751 lines
27 KiB
Python
751 lines
27 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Apache License, Version 2.0:
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# MIT License:
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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"""Inference-only Flash model compatible with HuggingFace weights."""
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import typing
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from collections.abc import Callable, 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 transformers import PretrainedConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group
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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.fused_moe import FusedMoE
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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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ReplicatedLinear,
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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.int8_utils import block_dequant
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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 default_weight_loader
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from vllm.model_executor.models.deepseek_v2 import DeepseekV2MLAAttention
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (
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PPMissingLayer,
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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 FlashConfig(PretrainedConfig):
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"""Flash model configuration."""
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model_type = "longcat_flash"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=131072,
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hidden_size=4096,
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intermediate_size=8192,
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num_layers=28,
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num_hidden_layers=None,
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num_attention_heads=96,
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num_key_value_heads=128,
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ep_size=1,
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kv_lora_rank=512,
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q_lora_rank=1536,
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qk_rope_head_dim=64,
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v_head_dim=128,
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qk_nope_head_dim=128,
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num_experts_per_tok=None,
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norm_topk_prob=False,
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max_position_embeddings=8192,
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initializer_range=0.02,
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rms_norm_eps=1e-05,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=100000,
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eos_token_id=100001,
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pretraining_tp=1,
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tie_word_embeddings=False,
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rope_theta=1000000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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mla_scale_q_lora=False,
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mla_scale_kv_lora=False,
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dtype="bfloat16",
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params_dtype="bfloat16",
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router_dtype="float32",
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router_bias=False,
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topk_method=None,
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routed_scaling_factor=None,
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zero_expert_num=0,
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zero_expert_type=None,
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nextn_use_scmoe=False,
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**kwargs,
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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dtype=dtype,
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params_dtype=params_dtype,
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router_dtype=router_dtype,
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topk_method=topk_method,
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router_bias=router_bias,
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nextn_use_scmoe=nextn_use_scmoe,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.num_hidden_layers = (
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num_hidden_layers if num_hidden_layers is not None else num_layers
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)
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self.num_attention_heads = num_attention_heads
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self.ep_size = ep_size
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self.kv_lora_rank = kv_lora_rank
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self.q_lora_rank = q_lora_rank
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self.qk_rope_head_dim = qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.qk_nope_head_dim = qk_nope_head_dim
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self.num_experts_per_tok = num_experts_per_tok
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self.norm_topk_prob = norm_topk_prob
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mla_scale_q_lora = mla_scale_q_lora
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self.mla_scale_kv_lora = mla_scale_kv_lora
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self.zero_expert_num = zero_expert_num
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self.zero_expert_type = zero_expert_type
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self.routed_scaling_factor = routed_scaling_factor
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self.hidden_act = "silu"
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self.intermediate_size = (
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self.ffn_hidden_size
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if hasattr(self, "ffn_hidden_size")
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else self.intermediate_size
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)
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if hasattr(self, "moe_intermediate_size"):
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self.moe_intermediate_size = self.moe_intermediate_size
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elif hasattr(self, "expert_ffn_hidden_size"):
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self.moe_intermediate_size = self.expert_ffn_hidden_size
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else:
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self.moe_intermediate_size = self.intermediate_size
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class FlashMLP(nn.Module):
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"""Flash MLP layer."""
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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: torch.Tensor) -> torch.Tensor:
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if x.numel() == 0:
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return 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 LongcatRouter(nn.Module):
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def __init__(
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self,
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config,
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zero_expert_num=0,
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rounter_params_dtype=torch.bfloat16,
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prefix: str = "",
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):
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super().__init__()
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self.n_routed_experts = (
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config.n_routed_experts
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if hasattr(config, "n_routed_experts")
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else config.num_experts[0]
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)
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self.n_routed_experts = self.n_routed_experts + zero_expert_num
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self.classifier = ReplicatedLinear(
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config.hidden_size,
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self.n_routed_experts,
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bias=config.router_bias,
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params_dtype=rounter_params_dtype,
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quant_config=None,
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prefix=f"{prefix}.classifier",
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)
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self.e_score_correction_bias = nn.Parameter(
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torch.zeros((self.n_routed_experts), dtype=rounter_params_dtype)
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)
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def forward(self, hidden_states):
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logits, _ = self.classifier(hidden_states)
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return logits
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class LongcatMoe(nn.Module):
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def __init__(
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self,
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config: FlashConfig,
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num_experts: int,
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top_k: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: torch.dtype | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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enable_eplb: bool = False,
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.zero_expert_num = config.zero_expert_num
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self.zero_expert_type = config.zero_expert_type
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self.routed_scaling_factor = config.routed_scaling_factor
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self.enable_eplb = enable_eplb
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# Gate always runs at half / full precision for now.
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self.rounter_params_dtype = params_dtype
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if config.router_dtype == "float32":
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self.rounter_params_dtype = torch.float32
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self.router = LongcatRouter(
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config=config,
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zero_expert_num=self.zero_expert_num,
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rounter_params_dtype=self.rounter_params_dtype,
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prefix=f"{prefix}.gate",
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)
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self.experts = FusedMoE(
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num_experts=num_experts,
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top_k=top_k,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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reduce_results=True,
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params_dtype=params_dtype,
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e_score_correction_bias=self.router.e_score_correction_bias,
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renormalize=False,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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zero_expert_num=self.zero_expert_num,
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zero_expert_type=self.zero_expert_type,
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enable_eplb=self.enable_eplb,
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routed_scaling_factor=config.routed_scaling_factor,
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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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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router_logits = self.router(hidden_states.to(self.rounter_params_dtype))
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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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return final_hidden_states.view(num_tokens, hidden_dim)
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class FlashDecoderLayer(nn.Module):
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"""Flash decoder layer with dual attention and MLP structure."""
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def __init__(
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self,
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vllm_config: VllmConfig,
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config: FlashConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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enable_eplb: bool = False,
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) -> None:
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super().__init__()
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self.layer_idx = int(prefix.split(sep=".")[-1])
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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if rope_scaling is not None and getattr(
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config, "original_max_position_embeddings", None
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):
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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings
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)
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# Dual attention structure
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self.self_attn = nn.ModuleList(
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[
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DeepseekV2MLAAttention(
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vllm_config=vllm_config,
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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qk_nope_head_dim=config.qk_nope_head_dim,
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qk_rope_head_dim=config.qk_rope_head_dim,
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v_head_dim=config.v_head_dim,
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q_lora_rank=(
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config.q_lora_rank if hasattr(config, "q_lora_rank") else None
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),
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kv_lora_rank=config.kv_lora_rank,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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cache_config=cache_config,
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quant_config=None
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if "self_attn" in getattr(config, "disable_quant_module", [])
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else quant_config,
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prefix=f"{prefix}.self_attn.{i}",
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)
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for i in range(2)
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]
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)
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self.input_layernorm = nn.ModuleList(
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[RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
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)
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self.post_attention_layernorm = nn.ModuleList(
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[RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
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)
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# Dual MLP structure
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self.mlps = nn.ModuleList(
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[
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FlashMLP(
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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=None
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if "mlps" in getattr(config, "disable_quant_module", [])
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else quant_config,
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prefix=f"{prefix}.mlps.{i}",
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)
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for i in range(2)
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]
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)
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self.mlp = LongcatMoe(
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config=config,
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num_experts=config.n_routed_experts
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if hasattr(config, "n_routed_experts")
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else config.num_experts[self.layer_idx],
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top_k=config.moe_topk
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if hasattr(config, "moe_topk")
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else 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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quant_config=quant_config,
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prefix=(f"{prefix}.mlp"),
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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[0](hidden_states)
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else:
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hidden_states, residual = self.input_layernorm[0](hidden_states, residual)
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hidden_states = self.self_attn[0](
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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[0](
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hidden_states, residual
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)
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# moe
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hidden_states_copy = hidden_states.clone()
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moe_hidden_states = self.mlp(hidden_states_copy)
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# first mlp
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hidden_states = self.mlps[0](hidden_states)
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hidden_states, residual = self.input_layernorm[1](hidden_states, residual)
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# second_attn
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hidden_states = self.self_attn[1](
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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[1](
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hidden_states, residual
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)
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# second_mlp
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hidden_states = self.mlps[1](hidden_states)
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hidden_states = hidden_states + moe_hidden_states
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return hidden_states, residual
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@support_torch_compile
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class FlashModel(nn.Module):
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"""Flash model."""
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.padding_idx = getattr(config, "pad_token_id", None)
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self.vocab_size = config.vocab_size
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if get_pp_group().is_first_rank:
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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prefix=maybe_prefix(prefix, "embed_tokens"),
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: FlashDecoderLayer(
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vllm_config,
|
|
config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=prefix,
|
|
),
|
|
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()
|
|
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
|
|
["hidden_states", "residual"], config.hidden_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,
|
|
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.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 islice(self.layers, self.start_layer, self.end_layer):
|
|
hidden_states, residual = layer(
|
|
positions,
|
|
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)
|
|
return hidden_states
|
|
|
|
|
|
class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
|
"""Flash model for causal language modeling."""
|
|
|
|
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 = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
|
|
quant_config = vllm_config.quant_config
|
|
lora_config = vllm_config.lora_config
|
|
|
|
self.config = config
|
|
config.intermediate_size = (
|
|
config.ffn_hidden_size
|
|
if hasattr(config, "ffn_hidden_size")
|
|
else config.intermediate_size
|
|
)
|
|
self.lora_config = lora_config
|
|
self.quant_config = quant_config
|
|
|
|
self.model = FlashModel(
|
|
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 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,
|
|
) -> 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]]:
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
return FusedMoE.make_expert_params_mapping(
|
|
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
|
|
if hasattr(self.config, "n_routed_experts")
|
|
else self.config.num_experts[0],
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
("fused_qkv_a_proj", "q_a_proj", 0),
|
|
("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
|
|
(".gate_up_proj", ".gate_proj", 0),
|
|
(".gate_up_proj", ".up_proj", 1),
|
|
]
|
|
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
loaded_params: set[str] = set()
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
if "mlp" in name and "mlps" not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if (
|
|
name.endswith(".bias") or name.endswith("_bias")
|
|
) and name not in params_dict:
|
|
continue
|
|
# Skip mtp
|
|
if ".mtp." in name:
|
|
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:
|
|
is_expert_weight = False
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
is_expert_weight = True
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
# Skip mtp
|
|
if ".mtp." in name_mapped:
|
|
continue
|
|
if (
|
|
name_mapped.endswith(".bias") or name_mapped.endswith("_bias")
|
|
) and name not in params_dict:
|
|
continue
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
param = params_dict[name_mapped]
|
|
weight_loader = param.weight_loader
|
|
weight_loader = typing.cast(
|
|
Callable[..., bool], param.weight_loader
|
|
)
|
|
success = weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name_mapped,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
return_success=True,
|
|
)
|
|
if success:
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# We've checked that this is an expert weight
|
|
# However it's not mapped locally to this rank
|
|
# So we simply skip it
|
|
continue
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Skip loading kv_scale from ckpts towards new design.
|
|
if name.endswith(".kv_scale") and name not in params_dict:
|
|
continue
|
|
# Skip mtp
|
|
if ".mtp." in name:
|
|
continue
|
|
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)
|
|
loaded_params.add(name)
|
|
for layer_id in range(self.config.num_hidden_layers):
|
|
for i in range(2):
|
|
if isinstance(self.model.layers[layer_id], PPMissingLayer):
|
|
continue
|
|
self_attn = self.model.layers[layer_id].self_attn[i]
|
|
if hasattr(
|
|
self.quant_config, "weight_block_size"
|
|
) and self_attn.kv_b_proj.weight.dtype in (
|
|
torch.float8_e4m3fn,
|
|
torch.float8_e4m3fnuz,
|
|
):
|
|
weight_block_size = self.quant_config.weight_block_size
|
|
if weight_block_size is not None:
|
|
assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
|
|
dtype = torch.get_default_dtype()
|
|
w = block_dequant(
|
|
self_attn.kv_b_proj.weight,
|
|
self_attn.kv_b_proj.weight_scale_inv,
|
|
weight_block_size,
|
|
).to(dtype)
|
|
else:
|
|
w = self_attn.kv_b_proj.weight
|
|
|
|
w_kc, w_vc = w.unflatten(
|
|
0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
|
|
).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
|
|
self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
|
|
self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
|
|
if self.config.mla_scale_q_lora:
|
|
self_attn.q_a_layernorm.weight.data *= (
|
|
self.config.hidden_size / self.config.q_lora_rank
|
|
) ** 0.5
|
|
if self.config.mla_scale_kv_lora:
|
|
self_attn.kv_a_layernorm.weight.data *= (
|
|
self.config.hidden_size / self.config.kv_lora_rank
|
|
) ** 0.5
|
|
return loaded_params
|