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Results 41 - 50 of 93 for getAxes (0.23 sec)
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tensorflow/compiler/mlir/tensorflow/transforms/tpu_resource_partitioning.cc
xla::OpSharding sharding; sharding.ParseFromString( old_partitioned_input.get_XlaShardingAttr().getValue().str()); for (OpOperand& read_use : llvm::make_early_inc_range(old_read.getValue().getUses())) { if (dyn_cast_or_null<tf_device::ClusterFuncOp>(read_use.getOwner())) { // ClusterFunc's use of the Read is replaced with use of the // TPUPartitionedInputV2. read_use.set(new_partitioned_input);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 06 19:12:29 UTC 2023 - 11.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/passes/tpu_cluster_formation.cc
for (auto ret_vals : llvm::zip(results, cluster.getResults())) { Value old_ret = std::get<0>(ret_vals); Value new_ret = std::get<1>(ret_vals); for (auto& use : llvm::make_early_inc_range(old_ret.getUses())) { Operation* user = use.getOwner(); if (!body->findAncestorOpInBlock(*user)) use.set(new_ret); } } // Move ops that depend on something in the cluster behind the cluster.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 22:03:30 UTC 2024 - 39.3K bytes - Viewed (0) -
src/main/java/jcifs/smb/NtlmUtil.java
Registered: Wed Jun 12 15:45:55 UTC 2024 - Last Modified: Tue Jul 07 12:07:20 UTC 2020 - 9.7K bytes - Viewed (0) -
pkg/kubelet/volume_host.go
return kvh.kubelet.nodeName } func (kvh *kubeletVolumeHost) GetEventRecorder() record.EventRecorder { return kvh.kubelet.recorder } func (kvh *kubeletVolumeHost) GetExec(pluginName string) utilexec.Interface { return kvh.exec
Registered: Sat Jun 15 01:39:40 UTC 2024 - Last Modified: Thu Apr 18 11:00:37 UTC 2024 - 10K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantize_weights.cc
while (!uses_at_current_level.empty()) { llvm::SmallVector<mlir::Value> next_values_to_visit; for (auto cur_op : uses_at_current_level) { for (auto& cur_op_use : cur_op.getUses()) { Operation* next_op = cur_op_use.getOwner(); int next_op_operand_num = cur_op_use.getOperandNumber(); if (auto call_op = llvm::dyn_cast<mlir::CallOpInterface>(next_op)) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 07:39:40 UTC 2024 - 11.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/odml_converter/transforms/outline_composites.cc
// if there is only one. Operation* GetUserIfOnlyOne(Operation* op) { if (op->getNumResults() != 1) return nullptr; auto result = op->getResult(0); if (!result.hasOneUse()) return nullptr; return (*result.getUses().begin()).getOwner(); } // Gets operation providing value for the given operand of given operation // if the given operation is the only user. Operation* GetInputOpWithOneUse(Operation* op, int opr_num) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 9.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/insert_weight_param.cc
if (!type || !type.getElementType().isF32()) { return failure(); } return success( op->hasOneUse() && IsWeightQuantizableFunction(*op->getUses().begin(), type.getRank())); } // Checks if the operand is second operand of `tf.XlaCallModule` op for // `stablehlo.convolution` or `stablehlo.dot_general` with fully_quantizable // trait.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 10.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/default_quant_params.cc
// bias is used immediately by the user. This assumption is always correct // after constant folding. bool UsedAsBias(Value value) { for (auto &use : value.getUses()) { auto biases = TFL::GetOpQuantSpec(use.getOwner())->biases_params; if (biases.find(use.getOperandNumber()) != biases.end()) return true; } return false; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 9.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize_drq.cc
if (!type || !type.getElementType().isF32()) return false; Value value = op.getResult(); // Check whether dynamic range quantization can be applied. for (auto& use : value.getUses()) { Operation* user = use.getOwner(); int operand_num = use.getOperandNumber(); std::unique_ptr<OpQuantSpec> spec = GetTFOpQuantSpec(user);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/legalize_tf.cc
auto values = tf_concat_op.getValues(); auto output_type = tf_concat_op.getOutput().getType(); // Extract axis attribute from constant axis tensor ElementsAttr axis; if (!matchPattern(tf_concat_op.getAxis(), m_Constant(&axis))) return failure(); IntegerAttr axis_int = ExtractSingleElementAsInteger(axis); // "axis" operand could be a i64 tensor. Resolve it here. IntegerAttr axis_i32;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 20 20:06:54 UTC 2024 - 45.2K bytes - Viewed (0)