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

Here's the equivalent JAX implementation: 

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

def init_linear_layer(key, in_features, out_features):
    k1, k2 = random.split(key)
    # Using He initialization
    w = random.normal(k1, (in_features, out_features)) * jnp.sqrt(2.0 / in_features)
    b = random.normal(k2, (out_features,)) * 0.01
    return {'weight': w, 'bias': b}

def linear(params, x):
    return x @ params['weight'] + params['bias']

def relu(x):
    return jnp.maximum(0, x)

def softmax(x):
    exp_x = jnp.exp(x - jnp.max(x, axis=1, keepdims=True))
    return exp_x / jnp.sum(exp_x, axis=1, keepdims=True)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key1, key2 = random.split(key)
        self.params = {
            'linear1': init_linear_layer(key1, input_size, hidden_size),
            'linear2': init_linear_layer(key2, hidden_size, output_size)
        }

    def __call__(self, x):
        x = linear(self.params['linear1'], x)
        x = relu(x)
        x = linear(self.params['linear2'], x)
        return softmax(x)

# Initialize model and generate random input
key = random.PRNGKey(0)
key1, key2 = random.split(key)

model = MLP(key1, 10, 20, 30)
x = random.normal(key2, (5, 10))
y = model(x)
print(list(y.flatten().sum()))
Key differences and notes: 1. JAX uses functional programming style rather than PyTorch's object-oriented approach 2. Instead of torch.nn.Linear, we implement our own linear layer function 3. Parameters are explicitly managed through dictionaries 4. Random number generation in JAX requires explicit key management 5. JAX's numpy-like operations (jnp) are used instead of torch operations 6. The softmax implementation is slightly different but mathematically equivalent 7. The output might differ due to different random initialization and numerical precision The main conceptual difference is that JAX is more functional in nature, while PyTorch is more object-oriented. In JAX, we explicitly manage the parameters and pass them through functions, whereas PyTorch encapsulates this in classes with state. Also note that the output will be different from the PyTorch version because: 1. Different random number generation 2. Different parameter initialization schemes 3. Different numerical precision in computations But the structure and functionality of the network remain the same.

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

def init_linear_layer(key, in_features, out_features):
    k1, k2 = random.split(key)
    # Using He initialization
    w = random.normal(k1, (in_features, out_features)) * jnp.sqrt(2.0 / in_features)
    b = random.normal(k2, (out_features,)) * 0.01
    return {'weight': w, 'bias': b}

def linear(params, x):
    return x @ params['weight'] + params['bias']

def relu(x):
    return jnp.maximum(0, x)

def softmax(x):
    exp_x = jnp.exp(x - jnp.max(x, axis=1, keepdims=True))
    return exp_x / jnp.sum(exp_x, axis=1, keepdims=True)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key1, key2 = random.split(key)
        self.params = {
            'linear1': init_linear_layer(key1, input_size, hidden_size),
            'linear2': init_linear_layer(key2, hidden_size, output_size)
        }

    def __call__(self, x):
        x = linear(self.params['linear1'], x)
        x = relu(x)
        x = linear(self.params['linear2'], x)
        return softmax(x)

# Initialize model and generate random input
key = random.PRNGKey(0)
key1, key2 = random.split(key)

model = MLP(key1, 10, 20, 30)
x = random.normal(key2, (5, 10))
y = model(x)
print(list(y.flatten().sum()))

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

def init_linear_layer(key, in_features, out_features):
    k1, k2 = random.split(key)
    # Using He initialization
    w = random.normal(k1, (in_features, out_features)) * jnp.sqrt(2.0 / in_features)
    b = random.normal(k2, (out_features,)) * 0.01
    return {'weight': w, 'bias': b}

def linear(params, x):
    return x @ params['weight'] + params['bias']

def relu(x):
    return jnp.maximum(0, x)

def softmax(x):
    exp_x = jnp.exp(x - jnp.max(x, axis=1, keepdims=True))
    return exp_x / jnp.sum(exp_x, axis=1, keepdims=True)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key1, key2 = random.split(key)
        self.params = {
            'linear1': init_linear_layer(key1, input_size, hidden_size),
            'linear2': init_linear_layer(key2, hidden_size, output_size)
        }

    def __call__(self, x):
        x = linear(self.params['linear1'], x)
        x = relu(x)
        x = linear(self.params['linear2'], x)
        return softmax(x)

# Initialize model and generate random input
key = random.PRNGKey(0)
key1, key2 = random.split(key)

model = MLP(key1, 10, 20, 30)
x = random.normal(key2, (5, 10))
y = model(x)
print(list(y.flatten().sum()))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 44, in <module> print(list(y.flatten().sum())) ^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/jax/_src/array.py", line 374, in __iter__ raise TypeError("iteration over a 0-d array") # same as numpy error ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: iteration over a 0-d array

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

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: False

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: False

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

Here's the equivalent code using pure JAX: 

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Split the key for different weight initializations
        key1, key2 = random.split(key)
        
        # Initialize weights and biases
        scale = 1.0 / jnp.sqrt(input_size)
        self.w1 = random.normal(key1, (input_size, hidden_size)) * scale
        self.b1 = jnp.zeros(hidden_size)
        
        scale = 1.0 / jnp.sqrt(hidden_size)
        self.w2 = random.normal(key2, (hidden_size, output_size)) * scale
        self.b2 = jnp.zeros(output_size)

    def __call__(self, x):
        # First linear layer
        x = jnp.dot(x, self.w1) + self.b1
        # ReLU activation
        x = jnp.maximum(0, x)
        # Second linear layer
        x = jnp.dot(x, self.w2) + self.b2
        # Softmax activation
        x = jax.nn.softmax(x, axis=1)
        return x

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

# Create model
model = MLP(10, 20, 30, key)

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

# Forward pass
y = model(x)
print(list(y.flatten().sum()))
Key differences and notes: 1. JAX uses functional programming paradigms rather than PyTorch's object-oriented approach. 2. Instead of `torch.nn.Linear`, we explicitly implement the linear layers using matrix multiplication (`jnp.dot`) and bias addition. 3. Weight initialization is done manually using JAX's random number generators. 4. JAX uses `jax.nn.softmax` instead of `torch.nn.functional.softmax`. 5. ReLU activation is implemented using `jnp.maximum(0, x)` instead of `torch.relu`. 6. JAX arrays are immutable, unlike PyTorch tensors. 7. The random number generation in JAX is explicit and requires passing and splitting keys. The output might differ from the PyTorch version due to: - Different random number generation - Different weight initialization schemes - Different numerical precision handling This implementation maintains the same basic structure and functionality as the PyTorch version while using JAX's functional programming style and array operations.

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
from functools import partial

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Split the key for different weight initializations
        key1, key2 = random.split(key)
        
        # Initialize weights and biases
        scale = 1.0 / jnp.sqrt(input_size)
        self.w1 = random.normal(key1, (input_size, hidden_size)) * scale
        self.b1 = jnp.zeros(hidden_size)
        
        scale = 1.0 / jnp.sqrt(hidden_size)
        self.w2 = random.normal(key2, (hidden_size, output_size)) * scale
        self.b2 = jnp.zeros(output_size)

    def __call__(self, x):
        # First linear layer
        x = jnp.dot(x, self.w1) + self.b1
        # ReLU activation
        x = jnp.maximum(0, x)
        # Second linear layer
        x = jnp.dot(x, self.w2) + self.b2
        # Softmax activation
        x = jax.nn.softmax(x, axis=1)
        return x

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

# Create model
model = MLP(10, 20, 30, key)

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

# Forward pass
y = model(x)
print(list(y.flatten().sum()))

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
from functools import partial

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Split the key for different weight initializations
        key1, key2 = random.split(key)
        
        # Initialize weights and biases
        scale = 1.0 / jnp.sqrt(input_size)
        self.w1 = random.normal(key1, (input_size, hidden_size)) * scale
        self.b1 = jnp.zeros(hidden_size)
        
        scale = 1.0 / jnp.sqrt(hidden_size)
        self.w2 = random.normal(key2, (hidden_size, output_size)) * scale
        self.b2 = jnp.zeros(output_size)

    def __call__(self, x):
        # First linear layer
        x = jnp.dot(x, self.w1) + self.b1
        # ReLU activation
        x = jnp.maximum(0, x)
        # Second linear layer
        x = jnp.dot(x, self.w2) + self.b2
        # Softmax activation
        x = jax.nn.softmax(x, axis=1)
        return x

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

# Create model
model = MLP(10, 20, 30, key)

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

# Forward pass
y = model(x)
print(list(y.flatten().sum()))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 44, in <module> print(list(y.flatten().sum())) ^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/jax/_src/array.py", line 374, in __iter__ raise TypeError("iteration over a 0-d array") # same as numpy error ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: iteration over a 0-d array

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

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: False

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: False

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

Here's the equivalent code written in pure JAX: 

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

def init_linear_layer(key: jax.random.PRNGKey, 
                     in_dim: int, 
                     out_dim: int) -> Tuple[jnp.ndarray, jnp.ndarray]:
    k1, k2 = random.split(key)
    weight = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2.0 / in_dim)
    bias = random.normal(k2, (out_dim,)) * 0.01
    return weight, bias

class MLP:
    def __init__(self, key: jax.random.PRNGKey, input_size: int, hidden_size: int, output_size: int):
        # Split the PRNG key
        k1, k2 = random.split(key)
        
        # Initialize the layers
        self.w1, self.b1 = init_linear_layer(k1, input_size, hidden_size)
        self.w2, self.b2 = init_linear_layer(k2, hidden_size, output_size)
    
    def linear(self, x: jnp.ndarray, w: jnp.ndarray, b: jnp.ndarray) -> jnp.ndarray:
        return jnp.dot(x, w) + b
    
    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        x = self.linear(x, self.w1, self.b1)
        x = jax.nn.relu(x)
        x = self.linear(x, self.w2, self.b2)
        return jax.nn.softmax(x, axis=1)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

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

# Forward pass
y = model(x)
print(jnp.sum(y.flatten()))
Key differences and notes: 1. JAX uses functional programming paradigms, so the class implementation is slightly different from PyTorch's object-oriented approach. 2. Instead of torch.nn.Linear, we implement our own linear layer using matrix multiplication (jnp.dot). 3. We explicitly handle random number generation using JAX's PRNG system with keys. 4. Weight initialization follows a similar pattern to PyTorch's default initialization (He initialization for weights). 5. JAX's numpy-like interface (jnp) is used instead of torch's tensors. 6. The activation functions come from jax.nn instead of torch.nn.functional. 7. The output will be slightly different due to: - Different random number generation - Different default initialization schemes - Potential small numerical differences in implementation 8. JAX arrays are immutable, unlike PyTorch tensors. This implementation provides the same functionality as the PyTorch code but uses JAX's pure functional approach. Note that in real applications, you might want to use higher-level JAX libraries like Flax or Haiku for more complex neural networks, but this shows the pure JAX implementation.

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
from typing import Tuple

def init_linear_layer(key: jax.random.PRNGKey, 
                     in_dim: int, 
                     out_dim: int) -> Tuple[jnp.ndarray, jnp.ndarray]:
    k1, k2 = random.split(key)
    weight = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2.0 / in_dim)
    bias = random.normal(k2, (out_dim,)) * 0.01
    return weight, bias

class MLP:
    def __init__(self, key: jax.random.PRNGKey, input_size: int, hidden_size: int, output_size: int):
        # Split the PRNG key
        k1, k2 = random.split(key)
        
        # Initialize the layers
        self.w1, self.b1 = init_linear_layer(k1, input_size, hidden_size)
        self.w2, self.b2 = init_linear_layer(k2, hidden_size, output_size)
    
    def linear(self, x: jnp.ndarray, w: jnp.ndarray, b: jnp.ndarray) -> jnp.ndarray:
        return jnp.dot(x, w) + b
    
    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        x = self.linear(x, self.w1, self.b1)
        x = jax.nn.relu(x)
        x = self.linear(x, self.w2, self.b2)
        return jax.nn.softmax(x, axis=1)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

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

# Forward pass
y = model(x)
print(jnp.sum(y.flatten()))

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
from typing import Tuple

def init_linear_layer(key: jax.random.PRNGKey, 
                     in_dim: int, 
                     out_dim: int) -> Tuple[jnp.ndarray, jnp.ndarray]:
    k1, k2 = random.split(key)
    weight = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2.0 / in_dim)
    bias = random.normal(k2, (out_dim,)) * 0.01
    return weight, bias

class MLP:
    def __init__(self, key: jax.random.PRNGKey, input_size: int, hidden_size: int, output_size: int):
        # Split the PRNG key
        k1, k2 = random.split(key)
        
        # Initialize the layers
        self.w1, self.b1 = init_linear_layer(k1, input_size, hidden_size)
        self.w2, self.b2 = init_linear_layer(k2, hidden_size, output_size)
    
    def linear(self, x: jnp.ndarray, w: jnp.ndarray, b: jnp.ndarray) -> jnp.ndarray:
        return jnp.dot(x, w) + b
    
    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        x = self.linear(x, self.w1, self.b1)
        x = jax.nn.relu(x)
        x = self.linear(x, self.w2, self.b2)
        return jax.nn.softmax(x, axis=1)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

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

# Forward pass
y = model(x)
print(jnp.sum(y.flatten()))
And got the output: 5.0

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

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: False

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: False

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

Here's the equivalent code in pure JAX: 

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

def init_linear_layer(key, in_dim, out_dim):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_dim, out_dim)) * (1.0 / jnp.sqrt(in_dim))
    b = random.normal(k2, (out_dim,)) * 0.1
    return {'w': w, 'b': b}

def linear(params, x):
    return jnp.dot(x, params['w']) + params['b']

def relu(x):
    return jnp.maximum(0, x)

def softmax(x):
    # Subtract max for numerical stability
    exp_x = jnp.exp(x - jnp.max(x, axis=1, keepdims=True))
    return exp_x / jnp.sum(exp_x, axis=1, keepdims=True)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key1, key2 = random.split(key)
        self.params = {
            'linear1': init_linear_layer(key1, input_size, hidden_size),
            'linear2': init_linear_layer(key2, hidden_size, output_size)
        }

    def __call__(self, x):
        x = linear(self.params['linear1'], x)
        x = relu(x)
        x = linear(self.params['linear2'], x)
        return softmax(x)

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

# Create model
model = MLP(key, 10, 20, 30)

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

# Forward pass
y = model(x)

# Print sum (note: JAX arrays are already numpy-like)
print(list(y.flatten().sum()))
Key differences and notes: 1. JAX uses functional programming style instead of PyTorch's object-oriented style. 2. Instead of torch.nn.Linear, we implement our own linear layer initialization and forward pass. 3. The random number generation in JAX is explicit and requires passing and splitting keys. 4. Weight initialization follows a standard practice (scaled normal distribution). 5. JAX arrays (created by jnp) are immutable, unlike PyTorch tensors. 6. The softmax implementation includes the standard numerical stability trick of subtracting the maximum value. 7. JAX doesn't have a direct equivalent to torch.nn.functional.softmax, so we implement it manually. The output might differ from the PyTorch version due to: - Different random number generation - Slightly different initialization schemes - Potential minor numerical differences in implementations This implementation maintains the same basic structure and functionality as the PyTorch code while using JAX's functional programming paradigm.

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

def init_linear_layer(key, in_dim, out_dim):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_dim, out_dim)) * (1.0 / jnp.sqrt(in_dim))
    b = random.normal(k2, (out_dim,)) * 0.1
    return {'w': w, 'b': b}

def linear(params, x):
    return jnp.dot(x, params['w']) + params['b']

def relu(x):
    return jnp.maximum(0, x)

def softmax(x):
    # Subtract max for numerical stability
    exp_x = jnp.exp(x - jnp.max(x, axis=1, keepdims=True))
    return exp_x / jnp.sum(exp_x, axis=1, keepdims=True)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key1, key2 = random.split(key)
        self.params = {
            'linear1': init_linear_layer(key1, input_size, hidden_size),
            'linear2': init_linear_layer(key2, hidden_size, output_size)
        }

    def __call__(self, x):
        x = linear(self.params['linear1'], x)
        x = relu(x)
        x = linear(self.params['linear2'], x)
        return softmax(x)

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

# Create model
model = MLP(key, 10, 20, 30)

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

# Forward pass
y = model(x)

# Print sum (note: JAX arrays are already numpy-like)
print(list(y.flatten().sum()))

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

def init_linear_layer(key, in_dim, out_dim):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_dim, out_dim)) * (1.0 / jnp.sqrt(in_dim))
    b = random.normal(k2, (out_dim,)) * 0.1
    return {'w': w, 'b': b}

def linear(params, x):
    return jnp.dot(x, params['w']) + params['b']

def relu(x):
    return jnp.maximum(0, x)

def softmax(x):
    # Subtract max for numerical stability
    exp_x = jnp.exp(x - jnp.max(x, axis=1, keepdims=True))
    return exp_x / jnp.sum(exp_x, axis=1, keepdims=True)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key1, key2 = random.split(key)
        self.params = {
            'linear1': init_linear_layer(key1, input_size, hidden_size),
            'linear2': init_linear_layer(key2, hidden_size, output_size)
        }

    def __call__(self, x):
        x = linear(self.params['linear1'], x)
        x = relu(x)
        x = linear(self.params['linear2'], x)
        return softmax(x)

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

# Create model
model = MLP(key, 10, 20, 30)

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

# Forward pass
y = model(x)

# Print sum (note: JAX arrays are already numpy-like)
print(list(y.flatten().sum()))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 51, in <module> print(list(y.flatten().sum())) ^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/jax/_src/array.py", line 374, in __iter__ raise TypeError("iteration over a 0-d array") # same as numpy error ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: iteration over a 0-d array

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

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: False

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: False

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

Here's the equivalent JAX implementation: 

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

def init_layer_params(key: random.PRNGKey, 
                     in_dim: int, 
                     out_dim: int) -> Tuple[jnp.ndarray, jnp.ndarray]:
    # Initialize weights and biases for a linear layer
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2.0 / in_dim)
    b = random.normal(k2, (out_dim,)) * 0.01
    return w, b

class MLP:
    def __init__(self, key: random.PRNGKey, input_size: int, hidden_size: int, output_size: int):
        # Initialize parameters for both layers
        key1, key2 = random.split(key)
        self.params1 = init_layer_params(key1, input_size, hidden_size)
        self.params2 = init_layer_params(key2, hidden_size, output_size)
    
    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        # First layer
        w1, b1 = self.params1
        x = jnp.dot(x, w1) + b1
        x = jax.nn.relu(x)
        
        # Second layer
        w2, b2 = self.params2
        x = jnp.dot(x, w2) + b2
        
        # Softmax output
        return jax.nn.softmax(x, axis=1)

# Initialize model and generate random input
key = random.PRNGKey(0)
key1, key2 = random.split(key)

model = MLP(key1, 10, 20, 30)
x = random.normal(key2, (5, 10))

# Forward pass
y = model(x)
print(jnp.sum(y.flatten()))
Key differences and notes: 1. Instead of using torch.nn.Module, we implement a plain Python class. 2. Parameters (weights and biases) are explicitly initialized using JAX's random number generators. 3. Linear layers are implemented manually using matrix multiplication (jnp.dot). 4. JAX uses function transformations rather than class inheritance for its features. 5. The implementation uses JAX's numpy-like interface (jnp) instead of torch's tensors. 6. JAX requires explicit handling of random keys for reproducibility. 7. The activation functions come from jax.nn instead of torch.nn.functional. 8. The output will be slightly different from the PyTorch version due to different initialization schemes and random number generation. This implementation maintains the same basic structure and functionality as the PyTorch code but uses JAX's functional programming paradigm and numpy-like interface. Note that JAX is more functional in nature compared to PyTorch's object-oriented approach.

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
from typing import Tuple

def init_layer_params(key: random.PRNGKey, 
                     in_dim: int, 
                     out_dim: int) -> Tuple[jnp.ndarray, jnp.ndarray]:
    # Initialize weights and biases for a linear layer
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2.0 / in_dim)
    b = random.normal(k2, (out_dim,)) * 0.01
    return w, b

class MLP:
    def __init__(self, key: random.PRNGKey, input_size: int, hidden_size: int, output_size: int):
        # Initialize parameters for both layers
        key1, key2 = random.split(key)
        self.params1 = init_layer_params(key1, input_size, hidden_size)
        self.params2 = init_layer_params(key2, hidden_size, output_size)
    
    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        # First layer
        w1, b1 = self.params1
        x = jnp.dot(x, w1) + b1
        x = jax.nn.relu(x)
        
        # Second layer
        w2, b2 = self.params2
        x = jnp.dot(x, w2) + b2
        
        # Softmax output
        return jax.nn.softmax(x, axis=1)

# Initialize model and generate random input
key = random.PRNGKey(0)
key1, key2 = random.split(key)

model = MLP(key1, 10, 20, 30)
x = random.normal(key2, (5, 10))

# Forward pass
y = model(x)
print(jnp.sum(y.flatten()))

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
from typing import Tuple

def init_layer_params(key: random.PRNGKey, 
                     in_dim: int, 
                     out_dim: int) -> Tuple[jnp.ndarray, jnp.ndarray]:
    # Initialize weights and biases for a linear layer
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2.0 / in_dim)
    b = random.normal(k2, (out_dim,)) * 0.01
    return w, b

class MLP:
    def __init__(self, key: random.PRNGKey, input_size: int, hidden_size: int, output_size: int):
        # Initialize parameters for both layers
        key1, key2 = random.split(key)
        self.params1 = init_layer_params(key1, input_size, hidden_size)
        self.params2 = init_layer_params(key2, hidden_size, output_size)
    
    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        # First layer
        w1, b1 = self.params1
        x = jnp.dot(x, w1) + b1
        x = jax.nn.relu(x)
        
        # Second layer
        w2, b2 = self.params2
        x = jnp.dot(x, w2) + b2
        
        # Softmax output
        return jax.nn.softmax(x, axis=1)

# Initialize model and generate random input
key = random.PRNGKey(0)
key1, key2 = random.split(key)

model = MLP(key1, 10, 20, 30)
x = random.normal(key2, (5, 10))

# Forward pass
y = model(x)
print(jnp.sum(y.flatten()))
And got the output: 5.0

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

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: False

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: False