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generation.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import json
import os
import sys
import time
from pathlib import Path
from typing import List, Literal, Optional, Tuple, TypedDict
import torch
import torch.nn.functional as F
from fairscale.nn.model_parallel.initialize import (
get_model_parallel_rank,
initialize_model_parallel,
model_parallel_is_initialized,
)
from llama.model import ModelArgs, Transformer
from llama.tokenizer import Tokenizer
Role = Literal["system", "user", "assistant"]
class Message(TypedDict):
role: Role
content: str
class CompletionPrediction(TypedDict, total=False):
generation: str
tokens: List[str] # not required
logprobs: List[float] # not required
class ChatPrediction(TypedDict, total=False):
generation: Message
tokens: List[str] # not required
logprobs: List[float] # not required
Dialog = List[Message]
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
class Llama:
@staticmethod
def build(
ckpt_dir: str,
tokenizer_path: str,
max_seq_len: int,
max_batch_size: int,
model_parallel_size: Optional[int] = None,
) -> "Llama":
if not torch.distributed.is_initialized():
torch.distributed.init_process_group("nccl")
if not model_parallel_is_initialized():
if model_parallel_size is None:
model_parallel_size = int(os.environ.get("WORLD_SIZE", 1))
initialize_model_parallel(model_parallel_size)
local_rank = int(os.environ.get("LOCAL_RANK", 0))
torch.cuda.set_device(local_rank)
# seed must be the same in all processes
torch.manual_seed(1)
if local_rank > 0:
sys.stdout = open(os.devnull, "w")
start_time = time.time()
checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
assert model_parallel_size == len(
checkpoints
), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
ckpt_path = checkpoints[get_model_parallel_rank()]
checkpoint = torch.load(ckpt_path, map_location="cpu")
with open(Path(ckpt_dir) / "params.json", "r") as f:
params = json.loads(f.read())
model_args: ModelArgs = ModelArgs(
max_seq_len=max_seq_len,
max_batch_size=max_batch_size,
**params,
)
tokenizer = Tokenizer(model_path=tokenizer_path)
model_args.vocab_size = tokenizer.n_words
torch.set_default_tensor_type(torch.cuda.HalfTensor)
model = Transformer(model_args)
model.load_state_dict(checkpoint, strict=False)
print(f"Loaded in {time.time() - start_time:.2f} seconds")
return Llama(model, tokenizer)
def __init__(self, model: Transformer, tokenizer: Tokenizer):
self.model = model
self.tokenizer = tokenizer
@torch.inference_mode()
def generate(
self,
prompt_tokens: List[List[int]],
max_gen_len: int,
temperature: float = 0.6,
top_p: float = 0.9,
logprobs: bool = False,
echo: bool = False,
) -> Tuple[List[List[int]], Optional[List[List[float]]]]:
params = self.model.params
bsz = len(prompt_tokens)
assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
min_prompt_len = min(len(t) for t in prompt_tokens)
max_prompt_len = max(len(t) for t in prompt_tokens)
assert max_prompt_len <= params.max_seq_len
total_len = min(params.max_seq_len, max_gen_len + max_prompt_len)
pad_id = self.tokenizer.pad_id
tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
for k, t in enumerate(prompt_tokens):
tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
if logprobs:
token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
prev_pos = 0
eos_reached = torch.tensor([False] * bsz, device="cuda")
input_text_mask = tokens != pad_id
for cur_pos in range(min_prompt_len, total_len):
logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
if logprobs:
token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
input=logits.transpose(1, 2),
target=tokens[:, prev_pos + 1 : cur_pos + 1],
reduction="none",
ignore_index=pad_id,
)
if temperature > 0:
probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
next_token = sample_top_p(probs, top_p)
else:
next_token = torch.argmax(logits[:, -1], dim=-1)
next_token = next_token.reshape(-1)
# only replace token if prompt has already been generated
next_token = torch.where(
input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
)
tokens[:, cur_pos] = next_token
eos_reached |= (~input_text_mask[:, cur_pos]) & (
next_token == self.tokenizer.eos_id
)
prev_pos = cur_pos
if all(eos_reached):
break
if logprobs:
token_logprobs = token_logprobs.tolist()
out_tokens, out_logprobs = [], []
for i, toks in enumerate(tokens.tolist()):
# cut to max gen len
start = 0 if echo else len(prompt_tokens[i])
toks = toks[start : len(prompt_tokens[i]) + max_gen_len]
if logprobs:
probs = token_logprobs[i][start : len(prompt_tokens[i]) + max_gen_len]
# cut to eos tok if any
if self.tokenizer.eos_id in toks:
eos_idx = toks.index(self.tokenizer.eos_id)
toks = toks[:eos_idx]
probs = probs[:eos_idx] if logprobs else None
out_tokens.append(toks)
out_logprobs.append(probs)
return (out_tokens, out_logprobs if logprobs else None)
def text_completion(
self,
prompts: List[str],
temperature: float = 0.6,
top_p: float = 0.9,
max_gen_len: Optional[int] = None,
logprobs: bool = False,
echo: bool = False,
) -> List[CompletionPrediction]:
if max_gen_len is None:
max_gen_len = self.model.params.max_seq_len - 1
prompt_tokens = [self.tokenizer.encode(x, bos=True, eos=False) for x in prompts]
generation_tokens, generation_logprobs = self.generate(
prompt_tokens=prompt_tokens,
max_gen_len=max_gen_len,
temperature=temperature,
top_p=top_p,
logprobs=logprobs,
echo=echo,
)
if logprobs:
return [
{
"generation": self.tokenizer.decode(t),
"tokens": [self.tokenizer.decode(x) for x in t],
"logprobs": logprobs_i,
}
for t, logprobs_i in zip(generation_tokens, generation_logprobs)
]
return [{"generation": self.tokenizer.decode(t)} for t in generation_tokens]
def chat_completion(
self,
dialogs: List[Dialog],
temperature: float = 0.6,
top_p: float = 0.9,
max_gen_len: Optional[int] = None,
logprobs: bool = False,
) -> List[ChatPrediction]:
if max_gen_len is None:
max_gen_len = self.model.params.max_seq_len - 1
prompt_tokens = []
for dialog in dialogs:
if dialog[0]["role"] != "system":
dialog = [
{
"role": "system",
"content": DEFAULT_SYSTEM_PROMPT,
}
] + dialog
dialog = [
{
"role": dialog[1]["role"],
"content": B_SYS
+ dialog[0]["content"]
+ E_SYS
+ dialog[1]["content"],
}
] + dialog[2:]
assert all([msg["role"] == "user" for msg in dialog[::2]]) and all(
[msg["role"] == "assistant" for msg in dialog[1::2]]
), (
"model only supports 'system', 'user' and 'assistant' roles, "
"starting with 'system', then 'user' and alternating (u/a/u/a/u...)"
)
dialog_tokens: List[int] = sum(
[
self.tokenizer.encode(
f"{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} ",
bos=True,
eos=True,
)
for prompt, answer in zip(
dialog[::2],
dialog[1::2],
)
],
[],
)
assert (
dialog[-1]["role"] == "user"
), f"Last message must be from user, got {dialog[-1]['role']}"
dialog_tokens += self.tokenizer.encode(
f"{B_INST} {(dialog[-1]['content']).strip()} {E_INST}",
bos=True,
eos=False,
)
prompt_tokens.append(dialog_tokens)
generation_tokens, generation_logprobs = self.generate(
prompt_tokens=prompt_tokens,
max_gen_len=max_gen_len,
temperature=temperature,
top_p=top_p,
logprobs=logprobs,
)
if logprobs:
return [
{
"generation": {
"role": "assistant",
"content": self.tokenizer.decode(t),
},
"tokens": [self.tokenizer.decode(x) for x in t],
"logprobs": logprobs_i,
}
for t, logprobs_i in zip(generation_tokens, generation_logprobs)
]
return [
{"generation": {"role": "assistant", "content": self.tokenizer.decode(t)}}
for t in generation_tokens
]
def sample_top_p(probs, p):
probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
probs_sum = torch.cumsum(probs_sort, dim=-1)
mask = probs_sum - probs_sort > p
probs_sort[mask] = 0.0
probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
next_token = torch.multinomial(probs_sort, num_samples=1)
next_token = torch.gather(probs_idx, -1, next_token)
return next_token