fix: use miniCPM's way to init model
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@ -346,91 +346,7 @@ class HairuoModel(nn.Module):
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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"""
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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if ("rotary_emb.cos_cached" in name
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or "rotary_emb.sin_cached" in name):
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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continue
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if scale_name := get_compressed_tensors_cache_scale(name):
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# Loading kv cache scales for compressed-tensors quantization
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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loaded_weight = loaded_weight[0]
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weight_loader(param, loaded_weight)
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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# Remapping the name of FP8 kv-scale.
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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# If this function is called, it should always initialize KV cache scale
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# factors (or else raise an exception). Thus, handled exceptions should
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# make sure to leave KV cache scale factors in a known good (dummy) state
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def load_kv_cache_scales(self, quantization_param_path: str) -> None:
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tp_size = get_tensor_model_parallel_world_size()
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tp_rank = get_tensor_model_parallel_rank()
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for layer_idx, scaling_factor in kv_cache_scales_loader(
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quantization_param_path, tp_rank, tp_size,
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self.config.num_hidden_layers,
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self.config.__class__.model_type):
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if not isinstance(self.layers[layer_idx], nn.Identity):
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layer_self_attn = self.layers[layer_idx].self_attn
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if is_hip():
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# The scaling factor convention we are assuming is
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# quantized_value * scaling_factor ~= true_value
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# which is consistent with the practice of setting
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# scaling_factor = tensor_amax / FPtype_max
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scaling_factor *= 2
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if hasattr(layer_self_attn, "kv_scale"):
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layer_self_attn.attn._kv_scale = scaling_factor
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else:
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raise RuntimeError("Self attention has no KV cache scaling "
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"factor attribute!")
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"""
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class HairuoForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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@ -568,11 +484,11 @@ class HairuoForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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@ -583,87 +499,47 @@ class HairuoForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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continue
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if scale_name := get_compressed_tensors_cache_scale(name):
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# Loading kv cache scales for compressed-tensors quantization
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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loaded_weight = loaded_weight[0]
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weight_loader(param, loaded_weight)
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# With tie_word_embeddings, we can skip lm_head.weight
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# The weight might appear unnecessarily in the files if the model is
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# processed with quantization, LoRA, fine-tuning, etc.
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if self.config.tie_word_embeddings and "lm_head.weight" in name:
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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"""
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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# Remapping the name of FP8 kv-scale.
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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"""
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loader = AutoWeightsLoader(
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self,
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skip_prefixes=(["lm_head."]
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if self.config.tie_word_embeddings else None),
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)
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loader.load_weights(
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self.maybe_remap_mistral(name, loaded_weight)
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for name, loaded_weight in weights)
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"""
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def maybe_remap_mistral(
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self,
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name: str,
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loaded_weight: torch.Tensor,
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) -> Tuple[str, torch.Tensor]:
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def permute(w: torch.Tensor, n_heads: int):
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attn_in = self.config.head_dim * n_heads
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attn_out = self.config.hidden_size
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return w.view(n_heads, attn_in // n_heads // 2, 2,
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attn_out).transpose(1, 2).reshape(attn_in, attn_out)
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mapping = self.mistral_mapping
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modules = name.split(".")
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# rotary embeds should be sliced
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if "wk" in modules:
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loaded_weight = permute(loaded_weight,
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self.config.num_key_value_heads)
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elif "wq" in modules:
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loaded_weight = permute(loaded_weight,
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self.config.num_attention_heads)
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for item in modules:
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if item in mapping and mapping[item] not in name:
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name = name.replace(item, mapping[item])
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return name, loaded_weight
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for param_name, weight_name, expert_id in expert_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param,
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loaded_weight,
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weight_name,
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expert_id=expert_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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"""
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