Initial Query

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

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

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

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

Here's the equivalent code in pure JAX without using any other libraries: 

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import jax.numpy as jnp
from jax import random, grad, jit, vmap

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        key = random.PRNGKey(0)
        self.params = self.init_params(key, input_size, hidden_size, output_size)

    def init_params(self, key, input_size, hidden_size, output_size):
        key, subkey = random.split(key)
        w1 = random.normal(subkey, (input_size, hidden_size))
        key, subkey = random.split(key)
        b1 = random.normal(subkey, (hidden_size,))
        key, subkey = random.split(key)
        w2 = random.normal(subkey, (hidden_size, output_size))
        key, subkey = random.split(key)
        b2 = random.normal(subkey, (output_size,))
        return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

    def __call__(self, params, x):
        x = jnp.dot(x, params['w1']) + params['b1']
        x = jnp.maximum(0, x)  # ReLU activation
        x = jnp.dot(x, params['w2']) + params['b2']
        return jax.nn.softmax(x, axis=-1)

model = MLP(10, 20, 30)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(model.params, x)
print(list(y.flatten().sum(axis=-1)))
Explanation: - We define the `MLP` class with the same structure as the PyTorch version, but instead of using PyTorch's `nn.Module`, we define the parameters using pure JAX. - In the `__init__` method, we initialize the parameters using `self.init_params`, which takes a random key and the layer sizes as inputs. - The `init_params` method uses `jax.random.normal` to generate random weights and biases for the linear layers. - The `__call__` method performs the forward pass of the MLP. We use `jnp.dot` for matrix multiplication and `jnp.maximum` for the ReLU activation. - We use `jax.nn.softmax` for the softmax activation in the output layer. - Finally, we create an instance of the `MLP` class, generate random input data `x` using `jax.random.normal`, and pass it through the model to get the output `y`. - We print the sum of the flattened output tensor along the last axis. Note that in JAX, we need to explicitly pass the parameters to the model when calling it, unlike in PyTorch where the parameters are automatically managed by the `nn.Module` class.

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import random, grad, jit, vmap

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        key = random.PRNGKey(0)
        self.params = self.init_params(key, input_size, hidden_size, output_size)

    def init_params(self, key, input_size, hidden_size, output_size):
        key, subkey = random.split(key)
        w1 = random.normal(subkey, (input_size, hidden_size))
        key, subkey = random.split(key)
        b1 = random.normal(subkey, (hidden_size,))
        key, subkey = random.split(key)
        w2 = random.normal(subkey, (hidden_size, output_size))
        key, subkey = random.split(key)
        b2 = random.normal(subkey, (output_size,))
        return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

    def __call__(self, params, x):
        x = jnp.dot(x, params['w1']) + params['b1']
        x = jnp.maximum(0, x)  # ReLU activation
        x = jnp.dot(x, params['w2']) + params['b2']
        return jax.nn.softmax(x, axis=-1)

model = MLP(10, 20, 30)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(model.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.numpy as jnp
from jax import random, grad, jit, vmap

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        key = random.PRNGKey(0)
        self.params = self.init_params(key, input_size, hidden_size, output_size)

    def init_params(self, key, input_size, hidden_size, output_size):
        key, subkey = random.split(key)
        w1 = random.normal(subkey, (input_size, hidden_size))
        key, subkey = random.split(key)
        b1 = random.normal(subkey, (hidden_size,))
        key, subkey = random.split(key)
        w2 = random.normal(subkey, (hidden_size, output_size))
        key, subkey = random.split(key)
        b2 = random.normal(subkey, (output_size,))
        return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

    def __call__(self, params, x):
        x = jnp.dot(x, params['w1']) + params['b1']
        x = jnp.maximum(0, x)  # ReLU activation
        x = jnp.dot(x, params['w2']) + params['b2']
        return jax.nn.softmax(x, axis=-1)

model = MLP(10, 20, 30)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(model.params, x)
print(list(y.flatten().sum(axis=-1)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 29, in <module> y = model(model.params, x) ^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 25, in __call__ return jax.nn.softmax(x, axis=-1) ^^^ NameError: name 'jax' is not defined. Did you mean: 'max'?

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: False

Substring Evaluation

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

Final Answer: False

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: False

Initial Query

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

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

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

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

Here's the equivalent code in pure JAX without using any other libraries: 

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

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

def softmax(x):
    exp_x = jnp.exp(x)
    return exp_x / jnp.sum(exp_x, axis=-1, keepdims=True)

def init_mlp(key, input_size, hidden_size, output_size):
    key1, key2 = random.split(key)
    weights1 = random.normal(key1, (input_size, hidden_size))
    weights2 = random.normal(key2, (hidden_size, output_size))
    return (weights1, weights2)

def apply_mlp(params, x):
    weights1, weights2 = params
    x = jnp.dot(x, weights1)
    x = relu(x)
    x = jnp.dot(x, weights2)
    return softmax(x)

key = random.PRNGKey(0)
input_size = 10
hidden_size = 20
output_size = 30
params = init_mlp(key, input_size, hidden_size, output_size)

x = random.normal(key, (5, 10))
y = apply_mlp(params, x)
print(list(y.flatten().sum(axis=1)))
Explanation: - We define the `relu` and `softmax` functions separately since JAX doesn't have built-in modules like PyTorch. - The `init_mlp` function initializes the weights of the MLP using `random.normal` from JAX. It takes a random key and the sizes of the input, hidden, and output layers as arguments. - The `apply_mlp` function applies the MLP to an input `x` using the provided parameters (weights). It performs matrix multiplication (`jnp.dot`) between the input and the weights, applies the ReLU activation function, and then performs another matrix multiplication with the second set of weights. Finally, it applies the softmax function to the output. - We generate a random key using `random.PRNGKey` and initialize the parameters of the MLP using `init_mlp`. - We generate a random input `x` using `random.normal` with the same shape as in the PyTorch code. - We apply the MLP to the input `x` using `apply_mlp` and print the sum of the flattened output along axis 1, similar to the PyTorch code. Note that JAX uses functional programming paradigms, so we don't define a class for the MLP like in PyTorch. Instead, we define separate functions for initialization and forward pass.

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 relu(x):
    return jnp.maximum(0, x)

def softmax(x):
    exp_x = jnp.exp(x)
    return exp_x / jnp.sum(exp_x, axis=-1, keepdims=True)

def init_mlp(key, input_size, hidden_size, output_size):
    key1, key2 = random.split(key)
    weights1 = random.normal(key1, (input_size, hidden_size))
    weights2 = random.normal(key2, (hidden_size, output_size))
    return (weights1, weights2)

def apply_mlp(params, x):
    weights1, weights2 = params
    x = jnp.dot(x, weights1)
    x = relu(x)
    x = jnp.dot(x, weights2)
    return softmax(x)

key = random.PRNGKey(0)
input_size = 10
hidden_size = 20
output_size = 30
params = init_mlp(key, input_size, hidden_size, output_size)

x = random.normal(key, (5, 10))
y = apply_mlp(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

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

def softmax(x):
    exp_x = jnp.exp(x)
    return exp_x / jnp.sum(exp_x, axis=-1, keepdims=True)

def init_mlp(key, input_size, hidden_size, output_size):
    key1, key2 = random.split(key)
    weights1 = random.normal(key1, (input_size, hidden_size))
    weights2 = random.normal(key2, (hidden_size, output_size))
    return (weights1, weights2)

def apply_mlp(params, x):
    weights1, weights2 = params
    x = jnp.dot(x, weights1)
    x = relu(x)
    x = jnp.dot(x, weights2)
    return softmax(x)

key = random.PRNGKey(0)
input_size = 10
hidden_size = 20
output_size = 30
params = init_mlp(key, input_size, hidden_size, output_size)

x = random.normal(key, (5, 10))
y = apply_mlp(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 34, 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

Here's the equivalent code in pure JAX without using any other libraries: 

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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):
        key, subkey = random.split(key)
        self.params = {
            'linear1': {
                'weights': random.normal(subkey, (input_size, hidden_size)),
                'bias': jnp.zeros(hidden_size)
            },
            'linear2': {
                'weights': random.normal(subkey, (hidden_size, output_size)),
                'bias': jnp.zeros(output_size)
            }
        }
        
    def __call__(self, 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']
        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(model.params, x)
print(list(y.flatten().sum(axis=1)))
Explanation: 1. We import the necessary modules from JAX: `jax`, `jax.numpy` as `jnp`, and `random` for random number generation. 2. We define the `MLP` class with `__init__` and `__call__` methods similar to the PyTorch version. - In `__init__`, we initialize the parameters of the linear layers using `random.normal` for the weights and `jnp.zeros` for the biases. - In `__call__`, we perform the forward pass by applying the linear transformations, ReLU activation, and softmax function using JAX's `jnp.dot`, `jax.nn.relu`, and `jax.nn.softmax`, respectively. 3. We create an instance of the `MLP` class by providing the input size, hidden size, output size, and a random key. 4. We generate random input data `x` using `random.normal`. 5. We call the model with the parameters and input data to obtain the output `y`. 6. Finally, we print the sum of the flattened output along axis 1 using `y.flatten().sum(axis=1)`. Note that in JAX, we need to explicitly pass the parameters to the model when calling it, unlike in PyTorch where the parameters are automatically managed by the `torch.nn.Module` class. Also, JAX uses `jax.numpy` instead of `numpy`, which provides a NumPy-like API that is compatible with JAX's autograd and JIT compilation.

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):
        key, subkey = random.split(key)
        self.params = {
            'linear1': {
                'weights': random.normal(subkey, (input_size, hidden_size)),
                'bias': jnp.zeros(hidden_size)
            },
            'linear2': {
                'weights': random.normal(subkey, (hidden_size, output_size)),
                'bias': jnp.zeros(output_size)
            }
        }
        
    def __call__(self, 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']
        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(model.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, key):
        key, subkey = random.split(key)
        self.params = {
            'linear1': {
                'weights': random.normal(subkey, (input_size, hidden_size)),
                'bias': jnp.zeros(hidden_size)
            },
            'linear2': {
                'weights': random.normal(subkey, (hidden_size, output_size)),
                'bias': jnp.zeros(output_size)
            }
        }
        
    def __call__(self, 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']
        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(model.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 31, 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

Here's the equivalent code in pure JAX: 

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

def mlp_init(input_size, hidden_size, output_size):
    def init(rng_key):
        key1, key2 = jax.random.split(rng_key)
        weights1 = jax.random.normal(key1, (input_size, hidden_size)) * jnp.sqrt(2 / input_size)
        weights2 = jax.random.normal(key2, (hidden_size, output_size)) * jnp.sqrt(2 / hidden_size)
        biases1 = jnp.zeros((hidden_size,))
        biases2 = jnp.zeros((output_size,))
        return (weights1, biases1), (weights2, biases2)
    return init

def mlp_apply(params, x):
    (weights1, biases1), (weights2, biases2) = params
    x = jnp.dot(x, weights1) + biases1
    x = relu(x)
    x = jnp.dot(x, weights2) + biases2
    return softmax(x, axis=-1)

input_size = 10
hidden_size = 20
output_size = 30

rng_key = jax.random.PRNGKey(0)
params = mlp_init(input_size, hidden_size, output_size)(rng_key)

x = jax.random.normal(rng_key, (5, 10))
y = mlp_apply(params, x)
print(list(y.sum(axis=1)))
Explanation: - We define an `mlp_init` function that takes the input size, hidden size, and output size as arguments and returns a function that initializes the parameters (weights and biases) of the MLP using `jax.random.normal`. - The `mlp_apply` function takes the parameters and input `x` and applies the MLP layers using `jnp.dot` for matrix multiplication, `relu` for the activation function, and `softmax` for the output layer. - We create the MLP with the specified input size, hidden size, and output size, and initialize the parameters using `mlp_init` and a random key generated by `jax.random.PRNGKey`. - We generate random input data `x` using `jax.random.normal` and apply the MLP to it using `mlp_apply`. - Finally, we print the sum of the output probabilities along the second axis (equivalent to summing over the flattened tensor in the PyTorch version). Note that JAX uses a functional programming style, so we separate the parameter initialization (`mlp_init`) from the forward pass (`mlp_apply`). The `mlp_init` function returns a function that takes a random key and returns the initialized parameters, which we then pass to `mlp_apply` along with the input data.

Extract Code

I extracted the following code from that output: 

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

def mlp_init(input_size, hidden_size, output_size):
    def init(rng_key):
        key1, key2 = jax.random.split(rng_key)
        weights1 = jax.random.normal(key1, (input_size, hidden_size)) * jnp.sqrt(2 / input_size)
        weights2 = jax.random.normal(key2, (hidden_size, output_size)) * jnp.sqrt(2 / hidden_size)
        biases1 = jnp.zeros((hidden_size,))
        biases2 = jnp.zeros((output_size,))
        return (weights1, biases1), (weights2, biases2)
    return init

def mlp_apply(params, x):
    (weights1, biases1), (weights2, biases2) = params
    x = jnp.dot(x, weights1) + biases1
    x = relu(x)
    x = jnp.dot(x, weights2) + biases2
    return softmax(x, axis=-1)

input_size = 10
hidden_size = 20
output_size = 30

rng_key = jax.random.PRNGKey(0)
params = mlp_init(input_size, hidden_size, output_size)(rng_key)

x = jax.random.normal(rng_key, (5, 10))
y = mlp_apply(params, x)
print(list(y.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.nn import relu, softmax

def mlp_init(input_size, hidden_size, output_size):
    def init(rng_key):
        key1, key2 = jax.random.split(rng_key)
        weights1 = jax.random.normal(key1, (input_size, hidden_size)) * jnp.sqrt(2 / input_size)
        weights2 = jax.random.normal(key2, (hidden_size, output_size)) * jnp.sqrt(2 / hidden_size)
        biases1 = jnp.zeros((hidden_size,))
        biases2 = jnp.zeros((output_size,))
        return (weights1, biases1), (weights2, biases2)
    return init

def mlp_apply(params, x):
    (weights1, biases1), (weights2, biases2) = params
    x = jnp.dot(x, weights1) + biases1
    x = relu(x)
    x = jnp.dot(x, weights2) + biases2
    return softmax(x, axis=-1)

input_size = 10
hidden_size = 20
output_size = 30

rng_key = jax.random.PRNGKey(0)
params = mlp_init(input_size, hidden_size, output_size)(rng_key)

x = jax.random.normal(rng_key, (5, 10))
y = mlp_apply(params, x)
print(list(y.sum(axis=1)))
And got the output: [Array(0.99999994, dtype=float32), Array(1., dtype=float32), Array(1., dtype=float32), Array(1., 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: 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

Here's the equivalent code in pure JAX without using any other libraries: 

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import jax.numpy as jnp
from jax import random, grad, vmap, jit

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        key = random.PRNGKey(0)
        self.params = self.init_params(key, input_size, hidden_size, output_size)

    def init_params(self, key, input_size, hidden_size, output_size):
        key, subkey1, subkey2 = random.split(key, 3)
        params = {
            'linear1': {
                'weights': random.normal(subkey1, (input_size, hidden_size)),
                'bias': jnp.zeros(hidden_size)
            },
            'linear2': {
                'weights': random.normal(subkey2, (hidden_size, output_size)),
                'bias': jnp.zeros(output_size)
            }
        }
        return params

    def __call__(self, params, x):
        x = jnp.dot(x, params['linear1']['weights']) + params['linear1']['bias']
        x = jnp.maximum(x, 0)
        x = jnp.dot(x, params['linear2']['weights']) + params['linear2']['bias']
        return jax.nn.softmax(x, axis=-1)

model = MLP(10, 20, 30)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(model.params, x)
print(y.sum(axis=1))
Explanation: - We define the `MLP` class with an `__init__` method that initializes the model parameters using `init_params`. - The `init_params` method takes the input size, hidden size, and output size and returns a dictionary containing the randomly initialized weights and biases for the linear layers. - The `__call__` method defines the forward pass of the model. It takes the model parameters and the input `x`, applies the linear transformations, ReLU activation, and softmax activation. - We create an instance of the `MLP` class with the specified input size, hidden size, and output size. - We generate a random input tensor `x` using `random.normal`. - We call the model with the input `x` to get the output `y`. - Finally, we print the sum of the output along the second axis (corresponding to `dim=1` in PyTorch). Note that JAX uses functional programming paradigms, so we pass the model parameters explicitly to the `__call__` method instead of using `self.params` directly.

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import random, grad, vmap, jit

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        key = random.PRNGKey(0)
        self.params = self.init_params(key, input_size, hidden_size, output_size)

    def init_params(self, key, input_size, hidden_size, output_size):
        key, subkey1, subkey2 = random.split(key, 3)
        params = {
            'linear1': {
                'weights': random.normal(subkey1, (input_size, hidden_size)),
                'bias': jnp.zeros(hidden_size)
            },
            'linear2': {
                'weights': random.normal(subkey2, (hidden_size, output_size)),
                'bias': jnp.zeros(output_size)
            }
        }
        return params

    def __call__(self, params, x):
        x = jnp.dot(x, params['linear1']['weights']) + params['linear1']['bias']
        x = jnp.maximum(x, 0)
        x = jnp.dot(x, params['linear2']['weights']) + params['linear2']['bias']
        return jax.nn.softmax(x, axis=-1)

model = MLP(10, 20, 30)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(model.params, x)
print(y.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.numpy as jnp
from jax import random, grad, vmap, jit

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        key = random.PRNGKey(0)
        self.params = self.init_params(key, input_size, hidden_size, output_size)

    def init_params(self, key, input_size, hidden_size, output_size):
        key, subkey1, subkey2 = random.split(key, 3)
        params = {
            'linear1': {
                'weights': random.normal(subkey1, (input_size, hidden_size)),
                'bias': jnp.zeros(hidden_size)
            },
            'linear2': {
                'weights': random.normal(subkey2, (hidden_size, output_size)),
                'bias': jnp.zeros(output_size)
            }
        }
        return params

    def __call__(self, params, x):
        x = jnp.dot(x, params['linear1']['weights']) + params['linear1']['bias']
        x = jnp.maximum(x, 0)
        x = jnp.dot(x, params['linear2']['weights']) + params['linear2']['bias']
        return jax.nn.softmax(x, axis=-1)

model = MLP(10, 20, 30)
x = random.normal(random.PRNGKey(1), (5, 10))
y = model(model.params, x)
print(y.sum(axis=1))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 32, in <module> y = model(model.params, x) ^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 28, in __call__ return jax.nn.softmax(x, axis=-1) ^^^ NameError: name 'jax' is not defined. Did you mean: 'max'?

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