[Model] Introduce Kimi Linear to vLLM (#27809)
Signed-off-by: lizhiyuan <lizhiyuan@moonshot.cn> Signed-off-by: Zhiyuan Li <uniartisan2017@gmail.com>
This commit is contained in:
Zhiyuan Li
@@ -1304,7 +1304,7 @@ def kda_gate_fwd_kernel(
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tl.store(y_ptr, b_y.to(y.dtype.element_ty), boundary_check=(0, 1))
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def kda_gate_fwd(
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def fused_kda_gate(
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g: torch.Tensor,
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A: torch.Tensor,
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head_k_dim: int,
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426
vllm/model_executor/layers/kda.py
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426
vllm/model_executor/layers/kda.py
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@@ -0,0 +1,426 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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from einops import rearrange
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from torch import nn
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from vllm.attention import AttentionBackend
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from vllm.attention.backends.abstract import AttentionMetadata
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from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
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from vllm.distributed import (
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divide,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.model_loader.weight_utils import sharded_weight_loader
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.utils.torch_utils import direct_register_custom_op
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
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from .fla.ops.kda import (
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FusedRMSNormGated,
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chunk_kda,
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fused_kda_gate,
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fused_recurrent_kda,
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)
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from .linear import (
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ColumnParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from .mamba.abstract import MambaBase
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from .mamba.mamba_utils import MambaStateDtypeCalculator, MambaStateShapeCalculator
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from .mamba.ops.causal_conv1d import causal_conv1d_fn, causal_conv1d_update
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from .quantization.base_config import QuantizationConfig
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logger = init_logger(__name__)
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def kda_attention(
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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layer_name: str,
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) -> None:
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forward_context: ForwardContext = get_forward_context()
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self = forward_context.no_compile_layers[layer_name]
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self._forward(hidden_states=hidden_states, output=output)
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def kda_attention_fake(
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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layer_name: str,
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) -> None:
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return
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direct_register_custom_op(
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op_name="kda_attention",
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op_func=kda_attention,
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mutates_args=["output"],
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fake_impl=kda_attention_fake,
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)
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class KimiDeltaAttention(nn.Module, MambaBase):
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@property
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def mamba_type(self) -> str:
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return "linear_attention"
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def get_attn_backend(self) -> type["AttentionBackend"]:
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionBackend
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return GDNAttentionBackend
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def get_state_dtype(
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self,
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) -> tuple[torch.dtype, torch.dtype, torch.dtype, torch.dtype]:
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if self.model_config is None or self.cache_config is None:
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raise ValueError("model_config and cache_config must be set")
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return MambaStateDtypeCalculator.kda_state_dtype(
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self.model_config.dtype, self.cache_config.mamba_cache_dtype
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)
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def get_state_shape(
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self,
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) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
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return MambaStateShapeCalculator.kda_state_shape(
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self.tp_size, self.num_heads, self.head_dim, conv_kernel_size=self.conv_size
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)
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def __init__(
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self,
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layer_idx: int,
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hidden_size: int,
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quant_config: QuantizationConfig | None = None,
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cache_config: CacheConfig | None = None,
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model_config: ModelConfig | None = None,
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rms_norm_eps: float = 1e-5,
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prefix: str = "",
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**kwargs,
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) -> None:
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.tp_rank = get_tensor_model_parallel_rank()
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self.hidden_size = hidden_size
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self.model_config = model_config
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self.cache_config = cache_config
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if model_config is None:
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raise ValueError("model_config must be provided")
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kda_config = model_config.linear_attn_config
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self.head_dim = kda_config["head_dim"]
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self.num_heads = kda_config["num_heads"]
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self.layer_idx = layer_idx
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self.prefix = prefix
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assert self.num_heads % self.tp_size == 0
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self.local_num_heads = divide(self.num_heads, self.tp_size)
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projection_size = self.head_dim * self.num_heads
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self.conv_size = kda_config["short_conv_kernel_size"]
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self.q_proj = ColumnParallelLinear(
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self.hidden_size,
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projection_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_proj",
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)
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self.k_proj = ColumnParallelLinear(
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self.hidden_size,
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projection_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.k_proj",
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)
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self.v_proj = ColumnParallelLinear(
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self.hidden_size,
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projection_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.v_proj",
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)
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self.f_a_proj = ReplicatedLinear(
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self.hidden_size,
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self.head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.f_a_proj",
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)
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self.f_b_proj = ColumnParallelLinear(
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self.head_dim,
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projection_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.f_b_proj",
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)
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self.dt_bias = nn.Parameter(
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torch.empty(divide(projection_size, self.tp_size), dtype=torch.float32)
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)
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set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
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self.b_proj = ColumnParallelLinear(
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self.hidden_size,
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self.num_heads,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.b_proj",
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)
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self.q_conv1d = ColumnParallelLinear(
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input_size=self.conv_size,
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output_size=projection_size,
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bias=False,
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params_dtype=torch.float32,
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prefix=f"{prefix}.q_conv1d",
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)
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self.k_conv1d = ColumnParallelLinear(
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input_size=self.conv_size,
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output_size=projection_size,
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bias=False,
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params_dtype=torch.float32,
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prefix=f"{prefix}.k_conv1d",
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)
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self.v_conv1d = ColumnParallelLinear(
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input_size=self.conv_size,
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output_size=projection_size,
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bias=False,
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params_dtype=torch.float32,
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prefix=f"{prefix}.v_conv1d",
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)
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# unsqueeze to fit conv1d weights shape into the linear weights shape.
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# Can't do this in `weight_loader` since it already exists in
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# `ColumnParallelLinear` and `set_weight_attrs`
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# doesn't allow to override it
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self.q_conv1d.weight.data = self.q_conv1d.weight.data.unsqueeze(1)
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self.k_conv1d.weight.data = self.k_conv1d.weight.data.unsqueeze(1)
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self.v_conv1d.weight.data = self.v_conv1d.weight.data.unsqueeze(1)
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self.A_log = nn.Parameter(
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torch.empty(1, 1, self.local_num_heads, 1, dtype=torch.float32)
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)
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set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(2)})
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self.g_a_proj = ReplicatedLinear(
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self.hidden_size,
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self.head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.g_a_proj",
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)
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self.g_b_proj = ColumnParallelLinear(
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self.head_dim,
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projection_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.g_b_proj",
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)
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self.o_norm = FusedRMSNormGated(
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self.head_dim, eps=rms_norm_eps, activation="sigmoid"
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)
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self.o_proj = RowParallelLinear(
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projection_size,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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compilation_config = get_current_vllm_config().compilation_config
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if prefix in compilation_config.static_forward_context:
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raise ValueError(f"Duplicate layer name: {prefix}")
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compilation_config.static_forward_context[prefix] = self
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def forward(
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self,
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hidden_states: torch.Tensor,
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positions: torch.Tensor,
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output: torch.Tensor,
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) -> None:
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return torch.ops.vllm.kda_attention(
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hidden_states,
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output,
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self.prefix,
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)
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def _forward(
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self,
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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) -> None:
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forward_context = get_forward_context()
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attn_metadata: AttentionMetadata = forward_context.attn_metadata
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if attn_metadata is None:
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# V1 profile run
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# Mimic the memory allocation in the real run
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q = torch.empty_like(hidden_states)
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k = torch.empty_like(hidden_states)
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v = torch.empty_like(hidden_states)
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g = hidden_states.new_empty(
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hidden_states.size(0),
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self.local_num_heads,
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self.head_dim,
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dtype=torch.float32,
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)
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beta = torch.empty(
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hidden_states.size(0), self.local_num_heads, dtype=torch.float32
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)
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core_attn_out = torch.empty_like(hidden_states)
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return
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assert isinstance(attn_metadata, dict)
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attn_metadata = attn_metadata[self.prefix]
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assert isinstance(attn_metadata, GDNAttentionMetadata)
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has_initial_state = attn_metadata.has_initial_state
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non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
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non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor # noqa: E501
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constant_caches = self.kv_cache[forward_context.virtual_engine]
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(conv_state_q, conv_state_k, conv_state_v, recurrent_state) = constant_caches
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# deal with strides
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conv_state_q = conv_state_q.transpose(-1, -2)
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conv_state_k = conv_state_k.transpose(-1, -2)
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conv_state_v = conv_state_v.transpose(-1, -2)
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q_proj_states = self.q_proj(hidden_states)[0]
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k_proj_states = self.k_proj(hidden_states)[0]
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v_proj_states = self.v_proj(hidden_states)[0]
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q_conv_weights = self.q_conv1d.weight.view(
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self.q_conv1d.weight.size(0), self.q_conv1d.weight.size(2)
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)
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k_conv_weights = self.k_conv1d.weight.view(
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self.k_conv1d.weight.size(0), self.k_conv1d.weight.size(2)
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)
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v_conv_weights = self.v_conv1d.weight.view(
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self.v_conv1d.weight.size(0), self.v_conv1d.weight.size(2)
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)
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if attn_metadata.num_prefills > 0:
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q_proj_states = q_proj_states.transpose(0, 1)
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k_proj_states = k_proj_states.transpose(0, 1)
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v_proj_states = v_proj_states.transpose(0, 1)
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q = causal_conv1d_fn(
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q_proj_states,
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q_conv_weights,
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self.q_conv1d.bias,
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activation="silu",
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conv_states=conv_state_q,
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has_initial_state=has_initial_state,
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cache_indices=non_spec_state_indices_tensor,
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query_start_loc=non_spec_query_start_loc,
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metadata=attn_metadata,
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).transpose(0, 1)
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k = causal_conv1d_fn(
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k_proj_states,
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k_conv_weights,
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self.k_conv1d.bias,
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activation="silu",
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conv_states=conv_state_k,
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has_initial_state=has_initial_state,
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cache_indices=non_spec_state_indices_tensor,
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query_start_loc=non_spec_query_start_loc,
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metadata=attn_metadata,
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).transpose(0, 1)
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v = causal_conv1d_fn(
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v_proj_states,
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v_conv_weights,
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self.v_conv1d.bias,
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activation="silu",
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conv_states=conv_state_v,
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has_initial_state=has_initial_state,
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cache_indices=non_spec_state_indices_tensor,
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query_start_loc=non_spec_query_start_loc,
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metadata=attn_metadata,
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).transpose(0, 1)
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else:
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decode_conv_indices = non_spec_state_indices_tensor[
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: attn_metadata.num_decodes
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]
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q = causal_conv1d_update(
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q_proj_states,
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conv_state_q,
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q_conv_weights,
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self.q_conv1d.bias,
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activation="silu",
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conv_state_indices=decode_conv_indices,
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validate_data=True,
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)
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k = causal_conv1d_update(
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k_proj_states,
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conv_state_k,
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k_conv_weights,
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self.k_conv1d.bias,
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activation="silu",
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conv_state_indices=decode_conv_indices,
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validate_data=True,
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)
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v = causal_conv1d_update(
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v_proj_states,
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conv_state_v,
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v_conv_weights,
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self.v_conv1d.bias,
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activation="silu",
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conv_state_indices=decode_conv_indices,
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validate_data=True,
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)
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q, k, v = map(
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lambda x: rearrange(x, "n (h d) -> 1 n h d", d=self.head_dim), (q, k, v)
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)
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beta = self.b_proj(hidden_states)[0].float().sigmoid()
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g = self.f_b_proj(self.f_a_proj(hidden_states)[0])[0]
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g = fused_kda_gate(g, self.A_log, self.head_dim, g_bias=self.dt_bias)
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beta = beta.unsqueeze(0)
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g = g.unsqueeze(0)
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if attn_metadata.num_prefills > 0:
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zero_idx = non_spec_state_indices_tensor[~has_initial_state]
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recurrent_state[zero_idx] = 0
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initial_state = recurrent_state[non_spec_state_indices_tensor].contiguous()
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(
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core_attn_out_non_spec,
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last_recurrent_state,
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) = chunk_kda(
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q=q,
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k=k,
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v=v,
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g=g,
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beta=beta,
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initial_state=initial_state,
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output_final_state=True,
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use_qk_l2norm_in_kernel=True,
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cu_seqlens=non_spec_query_start_loc,
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)
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# Init cache
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recurrent_state[non_spec_state_indices_tensor] = last_recurrent_state
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else:
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(
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core_attn_out_non_spec,
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last_recurrent_state,
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) = fused_recurrent_kda(
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q=q,
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k=k,
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v=v,
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g=g,
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beta=beta,
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initial_state=recurrent_state,
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use_qk_l2norm_in_kernel=True,
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cu_seqlens=non_spec_query_start_loc,
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ssm_state_indices=non_spec_state_indices_tensor,
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)
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g_proj_states = self.g_b_proj(self.g_a_proj(hidden_states)[0])[0]
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g = rearrange(g_proj_states, "... (h d) -> ... h d", d=self.head_dim)
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core_attn_out = self.o_norm(core_attn_out_non_spec, g)
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core_attn_out = rearrange(core_attn_out, "1 n h d -> n (h d)")
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output[:] = self.o_proj(core_attn_out)[0]
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@@ -80,6 +80,15 @@ class MambaStateDtypeCalculator:
|
||||
state_dtype = get_kv_cache_torch_dtype(mamba_cache_dtype, model_dtype)
|
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return (state_dtype, state_dtype)
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|
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@classmethod
|
||||
def kda_state_dtype(
|
||||
cls,
|
||||
model_dtype: ModelDType | torch.dtype,
|
||||
mamba_cache_dtype: MambaDType,
|
||||
):
|
||||
state_dtype = get_kv_cache_torch_dtype(mamba_cache_dtype, model_dtype)
|
||||
return (state_dtype, state_dtype, state_dtype, torch.float32)
|
||||
|
||||
|
||||
class MambaStateShapeCalculator:
|
||||
@classmethod
|
||||
@@ -182,3 +191,35 @@ class MambaStateShapeCalculator:
|
||||
head_v_dim,
|
||||
)
|
||||
return conv_state_shape, temporal_state_shape
|
||||
|
||||
@classmethod
|
||||
def kda_state_shape(
|
||||
cls,
|
||||
tp_world_size: int,
|
||||
num_heads: int,
|
||||
head_dim: int,
|
||||
num_k_heads: int | None = None,
|
||||
head_k_dim: int | None = None,
|
||||
conv_kernel_size: int = 4,
|
||||
num_spec: int = 0,
|
||||
) -> tuple[tuple[int, int], tuple[int, int], tuple[int, int], tuple[int, int, int]]:
|
||||
if num_k_heads is None:
|
||||
num_k_heads = num_heads
|
||||
if head_k_dim is None:
|
||||
head_k_dim = head_dim
|
||||
|
||||
proj_size = num_heads * head_dim
|
||||
proj_k_size = num_k_heads * head_k_dim
|
||||
|
||||
conv_state_shape = (divide(proj_size, tp_world_size), conv_kernel_size - 1)
|
||||
conv_state_k_shape = (divide(proj_k_size, tp_world_size), conv_kernel_size - 1)
|
||||
recurrent_state_shape = (divide(num_heads, tp_world_size), head_dim, head_dim)
|
||||
|
||||
conv_state_shape = conv_state_shape[1], conv_state_shape[0]
|
||||
conv_state_k_shape = conv_state_k_shape[1], conv_state_k_shape[0]
|
||||
return (
|
||||
conv_state_shape,
|
||||
conv_state_k_shape,
|
||||
conv_state_k_shape,
|
||||
recurrent_state_shape,
|
||||
)
|
||||
|
||||
@@ -147,9 +147,10 @@ class MultiHeadLatentAttentionWrapper(CustomOp):
|
||||
# Add head dim of 1 to k_pe
|
||||
k_pe = k_pe.unsqueeze(1)
|
||||
|
||||
q[..., self.qk_nope_head_dim :], k_pe = self.rotary_emb(
|
||||
positions, q[..., self.qk_nope_head_dim :], k_pe
|
||||
)
|
||||
if self.rotary_emb is not None:
|
||||
q[..., self.qk_nope_head_dim :], k_pe = self.rotary_emb(
|
||||
positions, q[..., self.qk_nope_head_dim :], k_pe
|
||||
)
|
||||
|
||||
if self.indexer and self.is_sparse:
|
||||
_topk_indices = self.indexer(hidden_states, q_c, positions, self.rotary_emb)
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
from copy import deepcopy
|
||||
from math import lcm
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import vllm.envs as envs
|
||||
@@ -8,7 +9,7 @@ from vllm.logger import init_logger
|
||||
from vllm.model_executor.models import ModelRegistry
|
||||
from vllm.utils.math_utils import cdiv, round_up
|
||||
from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
|
||||
from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec
|
||||
from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec, MLAAttentionSpec
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from vllm.config import VllmConfig
|
||||
@@ -347,12 +348,28 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
|
||||
kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
|
||||
|
||||
# get attention page size (for 1 token)
|
||||
attn_page_size_1_token = FullAttentionSpec(
|
||||
block_size=1,
|
||||
num_kv_heads=model_config.get_num_kv_heads(parallel_config),
|
||||
head_size=model_config.get_head_size(),
|
||||
dtype=kv_cache_dtype,
|
||||
).page_size_bytes
|
||||
# Attention backend constraints:
|
||||
# - FlashAttention (FA) requires block size to be multiple of 16
|
||||
# - MLA (Multi-head Latent Attention) requires larger alignment:
|
||||
# * CUTLASS_MLA backend: kernel_block_size 128 alignment
|
||||
# * Other MLA backends: kernel_block_size 64 alignment
|
||||
if model_config.use_mla:
|
||||
use_cutlass_mla = envs.VLLM_ATTENTION_BACKEND == "CUTLASS_MLA"
|
||||
kernel_block_alignment_size = 128 if use_cutlass_mla else 64
|
||||
attn_page_size_1_token = MLAAttentionSpec(
|
||||
block_size=1,
|
||||
num_kv_heads=model_config.get_num_kv_heads(parallel_config),
|
||||
head_size=model_config.get_head_size(),
|
||||
dtype=kv_cache_dtype,
|
||||
).page_size_bytes
|
||||
else:
|
||||
kernel_block_alignment_size = 16
|
||||
attn_page_size_1_token = FullAttentionSpec(
|
||||
block_size=1,
|
||||
num_kv_heads=model_config.get_num_kv_heads(parallel_config),
|
||||
head_size=model_config.get_head_size(),
|
||||
dtype=kv_cache_dtype,
|
||||
).page_size_bytes
|
||||
|
||||
model_cls, _ = ModelRegistry.resolve_model_cls(
|
||||
model_config.architecture,
|
||||
@@ -372,17 +389,6 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
|
||||
if mamba_page_size == 0:
|
||||
return
|
||||
|
||||
# Attention backend constraints:
|
||||
# - FlashAttention (FA) requires block size to be multiple of 16
|
||||
# - MLA (Multi-head Latent Attention) requires larger alignment:
|
||||
# * CUTLASS_MLA backend: 128-byte alignment
|
||||
# * Other MLA backends: 64-byte alignment
|
||||
if model_config.use_mla:
|
||||
use_cutlass_mla = envs.VLLM_ATTENTION_BACKEND == "CUTLASS_MLA"
|
||||
kernel_block_alignment_size = 128 if use_cutlass_mla else 64
|
||||
else:
|
||||
kernel_block_alignment_size = 16
|
||||
|
||||
if cache_config.enable_prefix_caching:
|
||||
# With prefix caching, select attention block size to
|
||||
# optimize for mamba kernel performance
|
||||
@@ -400,15 +406,8 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
|
||||
# easily by changing the way we layout chunks in the
|
||||
# mamba2 kernels.
|
||||
|
||||
from math import gcd
|
||||
|
||||
def lcm(a, b):
|
||||
return a * b // gcd(a, b)
|
||||
|
||||
base_chunk_size = mamba_block_size or model_config.get_mamba_chunk_size()
|
||||
|
||||
base_chunk_size = model_config.get_mamba_chunk_size()
|
||||
attn_tokens_per_mamba_state = cdiv(mamba_page_size, attn_page_size_1_token)
|
||||
|
||||
chunk_size = lcm(base_chunk_size, kernel_block_alignment_size)
|
||||
attn_block_size = chunk_size * cdiv(attn_tokens_per_mamba_state, chunk_size)
|
||||
cache_config.mamba_block_size = attn_block_size
|
||||
|
||||
663
vllm/model_executor/models/kimi_linear.py
Normal file
663
vllm/model_executor/models/kimi_linear.py
Normal file
@@ -0,0 +1,663 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
from collections.abc import Iterable
|
||||
from typing import Any
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from vllm.compilation.decorators import support_torch_compile
|
||||
from vllm.config import CacheConfig, ModelConfig, ParallelConfig, VllmConfig
|
||||
from vllm.distributed import (
|
||||
get_pp_group,
|
||||
get_tensor_model_parallel_world_size,
|
||||
tensor_model_parallel_all_reduce,
|
||||
)
|
||||
from vllm.logger import init_logger
|
||||
from vllm.model_executor.layers.activation import SiluAndMul
|
||||
from vllm.model_executor.layers.fused_moe import FusedMoE
|
||||
from vllm.model_executor.layers.kda import KimiDeltaAttention
|
||||
from vllm.model_executor.layers.layernorm import RMSNorm
|
||||
from vllm.model_executor.layers.linear import (
|
||||
ColumnParallelLinear,
|
||||
MergedColumnParallelLinear,
|
||||
QKVParallelLinear,
|
||||
ReplicatedLinear,
|
||||
RowParallelLinear,
|
||||
)
|
||||
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
||||
from vllm.model_executor.layers.mamba.mamba_utils import (
|
||||
MambaStateDtypeCalculator,
|
||||
MambaStateShapeCalculator,
|
||||
)
|
||||
from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttentionWrapper
|
||||
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
|
||||
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
||||
ParallelLMHead,
|
||||
VocabParallelEmbedding,
|
||||
)
|
||||
from vllm.model_executor.model_loader.weight_utils import (
|
||||
default_weight_loader,
|
||||
maybe_remap_kv_scale_name,
|
||||
)
|
||||
from vllm.sequence import IntermediateTensors
|
||||
from vllm.transformers_utils.configs.kimi_linear import KimiLinearConfig
|
||||
|
||||
from .interfaces import HasInnerState, IsHybrid, MixtureOfExperts, SupportsPP
|
||||
from .utils import (
|
||||
PPMissingLayer,
|
||||
is_pp_missing_parameter,
|
||||
make_layers,
|
||||
maybe_prefix,
|
||||
)
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class KimiMLP(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size: int,
|
||||
intermediate_size: int,
|
||||
hidden_act: str,
|
||||
quant_config: QKVParallelLinear | None = None,
|
||||
reduce_results: bool = True,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.gate_up_proj = MergedColumnParallelLinear(
|
||||
hidden_size,
|
||||
[intermediate_size] * 2,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.gate_up_proj",
|
||||
)
|
||||
self.down_proj = RowParallelLinear(
|
||||
intermediate_size,
|
||||
hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
reduce_results=reduce_results,
|
||||
prefix=f"{prefix}.down_proj",
|
||||
)
|
||||
if hidden_act != "silu":
|
||||
raise ValueError(
|
||||
f"Unsupported activation: {hidden_act}. Only silu is supported for now."
|
||||
)
|
||||
self.act_fn = SiluAndMul()
|
||||
|
||||
def forward(self, x):
|
||||
gate_up, _ = self.gate_up_proj(x)
|
||||
x = self.act_fn(gate_up)
|
||||
x, _ = self.down_proj(x)
|
||||
return x
|
||||
|
||||
|
||||
class KimiMoE(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: KimiLinearConfig,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
prefix: str = "",
|
||||
layer_idx: int = 0,
|
||||
):
|
||||
super().__init__()
|
||||
hidden_size = config.hidden_size
|
||||
intermediate_size = config.intermediate_size
|
||||
moe_intermediate_size = config.moe_intermediate_size
|
||||
num_experts = config.num_experts
|
||||
moe_renormalize = config.moe_renormalize
|
||||
self.tp_size = get_tensor_model_parallel_world_size()
|
||||
self.routed_scaling_factor = config.routed_scaling_factor
|
||||
self.num_shared_experts = config.num_shared_experts
|
||||
self.layer_idx = layer_idx
|
||||
|
||||
if config.hidden_act != "silu":
|
||||
raise ValueError(
|
||||
f"Unsupported activation: {config.hidden_act}. "
|
||||
"Only silu is supported for now."
|
||||
)
|
||||
|
||||
# Gate always runs at half / full precision for now.
|
||||
self.gate = ReplicatedLinear(
|
||||
hidden_size,
|
||||
num_experts,
|
||||
bias=False,
|
||||
quant_config=None,
|
||||
prefix=f"{prefix}.gate",
|
||||
)
|
||||
|
||||
self.gate.e_score_correction_bias = nn.Parameter(torch.empty(num_experts))
|
||||
|
||||
self.experts = FusedMoE(
|
||||
num_experts=num_experts,
|
||||
top_k=config.num_experts_per_token,
|
||||
hidden_size=hidden_size,
|
||||
intermediate_size=moe_intermediate_size,
|
||||
reduce_results=False,
|
||||
renormalize=moe_renormalize,
|
||||
quant_config=quant_config,
|
||||
use_grouped_topk=config.use_grouped_topk,
|
||||
num_expert_group=config.num_expert_group,
|
||||
topk_group=config.topk_group,
|
||||
prefix=f"{prefix}.experts",
|
||||
scoring_func=config.moe_router_activation_func,
|
||||
e_score_correction_bias=self.gate.e_score_correction_bias,
|
||||
)
|
||||
|
||||
if self.num_shared_experts is not None:
|
||||
intermediate_size = moe_intermediate_size * self.num_shared_experts
|
||||
self.shared_experts = KimiMLP(
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
reduce_results=False,
|
||||
)
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
num_tokens, hidden_size = hidden_states.shape
|
||||
hidden_states = hidden_states.view(-1, hidden_size)
|
||||
if self.num_shared_experts is not None:
|
||||
shared_output = self.shared_experts(hidden_states)
|
||||
router_logits, _ = self.gate(hidden_states)
|
||||
final_hidden_states = (
|
||||
self.experts(hidden_states=hidden_states, router_logits=router_logits)
|
||||
* self.routed_scaling_factor
|
||||
)
|
||||
if shared_output is not None:
|
||||
final_hidden_states = final_hidden_states + shared_output
|
||||
|
||||
if self.tp_size > 1:
|
||||
final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
|
||||
return final_hidden_states.view(num_tokens, hidden_size)
|
||||
|
||||
|
||||
class KimiMLAAttention(nn.Module):
|
||||
"""
|
||||
Main reference: DeepseekV2 vllm Implementation
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: KimiLinearConfig,
|
||||
hidden_size: int,
|
||||
num_heads: int,
|
||||
qk_nope_head_dim: int,
|
||||
qk_rope_head_dim: int,
|
||||
v_head_dim: int,
|
||||
q_lora_rank: int | None,
|
||||
kv_lora_rank: int,
|
||||
rope_theta: float = 10000,
|
||||
use_nope: bool = False,
|
||||
rope_scaling: dict[str, Any] | None = None,
|
||||
cache_config: CacheConfig | None = None,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
prefix: str = "",
|
||||
**kwargs,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = hidden_size
|
||||
self.qk_nope_head_dim = qk_nope_head_dim
|
||||
self.qk_rope_head_dim = qk_rope_head_dim
|
||||
self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
|
||||
self.v_head_dim = v_head_dim
|
||||
self.q_lora_rank = q_lora_rank
|
||||
self.kv_lora_rank = kv_lora_rank
|
||||
self.num_heads = num_heads
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
self.num_local_heads = num_heads // tp_size
|
||||
self.scaling = self.qk_head_dim**-0.5
|
||||
self.rope_theta = rope_theta
|
||||
self.use_nope = use_nope
|
||||
assert self.use_nope is True
|
||||
assert self.q_lora_rank is None
|
||||
assert rope_scaling is None
|
||||
assert num_heads % tp_size == 0
|
||||
self.kv_a_proj_with_mqa = ReplicatedLinear(
|
||||
self.hidden_size,
|
||||
self.kv_lora_rank + self.qk_rope_head_dim,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.kv_a_proj_with_mqa",
|
||||
)
|
||||
self.q_proj = ColumnParallelLinear(
|
||||
self.hidden_size,
|
||||
self.num_heads * self.qk_head_dim,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.q_proj",
|
||||
)
|
||||
self.kv_a_layernorm = RMSNorm(
|
||||
self.kv_lora_rank,
|
||||
eps=config.rms_norm_eps,
|
||||
)
|
||||
self.kv_b_proj = ColumnParallelLinear(
|
||||
self.kv_lora_rank,
|
||||
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.kv_b_proj",
|
||||
)
|
||||
self.o_proj = RowParallelLinear(
|
||||
self.num_heads * self.v_head_dim,
|
||||
self.hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.o_proj",
|
||||
)
|
||||
|
||||
mla_modules = MLAModules(
|
||||
kv_a_layernorm=self.kv_a_layernorm,
|
||||
kv_b_proj=self.kv_b_proj,
|
||||
rotary_emb=None,
|
||||
o_proj=self.o_proj,
|
||||
fused_qkv_a_proj=None,
|
||||
kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
|
||||
q_a_layernorm=None,
|
||||
q_b_proj=None,
|
||||
q_proj=self.q_proj,
|
||||
indexer=None,
|
||||
is_sparse=False,
|
||||
topk_indices_buffer=None,
|
||||
)
|
||||
self.mla_attn = MultiHeadLatentAttentionWrapper(
|
||||
self.hidden_size,
|
||||
self.num_local_heads,
|
||||
self.scaling,
|
||||
self.qk_nope_head_dim,
|
||||
self.qk_rope_head_dim,
|
||||
self.v_head_dim,
|
||||
self.q_lora_rank,
|
||||
self.kv_lora_rank,
|
||||
mla_modules,
|
||||
cache_config,
|
||||
quant_config,
|
||||
prefix,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
output: torch.Tensor,
|
||||
) -> None:
|
||||
output[:] = self.mla_attn(positions, hidden_states)
|
||||
|
||||
|
||||
class KimiDecoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: KimiLinearConfig,
|
||||
layer_idx: int,
|
||||
cache_config: CacheConfig | None = None,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
parallel_config: ParallelConfig | None = None,
|
||||
model_config: ModelConfig | None = None,
|
||||
prefix: str = "",
|
||||
**kwargs,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
|
||||
self.is_moe = config.is_moe
|
||||
|
||||
if config.is_kda_layer(layer_idx):
|
||||
self.self_attn = KimiDeltaAttention(
|
||||
layer_idx=layer_idx,
|
||||
hidden_size=config.hidden_size,
|
||||
quant_config=quant_config,
|
||||
cache_config=cache_config,
|
||||
model_config=config,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
)
|
||||
else:
|
||||
self.self_attn = KimiMLAAttention(
|
||||
layer_idx=layer_idx,
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=config.num_attention_heads,
|
||||
quant_config=quant_config,
|
||||
cache_config=cache_config,
|
||||
model_config=model_config,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
config=config,
|
||||
qk_nope_head_dim=config.qk_nope_head_dim,
|
||||
qk_rope_head_dim=config.qk_rope_head_dim,
|
||||
v_head_dim=config.v_head_dim,
|
||||
q_lora_rank=config.q_lora_rank,
|
||||
kv_lora_rank=config.kv_lora_rank,
|
||||
use_nope=config.mla_use_nope,
|
||||
)
|
||||
|
||||
if (
|
||||
self.is_moe
|
||||
and config.num_experts is not None
|
||||
and layer_idx >= config.first_k_dense_replace
|
||||
and layer_idx % config.moe_layer_freq == 0
|
||||
):
|
||||
self.block_sparse_moe = KimiMoE(
|
||||
config=config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
self.mlp = self.block_sparse_moe
|
||||
else:
|
||||
self.mlp = KimiMLP(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = RMSNorm(
|
||||
config.hidden_size, eps=config.rms_norm_eps
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
residual: torch.Tensor | None,
|
||||
**kwargs,
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
# Self Attention
|
||||
if residual is None:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
else:
|
||||
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
||||
|
||||
attn_output = torch.empty_like(hidden_states)
|
||||
self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
positions=positions,
|
||||
output=attn_output,
|
||||
)
|
||||
hidden_states = attn_output
|
||||
|
||||
# Fully Connected
|
||||
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
@support_torch_compile
|
||||
class KimiLinearModel(nn.Module):
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
|
||||
config = vllm_config.model_config.hf_text_config
|
||||
model_config = vllm_config.model_config
|
||||
cache_config = vllm_config.cache_config
|
||||
quant_config = vllm_config.quant_config
|
||||
parallel_config = vllm_config.parallel_config
|
||||
self.config = config
|
||||
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
if get_pp_group().is_first_rank:
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
prefix=f"{prefix}.embed_tokens",
|
||||
)
|
||||
else:
|
||||
self.embed_tokens = PPMissingLayer()
|
||||
|
||||
extra_kwargs = {}
|
||||
|
||||
def get_layer(prefix: str):
|
||||
layer_idx = int(prefix.rsplit(".", 1)[1])
|
||||
return KimiDecoderLayer(
|
||||
config,
|
||||
layer_idx,
|
||||
cache_config,
|
||||
quant_config,
|
||||
parallel_config,
|
||||
model_config,
|
||||
prefix,
|
||||
**extra_kwargs,
|
||||
)
|
||||
|
||||
self.start_layer, self.end_layer, self.layers = make_layers(
|
||||
config.num_hidden_layers,
|
||||
get_layer,
|
||||
prefix=f"{prefix}.layers",
|
||||
)
|
||||
|
||||
if get_pp_group().is_last_rank:
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
else:
|
||||
self.norm = PPMissingLayer()
|
||||
|
||||
world_size = get_tensor_model_parallel_world_size()
|
||||
assert config.num_attention_heads % world_size == 0, (
|
||||
"num_attention_heads must be divisible by world_size"
|
||||
)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor | None,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is not None:
|
||||
hidden_states = inputs_embeds
|
||||
else:
|
||||
hidden_states = self.get_input_embeddings(input_ids)
|
||||
residual = None
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
residual = intermediate_tensors["residual"]
|
||||
|
||||
for _, layer in enumerate(self.layers[self.start_layer : self.end_layer]):
|
||||
hidden_states, residual = layer(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
residual=residual,
|
||||
)
|
||||
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors(
|
||||
{"hidden_states": hidden_states, "residual": residual}
|
||||
)
|
||||
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class KimiLinearForCausalLM(
|
||||
nn.Module, HasInnerState, SupportsPP, MixtureOfExperts, IsHybrid
|
||||
):
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
self.model_config = vllm_config.model_config
|
||||
self.vllm_config = vllm_config
|
||||
self.config = self.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
self.quant_config = quant_config
|
||||
self.model = KimiLinearModel(
|
||||
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
||||
)
|
||||
if get_pp_group().is_last_rank:
|
||||
self.lm_head = ParallelLMHead(
|
||||
self.config.vocab_size,
|
||||
self.config.hidden_size,
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "lm_head"),
|
||||
)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
logit_scale = getattr(self.config, "logit_scale", 1.0)
|
||||
self.logits_processor = LogitsProcessor(
|
||||
self.config.vocab_size, scale=logit_scale
|
||||
)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.get_input_embeddings(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
hidden_states = self.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds, **kwargs
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
@classmethod
|
||||
def get_mamba_state_dtype_from_config(
|
||||
cls,
|
||||
vllm_config: "VllmConfig",
|
||||
) -> tuple[torch.dtype, torch.dtype, torch.dtype, torch.dtype]:
|
||||
return MambaStateDtypeCalculator.kda_state_dtype(
|
||||
vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_mamba_state_shape_from_config(
|
||||
cls, vllm_config: "VllmConfig"
|
||||
) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
|
||||
parallel_config = vllm_config.parallel_config
|
||||
hf_config = vllm_config.model_config.hf_config
|
||||
tp_size = parallel_config.tensor_parallel_size
|
||||
num_spec = (
|
||||
vllm_config.speculative_config.num_speculative_tokens
|
||||
if vllm_config.speculative_config
|
||||
else 0
|
||||
)
|
||||
return MambaStateShapeCalculator.kda_state_shape(
|
||||
tp_size,
|
||||
hf_config.linear_attn_config["num_heads"],
|
||||
hf_config.linear_attn_config["head_dim"],
|
||||
conv_kernel_size=hf_config.linear_attn_config["short_conv_kernel_size"],
|
||||
num_spec=num_spec,
|
||||
)
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
return self.logits_processor(self.lm_head, hidden_states)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
(".gate_up_proj", ".gate_proj", 0),
|
||||
(".gate_up_proj", ".up_proj", 1),
|
||||
]
|
||||
if self.config.is_moe:
|
||||
# Params for weights, fp8 weight scales, fp8 activation scales
|
||||
# (param_name, weight_name, expert_id, shard_id)
|
||||
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
||||
ckpt_gate_proj_name="w1",
|
||||
ckpt_down_proj_name="w2",
|
||||
ckpt_up_proj_name="w3",
|
||||
num_experts=self.config.num_experts,
|
||||
)
|
||||
else:
|
||||
expert_params_mapping = []
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: set[str] = set()
|
||||
for args in weights:
|
||||
name, loaded_weight = args[:2]
|
||||
kwargs = args[2] if len(args) > 2 else {}
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
|
||||
if spec_layer is not None:
|
||||
continue # skip spec decode layers for main model
|
||||
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
|
||||
# Models trained using ColossalAI may include these tensors in
|
||||
# the checkpoint. Skip them.
|
||||
continue
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
# We have mlp.experts[0].gate_proj in the checkpoint.
|
||||
# Since we handle the experts below in expert_params_mapping,
|
||||
# we need to skip here BEFORE we update the name, otherwise
|
||||
# name will be updated to mlp.experts[0].gate_up_proj, which
|
||||
# will then be updated below in expert_params_mapping
|
||||
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
||||
if ("mlp.experts." in name) and name not in params_dict:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
for idx, (param_name, weight_name, expert_id, shard_id) in enumerate(
|
||||
expert_params_mapping
|
||||
):
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(
|
||||
param,
|
||||
loaded_weight,
|
||||
name,
|
||||
expert_id=expert_id,
|
||||
shard_id=shard_id,
|
||||
)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if (
|
||||
name.endswith(".bias")
|
||||
and name not in params_dict
|
||||
and not self.config.is_linear_attn
|
||||
): # noqa: E501
|
||||
continue
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
name = maybe_remap_kv_scale_name(name, params_dict)
|
||||
if name is None:
|
||||
continue
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight, **kwargs)
|
||||
loaded_params.add(name)
|
||||
|
||||
|
||||
def get_spec_layer_idx_from_weight_name(
|
||||
config: KimiLinearConfig, weight_name: str
|
||||
) -> int | None:
|
||||
if hasattr(config, "num_nextn_predict_layers") and (
|
||||
config.num_nextn_predict_layers > 0
|
||||
):
|
||||
layer_idx = config.num_hidden_layers
|
||||
for i in range(config.num_nextn_predict_layers):
|
||||
if weight_name.startswith(f"model.layers.{layer_idx + i}."):
|
||||
return layer_idx + i
|
||||
return None
|
||||
@@ -118,6 +118,7 @@ _TEXT_GENERATION_MODELS = {
|
||||
"InternLM3ForCausalLM": ("llama", "LlamaForCausalLM"),
|
||||
"JAISLMHeadModel": ("jais", "JAISLMHeadModel"),
|
||||
"JambaForCausalLM": ("jamba", "JambaForCausalLM"),
|
||||
"KimiLinearForCausalLM": ("kimi_linear", "KimiLinearForCausalLM"), # noqa: E501
|
||||
"Lfm2ForCausalLM": ("lfm2", "Lfm2ForCausalLM"),
|
||||
"Lfm2MoeForCausalLM": ("lfm2_moe", "Lfm2MoeForCausalLM"),
|
||||
"LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
|
||||
|
||||
Reference in New Issue
Block a user