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https://github.com/vllm-project/vllm.git
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[Model] use AutoWeightsLoader for commandr (#19399)
Signed-off-by: py-andy-c <pychen1017@gmail.com>
This commit is contained in:
py-andy-c
GitHub
@ -51,7 +51,8 @@ from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
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from .utils import (extract_layer_index, is_pp_missing_parameter,
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from .utils import (AutoWeightsLoader, extract_layer_index,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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@ -286,6 +287,7 @@ class CohereModel(nn.Module):
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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self.quant_config = quant_config
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self.config = config
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lora_vocab = (lora_config.lora_extra_vocab_size *
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@ -339,6 +341,62 @@ class CohereModel(nn.Module):
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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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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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if (self.quant_config is not None and
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(scale_name := self.quant_config.get_cache_scale(name))):
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# Loading kv cache quantization scales
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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 if loaded_weight.dim() == 0 else
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loaded_weight[0])
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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for param_name, shard_name, shard_id in stacked_params_mapping:
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if shard_name not in name:
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continue
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name = name.replace(shard_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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loaded_params.add(name)
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return loaded_params
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class CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsQuant):
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packed_modules_mapping = {
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@ -408,65 +466,6 @@ class CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsQuant):
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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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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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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# Skip loading rotary embeddings since vLLM has its own
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if "rotary_emb.inv_freq" in name:
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continue
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if (self.quant_config is not None and
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(scale_name := self.quant_config.get_cache_scale(name))):
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# Loading kv cache quantization scales
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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 if loaded_weight.dim() == 0 else
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loaded_weight[0])
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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for param_name, shard_name, shard_id in stacked_params_mapping:
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if shard_name not in name:
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continue
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name = name.replace(shard_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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# lm_head is not used in vllm as it is tied with embed_token.
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# To prevent errors, skip loading lm_head.weight.
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if "lm_head.weight" in name:
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continue
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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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loaded_params.add(name)
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return loaded_params
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loader = AutoWeightsLoader(
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self, skip_prefixes=["lm_head", "rotary_emb.inv_freq"])
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return loader.load_weights(weights)
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