wehub-resource-sync
907 lines
32 KiB
Python
907 lines
32 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable
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from itertools import islice
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import torch
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from torch import nn
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (
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CacheConfig,
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VllmConfig,
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get_current_vllm_config,
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str_dtype_to_torch_dtype,
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)
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from vllm.distributed import (
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get_ep_group,
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_gather,
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)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fused_moe import (
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FusedMoE,
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fused_moe_make_expert_params_mapping,
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)
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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GroupShape,
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scaled_quantize,
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)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.model_executor.models.utils import sequence_parallel_chunk
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from vllm.sequence import IntermediateTensors
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from vllm.v1.attention.backend import AttentionType
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from vllm.v1.attention.backends.registry import AttentionBackendEnum
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from .interfaces import (
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EagleModelMixin,
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MixtureOfExperts,
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SupportsEagle3,
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SupportsPP,
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)
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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extract_layer_index,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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logger = init_logger(__name__)
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class MiMoV2MLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: QuantizationConfig | None = None,
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reduce_results: bool = True,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class MiMoV2MoE(nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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prefix: str = "",
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is_nextn: bool = False,
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):
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super().__init__()
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config = vllm_config.model_config.hf_text_config
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parallel_config = vllm_config.parallel_config
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quant_config = vllm_config.quant_config
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self.tp_size = get_tensor_model_parallel_world_size()
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self.ep_group = get_ep_group().device_group
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self.ep_rank = get_ep_group().rank_in_group
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self.ep_size = self.ep_group.size()
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self.n_routed_experts = config.n_routed_experts
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self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
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if self.tp_size > config.n_routed_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.n_routed_experts}."
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)
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if config.hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now."
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)
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vllm_config = get_current_vllm_config()
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eplb_config = vllm_config.parallel_config.eplb_config
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self.enable_eplb = parallel_config.enable_eplb
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self.n_logical_experts = self.n_routed_experts
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self.n_redundant_experts = eplb_config.num_redundant_experts
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self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
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self.n_local_physical_experts = self.n_physical_experts // self.ep_size
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self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
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self.physical_expert_end = (
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self.physical_expert_start + self.n_local_physical_experts
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)
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dtype = getattr(config, "moe_router_dtype", "float32")
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self.gate_dtype = str_dtype_to_torch_dtype(dtype)
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self.gate = nn.Linear(
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config.hidden_size,
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config.n_routed_experts,
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bias=False,
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dtype=self.gate_dtype,
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)
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self.gate.e_score_correction_bias = nn.Parameter(
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torch.empty(config.n_routed_experts, dtype=self.gate_dtype)
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)
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self.experts = FusedMoE(
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num_experts=self.n_routed_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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e_score_correction_bias=self.gate.e_score_correction_bias,
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enable_eplb=self.enable_eplb,
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num_redundant_experts=self.n_redundant_experts,
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is_sequence_parallel=self.is_sequence_parallel,
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use_grouped_topk=True,
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num_expert_group=config.n_group,
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topk_group=config.topk_group,
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scoring_func="sigmoid",
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router_logits_dtype=self.gate_dtype,
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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assert hidden_states.dim() <= 2, "MiMoV2MoE only supports 1D or 2D inputs"
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is_input_1d = hidden_states.dim() == 1
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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if self.is_sequence_parallel:
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hidden_states = sequence_parallel_chunk(hidden_states)
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if self.gate_dtype is not None:
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gate_input = hidden_states.to(self.gate_dtype)
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else:
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gate_input = hidden_states
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router_logits = self.gate(gate_input)
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final_hidden_states = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
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)
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if self.is_sequence_parallel:
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final_hidden_states = tensor_model_parallel_all_gather(
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final_hidden_states, 0
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)
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final_hidden_states = final_hidden_states[:num_tokens]
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return final_hidden_states.squeeze(0) if is_input_1d else final_hidden_states
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class MiMoV2Attention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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head_dim: int,
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v_head_dim: int | None = None,
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v_scale: float | None = None,
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sliding_window_size: int = -1,
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attention_bias: bool = False,
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add_swa_attention_sink_bias: bool = False,
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layer_id: int = 0,
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rope_theta: float = 1000000,
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max_position_embeddings: int = 32768,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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partial_rotary_factor: float = 1.0,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.layer_id = layer_id
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = head_dim
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self.v_head_dim = v_head_dim if v_head_dim is not None else head_dim
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self.q_size = self.num_heads * self.head_dim
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self.k_size = self.num_kv_heads * self.head_dim
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self.v_size = self.num_kv_heads * self.v_head_dim
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self.v_scale = v_scale
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=attention_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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v_head_size=self.v_head_dim,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.v_head_dim,
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hidden_size,
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bias=False,
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quant_config=quant_config if "mtp.layers" not in prefix else None,
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reduce_results=True,
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prefix=f"{prefix}.o_proj",
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)
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self.rotary_emb = get_rope(
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head_size=self.head_dim,
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max_position=max_position_embeddings,
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rope_parameters={
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"rope_type": "default",
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"rope_theta": rope_theta,
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"partial_rotary_factor": partial_rotary_factor,
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},
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)
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self.attention_sink_bias = (
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torch.nn.Parameter(torch.empty(self.num_heads), requires_grad=False)
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if add_swa_attention_sink_bias
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else None
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)
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sliding_window = sliding_window_size if sliding_window_size > -1 else None
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# Use DiffKV backend when V has a different head dim than K.
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# Auto-pick FA-DiffKV when FA3/4 is usable on this device, else fall
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# back to TRITON_ATTN_DIFFKV. Users can force a choice via
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# `--attention-backend <FLASH_ATTN_DIFFKV|TRITON_ATTN_DIFFKV>`.
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if self.v_head_dim != self.head_dim:
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requested = get_current_vllm_config().attention_config.backend
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if requested is not None and requested.name.endswith("_DIFFKV"):
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backend_enum = requested
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else:
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fa_backend = AttentionBackendEnum.FLASH_ATTN_DIFFKV.get_class()
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if fa_backend.is_supported_on_current_device(
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head_size=self.head_dim,
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head_size_v=self.v_head_dim,
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has_sinks=self.attention_sink_bias is not None,
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):
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backend_enum = AttentionBackendEnum.FLASH_ATTN_DIFFKV
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else:
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backend_enum = AttentionBackendEnum.TRITON_ATTN_DIFFKV
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attn_backend = backend_enum.get_class()
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attn_backend.set_head_size_v(self.v_head_dim)
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logger.info_once("Using %s for attention.", attn_backend.get_name())
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else:
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attn_backend = None
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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per_layer_sliding_window=sliding_window,
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attn_type=AttentionType.DECODER,
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prefix=f"{prefix}.attn",
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sinks=self.attention_sink_bias,
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attn_backend=attn_backend,
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head_size_v=self.v_head_dim,
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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# Apply v_scale before attention
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if self.v_scale is not None:
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v = v * self.v_scale
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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class MiMoV2FlashDecoderLayer(nn.Module):
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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_text_config
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quant_config = vllm_config.quant_config
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layer_id = extract_layer_index(prefix)
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self.hidden_size = config.hidden_size
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self.config = config
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self.layer_id = layer_id
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rope_theta = getattr(config, "rope_theta", 1000000)
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max_position_embeddings = getattr(config, "max_position_embeddings", 32768)
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v_scale = getattr(config, "attention_value_scale", None)
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if self.is_compressed_softmax_layer():
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self.self_attn = MiMoV2Attention(
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hidden_size=self.hidden_size,
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num_heads=config.swa_num_attention_heads,
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num_kv_heads=config.swa_num_key_value_heads,
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head_dim=config.swa_head_dim,
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v_head_dim=getattr(config, "swa_v_head_dim", None),
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v_scale=v_scale,
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sliding_window_size=config.sliding_window_size,
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attention_bias=config.attention_bias,
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add_swa_attention_sink_bias=getattr(
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config, "add_swa_attention_sink_bias", False
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),
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layer_id=layer_id,
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rope_theta=getattr(config, "swa_rope_theta", rope_theta),
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
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prefix=f"{prefix}.self_attn",
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)
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else:
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self.self_attn = MiMoV2Attention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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head_dim=config.head_dim,
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v_head_dim=getattr(config, "v_head_dim", None),
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v_scale=v_scale,
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sliding_window_size=-1, # normal attention
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attention_bias=config.attention_bias,
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layer_id=layer_id,
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rope_theta=rope_theta,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
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prefix=f"{prefix}.self_attn",
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)
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self.is_layer_sparse = self.is_moe_layer(layer_id)
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if self.is_layer_sparse:
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self.mlp = MiMoV2MoE(
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vllm_config=vllm_config,
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prefix=f"{prefix}.mlp",
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)
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else:
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self.mlp = MiMoV2MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.layernorm_epsilon
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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def is_moe_layer(self, layer_idx: int) -> bool:
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return (
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hasattr(self.config, "moe_layer_freq")
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and layer_idx >= 0
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and not isinstance(self.config.moe_layer_freq, int)
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and self.config.moe_layer_freq[layer_idx]
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)
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def is_compressed_softmax_layer(self) -> bool:
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return self.config.hybrid_layer_pattern[self.layer_id] == 1
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def _shard_fp8_qkv_proj(
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w_full: torch.Tensor,
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s_full: torch.Tensor,
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num_heads: int,
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num_kv_heads: int,
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head_dim: int,
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v_head_dim: int,
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tp_rank: int,
|
|
tp_size: int,
|
|
block: int = 128,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""Shard the fp8 qkv_proj weights for ``tp_rank``.
|
|
|
|
The checkpoint stores the fused QKV as ``num_kv_heads`` contiguous groups
|
|
(one per KV head; ``n`` below), each ordered ``[Q | K | V]``:
|
|
|
|
[Q_1 | K_1 | V_1 | Q_2 | K_2 | V_2 | ... | Q_n | K_n | V_n]
|
|
|
|
Per group, Q has ``(num_heads / num_kv_heads) * head_dim`` rows, K has
|
|
``head_dim`` rows, and V has ``v_head_dim`` rows.
|
|
|
|
Each TP rank owns ``g = num_kv_heads / tp_size`` of these groups, and the
|
|
forward expects them de-interleaved into a single Q, K, and V block:
|
|
|
|
[Q_1 | Q_2 | ... | Q_g | K_1 | K_2 | ... | K_g | V_1 | V_2 | ... | V_g]
|
|
|
|
When ``g == 1`` the rank's slice is already ``[Q | K | V]``, so a plain
|
|
chunk suffices. When ``g > 1`` we cannot reach the de-interleaved layout by
|
|
re-permuting the fp8 block scales: each scale covers a 128-row block, and
|
|
since K is 192 rows (1.5 blocks) a block straddles the K/V boundary, so no
|
|
whole-block permutation produces it. Instead we dequantize this rank's
|
|
groups to float (dropping the block constraint), reorder the rows into the
|
|
layout above (Q, K, and V then each span a whole number of blocks), and
|
|
re-quantize to fp8.
|
|
"""
|
|
assert tp_size <= num_kv_heads and num_kv_heads % tp_size == 0, (
|
|
"TP size must evenly split the number of KV heads."
|
|
)
|
|
|
|
kv_heads_per_rank = num_kv_heads // tp_size
|
|
if kv_heads_per_rank == 1:
|
|
# One KV head per rank. The weights and scale can be trivially sharded
|
|
# without re-quantization.
|
|
w = w_full.chunk(tp_size, dim=0)[tp_rank]
|
|
s = s_full.chunk(tp_size, dim=0)[tp_rank]
|
|
return w, s
|
|
|
|
q_rows_per_group = (num_heads // num_kv_heads) * head_dim
|
|
k_rows_per_group = head_dim
|
|
v_rows_per_group = v_head_dim
|
|
rows_per_group = q_rows_per_group + k_rows_per_group + v_rows_per_group
|
|
scale_rows_per_group = s_full.shape[0] // num_kv_heads
|
|
qs, ks, vs = [], [], []
|
|
for g_idx in range(tp_rank * kv_heads_per_rank, (tp_rank + 1) * kv_heads_per_rank):
|
|
row_start = g_idx * rows_per_group
|
|
scale_row_start = g_idx * scale_rows_per_group
|
|
# Dequantize this group's weights.
|
|
w_g = w_full[row_start : row_start + rows_per_group].to(torch.float32)
|
|
s_g = s_full[scale_row_start : scale_row_start + scale_rows_per_group].to(
|
|
torch.float32
|
|
)
|
|
s_g_expanded = s_g.repeat_interleave(block, dim=0).repeat_interleave(
|
|
block, dim=1
|
|
)[:rows_per_group]
|
|
w_g_dequant = w_g * s_g_expanded
|
|
# Track the dequantized q, k, and v weights separately.
|
|
qs.append(w_g_dequant[:q_rows_per_group])
|
|
ks.append(w_g_dequant[q_rows_per_group : q_rows_per_group + k_rows_per_group])
|
|
vs.append(w_g_dequant[q_rows_per_group + k_rows_per_group :])
|
|
|
|
# Combine the q, k, and v weights into the following layout:
|
|
# [Q_1, Q_2, .., Q_g, K_1, K_2, ..., K_g, V_1, V_2, ..., V_g]
|
|
grouped = torch.cat([torch.cat(qs), torch.cat(ks), torch.cat(vs)], dim=0)
|
|
# Quantize back to fp8.
|
|
return scaled_quantize(
|
|
grouped, GroupShape(block, block), w_full.dtype, compute_dtype=torch.float32
|
|
)
|
|
|
|
|
|
@support_torch_compile
|
|
class MiMoV2Model(nn.Module, EagleModelMixin):
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_config.get_text_config()
|
|
quant_config = vllm_config.quant_config
|
|
eplb_config = vllm_config.parallel_config.eplb_config
|
|
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.vocab_size = config.vocab_size
|
|
self.num_redundant_experts = eplb_config.num_redundant_experts
|
|
|
|
if get_pp_group().is_first_rank or (
|
|
config.tie_word_embeddings and get_pp_group().is_last_rank
|
|
):
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.embed_tokens",
|
|
)
|
|
else:
|
|
self.embed_tokens = PPMissingLayer()
|
|
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda prefix: MiMoV2FlashDecoderLayer(
|
|
vllm_config=vllm_config,
|
|
prefix=prefix,
|
|
),
|
|
prefix=f"{prefix}.layers",
|
|
)
|
|
|
|
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
|
|
["hidden_states", "residual"], config.hidden_size
|
|
)
|
|
if get_pp_group().is_last_rank:
|
|
self.norm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
|
|
else:
|
|
self.norm = PPMissingLayer()
|
|
|
|
def embed_input_ids(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 = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.embed_input_ids(input_ids)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
|
|
aux_hidden_states = self._maybe_add_hidden_state(
|
|
[], self.start_layer, hidden_states, residual
|
|
)
|
|
for idx, layer in enumerate(
|
|
islice(self.layers, self.start_layer, self.end_layer)
|
|
):
|
|
hidden_states, residual = layer(positions, hidden_states, residual)
|
|
self._maybe_add_hidden_state(
|
|
aux_hidden_states, idx + 1, hidden_states, residual
|
|
)
|
|
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors(
|
|
{"hidden_states": hidden_states, "residual": residual}
|
|
)
|
|
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
if len(aux_hidden_states) > 0:
|
|
return hidden_states, aux_hidden_states
|
|
return hidden_states
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
return fused_moe_make_expert_params_mapping(
|
|
self,
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.n_routed_experts,
|
|
num_redundant_experts=self.num_redundant_experts,
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
loaded_params: set[str] = set()
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
# Pro-format fused qkv_proj arrives as two tensors (weight and
|
|
# weight_scale_inv). Store them per-layer so that they can be
|
|
# sharded together.
|
|
pending_fp8_qkv_proj: dict[str, dict[str, torch.Tensor]] = {}
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
|
|
continue
|
|
if "mtp" in name:
|
|
continue
|
|
|
|
expert_matched = False
|
|
for param_name, weight_name, expert_id, shard_id in expert_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
|
|
name_rewritten = name.replace(weight_name, param_name)
|
|
|
|
if is_pp_missing_parameter(name_rewritten, self):
|
|
continue
|
|
|
|
if (
|
|
name_rewritten.endswith(".bias") or name_rewritten.endswith("_bias")
|
|
) and name_rewritten not in params_dict:
|
|
continue
|
|
|
|
if name_rewritten not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name_rewritten]
|
|
weight_loader = param.weight_loader
|
|
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name_rewritten,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
loaded_params.add(name_rewritten)
|
|
expert_matched = True
|
|
break
|
|
|
|
if expert_matched:
|
|
continue
|
|
# Support fused qkv_proj checkpoint (Pro format)
|
|
if self._try_load_fp8_qkv_proj(
|
|
name,
|
|
loaded_weight,
|
|
pending_fp8_qkv_proj,
|
|
params_dict,
|
|
loaded_params,
|
|
tp_rank,
|
|
tp_size,
|
|
):
|
|
continue
|
|
stacked_matched = False
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name_rewritten = name.replace(weight_name, param_name)
|
|
|
|
if (
|
|
name_rewritten.endswith(".bias")
|
|
and name_rewritten not in params_dict
|
|
):
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name_rewritten, self):
|
|
continue
|
|
|
|
if name_rewritten not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name_rewritten]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
loaded_params.add(name_rewritten)
|
|
|
|
stacked_matched = True
|
|
break
|
|
|
|
if stacked_matched:
|
|
continue
|
|
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
orig_name = name
|
|
mapped_name = maybe_remap_kv_scale_name(name, params_dict)
|
|
name = mapped_name if mapped_name is not None else orig_name
|
|
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
|
|
if "attention_sink_bias" in name:
|
|
total_heads = loaded_weight.shape[0]
|
|
heads_per_rank = total_heads // tp_size
|
|
head_start = tp_rank * heads_per_rank
|
|
narrow_weight = loaded_weight.narrow(0, head_start, heads_per_rank)
|
|
|
|
param.data.copy_(narrow_weight)
|
|
loaded_params.add(name)
|
|
else:
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
|
|
return loaded_params
|
|
|
|
def _try_load_fp8_qkv_proj(
|
|
self,
|
|
name: str,
|
|
tensor: torch.Tensor,
|
|
fp8_qkv_proj_dict: dict[str, dict[str, torch.Tensor]],
|
|
params_dict: dict[str, torch.nn.Parameter],
|
|
loaded_params: set[str],
|
|
tp_rank: int,
|
|
tp_size: int,
|
|
) -> bool:
|
|
"""
|
|
The fused fp8 QKV projection weights and scale are stored separately.
|
|
Special care must be taken while sharding these tensors across TP ranks.
|
|
See _shard_fp8_qkv_proj for more details.
|
|
|
|
Returns:
|
|
True if ``tensor`` was an fp8 qkv_proj weight/scale and was consumed
|
|
(caller should skip it); False otherwise, so the caller falls
|
|
through to its normal loading path.
|
|
"""
|
|
is_weight = (
|
|
name.endswith("qkv_proj.weight") and tensor.dtype == torch.float8_e4m3fn
|
|
)
|
|
is_scale = name.endswith("qkv_proj.weight_scale_inv")
|
|
if not is_weight and not is_scale:
|
|
# Weight is not in FP8 format. Ignore.
|
|
return False
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
# This qkv_proj is for a layer not on this PP rank.
|
|
return True
|
|
|
|
prefix, qkv_kind = name.rsplit(".", 1)
|
|
entry = fp8_qkv_proj_dict.setdefault(prefix, {})
|
|
entry[qkv_kind] = tensor
|
|
if "weight" not in entry or "weight_scale_inv" not in entry:
|
|
# Still waiting for the other param.
|
|
return True
|
|
del fp8_qkv_proj_dict[prefix]
|
|
|
|
# Get self_attn module, which is a parent of qkv_proj.
|
|
attn = self.get_submodule(prefix.rsplit(".", 1)[0])
|
|
|
|
# Shard the qkv_proj per-rank.
|
|
w_rank, s_rank = _shard_fp8_qkv_proj(
|
|
entry["weight"],
|
|
entry["weight_scale_inv"],
|
|
num_heads=attn.total_num_heads,
|
|
num_kv_heads=attn.total_num_kv_heads,
|
|
head_dim=attn.head_dim,
|
|
v_head_dim=attn.v_head_dim,
|
|
tp_rank=tp_rank,
|
|
tp_size=tp_size,
|
|
)
|
|
sharded = {"weight": w_rank, "weight_scale_inv": s_rank}
|
|
for kind, tensor in sharded.items():
|
|
param_name = f"{prefix}.{kind}"
|
|
param = params_dict[param_name]
|
|
if tensor.shape[0] > param.shape[0]:
|
|
tensor = tensor[: param.shape[0]]
|
|
default_weight_loader(param, tensor)
|
|
loaded_params.add(param_name)
|
|
return True
|
|
|
|
|
|
class MiMoV2FlashForCausalLM(nn.Module, SupportsPP, MixtureOfExperts, SupportsEagle3):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = MiMoV2Model(
|
|
vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"),
|
|
)
|
|
|
|
if get_pp_group().is_last_rank:
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
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,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|
|
|
|
|
|
class MiMoV2ForCausalLM(MiMoV2FlashForCausalLM):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": ["qkv_proj"],
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
}
|