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

key = random.PRNGKey(0)

def init_layer(key, m, n):
    key, subkey = random.split(key)
    w_key, b_key = random.split(subkey)
    w = random.normal(w_key, (n, m)) * (2 / m)**0.5  # Kaiming initialization
    b = random.normal(b_key, (n,))
    return key, (w, b)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key, layer1 = init_layer(key, input_size, hidden_size)
        key, layer2 = init_layer(key, hidden_size, output_size)
        self.layer1 = layer1
        self.layer2 = layer2

    def __call__(self, x):
        w1, b1 = self.layer1
        x = jnp.dot(x, w1.T) + b1
        x = jax.nn.relu(x)
        w2, b2 = self.layer2
        x = jnp.dot(x, w2.T) + b2
        return jax.nn.softmax(x, axis=1)

input_size = 10
hidden_size = 20
output_size = 30

model = MLP(key, input_size, hidden_size, output_size)
x = random.normal(key, (5, input_size))
y = model(x)


print(list(y.flatten().sum(1)))  # No need to convert to numpy in JAX

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

key = random.PRNGKey(0)

def init_layer(key, m, n):
    key, subkey = random.split(key)
    w_key, b_key = random.split(subkey)
    w = random.normal(w_key, (n, m)) * (2 / m)**0.5  # Kaiming initialization
    b = random.normal(b_key, (n,))
    return key, (w, b)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key, layer1 = init_layer(key, input_size, hidden_size)
        key, layer2 = init_layer(key, hidden_size, output_size)
        self.layer1 = layer1
        self.layer2 = layer2

    def __call__(self, x):
        w1, b1 = self.layer1
        x = jnp.dot(x, w1.T) + b1
        x = jax.nn.relu(x)
        w2, b2 = self.layer2
        x = jnp.dot(x, w2.T) + b2
        return jax.nn.softmax(x, axis=1)

input_size = 10
hidden_size = 20
output_size = 30

model = MLP(key, input_size, hidden_size, output_size)
x = random.normal(key, (5, input_size))
y = model(x)


print(list(y.flatten().sum(1)))  # No need to convert to numpy in JAX

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

key = random.PRNGKey(0)

def init_layer(key, m, n):
    key, subkey = random.split(key)
    w_key, b_key = random.split(subkey)
    w = random.normal(w_key, (n, m)) * (2 / m)**0.5  # Kaiming initialization
    b = random.normal(b_key, (n,))
    return key, (w, b)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key, layer1 = init_layer(key, input_size, hidden_size)
        key, layer2 = init_layer(key, hidden_size, output_size)
        self.layer1 = layer1
        self.layer2 = layer2

    def __call__(self, x):
        w1, b1 = self.layer1
        x = jnp.dot(x, w1.T) + b1
        x = jax.nn.relu(x)
        w2, b2 = self.layer2
        x = jnp.dot(x, w2.T) + b2
        return jax.nn.softmax(x, axis=1)

input_size = 10
hidden_size = 20
output_size = 30

model = MLP(key, input_size, hidden_size, output_size)
x = random.normal(key, (5, input_size))
y = model(x)


print(list(y.flatten().sum(1)))  # No need to convert to numpy in JAX
And got the output: 
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Traceback (most recent call last):
  File "/usr/src/app/main.py", line 39, in <module>
    print(list(y.flatten().sum(1)))  # No need to convert to numpy in JAX
               ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/array_methods.py", line 363, in _sum
    return reductions.sum(self, axis=axis, dtype=dtype, out=out, keepdims=keepdims,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 289, in sum
    return _reduce_sum(a, axis=_ensure_optional_axes(axis), dtype=dtype, out=out,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 213, in _reduce_sum
    return _reduction(a, "sum", np.sum, lax.add, 0, preproc=_cast_to_numeric,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 111, in _reduction
    pos_dims, dims = _reduction_dims(a, axis)
                     ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 160, in _reduction_dims
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 160, in <genexpr>
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 153, in _canonicalize_axis_allow_named
    return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 153, 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
from jax import random

key = random.PRNGKey(0)


class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        key1, key2 = random.split(key)
        self.linear1 = Linear(input_size, hidden_size, key1)
        self.linear2 = Linear(hidden_size, output_size, key2)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=1)

class Linear:
    def __init__(self, input_size, output_size, key):
        w_key, b_key = random.split(key)
        self.w = random.normal(w_key, (input_size, output_size)) * jax.lax.rsqrt(input_size)  # Xavier initialization
        self.b = jnp.zeros((output_size,))

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



model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))
y = model(x)
print(list(y.flatten().sum(1))) # No numpy conversion needed in JAX

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

key = random.PRNGKey(0)


class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        key1, key2 = random.split(key)
        self.linear1 = Linear(input_size, hidden_size, key1)
        self.linear2 = Linear(hidden_size, output_size, key2)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=1)

class Linear:
    def __init__(self, input_size, output_size, key):
        w_key, b_key = random.split(key)
        self.w = random.normal(w_key, (input_size, output_size)) * jax.lax.rsqrt(input_size)  # Xavier initialization
        self.b = jnp.zeros((output_size,))

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



model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))
y = model(x)
print(list(y.flatten().sum(1))) # No numpy conversion needed in JAX

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

key = random.PRNGKey(0)


class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        key1, key2 = random.split(key)
        self.linear1 = Linear(input_size, hidden_size, key1)
        self.linear2 = Linear(hidden_size, output_size, key2)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=1)

class Linear:
    def __init__(self, input_size, output_size, key):
        w_key, b_key = random.split(key)
        self.w = random.normal(w_key, (input_size, output_size)) * jax.lax.rsqrt(input_size)  # Xavier initialization
        self.b = jnp.zeros((output_size,))

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



model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))
y = model(x)
print(list(y.flatten().sum(1))) # No numpy conversion needed in JAX
And got the output: 
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Traceback (most recent call last):
  File "/usr/src/app/main.py", line 32, in <module>
    model = MLP(10, 20, 30, key)
            ^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 12, in <strong>init</strong>
    self.linear1 = Linear(input_size, hidden_size, key1)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 24, in <strong>init</strong>
    self.w = random.normal(w_key, (input_size, output_size)) * jax.lax.rsqrt(input_size)  # Xavier initialization
                                                               ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/lax/lax.py", line 395, in rsqrt
    return rsqrt_p.bind(x)
           ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/core.py", line 438, in bind
    return self.bind_with_trace(find_top_trace(args), args, params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/core.py", line 442, in bind_with_trace
    out = trace.process_primitive(self, map(trace.full_raise, args), params)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/core.py", line 955, in process_primitive
    return primitive.impl(<em>tracers, </em><em>params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/dispatch.py", line 91, in apply_primitive
    outs = fun(</em>args)
           ^^^^^^^^^^
TypeError: rsqrt does not accept dtype int32. Accepted dtypes are subtypes of floating, complexfloating.</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
from jax import random

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        k1, k2 = random.split(key)
        self.linear1_w = random.normal(k1, (input_size, hidden_size))
        self.linear1_b = jnp.zeros(hidden_size)
        self.linear2_w = random.normal(k2, (hidden_size, output_size))
        self.linear2_b = jnp.zeros(output_size)

    def __call__(self, x):
        x = jnp.dot(x, self.linear1_w) + self.linear1_b
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_w) + self.linear2_b
        return jax.nn.softmax(x, axis=1)

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        k1, k2 = random.split(key)
        self.linear1_w = random.normal(k1, (input_size, hidden_size))
        self.linear1_b = jnp.zeros(hidden_size)
        self.linear2_w = random.normal(k2, (hidden_size, output_size))
        self.linear2_b = jnp.zeros(output_size)

    def __call__(self, x):
        x = jnp.dot(x, self.linear1_w) + self.linear1_b
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_w) + self.linear2_b
        return jax.nn.softmax(x, axis=1)

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        k1, k2 = random.split(key)
        self.linear1_w = random.normal(k1, (input_size, hidden_size))
        self.linear1_b = jnp.zeros(hidden_size)
        self.linear2_w = random.normal(k2, (hidden_size, output_size))
        self.linear2_b = jnp.zeros(output_size)

    def __call__(self, x):
        x = jnp.dot(x, self.linear1_w) + self.linear1_b
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_w) + self.linear2_b
        return jax.nn.softmax(x, axis=1)

key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))
y = model(x)
print(list(y.flatten().sum(1)))
And got the output: 
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Traceback (most recent call last):
  File "/usr/src/app/main.py", line 24, in <module>
    print(list(y.flatten().sum(1))) 
               ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/array_methods.py", line 363, in _sum
    return reductions.sum(self, axis=axis, dtype=dtype, out=out, keepdims=keepdims,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 289, in sum
    return _reduce_sum(a, axis=_ensure_optional_axes(axis), dtype=dtype, out=out,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 213, in _reduce_sum
    return _reduction(a, "sum", np.sum, lax.add, 0, preproc=_cast_to_numeric,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 111, in _reduction
    pos_dims, dims = _reduction_dims(a, axis)
                     ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 160, in _reduction_dims
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 160, in <genexpr>
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 153, in _canonicalize_axis_allow_named
    return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 153, 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, key):
        k1, k2 = jax.random.split(key)
        self.linear1_w = jax.random.normal(k1, (hidden_size, input_size))
        self.linear1_b = jnp.zeros((hidden_size,))
        self.linear2_w = jax.random.normal(k2, (output_size, hidden_size))
        self.linear2_b = jnp.zeros((output_size,))

    def __call__(self, x):
        x = jnp.dot(x, self.linear1_w.T) + self.linear1_b
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_w.T) + self.linear2_b
        return jax.nn.softmax(x, axis=1)

key = jax.random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = jax.random.normal(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        k1, k2 = jax.random.split(key)
        self.linear1_w = jax.random.normal(k1, (hidden_size, input_size))
        self.linear1_b = jnp.zeros((hidden_size,))
        self.linear2_w = jax.random.normal(k2, (output_size, hidden_size))
        self.linear2_b = jnp.zeros((output_size,))

    def __call__(self, x):
        x = jnp.dot(x, self.linear1_w.T) + self.linear1_b
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_w.T) + self.linear2_b
        return jax.nn.softmax(x, axis=1)

key = jax.random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = jax.random.normal(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        k1, k2 = jax.random.split(key)
        self.linear1_w = jax.random.normal(k1, (hidden_size, input_size))
        self.linear1_b = jnp.zeros((hidden_size,))
        self.linear2_w = jax.random.normal(k2, (output_size, hidden_size))
        self.linear2_b = jnp.zeros((output_size,))

    def __call__(self, x):
        x = jnp.dot(x, self.linear1_w.T) + self.linear1_b
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2_w.T) + self.linear2_b
        return jax.nn.softmax(x, axis=1)

key = jax.random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = jax.random.normal(key, (5, 10))
y = model(x)
print(jnp.sum(y, axis=1))
And got the output: [0.99999994 0.99999994 1. 1. 1.0000001 ]

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, key):
        k1, k2 = random.split(key)
        self.linear1 = Linear(input_size, hidden_size, k1)
        self.linear2 = Linear(hidden_size, output_size, k2)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=1)


class Linear:
    def __init__(self, in_features, out_features, key):
        wkey, bkey = random.split(key)
        self.w = random.normal(wkey, (out_features, in_features))
        self.b = random.normal(bkey, (out_features,))

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


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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        k1, k2 = random.split(key)
        self.linear1 = Linear(input_size, hidden_size, k1)
        self.linear2 = Linear(hidden_size, output_size, k2)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=1)


class Linear:
    def __init__(self, in_features, out_features, key):
        wkey, bkey = random.split(key)
        self.w = random.normal(wkey, (out_features, in_features))
        self.b = random.normal(bkey, (out_features,))

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


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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        k1, k2 = random.split(key)
        self.linear1 = Linear(input_size, hidden_size, k1)
        self.linear2 = Linear(hidden_size, output_size, k2)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=1)


class Linear:
    def __init__(self, in_features, out_features, key):
        wkey, bkey = random.split(key)
        self.w = random.normal(wkey, (out_features, in_features))
        self.b = random.normal(bkey, (out_features,))

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


key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(x)
print(list(y.flatten().sum(1)))
And got the output: 
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Traceback (most recent call last):
  File "/usr/src/app/main.py", line 33, in <module>
    print(list(y.flatten().sum(1)))
               ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/array_methods.py", line 363, in _sum
    return reductions.sum(self, axis=axis, dtype=dtype, out=out, keepdims=keepdims,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 289, in sum
    return _reduce_sum(a, axis=_ensure_optional_axes(axis), dtype=dtype, out=out,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 213, in _reduce_sum
    return _reduction(a, "sum", np.sum, lax.add, 0, preproc=_cast_to_numeric,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 111, in _reduction
    pos_dims, dims = _reduction_dims(a, axis)
                     ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 160, in _reduction_dims
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 160, in <genexpr>
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 153, in _canonicalize_axis_allow_named
    return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/reductions.py", line 153, 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