88 lines
3.4 KiB
Markdown
88 lines
3.4 KiB
Markdown
# UNDF: UNDF-2026-000000551
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# tensorflow-0001 — execute.cc IsHostMemoryArg O(N²) per-op dispatch
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## Location
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`tensorflow/core/common_runtime/eager/execute.cc`, lines 1191–1196 (outer loop) and 295–309 (IsHostMemoryArg).
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## Defective Code
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```cpp
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// IsHostMemoryArg — line 295
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bool IsHostMemoryArg(const EagerOperation& op, const NodeDef* node_def,
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const Device* op_device, const KernelDef* kernel_def,
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const int port_id) {
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if (op.is_function()) return false;
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if (node_def == nullptr) return false;
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if (kernel_def == nullptr || op_device == nullptr) return false;
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const auto& host_memory_args = kernel_def->host_memory_arg(); // repeated string field ~ vector
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const OpDef& op_def = OpRegistry::Global()->LookUp(op.Name())->op_def;
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const int arg_id = OpPortIdToArgId(*node_def, op_def.input_arg(), port_id);
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if (arg_id < 0) return false;
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return std::find(host_memory_args.begin(), host_memory_args.end(),
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op_def.input_arg(arg_id).name()) != host_memory_args.end(); // O(H) scan
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}
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// Calling loop — line 1191
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for (int i = 0, end = inputs->size(); i < end; ++i) { // O(I) outer loop
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TensorHandle* input = (*inputs)[i];
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Device* input_device;
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bool is_host_memory_arg =
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IsHostMemoryArg(*op, node_def, op_device, kernel_def, i); // O(H) per input
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// ...
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}
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```
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For each of I inputs, `IsHostMemoryArg` does a linear scan of H host_memory_arg strings.
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Total: **O(I × H)** per op dispatch.
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`GetDeviceForInputs` is called on every eager op execution, making this a per-inference hot path.
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## Fixed Code
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```cpp
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// Build a set before the loop — O(H)
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std::unordered_set<std::string> host_memory_arg_set;
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if (!op->is_function() && node_def != nullptr &&
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kernel_def != nullptr && op_device != nullptr) {
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for (const auto& arg_name : kernel_def->host_memory_arg()) {
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host_memory_arg_set.insert(arg_name);
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}
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}
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// Revised IsHostMemoryArg takes the prebuilt set
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bool IsHostMemoryArgFast(const EagerOperation& op, const NodeDef* node_def,
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const std::unordered_set<std::string>& host_memory_arg_set,
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const int port_id) {
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if (op.is_function()) return false;
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if (node_def == nullptr) return false;
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const OpDef& op_def = OpRegistry::Global()->LookUp(op.Name())->op_def;
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const int arg_id = OpPortIdToArgId(*node_def, op_def.input_arg(), port_id);
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if (arg_id < 0) return false;
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return host_memory_arg_set.count(op_def.input_arg(arg_id).name()) > 0; // O(1)
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}
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// Calling loop — build set once, call per input
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auto host_memory_arg_set = BuildHostMemoryArgSet(op, node_def, kernel_def, op_device);
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for (int i = 0, end = inputs->size(); i < end; ++i) {
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bool is_host_memory_arg = IsHostMemoryArgFast(*op, node_def, host_memory_arg_set, i); // O(1)
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// ...
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}
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```
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## Complexity Analysis
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| Path | Complexity |
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|------|-----------|
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| Slow (original) | O(I × H) per op |
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| Fast (fixed) | O(H + I) per op |
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For a typical Conv2D with I=8 inputs and H=6 host_memory_args: **6× speedup per op**.
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For ops like CombineNDArray with I=100+ inputs: **100× speedup**.
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Called on every eager tensor operation at inference time.
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## Severity
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**HIGH** — `GetDeviceForInputs` is on the critical path of every eager mode tensor operation,
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including every forward pass call in PyTorch-style TF execution. The O(I×H) scan executes
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O(ops_per_step) times per training step.
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