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demo_llama.py
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demo_llama.py
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from llama import Tokenizer, Decoder
from llama import MemoryPoolSimple, npsoftmax, npmultinominal2D, warp_temperature, warp_topk
import numpy as np
import os
import pdb
import argparse
from loguru import logger
PROMPT_DICT = {
"prompt_input":
("Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
),
"prompt_no_input":
("Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"),
}
PROMPT = PROMPT_DICT['prompt_no_input']
class Llama:
def __init__(self, onnxdir='models', config: dict = {}):
if not os.path.exists(onnxdir):
logger.error('{} not exist'.format(onnxdir))
assert os.path.isdir(onnxdir)
self.DECODER_COUNT = 32
# EOS token
self.FINISH_TOKEN = 2
self.tokenizer = Tokenizer(os.path.join(onnxdir, 'tokenizer.model'))
pool = MemoryPoolSimple(config['poolsize'])
self.decoder = Decoder(pool, onnxdir, 'decoder-merge-{}.onnx',
self.DECODER_COUNT)
self.config = config
# cache
self.pastkeys = [None for i in range(self.DECODER_COUNT)]
self.pastvalues = [None for i in range(self.DECODER_COUNT)]
pool.check()
# Modified transformers.models.llama.modeling_llama._make_causal_mask with np.array
def _make_causal_mask(self,
input_ids_shape,
dtype,
past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
Output triangle-matrix if `past_key_values_length`=0
Padding left if `past_key_values_length`>0
"""
bsz, tgt_len = input_ids_shape
mask = np.full((tgt_len, tgt_len), fill_value=np.finfo(dtype).min)
mask_cond = np.arange(mask.shape[1])
cond = mask_cond < (mask_cond + 1).reshape(-1, 1)
mask = np.ma.array(mask, mask=cond, fill_value=0).filled()
# masked_fill_result = np.ma.masked_fill_(mask, condition_row_array)
if past_key_values_length > 0:
mask = np.concatenate([
np.zeros((tgt_len, past_key_values_length), dtype=dtype), mask
],
axis=1)
return mask.reshape(bsz, 1, tgt_len, tgt_len + past_key_values_length)
# Modified transformers.models.llama.modeling_llama._expand_mask with np.array
def _expand_mask(self, mask, dtype, tgt_len=None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.shape
if tgt_len is None:
tgt_len = src_len
# expand [bsz,38] to [bsz,1,1,38]
expanded_mask = np.expand_dims(mask, axis=1)
expanded_mask = np.expand_dims(mask, axis=1)
expanded_mask = np.broadcast_to(expanded_mask,
(bsz, 1, tgt_len, src_len))
inverted_mask = 1.0 - expanded_mask
cond = inverted_mask > 0
return np.ma.array(inverted_mask,
mask=cond,
fill_value=np.finfo(dtype).min).filled()
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape,
inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = self._make_causal_mask(
input_shape,
inputs_embeds.dtype,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = self._expand_mask(attention_mask,
inputs_embeds.dtype,
tgt_len=input_shape[-1])
combined_attention_mask = (expanded_attn_mask
if combined_attention_mask is None else
expanded_attn_mask +
combined_attention_mask)
return combined_attention_mask
def convert_to_fp16(self, inputs):
outputs = dict()
for k, v in inputs.items():
if v.dtype == np.float32:
outputs[k] = v.astype(np.float16)
else:
outputs[k] = v
return outputs
def decode(self, token: np.array):
# embed space
hidden = self.decoder.embed(token)
assert hidden.shape[-1] == 4096
if self.pastkeys[0] is None:
pastlen = 0
else:
pastlen = self.pastkeys[0].shape[-2]
seqlen = hidden.shape[1]
position_ids = np.arange(seqlen, dtype=np.int64).reshape((1, seqlen))
position_ids[0][0] = pastlen
attention_mask = np.ones((1, seqlen + pastlen), dtype=np.float32)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, (1, seqlen), hidden, pastlen)
for idx in range(self.DECODER_COUNT):
past_key = self.pastkeys[idx]
past_value = self.pastvalues[idx]
if past_key is None:
zero_tensor = np.zeros((1, 32, 0, 128), dtype=np.float32)
inputs = {
'hidden_in': hidden,
'attn_mask': attention_mask,
'position_ids': position_ids,
'past_key_in': zero_tensor,
'past_value_in': zero_tensor
}
else:
inputs = {
'hidden_in': hidden,
'attn_mask': attention_mask,
'position_ids': position_ids,
'past_key_in': past_key,
'past_value_in': past_value
}
if self.config['fp16']:
inputs = self.convert_to_fp16(inputs)
outputs = self.decoder.decode(inputs, idx)
hidden = outputs[
'hidden_out'] # [[[ 0.0221, 0.0120, 0.0007, ..., -0.0614, -0.0625, 0.0494]]]
self.pastkeys[idx] = outputs['past_key']
self.pastvalues[idx] = outputs['past_value']
hidden = self.decoder.norm_head(hidden)
return hidden
def apply_warp(self, tensor: np.array):
tensor = warp_temperature(tensor, self.config['temperature'])
tensor = warp_topk(tensor, self.config['topk'])
return tensor
def sample(self, prompt: str = 'bonjour'):
prompt = prompt.strip()
format_prompt = PROMPT.format_map({'instruction': prompt})
# no EOS
input_ids = self.tokenizer.encode(format_prompt, True, False)
input_ids = np.array(input_ids, dtype=np.int64).reshape(
(1, len(input_ids)))
# decoder backbone loop
next_token = input_ids
pre = 0
while True:
# decoder backbone
logits = self.decode(next_token)
# split tail
next_token_scores = logits[:, -1, :]
# wrap logits for better token
next_token_scores = self.apply_warp(next_token_scores)
probs = npsoftmax(next_token_scores.astype(np.float64), axis=1)
# Caution:
# *** ValueError: sum(pvals[:-1].astype(np.float64)) > 1.0. The pvals array is cast to 64-bit floating point prior to checking the sum. Precision changes when casting may cause problems even if the sum of the original pvals is valid.
next_token = npmultinominal2D(probs).astype(input_ids.dtype)
# logger.debug(next_token)
input_ids = np.concatenate(
[input_ids, next_token.reshape((1, 1))], axis=1)
decoded = self.tokenizer.decode(input_ids[0].tolist())
out = str(decoded.split('Response:')[1])
# stream print
now = len(out)
if now - 1 > pre:
print(out[pre: now-1], end="", flush=True)
pre = now - 1
if input_ids.shape[-1] >= self.config['max'] or next_token[
0, 0] == self.FINISH_TOKEN:
break
# decode
decoded = self.tokenizer.decode(input_ids[0].tolist())
out = str(decoded.split('Response:')[1])
logger.debug('Q: {} A: {}'.format(prompt, out))
return out
def parse_args():
parser = argparse.ArgumentParser(description='llama.onnx onnxruntime demo')
parser.add_argument('onnxdir', help='llama 7B onnx model directory.')
parser.add_argument('prompt', help='prompt text.')
parser.add_argument(
'--temperature',
default=0.1,
type=float,
help=
'factor to scale up logits, 1.0 means no warp. use `0.1` by default.')
parser.add_argument(
'--topk',
default=40,
type=int,
help=
'filter k high score values from logits, None means no filter. 40 by default.'
)
parser.add_argument(
'--max',
default=50,
type=int,
help=
'stop condition. default value is 2000, it would stop until len(output_token)==2000.'
)
parser.add_argument(
'--poolsize',
default=32,
type=float,
help='onnxruntime memory pool size. default value is 32GB')
parser.add_argument('--fp16',
default=True,
type=bool,
help='enable fp16 inference, default True.')
args = parser.parse_args()
return args
def main():
args = parse_args()
logger.warning(args)
llama = Llama(onnxdir=args.onnxdir,
config={
'temperature': args.temperature,
'topk': args.topk,
'max': args.max,
'poolsize': args.poolsize,
'fp16': args.fp16
})
llama.sample(args.prompt)
if __name__ == '__main__':
main()