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 converted to pure JAX: 

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

def init_linear(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features))
    b = random.normal(k2, (out_features,))
    return w, b

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

    def __call__(self, x):
        x = jnp.dot(x, self.linear1[0]) + self.linear1[1]
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2[0]) + self.linear2[1]
        return jax.nn.softmax(x, axis=1)

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

# Create model
model = MLP(key, 10, 20, 30)

# Generate random input
key, subkey = random.split(key)
x = random.normal(subkey, (5, 10))

# Forward pass
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))
Key differences and explanations: 1. We use `jax` and `jax.numpy` instead of `torch`. 2. JAX doesn't have built-in modules like PyTorch, so we implement the linear layers manually using `init_linear` function. 3. The `MLP` class is now a regular Python class, not inheriting from any module. 4. We use `jax.nn.relu` and `jax.nn.softmax` for activation functions. 5. Matrix multiplication is done using `jnp.dot` instead of the `@` operator or a separate function. 6. We use JAX's random number generation functions for initializing weights and creating random input. 7. The `flatten().numpy().sum(1)` operation in PyTorch is replaced with `flatten().sum(0)` in JAX, as JAX arrays are already NumPy-like. 8. We don't need to explicitly convert to NumPy as JAX arrays can be directly used in Python's `list` function. This JAX implementation should produce similar results to the PyTorch version, with the main differences being in the random number generation and initialization.

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 init_linear(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features))
    b = random.normal(k2, (out_features,))
    return w, b

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

    def __call__(self, x):
        x = jnp.dot(x, self.linear1[0]) + self.linear1[1]
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2[0]) + self.linear2[1]
        return jax.nn.softmax(x, axis=1)

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

# Create model
model = MLP(key, 10, 20, 30)

# Generate random input
key, subkey = random.split(key)
x = random.normal(subkey, (5, 10))

# Forward pass
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))

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 init_linear(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features))
    b = random.normal(k2, (out_features,))
    return w, b

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

    def __call__(self, x):
        x = jnp.dot(x, self.linear1[0]) + self.linear1[1]
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2[0]) + self.linear2[1]
        return jax.nn.softmax(x, axis=1)

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

# Create model
model = MLP(key, 10, 20, 30)

# Generate random input
key, subkey = random.split(key)
x = random.normal(subkey, (5, 10))

# Forward pass
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 38, in <module> print(list(y.flatten().sum(0))) File "/usr/local/lib/python3.10/dist-packages/jax/_src/array.py", line 340, in __iter__ raise TypeError("iteration over a 0-d array") # same as numpy error TypeError: iteration over a 0-d array

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 import random

def init_linear(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features)) * (2 / in_features)**0.5
    b = random.normal(k2, (out_features,)) * 0.01
    return w, b

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

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

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
key, subkey = random.split(key)
x = random.normal(subkey, (5, 10))

# Run the model
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))
Key differences and explanations: 1. We use `jax` and `jax.numpy` instead of `torch`. 2. JAX doesn't have built-in `nn.Module` or `nn.Linear`, so we implement the linear layers manually using `init_linear` function. 3. The `MLP` class is now a regular Python class, not inheriting from any JAX-specific class. 4. We use JAX's `random` module for all random operations, including weight initialization and input generation. 5. JAX uses function transformations, so we don't need to explicitly define backward passes or optimizers in this simple example. 6. We use `jax.nn.relu` and `jax.nn.softmax` instead of PyTorch's versions. 7. The output is slightly different due to different random initializations and computations, but the structure and functionality are equivalent. 8. In the final print statement, we use `sum(0)` instead of `sum(1)` because JAX's default is row-major order, while PyTorch uses column-major order by default. This JAX version provides the same functionality as the PyTorch code, with equivalent layer structures and activation functions.

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 init_linear(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features)) * (2 / in_features)**0.5
    b = random.normal(k2, (out_features,)) * 0.01
    return w, b

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

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

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
key, subkey = random.split(key)
x = random.normal(subkey, (5, 10))

# Run the model
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))

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 init_linear(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features)) * (2 / in_features)**0.5
    b = random.normal(k2, (out_features,)) * 0.01
    return w, b

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

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

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
key, subkey = random.split(key)
x = random.normal(subkey, (5, 10))

# Run the model
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 36, in <module> print(list(y.flatten().sum(0))) File "/usr/local/lib/python3.10/dist-packages/jax/_src/array.py", line 340, in __iter__ raise TypeError("iteration over a 0-d array") # same as numpy error TypeError: iteration over a 0-d array

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 JAX code for the given PyTorch code: 

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

def init_linear_layer(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features)) * jnp.sqrt(2.0 / in_features)
    b = random.normal(k2, (out_features,)) * 0.01
    return {'w': w, 'b': b}

def linear(params, x):
    return jnp.dot(x, params['w']) + params['b']

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

def softmax(x):
    return jax.nn.softmax(x, axis=-1)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        k1, k2 = random.split(key)
        self.params = {
            'linear1': init_linear_layer(k1, input_size, hidden_size),
            'linear2': init_linear_layer(k2, hidden_size, output_size)
        }

    def __call__(self, x):
        x = linear(self.params['linear1'], x)
        x = relu(x)
        x = linear(self.params['linear2'], x)
        return softmax(x)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
subkey = random.split(key)[0]
x = random.normal(subkey, (5, 10))

# Forward pass
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))
Key differences and explanations: 1. We use `jax.numpy` instead of `torch` for array operations. 2. The `Linear` layer is implemented manually using `init_linear_layer` and `linear` functions. 3. We use JAX's `random` module for generating random numbers and initializing weights. 4. The `MLP` class is implemented as a regular Python class instead of inheriting from `nn.Module`. 5. We use JAX's `jax.nn.softmax` instead of `torch.nn.functional.softmax`. 6. The `relu` function is implemented manually using `jnp.maximum`. 7. We use `jax.random.PRNGKey` to manage random number generation. 8. The output summation is slightly different due to JAX's default behavior. We sum along axis 0 instead of 1 to match the PyTorch code's output shape. This JAX implementation should produce similar results to the PyTorch version, with some differences due to random initialization and floating-point arithmetic differences between the two frameworks.

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 init_linear_layer(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features)) * jnp.sqrt(2.0 / in_features)
    b = random.normal(k2, (out_features,)) * 0.01
    return {'w': w, 'b': b}

def linear(params, x):
    return jnp.dot(x, params['w']) + params['b']

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

def softmax(x):
    return jax.nn.softmax(x, axis=-1)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        k1, k2 = random.split(key)
        self.params = {
            'linear1': init_linear_layer(k1, input_size, hidden_size),
            'linear2': init_linear_layer(k2, hidden_size, output_size)
        }

    def __call__(self, x):
        x = linear(self.params['linear1'], x)
        x = relu(x)
        x = linear(self.params['linear2'], x)
        return softmax(x)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
subkey = random.split(key)[0]
x = random.normal(subkey, (5, 10))

# Forward pass
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))

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 init_linear_layer(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features)) * jnp.sqrt(2.0 / in_features)
    b = random.normal(k2, (out_features,)) * 0.01
    return {'w': w, 'b': b}

def linear(params, x):
    return jnp.dot(x, params['w']) + params['b']

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

def softmax(x):
    return jax.nn.softmax(x, axis=-1)

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        k1, k2 = random.split(key)
        self.params = {
            'linear1': init_linear_layer(k1, input_size, hidden_size),
            'linear2': init_linear_layer(k2, hidden_size, output_size)
        }

    def __call__(self, x):
        x = linear(self.params['linear1'], x)
        x = relu(x)
        x = linear(self.params['linear2'], x)
        return softmax(x)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
subkey = random.split(key)[0]
x = random.normal(subkey, (5, 10))

# Forward pass
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 47, in <module> print(list(y.flatten().sum(0))) File "/usr/local/lib/python3.10/dist-packages/jax/_src/array.py", line 340, in __iter__ raise TypeError("iteration over a 0-d array") # same as numpy error TypeError: iteration over a 0-d array

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 JAX code for the PyTorch snippet you provided: 

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

def init_linear_layer(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features)) * (2 / in_features)**0.5
    b = random.normal(k2, (out_features,)) * 0.01
    return w, b

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key1, key2 = random.split(key)
        self.linear1 = init_linear_layer(key1, input_size, hidden_size)
        self.linear2 = init_linear_layer(key2, hidden_size, output_size)
    
    def __call__(self, x):
        x = jnp.dot(x, self.linear1[0]) + self.linear1[1]
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2[0]) + self.linear2[1]
        return jax.nn.softmax(x, axis=1)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
key, subkey = random.split(key)
x = random.normal(subkey, (5, 10))

# Run the model
y = model(x)

# Print the sum
print(list(y.flatten().sum()))
Key differences and explanations: 1. We use `jax` and `jax.numpy` instead of `torch`. 2. JAX doesn't have built-in modules like PyTorch, so we implement the linear layers manually using `init_linear_layer` function. 3. The `MLP` class is now a regular Python class, not inheriting from any base class. 4. We use `jax.random` for random number generation and initialization. 5. Matrix multiplication is done using `jnp.dot` instead of the `@` operator or a separate linear layer function. 6. We use `jax.nn.relu` and `jax.nn.softmax` for the activation functions. 7. The model initialization takes a random key as an argument for reproducibility. 8. The input `x` is generated using JAX's random functions. 9. The final print statement is slightly different because JAX arrays behave differently from PyTorch tensors. We don't need to convert to numpy, and we sum all elements at once. This JAX code should produce similar results to the PyTorch code, with the main differences being in the exact random number generation and initialization. The overall structure and functionality remain the same.

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 init_linear_layer(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features)) * (2 / in_features)**0.5
    b = random.normal(k2, (out_features,)) * 0.01
    return w, b

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key1, key2 = random.split(key)
        self.linear1 = init_linear_layer(key1, input_size, hidden_size)
        self.linear2 = init_linear_layer(key2, hidden_size, output_size)
    
    def __call__(self, x):
        x = jnp.dot(x, self.linear1[0]) + self.linear1[1]
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2[0]) + self.linear2[1]
        return jax.nn.softmax(x, axis=1)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
key, subkey = random.split(key)
x = random.normal(subkey, (5, 10))

# Run the model
y = model(x)

# Print the sum
print(list(y.flatten().sum()))

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 init_linear_layer(key, in_features, out_features):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_features, out_features)) * (2 / in_features)**0.5
    b = random.normal(k2, (out_features,)) * 0.01
    return w, b

class MLP:
    def __init__(self, key, input_size, hidden_size, output_size):
        key1, key2 = random.split(key)
        self.linear1 = init_linear_layer(key1, input_size, hidden_size)
        self.linear2 = init_linear_layer(key2, hidden_size, output_size)
    
    def __call__(self, x):
        x = jnp.dot(x, self.linear1[0]) + self.linear1[1]
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.linear2[0]) + self.linear2[1]
        return jax.nn.softmax(x, axis=1)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
key, subkey = random.split(key)
x = random.normal(subkey, (5, 10))

# Run the model
y = model(x)

# Print the sum
print(list(y.flatten().sum()))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 36, in <module> print(list(y.flatten().sum())) File "/usr/local/lib/python3.10/dist-packages/jax/_src/array.py", line 340, in __iter__ raise TypeError("iteration over a 0-d array") # same as numpy error TypeError: iteration over a 0-d array

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 JAX code for the given PyTorch implementation: 

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

def init_linear_layer(key, in_dim, out_dim):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2.0 / in_dim)
    b = random.normal(k2, (out_dim,)) * 0.01
    return {'w': w, 'b': b}

def linear(params, x):
    return jnp.dot(x, params['w']) + params['b']

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

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

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

    def __call__(self, x):
        x = linear(self.linear1, x)
        x = relu(x)
        x = linear(self.linear2, x)
        return softmax(x)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
subkey = random.split(key)[0]
x = random.normal(subkey, (5, 10))

# Forward pass
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))
This JAX implementation closely mirrors the functionality of the PyTorch code. Here are the key differences and explanations: 1. We use `jax.numpy` instead of `torch` for array operations. 2. The `MLP` class is implemented as a regular Python class rather than inheriting from a module. 3. Linear layers are implemented manually using `init_linear_layer` and `linear` functions. 4. `relu` and `softmax` functions are implemented directly using JAX operations. 5. The model initialization uses JAX's random number generation for weight initialization. 6. The forward pass is implemented in the `__call__` method, similar to PyTorch. 7. We use JAX's random number generation to create the input tensor. 8. The final print statement sums over axis 0 instead of 1 due to JAX's default behavior, but the result should be equivalent. This JAX implementation should produce similar results to the PyTorch version, with potential minor differences due to random initialization and floating-point arithmetic variations between the two frameworks.

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 init_linear_layer(key, in_dim, out_dim):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2.0 / in_dim)
    b = random.normal(k2, (out_dim,)) * 0.01
    return {'w': w, 'b': b}

def linear(params, x):
    return jnp.dot(x, params['w']) + params['b']

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

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

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

    def __call__(self, x):
        x = linear(self.linear1, x)
        x = relu(x)
        x = linear(self.linear2, x)
        return softmax(x)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
subkey = random.split(key)[0]
x = random.normal(subkey, (5, 10))

# Forward pass
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))

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 init_linear_layer(key, in_dim, out_dim):
    k1, k2 = random.split(key)
    w = random.normal(k1, (in_dim, out_dim)) * jnp.sqrt(2.0 / in_dim)
    b = random.normal(k2, (out_dim,)) * 0.01
    return {'w': w, 'b': b}

def linear(params, x):
    return jnp.dot(x, params['w']) + params['b']

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

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

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

    def __call__(self, x):
        x = linear(self.linear1, x)
        x = relu(x)
        x = linear(self.linear2, x)
        return softmax(x)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(key, 10, 20, 30)

# Generate random input
subkey = random.split(key)[0]
x = random.normal(subkey, (5, 10))

# Forward pass
y = model(x)

# Print the sum of flattened output
print(list(y.flatten().sum(0)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 46, in <module> print(list(y.flatten().sum(0))) File "/usr/local/lib/python3.10/dist-packages/jax/_src/array.py", line 340, in __iter__ raise TypeError("iteration over a 0-d array") # same as numpy error TypeError: iteration over a 0-d array

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