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@codeflash-ai codeflash-ai bot commented Oct 21, 2025

📄 677% (6.77x) speedup for get_dummy_client in framework/py/flwr/simulation/ray_transport/ray_client_proxy_test.py

⏱️ Runtime : 5.06 milliseconds 651 microseconds (best of 360 runs)

📝 Explanation and details

The optimization introduces a wrapper class caching mechanism to avoid expensive dynamic class creation overhead in the to_client() method.

Key Changes:

  • Added _wrapper_cache dictionary to store dynamically created wrapper classes by client type
  • Modified to_client() to check cache first before calling _wrap_numpy_client()
  • Only performs expensive wrapper class creation once per client class type, then reuses the cached class

Why This Provides a 677% Speedup:
The original code called _wrap_numpy_client() on every invocation, which dynamically creates a new class using type() - an expensive operation involving reflection and dictionary construction. The optimization caches the wrapper class type after first creation, so subsequent calls only need a fast dictionary lookup and constructor call.

Performance Analysis:

  • Line profiler shows the expensive _wrap_numpy_client() call (28,492 ns) now only executes once instead of 1,034 times
  • Cache lookup (cls not in _wrapper_cache) is much faster at 241.6 ns per hit
  • The cached constructor call (_wrapper_cache[cls](numpy_client=self)) at 704.2 ns is significantly cheaper than full wrapper creation

Ideal Test Cases:
This optimization excels when the same client class types are converted repeatedly (as shown in the test results), which is typical in federated learning scenarios where multiple instances of the same client class need conversion to the Client interface.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 1035 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import pytest  # used for our unit tests
from simulation.ray_transport.ray_client_proxy_test import get_dummy_client


# Minimal stubs for required classes, since we can't import flwr in this test environment.
class Client:
    """Minimal Client base class."""
    def __init__(self, client_id=None):
        self.client_id = client_id

class DummyClient:
    """DummyClient that mimics expected behavior for testing."""
    def __init__(self, node_id, state):
        self.node_id = node_id
        self.state = state

    def to_client(self):
        # Returns a Client instance with client_id set to node_id for testability
        return Client(client_id=self.node_id)

class Context:
    """Context stub with node_id and state."""
    def __init__(self, node_id, state):
        self.node_id = node_id
        self.state = state
from simulation.ray_transport.ray_client_proxy_test import get_dummy_client

# unit tests

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

def test_basic_valid_context():
    """Test that get_dummy_client returns a Client with correct client_id for normal input."""
    context = Context(node_id=123, state="active")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_basic_string_node_id():
    """Test with node_id as a string."""
    context = Context(node_id="abc", state="ready")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_basic_state_none():
    """Test with state as None."""
    context = Context(node_id=456, state=None)
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_basic_state_empty_string():
    """Test with state as empty string."""
    context = Context(node_id=789, state="")
    codeflash_output = get_dummy_client(context); client = codeflash_output

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

def test_edge_node_id_zero():
    """Test with node_id as zero."""
    context = Context(node_id=0, state="inactive")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_node_id_negative():
    """Test with node_id as negative integer."""
    context = Context(node_id=-1, state="error")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_node_id_empty_string():
    """Test with node_id as empty string."""
    context = Context(node_id="", state="empty")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_state_large_string():
    """Test with state as a very large string."""
    large_state = "x" * 512
    context = Context(node_id=1, state=large_state)
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_node_id_none():
    """Test with node_id as None."""
    context = Context(node_id=None, state="none")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_state_object():
    """Test with state as an object."""
    class DummyState:
        pass
    state_obj = DummyState()
    context = Context(node_id=999, state=state_obj)
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_node_id_float():
    """Test with node_id as a float."""
    context = Context(node_id=3.1415, state="float")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_state_list():
    """Test with state as a list."""
    context = Context(node_id=888, state=[1,2,3])
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_context_missing_attributes():
    """Test with context missing node_id/state attributes."""
    class BadContext:
        def __init__(self):
            pass
    bad_context = BadContext()
    with pytest.raises(AttributeError):
        get_dummy_client(bad_context)

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


def test_large_scale_large_node_id():
    """Test with very large node_id value."""
    large_id = 10**18
    context = Context(node_id=large_id, state="large")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_large_scale_long_state():
    """Test with state as a long list."""
    long_state = list(range(1000))
    context = Context(node_id=42, state=long_state)
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_large_scale_unicode_node_id():
    """Test with node_id as a unicode string."""
    context = Context(node_id="用户42", state="unicode_state")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_large_scale_varied_types():
    """Test with node_id and state as various types in a batch."""
    samples = [
        (None, None),
        (1, {}),
        ("id", []),
        (2.5, set([1,2])),
        (True, False),
        (b"bytes", b"bytes_state"),
    ]
    for node_id, state in samples:
        context = Context(node_id=node_id, state=state)
        codeflash_output = get_dummy_client(context); client = codeflash_output
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import pytest  # used for our unit tests
from simulation.ray_transport.ray_client_proxy_test import get_dummy_client

# --- Function to test (copied from ray_client_proxy_test.py) ---

# Minimal stand-in for Context, DummyClient, and Client to make tests self-contained.
class Client:
    """Minimal Client base class."""
    def __init__(self, node_id=None, state=None):
        self.node_id = node_id
        self.state = state

class DummyClient:
    """DummyClient simulates a federated learning client."""
    def __init__(self, node_id, state):
        self.node_id = node_id
        self.state = state

    def to_client(self):
        # Returns a Client instance with the same node_id and state
        return Client(node_id=self.node_id, state=self.state)

class Context:
    """Context holds node_id and state information."""
    def __init__(self, node_id, state):
        self.node_id = node_id
        self.state = state
from simulation.ray_transport.ray_client_proxy_test import get_dummy_client

# --- Unit Tests ---

# BASIC TEST CASES

def test_basic_returns_client_instance():
    """Test that get_dummy_client returns an instance of Client."""
    context = Context(node_id=1, state="active")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_basic_node_id_and_state_preserved():
    """Test that node_id and state are correctly passed through."""
    context = Context(node_id=42, state="ready")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_basic_different_types_for_state():
    """Test that state can be of any type (string, int, dict)."""
    context = Context(node_id=2, state=123)
    codeflash_output = get_dummy_client(context); client = codeflash_output

    context = Context(node_id=3, state={"foo": "bar"})
    codeflash_output = get_dummy_client(context); client = codeflash_output

# EDGE TEST CASES

def test_edge_node_id_zero_and_none():
    """Test edge values for node_id: zero and None."""
    context = Context(node_id=0, state="idle")
    codeflash_output = get_dummy_client(context); client = codeflash_output

    context = Context(node_id=None, state="unknown")
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_state_none_and_empty():
    """Test edge values for state: None and empty string/dict."""
    context = Context(node_id=1, state=None)
    codeflash_output = get_dummy_client(context); client = codeflash_output

    context = Context(node_id=1, state="")
    codeflash_output = get_dummy_client(context); client = codeflash_output

    context = Context(node_id=1, state={})
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_large_node_id_and_state():
    """Test very large node_id and state values."""
    large_id = 10**18
    large_state = "x" * 1000  # 1000-char string
    context = Context(node_id=large_id, state=large_state)
    codeflash_output = get_dummy_client(context); client = codeflash_output

def test_edge_state_mutable_object():
    """Test that mutable state objects are handled (e.g., list)."""
    state_list = [1, 2, 3]
    context = Context(node_id=5, state=state_list)
    codeflash_output = get_dummy_client(context); client = codeflash_output

    # Mutate original state and check that client.state is not affected (shallow copy)
    state_list.append(4)

def test_edge_state_custom_object():
    """Test that custom objects can be used as state."""
    class CustomState:
        def __init__(self, value):
            self.value = value
        def __eq__(self, other):
            return isinstance(other, CustomState) and self.value == other.value
    custom_state = CustomState(99)
    context = Context(node_id=7, state=custom_state)
    codeflash_output = get_dummy_client(context); client = codeflash_output

# LARGE SCALE TEST CASES


def test_large_scale_large_state_object():
    """Test with a large state object (large dict)."""
    large_dict = {str(i): i for i in range(1000)}
    context = Context(node_id=123, state=large_dict)
    codeflash_output = get_dummy_client(context); client = codeflash_output
    # Ensure state is not mutated by reference
    large_dict["new_key"] = "new_value"

To edit these changes git checkout codeflash/optimize-get_dummy_client-mh16sbsw and push.

Codeflash

The optimization introduces a **wrapper class caching mechanism** to avoid expensive dynamic class creation overhead in the `to_client()` method.

**Key Changes:**
- Added `_wrapper_cache` dictionary to store dynamically created wrapper classes by client type
- Modified `to_client()` to check cache first before calling `_wrap_numpy_client()`
- Only performs expensive wrapper class creation once per client class type, then reuses the cached class

**Why This Provides a 677% Speedup:**
The original code called `_wrap_numpy_client()` on every invocation, which dynamically creates a new class using `type()` - an expensive operation involving reflection and dictionary construction. The optimization caches the wrapper class type after first creation, so subsequent calls only need a fast dictionary lookup and constructor call.

**Performance Analysis:**
- Line profiler shows the expensive `_wrap_numpy_client()` call (28,492 ns) now only executes once instead of 1,034 times
- Cache lookup (`cls not in _wrapper_cache`) is much faster at 241.6 ns per hit
- The cached constructor call (`_wrapper_cache[cls](numpy_client=self)`) at 704.2 ns is significantly cheaper than full wrapper creation

**Ideal Test Cases:**
This optimization excels when the same client class types are converted repeatedly (as shown in the test results), which is typical in federated learning scenarios where multiple instances of the same client class need conversion to the Client interface.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 21, 2025 23:19
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 21, 2025
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