Initial Query

I have list of indexes and I want to convert it to one hot vector using jax and the function should be jittable and the function should be jitted. name the function one_hot and it should get two arguments the first one is the indexes and the second one is the number of possible labeles. Just give me the code

LLM Generation

Query

I have list of indexes and I want to convert it to one hot vector using jax and the function should be jittable and the function should be jitted. name the function one_hot and it should get two arguments the first one is the indexes and the second one is the number of possible labeles. Just give me the code

Output

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import jax
import jax.numpy as jnp
import jax.nn as jnn # Contains the one_hot function

# Define the core function implementation
def _one_hot_impl(indexes, num_labels):
  """
  Converts a list or array of indices to one-hot vectors using JAX.

  Args:
    indexes: A JAX array (or array-like, e.g., list, numpy array)
             of non-negative integers representing the indices.
    num_labels: An integer representing the total number of possible labels
                (the depth of the one-hot vector).

  Returns:
    A JAX array where each row corresponds to an index from the input,
    represented as a one-hot vector. The shape will be (len(indexes), num_labels).
  """
  # Ensure indexes is a JAX array for potential performance benefits,
  # although jax.nn.one_hot often handles conversion implicitly.
  # indexes = jnp.asarray(indexes) # Optional explicit conversion

  # Use jax.nn.one_hot for efficient implementation
  # It automatically handles the creation of the one-hot matrix.
  # Default dtype is float based on jax_enable_x64 status (usually float32)
  return jnn.one_hot(indexes, num_classes=num_labels)

# Create the jitted version of the function
one_hot = jax.jit(_one_hot_impl)

# --- Example Usage ---
if __name__ == "__main__":
  # Example list of indices
  index_list = [0, 2, 1, 3, 0]
  # Can also use a jax array or numpy array
  # index_array = jnp.array([0, 2, 1, 3, 0])

  # Number of possible labels (classes)
  num_classes = 5

  # Convert using the jitted function
  # The first call will trigger JIT compilation, subsequent calls will be faster.
  print(f"Input Indexes: {index_list}")
  print(f"Number of Labels: {num_classes}")

  one_hot_vectors = one_hot(index_list, num_classes)
  # one_hot_vectors = one_hot(index_array, num_classes) # Works the same

  print("\nOutput One-Hot Vectors:")
  print(one_hot_vectors)
  print(f"\nOutput Shape: {one_hot_vectors.shape}")
  print(f"Output Dtype: {one_hot_vectors.dtype}")

  # Verify the output for the first index (0)
  print(f"\nVerification: First vector corresponds to index {index_list[0]}: {one_hot_vectors[0]}")
  # Verify the output for the second index (2)
  print(f"Verification: Second vector corresponds to index {index_list[1]}: {one_hot_vectors[1]}")

  # Example with different data
  indices_2 = jnp.array([4, 0, 2])
  num_classes_2 = 6
  one_hot_2 = one_hot(indices_2, num_classes_2)
  print("\n--- Another Example ---")
  print(f"Input Indexes: {indices_2}")
  print(f"Number of Labels: {num_classes_2}")
  print("Output One-Hot Vectors:")
  print(one_hot_2)
**Explanation:** 1. **Import necessary libraries:** `jax`, `jax.numpy` (commonly aliased as `jnp`), and `jax.nn` (which contains `one_hot`, commonly aliased as `jnn`). 2. **`_one_hot_impl(indexes, num_labels)`:** This is the core Python function that performs the logic. * It takes `indexes` (your list or array) and `num_labels` as input. * It directly uses `jax.nn.one_hot(indexes, num_classes=num_labels)`. This is the standard, efficient JAX function for this exact purpose. It handles creating the output array of the correct shape `(len(indexes), num_labels)` and placing the `1.0` at the correct positions. 3. **`one_hot = jax.jit(_one_hot_impl)`:** This line creates the final `one_hot` function you requested. It takes the implementation function `_one_hot_impl` and applies `jax.jit` to it. This means JAX will compile this function to highly optimized XLA code the first time it's called with specific input shapes and dtypes. Subsequent calls with compatible inputs will use the cached compiled code, making them much faster. 4. **Example Usage (`if __name__ == "__main__":`)**: This block demonstrates how to use the `one_hot` function and prints the results for verification. It shows that you can pass a standard Python list directly to the jitted function.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp
import jax.nn as jnn # Contains the one_hot function

# Define the core function implementation
def _one_hot_impl(indexes, num_labels):
  """
  Converts a list or array of indices to one-hot vectors using JAX.

  Args:
    indexes: A JAX array (or array-like, e.g., list, numpy array)
             of non-negative integers representing the indices.
    num_labels: An integer representing the total number of possible labels
                (the depth of the one-hot vector).

  Returns:
    A JAX array where each row corresponds to an index from the input,
    represented as a one-hot vector. The shape will be (len(indexes), num_labels).
  """
  # Ensure indexes is a JAX array for potential performance benefits,
  # although jax.nn.one_hot often handles conversion implicitly.
  # indexes = jnp.asarray(indexes) # Optional explicit conversion

  # Use jax.nn.one_hot for efficient implementation
  # It automatically handles the creation of the one-hot matrix.
  # Default dtype is float based on jax_enable_x64 status (usually float32)
  return jnn.one_hot(indexes, num_classes=num_labels)

# Create the jitted version of the function
one_hot = jax.jit(_one_hot_impl)

# --- Example Usage ---
if __name__ == "__main__":
  # Example list of indices
  index_list = [0, 2, 1, 3, 0]
  # Can also use a jax array or numpy array
  # index_array = jnp.array([0, 2, 1, 3, 0])

  # Number of possible labels (classes)
  num_classes = 5

  # Convert using the jitted function
  # The first call will trigger JIT compilation, subsequent calls will be faster.
  print(f"Input Indexes: {index_list}")
  print(f"Number of Labels: {num_classes}")

  one_hot_vectors = one_hot(index_list, num_classes)
  # one_hot_vectors = one_hot(index_array, num_classes) # Works the same

  print("\nOutput One-Hot Vectors:")
  print(one_hot_vectors)
  print(f"\nOutput Shape: {one_hot_vectors.shape}")
  print(f"Output Dtype: {one_hot_vectors.dtype}")

  # Verify the output for the first index (0)
  print(f"\nVerification: First vector corresponds to index {index_list[0]}: {one_hot_vectors[0]}")
  # Verify the output for the second index (2)
  print(f"Verification: Second vector corresponds to index {index_list[1]}: {one_hot_vectors[1]}")

  # Example with different data
  indices_2 = jnp.array([4, 0, 2])
  num_classes_2 = 6
  one_hot_2 = one_hot(indices_2, num_classes_2)
  print("\n--- Another Example ---")
  print(f"Input Indexes: {indices_2}")
  print(f"Number of Labels: {num_classes_2}")
  print("Output One-Hot Vectors:")
  print(one_hot_2)

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp
import jax.nn as jnn # Contains the one_hot function

# Define the core function implementation
def _one_hot_impl(indexes, num_labels):
  """
  Converts a list or array of indices to one-hot vectors using JAX.

  Args:
    indexes: A JAX array (or array-like, e.g., list, numpy array)
             of non-negative integers representing the indices.
    num_labels: An integer representing the total number of possible labels
                (the depth of the one-hot vector).

  Returns:
    A JAX array where each row corresponds to an index from the input,
    represented as a one-hot vector. The shape will be (len(indexes), num_labels).
  """
  # Ensure indexes is a JAX array for potential performance benefits,
  # although jax.nn.one_hot often handles conversion implicitly.
  # indexes = jnp.asarray(indexes) # Optional explicit conversion

  # Use jax.nn.one_hot for efficient implementation
  # It automatically handles the creation of the one-hot matrix.
  # Default dtype is float based on jax_enable_x64 status (usually float32)
  return jnn.one_hot(indexes, num_classes=num_labels)

# Create the jitted version of the function
one_hot = jax.jit(_one_hot_impl)

# --- Example Usage ---
if __name__ == "__main__":
  # Example list of indices
  index_list = [0, 2, 1, 3, 0]
  # Can also use a jax array or numpy array
  # index_array = jnp.array([0, 2, 1, 3, 0])

  # Number of possible labels (classes)
  num_classes = 5

  # Convert using the jitted function
  # The first call will trigger JIT compilation, subsequent calls will be faster.
  print(f"Input Indexes: {index_list}")
  print(f"Number of Labels: {num_classes}")

  one_hot_vectors = one_hot(index_list, num_classes)
  # one_hot_vectors = one_hot(index_array, num_classes) # Works the same

  print("\nOutput One-Hot Vectors:")
  print(one_hot_vectors)
  print(f"\nOutput Shape: {one_hot_vectors.shape}")
  print(f"Output Dtype: {one_hot_vectors.dtype}")

  # Verify the output for the first index (0)
  print(f"\nVerification: First vector corresponds to index {index_list[0]}: {one_hot_vectors[0]}")
  # Verify the output for the second index (2)
  print(f"Verification: Second vector corresponds to index {index_list[1]}: {one_hot_vectors[1]}")

  # Example with different data
  indices_2 = jnp.array([4, 0, 2])
  num_classes_2 = 6
  one_hot_2 = one_hot(indices_2, num_classes_2)
  print("\n--- Another Example ---")
  print(f"Input Indexes: {indices_2}")
  print(f"Number of Labels: {num_classes_2}")
  print("Output One-Hot Vectors:")
  print(one_hot_2)



import jax.numpy as jnp

answer = str(one_hot(jnp.array([1,2,0]),5))
expected = str(jnp.array([[0.0,1.0,0.0,0.0,0.0],[0.0,0.0,1.0,0.0,0.0],[1.0,0.0,0.0,0.0,0.0]]))
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: `` Input Indexes: [0, 2, 1, 3, 0] Number of Labels: 5 Traceback (most recent call last): File "/usr/src/app/main.py", line 48, in <module> one_hot_vectors = one_hot(index_list, num_classes) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 28, in _one_hot_impl return jnn.one_hot(indexes, num_classes=num_labels) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/jax/_src/nn/functions.py", line 706, in one_hot num_classes = core.concrete_dim_or_error( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ jax.errors.ConcretizationTypeError: Abstract tracer value encountered where concrete value is expected: traced array with shape int32[] The error arose in jax.nn.one_hot argumentnum_classes`. The error occurred while tracing the function _one_hot_impl at /usr/src/app/main.py:7 for jit. This concrete value was not available in Python because it depends on the value of the argument num_labels. See https://jax.readthedocs.io/en/latest/errors.html#jax.errors.ConcretizationTypeError

For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these. 

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<h1>Substring Evaluation</h1>
<p>Testing if the previous output contains the string <code>All tests passed</code>: False</p>