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

# PyTorch to JAX Conversion Here's the PyTorch code converted to pure JAX: 

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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=random.PRNGKey(0)):
        # Split the key for initializing different layers
        key1, key2 = random.split(key)
        
        # Initialize first layer parameters (equivalent to nn.Linear)
        scale1 = 1 / jnp.sqrt(input_size)
        self.w1 = random.normal(key1, (input_size, hidden_size)) * scale1
        self.b1 = jnp.zeros(hidden_size)
        
        # Initialize second layer parameters
        scale2 = 1 / jnp.sqrt(hidden_size)
        self.w2 = random.normal(key2, (hidden_size, output_size)) * scale2
        self.b2 = jnp.zeros(output_size)
    
    def __call__(self, x):
        # First linear layer
        x = jnp.matmul(x, self.w1) + self.b1
        
        # ReLU activation
        x = jax.nn.relu(x)
        
        # Second linear layer
        x = jnp.matmul(x, self.w2) + self.b2
        
        # Softmax activation (equivalent to nn.functional.softmax)
        return jax.nn.softmax(x, axis=1)

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

# Generate random input (equivalent to torch.randn)
key = random.PRNGKey(0)
x = random.normal(key, (5, 10))

# Forward pass
y = model(x)

# Print results (flattening and summing)
# Note: In PyTorch code, y.flatten().numpy().sum(1) would raise an error
# as there's no axis 1 after flattening, so we're using sum() to match intent
print(list(y.flatten()))
## Notes on the conversion: 1. JAX doesn't have built-in modules like PyTorch, so the class implementation is different 2. We manually initialize parameters using JAX random functions 3. We use `jnp.matmul` instead of PyTorch's `Linear` layers 4. We use `jax.nn.relu` and `jax.nn.softmax` for activations 5. The print statement in the original code had an issue - `y.flatten().numpy().sum(1)` would raise an error because a flattened tensor doesn't have an axis 1. I've adjusted the output to simply show the flattened array.

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=random.PRNGKey(0)):
        # Split the key for initializing different layers
        key1, key2 = random.split(key)
        
        # Initialize first layer parameters (equivalent to nn.Linear)
        scale1 = 1 / jnp.sqrt(input_size)
        self.w1 = random.normal(key1, (input_size, hidden_size)) * scale1
        self.b1 = jnp.zeros(hidden_size)
        
        # Initialize second layer parameters
        scale2 = 1 / jnp.sqrt(hidden_size)
        self.w2 = random.normal(key2, (hidden_size, output_size)) * scale2
        self.b2 = jnp.zeros(output_size)
    
    def __call__(self, x):
        # First linear layer
        x = jnp.matmul(x, self.w1) + self.b1
        
        # ReLU activation
        x = jax.nn.relu(x)
        
        # Second linear layer
        x = jnp.matmul(x, self.w2) + self.b2
        
        # Softmax activation (equivalent to nn.functional.softmax)
        return jax.nn.softmax(x, axis=1)

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

# Generate random input (equivalent to torch.randn)
key = random.PRNGKey(0)
x = random.normal(key, (5, 10))

# Forward pass
y = model(x)

# Print results (flattening and summing)
# Note: In PyTorch code, y.flatten().numpy().sum(1) would raise an error
# as there's no axis 1 after flattening, so we're using sum() to match intent
print(list(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key=random.PRNGKey(0)):
        # Split the key for initializing different layers
        key1, key2 = random.split(key)
        
        # Initialize first layer parameters (equivalent to nn.Linear)
        scale1 = 1 / jnp.sqrt(input_size)
        self.w1 = random.normal(key1, (input_size, hidden_size)) * scale1
        self.b1 = jnp.zeros(hidden_size)
        
        # Initialize second layer parameters
        scale2 = 1 / jnp.sqrt(hidden_size)
        self.w2 = random.normal(key2, (hidden_size, output_size)) * scale2
        self.b2 = jnp.zeros(output_size)
    
    def __call__(self, x):
        # First linear layer
        x = jnp.matmul(x, self.w1) + self.b1
        
        # ReLU activation
        x = jax.nn.relu(x)
        
        # Second linear layer
        x = jnp.matmul(x, self.w2) + self.b2
        
        # Softmax activation (equivalent to nn.functional.softmax)
        return jax.nn.softmax(x, axis=1)

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

# Generate random input (equivalent to torch.randn)
key = random.PRNGKey(0)
x = random.normal(key, (5, 10))

# Forward pass
y = model(x)

# Print results (flattening and summing)
# Note: In PyTorch code, y.flatten().numpy().sum(1) would raise an error
# as there's no axis 1 after flattening, so we're using sum() to match intent
print(list(y.flatten()))
And got the output: [Array(0.02978029, dtype=float32), Array(0.02947385, dtype=float32), Array(0.0360859, dtype=float32), Array(0.02371032, dtype=float32), Array(0.03604907, dtype=float32), Array(0.02008254, dtype=float32), Array(0.03528371, dtype=float32), Array(0.02001837, dtype=float32), Array(0.0458555, dtype=float32), Array(0.03216681, dtype=float32), Array(0.04036855, dtype=float32), Array(0.05167134, dtype=float32), Array(0.02483863, dtype=float32), Array(0.0239955, dtype=float32), Array(0.030925, dtype=float32), Array(0.01903713, dtype=float32), Array(0.03030777, dtype=float32), Array(0.02585757, dtype=float32), Array(0.01700659, dtype=float32), Array(0.04709048, dtype=float32), Array(0.02024409, dtype=float32), Array(0.04924434, dtype=float32), Array(0.04976657, dtype=float32), Array(0.02787153, dtype=float32), Array(0.04985207, dtype=float32), Array(0.03500708, dtype=float32), Array(0.02502094, dtype=float32), Array(0.03012683, dtype=float32), Array(0.03453487, dtype=float32), Array(0.05872674, dtype=float32), Array(0.02294744, dtype=float32), Array(0.0314311, dtype=float32), Array(0.03213862, dtype=float32), Array(0.04102233, dtype=float32), Array(0.03374826, dtype=float32), Array(0.0225282, dtype=float32), Array(0.01969043, dtype=float32), Array(0.05933901, dtype=float32), Array(0.03451744, dtype=float32), Array(0.05634241, dtype=float32), Array(0.01174861, dtype=float32), Array(0.02297119, dtype=float32), Array(0.03117236, dtype=float32), Array(0.01497503, dtype=float32), Array(0.02693945, dtype=float32), Array(0.01902682, dtype=float32), Array(0.0311462, dtype=float32), Array(0.02265472, dtype=float32), Array(0.04730824, dtype=float32), Array(0.05614844, dtype=float32), Array(0.01320447, dtype=float32), Array(0.04779796, dtype=float32), Array(0.02832101, dtype=float32), Array(0.02107547, dtype=float32), Array(0.07421254, dtype=float32), Array(0.03186742, dtype=float32), Array(0.03184121, dtype=float32), Array(0.02509961, dtype=float32), Array(0.03110005, dtype=float32), Array(0.05768394, dtype=float32), Array(0.03390025, dtype=float32), Array(0.01129195, dtype=float32), Array(0.01927331, dtype=float32), Array(0.06109216, dtype=float32), Array(0.02890636, dtype=float32), Array(0.0507016, dtype=float32), Array(0.03922603, dtype=float32), Array(0.0567082, dtype=float32), Array(0.02226824, dtype=float32), Array(0.06041918, dtype=float32), Array(0.00718206, dtype=float32), Array(0.03937722, dtype=float32), Array(0.00775642, dtype=float32), Array(0.00590406, dtype=float32), Array(0.01021772, dtype=float32), Array(0.02883537, dtype=float32), Array(0.06474207, dtype=float32), Array(0.00748337, dtype=float32), Array(0.02709774, dtype=float32), Array(0.05556064, dtype=float32), Array(0.00560176, dtype=float32), Array(0.03699937, dtype=float32), Array(0.02812836, dtype=float32), Array(0.02047058, dtype=float32), Array(0.06706382, dtype=float32), Array(0.03065846, dtype=float32), Array(0.01261212, dtype=float32), Array(0.02105441, dtype=float32), Array(0.00928156, dtype=float32), Array(0.13018562, dtype=float32), Array(0.0408042, dtype=float32), Array(0.03728081, dtype=float32), Array(0.0279179, dtype=float32), Array(0.02849475, dtype=float32), Array(0.03828882, dtype=float32), Array(0.02153988, dtype=float32), Array(0.03726659, dtype=float32), Array(0.02076633, dtype=float32), Array(0.04753922, dtype=float32), Array(0.03203461, dtype=float32), Array(0.05366454, dtype=float32), Array(0.04307998, dtype=float32), Array(0.02431505, dtype=float32), Array(0.03260963, dtype=float32), Array(0.02926573, dtype=float32), Array(0.0274394, dtype=float32), Array(0.03759709, dtype=float32), Array(0.03483219, dtype=float32), Array(0.02604786, dtype=float32), Array(0.02533469, dtype=float32), Array(0.02557811, dtype=float32), Array(0.03284914, dtype=float32), Array(0.04181066, dtype=float32), Array(0.02336052, dtype=float32), Array(0.04146308, dtype=float32), Array(0.03249579, dtype=float32), Array(0.02683526, dtype=float32), Array(0.03389003, dtype=float32), Array(0.02463588, dtype=float32), Array(0.05096227, dtype=float32), Array(0.04294204, dtype=float32), Array(0.01604186, dtype=float32), Array(0.03837278, dtype=float32), Array(0.03253577, dtype=float32), Array(0.03705489, dtype=float32), Array(0.01456863, dtype=float32), Array(0.01789336, dtype=float32), Array(0.02875433, dtype=float32), Array(0.03199888, dtype=float32), Array(0.02607664, dtype=float32), Array(0.02499624, dtype=float32), Array(0.0410391, dtype=float32), Array(0.02531772, dtype=float32), Array(0.0079244, dtype=float32), Array(0.01123373, dtype=float32), Array(0.01991546, dtype=float32), Array(0.02816455, dtype=float32), Array(0.01533127, dtype=float32), Array(0.01307241, dtype=float32), Array(0.06480767, dtype=float32), Array(0.01026748, dtype=float32), Array(0.05408802, dtype=float32), Array(0.04165613, dtype=float32), Array(0.02023649, dtype=float32), Array(0.08419468, dtype=float32), Array(0.04154534, dtype=float32), Array(0.02325782, dtype=float32), Array(0.03649699, dtype=float32), Array(0.04216737, dtype=float32), Array(0.10804794, dtype=float32)]

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

# PyTorch to JAX Conversion Here's the equivalent code using pure JAX: 

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

def init_mlp_params(input_size, hidden_size, output_size, key):
    # Split the key for different layers
    key1, key2 = random.split(key)
    
    # Initialize parameters for linear layers
    # Using Xavier/Glorot initialization similar to PyTorch defaults
    w1_scale = jnp.sqrt(2.0 / (input_size + hidden_size))
    w2_scale = jnp.sqrt(2.0 / (hidden_size + output_size))
    
    linear1 = {
        'weight': random.normal(key1, (input_size, hidden_size)) * w1_scale,
        'bias': jnp.zeros(hidden_size)
    }
    
    linear2 = {
        'weight': random.normal(key2, (hidden_size, output_size)) * w2_scale,
        'bias': jnp.zeros(output_size)
    }
    
    return {'linear1': linear1, 'linear2': linear2}

def mlp_forward(params, x):
    # First linear layer
    x = jnp.matmul(x, params['linear1']['weight']) + params['linear1']['bias']
    
    # ReLU activation
    x = jax.nn.relu(x)
    
    # Second linear layer
    x = jnp.matmul(x, params['linear2']['weight']) + params['linear2']['bias']
    
    # Softmax activation
    return jax.nn.softmax(x, axis=1)

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

# Initialize model parameters
params = init_mlp_params(10, 20, 30, key)

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

# Forward pass
y = mlp_forward(params, x)

# Note: The original PyTorch code appears to have an issue.
# In PyTorch, y.flatten().numpy().sum(1) would raise an error 
# as a flattened tensor is 1D and doesn't have axis 1.
# The equivalent code should probably be:
print(list(jnp.sum(y, axis=1)))

# If the original code really intended to flatten first:
print(list(jnp.sum(y.flatten())))
The main differences from PyTorch: 1. JAX uses a functional approach instead of classes with state 2. Parameters are explicitly initialized and passed to functions 3. Matrix multiplication is done with `jnp.matmul` instead of PyTorch's built-in linear layers 4. Activations come from `jax.nn` instead of `torch.nn.functional` 5. JAX uses `axis` parameter instead of PyTorch's `dim` The original code's print statement appears problematic, so I've provided two possible interpretations.

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_mlp_params(input_size, hidden_size, output_size, key):
    # Split the key for different layers
    key1, key2 = random.split(key)
    
    # Initialize parameters for linear layers
    # Using Xavier/Glorot initialization similar to PyTorch defaults
    w1_scale = jnp.sqrt(2.0 / (input_size + hidden_size))
    w2_scale = jnp.sqrt(2.0 / (hidden_size + output_size))
    
    linear1 = {
        'weight': random.normal(key1, (input_size, hidden_size)) * w1_scale,
        'bias': jnp.zeros(hidden_size)
    }
    
    linear2 = {
        'weight': random.normal(key2, (hidden_size, output_size)) * w2_scale,
        'bias': jnp.zeros(output_size)
    }
    
    return {'linear1': linear1, 'linear2': linear2}

def mlp_forward(params, x):
    # First linear layer
    x = jnp.matmul(x, params['linear1']['weight']) + params['linear1']['bias']
    
    # ReLU activation
    x = jax.nn.relu(x)
    
    # Second linear layer
    x = jnp.matmul(x, params['linear2']['weight']) + params['linear2']['bias']
    
    # Softmax activation
    return jax.nn.softmax(x, axis=1)

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

# Initialize model parameters
params = init_mlp_params(10, 20, 30, key)

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

# Forward pass
y = mlp_forward(params, x)

# Note: The original PyTorch code appears to have an issue.
# In PyTorch, y.flatten().numpy().sum(1) would raise an error 
# as a flattened tensor is 1D and doesn't have axis 1.
# The equivalent code should probably be:
print(list(jnp.sum(y, axis=1)))

# If the original code really intended to flatten first:
print(list(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

def init_mlp_params(input_size, hidden_size, output_size, key):
    # Split the key for different layers
    key1, key2 = random.split(key)
    
    # Initialize parameters for linear layers
    # Using Xavier/Glorot initialization similar to PyTorch defaults
    w1_scale = jnp.sqrt(2.0 / (input_size + hidden_size))
    w2_scale = jnp.sqrt(2.0 / (hidden_size + output_size))
    
    linear1 = {
        'weight': random.normal(key1, (input_size, hidden_size)) * w1_scale,
        'bias': jnp.zeros(hidden_size)
    }
    
    linear2 = {
        'weight': random.normal(key2, (hidden_size, output_size)) * w2_scale,
        'bias': jnp.zeros(output_size)
    }
    
    return {'linear1': linear1, 'linear2': linear2}

def mlp_forward(params, x):
    # First linear layer
    x = jnp.matmul(x, params['linear1']['weight']) + params['linear1']['bias']
    
    # ReLU activation
    x = jax.nn.relu(x)
    
    # Second linear layer
    x = jnp.matmul(x, params['linear2']['weight']) + params['linear2']['bias']
    
    # Softmax activation
    return jax.nn.softmax(x, axis=1)

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

# Initialize model parameters
params = init_mlp_params(10, 20, 30, key)

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

# Forward pass
y = mlp_forward(params, x)

# Note: The original PyTorch code appears to have an issue.
# In PyTorch, y.flatten().numpy().sum(1) would raise an error 
# as a flattened tensor is 1D and doesn't have axis 1.
# The equivalent code should probably be:
print(list(jnp.sum(y, axis=1)))

# If the original code really intended to flatten first:
print(list(jnp.sum(y.flatten())))
And got the output: [Array(1., dtype=float32), Array(1., dtype=float32), Array(0.9999999, dtype=float32), Array(0.99999994, dtype=float32), Array(0.9999999, dtype=float32)] Traceback (most recent call last): File "/usr/src/app/main.py", line 59, in <module> print(list(jnp.sum(y.flatten()))) ^^^^^^^^^^^^^^^^^^^^^^^^^^ 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: 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