Initial Query

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
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. 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
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

Below is one way to rewrite the code in pure JAX without using any extra libraries: ------------------------------------------------------------ #!/usr/bin/env python # Pure JAX implementation of the MLP example import jax import jax.numpy as jnp from jax import random # Initialize parameters for a fully-connected (linear) layer. def init_linear_params(key, in_dim, out_dim): # Use a simple Xavier/He-like initialization. k1, _ = random.split(key) # Scale by sqrt(2/in_dim) (you can adjust the scaling as needed). weight = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2 / in_dim) bias = jnp.zeros((out_dim,)) return weight, bias # Initialize parameters for the two-layer MLP. def init_mlp_params(key, input_size, hidden_size, output_size): key1, key2 = random.split(key) W1, b1 = init_linear_params(key1, input_size, hidden_size) W2, b2 = init_linear_params(key2, hidden_size, output_size) return {"W1": W1, "b1": b1, "W2": W2, "b2": b2} # Define the MLP forward pass. def mlp_forward(params, x): # First linear layer: x @ W1 + b1 x = jnp.dot(x, params["W1"]) + params["b1"] # Apply ReLU nonlinearity. x = jnp.maximum(x, 0) # Second linear layer: x @ W2 + b2 x = jnp.dot(x, params["W2"]) + params["b2"] # Compute the softmax along axis 1. # For numerical stability, subtract the maximum value in each row. x_max = jnp.max(x, axis=1, keepdims=True) exps = jnp.exp(x - x_max) softmax = exps / jnp.sum(exps, axis=1, keepdims=True) return softmax # Main execution: def main(): # Set up a random key. rng = random.PRNGKey(0) # Initialize MLP parameters for an MLP with # input size of 10, hidden size of 20, and output size of 30. params = init_mlp_params(rng, input_size=10, hidden_size=20, output_size=30) # Generate a random input tensor like torch.randn(5, 10) x = random.normal(rng, (5, 10)) # Compute the model output. y = mlp_forward(params, x) # Print the sum over each row. (The original code printed the sum for each row.) # Note: In the PyTorch code there was a call to y.flatten().numpy().sum(1) # which is a bit unusual; here we simply sum each row of y. print(list(y.sum(axis=1))) if __name__ == "__main__": main() ------------------------------------------------------------ Explanation: 1. We import jax and jax.numpy as jnp. 2. The function init_linear_params builds each fully connected layer by drawing random normal weights and zeros for biases. 3. The function init_mlp_params splits the random key to initialize the two layers. 4. The mlp_forward function computes the two linear transformations with a ReLU activation after the first layer and applies a softmax (computed manually for numerical stability) on the final output. 5. In the main function we generate a random input with shape (5, 10), run the model, and then print a list of row-sums of the softmax output. This pure JAX code does not depend on any extra libraries aside from JAX itself.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
#!/usr/bin/env python
# Pure JAX implementation of the MLP example

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

# Initialize parameters for a fully-connected (linear) layer.
def init_linear_params(key, in_dim, out_dim):
    # Use a simple Xavier/He-like initialization.
    k1, _ = random.split(key)
    # Scale by sqrt(2/in_dim) (you can adjust the scaling as needed).
    weight = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2 / in_dim)
    bias = jnp.zeros((out_dim,))
    return weight, bias

# Initialize parameters for the two-layer MLP.
def init_mlp_params(key, input_size, hidden_size, output_size):
    key1, key2 = random.split(key)
    W1, b1 = init_linear_params(key1, input_size, hidden_size)
    W2, b2 = init_linear_params(key2, hidden_size, output_size)
    return {"W1": W1, "b1": b1, "W2": W2, "b2": b2}

# Define the MLP forward pass.
def mlp_forward(params, x):
    # First linear layer: x @ W1 + b1
    x = jnp.dot(x, params["W1"]) + params["b1"]
    # Apply ReLU nonlinearity.
    x = jnp.maximum(x, 0)
    # Second linear layer: x @ W2 + b2
    x = jnp.dot(x, params["W2"]) + params["b2"]
    # Compute the softmax along axis 1.
    # For numerical stability, subtract the maximum value in each row.
    x_max = jnp.max(x, axis=1, keepdims=True)
    exps = jnp.exp(x - x_max)
    softmax = exps / jnp.sum(exps, axis=1, keepdims=True)
    return softmax

# Main execution:
def main():
    # Set up a random key.
    rng = random.PRNGKey(0)
    # Initialize MLP parameters for an MLP with
    # input size of 10, hidden size of 20, and output size of 30.
    params = init_mlp_params(rng, input_size=10, hidden_size=20, output_size=30)
    # Generate a random input tensor like torch.randn(5, 10)
    x = random.normal(rng, (5, 10))
    # Compute the model output.
    y = mlp_forward(params, x)
    
    # Print the sum over each row. (The original code printed the sum for each row.)
    # Note: In the PyTorch code there was a call to y.flatten().numpy().sum(1)
    # which is a bit unusual; here we simply sum each row of y.
    print(list(y.sum(axis=1)))

if __name__ == "__main__":
    main()

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
#!/usr/bin/env python
# Pure JAX implementation of the MLP example

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

# Initialize parameters for a fully-connected (linear) layer.
def init_linear_params(key, in_dim, out_dim):
    # Use a simple Xavier/He-like initialization.
    k1, _ = random.split(key)
    # Scale by sqrt(2/in_dim) (you can adjust the scaling as needed).
    weight = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2 / in_dim)
    bias = jnp.zeros((out_dim,))
    return weight, bias

# Initialize parameters for the two-layer MLP.
def init_mlp_params(key, input_size, hidden_size, output_size):
    key1, key2 = random.split(key)
    W1, b1 = init_linear_params(key1, input_size, hidden_size)
    W2, b2 = init_linear_params(key2, hidden_size, output_size)
    return {"W1": W1, "b1": b1, "W2": W2, "b2": b2}

# Define the MLP forward pass.
def mlp_forward(params, x):
    # First linear layer: x @ W1 + b1
    x = jnp.dot(x, params["W1"]) + params["b1"]
    # Apply ReLU nonlinearity.
    x = jnp.maximum(x, 0)
    # Second linear layer: x @ W2 + b2
    x = jnp.dot(x, params["W2"]) + params["b2"]
    # Compute the softmax along axis 1.
    # For numerical stability, subtract the maximum value in each row.
    x_max = jnp.max(x, axis=1, keepdims=True)
    exps = jnp.exp(x - x_max)
    softmax = exps / jnp.sum(exps, axis=1, keepdims=True)
    return softmax

# Main execution:
def main():
    # Set up a random key.
    rng = random.PRNGKey(0)
    # Initialize MLP parameters for an MLP with
    # input size of 10, hidden size of 20, and output size of 30.
    params = init_mlp_params(rng, input_size=10, hidden_size=20, output_size=30)
    # Generate a random input tensor like torch.randn(5, 10)
    x = random.normal(rng, (5, 10))
    # Compute the model output.
    y = mlp_forward(params, x)
    
    # Print the sum over each row. (The original code printed the sum for each row.)
    # Note: In the PyTorch code there was a call to y.flatten().numpy().sum(1)
    # which is a bit unusual; here we simply sum each row of y.
    print(list(y.sum(axis=1)))

if __name__ == "__main__":
    main()
And got the output: [Array(1., dtype=float32), Array(0.99999994, dtype=float32), Array(1., dtype=float32), Array(1., dtype=float32), Array(1., 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: False

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

Initial Query

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
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. 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
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

Below is one way to write the same model and computation in pure JAX (using only JAX, no other external libraries): ------------------------------------------------------------ #!/usr/bin/env python3 import jax import jax.numpy as jnp # Initialize parameters for a two‐layer fully connected network. def init_mlp_params(key, input_size, hidden_size, output_size): # Split the PRNG key for reproducible independent initializations. key1, key2 = jax.random.split(key, 2) # For the first linear layer, we create weights of shape (input_size, hidden_size) # and biases of shape (hidden_size,). w1 = jax.random.normal(key1, (input_size, hidden_size)) b1 = jnp.zeros((hidden_size,)) # For the second linear layer, we create weights of shape (hidden_size, output_size) # and biases of shape (output_size,). w2 = jax.random.normal(key2, (hidden_size, output_size)) b2 = jnp.zeros((output_size,)) return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2} # Define the forward pass of the MLP: Linear -> ReLU -> Linear -> Softmax. def mlp_apply(params, x): # First linear layer: x @ W + b x = jnp.dot(x, params['w1']) + params['b1'] # Apply ReLU activation. x = jnp.maximum(x, 0) # Second linear layer. x = jnp.dot(x, params['w2']) + params['b2'] # Compute softmax over the last axis (axis=1) to obtain probabilities. x = jax.nn.softmax(x, axis=1) return x def main(): # Create an initial PRNG key. key = jax.random.PRNGKey(0) # Initialize the MLP parameters with input size=10, hidden size=20, output size=30. params = init_mlp_params(key, 10, 20, 30) # Generate a random input tensor, equivalent to torch.randn(5, 10). key, subkey = jax.random.split(key) x = jax.random.normal(subkey, (5, 10)) # Compute the model's output. y = mlp_apply(params, x) # In PyTorch the code printed: # list(y.flatten().numpy().sum(1)) # Since softmax makes each row sum to 1, we compute the row‐sums directly. row_sums = jnp.sum(y, axis=1) # Convert to a Python list and print. print(list(row_sums)) if __name__ == '__main__': main() ------------------------------------------------------------ Explanation: 1. The function init_mlp_params splits a PRNG key into two parts and randomly initializes the weights for each linear layer while setting the biases to 0. 2. The mlp_apply function computes the forward pass: • It performs a dot product (i.e. a linear layer) using jnp.dot. • The ReLU is implemented via jnp.maximum. • The softmax is computed with jax.nn.softmax along axis 1. 3. In the main() function, a random 5×10 input is created with jax.random.normal, the model output is calculated, and the row‐sums (which should be 1.0 for each sample) are printed. This code is written entirely using JAX and does not rely on any additional libraries.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
#!/usr/bin/env python3
import jax
import jax.numpy as jnp

# Initialize parameters for a two‐layer fully connected network.
def init_mlp_params(key, input_size, hidden_size, output_size):
    # Split the PRNG key for reproducible independent initializations.
    key1, key2 = jax.random.split(key, 2)
    # For the first linear layer, we create weights of shape (input_size, hidden_size)
    # and biases of shape (hidden_size,).
    w1 = jax.random.normal(key1, (input_size, hidden_size))
    b1 = jnp.zeros((hidden_size,))
    # For the second linear layer, we create weights of shape (hidden_size, output_size)
    # and biases of shape (output_size,).
    w2 = jax.random.normal(key2, (hidden_size, output_size))
    b2 = jnp.zeros((output_size,))
    return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

# Define the forward pass of the MLP: Linear -> ReLU -> Linear -> Softmax.
def mlp_apply(params, x):
    # First linear layer: x @ W + b
    x = jnp.dot(x, params['w1']) + params['b1']
    # Apply ReLU activation.
    x = jnp.maximum(x, 0)
    # Second linear layer.
    x = jnp.dot(x, params['w2']) + params['b2']
    # Compute softmax over the last axis (axis=1) to obtain probabilities.
    x = jax.nn.softmax(x, axis=1)
    return x

def main():
    # Create an initial PRNG key.
    key = jax.random.PRNGKey(0)
    # Initialize the MLP parameters with input size=10, hidden size=20, output size=30.
    params = init_mlp_params(key, 10, 20, 30)
    # Generate a random input tensor, equivalent to torch.randn(5, 10).
    key, subkey = jax.random.split(key)
    x = jax.random.normal(subkey, (5, 10))
    # Compute the model's output.
    y = mlp_apply(params, x)
    # In PyTorch the code printed:
    #   list(y.flatten().numpy().sum(1))
    # Since softmax makes each row sum to 1, we compute the row‐sums directly.
    row_sums = jnp.sum(y, axis=1)
    # Convert to a Python list and print.
    print(list(row_sums))

if __name__ == '__main__':
    main()

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
#!/usr/bin/env python3
import jax
import jax.numpy as jnp

# Initialize parameters for a two‐layer fully connected network.
def init_mlp_params(key, input_size, hidden_size, output_size):
    # Split the PRNG key for reproducible independent initializations.
    key1, key2 = jax.random.split(key, 2)
    # For the first linear layer, we create weights of shape (input_size, hidden_size)
    # and biases of shape (hidden_size,).
    w1 = jax.random.normal(key1, (input_size, hidden_size))
    b1 = jnp.zeros((hidden_size,))
    # For the second linear layer, we create weights of shape (hidden_size, output_size)
    # and biases of shape (output_size,).
    w2 = jax.random.normal(key2, (hidden_size, output_size))
    b2 = jnp.zeros((output_size,))
    return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

# Define the forward pass of the MLP: Linear -> ReLU -> Linear -> Softmax.
def mlp_apply(params, x):
    # First linear layer: x @ W + b
    x = jnp.dot(x, params['w1']) + params['b1']
    # Apply ReLU activation.
    x = jnp.maximum(x, 0)
    # Second linear layer.
    x = jnp.dot(x, params['w2']) + params['b2']
    # Compute softmax over the last axis (axis=1) to obtain probabilities.
    x = jax.nn.softmax(x, axis=1)
    return x

def main():
    # Create an initial PRNG key.
    key = jax.random.PRNGKey(0)
    # Initialize the MLP parameters with input size=10, hidden size=20, output size=30.
    params = init_mlp_params(key, 10, 20, 30)
    # Generate a random input tensor, equivalent to torch.randn(5, 10).
    key, subkey = jax.random.split(key)
    x = jax.random.normal(subkey, (5, 10))
    # Compute the model's output.
    y = mlp_apply(params, x)
    # In PyTorch the code printed:
    #   list(y.flatten().numpy().sum(1))
    # Since softmax makes each row sum to 1, we compute the row‐sums directly.
    row_sums = jnp.sum(y, axis=1)
    # Convert to a Python list and print.
    print(list(row_sums))

if __name__ == '__main__':
    main()
And got the output: [Array(1., dtype=float32), Array(0.99999994, dtype=float32), Array(1., dtype=float32), Array(0.99999994, dtype=float32), Array(1., 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: False

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