model: support solar (#8189)

docs/supported_models/generative_models.md

@@ -49,6 +49,9 @@ in the GitHub search bar.
 | **ERNIE-4.5** (4.5, 4.5MoE series) | `baidu/ERNIE-4.5-21B-A3B-PT`                    | Baidu's ERNIE-4.5 series which consists of MoE with 47B and 3B active parameters, with the largest model having 424B total parameters, as well as a 0.3B dense model. |
 | **Arcee AFM-4.5B**               | `arcee-ai/AFM-4.5B-Base`                         | Arcee's foundational model series for real world reliability and edge deployments. |
 | **Persimmon** (8B)               | `adept/persimmon-8b-chat`                         | Adept’s open 8B model with a 16K context window and fast inference; trained for broad usability and licensed under Apache 2.0. |
+
+| **Solar** (10.7B)               | `upstage/SOLAR-10.7B-Instruct-v1.0`                         | Upstage's 10.7B parameter model, optimized for instruction-following tasks. This architecture incorporates a depth-up scaling methodology, enhancing model performance. |
+
 | **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. |
 | **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. |
 | **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. |

python/sglang/srt/models/solar.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/solar.py
+from collections.abc import Iterable
+from typing import Any, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.python.sglang.srt.distributed.parallel_state import (
+    get_tensor_model_parallel_rank,
+)
+from sglang.python.sglang.srt.utils import add_prefix, make_layers
+from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
+from sglang.srt.layers.quantization import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.utils import PPMissingLayer
+from sglang.srt.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE,
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_loader.weight_utils import (
+    default_weight_loader,
+    kv_cache_scales_loader,
+)
+
+
+class SolarMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=hidden_size,
+            output_sizes=[intermediate_size] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. "
+                "Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class SolarAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+        layer_id: int = 0,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.head_dim = getattr(config, "head_dim", None)
+        if self.head_dim is None:
+            self.head_dim = self.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v, forward_batch=forward_batch)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class SolarDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+
+        if rope_scaling is not None and getattr(
+            config, "original_max_position_embeddings", None
+        ):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings
+            )
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+
+        attention_bias = getattr(config, "attention_bias", False) or getattr(
+            config, "bias", False
+        )
+        self.self_attn = SolarAttention(
+            config=config,
+            layer_id=layer_id,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=getattr(
+                config, "num_key_value_heads", config.num_attention_heads
+            ),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=attention_bias,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.mlp = SolarMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            bias=getattr(config, "mlp_bias", False),
+            prefix=f"{prefix}.mlp",
+        )
+        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,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class SolarModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+
+        self.vocab_size = config.vocab_size
+        self.org_vocab_size = config.vocab_size
+        self.pp_group = get_pp_group()
+        if self.pp_group.is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=add_prefix("embed_tokens", prefix),
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: SolarDecoderLayer(
+                config=config,
+                quant_config=quant_config,
+                layer_id=idx,
+                prefix=prefix,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]], PPProxyTensors]:
+        if self.pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert pp_proxy_tensors is not None
+
+            hidden_states = pp_proxy_tensors["hidden_states"]
+            residual = pp_proxy_tensors["residual"]
+
+        # Depth up-scaling mechanism: caches hidden states and residuals from intermediate layers and interpolates them with the states of later layers.
+        # `bskcn` stands for "backbone skip connection".
+        bskcn_h_1 = None
+        bskcn_h_2 = None
+        bskcn_r_1 = None
+        bskcn_r_2 = None
+        bskcn_tv = self.config.bskcn_tv[0] if self.training else self.config.bskcn_tv[1]
+
+        for i in range(self.start_layer, self.end_layer):
+            if i in self.config.bskcn_1:
+                bskcn_h_1 = hidden_states.clone()
+                bskcn_r_1 = residual.clone() if residual is not None else None
+            if i in self.config.bskcn_2:
+                bskcn_h_2 = hidden_states.clone()
+                bskcn_r_2 = residual.clone() if residual is not None else None
+            if i in self.config.bskcn_3:
+                hidden_states = bskcn_h_1 * bskcn_tv + hidden_states * (1 - bskcn_tv)
+                if bskcn_r_1 is not None and residual is not None:
+                    residual = bskcn_r_1 * bskcn_tv + residual * (1 - bskcn_tv)
+            if i in self.config.bskcn_4:
+                hidden_states = bskcn_h_2 * bskcn_tv + hidden_states * (1 - bskcn_tv)
+                if bskcn_r_2 is not None and residual is not None:
+                    residual = bskcn_r_2 * bskcn_tv + residual * (1 - bskcn_tv)
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                forward_batch=forward_batch,
+                residual=residual,
+            )
+
+        if not self.pp_group().is_last_rank:
+            return PPProxyTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
+        tp_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        for layer_idx, scaling_factor in kv_cache_scales_loader(
+            quantization_param_path,
+            tp_rank,
+            tp_size,
+            self.config.num_hidden_layers,
+            self.config.__class__.model_type,
+        ):
+            if not isinstance(self.layers[layer_idx], nn.Identity):
+                layer_self_attn = self.layers[layer_idx].self_attn
+
+            if hasattr(layer_self_attn.attn, "k_scale"):
+                layer_self_attn.attn.k_scale = scaling_factor
+                layer_self_attn.attn.v_scale = scaling_factor
+            else:
+                raise RuntimeError(
+                    "Self attention has no KV cache scaling " "factor attribute!"
+                )
+
+
+class SolarForCausalLM(nn.Module):
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            ("q_proj", "q"),
+            ("k_proj", "k"),
+            ("v_proj", "v"),
+        ],
+        "gate_up_proj": [
+            ("gate_proj", 0),
+            ("up_proj", 1),
+        ],
+    }
+
+    default_bitsandbytes_target_modules = [
+        ".gate_proj.",
+        ".down_proj.",
+        ".up_proj.",
+        ".q_proj.",
+        ".k_proj.",
+        ".v_proj.",
+        ".o_proj.",
+    ]
+    column_parallel_weights_modules = [".down_proj.", ".o_proj."]
+    bitsandbytes_stacked_params_mapping = {
+        ".q_proj": (".qkv_proj", 0),
+        ".k_proj": (".qkv_proj", 1),
+        ".v_proj": (".qkv_proj", 2),
+        ".gate_proj": (".gate_up_proj", 0),
+        ".up_proj": (".gate_up_proj", 1),
+    }
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.pp_group = get_pp_group()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = SolarModel(
+            config=config,
+            quant_config=self.quant_config,
+            prefix=add_prefix("model", prefix),
+        )
+
+        if self.pp_group.is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                quant_config=quant_config,
+            )
+            if config.tie_word_embeddings and self.pp_group.is_first_rank:
+                self.lm_head.weight = self.model.embed_tokens.weight
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(
+                self.unpadded_vocab_size, config.vocab_size, logit_scale
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, LogitsProcessorOutput]:
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            forward_batch=forward_batch,
+            inputs_embeds=inputs_embeds,
+        )
+
+        if self.pp_group().is_last_rank:
+            logits = self.logits_processor(self.lm_head, hidden_states, forward_batch)
+            return logits
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+
+            is_packed = False
+            for packed_name, sources in self.packed_modules_mapping.items():
+                for src_name, shard_id in sources:
+                    if src_name in name:
+
+                        model_param_name = name.replace(src_name, packed_name)
+
+                        if model_param_name in params_dict:
+                            param = params_dict[model_param_name]
+                            weight_loader = getattr(
+                                param, "weight_loader", default_weight_loader
+                            )
+                            weight_loader(param, loaded_weight, shard_id)
+                            is_packed = True
+                            break
+                if is_packed:
+                    break
+
+            if is_packed:
+                continue
+
+            if name in params_dict:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = SolarForCausalLM

test/srt/models/test_generation_models.py

@@ -67,7 +67,11 @@ ALL_MODELS = [
     ModelCase("openai-community/gpt2"),
     ModelCase("microsoft/phi-1_5", trust_remote_code=True),
     ModelCase("adept/persimmon-8b-chat"),
+
+    ModelCase("upstage/SOLAR-10.7B-Instruct-v1.0"),
+
     ModelCase("inclusionAI/Ling-lite", trust_remote_code=True),
+
     ModelCase("microsoft/Phi-3-small-8k-instruct", trust_remote_code=True),
     ModelCase("allenai/OLMo-2-1124-7B-Instruct", skip_long_prompt=True),
     ModelCase("ibm-granite/granite-3.0-2b-instruct", skip_long_prompt=True),