); const int64_t filter_rank = mlir::dyn_cast<ShapedType>(dot_general_op.getOperand(1).getType()) .getRank(); // The following conditions are such requirements: // - rank(lhs) is 1 or 2 // - rank(rhs) = 2 // - size(lhs_contracting_dimensions) = 1
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions/scatter.h
permutation_and_shape.shape.getRank() - inserted_window_dims.size(); int64_t num_updates = indices_type.getDimSize(0); // For TF::TensorScatterUpdateOp, `indices` must have at least 2 axes: // `(num_updates, index_depth)`. Reshape indices and updates if necessary. if (std::is_same<TfOp, TF::TensorScatterUpdateOp>::value && indices_type.getRank() == 1 && updates_type.getRank() == 1 &&
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/push_transpose_through_ewise.cc
llvm::dyn_cast<RankedTensorType>(tpose_arg1->getResultTypes()[0]); auto tpose_arg2_type = llvm::dyn_cast<RankedTensorType>(tpose_arg2->getResultTypes()[0]); if (tpose_arg1_type.getRank() != tpose_arg2_type.getRank()) { return failure(); } if (llvm::isa<BlockArgument>(tpose_arg1.getPerm()) || llvm::isa<BlockArgument>(tpose_arg2.getPerm())) { return failure(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 12.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/unfuse_batch_norm_pass.cc
// Compute mean int64_t input_last_dim = input_type.getRank() - 1; auto dims_type = RankedTensorType::get(/*shape=*/{input_last_dim}, rewriter.getIntegerType(32)); ::mlir::SmallVector<int32_t> reduce_dim_axes; for (int i = 0; i < input_type.getRank(); ++i) { if (i != feature_index) { reduce_dim_axes.push_back(i); } }
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/stablehlo/transforms/uniform_quantized_stablehlo_to_tfl_pass.cc
// before broadcasting. if (operand_type.getRank() < output_type.getRank()) { input = InsertExpandDimsOp(op, rewriter, input, output_type.getRank()); } SmallVector<int32_t> broadcast_shape = CastI64ArrayToI32(output_type.getShape()).value(); TensorType broadcast_shape_type = output_type.cloneWith({output_type.getRank()}, rewriter.getI32Type()); auto broadcast_shape_attr =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 09:00:19 UTC 2024 - 99.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/perception_ops_utils.cc
if (!image_type || !image_type.getElementType().isF32() || image_type.getRank() != 4) { return func_.emitWarning() << "Image should be a 4D float tensor"; } auto flow_type = mlir::dyn_cast_or_null<RankedTensorType>( func_.getFunctionType().getInput(1)); if (!flow_type || !flow_type.getElementType().isF32() || flow_type.getRank() != 4) { return func_.emitWarning() << "Flow should be a 4D float tensor";
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 8.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc
if (!output_ty) return success(); int64_t expected_output_rank = std::max(x_ty.getRank(), y_ty.getRank()); if (output_ty.getRank() != expected_output_rank) return op.emitOpError() << "found invalid output rank, expected " << expected_output_rank << " but got " << output_ty.getRank(); // Check output batch dim with potential broadcasting.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 146.7K bytes - Viewed (0)