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, which mirrors the structure and functionality of the original PyTorch code. I'll use JAX's `jax.numpy` for array operations and `jax.nn` for the activation functions. 

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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):
        # Initialize weights and biases for the first layer
        self.w1 = jax.random.normal(random.PRNGKey(0), (input_size, hidden_size))
        self.b1 = jnp.zeros(hidden_size)
        
        # Initialize weights and biases for the second layer
        self.w2 = jax.random.normal(random.PRNGKey(1), (hidden_size, output_size))
        self.b2 = jnp.zeros(output_size)

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

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

# Generate random input
x = jax.random.normal(random.PRNGKey(2), (5, 10))

# Get the output
y = model(x)

# Print the sum of the flattened output
print(jnp.sum(y.flatten()))
### Key Changes: 1. **Model Initialization**: In JAX, we manually initialize weights and biases using random normal distributions. 2. **Dot Product**: We use `jnp.dot()` for matrix multiplication. 3. **Activation Functions**: JAX provides `jax.nn.relu` for the ReLU activation function and `jax.nn.softmax` for the softmax function. 4. **Random Keys**: JAX uses random keys for generating random numbers, which allows for reproducibility. 5. **No `torch.nn.Module`**: JAX does not have a built-in module system like PyTorch, so we define the model class without inheriting from any base class. This JAX implementation preserves the logic of the original PyTorch model while adhering to JAX conventions.

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):
        # Initialize weights and biases for the first layer
        self.w1 = jax.random.normal(random.PRNGKey(0), (input_size, hidden_size))
        self.b1 = jnp.zeros(hidden_size)
        
        # Initialize weights and biases for the second layer
        self.w2 = jax.random.normal(random.PRNGKey(1), (hidden_size, output_size))
        self.b2 = jnp.zeros(output_size)

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

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

# Generate random input
x = jax.random.normal(random.PRNGKey(2), (5, 10))

# Get the output
y = model(x)

# Print the sum of the flattened output
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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        # Initialize weights and biases for the first layer
        self.w1 = jax.random.normal(random.PRNGKey(0), (input_size, hidden_size))
        self.b1 = jnp.zeros(hidden_size)
        
        # Initialize weights and biases for the second layer
        self.w2 = jax.random.normal(random.PRNGKey(1), (hidden_size, output_size))
        self.b2 = jnp.zeros(output_size)

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

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

# Generate random input
x = jax.random.normal(random.PRNGKey(2), (5, 10))

# Get the output
y = model(x)

# Print the sum of the flattened output
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

To convert the given PyTorch code to pure JAX, we need to replace the PyTorch-specific operations with equivalent JAX operations. JAX does not have a built-in module system like PyTorch's `nn.Module`, so we will create a similar functional approach. Here's the equivalent JAX code: 

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

def init_weights(input_size, hidden_size, output_size, key):
    key1, key2, key3 = random.split(key, 3)
    w1 = random.normal(key1, (input_size, hidden_size))
    b1 = jnp.zeros(hidden_size)
    w2 = random.normal(key2, (hidden_size, output_size))
    b2 = jnp.zeros(output_size)
    return (w1, b1, w2, b2)

def mlp(params, x):
    w1, b1, w2, b2 = params
    x = jnp.dot(x, w1) + b1
    x = jax.nn.relu(x)
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x)

# Initialize model parameters
key = random.PRNGKey(0)  # Random key for initialization
params = init_weights(10, 20, 30, key)

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

# Forward pass
y = mlp(params, x)

# Print the flattened sum
print(jnp.sum(y.flatten()))
### Explanation of Changes: 1. **Weight Initialization**: Instead of using `torch.nn.Linear`, we create a function `init_weights` to initialize weights and biases using JAX's random number generation. 2. **MLP Function**: We define an `mlp` function that takes the model parameters and input data, performs the matrix multiplications and biases, applies ReLU activation, and then computes the softmax. 3. **Random Key**: JAX uses a functional approach to random number generation, so we create a `PRNGKey` for random operations. 4. **JAX Functions**: We use `jax.numpy` (`jnp`) for array operations and `jax.nn.relu` and `jax.nn.softmax` for activation and output functions. 5. **Flattening and Summing**: The print statement has been modified to use JAX's array operations. This code is a direct translation of the functionality from PyTorch to JAX, maintaining the structure and intent of the original code.

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_weights(input_size, hidden_size, output_size, key):
    key1, key2, key3 = random.split(key, 3)
    w1 = random.normal(key1, (input_size, hidden_size))
    b1 = jnp.zeros(hidden_size)
    w2 = random.normal(key2, (hidden_size, output_size))
    b2 = jnp.zeros(output_size)
    return (w1, b1, w2, b2)

def mlp(params, x):
    w1, b1, w2, b2 = params
    x = jnp.dot(x, w1) + b1
    x = jax.nn.relu(x)
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x)

# Initialize model parameters
key = random.PRNGKey(0)  # Random key for initialization
params = init_weights(10, 20, 30, key)

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

# Forward pass
y = mlp(params, x)

# Print the flattened sum
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

def init_weights(input_size, hidden_size, output_size, key):
    key1, key2, key3 = random.split(key, 3)
    w1 = random.normal(key1, (input_size, hidden_size))
    b1 = jnp.zeros(hidden_size)
    w2 = random.normal(key2, (hidden_size, output_size))
    b2 = jnp.zeros(output_size)
    return (w1, b1, w2, b2)

def mlp(params, x):
    w1, b1, w2, b2 = params
    x = jnp.dot(x, w1) + b1
    x = jax.nn.relu(x)
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x)

# Initialize model parameters
key = random.PRNGKey(0)  # Random key for initialization
params = init_weights(10, 20, 30, key)

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

# Forward pass
y = mlp(params, x)

# Print the flattened sum
print(jnp.sum(y.flatten()))
And got the output: 4.9999995

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

You can convert the provided PyTorch code to pure JAX as follows. In JAX, we typically use `jax.numpy` (imported as `jnp`) for numerical operations and `jax.nn` for activation functions. Here's the equivalent code: 

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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):
        # Initialize weights and biases
        self.linear1_weights = jax.random.normal(random.PRNGKey(0), (input_size, hidden_size))
        self.linear1_bias = jnp.zeros(hidden_size)
        self.linear2_weights = jax.random.normal(random.PRNGKey(1), (hidden_size, output_size))
        self.linear2_bias = jnp.zeros(output_size)

    def __call__(self, x):
        # Forward pass
        x = jnp.dot(x, self.linear1_weights) + self.linear1_bias
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_weights) + self.linear2_bias
        return jax.nn.softmax(x)

# Initialize the model
model = MLP(10, 20, 30)

# Create a random input
x = jax.random.normal(random.PRNGKey(2), (5, 10))

# Get the output
y = model(x)

# Print the sum of elements in the output
print(jnp.sum(y.flatten()))
### Key Points: 1. **Weights Initialization**: In JAX, we manually initialize the weights and biases using `jax.random.normal`. 2. **Forward Pass**: We use `jnp.dot` for matrix multiplication and `jax.nn.relu` for the activation function. 3. **Softmax**: The softmax function is applied using `jax.nn.softmax`. 4. **Random Number Generation**: JAX uses a functional approach to random number generation, requiring PRNG keys for reproducibility. ### Note: - Ensure that the random keys used for initializing weights are unique to avoid overlapping random values.

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):
        # Initialize weights and biases
        self.linear1_weights = jax.random.normal(random.PRNGKey(0), (input_size, hidden_size))
        self.linear1_bias = jnp.zeros(hidden_size)
        self.linear2_weights = jax.random.normal(random.PRNGKey(1), (hidden_size, output_size))
        self.linear2_bias = jnp.zeros(output_size)

    def __call__(self, x):
        # Forward pass
        x = jnp.dot(x, self.linear1_weights) + self.linear1_bias
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_weights) + self.linear2_bias
        return jax.nn.softmax(x)

# Initialize the model
model = MLP(10, 20, 30)

# Create a random input
x = jax.random.normal(random.PRNGKey(2), (5, 10))

# Get the output
y = model(x)

# Print the sum of elements in the output
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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        # Initialize weights and biases
        self.linear1_weights = jax.random.normal(random.PRNGKey(0), (input_size, hidden_size))
        self.linear1_bias = jnp.zeros(hidden_size)
        self.linear2_weights = jax.random.normal(random.PRNGKey(1), (hidden_size, output_size))
        self.linear2_bias = jnp.zeros(output_size)

    def __call__(self, x):
        # Forward pass
        x = jnp.dot(x, self.linear1_weights) + self.linear1_bias
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_weights) + self.linear2_bias
        return jax.nn.softmax(x)

# Initialize the model
model = MLP(10, 20, 30)

# Create a random input
x = jax.random.normal(random.PRNGKey(2), (5, 10))

# Get the output
y = model(x)

# Print the sum of elements in the output
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 is the equivalent implementation of the provided PyTorch code in 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):
        # Initialize weights and biases for the two layers
        self.params = {
            'linear1': {
                'weight': jax.random.normal(random.PRNGKey(0), (input_size, hidden_size)) * 0.01,
                'bias': jnp.zeros((hidden_size,))
            },
            'linear2': {
                'weight': jax.random.normal(random.PRNGKey(1), (hidden_size, output_size)) * 0.01,
                'bias': jnp.zeros((output_size,))
            }
        }

    def __call__(self, x):
        # Forward pass through the network
        x = jnp.dot(x, self.params['linear1']['weight']) + self.params['linear1']['bias']
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.params['linear2']['weight']) + self.params['linear2']['bias']
        return jax.nn.softmax(x)

# Initialize the model
model = MLP(10, 20, 30)

# Generate random input data
x = jax.random.normal(random.PRNGKey(2), (5, 10))

# Perform the forward pass
y = model(x)

# Print the flattened sum of the output
print(jnp.sum(y.flatten()))
### Explanation: 1. **Initialization**: In the `MLP` class, we manually initialize weights and biases for each layer using random normal distribution scaled by a small factor (0.01) to avoid large initial values. JAX requires a random key (`PRNGKey`) for randomness. 2. **Forward Pass**: The `__call__` method implements the forward pass where: - We use `jnp.dot` to perform matrix multiplication for the layers. - We add biases to the output of the first layer. - We apply the ReLU activation function using `jax.nn.relu`. - Lastly, we apply another `jnp.dot` for the second layer and compute the softmax of the output. 3. **Input Generation**: Random input data is generated using `jax.random.normal`. 4. **Output**: The output is flattened and summed, similar to the PyTorch example. This code is a direct conversion of the PyTorch example while utilizing only JAX functionalities.

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):
        # Initialize weights and biases for the two layers
        self.params = {
            'linear1': {
                'weight': jax.random.normal(random.PRNGKey(0), (input_size, hidden_size)) * 0.01,
                'bias': jnp.zeros((hidden_size,))
            },
            'linear2': {
                'weight': jax.random.normal(random.PRNGKey(1), (hidden_size, output_size)) * 0.01,
                'bias': jnp.zeros((output_size,))
            }
        }

    def __call__(self, x):
        # Forward pass through the network
        x = jnp.dot(x, self.params['linear1']['weight']) + self.params['linear1']['bias']
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.params['linear2']['weight']) + self.params['linear2']['bias']
        return jax.nn.softmax(x)

# Initialize the model
model = MLP(10, 20, 30)

# Generate random input data
x = jax.random.normal(random.PRNGKey(2), (5, 10))

# Perform the forward pass
y = model(x)

# Print the flattened sum of the output
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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        # Initialize weights and biases for the two layers
        self.params = {
            'linear1': {
                'weight': jax.random.normal(random.PRNGKey(0), (input_size, hidden_size)) * 0.01,
                'bias': jnp.zeros((hidden_size,))
            },
            'linear2': {
                'weight': jax.random.normal(random.PRNGKey(1), (hidden_size, output_size)) * 0.01,
                'bias': jnp.zeros((output_size,))
            }
        }

    def __call__(self, x):
        # Forward pass through the network
        x = jnp.dot(x, self.params['linear1']['weight']) + self.params['linear1']['bias']
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.params['linear2']['weight']) + self.params['linear2']['bias']
        return jax.nn.softmax(x)

# Initialize the model
model = MLP(10, 20, 30)

# Generate random input data
x = jax.random.normal(random.PRNGKey(2), (5, 10))

# Perform the forward pass
y = model(x)

# Print the flattened sum of the output
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 converted from PyTorch to pure JAX. In this code, we define a similar multi-layer perceptron (MLP) using JAX's functionalities: 

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1_weights = jax.random.normal(jax.random.PRNGKey(0), (input_size, hidden_size)) * jnp.sqrt(2.0 / input_size)
        self.linear1_bias = jnp.zeros(hidden_size)
        self.linear2_weights = jax.random.normal(jax.random.PRNGKey(1), (hidden_size, output_size)) * jnp.sqrt(2.0 / hidden_size)
        self.linear2_bias = jnp.zeros(output_size)

    def __call__(self, x):
        x = jnp.dot(x, self.linear1_weights) + self.linear1_bias
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_weights) + self.linear2_bias
        return jax.nn.softmax(x)

# Initialize the model
model = MLP(10, 20, 30)

# Create random input data
x = jax.random.normal(jax.random.PRNGKey(2), (5, 10))

# Get the model output
y = model(x)

# Print the sum of the flattened output
print(jnp.sum(y.flatten()))
### Key Changes: 1. **Initialization**: JAX does not have a built-in module system like PyTorch, so we manually initialize weights and biases. 2. **Random Key**: JAX uses a functional approach to randomness. We generate weights using `jax.random.normal` with a random key. 3. **Activation Functions**: We use `jax.nn.relu` and `jax.nn.softmax` for activation functions. 4. **Matrix Operations**: We use `jnp.dot` for matrix multiplication, similar to PyTorch's `torch.matmul`. 5. **Output Handling**: JAX uses JAX's `jnp.sum` to compute the sum of the flattened output. Make sure you have JAX installed in your environment to run this code, and you can install it using: 
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pip install jax jaxlib

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1_weights = jax.random.normal(jax.random.PRNGKey(0), (input_size, hidden_size)) * jnp.sqrt(2.0 / input_size)
        self.linear1_bias = jnp.zeros(hidden_size)
        self.linear2_weights = jax.random.normal(jax.random.PRNGKey(1), (hidden_size, output_size)) * jnp.sqrt(2.0 / hidden_size)
        self.linear2_bias = jnp.zeros(output_size)

    def __call__(self, x):
        x = jnp.dot(x, self.linear1_weights) + self.linear1_bias
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_weights) + self.linear2_bias
        return jax.nn.softmax(x)

# Initialize the model
model = MLP(10, 20, 30)

# Create random input data
x = jax.random.normal(jax.random.PRNGKey(2), (5, 10))

# Get the model output
y = model(x)

# Print the sum of the flattened output
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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1_weights = jax.random.normal(jax.random.PRNGKey(0), (input_size, hidden_size)) * jnp.sqrt(2.0 / input_size)
        self.linear1_bias = jnp.zeros(hidden_size)
        self.linear2_weights = jax.random.normal(jax.random.PRNGKey(1), (hidden_size, output_size)) * jnp.sqrt(2.0 / hidden_size)
        self.linear2_bias = jnp.zeros(output_size)

    def __call__(self, x):
        x = jnp.dot(x, self.linear1_weights) + self.linear1_bias
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_weights) + self.linear2_bias
        return jax.nn.softmax(x)

# Initialize the model
model = MLP(10, 20, 30)

# Create random input data
x = jax.random.normal(jax.random.PRNGKey(2), (5, 10))

# Get the model output
y = model(x)

# Print the sum of the flattened output
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