vbatts/llama.cpp
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Move QK norm stack to private function so it's easier to read

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Ashish 2024-04-13 23:56:32 -07:00
parent f7b40d7650
commit e3f73604d5
1 changed files with 26 additions and 48 deletions
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74
convert-hf-to-gguf.py
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@ -1232,60 +1232,14 @@ class StableLMModel(Model):
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)
self._stack_qk_norm(block_count, name, tensor_map, n_head, q_norms, n_dims, layer_name="q_layernorm")
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)
self._stack_qk_norm(block_count, name, tensor_map, n_kv_head, k_norms, n_dims, layer_name="k_layernorm")
continue
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
@ -1309,6 +1263,30 @@ class StableLMModel(Model):
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
def _stack_qk_norm(self, block_count, name, tensor_map, n_head, norms, n_dims, layer_name="q_layernorm"):
for bid in range(block_count):
datas = []
for xid in range(n_head):
ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight"
datas.append(norms[ename])
del norms[ename]
data = np.stack(datas, axis=0)
data_dtype = data.dtype
merged_name = f"model.layers.{bid}.self_attn.{layer_name}.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)
@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")