Qwen1_5/speculative_sample_demo/export_onnx.py

#!/usr/bin/env python3
# ==============================================================================
#
# Copyright (C) 2023 Sophgo Technologies Inc. All rights reserved.
#
# TPU-MLIR is licensed under the 2-Clause BSD License except for the
# third-party components.
#
# ==============================================================================

import os
import torch
import argparse
from tqdm import tqdm
from transformers import AutoModelForCausalLM
torch.set_grad_enabled(False)

parser = argparse.ArgumentParser(description='export onnx')
parser.add_argument('-m', '--model_path', type=str, help='path to the torch model')
parser.add_argument('-s', '--seq_length', type=int, default=512, help="sequence length")
parser.add_argument('-d', '--device', type=str, choices=["cpu", "cuda"], default="cpu")
parser.add_argument('--save_dir', type=str, default=f"./tmp/onnx")
parser.add_argument('--guess_length', type=int, default=5)

args = parser.parse_args()

model_path = args.model_path
folder = args.save_dir

device = torch.device(args.device)
origin_model = AutoModelForCausalLM.from_pretrained(
 model_path, trust_remote_code=True,
 torch_dtype=torch.float).eval()

for param in origin_model.parameters():
 param.requires_grad = False

config = origin_model.config
transformer = origin_model.model
layers = transformer.layers

SEQ_LENGTH = args.seq_length
NUM_LAYERS = config.num_hidden_layers
HIDDEN_SIZE = config.hidden_size
NUM_ATTENTION_HEADS = config.num_attention_heads
HEAD_DIM = HIDDEN_SIZE // NUM_ATTENTION_HEADS
VOCAB_SIZE = config.vocab_size
GUESS_LENGTH = args.guess_length

print(f'Layers: {NUM_LAYERS}\nHidden size: {HIDDEN_SIZE}\n')

class Embedding(torch.nn.Module):

 def __init__(self):
 super().__init__()

 def forward(self, input_ids):
 out = transformer.embed_tokens(input_ids)
 return out.float()


class QwenBlock(torch.nn.Module):

 def __init__(self, layer_id):
 super().__init__()
 self.layer_id = layer_id
 self.layer = layers[layer_id]

 def forward(self, hidden_states, position_ids, attention_mask):
 hidden_states, past_kv = self.layer(
 hidden_states,
 attention_mask=attention_mask,
 position_ids=position_ids,
 use_cache=True)
 present_k, present_v = past_kv
 return hidden_states.float(), present_k.float(), present_v.float()


class QwenBlockCache(torch.nn.Module):

 def __init__(self, layer_id):
 super().__init__()
 self.layer_id = layer_id
 self.layer = layers[layer_id]

 def forward(self, hidden_states, position_ids, attention_mask, past_k,
 past_v):
 hidden_states, past_kv = self.layer(
 hidden_states,
 past_key_value=(past_k, past_v),
 position_ids=position_ids,
 attention_mask=attention_mask,
 use_cache=True)
 present_k, present_v = past_kv
 return hidden_states.float(), present_k.float(), present_v.float()


class LmHead(torch.nn.Module):

 def __init__(self):
 super().__init__()

 def forward(self, hidden_states):
 hidden_states = transformer.norm(hidden_states)
 m_logits = origin_model.lm_head(hidden_states)
 return m_logits
 

class GreedyHead(torch.nn.Module):

 def __init__(self):
 super().__init__()

 def forward(self, m_logits):
 _, token = torch.topk(m_logits.float(), 1, dim=1)
 return token

 
# refs:https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py
class PenaltySampleHead(torch.nn.Module):

 def __init__(self, top_k = 50, min_tokens_to_keep = 5):
 super().__init__()
 self.top_k = top_k
 self.min_tokens_to_keep = min_tokens_to_keep
 self.keep_matrix = torch.zeros((1, self.top_k), dtype=torch.bool)
 self.keep_matrix[0, :self.min_tokens_to_keep] = True

 def forward(self, m_logits, input_ids, top_p, temperature, penalty):
 # repeat penalty
 logits = torch.gather(m_logits, 1, input_ids)
 logits = torch.where(logits < 0, logits * penalty, logits / penalty)
 m_logits.scatter_(1, input_ids, logits)

 # top_k
 logits, token = torch.topk(m_logits.float(), self.top_k, dim=1)

 # temperature
 logits = logits / temperature

 # top_p
 cumulative_probs = logits.softmax(dim=1).cumsum(dim=1)
 mask = cumulative_probs < top_p
 mask = mask + self.keep_matrix
 filtered_logits = torch.where(mask, logits, torch.FloatTensor([-1000.]))
 probs = filtered_logits.softmax(dim=1)
 return probs, token


def convert_block(layer_id):
 model = QwenBlock(layer_id)
 hidden_states = torch.randn(
 (1, SEQ_LENGTH, HIDDEN_SIZE)).to(torch.float).to(device)
 position_ids = torch.tensor(
 [range(SEQ_LENGTH)], dtype=torch.long).to(device)
 attention_mask = torch.randn(
 (1, 1, SEQ_LENGTH, SEQ_LENGTH)).to(torch.float).to(device)

 torch.onnx.export(
 model, (hidden_states, position_ids, attention_mask),
 f'{folder}/block_{layer_id}.onnx',
 verbose=False,
 input_names=['input_states', 'position_ids', 'attention_mask'],
 output_names=['hidden_states', 'past_k', 'past_v'],
 do_constant_folding=True,
 opset_version=15)


def convert_block_cache(layer_id):
 model = QwenBlockCache(layer_id)
 hidden_states = torch.randn((1, GUESS_LENGTH, HIDDEN_SIZE)).to(torch.float).to(device)
 position_ids = torch.tensor([range(GUESS_LENGTH)], dtype=torch.long).to(device)
 attention_mask = torch.ones(
 (1, 1, GUESS_LENGTH, SEQ_LENGTH + GUESS_LENGTH)).to(torch.float).to(device)
 past_k = torch.randn((1, SEQ_LENGTH, NUM_ATTENTION_HEADS, HEAD_DIM)).to(torch.float).to(device)
 past_v = torch.randn((1, SEQ_LENGTH, NUM_ATTENTION_HEADS, HEAD_DIM)).to(torch.float).to(device)

 torch.onnx.export(
 model, (hidden_states, position_ids, attention_mask, past_k, past_v),
 f'{folder}/block_cache_{layer_id}.onnx',
 verbose=False,
 input_names=[
 'input_states', 'position_ids', 'attention_mask', 'history_k',
 'history_v'
 ],
 output_names=['hidden_states', 'past_k', 'past_v'],
 do_constant_folding=True,
 opset_version=15)


def convert_embedding():
 model = Embedding()
 input_ids = torch.tensor([range(SEQ_LENGTH)]).to(device)

 torch.onnx.export(model, (input_ids),
 f'{folder}/embedding.onnx',
 verbose=False,
 input_names=['input_ids'],
 output_names=['input_embed'],
 do_constant_folding=True,
 opset_version=15)
 

def convert_lm_head():
 model = LmHead()
 hidden_states = torch.randn(GUESS_LENGTH, HIDDEN_SIZE).to(device)

 torch.onnx.export(model, (hidden_states),
 f'{folder}/lm_head.onnx',
 verbose=False,
 input_names=['hidden_states'],
 output_names=['m_logits'],
 do_constant_folding=True,
 opset_version=15)


def convert_greedy_head():
 model = GreedyHead()
 m_logits = torch.randn(GUESS_LENGTH, VOCAB_SIZE)

 torch.onnx.export(
 model, (m_logits),
 f'{folder}/greedy_head.onnx',
 verbose=False,
 input_names=['m_logits'],
 output_names=['token'],
 do_constant_folding=True,
 opset_version=15)



def convert_penalty_sample_head():
 model = PenaltySampleHead()
 m_logits = torch.randn(GUESS_LENGTH, VOCAB_SIZE)
 input_ids = torch.tensor([range(SEQ_LENGTH)])
 top_p = torch.tensor([0.8])
 temperature = torch.tensor([0.98])
 penalty = torch.tensor([0.98])

 torch.onnx.export(
 model, (m_logits, input_ids, top_p, temperature, penalty),
 f'{folder}/penalty_sample_head.onnx',
 verbose=False,
 input_names=[
 'm_logits', 'input_ids', 'top_p', 'temperature',
 'penalty'
 ],
 output_names=['probs', 'token'],
 do_constant_folding=True,
 opset_version=15)


# create folder to store onnx
if not os.path.exists(folder):
 os.makedirs(folder)

# export models
print(f'Convert block & block_cache')
for i in tqdm(range(NUM_LAYERS)):
 convert_block(i)
 convert_block_cache(i)

print(f'Convert embedding')
convert_embedding()

print(f'Convert lm_head')
convert_lm_head()
convert_greedy_head()
convert_penalty_sample_head()