Initial Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

Sure! Below is the equivalent JAX implementation of your PyTorch `MLP` model. This version uses only JAX without any additional libraries. The `MLP` class initializes the weights and biases for two linear layers, applies the ReLU activation, and finally applies the softmax function. The input tensor `x` is generated using JAX's random number generation. 

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import jax
import jax.numpy as jnp
from jax import random

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Split the key for initializing different layers
        key1, key2 = random.split(key)
        
        # Initialize weights and biases for the first linear layer
        self.W1 = random.normal(key1, (hidden_size, input_size))
        self.b1 = jnp.zeros(hidden_size)
        
        # Initialize weights and biases for the second linear layer
        self.W2 = random.normal(key2, (output_size, hidden_size))
        self.b2 = jnp.zeros(output_size)
    
    def __call__(self, x):
        # First linear transformation
        x = jnp.dot(x, self.W1.T) + self.b1
        # Apply ReLU activation
        x = jnp.maximum(x, 0)
        # Second linear transformation
        x = jnp.dot(x, self.W2.T) + self.b2
        # Apply softmax activation
        x = jax.nn.softmax(x, axis=1)
        return x

# Initialize a random key for reproducibility
key = random.PRNGKey(0)

# Create an instance of the MLP model
model = MLP(input_size=10, hidden_size=20, output_size=30, key=key)

# Generate a new key for input data
key, subkey = random.split(key)

# Create random input data
x = random.normal(subkey, (5, 10))

# Perform a forward pass through the model
y = model(x)

# Sum the softmax outputs along axis 1 and convert to a Python list
output_sums = y.sum(axis=1).tolist()

# Print the result
print(output_sums)
### Explanation: 1. **Imports:** - `jax` and `jax.numpy` (`jnp`) are imported for numerical operations. - `random` from `jax` is used for random number generation. 2. **MLP Class:** - **Initialization (`__init__`):** - **Random Key Splitting:** JAX uses functional randomness. The `key` is split into `key1` and `key2` to independently initialize weights for each layer. - **Weights and Biases:** - `W1`: Weight matrix for the first linear layer with shape `(hidden_size, input_size)`. - `b1`: Bias vector for the first linear layer with shape `(hidden_size,)`. - `W2`: Weight matrix for the second linear layer with shape `(output_size, hidden_size)`. - `b2`: Bias vector for the second linear layer with shape `(output_size,)`. - **Forward Pass (`__call__`):** - **First Linear Layer:** Computes `x @ W1.T + b1`. - **ReLU Activation:** Applies the ReLU function using `jnp.maximum`. - **Second Linear Layer:** Computes `x @ W2.T + b2`. - **Softmax Activation:** Applies the softmax function along the feature axis (`axis=1`). 3. **Model Initialization and Data Generation:** - A random key is initialized for reproducibility. - An instance of the `MLP` class is created with input size `10`, hidden size `20`, and output size `30`. - A new key is generated for creating the input data `x`, which has a shape of `(5, 10)`. 4. **Forward Pass and Output:** - The input `x` is passed through the model to get the output `y`. - The softmax outputs are summed along `axis=1` to verify that they sum to 1 (as expected from softmax). - The result is converted to a Python list and printed. ### Sample Output: Since the weights are initialized randomly, the exact output will vary each time you run the code. However, you should expect each element in the printed list to be approximately `1.0`, confirming that the softmax function is correctly normalizing the outputs. 
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[1.0, 1.0, 1.0, 1.0, 1.0]
This JAX implementation mirrors the structure and functionality of your original PyTorch code, ensuring that the `MLP` model behaves similarly in both frameworks.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp
from jax import random

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Split the key for initializing different layers
        key1, key2 = random.split(key)
        
        # Initialize weights and biases for the first linear layer
        self.W1 = random.normal(key1, (hidden_size, input_size))
        self.b1 = jnp.zeros(hidden_size)
        
        # Initialize weights and biases for the second linear layer
        self.W2 = random.normal(key2, (output_size, hidden_size))
        self.b2 = jnp.zeros(output_size)
    
    def __call__(self, x):
        # First linear transformation
        x = jnp.dot(x, self.W1.T) + self.b1
        # Apply ReLU activation
        x = jnp.maximum(x, 0)
        # Second linear transformation
        x = jnp.dot(x, self.W2.T) + self.b2
        # Apply softmax activation
        x = jax.nn.softmax(x, axis=1)
        return x

# Initialize a random key for reproducibility
key = random.PRNGKey(0)

# Create an instance of the MLP model
model = MLP(input_size=10, hidden_size=20, output_size=30, key=key)

# Generate a new key for input data
key, subkey = random.split(key)

# Create random input data
x = random.normal(subkey, (5, 10))

# Perform a forward pass through the model
y = model(x)

# Sum the softmax outputs along axis 1 and convert to a Python list
output_sums = y.sum(axis=1).tolist()

# Print the result
print(output_sums)

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp
from jax import random

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Split the key for initializing different layers
        key1, key2 = random.split(key)
        
        # Initialize weights and biases for the first linear layer
        self.W1 = random.normal(key1, (hidden_size, input_size))
        self.b1 = jnp.zeros(hidden_size)
        
        # Initialize weights and biases for the second linear layer
        self.W2 = random.normal(key2, (output_size, hidden_size))
        self.b2 = jnp.zeros(output_size)
    
    def __call__(self, x):
        # First linear transformation
        x = jnp.dot(x, self.W1.T) + self.b1
        # Apply ReLU activation
        x = jnp.maximum(x, 0)
        # Second linear transformation
        x = jnp.dot(x, self.W2.T) + self.b2
        # Apply softmax activation
        x = jax.nn.softmax(x, axis=1)
        return x

# Initialize a random key for reproducibility
key = random.PRNGKey(0)

# Create an instance of the MLP model
model = MLP(input_size=10, hidden_size=20, output_size=30, key=key)

# Generate a new key for input data
key, subkey = random.split(key)

# Create random input data
x = random.normal(subkey, (5, 10))

# Perform a forward pass through the model
y = model(x)

# Sum the softmax outputs along axis 1 and convert to a Python list
output_sums = y.sum(axis=1).tolist()

# Print the result
print(output_sums)
And got the output: [1.0, 0.9999999403953552, 0.9999999403953552, 0.9999999403953552, 1.000000238418579]

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: True

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: True

Substring Evaluation

Testing if the previous output contains the string 1.0,: True

Final Answer: True

Check this condition is not true:

Substring Evaluation

Testing if the previous output contains the string import torch: False

Final Answer: True

Final Answer: True

Initial Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

Certainly! Below is the equivalent JAX code that replicates the functionality of your PyTorch `MLP` class. This implementation uses pure JAX without relying on any additional libraries. ### Explanation: 1. **Linear Layer Implementation**: - **Initialization**: The `Linear` class initializes weights (`W`) using Xavier (Glorot) uniform initialization, which is commonly used for neural networks to keep the scale of the gradients roughly the same in all layers. Biases (`b`) are initialized to zeros. - **Forward Pass**: The `__call__` method performs the linear transformation: \( xW + b \). 2. **MLP Class**: - **Initialization**: The `MLP` class initializes two linear layers (`linear1` and `linear2`) with specified input, hidden, and output sizes. - **Forward Pass**: - Applies the first linear layer. - Applies the ReLU activation function. - Applies the second linear layer. - Applies the softmax function along the second axis to obtain probability distributions. 3. **Model Usage**: - Initializes the model with input size 10, hidden size 20, and output size 30. - Creates a random input tensor `x` with shape `(5, 10)`. - Performs a forward pass to obtain the output `y`. - Prints the sum of the softmax probabilities for each sample in the batch to verify that they sum to 1. ### JAX Implementation: 

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import jax
import jax.numpy as jnp
from jax import random

# Define a simple Linear layer
class Linear:
    def __init__(self, key, in_features, out_features):
        """
        Initializes the Linear layer with random weights and zero biases.

        Args:
            key: JAX random key for initialization.
            in_features: Size of each input sample.
            out_features: Size of each output sample.
        """
        key_w, key_b = random.split(key)
        # Xavier uniform initialization
        limit = jnp.sqrt(6 / (in_features + out_features))
        self.W = random.uniform(key_w, (in_features, out_features), minval=-limit, maxval=limit)
        self.b = jnp.zeros(out_features)

    def __call__(self, x):
        """
        Performs the linear transformation.

        Args:
            x: Input tensor of shape (batch_size, in_features).

        Returns:
            Tensor of shape (batch_size, out_features).
        """
        return jnp.dot(x, self.W) + self.b

# Define the MLP model
class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        """
        Initializes the MLP with two linear layers.

        Args:
            key: JAX random key for initialization.
            input_size: Size of the input features.
            hidden_size: Size of the hidden layer.
            output_size: Size of the output layer.
        """
        key1, key2 = random.split(key)
        self.linear1 = Linear(key1, input_size, hidden_size)
        self.linear2 = Linear(key2, hidden_size, output_size)

    def __call__(self, x):
        """
        Performs a forward pass through the MLP.

        Args:
            x: Input tensor of shape (batch_size, input_size).

        Returns:
            Tensor of shape (batch_size, output_size) after softmax.
        """
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        x = jax.nn.softmax(x, axis=1)
        return x

# Initialize random key
key = random.PRNGKey(0)

# Create the MLP model
model = MLP(key, input_size=10, hidden_size=20, output_size=30)

# Generate random input data
input_key, _ = random.split(key)
x = random.normal(input_key, (5, 10))  # Shape: (5, 10)

# Perform a forward pass
y = model(x)

# Compute the sum of softmax probabilities for each sample
sums = jnp.sum(y, axis=1)

# Convert to a Python list and print
print(list(sums))
### Output: When you run the above code, you should see output similar to the following, indicating that the softmax probabilities for each sample sum to 1: 
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[1.0000001, 1.0, 1.0, 1.0, 1.0]
**Note**: Due to floating-point precision, the sums may not be exactly 1 but should be very close, as shown in the first element of the list. ### Additional Notes: - **Random Key Management**: JAX requires explicit management of random keys for reproducibility. The `random.split` function is used to generate new keys for different parts of the model to ensure that weights are initialized independently. - **Functional Programming Paradigm**: Unlike PyTorch's object-oriented approach, JAX encourages a more functional programming style. However, for clarity and similarity to your original PyTorch code, a class-based approach is used here. - **No Autograd or Optimizer**: This implementation focuses solely on the forward pass. If you intend to train the model, you'll need to implement an optimization loop and compute gradients using `jax.grad` or other related functions. Feel free to modify the `input_size`, `hidden_size`, `output_size`, and input data `x` as needed for your specific use case.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp
from jax import random

# Define a simple Linear layer
class Linear:
    def __init__(self, key, in_features, out_features):
        """
        Initializes the Linear layer with random weights and zero biases.

        Args:
            key: JAX random key for initialization.
            in_features: Size of each input sample.
            out_features: Size of each output sample.
        """
        key_w, key_b = random.split(key)
        # Xavier uniform initialization
        limit = jnp.sqrt(6 / (in_features + out_features))
        self.W = random.uniform(key_w, (in_features, out_features), minval=-limit, maxval=limit)
        self.b = jnp.zeros(out_features)

    def __call__(self, x):
        """
        Performs the linear transformation.

        Args:
            x: Input tensor of shape (batch_size, in_features).

        Returns:
            Tensor of shape (batch_size, out_features).
        """
        return jnp.dot(x, self.W) + self.b

# Define the MLP model
class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        """
        Initializes the MLP with two linear layers.

        Args:
            key: JAX random key for initialization.
            input_size: Size of the input features.
            hidden_size: Size of the hidden layer.
            output_size: Size of the output layer.
        """
        key1, key2 = random.split(key)
        self.linear1 = Linear(key1, input_size, hidden_size)
        self.linear2 = Linear(key2, hidden_size, output_size)

    def __call__(self, x):
        """
        Performs a forward pass through the MLP.

        Args:
            x: Input tensor of shape (batch_size, input_size).

        Returns:
            Tensor of shape (batch_size, output_size) after softmax.
        """
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        x = jax.nn.softmax(x, axis=1)
        return x

def main():
    # Initialize random key
    key = random.PRNGKey(0)

    # Create the MLP model
    model = MLP(key, input_size=10, hidden_size=20, output_size=30)

    # Generate random input data
    input_key, _ = random.split(key)
    x = random.normal(input_key, (5, 10))  # Shape: (5, 10)

    # Perform a forward pass
    y = model(x)

    # Compute the sum of softmax probabilities for each sample
    sums = jnp.sum(y, axis=1)

    # Convert to a Python list and print
    print(list(sums))

if __name__ == "__main__":
    main()

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp
from jax import random

# Define a simple Linear layer
class Linear:
    def __init__(self, key, in_features, out_features):
        """
        Initializes the Linear layer with random weights and zero biases.

        Args:
            key: JAX random key for initialization.
            in_features: Size of each input sample.
            out_features: Size of each output sample.
        """
        key_w, key_b = random.split(key)
        # Xavier uniform initialization
        limit = jnp.sqrt(6 / (in_features + out_features))
        self.W = random.uniform(key_w, (in_features, out_features), minval=-limit, maxval=limit)
        self.b = jnp.zeros(out_features)

    def __call__(self, x):
        """
        Performs the linear transformation.

        Args:
            x: Input tensor of shape (batch_size, in_features).

        Returns:
            Tensor of shape (batch_size, out_features).
        """
        return jnp.dot(x, self.W) + self.b

# Define the MLP model
class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        """
        Initializes the MLP with two linear layers.

        Args:
            key: JAX random key for initialization.
            input_size: Size of the input features.
            hidden_size: Size of the hidden layer.
            output_size: Size of the output layer.
        """
        key1, key2 = random.split(key)
        self.linear1 = Linear(key1, input_size, hidden_size)
        self.linear2 = Linear(key2, hidden_size, output_size)

    def __call__(self, x):
        """
        Performs a forward pass through the MLP.

        Args:
            x: Input tensor of shape (batch_size, input_size).

        Returns:
            Tensor of shape (batch_size, output_size) after softmax.
        """
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        x = jax.nn.softmax(x, axis=1)
        return x

def main():
    # Initialize random key
    key = random.PRNGKey(0)

    # Create the MLP model
    model = MLP(key, input_size=10, hidden_size=20, output_size=30)

    # Generate random input data
    input_key, _ = random.split(key)
    x = random.normal(input_key, (5, 10))  # Shape: (5, 10)

    # Perform a forward pass
    y = model(x)

    # Compute the sum of softmax probabilities for each sample
    sums = jnp.sum(y, axis=1)

    # Convert to a Python list and print
    print(list(sums))

if __name__ == "__main__":
    main()
And got the output: [Array(0.9999999, dtype=float32), Array(1., dtype=float32), Array(0.9999999, dtype=float32), Array(1.0000001, dtype=float32), Array(1.0000001, dtype=float32)]

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: True

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: True

Substring Evaluation

Testing if the previous output contains the string 1.0,: False

Final Answer: True

Check this condition is not true:

Substring Evaluation

Testing if the previous output contains the string import torch: False

Final Answer: True

Final Answer: True