fix: fix convert formatting
This commit is contained in:
Joan Martinez
parent
db7e8ce58f
commit
c1c0f4d883
1 changed files with 42 additions and 52 deletions
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@ -77,13 +77,11 @@ class Model(ABC):
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for part_name in self.part_names:
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print(f"gguf: loading model part '{part_name}'")
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ctx: ContextManager[Any]
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if self.is_safetensors:
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from safetensors import safe_open
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ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
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else:
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ctx = contextlib.nullcontext(
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torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
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ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
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with ctx as model_part:
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for name in model_part.keys():
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@ -120,8 +118,7 @@ class Model(ABC):
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if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
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self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
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print(f"gguf: rms norm epsilon = {f_rms_eps}")
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if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"],
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optional=True)) is not None:
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if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
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self.gguf_writer.add_layer_norm_eps(f_norm_eps)
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print(f"gguf: layer norm epsilon = {f_norm_eps}")
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if (n_experts := self.hparams.get("num_local_experts")) is not None:
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@ -209,7 +206,6 @@ class Model(ABC):
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for name in names:
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cls._model_classes[name] = modelcls
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return modelcls
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return func
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@classmethod
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@ -294,7 +290,7 @@ class Model(ABC):
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# for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
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added_vocab = tokenizer.special_tokens
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reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in (vocab | added_vocab).items()}
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reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in (vocab | added_vocab).items()}
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for i in range(vocab_size):
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if i not in reverse_vocab:
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@ -779,8 +775,8 @@ class BaichuanModel(Model):
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return (
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weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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.reshape(weights.shape)
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.swapaxes(1, 2)
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.reshape(weights.shape)
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)
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def _reverse_hf_permute_part(
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@ -931,8 +927,8 @@ class XverseModel(Model):
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return (
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weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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.reshape(weights.shape)
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.swapaxes(1, 2)
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.reshape(weights.shape)
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)
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@ -1209,8 +1205,7 @@ class StableLMModel(Model):
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
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rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
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self.gguf_writer.add_rope_dimension_count(
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int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
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self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
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self.gguf_writer.add_head_count(hparams["num_attention_heads"])
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self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
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self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
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@ -1304,7 +1299,7 @@ class LlamaModel(Model):
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def set_vocab(self):
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try:
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self._set_vocab_sentencepiece()
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self. _set_vocab_sentencepiece()
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except FileNotFoundError:
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try:
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self._set_vocab_llama_hf()
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@ -1653,8 +1648,8 @@ class MiniCPMModel(Model):
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return (
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weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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.reshape(weights.shape)
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.swapaxes(1, 2)
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.reshape(weights.shape)
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)
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def write_tensors(self):
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@ -1914,8 +1909,7 @@ class GPT2Model(Model):
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for name, data_torch in self.get_tensors():
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# we don't need these
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if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq",
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".attn.bias", ".attn.masked_bias")):
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if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")):
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continue
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if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
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@ -2300,8 +2294,7 @@ in chat mode so that the conversation can end normally.")
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bid = re.findall(qkv_pattern, name)[0]
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qkv = data_torch
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qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
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q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[...,
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q_per_kv + 1: q_per_kv + 2, :]
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q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
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# The model weights of q and k equire additional reshape.
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q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads)
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k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads)
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@ -2384,7 +2377,6 @@ class BertModel(Model):
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
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if new_name is None:
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print(f"Can not map tensor {name!r}")
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sys.exit()
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@ -2441,31 +2433,6 @@ class NomicBertModel(BertModel):
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self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
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@Model.register("JinaBertModel")
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class JinaBertModel(BertModel):
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model_arch = gguf.MODEL_ARCH.JINA_BERT
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.intermediate_size = self.hparams["intermediate_size"]
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def get_tensors(self):
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for name, data in super().get_tensors():
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if 'gated_layers' in name:
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d1 = data[:self.intermediate_size, :]
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name1 = name.replace('gated_layers', 'gated_layers_w')
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d2 = data[self.intermediate_size:, :]
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name2 = name.replace('gated_layers', 'gated_layers_v')
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yield name1, d1
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yield name2, d2
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continue
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yield name, data
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JinaBertForMaskedML = JinaBertModel
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@Model.register("GemmaForCausalLM")
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class GemmaModel(Model):
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model_arch = gguf.MODEL_ARCH.GEMMA
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@ -2493,8 +2460,7 @@ class GemmaModel(Model):
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
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self.gguf_writer.add_head_count(hparams["num_attention_heads"])
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self.gguf_writer.add_head_count_kv(
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self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
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self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
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self.gguf_writer.add_key_length(hparams["head_dim"])
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self.gguf_writer.add_value_length(hparams["head_dim"])
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@ -2604,10 +2570,10 @@ class MambaModel(Model):
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assert d_inner == 2 * d_model
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_context_length(2 ** 20) # arbitrary value; for those who use the default
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self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
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self.gguf_writer.add_embedding_length(d_model)
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self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
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self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
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self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
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self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
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self.gguf_writer.add_block_count(self.hparams["n_layer"])
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self.gguf_writer.add_ssm_conv_kernel(d_conv)
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self.gguf_writer.add_ssm_inner_size(d_inner)
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@ -2622,7 +2588,7 @@ class MambaModel(Model):
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tok_embd = None
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tok_embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.TOKEN_EMBD] + ".weight"
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output_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.OUTPUT] + ".weight"
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output_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.OUTPUT] + ".weight"
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for name, data_torch in self.get_tensors():
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old_dtype = data_torch.dtype
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@ -2748,6 +2714,29 @@ class OlmoModel(Model):
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self.gguf_writer.add_tensor(new_name, data)
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@Model.register("JinaBertModel")
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class JinaBertModel(BertModel):
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model_arch = gguf.MODEL_ARCH.JINA_BERT
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.intermediate_size = self.hparams["intermediate_size"]
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def get_tensors(self):
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for name, data in super().get_tensors():
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if 'gated_layers' in name:
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d1 = data[:self.intermediate_size, :]
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name1 = name.replace('gated_layers', 'gated_layers_w')
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d2 = data[self.intermediate_size:, :]
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name2 = name.replace('gated_layers', 'gated_layers_v')
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yield name1, d1
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yield name2, d2
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continue
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yield name, data
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JinaBertForMaskedML = JinaBertModel
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###### CONVERSION LOGIC ######
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@ -2816,6 +2805,7 @@ def main() -> None:
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print(f"Loading model: {dir_model.name}")
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hparams = Model.load_hparams(dir_model)
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with torch.inference_mode():
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model_class = Model.from_model_architecture(hparams["architectures"][0])
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model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file)
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