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

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

def MLP(input_size, hidden_size, output_size, key):
    key1, key2 = random.split(key)
    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,))
    params = {"w1": w1, "b1": b1, "w2": w2, "b2": b2}

    def forward(params, x):
        x = jax.nn.relu(jnp.dot(x, params["w1"]) + params["b1"])
        x = jnp.dot(x, params["w2"]) + params["b2"]
        return jax.nn.softmax(x, axis=1)

    return params, forward

key = random.PRNGKey(0)
params, forward = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = forward(params, x)
print(jnp.sum(y, axis=1))

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 MLP(input_size, hidden_size, output_size, key):
    key1, key2 = random.split(key)
    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,))
    params = {"w1": w1, "b1": b1, "w2": w2, "b2": b2}

    def forward(params, x):
        x = jax.nn.relu(jnp.dot(x, params["w1"]) + params["b1"])
        x = jnp.dot(x, params["w2"]) + params["b2"]
        return jax.nn.softmax(x, axis=1)

    return params, forward

key = random.PRNGKey(0)
params, forward = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = forward(params, x)
print(jnp.sum(y, axis=1))

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 MLP(input_size, hidden_size, output_size, key):
    key1, key2 = random.split(key)
    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,))
    params = {"w1": w1, "b1": b1, "w2": w2, "b2": b2}

    def forward(params, x):
        x = jax.nn.relu(jnp.dot(x, params["w1"]) + params["b1"])
        x = jnp.dot(x, params["w2"]) + params["b2"]
        return jax.nn.softmax(x, axis=1)

    return params, forward

key = random.PRNGKey(0)
params, forward = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = forward(params, x)
print(jnp.sum(y, axis=1))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 23, in <module> x = random.randn(key, (5, 10)) File "/usr/local/lib/python3.10/dist-packages/jax/_src/deprecations.py", line 54, in getattr raise AttributeError(f"module {module!r} has no attribute {name!r}") AttributeError: module 'jax.random' has no attribute 'randn'. Did you mean: 'randint'?

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

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

def MLP(input_size, hidden_size, output_size, key):
  key1, key2 = random.split(key)
  w1 = random.normal(key1, (input_size, hidden_size))
  b1 = random.normal(key1, (hidden_size,))
  w2 = random.normal(key2, (hidden_size, output_size))
  b2 = random.normal(key2, (output_size,))

  def apply(x):
    x = jnp.dot(x, w1) + b1
    x = jax.nn.relu(x)
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x, axis=1)

  return apply

key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = model(x)
print(jnp.sum(y, axis=1))

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 MLP(input_size, hidden_size, output_size, key):
  key1, key2 = random.split(key)
  w1 = random.normal(key1, (input_size, hidden_size))
  b1 = random.normal(key1, (hidden_size,))
  w2 = random.normal(key2, (hidden_size, output_size))
  b2 = random.normal(key2, (output_size,))

  def apply(x):
    x = jnp.dot(x, w1) + b1
    x = jax.nn.relu(x)
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x, axis=1)

  return apply

key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = model(x)
print(jnp.sum(y, axis=1))

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 MLP(input_size, hidden_size, output_size, key):
  key1, key2 = random.split(key)
  w1 = random.normal(key1, (input_size, hidden_size))
  b1 = random.normal(key1, (hidden_size,))
  w2 = random.normal(key2, (hidden_size, output_size))
  b2 = random.normal(key2, (output_size,))

  def apply(x):
    x = jnp.dot(x, w1) + b1
    x = jax.nn.relu(x)
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x, axis=1)

  return apply

key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = model(x)
print(jnp.sum(y, axis=1))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 23, in <module> x = random.randn(key, (5, 10)) File "/usr/local/lib/python3.10/dist-packages/jax/_src/deprecations.py", line 54, in getattr raise AttributeError(f"module {module!r} has no attribute {name!r}") AttributeError: module 'jax.random' has no attribute 'randn'. Did you mean: 'randint'?

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

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from jax import numpy as jnp
from jax import random
from jax.nn import relu, softmax

def MLP(input_size, hidden_size, output_size, key):
  k1, k2 = random.split(key, 2)
  linear1 = Linear(input_size, hidden_size, k1)
  linear2 = Linear(hidden_size, output_size, k2)
  def apply_fn(x):
    x = linear1(x)
    x = relu(x)
    x = linear2(x)
    return softmax(x, axis=1)
  return apply_fn

class Linear:
  def __init__(self, in_features, out_features, key):
    self.w = random.normal(key, (in_features, out_features))
    self.b = jnp.zeros((out_features,))

  def __call__(self, x):
    return jnp.dot(x, self.w) + self.b

key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = model(x)
print(jnp.sum(y, axis=1))

Extract Code

I extracted the following code from that output: 

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from jax import numpy as jnp
from jax import random
from jax.nn import relu, softmax

def MLP(input_size, hidden_size, output_size, key):
  k1, k2 = random.split(key, 2)
  linear1 = Linear(input_size, hidden_size, k1)
  linear2 = Linear(hidden_size, output_size, k2)
  def apply_fn(x):
    x = linear1(x)
    x = relu(x)
    x = linear2(x)
    return softmax(x, axis=1)
  return apply_fn

class Linear:
  def __init__(self, in_features, out_features, key):
    self.w = random.normal(key, (in_features, out_features))
    self.b = jnp.zeros((out_features,))

  def __call__(self, x):
    return jnp.dot(x, self.w) + self.b

key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = model(x)
print(jnp.sum(y, axis=1))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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from jax import numpy as jnp
from jax import random
from jax.nn import relu, softmax

def MLP(input_size, hidden_size, output_size, key):
  k1, k2 = random.split(key, 2)
  linear1 = Linear(input_size, hidden_size, k1)
  linear2 = Linear(hidden_size, output_size, k2)
  def apply_fn(x):
    x = linear1(x)
    x = relu(x)
    x = linear2(x)
    return softmax(x, axis=1)
  return apply_fn

class Linear:
  def __init__(self, in_features, out_features, key):
    self.w = random.normal(key, (in_features, out_features))
    self.b = jnp.zeros((out_features,))

  def __call__(self, x):
    return jnp.dot(x, self.w) + self.b

key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = model(x)
print(jnp.sum(y, axis=1))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 27, in <module> x = random.randn(key, (5, 10)) File "/usr/local/lib/python3.10/dist-packages/jax/_src/deprecations.py", line 54, in getattr raise AttributeError(f"module {module!r} has no attribute {name!r}") AttributeError: module 'jax.random' has no attribute 'randn'. Did you mean: 'randint'?

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

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

def MLP(input_size, hidden_size, output_size):
  key1, key2 = random.split(random.PRNGKey(0))
  w1 = random.normal(key1, (input_size, hidden_size))
  b1 = random.normal(key1, (hidden_size,))
  w2 = random.normal(key2, (hidden_size, output_size))
  b2 = random.normal(key2, (output_size,))

  def apply(x):
    x = jax.nn.relu(jnp.dot(x, w1) + b1)
    x = jax.nn.softmax(jnp.dot(x, w2) + b2, axis=1)
    return x
  return apply

model = MLP(10, 20, 30)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(x)
print(list(y.sum(1)))

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 MLP(input_size, hidden_size, output_size):
  key1, key2 = random.split(random.PRNGKey(0))
  w1 = random.normal(key1, (input_size, hidden_size))
  b1 = random.normal(key1, (hidden_size,))
  w2 = random.normal(key2, (hidden_size, output_size))
  b2 = random.normal(key2, (output_size,))

  def apply(x):
    x = jax.nn.relu(jnp.dot(x, w1) + b1)
    x = jax.nn.softmax(jnp.dot(x, w2) + b2, axis=1)
    return x
  return apply

model = MLP(10, 20, 30)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(x)
print(list(y.sum(1)))

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 MLP(input_size, hidden_size, output_size):
  key1, key2 = random.split(random.PRNGKey(0))
  w1 = random.normal(key1, (input_size, hidden_size))
  b1 = random.normal(key1, (hidden_size,))
  w2 = random.normal(key2, (hidden_size, output_size))
  b2 = random.normal(key2, (output_size,))

  def apply(x):
    x = jax.nn.relu(jnp.dot(x, w1) + b1)
    x = jax.nn.softmax(jnp.dot(x, w2) + b2, axis=1)
    return x
  return apply

model = MLP(10, 20, 30)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(x)
print(list(y.sum(1)))
And got the output: [Array(1., dtype=float32), Array(1., dtype=float32), Array(1., dtype=float32), Array(1., dtype=float32), Array(0.99999976, dtype=float32)]

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: True

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: False

Substring Evaluation

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

Final Answer: True

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: True

Initial Query

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

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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

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

def MLP(input_size, hidden_size, output_size, key):
  key1, key2 = random.split(key)
  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,))
  params = (w1, b1, w2, b2)

  def apply(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, axis=1)

  return params, apply

key = random.PRNGKey(0)
params, apply_fn = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = apply_fn(params, x)
print(jnp.sum(y, axis=1))

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 MLP(input_size, hidden_size, output_size, key):
  key1, key2 = random.split(key)
  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,))
  params = (w1, b1, w2, b2)

  def apply(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, axis=1)

  return params, apply

key = random.PRNGKey(0)
params, apply_fn = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = apply_fn(params, x)
print(jnp.sum(y, axis=1))

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 MLP(input_size, hidden_size, output_size, key):
  key1, key2 = random.split(key)
  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,))
  params = (w1, b1, w2, b2)

  def apply(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, axis=1)

  return params, apply

key = random.PRNGKey(0)
params, apply_fn = MLP(10, 20, 30, key)
x = random.randn(key, (5, 10))
y = apply_fn(params, x)
print(jnp.sum(y, axis=1))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 25, in <module> x = random.randn(key, (5, 10)) File "/usr/local/lib/python3.10/dist-packages/jax/_src/deprecations.py", line 54, in getattr raise AttributeError(f"module {module!r} has no attribute {name!r}") AttributeError: module 'jax.random' has no attribute 'randn'. Did you mean: 'randint'?

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