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 JAX version of the PyTorch code you provided: 

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

def init_mlp_params(key, input_size, hidden_size, output_size):
    keys = random.split(key, 2)
    w1_key, w2_key = keys

    w1 = random.normal(w1_key, (input_size, hidden_size)) / jnp.sqrt(input_size)
    b1 = jnp.zeros(hidden_size)

    w2 = random.normal(w2_key, (hidden_size, output_size)) / jnp.sqrt(hidden_size)
    b2 = jnp.zeros(output_size)

    return (w1, b1, w2, b2)

def mlp_forward(params, x):
    w1, b1, w2, b2 = params
    x = jnp.dot(x, w1) + b1
    x = relu(x)
    x = jnp.dot(x, w2) + b2
    return softmax(x)

key = random.PRNGKey(0)
params = init_mlp_params(key, 10, 20, 30)

x = random.normal(key, (5, 10))
y = vmap(mlp_forward, in_axes=(None, 0))(params, x)
print(list(y.flatten().sum(1)))
Here's a breakdown of the changes: 1. Instead of using PyTorch's `nn.Module` class, we define a simple function `init_mlp_params` to initialize the weights and biases for our MLP. 2. The `mlp_forward` function implements the forward pass of the MLP, taking in the parameters and input data. 3. Instead of PyTorch's `nn.Linear` module, we use `jnp.dot` to perform the linear transformations. 4. We use JAX's `relu` and `softmax` functions instead of PyTorch's versions. 5. Since JAX functions are pure and immutable, we don't need a separate `__call__` method. 6. We use `vmap` to apply the `mlp_forward` function to the batch of input data. Note that in this example, we're using JAX's `random.normal` function to generate random input data, but you can replace it with your own data.

Extract Code

I extracted the following code from that output: 

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

def init_mlp_params(key, input_size, hidden_size, output_size):
    keys = random.split(key, 2)
    w1_key, w2_key = keys

    w1 = random.normal(w1_key, (input_size, hidden_size)) / jnp.sqrt(input_size)
    b1 = jnp.zeros(hidden_size)

    w2 = random.normal(w2_key, (hidden_size, output_size)) / jnp.sqrt(hidden_size)
    b2 = jnp.zeros(output_size)

    return (w1, b1, w2, b2)

def mlp_forward(params, x):
    w1, b1, w2, b2 = params
    x = jnp.dot(x, w1) + b1
    x = relu(x)
    x = jnp.dot(x, w2) + b2
    return softmax(x)

key = random.PRNGKey(0)
params = init_mlp_params(key, 10, 20, 30)

x = random.normal(key, (5, 10))
y = vmap(mlp_forward, in_axes=(None, 0))(params, 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.numpy as jnp
from jax import random, vmap
from jax.nn import relu, softmax

def init_mlp_params(key, input_size, hidden_size, output_size):
    keys = random.split(key, 2)
    w1_key, w2_key = keys

    w1 = random.normal(w1_key, (input_size, hidden_size)) / jnp.sqrt(input_size)
    b1 = jnp.zeros(hidden_size)

    w2 = random.normal(w2_key, (hidden_size, output_size)) / jnp.sqrt(hidden_size)
    b2 = jnp.zeros(output_size)

    return (w1, b1, w2, b2)

def mlp_forward(params, x):
    w1, b1, w2, b2 = params
    x = jnp.dot(x, w1) + b1
    x = relu(x)
    x = jnp.dot(x, w2) + b2
    return softmax(x)

key = random.PRNGKey(0)
params = init_mlp_params(key, 10, 20, 30)

x = random.normal(key, (5, 10))
y = vmap(mlp_forward, in_axes=(None, 0))(params, 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 30, in <module>
    print(list(y.flatten().sum(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 JAX equivalent of the provided PyTorch code: 

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

# Define the MLP class
class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1 = random.normal(random.PRNGKey(0), (input_size, hidden_size))
        self.linear2 = random.normal(random.PRNGKey(1), (hidden_size, output_size))

    def __call__(self, x):
        x = jnp.dot(x, self.linear1)
        x = jnp.maximum(x, 0)  # ReLU activation
        x = jnp.dot(x, self.linear2)
        return jax.nn.softmax(x, axis=1)

# Create an instance of the MLP
model = MLP(10, 20, 30)

# Generate some random input data
key = random.PRNGKey(2)
x = random.normal(key, (5, 10))

# Forward pass through the MLP
y = model(x)

# Print the sum of the output logits for each example
print(list(jnp.sum(y, axis=1)))
Explanation: 1. We define the `MLP` class with the same architecture as the PyTorch version. However, instead of using PyTorch's `nn.Linear` module, we initialize the weight matrices `linear1` and `linear2` with random values using `jax.random.normal`. 2. In the `__call__` method, we perform the forward pass using JAX's NumPy-like operations: - `jnp.dot` is used for matrix multiplication (equivalent to `torch.mm` or `torch.nn.Linear`). - `jnp.maximum` is used for the ReLU activation (equivalent to `torch.relu`). - `jax.nn.softmax` is used for the softmax operation (equivalent to `torch.nn.functional.softmax`). 3. We create an instance of the `MLP` class with the same dimensions as in the PyTorch code. 4. We generate some random input data using `jax.random.normal`. 5. We perform the forward pass through the MLP by calling the instance with the input data: `y = model(x)`. 6. Finally, we print the sum of the output logits for each example using `jnp.sum` along the appropriate axis. Note that JAX doesn't have a built-in concept of "modules" like PyTorch's `nn.Module`. Instead, we define the MLP as a simple class with the weight matrices as attributes, and the forward pass is implemented in the `__call__` method. Also note that we haven't included any training or optimization logic in this example, as it focuses on converting the forward pass to 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

# Define the MLP class
class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1 = random.normal(random.PRNGKey(0), (input_size, hidden_size))
        self.linear2 = random.normal(random.PRNGKey(1), (hidden_size, output_size))

    def __call__(self, x):
        x = jnp.dot(x, self.linear1)
        x = jnp.maximum(x, 0)  # ReLU activation
        x = jnp.dot(x, self.linear2)
        return jax.nn.softmax(x, axis=1)

# Create an instance of the MLP
model = MLP(10, 20, 30)

# Generate some random input data
key = random.PRNGKey(2)
x = random.normal(key, (5, 10))

# Forward pass through the MLP
y = model(x)

# Print the sum of the output logits for each example
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
from jax import random

# Define the MLP class
class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1 = random.normal(random.PRNGKey(0), (input_size, hidden_size))
        self.linear2 = random.normal(random.PRNGKey(1), (hidden_size, output_size))

    def __call__(self, x):
        x = jnp.dot(x, self.linear1)
        x = jnp.maximum(x, 0)  # ReLU activation
        x = jnp.dot(x, self.linear2)
        return jax.nn.softmax(x, axis=1)

# Create an instance of the MLP
model = MLP(10, 20, 30)

# Generate some random input data
key = random.PRNGKey(2)
x = random.normal(key, (5, 10))

# Forward pass through the MLP
y = model(x)

# Print the sum of the output logits for each example
print(list(jnp.sum(y, axis=1)))
And got the output: [Array(0.99999994, dtype=float32), Array(0.9999999, dtype=float32), Array(0.9999999, 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

Sure, here's the equivalent code in pure JAX: 

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import jax.numpy as jnp
from jax import random

# Initialize RNG key
key = random.PRNGKey(0)

def init_weights(m, n, key):
    return random.normal(key, (m, n)) / jnp.sqrt(m)

def mlp(params, x):
    w1, b1, w2, b2 = params
    x = jnp.dot(x, w1) + b1
    x = jnp.maximum(x, 0)  # ReLU activation
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x, axis=1)

def init_mlp(input_size, hidden_size, output_size, key):
    keys = random.split(key, 4)
    return [init_weights(input_size, hidden_size, keys[0]),
            jnp.zeros(hidden_size),
            init_weights(hidden_size, output_size, keys[2]),
            jnp.zeros(output_size)]

# Initialize model parameters
params = init_mlp(10, 20, 30, key)

# Input data
x = random.normal(key, (5, 10))

# Forward pass
y = mlp(params, x)

# Print sum of logits for each example
print(list(jnp.sum(y, axis=1)))
Explanation: 1. We import the necessary JAX modules: `jax.numpy` for array operations and `jax.random` for random number generation. 2. We define a function `init_weights` to initialize weights for a fully-connected layer using a normal distribution. 3. The `mlp` function implements the forward pass of the multi-layer perceptron. It takes the model parameters and input data as arguments and returns the output logits after applying the softmax activation. 4. The `init_mlp` function initializes the model parameters (weights and biases) for the specified input, hidden, and output dimensions using the `init_weights` function. 5. We initialize the model parameters using `init_mlp` and generate some random input data using `jax.random.normal`. 6. We perform the forward pass by calling `mlp` with the initialized parameters and input data. 7. Finally, we print the sum of logits for each input example. Note that in this implementation, we explicitly define the forward pass and parameter initialization functions instead of using PyTorch's high-level abstractions like `nn.Module` and `nn.Linear`. JAX's design philosophy is to provide low-level primitives and encourage functional programming styles, which can sometimes lead to more verbose code compared to PyTorch.

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import random

# Initialize RNG key
key = random.PRNGKey(0)

def init_weights(m, n, key):
    return random.normal(key, (m, n)) / jnp.sqrt(m)

def mlp(params, x):
    w1, b1, w2, b2 = params
    x = jnp.dot(x, w1) + b1
    x = jnp.maximum(x, 0)  # ReLU activation
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x, axis=1)

def init_mlp(input_size, hidden_size, output_size, key):
    keys = random.split(key, 4)
    return [init_weights(input_size, hidden_size, keys[0]),
            jnp.zeros(hidden_size),
            init_weights(hidden_size, output_size, keys[2]),
            jnp.zeros(output_size)]

# Initialize model parameters
params = init_mlp(10, 20, 30, key)

# Input data
x = random.normal(key, (5, 10))

# Forward pass
y = mlp(params, x)

# Print sum of logits for each example
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.numpy as jnp
from jax import random

# Initialize RNG key
key = random.PRNGKey(0)

def init_weights(m, n, key):
    return random.normal(key, (m, n)) / jnp.sqrt(m)

def mlp(params, x):
    w1, b1, w2, b2 = params
    x = jnp.dot(x, w1) + b1
    x = jnp.maximum(x, 0)  # ReLU activation
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x, axis=1)

def init_mlp(input_size, hidden_size, output_size, key):
    keys = random.split(key, 4)
    return [init_weights(input_size, hidden_size, keys[0]),
            jnp.zeros(hidden_size),
            init_weights(hidden_size, output_size, keys[2]),
            jnp.zeros(output_size)]

# Initialize model parameters
params = init_mlp(10, 20, 30, key)

# Input data
x = random.normal(key, (5, 10))

# Forward pass
y = mlp(params, x)

# Print sum of logits for each example
print(list(jnp.sum(y, axis=1)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 32, in <module> y = mlp(params, x) ^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 16, in mlp 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: 

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

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

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

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

    def __call__(self, x):
        x = jnp.dot(x, self.linear1)
        x = relu(x)
        x = jnp.dot(x, self.linear2)
        return 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.sum(axis=1).flatten()))
In this code, we define the `relu` and `softmax` functions manually using JAX's NumPy (`jnp`). The `MLP` class is implemented by manually constructing the weight matrices using JAX's random number generation functionality (`random.normal`). The forward pass is implemented using matrix multiplication (`jnp.dot`) and the `relu` and `softmax` activation functions. Note that in this implementation, we use the `jit` function to compile the `__call__` method of the `MLP` class for better performance. However, since we are not using the compiled version in this example, it is not included in the code snippet. Also, keep in mind that this is a very basic implementation of an MLP, and in practice, you may want to use more advanced techniques like weight initialization strategies, batch normalization, and regularization.

Extract Code

I extracted the following code from that output: 

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

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

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

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

    def __call__(self, x):
        x = jnp.dot(x, self.linear1)
        x = relu(x)
        x = jnp.dot(x, self.linear2)
        return 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.sum(axis=1).flatten()))

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, jit

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

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

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

    def __call__(self, x):
        x = jnp.dot(x, self.linear1)
        x = relu(x)
        x = jnp.dot(x, self.linear2)
        return 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.sum(axis=1).flatten()))
And got the output: [Array(0.99999994, dtype=float32), Array(1., dtype=float32), Array(0.99999994, 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: 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 JAX code for the PyTorch code you provided: 

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

# Initialize weights and biases
key = random.PRNGKey(0)

def init_layer(key, input_size, output_size):
    w_key, b_key = random.split(key)
    w = random.normal(w_key, (input_size, output_size)) / jnp.sqrt(input_size)
    b = random.normal(b_key, (output_size,))
    return w, b

def mlp_forward(params, inputs):
    w1, b1, w2, b2 = params
    x = jnp.dot(inputs, w1) + b1
    x = jnp.maximum(0, x)  # ReLU activation
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x, axis=1)

# Initialize model parameters
input_size, hidden_size, output_size = 10, 20, 30
key1, key2, key3, key4 = random.split(key, 4)
w1, b1 = init_layer(key1, input_size, hidden_size)
w2, b2 = init_layer(key2, hidden_size, output_size)
params = (w1, b1, w2, b2)

# Forward pass
x = random.normal(key3, (5, 10))
y = mlp_forward(params, x)

# Print the sum of each row
print(list(y.sum(axis=1)))
Explanation: 1. We import the necessary JAX modules and functions. 2. We define the `init_layer` function to initialize the weights and biases for a single layer. 3. We define the `mlp_forward` function, which performs the forward pass of the MLP. 4. We initialize the model parameters using the `init_layer` function. 5. We create a random input tensor `x`. 6. We perform the forward pass by calling `mlp_forward` with the parameters and input tensor. 7. We print the sum of each row of the output tensor `y`. Note that in JAX, we don't have a built-in module like PyTorch's `nn.Module`. Instead, we define our own functions to initialize the parameters and perform the forward pass. JAX provides low-level operations for tensor manipulation, and we use these operations to implement the MLP.

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

# Initialize weights and biases
key = random.PRNGKey(0)

def init_layer(key, input_size, output_size):
    w_key, b_key = random.split(key)
    w = random.normal(w_key, (input_size, output_size)) / jnp.sqrt(input_size)
    b = random.normal(b_key, (output_size,))
    return w, b

def mlp_forward(params, inputs):
    w1, b1, w2, b2 = params
    x = jnp.dot(inputs, w1) + b1
    x = jnp.maximum(0, x)  # ReLU activation
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x, axis=1)

# Initialize model parameters
input_size, hidden_size, output_size = 10, 20, 30
key1, key2, key3, key4 = random.split(key, 4)
w1, b1 = init_layer(key1, input_size, hidden_size)
w2, b2 = init_layer(key2, hidden_size, output_size)
params = (w1, b1, w2, b2)

# Forward pass
x = random.normal(key3, (5, 10))
y = mlp_forward(params, x)

# Print the sum of each row
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 import random

# Initialize weights and biases
key = random.PRNGKey(0)

def init_layer(key, input_size, output_size):
    w_key, b_key = random.split(key)
    w = random.normal(w_key, (input_size, output_size)) / jnp.sqrt(input_size)
    b = random.normal(b_key, (output_size,))
    return w, b

def mlp_forward(params, inputs):
    w1, b1, w2, b2 = params
    x = jnp.dot(inputs, w1) + b1
    x = jnp.maximum(0, x)  # ReLU activation
    x = jnp.dot(x, w2) + b2
    return jax.nn.softmax(x, axis=1)

# Initialize model parameters
input_size, hidden_size, output_size = 10, 20, 30
key1, key2, key3, key4 = random.split(key, 4)
w1, b1 = init_layer(key1, input_size, hidden_size)
w2, b2 = init_layer(key2, hidden_size, output_size)
params = (w1, b1, w2, b2)

# Forward pass
x = random.normal(key3, (5, 10))
y = mlp_forward(params, x)

# Print the sum of each row
print(list(y.sum(axis=1)))
And got the output: [Array(1.0000001, dtype=float32), Array(1., dtype=float32), Array(0.99999994, dtype=float32), Array(0.99999994, 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