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Results 1 - 10 of 24 for dim2 (0.07 sec)
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tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc
for (const auto &item : llvm::enumerate(dims)) { int64_t index = item.index(); int64_t dim = item.value().getSExtValue(); if (dim < 0 || dim > output_rank) { return emitOptionalError(location, "out of range broadcast dim"); } if (is_broadcasted[dim]) { return emitOptionalError(location, "broadcast_dims has duplicates"); } broadcast_shape[dim] = min_rank_ty.getDimSize(index);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 170.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo.cc
const int64_t rank = type.getRank(); for (const int64_t dim : batch_dimensions) { batch_dimensions_.axes.push_back(dim); batch_dimensions_.sizes.push_back(type.getDimSize(dim)); } for (const int64_t dim : contracting_dimensions) { contracting_dimensions_.axes.push_back(dim); contracting_dimensions_.sizes.push_back(type.getDimSize(dim)); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 154.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
llvm::SmallVector<int64_t, 4> dims; dims.reserve(lhs.getRank()); for (auto dim : llvm::zip(lhs.getShape(), rhs.getShape())) { int64_t lhs_dim = std::get<0>(dim); if (lhs_dim == std::get<1>(dim)) { dims.push_back(lhs_dim); } else { dims.push_back(ShapedType::kDynamic); } } return tensorflow::GetTypeFromTFTensorShape( dims, GetElementTypeFromOperand(lhs, rhs)); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc
if (rank == 1) { int64_t dim0 = input_ty.getDimSize(0); if (dim0 != ShapedType::kDynamic && dim0 != 4 && dim0 != 2) return op.emitOpError("requires 1D input of size 4 or size 2"); } if (rank == 2) { int64_t dim0 = input_ty.getDimSize(0); if (dim0 != ShapedType::kDynamic && dim0 != 4) return op.emitOpError(
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/tests/prepare-composite-functions-tf.mlir
"tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], Tout = ["tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], _output_shapes = ["tfshape$dim { size: 9 } dim { size: 10 }", "tfshape$dim { size: -1 } dim { size: 9 } dim { size: 10 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$"], _read_only_resource_inputs = [], config = "", config_proto = "\0A\07\0A\03CPU\10\01\0A\07\0A\03GPU\10\002\02J\008\01", device = "", executor_type...
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 122.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
// Returns true if the n-th operand is ranked and has rank dim. class TFL_OperandHasKnownRank<int n, int dim> : And<[ CPred<"$_op.getOperand(" # n # ").getType().isa<RankedTensorType>()">, CPred<"$_op.getOperand(" # n # ").getType().cast<ShapedType>().getRank() == " # dim>]>; // True if operand n is ranked and has a rank > dim. class TFL_OperandIsRankedAndHasDimPred<int n, int dim> : And<[
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.cc
<< output_rank << ", got " << operand_rank; for (int64_t dim = 0; dim < output_rank; ++dim) { const int64_t operand_dim_size = operand_type.getDimSize(dim); const int64_t result_dim_size = result_dim_sizes[dim]; if (dim == axis) { if (ShapedType::isDynamic(operand_dim_size) || ShapedType::isDynamic(result_dim_size)) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 169.2K bytes - Viewed (0) -
tensorflow/c/c_api.cc
static char empty; int64_t nelems = 1; std::vector<int64_t> dims; dims.reserve(shape.dims()); for (int i = 0; i < shape.dims(); ++i) { dims.push_back(shape.dim_size(i)); nelems *= shape.dim_size(i); } CHECK_EQ(nelems, 0); return TF_NewTensor( dtype, reinterpret_cast<const int64_t*>(dims.data()), shape.dims(), reinterpret_cast<void*>(&empty), 0, [](void*, size_t, void*) {}, nullptr);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 15 03:35:10 UTC 2024 - 102.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/uniform_quantized_stablehlo_to_tfl_pass.cc
TensorType dim_type = input_type.cloneWith({static_cast<int64_t>(1)}, rewriter.getI32Type()); ArrayRef<int32_t> dims(dim_to_expand); auto dim_attr = DenseIntElementsAttr::get(dim_type, dims); auto dim = rewriter.create<arith::ConstantOp>(op.getLoc(), dim_attr); input_shape.insert(input_shape.begin() + dim_to_expand, 1);
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/stablehlo/tests/uniform-quantized-stablehlo-to-tfl.mlir
// CHECK: "tfl.batch_matmul"(%[[ARG]], %[[QCONST_0]]) <{adj_x = false, adj_y = true}> // ----- // Tests static range quantized dot_general with wrong batch dims func.func @dot_general_upstream_srq_too_many_batches(%arg0: tensor<1x1x1x2x3x4x!quant.uniform<i8:f32, 1.000000e+0>>) -> tensor<1x1x1x2x3x5x!quant.uniform<i8:f32, 4.000000e+0>> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 106.2K bytes - Viewed (0)