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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jnp.zeros((input_size, hidden_size)),
            'bias1': jnp.zeros((hidden_size,)),
            'linear2': jnp.zeros((hidden_size, output_size)),
            'bias2': jnp.zeros((output_size,))
        }

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

        x = jnp.dot(x, self.params['linear1']) + self.params['bias1']
        x = relu(x)
        x = jnp.dot(x, self.params['linear2']) + self.params['bias2']
        return jax.nn.softmax(x)

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

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.params = {
            'linear1': jnp.zeros((input_size, hidden_size)),
            'bias1': jnp.zeros((hidden_size,)),
            'linear2': jnp.zeros((hidden_size, output_size)),
            'bias2': jnp.zeros((output_size,))
        }

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

        x = jnp.dot(x, self.params['linear1']) + self.params['bias1']
        x = relu(x)
        x = jnp.dot(x, self.params['linear2']) + self.params['bias2']
        return jax.nn.softmax(x)

model = MLP(10, 20, 30)
x = jnp.random.randn(5, 10)
y = model(x)
print(list(y.flatten().sum(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jnp.zeros((input_size, hidden_size)),
            'bias1': jnp.zeros((hidden_size,)),
            'linear2': jnp.zeros((hidden_size, output_size)),
            'bias2': jnp.zeros((output_size,))
        }

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

        x = jnp.dot(x, self.params['linear1']) + self.params['bias1']
        x = relu(x)
        x = jnp.dot(x, self.params['linear2']) + self.params['bias2']
        return jax.nn.softmax(x)

model = MLP(10, 20, 30)
x = jnp.random.randn(5, 10)
y = model(x)
print(list(y.flatten().sum(axis=1)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 24, in <module> x = jnp.random.randn(5, 10) ^^^^^^^^^^ AttributeError: module 'jax.numpy' has no attribute 'random'

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jax.random.PRNGKey(0),
            'linear2': jax.random.PRNGKey(1)
        }
        self.params['linear1'], self.params['linear2'] = jax.random.split(self.params['linear1'])
        self.weights = {
            'linear1': jax.random.normal(self.params['linear1'], (input_size, hidden_size)),
            'linear2': jax.random.normal(self.params['linear2'], (hidden_size, output_size))
        }

    def __call__(self, x):
        x = jnp.dot(x, self.weights['linear1'])
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.weights['linear2'])
        return jax.nn.softmax(x, axis=1)

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

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.params = {
            'linear1': jax.random.PRNGKey(0),
            'linear2': jax.random.PRNGKey(1)
        }
        self.params['linear1'], self.params['linear2'] = jax.random.split(self.params['linear1'])
        self.weights = {
            'linear1': jax.random.normal(self.params['linear1'], (input_size, hidden_size)),
            'linear2': jax.random.normal(self.params['linear2'], (hidden_size, output_size))
        }

    def __call__(self, x):
        x = jnp.dot(x, self.weights['linear1'])
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.weights['linear2'])
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
key = jax.random.PRNGKey(0)
x = jax.random.normal(key, (5, 10))
y = model(x)
print(jnp.sum(y.flatten(), 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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jax.random.PRNGKey(0),
            'linear2': jax.random.PRNGKey(1)
        }
        self.params['linear1'], self.params['linear2'] = jax.random.split(self.params['linear1'])
        self.weights = {
            'linear1': jax.random.normal(self.params['linear1'], (input_size, hidden_size)),
            'linear2': jax.random.normal(self.params['linear2'], (hidden_size, output_size))
        }

    def __call__(self, x):
        x = jnp.dot(x, self.weights['linear1'])
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.weights['linear2'])
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
key = jax.random.PRNGKey(0)
x = jax.random.normal(key, (5, 10))
y = model(x)
print(jnp.sum(y.flatten(), axis=1))
And got the output: 
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Traceback (most recent call last):
  File "/usr/src/app/main.py", line 27, in <module>
    print(jnp.sum(y.flatten(), axis=1))
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 226, in sum
    return _reduce_sum(a, axis=_ensure_optional_axes(axis), dtype=dtype, out=out,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 216, in _reduce_sum
    return _reduction(a, "sum", np.sum, lax.add, 0, preproc=_cast_to_numeric,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 96, in _reduction
    pos_dims, dims = _reduction_dims(a, axis)
                     ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 154, in _reduction_dims
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 154, in <genexpr>
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 147, in _canonicalize_axis_allow_named
    return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 147, in <lambda>
    return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: axis 1 is out of bounds for array of dimension 1</p>
<hr />
<p>For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jax.random.PRNGKey(0),
            'linear2': jax.random.PRNGKey(1)
        }
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size

    def init(self, key):
        key, key1, key2 = jax.random.split(key, 3)
        w1 = jax.random.normal(key1, (self.input_size, self.hidden_size))
        b1 = jax.random.normal(key2, (self.hidden_size,))
        key, key1, key2 = jax.random.split(key, 3)
        w2 = jax.random.normal(key1, (self.hidden_size, self.output_size))
        b2 = jax.random.normal(key2, (self.output_size,))
        self.params = {
            'linear1': (w1, b1),
            'linear2': (w2, b2)
        }

    def forward(self, params, x):
        w1, b1 = params['linear1']
        w2, b2 = params['linear2']
        x = jnp.dot(x, w1) + b1
        x = jax.nn.relu(x)
        x = jnp.dot(x, w2) + b2
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
key = jax.random.PRNGKey(0)
model.init(key)
x = jax.random.normal(key, (5, 10))
params = model.params
y = model.forward(params, x)
print(list(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jax.random.PRNGKey(0),
            'linear2': jax.random.PRNGKey(1)
        }
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size

    def init(self, key):
        key, key1, key2 = jax.random.split(key, 3)
        w1 = jax.random.normal(key1, (self.input_size, self.hidden_size))
        b1 = jax.random.normal(key2, (self.hidden_size,))
        key, key1, key2 = jax.random.split(key, 3)
        w2 = jax.random.normal(key1, (self.hidden_size, self.output_size))
        b2 = jax.random.normal(key2, (self.output_size,))
        self.params = {
            'linear1': (w1, b1),
            'linear2': (w2, b2)
        }

    def forward(self, params, x):
        w1, b1 = params['linear1']
        w2, b2 = params['linear2']
        x = jnp.dot(x, w1) + b1
        x = jax.nn.relu(x)
        x = jnp.dot(x, w2) + b2
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
key = jax.random.PRNGKey(0)
model.init(key)
x = jax.random.normal(key, (5, 10))
params = model.params
y = model.forward(params, x)
print(list(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jax.random.PRNGKey(0),
            'linear2': jax.random.PRNGKey(1)
        }
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size

    def init(self, key):
        key, key1, key2 = jax.random.split(key, 3)
        w1 = jax.random.normal(key1, (self.input_size, self.hidden_size))
        b1 = jax.random.normal(key2, (self.hidden_size,))
        key, key1, key2 = jax.random.split(key, 3)
        w2 = jax.random.normal(key1, (self.hidden_size, self.output_size))
        b2 = jax.random.normal(key2, (self.output_size,))
        self.params = {
            'linear1': (w1, b1),
            'linear2': (w2, b2)
        }

    def forward(self, params, x):
        w1, b1 = params['linear1']
        w2, b2 = params['linear2']
        x = jnp.dot(x, w1) + b1
        x = jax.nn.relu(x)
        x = jnp.dot(x, w2) + b2
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
key = jax.random.PRNGKey(0)
model.init(key)
x = jax.random.normal(key, (5, 10))
params = model.params
y = model.forward(params, x)
print(list(jnp.sum(y, axis=1)))
And got the output: [Array(0.9999998, dtype=float32), Array(1., dtype=float32), Array(1.0000001, dtype=float32), Array(0.9999999, dtype=float32), Array(1., 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

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jnp.zeros((input_size, hidden_size)),
            'bias1': jnp.zeros((hidden_size,)),
            'linear2': jnp.zeros((hidden_size, output_size)),
            'bias2': jnp.zeros((output_size,))
        }

    def forward(self, params, x):
        linear1 = jnp.dot(x, params['linear1']) + params['bias1']
        relu = jax.nn.relu(linear1)
        linear2 = jnp.dot(relu, params['linear2']) + params['bias2']
        return jax.nn.softmax(linear2, axis=1)

model = MLP(10, 20, 30)
key = random.PRNGKey(0)
params = model.params
x = random.normal(key, (5, 10))
y = model.forward(params, x)
print(list(y.flatten().sum(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jnp.zeros((input_size, hidden_size)),
            'bias1': jnp.zeros((hidden_size,)),
            'linear2': jnp.zeros((hidden_size, output_size)),
            'bias2': jnp.zeros((output_size,))
        }

    def forward(self, params, x):
        linear1 = jnp.dot(x, params['linear1']) + params['bias1']
        relu = jax.nn.relu(linear1)
        linear2 = jnp.dot(relu, params['linear2']) + params['bias2']
        return jax.nn.softmax(linear2, axis=1)

model = MLP(10, 20, 30)
key = random.PRNGKey(0)
params = model.params
x = random.normal(key, (5, 10))
y = model.forward(params, x)
print(list(y.flatten().sum(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jnp.zeros((input_size, hidden_size)),
            'bias1': jnp.zeros((hidden_size,)),
            'linear2': jnp.zeros((hidden_size, output_size)),
            'bias2': jnp.zeros((output_size,))
        }

    def forward(self, params, x):
        linear1 = jnp.dot(x, params['linear1']) + params['bias1']
        relu = jax.nn.relu(linear1)
        linear2 = jnp.dot(relu, params['linear2']) + params['bias2']
        return jax.nn.softmax(linear2, axis=1)

model = MLP(10, 20, 30)
key = random.PRNGKey(0)
params = model.params
x = random.normal(key, (5, 10))
y = model.forward(params, x)
print(list(y.flatten().sum(axis=1)))
And got the output: 
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Traceback (most recent call last):
  File "/usr/src/app/main.py", line 26, in <module>
    print(list(y.flatten().sum(axis=1)))
               ^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 226, in sum
    return _reduce_sum(a, axis=_ensure_optional_axes(axis), dtype=dtype, out=out,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 216, in _reduce_sum
    return _reduction(a, "sum", np.sum, lax.add, 0, preproc=_cast_to_numeric,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 96, in _reduction
    pos_dims, dims = _reduction_dims(a, axis)
                     ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 154, in _reduction_dims
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 154, in <genexpr>
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 147, in _canonicalize_axis_allow_named
    return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 147, in <lambda>
    return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: axis 1 is out of bounds for array of dimension 1</p>
<hr />
<p>For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jax.random.PRNGKey(0),
            'linear2': jax.random.PRNGKey(1)
        }
        self.params['linear1'], self.params['linear2'] = jax.random.split(self.params['linear1'])
        self.params['linear1'] = jax.random.normal(self.params['linear1'], (hidden_size, input_size))
        self.params['linear2'] = jax.random.normal(self.params['linear2'], (output_size, hidden_size))

    def __call__(self, x):
        x = jnp.dot(self.params['linear1'], x)
        x = jax.nn.relu(x)
        x = jnp.dot(self.params['linear2'], x)
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
key = jax.random.PRNGKey(2)
x = jax.random.normal(key, (5, 10))
y = model(x)
print(list(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jax.random.PRNGKey(0),
            'linear2': jax.random.PRNGKey(1)
        }
        self.params['linear1'], self.params['linear2'] = jax.random.split(self.params['linear1'])
        self.params['linear1'] = jax.random.normal(self.params['linear1'], (hidden_size, input_size))
        self.params['linear2'] = jax.random.normal(self.params['linear2'], (output_size, hidden_size))

    def __call__(self, x):
        x = jnp.dot(self.params['linear1'], x)
        x = jax.nn.relu(x)
        x = jnp.dot(self.params['linear2'], x)
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
key = jax.random.PRNGKey(2)
x = jax.random.normal(key, (5, 10))
y = model(x)
print(list(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {
            'linear1': jax.random.PRNGKey(0),
            'linear2': jax.random.PRNGKey(1)
        }
        self.params['linear1'], self.params['linear2'] = jax.random.split(self.params['linear1'])
        self.params['linear1'] = jax.random.normal(self.params['linear1'], (hidden_size, input_size))
        self.params['linear2'] = jax.random.normal(self.params['linear2'], (output_size, hidden_size))

    def __call__(self, x):
        x = jnp.dot(self.params['linear1'], x)
        x = jax.nn.relu(x)
        x = jnp.dot(self.params['linear2'], x)
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
key = jax.random.PRNGKey(2)
x = jax.random.normal(key, (5, 10))
y = model(x)
print(list(jnp.sum(y, axis=1)))
And got the output: 
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Traceback (most recent call last):
  File "/usr/src/app/main.py", line 24, in <module>
    y = model(x)
        ^^^^^^^^
  File "/usr/src/app/main.py", line 16, in <strong>call</strong>
    x = jnp.dot(self.params['linear1'], x)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/lax_numpy.py", line 3326, in dot
    result = lax.dot_general(a, b, dimension_numbers=(contract_dims, batch_dims),
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: dot_general requires contracting dimensions to have the same shape, got (10,) and (5,).</p>
<hr />
<p>For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.

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