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

### JAX Implementation of the Provided PyTorch Code Below is the equivalent JAX implementation of the given PyTorch code. Note that JAX uses a functional programming paradigm, so we will define the model's forward pass as a function. 

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import jax
import jax.numpy as jnp

# Define the MLP model
def mlp_forward(params, x):
    # Unpack the parameters
    linear1_weights, linear1_bias, linear2_weights, linear2_bias = params

    # First linear layer
    x = jnp.dot(x, linear1_weights) + linear1_bias
    x = jax.nn.relu(x)

    # Second linear layer
    x = jnp.dot(x, linear2_weights) + linear2_bias

    # Softmax activation function
    return jax.nn.softmax(x, axis=1)

# Initialize the model parameters
def init_mlp(input_size, hidden_size, output_size, key):
    linear1_weights = jax.random.normal(key, (input_size, hidden_size))
    linear1_bias = jax.random.normal(key, (hidden_size,))
    linear2_weights = jax.random.normal(key, (hidden_size, output_size))
    linear2_bias = jax.random.normal(key, (output_size,))
    return (linear1_weights, linear1_bias, linear2_weights, linear2_bias)

# Main function
def main():
    # Set the random seed
    key = jax.random.PRNGKey(0)

    # Initialize the model
    input_size, hidden_size, output_size = 10, 20, 30
    params = init_mlp(input_size, hidden_size, output_size, key)

    # Generate a random input
    x = jax.random.normal(key, (5, input_size))

    # Forward pass
    y = mlp_forward(params, x)

    # Print the sum of the output along the last axis
    print(jnp.sum(y, axis=1))

if __name__ == "__main__":
    main()
### Notes on the Conversion 1. **Parameter Initialization**: In JAX, model parameters are typically initialized using `jax.random.normal`. We define a separate function `init_mlp` to initialize the model parameters. 2. **Linear Layers**: JAX does not have a built-in `Linear` module like PyTorch. Instead, we use matrix multiplication (`jnp.dot`) to implement linear layers. 3. **Activation Functions**: JAX provides implementations of common activation functions like `jax.nn.relu` and `jax.nn.softmax`. 4. **Random Number Generation**: JAX uses `jax.random.PRNGKey` to generate random numbers. 5. **Main Function**: We define a `main` function to demonstrate the usage of the `mlp_forward` and `init_mlp` functions.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

# Define the MLP model
def mlp_forward(params, x):
    # Unpack the parameters
    linear1_weights, linear1_bias, linear2_weights, linear2_bias = params

    # First linear layer
    x = jnp.dot(x, linear1_weights) + linear1_bias
    x = jax.nn.relu(x)

    # Second linear layer
    x = jnp.dot(x, linear2_weights) + linear2_bias

    # Softmax activation function
    return jax.nn.softmax(x, axis=1)

# Initialize the model parameters
def init_mlp(input_size, hidden_size, output_size, key):
    linear1_weights = jax.random.normal(key, (input_size, hidden_size))
    linear1_bias = jax.random.normal(key, (hidden_size,))
    linear2_weights = jax.random.normal(key, (hidden_size, output_size))
    linear2_bias = jax.random.normal(key, (output_size,))
    return (linear1_weights, linear1_bias, linear2_weights, linear2_bias)

# Main function
def main():
    # Set the random seed
    key = jax.random.PRNGKey(0)

    # Initialize the model
    input_size, hidden_size, output_size = 10, 20, 30
    params = init_mlp(input_size, hidden_size, output_size, key)

    # Generate a random input
    x = jax.random.normal(key, (5, input_size))

    # Forward pass
    y = mlp_forward(params, x)

    # Print the sum of the output along the last axis
    print(jnp.sum(y, axis=1))

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

# Define the MLP model
def mlp_forward(params, x):
    # Unpack the parameters
    linear1_weights, linear1_bias, linear2_weights, linear2_bias = params

    # First linear layer
    x = jnp.dot(x, linear1_weights) + linear1_bias
    x = jax.nn.relu(x)

    # Second linear layer
    x = jnp.dot(x, linear2_weights) + linear2_bias

    # Softmax activation function
    return jax.nn.softmax(x, axis=1)

# Initialize the model parameters
def init_mlp(input_size, hidden_size, output_size, key):
    linear1_weights = jax.random.normal(key, (input_size, hidden_size))
    linear1_bias = jax.random.normal(key, (hidden_size,))
    linear2_weights = jax.random.normal(key, (hidden_size, output_size))
    linear2_bias = jax.random.normal(key, (output_size,))
    return (linear1_weights, linear1_bias, linear2_weights, linear2_bias)

# Main function
def main():
    # Set the random seed
    key = jax.random.PRNGKey(0)

    # Initialize the model
    input_size, hidden_size, output_size = 10, 20, 30
    params = init_mlp(input_size, hidden_size, output_size, key)

    # Generate a random input
    x = jax.random.normal(key, (5, input_size))

    # Forward pass
    y = mlp_forward(params, x)

    # Print the sum of the output along the last axis
    print(jnp.sum(y, axis=1))

if __name__ == "__main__":
    main()
And got the output: [1. 0.99999994 1. 1.0000001 1. ]

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