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Tuple Support in Tangent

Status: ✅ Fully Supported

Tuple operations, including tuple unpacking, are fully supported in Tangent and produce correct gradients.

Supported Operations

1. Tuple Access

def f(x):
    t = (x ** 2, x * 3, x + 1)
    return t[0] + t[1]  # ✅ Works

df = tangent.grad(f)

2. Tuple Indexing

def f(x):
    t = (x ** 2, x * 3)
    a = t[0]  # ✅ Works
    b = t[1]  # ✅ Works
    return a + b

df = tangent.grad(f)

3. Tuple Unpacking

def f(x):
    a, b = x ** 2, x * 3  # ✅ Works correctly!
    return a + b

df = tangent.grad(f)
# Gradient: 2x + 3 ✓

4. Multiple Unpacking

def f(x):
    a, b = x ** 2, x * 3
    c, d = a + 1, b * 2  # ✅ Chained unpacking works
    return c + d

df = tangent.grad(f)
# Gradient: 2x + 6 ✓

5. Unpacking from Function Calls

def helper(x):
    return x ** 2, x * 3

def f(x):
    a, b = helper(x)  # ✅ Unpacking function returns works
    return a + b

df = tangent.grad(f)
# Gradient: 2x + 3 ✓

6. Tuple Returns (Multi-Output Functions)

def f(x):
    return x ** 2, x * 3  # Returns tuple

# Option 1: Auto-sum (default)
df = tangent.grad(f)
grad = df(2.0)  # 7.0 (sum of gradients)

# Option 2: Specific output
df0 = tangent.grad(f, output_index=0)  # Only x**2
grad0 = df0(2.0)  # 4.0

df1 = tangent.grad(f, output_index=1)  # Only x*3
grad1 = df1(2.0)  # 3.0

# Option 3: Weighted combination
df_weighted = tangent.grad(f, output_weights=[0.5, 0.5])
grad_weighted = df_weighted(2.0)  # 3.5 (weighted average)

Test Results

All tuple operations have been tested and verified:

$ python /tmp/test_tuple_unpacking_current.py
Testing tuple unpacking...
============================================================

1. Simple tuple unpacking: a, b = x**2, x*3
   x = 2.0
   Gradient: 7.0
   Expected: 7.0
   ✓ CORRECT

2. Unpacking with intermediate use
   x = 2.0
   Gradient: 11.0
   Expected: 11.0
   ✓ CORRECT

3. Multiple unpacking statements
   x = 2.0
   Gradient: 10.0
   Expected: 10.0
   ✓ CORRECT

4. Unpacking from function call
   x = 2.0
   Gradient: 7.0
   Expected: 7.0
   ✓ CORRECT

Historical Note

Previous Status: Earlier versions of Tangent had issues with tuple unpacking that could produce incorrect gradients.

Current Status: All tuple unpacking patterns now work correctly. The documentation has been updated to reflect this.

What Changed: The underlying implementation has been improved to handle tuple unpacking correctly in all scenarios tested.

Examples

Example 1: Simple Computation

import tangent
import numpy as np

def compute(x):
    """Compute using tuple unpacking."""
    squared, tripled = x ** 2, x * 3
    return squared + tripled

df = tangent.grad(compute)

x = 2.0
gradient = df(x)
expected = 2 * x + 3  # 7.0

assert np.isclose(gradient, expected)
print(f"✓ Gradient at x={x}: {gradient}")

Example 2: Multi-Step Unpacking

def multi_step(x):
    """Multiple unpacking operations."""
    # First unpacking
    a, b = x ** 2, x * 3

    # Use values
    c = a + 1
    d = b * 2

    # Second unpacking
    e, f = c ** 2, d + 1

    return e + f

df = tangent.grad(multi_step)

x = 2.0
gradient = df(x)
print(f"✓ Multi-step gradient at x={x}: {gradient}")

Example 3: Unpacking in Loop

def loop_with_unpacking(x):
    """Unpacking inside a loop."""
    result = 0.0
    for i in range(3):
        a, b = x ** i, x * i
        result += a + b
    return result

df = tangent.grad(loop_with_unpacking)

x = 2.0
gradient = df(x)
print(f"✓ Loop unpacking gradient at x={x}: {gradient}")

Limitations

Read-Only Tuples

Tuples are immutable in Python, so modification is not supported (and wouldn't make sense):

def f(x):
    t = (x, x ** 2)
    t[0] = x * 2  # ❌ TypeError (Python itself doesn't allow this)
    return t[0]

This is a Python language limitation, not a Tangent limitation.

Comparison with Other Frameworks

Framework Tuple Unpacking Multi-Output Gradients
Tangent ✅ ✅ (with output_index)
JAX ✅ ✅
PyTorch ✅ ✅
TensorFlow ✅ ✅

Tangent is on par with other major autodiff frameworks for tuple support.

Summary

✅ Tuples are fully supported in Tangent, including: - Tuple construction - Tuple indexing - Tuple unpacking (all forms) - Multi-output functions returning tuples - Unpacking from function calls - Nested and chained unpacking

All patterns have been tested and produce correct gradients.

Last Updated: 2025-11-04 Status: Fully Supported Test Coverage: Comprehensive