Support MiniCPM3 (#1371)
This commit is contained in:
William
@@ -483,11 +483,14 @@ def _decode_grouped_att_m_fwd(
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# shape constraints
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Lq, Lk = q.shape[-1], k_buffer.shape[-1]
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assert Lq == Lk
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assert Lk in {16, 32, 64, 96, 128, 256, 576}
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assert Lk in {16, 32, 64, 96, 128, 256, 576, 288}
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if Lk == 576:
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BLOCK_DMODEL = 512
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BLOCK_DPE = 64
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elif Lk == 288:
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BLOCK_DMODEL = 256
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BLOCK_DPE = 32
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else:
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BLOCK_DMODEL = triton.next_power_of_2(Lk)
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BLOCK_DPE = 0
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@@ -280,12 +280,15 @@ def extend_attention_fwd(
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assert Lq == Lk and Lv == Lo
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# TODO: is the assertion necessary?
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assert Lq in {16, 32, 64, 96, 128, 256, 576}
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assert Lq in {16, 32, 64, 96, 128, 256, 576, 288}
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assert Lv in {16, 32, 64, 96, 128, 256, 512}
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if Lq == 576:
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BLOCK_DMODEL = 512
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BLOCK_DPE = 64
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elif Lq == 288:
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BLOCK_DMODEL = 256
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BLOCK_DPE = 32
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else:
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BLOCK_DMODEL = triton.next_power_of_2(Lq)
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BLOCK_DPE = 0
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@@ -64,6 +64,11 @@ class ModelConfig:
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self.attention_arch = AttentionArch.MLA
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self.kv_lora_rank = self.hf_config.kv_lora_rank
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self.qk_rope_head_dim = self.hf_config.qk_rope_head_dim
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elif "MiniCPM3ForCausalLM" in self.hf_config.architectures:
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self.head_dim = 128
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self.attention_arch = AttentionArch.MLA
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self.kv_lora_rank = self.hf_config.kv_lora_rank
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self.qk_rope_head_dim = self.hf_config.qk_rope_head_dim
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else:
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self.attention_arch = AttentionArch.MHA
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669
python/sglang/srt/models/minicpm3.py
Normal file
669
python/sglang/srt/models/minicpm3.py
Normal file
@@ -0,0 +1,669 @@
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"""
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Copyright 2023-2024 SGLang Team
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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"""Inference-only MiniCPM3 model compatible with HuggingFace weights."""
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import math
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from typing import Any, Dict, Iterable, Optional, Tuple
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import torch
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from flashinfer import bmm_fp8
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.config import CacheConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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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.quantization.base_config import QuantizationConfig
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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 default_weight_loader
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.sampler import Sampler
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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from sglang.srt.model_executor.forward_batch_info import InputMetadata
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class MiniCPM3MLP(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: Optional[QuantizationConfig] = None,
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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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)
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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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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. "
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"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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def input_to_float8(x, dtype=torch.float8_e4m3fn):
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finfo = torch.finfo(dtype)
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min_val, max_val = x.aminmax()
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amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
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scale = finfo.max / amax
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x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
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return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()
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class MiniCPM3Attention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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v_head_dim: int,
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q_lora_rank: int,
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kv_lora_rank: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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layer_id=None,
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) -> None:
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super().__init__()
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self.layer_id = layer_id
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self.hidden_size = hidden_size
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_rope_head_dim = qk_rope_head_dim
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self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.q_lora_rank = q_lora_rank
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self.kv_lora_rank = kv_lora_rank
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self.num_heads = num_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert num_heads % tp_size == 0
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self.num_local_heads = num_heads // tp_size
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self.scaling = self.qk_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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if self.q_lora_rank is not None:
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self.q_a_proj = ReplicatedLinear(
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self.hidden_size,
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self.q_lora_rank,
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bias=False,
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quant_config=quant_config,
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)
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self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
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self.q_b_proj = ColumnParallelLinear(
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q_lora_rank,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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else:
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self.q_proj = ColumnParallelLinear(
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self.hidden_size,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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self.kv_a_proj_with_mqa = ReplicatedLinear(
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self.hidden_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
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self.kv_b_proj = ColumnParallelLinear(
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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)
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# O projection.
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self.o_proj = RowParallelLinear(
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self.num_heads * self.v_head_dim,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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self.rotary_emb = get_rope(
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qk_rope_head_dim,
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rotary_dim=qk_rope_head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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# TODO support head_size 96
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self.attn = RadixAttention(
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self.num_local_heads,
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128,
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self.scaling,
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num_kv_heads=self.num_local_heads,
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layer_id=layer_id,
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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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input_metadata: InputMetadata,
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) -> torch.Tensor:
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if self.q_lora_rank is not None:
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q = self.q_a_proj(hidden_states)[0]
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q = self.q_a_layernorm(q)
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q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
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else:
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q = self.q_proj(hidden_states)[0].view(
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-1, self.num_local_heads, self.qk_head_dim
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)
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_, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
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latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
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kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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latent_cache = latent_cache.unsqueeze(1)
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kv_a = self.kv_a_layernorm(kv_a.contiguous())
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kv = self.kv_b_proj(kv_a)[0]
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kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
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k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
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k_pe = latent_cache[:, :, self.kv_lora_rank :]
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original_shapes = [q_pe.shape, k_pe.shape]
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q_pe, k_pe = self.rotary_emb(
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positions, q_pe.reshape(q_pe.shape[0], -1), k_pe.reshape(k_pe.shape[0], -1)
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)
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q_pe, k_pe = q_pe.view(original_shapes[0]), k_pe.view(original_shapes[1])
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q[..., self.qk_nope_head_dim :] = q_pe
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k = torch.empty_like(q)
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k[..., : self.qk_nope_head_dim] = k_nope
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k[..., self.qk_nope_head_dim :] = k_pe
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q = torch.nn.functional.pad(q, [0, 128 - self.qk_head_dim], value=0).view(
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-1, self.num_local_heads * 128
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)
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k = torch.nn.functional.pad(k, [0, 128 - self.qk_head_dim], value=0).view(
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-1, self.num_local_heads * 128
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)
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v = torch.nn.functional.pad(v, [0, 128 - self.v_head_dim], value=0).view(
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-1, self.num_local_heads * 128
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)
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attn_output = self.attn(q, k, v, input_metadata)
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attn_output = attn_output.view(-1, self.num_local_heads, 128)[
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..., : self.v_head_dim
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].reshape(-1, self.num_local_heads * self.v_head_dim)
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output, _ = self.o_proj(attn_output)
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return output
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class MiniCPM3AttentionMLA(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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v_head_dim: int,
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q_lora_rank: int,
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kv_lora_rank: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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layer_id=None,
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) -> None:
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super().__init__()
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self.layer_id = layer_id
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self.hidden_size = hidden_size
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_rope_head_dim = qk_rope_head_dim
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self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.q_lora_rank = q_lora_rank
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self.kv_lora_rank = kv_lora_rank
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self.num_heads = num_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert num_heads % tp_size == 0
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self.num_local_heads = num_heads // tp_size
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self.scaling = self.qk_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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if self.q_lora_rank is not None:
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self.q_a_proj = ReplicatedLinear(
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self.hidden_size,
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self.q_lora_rank,
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bias=False,
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quant_config=quant_config,
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)
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self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
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self.q_b_proj = ColumnParallelLinear(
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q_lora_rank,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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else:
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self.q_proj = ColumnParallelLinear(
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self.hidden_size,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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self.kv_a_proj_with_mqa = ReplicatedLinear(
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self.hidden_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
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self.kv_b_proj = ColumnParallelLinear(
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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)
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# O projection.
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self.o_proj = RowParallelLinear(
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self.num_heads * self.v_head_dim,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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self.rotary_emb = get_rope(
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qk_rope_head_dim,
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rotary_dim=qk_rope_head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = RadixAttention(
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self.num_local_heads,
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self.kv_lora_rank + self.qk_rope_head_dim,
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self.scaling,
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num_kv_heads=1,
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layer_id=layer_id,
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v_head_dim=self.kv_lora_rank,
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)
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self.w_kc = None
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self.w_vc = None
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self.w_scale = None
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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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input_metadata: InputMetadata,
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) -> torch.Tensor:
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q_len = hidden_states.shape[0]
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q_input = hidden_states.new_empty(
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q_len, self.num_local_heads, self.kv_lora_rank + self.qk_rope_head_dim
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)
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if self.q_lora_rank is not None:
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q = self.q_a_proj(hidden_states)[0]
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q = self.q_a_layernorm(q)
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q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
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else:
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q = self.q_proj(hidden_states)[0].view(
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-1, self.num_local_heads, self.qk_head_dim
|
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)
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q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
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if self.w_kc.dtype == torch.float8_e4m3fn:
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q_nope_val, q_nope_scale = input_to_float8(
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q_nope.transpose(0, 1), torch.float8_e4m3fn
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)
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q_nope_out = bmm_fp8(
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q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16
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)
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else:
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q_nope_out = torch.bmm(q_nope.transpose(0, 1), self.w_kc)
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q_input[..., : self.kv_lora_rank] = q_nope_out.transpose(0, 1)
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|
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latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
|
||||
v_input = latent_cache[..., : self.kv_lora_rank]
|
||||
v_input = self.kv_a_layernorm(v_input.contiguous()).unsqueeze(1)
|
||||
k_input = latent_cache.unsqueeze(1)
|
||||
k_input[..., : self.kv_lora_rank] = v_input
|
||||
k_pe = k_input[..., self.kv_lora_rank :]
|
||||
|
||||
original_shapes = [q_pe.shape, k_pe.shape]
|
||||
q_pe, k_pe = self.rotary_emb(
|
||||
positions, q_pe.reshape(q_pe.shape[0], -1), k_pe.reshape(k_pe.shape[0], -1)
|
||||
)
|
||||
q_pe, k_pe = q_pe.view(original_shapes[0]), k_pe.view(original_shapes[1])
|
||||
q_input[..., self.kv_lora_rank :] = q_pe
|
||||
k_input[..., self.kv_lora_rank :] = k_pe
|
||||
|
||||
attn_output = self.attn(q_input, k_input, v_input, input_metadata)
|
||||
attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank)
|
||||
|
||||
if self.w_vc.dtype == torch.float8_e4m3fn:
|
||||
attn_output_val, attn_output_scale = input_to_float8(
|
||||
attn_output.transpose(0, 1), torch.float8_e4m3fn
|
||||
)
|
||||
attn_bmm_output = bmm_fp8(
|
||||
attn_output_val,
|
||||
self.w_vc,
|
||||
attn_output_scale,
|
||||
self.w_scale,
|
||||
torch.bfloat16,
|
||||
)
|
||||
else:
|
||||
attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc)
|
||||
attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
|
||||
output, _ = self.o_proj(attn_output)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
class MiniCPM3DecoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
layer_id: int,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.hidden_size = config.hidden_size
|
||||
rope_theta = getattr(config, "rope_theta", 10000)
|
||||
rope_scaling = getattr(config, "rope_scaling", None)
|
||||
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
||||
if global_server_args_dict["enable_mla"]:
|
||||
self.self_attn = MiniCPM3AttentionMLA(
|
||||
config=config,
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=config.num_attention_heads,
|
||||
qk_nope_head_dim=config.qk_nope_head_dim,
|
||||
qk_rope_head_dim=config.qk_rope_head_dim,
|
||||
v_head_dim=self.hidden_size // config.num_attention_heads,
|
||||
q_lora_rank=(
|
||||
config.q_lora_rank if hasattr(config, "q_lora_rank") else None
|
||||
),
|
||||
kv_lora_rank=config.kv_lora_rank,
|
||||
rope_theta=rope_theta,
|
||||
rope_scaling=rope_scaling,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
layer_id=layer_id,
|
||||
)
|
||||
else:
|
||||
self.self_attn = MiniCPM3Attention(
|
||||
config=config,
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=config.num_attention_heads,
|
||||
qk_nope_head_dim=config.qk_nope_head_dim,
|
||||
qk_rope_head_dim=config.qk_rope_head_dim,
|
||||
v_head_dim=self.hidden_size // config.num_attention_heads,
|
||||
q_lora_rank=(
|
||||
config.q_lora_rank if hasattr(config, "q_lora_rank") else None
|
||||
),
|
||||
kv_lora_rank=config.kv_lora_rank,
|
||||
rope_theta=rope_theta,
|
||||
rope_scaling=rope_scaling,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
layer_id=layer_id,
|
||||
)
|
||||
self.mlp = MiniCPM3MLP(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = RMSNorm(
|
||||
config.hidden_size, eps=config.rms_norm_eps
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
input_metadata: InputMetadata,
|
||||
residual: Optional[torch.Tensor],
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
# Self Attention
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
hidden_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
input_metadata=input_metadata,
|
||||
)
|
||||
hidden_states = residual + hidden_states * (
|
||||
self.config.scale_depth / math.sqrt(self.config.num_hidden_layers)
|
||||
)
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states * (
|
||||
self.config.scale_depth / math.sqrt(self.config.num_hidden_layers)
|
||||
)
|
||||
|
||||
return hidden_states, None
|
||||
|
||||
|
||||
class MiniCPM3Model(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
self.vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
)
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
MiniCPM3DecoderLayer(
|
||||
config, i, cache_config=cache_config, quant_config=quant_config
|
||||
)
|
||||
for i in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
input_metadata: InputMetadata,
|
||||
input_embeds: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
if input_embeds is None:
|
||||
hidden_states = self.embed_tokens(input_ids) * self.config.scale_emb
|
||||
else:
|
||||
hidden_states = input_embeds
|
||||
residual = None
|
||||
|
||||
for i in range(len(self.layers)):
|
||||
layer = self.layers[i]
|
||||
hidden_states, residual = layer(
|
||||
positions,
|
||||
hidden_states,
|
||||
input_metadata,
|
||||
residual,
|
||||
)
|
||||
hidden_states = self.norm(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class MiniCPM3ForCausalLM(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
|
||||
self.num_experts = getattr(self.config, "num_experts", 0)
|
||||
self.quant_config = quant_config
|
||||
self.model = MiniCPM3Model(
|
||||
config, cache_config=cache_config, quant_config=quant_config
|
||||
)
|
||||
# self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
|
||||
if not self.config.tie_word_embeddings:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
)
|
||||
|
||||
self.scale_width = self.config.hidden_size / self.config.dim_model_base
|
||||
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
self.sampler = Sampler()
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
input_metadata: InputMetadata,
|
||||
input_embeds: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
if input_embeds is not None:
|
||||
input_embeds = input_embeds * self.config.scale_emb
|
||||
hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
|
||||
hidden_states = hidden_states / self.scale_width
|
||||
if self.config.tie_word_embeddings:
|
||||
lm_head_weight = self.model.embed_tokens.weight
|
||||
else:
|
||||
lm_head_weight = self.lm_head.weight
|
||||
logits_output = self.logits_processor(
|
||||
input_ids, hidden_states, lm_head_weight, input_metadata
|
||||
)
|
||||
sample_output = self.sampler(logits_output, input_metadata.sampling_info)
|
||||
return sample_output, logits_output
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
expert_params_mapping = [
|
||||
# (param_name, weight_name, expert_id)
|
||||
(
|
||||
"ws" if weight_name in ["w1", "w3"] else "w2s",
|
||||
f"experts.{expert_id}.{weight_name}.weight",
|
||||
expert_id,
|
||||
)
|
||||
for expert_id in range(self.num_experts)
|
||||
for weight_name in ["w1", "w2", "w3"]
|
||||
]
|
||||
params_dict = dict(self.named_parameters())
|
||||
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:
|
||||
# Models trained using ColossalAI may include these tensors in
|
||||
# the checkpoint. Skip them.
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
for param_name, weight_name, expert_id in expert_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(
|
||||
param, loaded_weight, weight_name, expert_id=expert_id
|
||||
)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
if global_server_args_dict["enable_mla"]:
|
||||
for layer_id in range(self.config.num_hidden_layers):
|
||||
self_attn = self.model.layers[layer_id].self_attn
|
||||
w_kc, w_vc = self_attn.kv_b_proj.weight.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 hasattr(self_attn.kv_b_proj, "weight_scale"):
|
||||
self_attn.w_scale = self_attn.kv_b_proj.weight_scale
|
||||
del self_attn.kv_b_proj
|
||||
|
||||
|
||||
EntryClass = MiniCPM3ForCausalLM
|
||||
Reference in New Issue
Block a user