vbatts/llama.cpp
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Use torch.inference_mode

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Galunid 2023-11-23 00:58:05 +01:00
parent 436253f5a4
commit 28085f535e
1 changed files with 14 additions and 21 deletions
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35
convert-hf-to-gguf.py
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@ -51,7 +51,6 @@ class Model:
def set_vocab(self): def set_vocab(self):
self._set_vocab_gpt2() self._set_vocab_gpt2()
@torch.no_grad()
def get_tensors(self) -> Iterator[tuple[str, Tensor]]: def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
for part_name in self.part_names: for part_name in self.part_names:
print(f"gguf: loading model part '{part_name}'") print(f"gguf: loading model part '{part_name}'")
@ -82,7 +81,6 @@ class Model:
self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
@torch.no_grad()
def write_tensors(self): def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) 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) tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
@ -329,7 +327,6 @@ class BloomModel(Model):
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_file_type(self.ftype)
@torch.no_grad()
def write_tensors(self): def write_tensors(self):
block_count = self.hparams["n_layer"] block_count = self.hparams["n_layer"]
tensors = dict(self.get_tensors()) tensors = dict(self.get_tensors())
@ -424,7 +421,6 @@ class MPTModel(Model):
self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"]) self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"]) self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
@torch.no_grad()
def write_tensors(self): def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers")) block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers"))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
@ -510,7 +506,6 @@ class BaichuanModel(Model):
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
@torch.no_grad()
def write_tensors(self): def write_tensors(self):
# Collect tensors from generator object # Collect tensors from generator object
model_kv = dict(self.get_tensors()) model_kv = dict(self.get_tensors())
@ -613,7 +608,6 @@ class FalconModel(Model):
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_file_type(self.ftype)
@torch.no_grad()
def write_tensors(self): def write_tensors(self):
block_count = self.hparams.get("num_hidden_layers") block_count = self.hparams.get("num_hidden_layers")
if block_count is None: if block_count is None:
@ -719,7 +713,6 @@ class RefactModel(Model):
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_file_type(self.ftype)
@torch.no_grad()
def write_tensors(self): def write_tensors(self):
hidden_dim = self.hparams["n_embd"] hidden_dim = self.hparams["n_embd"]
inner_dim = 4 * hidden_dim inner_dim = 4 * hidden_dim
@ -805,7 +798,6 @@ class PersimmonModel(Model):
# self.gguf_writer.add_bos_token_id(71013) # self.gguf_writer.add_bos_token_id(71013)
# self.gguf_writer.add_eos_token_id(71013) # self.gguf_writer.add_eos_token_id(71013)
@torch.no_grad()
def write_tensors(self): def write_tensors(self):
block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
@ -888,20 +880,21 @@ print(f"Loading model: {dir_model.name}")
hparams = Model.load_hparams(dir_model) hparams = Model.load_hparams(dir_model)
model_class = Model.from_model_architecture(hparams["architectures"][0]) with torch.inference_mode():
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) model_class = Model.from_model_architecture(hparams["architectures"][0])
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)
print("Set model parameters") print("Set model parameters")
model_instance.set_gguf_parameters() model_instance.set_gguf_parameters()
print("Set model tokenizer") print("Set model tokenizer")
model_instance.set_vocab() model_instance.set_vocab()
if args.vocab_only: if args.vocab_only:
print(f"Exporting model vocab to '{fname_out}'") print(f"Exporting model vocab to '{fname_out}'")
model_instance.write_vocab() model_instance.write_vocab()
else: else:
print(f"Exporting model to '{fname_out}'") print(f"Exporting model to '{fname_out}'")
model_instance.write() model_instance.write()
print(f"Model successfully exported to '{fname_out}'") print(f"Model successfully exported to '{fname_out}'")