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#####################################################################################
# The MIT License (MIT)
#
# Copyright (c) 2015-2024 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#####################################################################################
from argparse import ArgumentParser
from transformers import LlamaTokenizer
import numpy as np
import migraphx as mgx
import os
import sys
import time
from functools import wraps
# measurement helper
def measure(fn):
@wraps(fn)
def measure_ms(*args, **kwargs):
start_time = time.perf_counter_ns()
result = fn(*args, **kwargs)
end_time = time.perf_counter_ns()
print(
f"Elapsed time for {fn.__name__}: {(end_time - start_time) * 1e-6:.4f} ms\n"
)
return result
return measure_ms
def get_args():
parser = ArgumentParser()
parser.add_argument(
"-p",
"--prompt",
type=str,
required=True,
help="Input prompt",
)
parser.add_argument(
"-l",
"--log-process",
action="store_true",
help="Print the current state of transcribing.",
)
parser.add_argument("-s",
"--max-seq-len",
type=int,
choices=[256, 512, 1024, 2048, 4096],
default=1024,
help="Max sequence length the model can handle")
return parser.parse_args()
class Llama2MGX():
def __init__(self, max_seq_len=1024):
model_id = "meta-llama/Llama-2-7b-chat-hf"
self.max_seq_len = max_seq_len
print("Load mgx model")
self.model = Llama2MGX.load_mgx_model(
max_seq_len, {
"input_ids": [1, max_seq_len],
"attention_mask": [1, max_seq_len],
"position_ids": [1, max_seq_len]
})
print(f"Load AutoTokenizer model from {model_id}")
self.tokenizer = LlamaTokenizer.from_pretrained(model_id)
@staticmethod
@measure
def load_mgx_model(max_seq_len, shapes):
file = "models/llama-2-7b-chat-hf/model"
print(f"Loading {max_seq_len} seq-len version model from {file}")
if os.path.isfile(f"{file}-{max_seq_len}.mxr"):
print("Found mxr, loading it...")
model = mgx.load(f"{file}-{max_seq_len}.mxr", format="msgpack")
elif os.path.isfile(f"{file}.onnx"):
print("Parsing from onnx file...")
model = mgx.parse_onnx(f"{file}.onnx", map_input_dims=shapes)
model.compile(mgx.get_target("gpu"))
print("Saving model to mxr file...")
mgx.save(model, f"{file}-{max_seq_len}.mxr", format="msgpack")
else:
print("No model found. Please download it and re-try.")
sys.exit(1)
return model
@measure
def tokenize(self, prompt):
return self.tokenizer(prompt, return_tensors="np").input_ids
@measure
def get_input_features_for_input_ids(self, input_ids):
input_ids_len = len(input_ids[0])
padding_len = self.max_seq_len - input_ids_len
input_ids = np.hstack([input_ids,
np.zeros((1, padding_len))]).astype(np.int64)
# 0 masked | 1 un-masked
attention_mask = np.array([1] * input_ids_len +
[0] * padding_len).astype(np.int64)
attention_mask = attention_mask[np.newaxis]
position_ids = np.arange(0, self.max_seq_len, dtype=np.int64)
position_ids = position_ids[np.newaxis]
return (input_ids, attention_mask, position_ids)
@measure
def decode_step(self, input_ids, attention_mask, position_ids):
return np.array(
self.model.run({
"input_ids": input_ids,
"attention_mask": attention_mask,
"position_ids": position_ids
})[0])
@measure
def decode_tokens(self, generated_tokens, skip_special_tokens=True):
return ''.join(
self.tokenizer.decode(generated_tokens,
skip_special_tokens=skip_special_tokens))
@measure
def generate(self, input_ids, log_process=False):
start_timestep = len(input_ids[0]) - 1
end_timestep = self.max_seq_len
input_ids, attention_mask, position_ids = self.get_input_features_for_input_ids(
input_ids)
print("Generating response...")
for timestep in range(start_timestep, self.max_seq_len):
# get logits for the current timestep
logits = self.decode_step(input_ids, attention_mask, position_ids)
# greedily get the highest probable token
new_token = np.argmax(logits[0][timestep])
# add it to the tokens and unmask it
input_ids[0][timestep + 1] = new_token
attention_mask[0][timestep + 1] = 1
if log_process:
print(self.decode_tokens(input_ids[0][:timestep + 2]))
if new_token == self.tokenizer.eos_token_id:
end_timestep = timestep + 1
break
return self.decode_tokens(input_ids[0][:end_timestep + 1])
if __name__ == "__main__":
args = get_args()
llama = Llama2MGX(args.max_seq_len)
print(f"Call tokenizer with \"{args.prompt}\"")
input_ids = llama.tokenize(args.prompt)
result = llama.generate(input_ids, log_process=args.log_process)
print(f"Result text: {result}")
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