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from typing import List
import torch
import torch.nn as nn
import json
import os
from .tokenizer import Tokenizer
from . import LLM
from fairscale.nn.model_parallel import initialize as fs_init
class MetaModel(nn.Module):
def __init__(self, llama_type, llama_config, llama_ckpt_dir=None, tokenizer_path=None):
super().__init__()
self.criterion = torch.nn.CrossEntropyLoss(ignore_index=0)
ModelArgs = LLM.__dict__[llama_type].ModelArgs
Transformer = LLM.__dict__[llama_type].Transformer
with open(llama_config, "r") as f:
params = json.loads(f.read())
model_args: ModelArgs = ModelArgs(
max_seq_len=2048, max_batch_size=32, **params
)
self.tokenizer = Tokenizer(model_path=tokenizer_path)
model_args.vocab_size = self.tokenizer.n_words
model = Transformer(model_args)
mp_rank = fs_init.get_model_parallel_rank()
if llama_ckpt_dir is not None:
ckpt_path = os.path.join(llama_ckpt_dir, f"consolidated.{mp_rank:02d}.pth")
if os.path.exists(ckpt_path):
checkpoint = torch.load(ckpt_path, map_location="cpu")
msg = model.load_state_dict(checkpoint, strict=False)
print(msg)
else:
print(f'Checkpoint not found at {ckpt_path}')
self.llma = model
for name, param in self.named_parameters():
if param.requires_grad:
print(f"Trainable param: {name}, {param.shape}, {param.dtype}")
count = sum(p.numel() for p in self.parameters() if p.requires_grad)
print(f"Parameter count : {count}")
def forward(self, examples, labels, image=None, modal='image'):
output = self.llma(examples, image=image, modal=modal)
output = output[:, :-1, :]
labels = labels[:, 1:]
if labels.sum() == 0:
c_loss = output.mean() * 0
else:
c_loss = self.criterion(output.reshape(-1, 32000), labels.flatten())
return c_loss
def generate(
self,
prompts: List[str],
images,
max_gen_len: int,
temperature: float = 0.8,
top_p: float = 0.95,
modal = ['image'],
) -> List[str]:
bsz = len(prompts)
params = self.llma.params
assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
prompt_tokens = [self.tokenizer.encode(
x, bos=True, eos=False) for x in prompts]
min_prompt_size = min([len(t) for t in prompt_tokens])
max_prompt_size = max([len(t) for t in prompt_tokens])
total_len = min(params.max_seq_len, max_gen_len + max_prompt_size)
tokens = torch.full(
(bsz, total_len), self.tokenizer.pad_id).cuda().long()
for k, t in enumerate(prompt_tokens):
tokens[k, : len(t)] = torch.tensor(t).long()
input_text_mask = tokens != self.tokenizer.pad_id
start_pos = min_prompt_size
prev_pos = 0
for cur_pos in range(start_pos, total_len):
logits = self.llma.forward_inference(tokens[:, prev_pos:cur_pos], prev_pos, images if prev_pos == 0 else None, modal=modal)
if temperature > 0:
probs = torch.softmax(logits / temperature, dim=-1)
next_token = self.sample_top_p(probs, top_p)
else:
next_token = torch.argmax(logits, 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
prev_pos = cur_pos
decoded = []
for i, t in enumerate(tokens.tolist()):
# cut to max gen len
t = t[: len(prompt_tokens[i]) + max_gen_len]
# cut to eos tok if any
try:
t = t[: t.index(self.tokenizer.eos_id)]
except ValueError:
pass
decoded.append(self.tokenizer.decode(t))
return decoded
@torch.inference_mode()
def stream_generate(
self,
prompt: str,
images,
max_gen_len: int,
temperature: float = 0.8,
top_p: float = 0.95,
modal = ['image'],
):
params = self.llma.params
prompt_tokens = self.tokenizer.encode(prompt, bos=True, eos=False)
# truncate from the left. leave some space for generation.
max_seq_len = params.max_seq_len
if images is not None:
max_seq_len -= self.llma.image_words
max_prompt_size = max_seq_len - max_gen_len
prompt_tokens = prompt_tokens[-max_prompt_size:]
prompt_size = len(prompt_tokens)
total_len = min(max_seq_len, max_gen_len + prompt_size)
tokens = torch.full([total_len], 0).cuda().long()
tokens[:len(prompt_tokens)] = torch.tensor(prompt_tokens).long()
start_pos = prompt_size
prev_pos = 0
generate_until = start_pos
for cur_pos in range(start_pos, total_len):
logits = self.llma.forward_inference(tokens[None, prev_pos:cur_pos], prev_pos, images if prev_pos == 0 else None, modal = modal)
if temperature > 0:
probs = torch.softmax(logits / temperature, dim=-1)
next_token = self.sample_top_p(probs, top_p)
else:
next_token = torch.argmax(logits, dim=-1)
next_token = next_token.item()
if next_token == self.tokenizer.eos_id:
break
tokens[cur_pos] = next_token
prev_pos = cur_pos
generate_until = cur_pos + 1
yield {"text": self.tokenizer.decode(tokens[start_pos:generate_until].tolist()), "end_of_content": False}
yield {"text": self.tokenizer.decode(tokens[start_pos:generate_until].tolist()), "end_of_content": True}
def sample_top_p(self, 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
def get_image_words(self):
return self.llma.image_words |