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fix codellama params and repeat_kv (#2181)

pull/2183/head
chenyu 2023-10-30 13:16:26 -04:00 committed by GitHub
parent c7f4dd6cb0
commit 8548b20b23
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1 changed files with 4 additions and 4 deletions
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examples/llama.py
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@ -46,7 +46,7 @@ def apply_rotary_emb(xq, xk, freqs_cis) -> Tuple[Tensor, Tensor]:
def repeat_kv(x:Tensor, n_rep:int) -> Tensor:
bs, seqlen, n_kv_heads, head_dim = x.shape
if n_rep == 1: return x
return x[:, :, :, None, :].expand(bs, seqlen, n_kv_heads, n_rep, head_dim).reshape(bs, seqlen, n_kv_heads * n_rep, head_dim)
return x.reshape(bs, seqlen, n_kv_heads, 1, head_dim).expand(bs, seqlen, n_kv_heads, n_rep, head_dim).reshape(bs, seqlen, n_kv_heads * n_rep, head_dim)
class RMSNorm:
def __init__(self, dim, eps=1e-6):
@ -224,11 +224,11 @@ MODEL_PARAMS = {
"files": 2,
},
"13B-Instruct": {
"args": {"dim": 5120, "n_layers": 40, "n_headvocab_sizes": 40, "multiple_of": 256, "ffn_dim_multiplier": 1.0, "norm_eps": 1e-5, "rope_theta": 1000000, "vocab_size": 32000},
"args": {"dim": 5120, "n_layers": 40, "n_heads": 40, "multiple_of": 256, "ffn_dim_multiplier": 1.0, "norm_eps": 1e-5, "rope_theta": 1000000, "vocab_size": 32016},
"files": 2,
},
"34B": {
"args": {"dim": 8192, "n_layers": 48, "n_heads": 64, "n_kv_heads": 8, "multiple_of": 256, "ffn_dim_multiplier": 1.0, "norm_eps": 1e-5, "rope_theta": 1000000, "vocab_size": 32016},
"args": {"dim": 8192, "n_layers": 48, "n_heads": 64, "n_kv_heads": 8, "multiple_of": 256, "ffn_dim_multiplier": 1.0, "norm_eps": 1e-5, "rope_theta": 1000000, "vocab_size": 32000},
"files": 4,
},
"34B-Python": {
@ -302,7 +302,7 @@ class LLaMa:
def build(model_path, tokenizer_path, model_gen="1", model_size="7B", quantize=False):
from sentencepiece import SentencePieceProcessor
sp_model = SentencePieceProcessor(model_file=str(tokenizer_path))
assert sp_model.vocab_size() == MODEL_PARAMS[model_gen][model_size]["args"]["vocab_size"]
assert sp_model.vocab_size() == MODEL_PARAMS[model_gen][model_size]["args"]["vocab_size"], f"{sp_model.vocab_size()=} not equal to {MODEL_PARAMS[model_gen][model_size]['args']['vocab_size']}"
params = MODEL_PARAMS[model_gen][model_size]
model = Transformer(**params["args"], linear=AbsmaxQuantizedLinear) if quantize else Transformer(**params["args"])