⚡️ Speed up function sort_chat_inputs_first by 13%

[codeflash-ai]

📄 13% (0.13x) speedup for sort_chat_inputs_first in src/dsa/nodes.py

⏱️ Runtime : 648 microseconds → 574 microseconds (best of 219 runs)

📝 Explanation and details

Here is a high-performance rewrite of your function incorporating recommendations from the profiler and making the core logic as efficient as possible.

Bottleneck Analysis

From your profiler.

• The main hotspots are.
  • "ChatInput" in vertex_id (done repeatedly per vertex, often for the same vertex twice)
  • self.get_vertex(vertex_id) and then self.get_predecessors(...) (done for every "ChatInput" in every layer, re-traversing graph data that may not have changed)
  • layer.remove(vertex_id) (removes items from lists during iteration, which is O(n) and risks skipping elements or doing slow searches)
• Two traversals are needed: first for dependency check, second for restructuring. Can these be merged? Yes.

Key Optimizations

1. One-pass collection: Do a single pass through all vertices, collecting "chat input" vertices and whether they have dependencies, remove them efficiently, and build new layers.
2. Avoid list.remove inside loop: Build new layers without "ChatInput" vertices rather than deleting-in-place, avoiding unnecessary list traversals.
3. Reduce repeated checks: Only call get_vertex/get_predecessors for those IDs that pass the substring check (fast path).
4. Short-circuit quickly: Immediately return if any ChatInput has dependencies.
5. Minimize allocations: Reuse objects and preallocate where possible.

Rewritten Code

Optimization Summary

• Single pass. Only one traversal over the data, both for dependency checking and collecting.
• No in-place removal. Instead of layer.remove, build new lists.
• Minimal checks. Only evaluate slow graph methods immediately before a possible short-circuit.
• No double iteration on "ChatInput" vertices per the original implementation; each is touched once.

Compatibility

• There are no installed libraries, so the above requires only core Python features.
• No changes to function signature or semantics — only the implementation is altered for much better runtime and memory behavior.

Let me know if you want a further-parallelized or NumPy-based approach, but in this data-shape (lists of IDs, graph methods), this is likely optimal.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 40 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest
from src.dsa.nodes import sort_chat_inputs_first

# ================================
# Fixtures and Mocks for testing
# ================================

class DummySelf:
    """
    Dummy class to mock self.get_vertex and self.get_predecessors.
    - vertices: dict mapping vertex_id to a dummy object (could just be the id)
    - predecessors: dict mapping vertex_id to a list of predecessor ids
    """
    def __init__(self, predecessors=None):
        self._vertices = {}
        self._predecessors = predecessors or {}

    def get_vertex(self, vertex_id):
        # Return the vertex object (here just the id)
        self._vertices.setdefault(vertex_id, vertex_id)
        return vertex_id

    def get_predecessors(self, vertex_id):
        # Return the list of predecessors for the given vertex_id
        return self._predecessors.get(vertex_id, [])

# ================================
# Basic Test Cases
# ================================

def test_no_chat_inputs_returns_original():
    """No ChatInput in any layer: should return the original layers unchanged."""
    layers = [["A", "B"], ["C", "D"]]
    dummy = DummySelf()
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 1.25μs -> 1.42μs (11.8% slower)

def test_single_chat_input_no_dependencies():
    """Single ChatInput, no dependencies: should move to first layer."""
    layers = [["A", "ChatInput1"], ["B", "C"]]
    dummy = DummySelf()
    expected = [["ChatInput1"], ["A"], ["B", "C"]]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 2.33μs -> 2.08μs (12.0% faster)

def test_multiple_chat_inputs_no_dependencies():
    """Multiple ChatInputs, no dependencies: all should move to first layer."""
    layers = [["ChatInput1", "A"], ["ChatInput2", "B"]]
    dummy = DummySelf()
    expected = [["ChatInput1", "ChatInput2"], ["A"], ["B"]]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 2.62μs -> 2.38μs (10.5% faster)

def test_chat_input_with_dependency():
    """ChatInput with a dependency: should NOT move any ChatInput."""
    layers = [["ChatInput1", "A"], ["B"]]
    # ChatInput1 has a predecessor, so should not be moved
    dummy = DummySelf(predecessors={"ChatInput1": ["X"]})
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 1.04μs -> 1.08μs (3.88% slower)

def test_mixed_chat_inputs_some_with_dependencies():
    """Some ChatInputs have dependencies, some don't: should NOT move any if any ChatInput has dependency."""
    layers = [["ChatInput1", "A"], ["ChatInput2", "B"]]
    # ChatInput2 has a dependency, so none should be moved
    dummy = DummySelf(predecessors={"ChatInput2": ["Y"]})
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 1.50μs -> 2.00μs (25.0% slower)

def test_chat_input_in_later_layer():
    """ChatInput appears in a non-first layer, no dependencies: should be moved to first."""
    layers = [["A", "B"], ["ChatInput1", "C"]]
    dummy = DummySelf()
    expected = [["ChatInput1"], ["A", "B"], ["C"]]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 1.96μs -> 1.96μs (0.051% slower)

def test_empty_layers():
    """Empty layers should be handled gracefully."""
    layers = [[], ["ChatInput1"], []]
    dummy = DummySelf()
    expected = [["ChatInput1"], [], []]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 2.21μs -> 2.04μs (8.13% faster)

# ================================
# Edge Test Cases
# ================================

def test_empty_input():
    """Empty input (no layers) should return empty list."""
    layers = []
    dummy = DummySelf()
    codeflash_output = sort_chat_inputs_first(dummy, []); result = codeflash_output # 417ns -> 542ns (23.1% slower)

def test_layers_with_only_chat_inputs():
    """All layers only have ChatInputs: should flatten all ChatInputs to first layer."""
    layers = [["ChatInput1"], ["ChatInput2"], ["ChatInput3"]]
    dummy = DummySelf()
    expected = [["ChatInput1", "ChatInput2", "ChatInput3"]]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 3.00μs -> 2.75μs (9.09% faster)

def test_layers_with_duplicate_chat_inputs():
    """Duplicate ChatInputs across layers: should collect all in first layer (duplicates preserved)."""
    layers = [["ChatInput1", "A"], ["ChatInput1", "B"]]
    dummy = DummySelf()
    expected = [["ChatInput1", "ChatInput1"], ["A"], ["B"]]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 2.42μs -> 2.29μs (5.45% faster)


def test_chat_input_with_empty_string_id():
    """ChatInput with empty string id: should not be moved."""
    layers = [["", "A"], ["ChatInput1"]]
    dummy = DummySelf()
    expected = [["ChatInput1"], ["", "A"]]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 2.04μs -> 2.12μs (3.91% slower)

def test_chat_input_substring_in_id():
    """Vertex id contains 'ChatInput' as a substring: should be moved."""
    layers = [["fooChatInputBar", "A"], ["B"]]
    dummy = DummySelf()
    expected = [["fooChatInputBar"], ["A"], ["B"]]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 2.17μs -> 2.04μs (6.12% faster)

def test_chat_input_with_self_dependency():
    """ChatInput with itself as predecessor: should NOT be moved."""
    layers = [["ChatInput1", "A"], ["B"]]
    dummy = DummySelf(predecessors={"ChatInput1": ["ChatInput1"]})
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 1.00μs -> 1.04μs (4.03% slower)

def test_chat_input_with_multiple_dependencies():
    """ChatInput with multiple predecessors: should NOT be moved."""
    layers = [["ChatInput1", "A"], ["B"]]
    dummy = DummySelf(predecessors={"ChatInput1": ["X", "Y"]})
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 958ns -> 1.08μs (11.6% slower)

def test_layers_with_empty_lists_and_chat_inputs():
    """Some layers empty, some with ChatInputs: should move ChatInputs to first layer, keep empty layers."""
    layers = [[], ["ChatInput1"], [], ["A", "ChatInput2"]]
    dummy = DummySelf()
    expected = [["ChatInput1", "ChatInput2"], [], [], ["A"]]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 3.00μs -> 2.79μs (7.49% faster)

# ================================
# Large Scale Test Cases
# ================================

def test_large_number_of_layers_and_chat_inputs():
    """Test with 100 layers, each with a ChatInput and another node, no dependencies."""
    n = 100
    layers = [[f"ChatInput{i}", f"Node{i}"] for i in range(n)]
    dummy = DummySelf()
    expected = [[f"ChatInput{i}" for i in range(n)]] + [[f"Node{i}"] for i in range(n)]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 47.5μs -> 41.5μs (14.3% faster)

def test_large_number_of_layers_no_chat_inputs():
    """Test with 100 layers, no ChatInputs at all."""
    n = 100
    layers = [[f"Node{i}", f"Node{i+1}"] for i in range(n)]
    dummy = DummySelf()
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 16.5μs -> 17.5μs (5.49% slower)

def test_large_number_of_layers_some_chat_inputs_with_dependencies():
    """Test with 100 layers, half ChatInputs have dependencies, none should move."""
    n = 100
    layers = []
    predecessors = {}
    for i in range(n):
        if i % 2 == 0:
            layers.append([f"ChatInput{i}", f"Node{i}"])
            predecessors[f"ChatInput{i}"] = ["X"]
        else:
            layers.append([f"ChatInput{i}", f"Node{i}"])
    dummy = DummySelf(predecessors=predecessors)
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 1.17μs -> 1.25μs (6.64% slower)

def test_large_number_of_layers_all_chat_inputs_no_dependencies():
    """Test with 1000 ChatInputs, all in separate layers, no dependencies."""
    n = 1000
    layers = [[f"ChatInput{i}"] for i in range(n)]
    dummy = DummySelf()
    expected = [[f"ChatInput{i}" for i in range(n)]]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 377μs -> 319μs (18.1% faster)

def test_large_layers_with_mixed_nodes():
    """Test with 10 layers, each with 100 nodes, some ChatInputs scattered, no dependencies."""
    n_layers = 10
    n_per_layer = 100
    layers = []
    chat_inputs = []
    for i in range(n_layers):
        layer = [f"Node{n_per_layer*i + j}" for j in range(n_per_layer)]
        # Add a ChatInput at the start of every even layer
        if i % 2 == 0:
            chat_id = f"ChatInput{i}"
            layer.insert(0, chat_id)
            chat_inputs.append(chat_id)
        layers.append(layer)
    dummy = DummySelf()
    expected = [chat_inputs] + [
        [v for v in layer if "ChatInput" not in v] for layer in layers
    ]
    codeflash_output = sort_chat_inputs_first(dummy, [layer.copy() for layer in layers]); result = codeflash_output # 28.1μs -> 30.5μs (7.92% slower)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from typing import List

# imports
import pytest
from src.dsa.nodes import sort_chat_inputs_first


# Helper class to mock the required methods and state for testing
class MockGraph:
    def __init__(self, predecessors: dict, vertices: dict = None):
        # predecessors: dict mapping vertex_id to list of predecessor ids
        self._predecessors = predecessors
        self._vertices = vertices or {}

    def get_vertex(self, vertex_id):
        # For this mock, just return the vertex_id itself
        return vertex_id

    def get_predecessors(self, vertex_id):
        # Return the list of predecessors for this vertex_id
        return self._predecessors.get(vertex_id, [])

# ---------------------------
# Basic Test Cases
# ---------------------------

def test_no_chat_inputs():
    # No ChatInput nodes at all
    graph = MockGraph(predecessors={})
    layers = [["A", "B"], ["C"]]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 1.08μs -> 1.33μs (18.8% slower)

def test_single_chat_input_no_dependencies():
    # Single ChatInput, no dependencies
    graph = MockGraph(predecessors={"ChatInput1": []})
    layers = [["ChatInput1", "A"], ["B"]]
    expected = [["ChatInput1"], ["A"], ["B"]]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 1.96μs -> 1.83μs (6.82% faster)

def test_multiple_chat_inputs_no_dependencies():
    # Multiple ChatInputs, none with dependencies
    graph = MockGraph(predecessors={"ChatInput1": [], "ChatInput2": []})
    layers = [["ChatInput1", "A"], ["ChatInput2", "B"]]
    expected = [["ChatInput1", "ChatInput2"], ["A"], ["B"]]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 2.21μs -> 2.04μs (8.13% faster)

def test_chat_input_with_dependency():
    # ChatInput node with dependencies; should not move any ChatInput
    graph = MockGraph(predecessors={"ChatInput1": ["X"]})
    layers = [["ChatInput1", "A"], ["B"]]
    input_layers = [layer.copy() for layer in layers]
    codeflash_output = sort_chat_inputs_first(graph, input_layers); result = codeflash_output # 833ns -> 958ns (13.0% slower)

def test_mixed_chat_inputs_some_with_dependencies():
    # Some ChatInputs with dependencies, some without; should not move any
    graph = MockGraph(predecessors={"ChatInput1": [], "ChatInput2": ["A"]})
    layers = [["ChatInput1", "A"], ["ChatInput2", "B"]]
    input_layers = [layer.copy() for layer in layers]
    codeflash_output = sort_chat_inputs_first(graph, input_layers); result = codeflash_output # 1.12μs -> 1.79μs (37.2% slower)

def test_chat_input_not_first_layer():
    # ChatInput node not in the first layer, no dependencies
    graph = MockGraph(predecessors={"ChatInput1": []})
    layers = [["A"], ["ChatInput1", "B"], ["C"]]
    expected = [["ChatInput1"], ["A"], ["B"], ["C"]]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 2.00μs -> 2.04μs (2.06% slower)

def test_chat_input_already_first_layer():
    # ChatInput already in the first layer, no dependencies
    graph = MockGraph(predecessors={"ChatInput1": []})
    layers = [["ChatInput1", "A"], ["B"]]
    expected = [["ChatInput1"], ["A"], ["B"]]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 1.75μs -> 1.75μs (0.000% faster)

def test_multiple_layers_multiple_chat_inputs():
    # Multiple ChatInputs in different layers, no dependencies
    graph = MockGraph(predecessors={"ChatInput1": [], "ChatInput2": [], "ChatInput3": []})
    layers = [["A"], ["ChatInput1", "B"], ["ChatInput2"], ["C", "ChatInput3"]]
    expected = [["ChatInput1", "ChatInput2", "ChatInput3"], ["A"], ["B"], ["C"]]
    # Note: The ChatInputs are collected in order of appearance
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 3.04μs -> 2.88μs (5.81% faster)

# ---------------------------
# Edge Test Cases
# ---------------------------

def test_empty_layers():
    # No layers at all
    graph = MockGraph(predecessors={})
    layers = []
    codeflash_output = sort_chat_inputs_first(graph, []); result = codeflash_output # 417ns -> 542ns (23.1% slower)

def test_layers_with_empty_lists():
    # Layers are empty lists, no ChatInputs
    graph = MockGraph(predecessors={})
    layers = [[], [], []]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 1.12μs -> 1.29μs (12.9% slower)

def test_layers_with_only_chat_inputs():
    # All layers only have ChatInputs, no dependencies
    graph = MockGraph(predecessors={"ChatInput1": [], "ChatInput2": []})
    layers = [["ChatInput1"], ["ChatInput2"]]
    expected = [["ChatInput1", "ChatInput2"]]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 2.04μs -> 2.00μs (2.10% faster)

def test_duplicate_chat_input_names():
    # Duplicate ChatInput names in different layers, no dependencies
    graph = MockGraph(predecessors={"ChatInput1": [], "ChatInput2": []})
    layers = [["ChatInput1"], ["ChatInput1", "ChatInput2"]]
    expected = [["ChatInput1", "ChatInput1", "ChatInput2"]]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 2.58μs -> 2.38μs (8.76% faster)


def test_chat_input_with_empty_dependency_list():
    # ChatInput with empty dependency list (should be moved)
    graph = MockGraph(predecessors={"ChatInput1": []})
    layers = [["A"], ["ChatInput1"]]
    expected = [["ChatInput1"], ["A"]]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 1.83μs -> 1.88μs (2.24% slower)

def test_chat_input_with_none_dependency():
    # ChatInput with None as dependencies (should treat as no dependencies)
    class MockGraphWithNone(MockGraph):
        def get_predecessors(self, vertex_id):
            val = self._predecessors.get(vertex_id, [])
            return [] if val is None else val
    graph = MockGraphWithNone(predecessors={"ChatInput1": None})
    layers = [["ChatInput1", "A"]]
    expected = [["ChatInput1"], ["A"]]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 2.12μs -> 2.00μs (6.25% faster)

def test_layers_are_mutated():
    # Ensure input layers are not mutated by the function
    graph = MockGraph(predecessors={"ChatInput1": []})
    layers = [["ChatInput1", "A"], ["B"]]
    layers_copy = [layer.copy() for layer in layers]
    sort_chat_inputs_first(graph, layers_copy)

# ---------------------------
# Large Scale Test Cases
# ---------------------------

def test_large_number_of_layers_and_vertices():
    # Many layers, each with many vertices, some ChatInputs
    num_layers = 50
    num_vertices_per_layer = 10
    chat_inputs = [f"ChatInput{i}" for i in range(10)]
    # Place one ChatInput in each of the first 10 layers
    layers = []
    for i in range(num_layers):
        layer = [f"V{i}_{j}" for j in range(num_vertices_per_layer)]
        if i < len(chat_inputs):
            layer.append(chat_inputs[i])
        layers.append(layer)
    # All ChatInputs have no dependencies
    predecessors = {cid: [] for cid in chat_inputs}
    graph = MockGraph(predecessors=predecessors)
    expected_first_layer = chat_inputs.copy()
    # Remove ChatInputs from original layers for expected output
    expected_layers = []
    for i, layer in enumerate(layers):
        expected_layer = [v for v in layer if v not in chat_inputs]
        if expected_layer:
            expected_layers.append(expected_layer)
    expected = [expected_first_layer] + expected_layers
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 24.3μs -> 21.6μs (12.5% faster)

def test_large_number_of_chat_inputs_with_dependencies():
    # Many ChatInputs, at least one with dependencies
    num_chat_inputs = 20
    chat_inputs = [f"ChatInput{i}" for i in range(num_chat_inputs)]
    layers = [[cid] for cid in chat_inputs]
    # Set one ChatInput to have dependencies
    predecessors = {cid: [] for cid in chat_inputs}
    predecessors["ChatInput10"] = ["A"]
    graph = MockGraph(predecessors=predecessors)
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 2.75μs -> 4.79μs (42.6% slower)

def test_large_layers_no_chat_inputs():
    # Large number of layers and vertices, no ChatInputs
    num_layers = 100
    num_vertices_per_layer = 8
    layers = [[f"V{i}_{j}" for j in range(num_vertices_per_layer)] for i in range(num_layers)]
    graph = MockGraph(predecessors={})
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 29.5μs -> 31.2μs (5.60% slower)

def test_large_layers_all_chat_inputs():
    # All vertices are ChatInputs, none with dependencies
    num_layers = 10
    num_chat_inputs_per_layer = 10
    chat_inputs = [f"ChatInput{i}_{j}" for i in range(num_layers) for j in range(num_chat_inputs_per_layer)]
    layers = []
    idx = 0
    for i in range(num_layers):
        layer = []
        for j in range(num_chat_inputs_per_layer):
            layer.append(chat_inputs[idx])
            idx += 1
        layers.append(layer)
    predecessors = {cid: [] for cid in chat_inputs}
    graph = MockGraph(predecessors=predecessors)
    expected = [chat_inputs]
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 25.5μs -> 19.5μs (31.0% faster)

def test_performance_with_maximum_elements():
    # Stress test with close to 1000 elements
    num_layers = 20
    num_vertices_per_layer = 50
    chat_inputs = [f"ChatInput{i}" for i in range(20)]
    layers = []
    for i in range(num_layers):
        layer = [f"V{i}_{j}" for j in range(num_vertices_per_layer)]
        if i < len(chat_inputs):
            layer.append(chat_inputs[i])
        layers.append(layer)
    predecessors = {cid: [] for cid in chat_inputs}
    graph = MockGraph(predecessors=predecessors)
    expected_first_layer = chat_inputs.copy()
    expected_layers = []
    for i, layer in enumerate(layers):
        expected_layer = [v for v in layer if v not in chat_inputs]
        if expected_layer:
            expected_layers.append(expected_layer)
    expected = [expected_first_layer] + expected_layers
    codeflash_output = sort_chat_inputs_first(graph, [layer.copy() for layer in layers]); result = codeflash_output # 41.4μs -> 32.1μs (29.1% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-sort_chat_inputs_first-mc8popkq and push.