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 converted JAX code: 

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

def init_params(key, input_size, hidden_size, output_size):
    k1, k2, k3, k4 = random.split(key, 4)
    a1 = jnp.sqrt(1.0 / input_size)
    w1 = random.uniform(k1, (input_size, hidden_size), minval=-a1, maxval=a1)
    b1 = random.uniform(k2, (hidden_size,), minval=-a1, maxval=a1)
    a2 = jnp.sqrt(1.0 / hidden_size)
    w2 = random.uniform(k3, (hidden_size, output_size), minval=-a2, maxval=a2)
    b2 = random.uniform(k4, (output_size,), minval=-a2, maxval=a2)
    params = {
        'linear1': {'weights': w1, 'bias': b1},
        'linear2': {'weights': w2, 'bias': b2},
    }
    return params

def mlp(params, x):
    x = jnp.dot(x, params['linear1']['weights']) + params['linear1']['bias']
    x = jax.nn.relu(x)
    x = jnp.dot(x, params['linear2']['weights']) + params['linear2']['bias']
    x = jax.nn.softmax(x, axis=1)
    return x

key = random.PRNGKey(0)
model_key, x_key = random.split(key)
params = init_params(model_key, 10, 20, 30)
x = random.normal(x_key, (5, 10))
y = mlp(params, x)

sums = jnp.sum(y, axis=1)
print(list(sums))
Key differences from PyTorch: 1. Explicit parameter management instead of using nn.Module 2. Manual parameter initialization using JAX's random module 3. Pure functional model definition (no class, parameters passed explicitly) 4. JAX's NumPy-style API for operations 5. Explicit PRNG key management for reproducibility The output will show five values very close to 1.0 (due to softmax normalization), similar to the original PyTorch code's intended behavior.

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_params(key, input_size, hidden_size, output_size):
    k1, k2, k3, k4 = random.split(key, 4)
    a1 = jnp.sqrt(1.0 / input_size)
    w1 = random.uniform(k1, (input_size, hidden_size), minval=-a1, maxval=a1)
    b1 = random.uniform(k2, (hidden_size,), minval=-a1, maxval=a1)
    a2 = jnp.sqrt(1.0 / hidden_size)
    w2 = random.uniform(k3, (hidden_size, output_size), minval=-a2, maxval=a2)
    b2 = random.uniform(k4, (output_size,), minval=-a2, maxval=a2)
    params = {
        'linear1': {'weights': w1, 'bias': b1},
        'linear2': {'weights': w2, 'bias': b2},
    }
    return params

def mlp(params, x):
    x = jnp.dot(x, params['linear1']['weights']) + params['linear1']['bias']
    x = jax.nn.relu(x)
    x = jnp.dot(x, params['linear2']['weights']) + params['linear2']['bias']
    x = jax.nn.softmax(x, axis=1)
    return x

key = random.PRNGKey(0)
model_key, x_key = random.split(key)
params = init_params(model_key, 10, 20, 30)
x = random.normal(x_key, (5, 10))
y = mlp(params, x)

sums = jnp.sum(y, axis=1)
print(list(sums))

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_params(key, input_size, hidden_size, output_size):
    k1, k2, k3, k4 = random.split(key, 4)
    a1 = jnp.sqrt(1.0 / input_size)
    w1 = random.uniform(k1, (input_size, hidden_size), minval=-a1, maxval=a1)
    b1 = random.uniform(k2, (hidden_size,), minval=-a1, maxval=a1)
    a2 = jnp.sqrt(1.0 / hidden_size)
    w2 = random.uniform(k3, (hidden_size, output_size), minval=-a2, maxval=a2)
    b2 = random.uniform(k4, (output_size,), minval=-a2, maxval=a2)
    params = {
        'linear1': {'weights': w1, 'bias': b1},
        'linear2': {'weights': w2, 'bias': b2},
    }
    return params

def mlp(params, x):
    x = jnp.dot(x, params['linear1']['weights']) + params['linear1']['bias']
    x = jax.nn.relu(x)
    x = jnp.dot(x, params['linear2']['weights']) + params['linear2']['bias']
    x = jax.nn.softmax(x, axis=1)
    return x

key = random.PRNGKey(0)
model_key, x_key = random.split(key)
params = init_params(model_key, 10, 20, 30)
x = random.normal(x_key, (5, 10))
y = mlp(params, x)

sums = jnp.sum(y, axis=1)
print(list(sums))
And got the output: [Array(0.99999994, dtype=float32), Array(1.0000001, dtype=float32), Array(1., dtype=float32), Array(1., dtype=float32), Array(0.99999994, 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: True

Substring Evaluation

Testing if the previous output contains the string 1.0,: False

Final Answer: True

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: True