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https://git.datalinker.icu/vllm-project/vllm.git
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[Quantization] Enable BNB support for more MoE models (#21370)
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
This commit is contained in:
Jee Jee Li
GitHub
parent
7311f74468
commit
5c3f2628d5
@ -54,8 +54,8 @@ from vllm.model_executor.model_loader.weight_utils import (
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsPP
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from .utils import (PPMissingLayer, is_pp_missing_parameter,
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, PPMissingLayer, 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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@ -327,6 +327,7 @@ class Dots1DecoderLayer(nn.Module):
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return hidden_states, residual
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@support_torch_compile
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class Dots1Model(nn.Module):
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fall_back_to_pt_during_load = False
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@ -404,68 +405,12 @@ class Dots1Model(nn.Module):
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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@support_torch_compile
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class Dots1ForCausalLM(nn.Module, SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.model = Dots1Model(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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if get_pp_group().is_last_rank:
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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quant_config=quant_config)
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else:
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self.lm_head = PPMissingLayer()
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(
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input_ids,
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positions,
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intermediate_tensors,
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inputs_embeds,
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)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def make_empty_intermediate_tensors(
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self, batch_size: int, dtype: torch.dtype,
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device: torch.device) -> IntermediateTensors:
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return IntermediateTensors({
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"hidden_states":
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torch.zeros((batch_size, self.config.hidden_size),
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dtype=dtype,
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device=device),
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"residual":
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torch.zeros((batch_size, self.config.hidden_size),
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dtype=dtype,
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device=device),
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})
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def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
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return FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.n_routed_experts)
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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@ -477,14 +422,9 @@ class Dots1ForCausalLM(nn.Module, SupportsPP):
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("gate_up_proj", "up_proj", 1),
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]
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expert_params_mapping = FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.n_routed_experts)
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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expert_params_mapping = self.get_expert_mapping()
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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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@ -534,3 +474,71 @@ class Dots1ForCausalLM(nn.Module, SupportsPP):
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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 Dots1ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.model = Dots1Model(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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if get_pp_group().is_last_rank:
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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quant_config=quant_config)
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else:
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self.lm_head = PPMissingLayer()
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(
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input_ids,
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positions,
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intermediate_tensors,
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inputs_embeds,
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)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
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return self.model.get_expert_mapping()
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@ -53,7 +53,7 @@ from vllm.model_executor.model_loader.weight_utils import (
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsPP
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, PPMissingLayer, 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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@ -461,6 +461,15 @@ class Glm4MoeModel(nn.Module):
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device=device),
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})
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def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
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# Params for weights, fp8 weight scales, fp8 activation scales
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# (param_name, weight_name, expert_id, shard_id)
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return FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.n_routed_experts)
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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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@ -472,16 +481,9 @@ class Glm4MoeModel(nn.Module):
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("gate_up_proj", "up_proj", 1),
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]
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# Params for weights, fp8 weight scales, fp8 activation scales
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# (param_name, weight_name, expert_id, shard_id)
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expert_params_mapping = FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.n_routed_experts)
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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expert_params_mapping = self.get_expert_mapping()
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for name, loaded_weight in weights:
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spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
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if spec_layer is not None:
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@ -570,7 +572,7 @@ class Glm4MoeModel(nn.Module):
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return loaded_params
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class Glm4MoeForCausalLM(nn.Module, SupportsPP):
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class Glm4MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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@ -677,6 +679,9 @@ class Glm4MoeForCausalLM(nn.Module, SupportsPP):
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
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return self.model.get_expert_mapping()
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def get_spec_layer_idx_from_weight_name(config: PretrainedConfig,
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weight_name: str) -> Optional[int]:
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