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Do QK norm stacking in model conversion step

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Ashish 2024-04-13 19:09:37 -07:00
parent 91a3db9e7d
commit 29d940b0d7
1 changed files with 101 additions and 1 deletions
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102
convert-hf-to-gguf.py
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@ -1208,7 +1208,6 @@ class StableLMModel(Model):
self._set_vocab_qwen()
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
block_count = hparams["num_hidden_layers"]
@ -1224,6 +1223,107 @@ class StableLMModel(Model):
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
n_head = self.hparams.get("num_attention_heads")
n_kv_head = self.hparams.get("num_key_value_heads")
q_norms = dict()
k_norms = dict()
for name, data_torch in self.get_tensors():
# we don't need these
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
continue
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
data = data_torch.squeeze().numpy()
n_dims = len(data.shape)
if name.find("q_layernorm.norms") != -1:
q_norms[name] = data
if len(q_norms) >= (block_count * n_head):
for bid in range(block_count):
datas = []
for xid in range(n_head):
ename = f"model.layers.{bid}.self_attn.q_layernorm.norms.{xid}.weight"
datas.append(q_norms[ename])
del q_norms[ename]
data = np.stack(datas, axis=0)
data_dtype = data.dtype
merged_name = f"model.layers.{bid}.self_attn.q_layernorm.weight"
new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")):
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2:
data = data.astype(np.float16)
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
continue
if name.find("k_layernorm.norms") != -1:
k_norms[name] = data
if len(k_norms) >= (block_count * n_kv_head):
for bid in range(block_count):
full = True
datas = []
for xid in range(n_kv_head):
ename = f"model.layers.{bid}.self_attn.k_layernorm.norms.{xid}.weight"
datas.append(k_norms[ename])
del k_norms[ename]
data = np.stack(datas, axis=0)
data_dtype = data.dtype
merged_name = f"model.layers.{bid}.self_attn.k_layernorm.weight"
new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")):
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2:
data = data.astype(np.float16)
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
continue
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")):
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
class LlamaModel(Model):