Initial Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

LLM Generation

Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

Output

1.0

Substring Evaluation

Testing if the previous output contains the string -0.03: False

Initial Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

LLM Generation

Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

Output

0.0

Substring Evaluation

Testing if the previous output contains the string -0.03: False

Initial Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

LLM Generation

Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

Output

The function will print the gradient of the tensor `g` after applying the `diff_round` function and calculating the loss.

Substring Evaluation

Testing if the previous output contains the string -0.03: False

Initial Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

LLM Generation

Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

Output

This function will print the gradient sum of the tensor `g` after applying the `diff_round` function and performing backpropagation. The gradient sum will be calculated based on the loss obtained from the `diff_round` function.

Substring Evaluation

Testing if the previous output contains the string -0.03: False

Initial Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

LLM Generation

Query

What will this function print 

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def diff_round(x, decimals=1):
    scale_factor = (10 ** decimals)
    x = x * scale_factor
    diff = (1 + 1e-2) * x - torch.floor(x)
    x = x - diff + (torch.floor(x) + torch.where(diff >= 0.5, 1, 0))
    x = x / scale_factor
    return x

g = torch.tensor([.99, 1.54, 1.9], dtype=torch.float32, requires_grad=True)
loss = torch.sum(diff_round(g, 1))
loss.backward()
print(g.grad.sum())

Output

1.0

Substring Evaluation

Testing if the previous output contains the string -0.03: False