GPTJForCausalLM Support (#7839)
Co-authored-by: b8zhong <b8zhong@uwaterloo.ca>
This commit is contained in:
Wenchen Lo
@@ -58,6 +58,7 @@ in the GitHub search bar.
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| **Ling** (16.8B–290B) | `inclusionAI/Ling-lite`, `inclusionAI/Ling-plus` | InclusionAI’s open MoE models. Ling-Lite has 16.8B total / 2.75B active parameters, and Ling-Plus has 290B total / 28.8B active parameters. They are designed for high performance on NLP and complex reasoning tasks. |
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| **Granite 3.0, 3.1** (IBM) | `ibm-granite/granite-3.1-8b-instruct` | IBM's open dense foundation models optimized for reasoning, code, and business AI use cases. Integrated with Red Hat and watsonx systems. |
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| **Granite 3.0 MoE** (IBM) | `ibm-granite/granite-3.0-3b-a800m-instruct` | IBM’s Mixture-of-Experts models offering strong performance with cost-efficiency. MoE expert routing designed for enterprise deployment at scale. |
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| **GPT-J** (6B) | `EleutherAI/gpt-j-6b` | EleutherAI's GPT-2-like causal language model (6B) trained on the [Pile](https://pile.eleuther.ai/) dataset. |
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| **Orion** (14B) | `OrionStarAI/Orion-14B-Base` | A series of open-source multilingual large language models by OrionStarAI, pretrained on a 2.5T token multilingual corpus including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. |
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| **Llama Nemotron Super** (v1, v1.5, NVIDIA) | `nvidia/Llama-3_3-Nemotron-Super-49B-v1`, `nvidia/Llama-3_3-Nemotron-Super-49B-v1_5` | The [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) family of multimodal models provides state-of-the-art reasoning models specifically designed for enterprise-ready AI agents. |
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| **Llama Nemotron Ultra** (v1, NVIDIA) | `nvidia/Llama-3_1-Nemotron-Ultra-253B-v1` | The [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) family of multimodal models provides state-of-the-art reasoning models specifically designed for enterprise-ready AI agents. |
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326
python/sglang/srt/models/gpt_j.py
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326
python/sglang/srt/models/gpt_j.py
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@@ -0,0 +1,326 @@
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# Copyright 2023-2025 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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# Adapted from
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# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/gpt_j.py
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"""Inference-only GPT-J model compatible with HuggingFace weights."""
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from typing import Iterable, Optional, Tuple, Type
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import torch
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from torch import nn
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from transformers import GPTJConfig
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from sglang.srt.distributed.parallel_state import get_tensor_model_parallel_world_size
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from sglang.srt.layers.activation import get_act_fn
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from sglang.srt.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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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 (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from sglang.srt.utils import add_prefix
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class GPTJAttention(nn.Module):
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def __init__(
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self,
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layer_id: int,
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config: GPTJConfig,
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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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total_num_heads = config.num_attention_heads
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hidden_size = config.hidden_size
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head_dim = hidden_size // total_num_heads
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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head_dim,
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total_num_heads,
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bias=False,
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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.out_proj = RowParallelLinear(
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hidden_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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prefix=add_prefix("out_proj", prefix),
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)
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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assert total_num_heads % tensor_model_parallel_world_size == 0
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num_heads = total_num_heads // tensor_model_parallel_world_size
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scaling = head_dim**-0.5
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assert getattr(config, "rotary", True)
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assert config.rotary_dim % 2 == 0
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rope_theta = getattr(config, "rope_theta", 10000)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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self.rotary_emb = get_rope(
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head_dim,
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rotary_dim=config.rotary_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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is_neox_style=False,
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)
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self.attn = RadixAttention(
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num_heads,
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head_dim,
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scaling=scaling,
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num_kv_heads=num_heads,
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layer_id=layer_id,
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quant_config=quant_config,
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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.chunk(chunks=3, dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, forward_batch)
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attn_output, _ = self.out_proj(attn_output)
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return attn_output
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class GPTJMLP(nn.Module):
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def __init__(
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self,
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intermediate_size: int,
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config: GPTJConfig,
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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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hidden_size = config.n_embd
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self.fc_in = ColumnParallelLinear(
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hidden_size,
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intermediate_size,
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quant_config=quant_config,
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prefix=add_prefix("fc_in", prefix),
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)
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self.fc_out = RowParallelLinear(
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intermediate_size,
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hidden_size,
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quant_config=quant_config,
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prefix=add_prefix("fc_out", prefix),
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)
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self.act = get_act_fn(config.activation_function)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.fc_in(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.fc_out(hidden_states)
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return hidden_states
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class GPTJBlock(nn.Module):
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def __init__(
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self,
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layer_id: int,
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config: GPTJConfig,
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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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inner_dim = 4 * config.n_embd if config.n_inner is None else config.n_inner
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self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.attn = GPTJAttention(
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layer_id,
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config,
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quant_config,
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prefix=add_prefix("attn", prefix),
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)
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self.mlp = GPTJMLP(
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inner_dim,
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config,
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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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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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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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attn_output = 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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mlp_output = self.mlp(hidden_states)
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hidden_states = attn_output + mlp_output + residual
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return hidden_states
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class GPTJModel(nn.Module):
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def __init__(
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self,
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config: GPTJConfig,
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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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embed_dim = config.n_embd
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self.wte = VocabParallelEmbedding(
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config.vocab_size,
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embed_dim,
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)
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self.h = nn.ModuleList(
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[
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GPTJBlock(
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i,
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config,
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quant_config=quant_config,
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prefix=add_prefix(f"h.{i}", prefix),
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)
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for i in range(config.n_layer)
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]
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)
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self.ln_f = nn.LayerNorm(embed_dim, eps=config.layer_norm_epsilon)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.wte(input_ids)
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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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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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for layer in self.h:
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hidden_states = layer(positions, hidden_states, forward_batch)
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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class GPTJForCausalLM(nn.Module):
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def __init__(
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self,
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config: GPTJConfig,
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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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assert not config.tie_word_embeddings
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self.quant_config = quant_config
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self.transformer = GPTJModel(
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config,
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quant_config,
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prefix=add_prefix("transformer", prefix),
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)
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.n_embd,
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bias=True,
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quant_config=quant_config,
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)
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self.logits_processor = LogitsProcessor(config)
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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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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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hidden_states = self.transformer(
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input_ids, positions, forward_batch, inputs_embeds
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)
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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, shard_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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]
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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 "attn.bias" in name or "attn.masked_bias" in name:
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continue
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if self.quant_config is not None and (
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scale_name := self.quant_config.get_cache_scale(name)
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):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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loaded_weight = (
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loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
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)
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weight_loader(param, loaded_weight)
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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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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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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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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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EntryClass = GPTJForCausalLM
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