standardize load_weights using AutoWeightsLoader for kimi_linear and minimax_text_01 (#37371)
Signed-off-by: XuLiu <xuliu40@gmail.com> Co-authored-by: XuLiu <xuliu40@gmail.com>
This commit is contained in:
XLiu-2000
@@ -46,6 +46,7 @@ from vllm.transformers_utils.configs.kimi_linear import KimiLinearConfig
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from .interfaces import HasInnerState, IsHybrid, MixtureOfExperts, SupportsPP
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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is_pp_missing_parameter,
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make_layers,
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@@ -472,94 +473,7 @@ class KimiLinearModel(nn.Module):
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class KimiLinearForCausalLM(
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nn.Module, HasInnerState, SupportsPP, MixtureOfExperts, IsHybrid
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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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self.model_config = vllm_config.model_config
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self.vllm_config = vllm_config
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self.config = self.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.quant_config = quant_config
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self.model = KimiLinearModel(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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if get_pp_group().is_last_rank:
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self.lm_head = ParallelLMHead(
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self.config.vocab_size,
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self.config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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else:
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self.lm_head = PPMissingLayer()
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logit_scale = getattr(self.config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(
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self.config.vocab_size, scale=logit_scale
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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**kwargs,
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) -> torch.Tensor | IntermediateTensors:
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hidden_states = self.model(
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input_ids, positions, intermediate_tensors, inputs_embeds, **kwargs
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)
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return hidden_states
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@classmethod
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def get_mamba_state_dtype_from_config(
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cls,
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vllm_config: "VllmConfig",
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) -> tuple[torch.dtype, torch.dtype, torch.dtype, torch.dtype]:
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return MambaStateDtypeCalculator.kda_state_dtype(
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vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype
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)
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@classmethod
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def get_mamba_state_shape_from_config(
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cls, vllm_config: "VllmConfig"
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) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
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parallel_config = vllm_config.parallel_config
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hf_config = vllm_config.model_config.hf_config
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tp_size = parallel_config.tensor_parallel_size
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num_spec = (
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vllm_config.speculative_config.num_speculative_tokens
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if vllm_config.speculative_config
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else 0
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)
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return MambaStateShapeCalculator.kda_state_shape(
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tp_size,
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hf_config.linear_attn_config["num_heads"],
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hf_config.linear_attn_config["head_dim"],
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conv_kernel_size=hf_config.linear_attn_config["short_conv_kernel_size"],
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num_spec=num_spec,
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)
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@classmethod
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def get_mamba_state_copy_func(
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cls,
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) -> tuple[
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MambaStateCopyFunc, MambaStateCopyFunc, MambaStateCopyFunc, MambaStateCopyFunc
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]:
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return MambaStateCopyFuncCalculator.kda_state_copy_func()
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor | None:
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return self.logits_processor(self.lm_head, hidden_states)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
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def load_weights(self, weights: Iterable[tuple[str, 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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(".gate_up_proj", ".gate_proj", 0),
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@@ -653,6 +567,101 @@ class KimiLinearForCausalLM(
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)
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weight_loader(param, loaded_weight, **kwargs)
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loaded_params.add(name)
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return loaded_params
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class KimiLinearForCausalLM(
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nn.Module, HasInnerState, SupportsPP, MixtureOfExperts, IsHybrid
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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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self.model_config = vllm_config.model_config
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self.vllm_config = vllm_config
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self.config = self.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.quant_config = quant_config
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self.model = KimiLinearModel(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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if get_pp_group().is_last_rank:
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self.lm_head = ParallelLMHead(
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self.config.vocab_size,
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self.config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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else:
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self.lm_head = PPMissingLayer()
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logit_scale = getattr(self.config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(
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self.config.vocab_size, scale=logit_scale
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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**kwargs,
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) -> torch.Tensor | IntermediateTensors:
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hidden_states = self.model(
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input_ids, positions, intermediate_tensors, inputs_embeds, **kwargs
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)
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return hidden_states
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@classmethod
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def get_mamba_state_dtype_from_config(
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cls,
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vllm_config: "VllmConfig",
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) -> tuple[torch.dtype, torch.dtype, torch.dtype, torch.dtype]:
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return MambaStateDtypeCalculator.kda_state_dtype(
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vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype
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)
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@classmethod
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def get_mamba_state_shape_from_config(
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cls, vllm_config: "VllmConfig"
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) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
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parallel_config = vllm_config.parallel_config
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hf_config = vllm_config.model_config.hf_config
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tp_size = parallel_config.tensor_parallel_size
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num_spec = (
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vllm_config.speculative_config.num_speculative_tokens
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if vllm_config.speculative_config
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else 0
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)
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return MambaStateShapeCalculator.kda_state_shape(
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tp_size,
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hf_config.linear_attn_config["num_heads"],
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hf_config.linear_attn_config["head_dim"],
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conv_kernel_size=hf_config.linear_attn_config["short_conv_kernel_size"],
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num_spec=num_spec,
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)
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@classmethod
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def get_mamba_state_copy_func(
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cls,
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) -> tuple[
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MambaStateCopyFunc, MambaStateCopyFunc, MambaStateCopyFunc, MambaStateCopyFunc
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]:
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return MambaStateCopyFuncCalculator.kda_state_copy_func()
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor | None:
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return self.logits_processor(self.lm_head, hidden_states)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(
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self,
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skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
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)
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return loader.load_weights(weights)
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def get_spec_layer_idx_from_weight_name(
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@@ -52,7 +52,12 @@ from vllm.sequence import IntermediateTensors
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from vllm.v1.attention.backend import AttentionMetadata
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from .interfaces import HasInnerState, IsHybrid
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from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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is_pp_missing_parameter,
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make_layers,
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)
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def replace_weight_name(
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@@ -494,6 +499,8 @@ class MiniMaxText01Model(nn.Module):
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quant_config = vllm_config.quant_config
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cache_config = vllm_config.cache_config
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scheduler_config = vllm_config.scheduler_config
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self.config = config
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self.CONCAT_FFN = True
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self.vocab_size = config.vocab_size
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@@ -620,128 +627,6 @@ class MiniMaxText01Model(nn.Module):
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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**kwargs,
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) -> torch.Tensor | IntermediateTensors:
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forward_context = get_forward_context()
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attn_metadata = forward_context.attn_metadata
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if get_pp_group().is_first_rank:
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if inputs_embeds is None:
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hidden_states = self.embed_scale * self.embed_tokens(input_ids)
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else:
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hidden_states = inputs_embeds
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for layer in islice(self.layers, self.start_layer, self.end_layer):
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hidden_states, residual = layer(
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hidden_states=hidden_states,
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positions=positions,
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attn_metadata=attn_metadata,
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residual=residual,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors(
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{"hidden_states": hidden_states, "residual": residual}
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)
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if residual is not None:
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hidden_states, _ = self.norm(hidden_states, residual)
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else:
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.config = config
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if not hasattr(config, "sliding_window"):
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config.sliding_window = None
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self.CONCAT_FFN = True
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if hasattr(vllm_config.model_config, "max_model_len"):
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self.config.max_model_len = vllm_config.model_config.max_model_len
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self.model = MiniMaxText01Model(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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if get_pp_group().is_last_rank:
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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self.config.hidden_size,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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self.logits_processor = LogitsProcessor(
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config.vocab_size, self.config.vocab_size
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)
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else:
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self.lm_head = PPMissingLayer()
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self.lm_head.float()
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flash_layer_count = sum(
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1 for attn_type in self.model.decoder_attention_types if attn_type == 1
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)
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self.kv_cache = [torch.tensor([]) for _ in range(flash_layer_count)]
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return
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def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
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return self.model.minimax_cache.copy_inputs_before_cuda_graphs(
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input_buffers, **kwargs
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)
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def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
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return self.model.minimax_cache.get_seqlen_agnostic_capture_inputs(batch_size)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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**kwargs,
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) -> torch.Tensor:
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hidden_states = self.model(
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input_ids, positions, intermediate_tensors, inputs_embeds, **kwargs
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)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head, hidden_states.float())
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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, device: torch.device
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) -> IntermediateTensors:
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return IntermediateTensors(
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{
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"hidden_states": torch.zeros(
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(batch_size, self.config.hidden_size), dtype=dtype, device=device
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),
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"residual": torch.zeros(
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(batch_size, self.config.hidden_size), dtype=dtype, device=device
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),
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}
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)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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@@ -753,17 +638,15 @@ class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
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return None
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def is_linear_attn_layer(layer_idx: int) -> bool:
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if layer_idx is None or layer_idx >= len(
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self.model.decoder_attention_types
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):
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if layer_idx is None or layer_idx >= len(self.decoder_attention_types):
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return False
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return self.model.decoder_attention_types[layer_idx] == 0
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return self.decoder_attention_types[layer_idx] == 0
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def is_moe_weight(name: str) -> bool:
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return "block_sparse_moe" in name and not name.endswith(".bias")
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def get_expert_id(param_name):
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pattern = r"model\.layers\.\d+\.block_sparse_moe\.experts\.(\d+)\."
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pattern = r"layers\.\d+\.block_sparse_moe\.experts\.(\d+)\."
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match = re.search(pattern, param_name)
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if match:
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return match.group(1)
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@@ -948,9 +831,7 @@ class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
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for name, loaded_weight in weights:
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weight_at_layer = which_layer(name)
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if weight_at_layer and weight_at_layer >= len(
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self.model.decoder_attention_types
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):
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if weight_at_layer and weight_at_layer >= len(self.decoder_attention_types):
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continue
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if is_layer_norm_weight(name):
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@@ -975,6 +856,128 @@ class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
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load_basic_weight(name, loaded_weight, self)
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return loaded_params
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def forward(
|
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self,
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input_ids: torch.Tensor | None,
|
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positions: torch.Tensor,
|
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intermediate_tensors: IntermediateTensors | None = None,
|
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inputs_embeds: torch.Tensor | None = None,
|
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**kwargs,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
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forward_context = get_forward_context()
|
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attn_metadata = forward_context.attn_metadata
|
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|
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if get_pp_group().is_first_rank:
|
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if inputs_embeds is None:
|
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hidden_states = self.embed_scale * self.embed_tokens(input_ids)
|
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else:
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hidden_states = inputs_embeds
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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|
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for layer in islice(self.layers, self.start_layer, self.end_layer):
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hidden_states, residual = layer(
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hidden_states=hidden_states,
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positions=positions,
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attn_metadata=attn_metadata,
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residual=residual,
|
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)
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if not get_pp_group().is_last_rank:
|
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return IntermediateTensors(
|
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{"hidden_states": hidden_states, "residual": residual}
|
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)
|
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if residual is not None:
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hidden_states, _ = self.norm(hidden_states, residual)
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else:
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hidden_states = self.norm(hidden_states)
|
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return hidden_states
|
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|
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|
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class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
|
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
|
||||
self.config = config
|
||||
|
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if not hasattr(config, "sliding_window"):
|
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config.sliding_window = None
|
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|
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self.CONCAT_FFN = True
|
||||
|
||||
if hasattr(vllm_config.model_config, "max_model_len"):
|
||||
self.config.max_model_len = vllm_config.model_config.max_model_len
|
||||
self.model = MiniMaxText01Model(
|
||||
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
||||
)
|
||||
if get_pp_group().is_last_rank:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
self.config.hidden_size,
|
||||
prefix=maybe_prefix(prefix, "lm_head"),
|
||||
)
|
||||
|
||||
self.logits_processor = LogitsProcessor(
|
||||
config.vocab_size, self.config.vocab_size
|
||||
)
|
||||
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
self.lm_head.float()
|
||||
flash_layer_count = sum(
|
||||
1 for attn_type in self.model.decoder_attention_types if attn_type == 1
|
||||
)
|
||||
self.kv_cache = [torch.tensor([]) for _ in range(flash_layer_count)]
|
||||
return
|
||||
|
||||
def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
|
||||
return self.model.minimax_cache.copy_inputs_before_cuda_graphs(
|
||||
input_buffers, **kwargs
|
||||
)
|
||||
|
||||
def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
|
||||
return self.model.minimax_cache.get_seqlen_agnostic_capture_inputs(batch_size)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.embed_input_ids(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor | None,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds, **kwargs
|
||||
)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
logits = self.logits_processor(self.lm_head, hidden_states.float())
|
||||
|
||||
return logits
|
||||
|
||||
def make_empty_intermediate_tensors(
|
||||
self, batch_size: int, dtype: torch.dtype, device: torch.device
|
||||
) -> IntermediateTensors:
|
||||
return IntermediateTensors(
|
||||
{
|
||||
"hidden_states": torch.zeros(
|
||||
(batch_size, self.config.hidden_size), dtype=dtype, device=device
|
||||
),
|
||||
"residual": torch.zeros(
|
||||
(batch_size, self.config.hidden_size), dtype=dtype, device=device
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_mamba_state_dtype_from_config(
|
||||
cls,
|
||||
@@ -1011,3 +1014,7 @@ class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
|
||||
@classmethod
|
||||
def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc]:
|
||||
return MambaStateCopyFuncCalculator.linear_attention_state_copy_func()
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(self)
|
||||
return loader.load_weights(weights)
|
||||
|
||||
Reference in New Issue
Block a user