Support THUDM/GLM-4-0414 (GLM-Z1) Glm4ForCausalLM architecture. (#5485)
This commit is contained in:
Wenbo Yang
312
python/sglang/srt/models/glm4.py
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312
python/sglang/srt/models/glm4.py
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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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#
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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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# Modeling from:
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# ./llama.py and
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm4/modular_glm4.py
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"""Inference-only GLM4 model compatible with THUDM weights."""
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from typing import Iterable, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from transformers import Glm4Config
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import QKVParallelLinear, RowParallelLinear
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.llama import LlamaMLP as Glm4MLP
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from sglang.srt.utils import add_prefix, make_layers
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class Glm4Attention(nn.Module):
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def __init__(
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self,
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config,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = config.num_key_value_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = config.hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = getattr(config, "rope_theta", 1000000)
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self.rope_scaling = getattr(config, "rope_scaling", None)
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self.qkv_proj = QKVParallelLinear(
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self.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=config.attention_bias,
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quant_config=quant_config,
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prefix=add_prefix("qkv_proj", prefix),
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.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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prefix=add_prefix("o_proj", prefix),
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=config.max_position_embeddings,
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base=self.rope_theta,
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rope_scaling=self.rope_scaling,
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partial_rotary_factor=partial_rotary_factor,
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is_neox_style=False,
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)
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self.attn = RadixAttention(
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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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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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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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forward_batch: ForwardBatch,
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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.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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context_layer = self.attn(
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q,
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k,
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v,
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forward_batch,
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)
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attn_output, _ = self.o_proj(context_layer)
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return attn_output
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class Glm4DecoderLayer(nn.Module):
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"""A single transformer layer.
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Transformer layer takes input with size [s, b, h] and returns an
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output of the same size.
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"""
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def __init__(
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self,
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config,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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# Self attention.
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self.self_attn = Glm4Attention(
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config, layer_id, quant_config, prefix=add_prefix("self_attn", prefix)
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)
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# MLP
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self.mlp = Glm4MLP(
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config.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=add_prefix("mlp", prefix),
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.post_self_attn_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.post_mlp_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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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forward_batch: ForwardBatch,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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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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forward_batch=forward_batch,
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)
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hidden_states = self.post_self_attn_layernorm(hidden_states)
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# Fully Connected
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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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hidden_states = self.post_mlp_layernorm(hidden_states)
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return hidden_states, residual
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class Glm4Model(nn.Module):
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def __init__(
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self,
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config: Glm4Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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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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quant_config=quant_config,
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prefix=add_prefix("embed_tokens", prefix),
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)
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self.layers = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: Glm4DecoderLayer(
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config=config, layer_id=idx, quant_config=quant_config, prefix=prefix
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),
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prefix="model.layers",
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
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if input_embeds is None:
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hidden_states = self.embed_tokens(input_ids)
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else:
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hidden_states = input_embeds
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residual = None
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for layer in self.layers:
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hidden_states, residual = layer(
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positions,
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hidden_states,
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forward_batch,
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residual,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class Glm4ForCausalLM(nn.Module):
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def __init__(
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self,
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config: Glm4Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.config: Glm4Config = config
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self.quant_config = quant_config
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self.model = Glm4Model(config, quant_config, add_prefix("model", prefix))
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if config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix="lm_head",
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)
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self.logits_processor = LogitsProcessor(config)
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, forward_batch)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, weight_name, shard_id)
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if self.config.tie_word_embeddings and "lm_head.weight" in name:
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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if name in params_dict.keys():
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight)
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else:
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raise KeyError(f"Parameter '{name}' not found in model.")
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EntryClass = [Glm4ForCausalLM]
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