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export.py
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export.py
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import argparse
from itertools import chain
import torch
import torch.nn as nn
from transformers import LlamaConfig, DynamicCache
from midi_model import MIDIModel, config_name_list, MIDIModelConfig
class MIDIModelBase(nn.Module):
def __init__(self, model):
super().__init__()
self.net = model.net
def forward(self, x, past_kv):
cache = DynamicCache.from_legacy_cache(past_kv)
x = self.net.embed_tokens(x)
x = x.sum(dim=-2)
x = self.net.forward(inputs_embeds=x,
past_key_values=cache,
use_cache=True)
return x.last_hidden_state, cache.to_legacy_cache()
class MIDIModelToken(nn.Module):
def __init__(self, model):
super().__init__()
self.net_token = model.net_token
self.lm_head = model.lm_head
def forward(self, hidden_state, x, past_kv):
cache = DynamicCache.from_legacy_cache(past_kv)
x = self.net_token.embed_tokens(x)
x = torch.cat([hidden_state, x], dim=1)
hidden_state = x
hidden_state = self.net_token.forward(inputs_embeds=hidden_state,
past_key_values=cache,
use_cache=True).last_hidden_state
return self.lm_head(hidden_state), cache.to_legacy_cache()
def export_onnx(model, model_inputs, input_names, output_names, dynamic_axes, meta_data, path):
import onnx
from onnxsim import simplify
torch.onnx.export(model, # model being run
model_inputs, # model input (or a tuple for multiple inputs)
path, # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
opset_version=14, # the ONNX version to export the model to
do_constant_folding=True, # whether to execute constant folding for optimization
input_names=input_names, # the model's input names
output_names=output_names, # the model's output names
verbose=True,
dynamic_axes=dynamic_axes
)
onnx_model = onnx.load(path)
model_simp, check = simplify(onnx_model)
assert check, "Simplified ONNX model could not be validated"
for k, v in meta_data.items():
meta = model_simp.metadata_props.add()
meta.key, meta.value = k, str(v)
onnx.save(model_simp, path)
print('finished exporting onnx')
def get_past_kv(config: LlamaConfig, batch_size=1, past_seq_len=16, torch_dtype= torch.float32, device="cpu"):
head_size = config.hidden_size // config.num_attention_heads
past_kv = [
(
torch.rand(batch_size, config.num_attention_heads,
past_seq_len, head_size, dtype=torch_dtype, device=device),
torch.rand(batch_size, config.num_attention_heads,
past_seq_len, head_size, dtype=torch_dtype, device=device),
)
for _ in range(config.num_hidden_layers)
]
input_names = list(
chain.from_iterable(
(f"past_key_values.{i}.key", f"past_key_values.{i}.value") for i in
range(config.num_hidden_layers)
)
)
output_names = list(
chain.from_iterable((f"present.{i}.key", f"present.{i}.value") for i in range(config.num_hidden_layers))
)
return past_kv, input_names, output_names
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
"--ckpt", type=str, default="model.ckpt", help="load ckpt"
)
parser.add_argument(
"--config", type=str, default="tv2o-medium", choices=config_name_list, help="model config"
)
parser.add_argument(
"--lora", type=str, default="", help="load lora"
)
parser.add_argument(
"--model-base-out", type=str, default="model_base.onnx", help="model base output path"
)
parser.add_argument(
"--model-token-out", type=str, default="model_token.onnx", help="model token output path"
)
opt = parser.parse_args()
config = MIDIModelConfig.from_name(opt.config)
tokenizer = config.tokenizer
model = MIDIModel(config).to(device="cpu")
ckpt = torch.load(opt.ckpt, map_location="cpu")
state_dict = ckpt.get("state_dict", ckpt)
model.load_state_dict(state_dict, strict=False)
if opt.lora != "":
model.load_merge_lora(opt.lora)
model.eval()
model_base = MIDIModelBase(model).eval()
model_token = MIDIModelToken(model).eval()
meta_data = {"config_name": opt.config, "config": config}
past_kv_shape = {0: "batch", 2: "past_seq"}
present_kv_shape = {0: "batch", 2: "present_seq"}
with torch.no_grad():
dynamic_axes = {
"x": {0: "batch", 1: "mid_seq", 2: "token_seq"},
"hidden": {0: "batch", 1: "mid_seq"}
}
x = torch.randint(tokenizer.vocab_size, (1, 16, tokenizer.max_token_seq), dtype=torch.int64, device="cpu")
past_kv, input_names, output_names= get_past_kv(config.net_config, past_seq_len=16,
torch_dtype=torch.float32)
for name in input_names:
dynamic_axes[name] = past_kv_shape
for name in output_names:
dynamic_axes[name] = present_kv_shape
input_names = [ "x"] + input_names
output_names = ["hidden"] + output_names
export_onnx(model_base, (x, past_kv),
input_names, output_names, dynamic_axes, meta_data, opt.model_base_out)
dynamic_axes = {
"x": {0: "batch", 1: "token_seq"},
"hidden": {0: "batch", 1: "states"},
"y": {0: "batch", 1: "token_seq1"}
}
hidden = torch.randn(1, 1, config.n_embd, device="cpu")
x = torch.randint(tokenizer.vocab_size, (1, tokenizer.max_token_seq //2), dtype=torch.int64, device="cpu")
past_kv, input_names, output_names = get_past_kv(config.net_token_config,
past_seq_len=(tokenizer.max_token_seq // 2),
torch_dtype=torch.float32)
for name in input_names:
dynamic_axes[name] = past_kv_shape
for name in output_names:
dynamic_axes[name] = present_kv_shape
input_names = ["hidden", "x"] + input_names
output_names = ["y"] + output_names
export_onnx(model_token, (hidden, x, past_kv),
input_names, output_names, dynamic_axes, meta_data, opt.model_token_out)