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…lama Export llama without llama
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""" | ||
This script exports the Llama 2 weights in llama2c.bin format. | ||
""" | ||
import sys | ||
import struct | ||
from pathlib import Path | ||
import json | ||
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Place it into the root directory of: | ||
https://github.com/facebookresearch/llama | ||
import torch | ||
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And then run it similar to their other examples, via torchrun sadly: | ||
torchrun --nproc_per_node 1 export_meta_llama_bin.py | ||
""" | ||
from model import precompute_freqs_cis | ||
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from llama import Llama | ||
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# ----------------------------------------------------------------------------- | ||
def export(self, filepath='model.bin'): | ||
def export(p, state_dict, filepath='model.bin'): | ||
"""export the model weights in fp32 into .bin file to be read from C""" | ||
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f = open(filepath, 'wb') | ||
import struct | ||
import numpy as np | ||
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def serialize(t): | ||
d = t.detach().cpu().view(-1).numpy().astype(np.float32) | ||
b = struct.pack(f'{len(d)}f', *d) | ||
f.write(b) | ||
def serialize(key): | ||
print(f"writing {key}...") | ||
t = state_dict[key].contiguous().view(-1).type(torch.float32).numpy() | ||
f.write(memoryview(t)) | ||
del state_dict[key] | ||
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# first write out the header | ||
hidden_dim = self.layers[0].feed_forward.w1.weight.shape[0] | ||
p = self.params | ||
n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads | ||
header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads, | ||
n_kv_heads, -p.vocab_size, p.max_seq_len) | ||
hidden_dim = state_dict['layers.0.feed_forward.w1.weight'].shape[0] | ||
p['vocab_size'] = 32000 | ||
p['max_seq_len'] = 2048 | ||
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n_kv_heads = p.get('n_kv_heads') or p['n_heads'] | ||
header = struct.pack( | ||
'iiiiiii', | ||
p['dim'], hidden_dim, p['n_layers'], p['n_heads'], | ||
n_kv_heads, -p['vocab_size'], p['max_seq_len'] | ||
) | ||
# NOTE ABOVE: -ve vocab_size is indicating that the classifier weights are present | ||
# in the checkpoint and should be loaded. | ||
f.write(header) | ||
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# next write out the embedding weights | ||
print("writing tok_embeddings...") | ||
serialize(self.tok_embeddings.weight) | ||
serialize('tok_embeddings.weight') | ||
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# now all the layers | ||
# attention weights | ||
for i, layer in enumerate(self.layers): | ||
print(f"writing attention_norm layer {i}...") | ||
serialize(layer.attention_norm.weight) | ||
for i, layer in enumerate(self.layers): | ||
print(f"writing attention.wq layer {i}...") | ||
serialize(layer.attention.wq.weight) | ||
for i, layer in enumerate(self.layers): | ||
print(f"writing attention.wk layer {i}...") | ||
serialize(layer.attention.wk.weight) | ||
for i, layer in enumerate(self.layers): | ||
print(f"writing attention.wv layer {i}...") | ||
serialize(layer.attention.wv.weight) | ||
for i, layer in enumerate(self.layers): | ||
print(f"writing attention.wo layer {i}...") | ||
serialize(layer.attention.wo.weight) | ||
for i in range(p['n_layers']): serialize(f'layers.{i}.attention_norm.weight') | ||
for i in range(p['n_layers']): serialize(f'layers.{i}.attention.wq.weight') | ||
for i in range(p['n_layers']): serialize(f'layers.{i}.attention.wk.weight') | ||
for i in range(p['n_layers']): serialize(f'layers.{i}.attention.wv.weight') | ||
for i in range(p['n_layers']): serialize(f'layers.{i}.attention.wo.weight') | ||
# ffn weights | ||
for i, layer in enumerate(self.layers): | ||
print(f"writing ffn_norm layer {i}...") | ||
serialize(layer.ffn_norm.weight) | ||
for i, layer in enumerate(self.layers): | ||
print(f"writing feed_forward.w1 layer {i}...") | ||
serialize(layer.feed_forward.w1.weight) | ||
for i, layer in enumerate(self.layers): | ||
print(f"writing feed_forward.w2 layer {i}...") | ||
serialize(layer.feed_forward.w2.weight) | ||
for i, layer in enumerate(self.layers): | ||
print(f"writing feed_forward.w3 layer {i}...") | ||
serialize(layer.feed_forward.w3.weight) | ||
for i in range(p['n_layers']): serialize(f'layers.{i}.ffn_norm.weight') | ||
for i in range(p['n_layers']): serialize(f'layers.{i}.feed_forward.w1.weight') | ||
for i in range(p['n_layers']): serialize(f'layers.{i}.feed_forward.w2.weight') | ||
for i in range(p['n_layers']): serialize(f'layers.{i}.feed_forward.w3.weight') | ||
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# final rmsnorm | ||
print("writing final rmsnorm, classifier and freq_cis...") | ||
serialize(self.norm.weight) | ||
serialize('norm.weight') | ||
# freqs_cis | ||
serialize(self.freqs_cis.real[:p.max_seq_len]) | ||
serialize(self.freqs_cis.imag[:p.max_seq_len]) | ||
freqs_cis = precompute_freqs_cis(p['dim'] // p['n_heads'], p['max_seq_len'] * 2) | ||
state_dict['freqs_cis.real'] = freqs_cis.real[:p['max_seq_len']] | ||
state_dict['freqs_cis.imag'] = freqs_cis.imag[:p['max_seq_len']] | ||
serialize('freqs_cis.real') | ||
serialize('freqs_cis.imag') | ||
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# finally write the output weights | ||
serialize(self.output.weight) | ||
serialize('output.weight') | ||
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# write to binary file | ||
f.close() | ||
print(f"wrote {filepath}") | ||
# ----------------------------------------------------------------------------- | ||
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# init Llama as normal | ||
generator = Llama.build( | ||
ckpt_dir="llama-2-7b", | ||
tokenizer_path="tokenizer.model", | ||
max_seq_len=4096, | ||
max_batch_size=1, | ||
) | ||
export(generator.model, "llama2_7b.bin") | ||
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def concat_weights(models): | ||
state_dict = {} | ||
for name in list(models[0]): | ||
tensors = [model[name] for model in models] | ||
if len(tensors) == 1 or len(tensors[0].shape) == 1: | ||
state_dict[name] = tensors[0] | ||
continue | ||
is_axis_1 = ( | ||
name.startswith('tok_embeddings.') | ||
or name.endswith('.attention.wo.weight') | ||
or name.endswith('.feed_forward.w2.weight') | ||
) | ||
axis = 1 if is_axis_1 else 0 | ||
state_dict[name] = torch.cat(tensors, dim=axis) | ||
for model in models: | ||
del model[name] | ||
return state_dict | ||
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def load_and_export(model_path, output_path): | ||
with open(model_path + 'params.json') as f: | ||
params = json.load(f) | ||
print(params) | ||
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model_paths = sorted(list(Path(model_path).glob('consolidated.*.pth'))) | ||
models = [] | ||
for i in model_paths: | ||
print(f'Loading {i}') | ||
models.append(torch.load(i, map_location='cpu')) | ||
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state_dict = concat_weights(models) | ||
del models | ||
export(params, state_dict, output_path) | ||
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if __name__ == '__main__': | ||
if len(sys.argv) == 1: | ||
print('[Llama model folder path] [output path]') | ||
exit() | ||
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model_path = sys.argv[1] | ||
output_path = sys.argv[2] | ||
load_and_export(model_path, output_path) |