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Running on Zero
Running on Zero
File size: 2,036 Bytes
9cde5e3 98d1e65 9cde5e3 6c01195 9cde5e3 98d1e65 9cde5e3 6c01195 9cde5e3 6c01195 9cde5e3 6c01195 9cde5e3 6c01195 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import torch
@torch.no_grad() #do not compute gradients
def prompt(
tokens,
model,
context_length,
max_new_tokens,
temperature=1.0,
top_p=None
):
if temperature <= 0:
raise ValueError("temperature must be greater than 0")
model.eval()
for _ in range(max_new_tokens):
context = tokens[:, -context_length:]
# ------------------------------------------
# Forward pass
# ------------------------------------------
logits = model(context)
next_token_logits = logits[:, -1, :]
next_token_logits = (
next_token_logits / temperature
)
if top_p is not None:
probabilities = get_top_p(top_p, next_token_logits)
else:
probabilities = torch.softmax(
next_token_logits,
dim=-1,
)
next_token = torch.multinomial( #watermark my be done here
probabilities,
num_samples=1,
)
tokens = torch.cat(
[tokens, next_token],
dim=1,
)
return tokens
def get_top_p(top_p,next_logits):
probabilities = torch.softmax(
next_logits,
dim=-1,
)
sorted_probabilities, sorted_indices = torch.sort(
probabilities,
descending=True,
)
cumulative_probabilities = torch.cumsum(
sorted_probabilities,
dim=-1,
)
remove = cumulative_probabilities > top_p
# Keep the first token that crosses the threshold.
remove[..., 1:] = remove[..., :-1].clone()
remove[..., 0] = False
sorted_probabilities = sorted_probabilities.masked_fill(
remove,
0.0,
)
probabilities = torch.zeros_like(probabilities)
probabilities.scatter_(
-1,
sorted_indices,
sorted_probabilities,
)
probabilities = probabilities / probabilities.sum(
dim=-1,
keepdim=True,
)
return probabilities |