[Model] Support IQuest-Coder-40B-Loop (#16348)

Co-authored-by: yxing <yxing@iquestlab.com>
Co-authored-by: yzhu <yzhu@ubiquant.com>
Co-authored-by: zelong518 <zelonghuang02@gmail.com>

python/sglang/srt/configs/model_config.py

@@ -463,6 +463,9 @@ class ModelConfig:
         self.num_attention_layers = self.num_hidden_layers
         if "LongcatFlashForCausalLM" in self.hf_config.architectures:
             self.num_attention_layers = self.num_hidden_layers * 2
+        if "IQuestLoopCoderForCausalLM" in self.hf_config.architectures:
+            loop_num = getattr(self.hf_text_config, "loop_num", 1)
+            self.num_attention_layers = int(self.num_hidden_layers * int(loop_num))
         self.num_nextn_predict_layers = getattr(
             self.hf_text_config, "num_nextn_predict_layers", None
         )

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -793,6 +793,13 @@ class FlashInferAttnBackend(AttentionBackend):
                 v_scale=layer.v_scale_float,
             )
         else:
+            # If `k`/`v` are not explicitly provided, fall back to the KV cache stored in
+            # `forward_batch.token_to_kv_pool` for this layer. This enables attention over
+            # previously cached context without re-materializing KV tensors (e.g., the
+            # IQuestLoopCoder path uses token_to_kv_pool as the KV source).
+            if k is None and v is None:
+                k = forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id)[0]
+                v = forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id)[1]
             causal = True
             if (
                 layer.is_cross_attention

python/sglang/srt/model_executor/model_runner.py

@@ -485,6 +485,12 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         self.start_layer = getattr(self.model, "start_layer", 0)
         self.end_layer = getattr(self.model, "end_layer", model_num_layers)
         self.num_effective_layers = self.end_layer - self.start_layer
+
+        # For LoopCoder models, each loop has its own layer_id, so we need to multiply by loop_num
+        loop_num = getattr(self.model_config.hf_config, "loop_num", 1)
+        if loop_num > 1:
+            self.num_effective_layers = self.num_effective_layers * loop_num
+
         assert (
             (not model_has_mtp_layers)
             or (self.spec_algorithm.is_none())

python/sglang/srt/models/iquest_loopcoder.py

@@ -0,0 +1,498 @@
+# Copyright 2023-2024 SGLang Team
+# 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.
+# ==============================================================================
+"""Inference-only LoopCoder model compatible with HuggingFace weights."""
+
+import logging
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.distributed import get_tensor_model_parallel_world_size
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.llama import LlamaMLP as LoopCoderMLP
+from sglang.srt.utils import add_prefix, make_layers
+
+logger = logging.getLogger(__name__)
+
+
+class LoopGateProjection(nn.Module):
+    """Gate projection for mixed attention in Loop 2+.
+
+    Computes: g = sigmoid(linear(Q)) for each head independently.
+    This gate determines how much to use Loop1's KV (global) vs current loop's KV (local).
+
+    Supports tensor parallelism: each GPU handles a subset of heads.
+    The weight matrix has shape [num_heads, head_dim] and is split along the head dimension.
+    """
+
+    def __init__(
+        self,
+        total_num_heads: int,
+        head_dim: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.total_num_heads = total_num_heads
+        self.head_dim = head_dim
+        tp_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+
+        self.gate_proj = ColumnParallelLinear(
+            head_dim,
+            self.total_num_heads,
+            bias=True,
+            gather_output=False,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_proj", prefix),
+        )
+
+    def forward(self, query: torch.Tensor) -> torch.Tensor:
+        """Compute gate values from query tensor.
+
+        Args:
+            query: [num_heads, num_tokens, head_dim]
+                where num_heads is the number of heads on this TP rank
+                and num_tokens = batch * seq_len
+
+        Returns:
+            gate: [num_tokens, num_heads * head_dim] (flattened format matching q shape)
+        """
+        num_heads, num_tokens, head_dim = query.shape
+
+        assert (
+            num_heads == self.num_heads
+        ), f"Expected {self.num_heads} heads, got {num_heads}"
+
+        query_flat = query.reshape(-1, head_dim)
+
+        gate_logits_flat, _ = self.gate_proj(query_flat)
+
+        gate_logits = gate_logits_flat.reshape(num_heads, num_tokens, self.num_heads)
+
+        # Extract diagonal: each head h's query should use output column h
+        gate_logits = torch.diagonal(gate_logits, dim1=0, dim2=2)
+        gate_logits = gate_logits.transpose(0, 1)
+        gate_logits = gate_logits.unsqueeze(-1)
+
+        # Apply sigmoid
+        gate = torch.sigmoid(gate_logits)
+
+        # Expand and reshape to match q shape: [num_tokens, num_heads * head_dim]
+        gate = gate.transpose(0, 1)
+        gate = gate.expand(-1, -1, head_dim)
+        gate = gate.reshape(num_tokens, num_heads * head_dim)
+
+        return gate
+
+
+class LoopCoderAttention(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        layer_id: int = 0,
+        max_position: int = 4096 * 32,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.layer_id = layer_id
+        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 = 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
+
+        # Get loop_num from config, default to 2 if not specified
+        self.loop_num = getattr(config, "loop_num", 2)
+        self.loop_window_size = getattr(config, "loop_window_size", 64)
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("qkv_proj", prefix),
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("o_proj", prefix),
+        )
+
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(
+            config, "max_position_embeddings", max_position
+        )
+
+        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,
+        )
+
+        # Create attention instances for each loop
+        # Loop 0: global attention without sliding window for full context
+        # Loop 1+: local attention with sliding window for recent tokens
+        # Each loop needs a unique layer_id to avoid KV cache conflicts
+        self.attn = nn.ModuleList()
+        total_layers = getattr(config, "num_hidden_layers", 24)
+        for loop_idx in range(self.loop_num):
+            sliding_window = -1 if loop_idx == 0 else self.loop_window_size
+            # Use unique layer_id for each loop: loop_idx * total_layers + layer_id
+            # This ensures each loop has its own KV cache space
+            unique_layer_id = loop_idx * total_layers + layer_id
+
+            self.attn.append(
+                RadixAttention(
+                    self.num_heads,
+                    self.head_dim,
+                    self.scaling,
+                    num_kv_heads=self.num_kv_heads,
+                    layer_id=unique_layer_id,  # Unique layer_id for each loop
+                    sliding_window_size=sliding_window,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"attn.{loop_idx}", prefix),
+                )
+            )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        loop_idx: int,
+        gate_proj: Optional[LoopGateProjection] = None,
+    ) -> 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)
+
+        if loop_idx == 0:
+            # First loop: standard global attention, save KV to cache
+            attn_output = self.attn[0](q, k, v, forward_batch)
+        else:
+            # Loop 2+: mixed attention with learned gating
+            # Global attention: read from Loop 0's KV cache without updating (save_kv_cache=False)
+            # This provides full context information
+            # Pass k=None, v=None to read from KV cache instead of recomputing
+            global_attn_output = self.attn[0](
+                q, None, None, forward_batch, save_kv_cache=False
+            )
+
+            # Local attention: use current loop's KV with sliding window
+            # This focuses on recent tokens within the window
+            local_attn_output = self.attn[loop_idx](q, k, v, forward_batch)
+
+            # Compute gating weights using query-dependent projection
+            assert gate_proj is not None, "gate_proj must be provided for loop_idx > 0"
+            num_tokens = q.shape[0]
+            q_reshaped = q.view(num_tokens, self.num_heads, self.head_dim).transpose(
+                0, 1
+            )
+            gate = gate_proj(q_reshaped)
+
+            # Mix global and local attention outputs with learned gate
+            # gate controls the balance between global context and local focus
+            attn_output = global_attn_output * gate + local_attn_output * (1 - gate)
+
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class LoopCoderDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.layer_id = layer_id
+
+        self.self_attn = LoopCoderAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            layer_id=layer_id,
+            max_position=getattr(config, "max_position_embeddings", 4096 * 32),
+            quant_config=quant_config,
+            prefix=add_prefix("self_attn", prefix),
+        )
+        self.mlp = LoopCoderMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+        )
+        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,
+        loop_idx: int,
+        gate_proj: Optional[LoopGateProjection] = None,
+    ) -> 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,
+            forward_batch=forward_batch,
+            loop_idx=loop_idx,
+            gate_proj=gate_proj,
+        )
+        hidden_states = hidden_states + residual
+
+        # MLP
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = hidden_states + residual
+
+        return hidden_states
+
+
+class IQuestLoopCoderModel(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=add_prefix("embed_tokens", prefix),
+        )
+
+        self.loop_num = getattr(self.config, "loop_num", 2)
+        self.window_size = getattr(self.config, "loop_window_size", 64)
+
+        # Gate projections for Loop 2+ (one per layer)
+        head_dim = config.hidden_size // config.num_attention_heads
+        gate_projections = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: LoopGateProjection(
+                total_num_heads=config.num_attention_heads,
+                head_dim=head_dim,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=add_prefix("gate_projections", prefix),
+        )
+        if isinstance(gate_projections, tuple):
+            self.start_layer, self.end_layer, self.gate_projections = gate_projections
+        else:
+            self.start_layer, self.end_layer = 0, config.num_hidden_layers
+            self.gate_projections = gate_projections
+
+        layers = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: LoopCoderDecoderLayer(
+                config=config,
+                layer_id=idx,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=add_prefix("layers", prefix),
+        )
+        if isinstance(layers, tuple):
+            self.start_layer, self.end_layer, self.layers = layers
+        else:
+            self.start_layer, self.end_layer = 0, config.num_hidden_layers
+            self.layers = layers
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        if input_embeds is not None:
+            hidden_states = input_embeds
+        else:
+            hidden_states = self.embed_tokens(input_ids)
+
+        # Multi-loop forward pass
+        for loop_idx in range(self.loop_num):
+            for layer_idx in range(self.start_layer, self.end_layer):
+                layer = self.layers[layer_idx]
+                # Get gate_proj for this layer (only for loop_idx > 0)
+                gate_proj = self.gate_projections[layer_idx] if loop_idx > 0 else None
+                hidden_states = layer(
+                    positions, hidden_states, forward_batch, loop_idx, gate_proj
+                )
+
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class IQuestLoopCoderForCausalLM(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+
+        self.model = IQuestLoopCoderModel(
+            config=config,
+            quant_config=quant_config,
+            prefix=add_prefix("model", prefix),
+        )
+
+        if config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=add_prefix("lm_head", prefix),
+            )
+
+        self.logits_processor = LogitsProcessor(config)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ):
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            # Handle gate_projections weights
+            if name.startswith("gate_projections."):
+                if name.endswith(".weight"):
+                    sglang_name = name.replace(".weight", ".gate_proj.weight")
+                elif name.endswith(".bias"):
+                    sglang_name = name.replace(".bias", ".gate_proj.bias")
+                else:
+                    continue
+
+                if sglang_name in params_dict:
+                    param = params_dict[sglang_name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+                continue
+
+            # Handle stacked parameters
+            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)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name in params_dict:
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Handle regular parameters
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name in params_dict:
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+
+# Entry class for model registration
+EntryClass = IQuestLoopCoderForCausalLM

python/sglang/srt/models/llama.py

@@ -794,4 +794,13 @@ class InternLM3ForCausalLM(LlamaForCausalLM):
     pass
 
 
-EntryClass = [LlamaForCausalLM, Phi3ForCausalLM, InternLM3ForCausalLM]
+class IQuestCoderForCausalLM(LlamaForCausalLM):
+    pass
+
+
+EntryClass = [
+    LlamaForCausalLM,
+    Phi3ForCausalLM,
+    InternLM3ForCausalLM,
+    IQuestCoderForCausalLM,
+]