port fp8 mixtral (#460)

python/sglang/srt/managers/router/model_rpc.py

@@ -69,20 +69,13 @@ class ModelRpcServer:
         )
 
         # For model end global settings
-        server_args_dict = {
-            "enable_flashinfer": server_args.enable_flashinfer,
-            "attention_reduce_in_fp32": server_args.attention_reduce_in_fp32,
-        }
-
         self.model_runner = ModelRunner(
             model_config=self.model_config,
             mem_fraction_static=server_args.mem_fraction_static,
             tp_rank=tp_rank,
             tp_size=server_args.tp_size,
             nccl_port=port_args.nccl_port,
-            load_format=server_args.load_format,
-            trust_remote_code=server_args.trust_remote_code,
-            server_args_dict=server_args_dict,
+            server_args=server_args,
         )
         if is_multimodal_model(server_args.model_path):
             self.processor = get_processor(

python/sglang/srt/managers/router/model_runner.py

@@ -17,6 +17,7 @@ from vllm.model_executor.models import ModelRegistry
 
 from sglang.srt.managers.router.infer_batch import Batch, ForwardMode
 from sglang.srt.memory_pool import ReqToTokenPool, TokenToKVPool
+from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import get_available_gpu_memory, is_multimodal_model
 
 
@@ -218,22 +219,23 @@ class ModelRunner:
         tp_rank,
         tp_size,
         nccl_port,
-        load_format="auto",
-        trust_remote_code=True,
-        server_args_dict: dict = {},
+        server_args: ServerArgs,
     ):
         self.model_config = model_config
         self.mem_fraction_static = mem_fraction_static
         self.tp_rank = tp_rank
         self.tp_size = tp_size
         self.nccl_port = nccl_port
-        self.load_format = load_format
-        self.trust_remote_code = trust_remote_code
+        self.server_args = server_args
 
         global global_server_args_dict
-        global_server_args_dict = server_args_dict
+        global_server_args_dict = {
+            "enable_flashinfer": server_args.enable_flashinfer,
+            "attention_reduce_in_fp32": server_args.attention_reduce_in_fp32,
+        }
 
         # Init torch distributed
+        logger.debug("Init torch begin.")
         torch.cuda.set_device(self.tp_rank)
         torch.distributed.init_process_group(
             backend="nccl",
@@ -241,13 +243,15 @@ class ModelRunner:
             rank=self.tp_rank,
             init_method=f"tcp://127.0.0.1:{self.nccl_port}",
         )
-
         initialize_model_parallel(tensor_model_parallel_size=self.tp_size)
+        logger.debug("Init torch end.")
 
         total_gpu_memory = get_available_gpu_memory(
             self.tp_rank, distributed=self.tp_size > 1
         ) * (1 << 30)
+        # logger.info(f"Before: {get_available_gpu_memory(self.tp_rank, False):.2f} GB")
         self.load_model()
+        # logger.info(f"After: {get_available_gpu_memory(self.tp_rank, False):.2f} GB")
         self.init_memory_pool(total_gpu_memory)
 
         self.is_multimodal_model = is_multimodal_model(self.model_config)
@@ -256,15 +260,15 @@ class ModelRunner:
         logger.info(f"Rank {self.tp_rank}: load weight begin.")
 
         device_config = DeviceConfig()
-        load_config = LoadConfig()
+        load_config = LoadConfig(load_format=self.server_args.load_format)
         vllm_model_config = VllmModelConfig(
-            model=self.model_config.path,
+            model=self.server_args.model_path,
+            quantization=self.server_args.quantization,
             tokenizer=None,
             tokenizer_mode=None,
-            trust_remote_code=self.model_config.trust_remote_code,
+            trust_remote_code=self.server_args.trust_remote_code,
             dtype=torch.float16,
             seed=42,
-            revision=self.model_config.revision,
             skip_tokenizer_init=True,
         )
         if self.model_config.model_overide_args is not None:
@@ -279,7 +283,7 @@ class ModelRunner:
             parallel_config=None,
             scheduler_config=None,
         )
-        logger.info(f"Rank {self.tp_rank}: load weight end.")
+        logger.info(f"Rank {self.tp_rank}: load weight end. {type(self.model)}")
 
     def profile_max_num_token(self, total_gpu_memory):
         available_gpu_memory = get_available_gpu_memory(

python/sglang/srt/models/mixtral.py

 # Adapted from
-# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/mixtral_quant.py#L1
+# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/mixtral.py#L1
 """Inference-only Mixtral model."""
 from typing import Iterable, Optional, Tuple
 
@@ -8,11 +8,13 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 from transformers import MixtralConfig
+from vllm import _custom_ops as ops
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
     tensor_model_parallel_all_reduce,
 )
+from vllm.model_executor.layers.fused_moe import fused_moe
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
@@ -20,12 +22,15 @@ from vllm.model_executor.layers.linear import (
     RowParallelLinear,
 )
 from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.quantization.fp8 import Fp8Config
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.utils import print_warning_once
 
 
 from sglang.srt.layers.logits_processor import LogitsProcessor
@@ -33,106 +38,196 @@ from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.managers.router.model_runner import InputMetadata
 
 
-class MixtralMLP(nn.Module):
-    def __init__(
-        self,
-        num_experts: int,
-        hidden_size: int,
-        intermediate_size: int,
-        quant_config: Optional[QuantizationConfig] = None,
-    ) -> None:
-        super().__init__()
-        self.num_experts = num_experts
-        self.ffn_dim = intermediate_size
-        self.hidden_dim = hidden_size
-
-        self.w1 = ReplicatedLinear(
-            self.hidden_dim, self.ffn_dim, bias=False, quant_config=quant_config
-        )
-        self.w2 = ReplicatedLinear(
-            self.ffn_dim, self.hidden_dim, bias=False, quant_config=quant_config
-        )
-        self.w3 = ReplicatedLinear(
-            self.hidden_dim, self.ffn_dim, bias=False, quant_config=quant_config
-        )
-
-        # TODO: Use vllm's SiluAndMul
-        self.act_fn = nn.SiLU()
 
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        w1_out, _ = self.w1(hidden_states)
-        w1_out = self.act_fn(w1_out)
-        w3_out, _ = self.w3(hidden_states)
-        current_hidden_states = w1_out * w3_out
-        current_hidden_states, _ = self.w2(current_hidden_states)
-        return current_hidden_states
+class MixtralMoE(nn.Module):
+    """A tensor-parallel MoE implementation for Mixtral that shards each expert
+    across all ranks.
 
+    Each expert's weights are sharded across all ranks and a fused MoE
+    kernel is used for the forward pass, and finally we reduce the outputs
+    across ranks.
+    """
 
-class MixtralMoE(nn.Module):
     def __init__(
         self,
-        config: MixtralConfig,
+        num_experts: int,
+        top_k: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: Optional[torch.dtype] = None,
+        tp_size: Optional[int] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
-        self.config = config
-        self.rank = get_tensor_model_parallel_rank()
-        self.tp_size = get_tensor_model_parallel_world_size()
-        self.num_total_experts = config.num_local_experts
-        self.top_k = config.num_experts_per_tok
-        if self.tp_size > self.num_total_experts:
-            raise ValueError(
-                f"Tensor parallel size {self.tp_size} is greater than "
-                f"the number of experts {self.num_total_experts}."
-            )
-        # Split experts equally between ranks
-        self.expert_indicies = np.array_split(
-            range(self.num_total_experts), self.tp_size
-        )[self.rank].tolist()
-        if not self.expert_indicies:
-            raise ValueError(f"Rank {self.rank} has no experts assigned to it.")
-
-        self.experts = nn.ModuleList(
-            [
-                (
-                    MixtralMLP(
-                        self.num_total_experts,
-                        config.hidden_size,
-                        config.intermediate_size,
-                        quant_config=quant_config,
-                    )
-                    if idx in self.expert_indicies
-                    else None
-                )
-                for idx in range(self.num_total_experts)
-            ]
-        )
-        self.gate = ReplicatedLinear(
-            config.hidden_size, self.num_total_experts, bias=False, quant_config=None
-        )
+        self.tp_size = tp_size or get_tensor_model_parallel_world_size()
+        self.num_total_experts = num_experts
+        self.top_k = top_k
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size // self.tp_size
+        self.quant_config = quant_config
+
+        # FIXME(pcmoritz): Make this more general to support different
+        # quantization schemes
+        self.use_fp8 = isinstance(quant_config, Fp8Config)
+
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.params_dtype = params_dtype
+
+        # Gate always runs at half / full precision for now.
+        self.gate = ReplicatedLinear(self.hidden_size,
+                                     self.num_total_experts,
+                                     bias=False,
+                                     params_dtype=self.params_dtype,
+                                     quant_config=None)
+
+        if self.use_fp8 and self.quant_config.is_checkpoint_fp8_serialized:
+            params_dtype = torch.float8_e4m3fn
+
+        self.w13_weight = nn.Parameter(
+            torch.empty(self.num_total_experts,
+                        2 * self.intermediate_size,
+                        self.hidden_size,
+                        dtype=params_dtype))
+        self.w2_weight = nn.Parameter(
+            torch.empty(self.num_total_experts,
+                        self.hidden_size,
+                        self.intermediate_size,
+                        dtype=params_dtype))
+
+        set_weight_attrs(self.w13_weight, {
+            "weight_loader": self.weight_loader,
+        })
+        set_weight_attrs(self.w2_weight, {
+            "weight_loader": self.weight_loader,
+        })
+
+        # Used for fp8.
+        self.w13_scale = None
+        self.w2_scale = None
+        self.a13_scale = None
+        self.a2_scale = None
+
+        if self.use_fp8:
+            # WEIGHT_SCALE (for fp8)
+            self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts,
+                                                     dtype=torch.float32),
+                                          requires_grad=False)
+            self.w2_scale = nn.Parameter(torch.ones(self.num_total_experts,
+                                                    dtype=torch.float32),
+                                         requires_grad=False)
+
+            # If loading fp8 checkpoint, pass the weight loaders.
+            # If loading an fp16 checkpoint, do not (we will quantize in
+            #   process_weights_after_loading()
+            if quant_config.is_checkpoint_fp8_serialized:
+                set_weight_attrs(self.w13_scale, {
+                    "weight_loader": self.weight_loader,
+                })
+                set_weight_attrs(self.w2_scale, {
+                    "weight_loader": self.weight_loader,
+                })
+
+            # ACT_SCALE (for fp8)
+            if quant_config.activation_scheme == "static":
+                if not quant_config.is_checkpoint_fp8_serialized:
+                    raise ValueError(
+                        "Found static activation scheme for checkpoint that "
+                        "was not serialized fp8.")
+                self.a13_scale = nn.Parameter(torch.zeros(
+                    self.num_total_experts, dtype=torch.float32),
+                                              requires_grad=False)
+                self.a2_scale = nn.Parameter(torch.zeros(
+                    self.num_total_experts, dtype=torch.float32),
+                                             requires_grad=False)
+
+                set_weight_attrs(self.a13_scale, {
+                    "weight_loader": self.weight_loader,
+                })
+                set_weight_attrs(self.a2_scale, {
+                    "weight_loader": self.weight_loader,
+                })
+
+    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
+                      weight_name: str, expert_id: int):
+        tp_rank = get_tensor_model_parallel_rank()
+        param_data = param.data
+        shard_size = self.intermediate_size
+        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+        if weight_name.endswith("w1.weight"):
+            param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
+        if weight_name.endswith("w3.weight"):
+            param_data[expert_id,
+                       shard_size:2 * shard_size, :] = loaded_weight[shard, :]
+        if weight_name.endswith("w2.weight"):
+            param_data[expert_id, :, :] = loaded_weight[:, shard]
+        if "act_scale" in weight_name or "weight_scale" in weight_name:
+            param_data[expert_id] = loaded_weight
+
+    def process_weights_after_loading(self):
+        # Fp8 is the only case where we need to process after loading.
+        if not self.use_fp8:
+            return
+
+        # If checkpoint is fp16, quantize here.
+        if not self.quant_config.is_checkpoint_fp8_serialized:
+            w13_weight = torch.empty_like(self.w13_weight.data,
+                                          dtype=torch.float8_e4m3fn)
+            w2_weight = torch.empty_like(self.w2_weight.data,
+                                         dtype=torch.float8_e4m3fn)
+            for expert in range(self.num_total_experts):
+                w13_weight[expert, :, :], self.w13_scale[
+                    expert] = ops.scaled_fp8_quant(
+                        self.w13_weight.data[expert, :, :])
+                w2_weight[expert, :, :], self.w2_scale[
+                    expert] = ops.scaled_fp8_quant(
+                        self.w2_weight.data[expert, :, :])
+            self.w13_weight = nn.Parameter(w13_weight, requires_grad=False)
+            self.w2_weight = nn.Parameter(w2_weight, requires_grad=False)
+
+        # If checkpoint is fp8 + static, cleanup act_scales.
+        #   Since state_dict has an act_scale per expert but our kernels
+        #   are passed one act_scale shared across all experts.
+        elif self.quant_config.activation_scheme == "static":
+            if self.a13_scale is None or self.a2_scale is None:
+                raise ValueError(
+                    "QuantConfig has static quantization, but found "
+                    "activation scales are None.")
+
+            if (not all_close_1d(self.a13_scale)
+                    or not all_close_1d(self.a2_scale)):
+                print_warning_once(
+                    "Found act_scales that are not equal for fp8 MoE layer. "
+                    "Using the maximum across experts for each layer. ")
+
+            self.a13_scale = nn.Parameter(self.a13_scale.max(),
+                                          requires_grad=False)
+            self.a2_scale = nn.Parameter(self.a2_scale.max(),
+                                         requires_grad=False)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
-
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(
-            routing_weights, self.top_k, dim=-1
-        )
-        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-
-        final_hidden_states = None
-        for expert_idx in self.expert_indicies:
-            expert_layer = self.experts[expert_idx]
-            expert_mask = selected_experts == expert_idx
-            expert_weights = (routing_weights * expert_mask).sum(dim=-1, keepdim=True)
-
-            current_hidden_states = expert_layer(hidden_states).mul_(expert_weights)
-            if final_hidden_states is None:
-                final_hidden_states = current_hidden_states
-            else:
-                final_hidden_states.add_(current_hidden_states)
-
-        return tensor_model_parallel_all_reduce(final_hidden_states)
+        final_hidden_states = fused_moe(hidden_states,
+                                        self.w13_weight,
+                                        self.w2_weight,
+                                        router_logits,
+                                        self.top_k,
+                                        renormalize=True,
+                                        inplace=True,
+                                        use_fp8=self.use_fp8,
+                                        w1_scale=self.w13_scale,
+                                        w2_scale=self.w2_scale,
+                                        a1_scale=self.a13_scale,
+                                        a2_scale=self.a2_scale)
+
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
+
+        return final_hidden_states.view(num_tokens, hidden_size)
 
 
 class MixtralAttention(nn.Module):
@@ -234,7 +329,12 @@ class MixtralDecoderLayer(nn.Module):
             sliding_window=config.sliding_window,
             quant_config=quant_config,
         )
-        self.block_sparse_moe = MixtralMoE(config=config, quant_config=quant_config)
+        self.block_sparse_moe = MixtralMoE(
+            num_experts=config.num_local_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            quant_config=quant_config)
         self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.post_attention_layernorm = RMSNorm(
             config.hidden_size, eps=config.rms_norm_eps
@@ -342,11 +442,35 @@ class MixtralForCausalLM(nn.Module):
             ("qkv_proj", "v_proj", "v"),
         ]
 
+        expert_params_mapping = [
+            # These are the weight scales for the experts
+            # (param_name, weight_name, expert_id)
+            ("w13_scale" if weight_name in ["w1", "w3"] else "w2_scale",
+             f"experts.{expert_id}.{weight_name}.weight_scale", expert_id)
+            for expert_id in range(self.config.num_local_experts)
+            for weight_name in ["w1", "w2", "w3"]
+        ] + [
+            # These are the weights for the experts
+            # (param_name, weight_name, expert_id)
+            ("w13_weight" if weight_name in ["w1", "w3"] else "w2_weight",
+             f"experts.{expert_id}.{weight_name}.weight", expert_id)
+            for expert_id in range(self.config.num_local_experts)
+            for weight_name in ["w1", "w2", "w3"]
+        ] + [
+            # These are the activation scales for the experts
+            # (param_name, weight_name, expert_id)
+            ("a13_scale" if weight_name in ["w1", "w3"] else "a2_scale",
+             f"experts.{expert_id}.{weight_name}.act_scale", expert_id)
+            for expert_id in range(self.config.num_local_experts)
+            for weight_name in ["w1", "w2", "w3"]
+        ]
+
         params_dict = dict(self.named_parameters())
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
-            for param_name, weight_name, shard_id in stacked_params_mapping:
+
+            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)
@@ -358,15 +482,30 @@ class MixtralForCausalLM(nn.Module):
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                # Skip experts that are not assigned to this worker.
-                if "block_sparse_moe.experts." in name and name not in params_dict:
-                    continue
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, loaded_weight)
+                for param_name, weight_name, expert_id in expert_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  weight_name,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+
+
+def all_close_1d(x: torch.Tensor) -> bool:
+    assert len(x.shape) == 1
+    return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0]))
 
 
 EntryClass = MixtralForCausalLM

python/sglang/srt/models/mixtral_quant.py

+# Adapted from
+# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/mixtral_quant.py#L1
+"""Inference-only Mixtral model."""
+from typing import Iterable, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import MixtralConfig
+from vllm.distributed import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_reduce,
+)
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+
+
+class MixtralMLP(nn.Module):
+    def __init__(
+        self,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.num_experts = num_experts
+        self.ffn_dim = intermediate_size
+        self.hidden_dim = hidden_size
+
+        self.w1 = ReplicatedLinear(
+            self.hidden_dim, self.ffn_dim, bias=False, quant_config=quant_config
+        )
+        self.w2 = ReplicatedLinear(
+            self.ffn_dim, self.hidden_dim, bias=False, quant_config=quant_config
+        )
+        self.w3 = ReplicatedLinear(
+            self.hidden_dim, self.ffn_dim, bias=False, quant_config=quant_config
+        )
+
+        # TODO: Use vllm's SiluAndMul
+        self.act_fn = nn.SiLU()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        w1_out, _ = self.w1(hidden_states)
+        w1_out = self.act_fn(w1_out)
+        w3_out, _ = self.w3(hidden_states)
+        current_hidden_states = w1_out * w3_out
+        current_hidden_states, _ = self.w2(current_hidden_states)
+        return current_hidden_states
+
+
+class MixtralMoE(nn.Module):
+    def __init__(
+        self,
+        config: MixtralConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.rank = get_tensor_model_parallel_rank()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_total_experts = config.num_local_experts
+        self.top_k = config.num_experts_per_tok
+        if self.tp_size > self.num_total_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {self.num_total_experts}."
+            )
+        # Split experts equally between ranks
+        self.expert_indicies = np.array_split(
+            range(self.num_total_experts), self.tp_size
+        )[self.rank].tolist()
+        if not self.expert_indicies:
+            raise ValueError(f"Rank {self.rank} has no experts assigned to it.")
+
+        self.experts = nn.ModuleList(
+            [
+                (
+                    MixtralMLP(
+                        self.num_total_experts,
+                        config.hidden_size,
+                        config.intermediate_size,
+                        quant_config=quant_config,
+                    )
+                    if idx in self.expert_indicies
+                    else None
+                )
+                for idx in range(self.num_total_experts)
+            ]
+        )
+        self.gate = ReplicatedLinear(
+            config.hidden_size, self.num_total_experts, bias=False, quant_config=None
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        router_logits, _ = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(
+            routing_weights, self.top_k, dim=-1
+        )
+        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+
+        final_hidden_states = None
+        for expert_idx in self.expert_indicies:
+            expert_layer = self.experts[expert_idx]
+            expert_mask = selected_experts == expert_idx
+            expert_weights = (routing_weights * expert_mask).sum(dim=-1, keepdim=True)
+
+            current_hidden_states = expert_layer(hidden_states).mul_(expert_weights)
+            if final_hidden_states is None:
+                final_hidden_states = current_hidden_states
+            else:
+                final_hidden_states.add_(current_hidden_states)
+
+        return tensor_model_parallel_all_reduce(final_hidden_states)
+
+
+class MixtralAttention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        layer_id: int = 0,
+        max_position: int = 4096 * 32,
+        rope_theta: float = 10000,
+        quant_config: Optional[QuantizationConfig] = None,
+        sliding_window: Optional[int] = None,
+    ) -> 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:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            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
+        self.rope_theta = rope_theta
+        self.sliding_window = sliding_window
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        input_metadata: InputMetadata,
+    ) -> 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, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MixtralDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: MixtralConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        self.self_attn = MixtralAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            layer_id=layer_id,
+            rope_theta=rope_theta,
+            sliding_window=config.sliding_window,
+            quant_config=quant_config,
+        )
+        self.block_sparse_moe = MixtralMoE(config=config, quant_config=quant_config)
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> 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,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.block_sparse_moe(hidden_states)
+        return hidden_states, residual
+
+
+class MixtralModel(nn.Module):
+    def __init__(
+        self,
+        config: MixtralConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList(
+            [
+                MixtralDecoderLayer(config, i, quant_config=quant_config)
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_metadata: InputMetadata,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        if input_embeds is None:
+            hidden_states = self.embed_tokens(input_ids)
+        else:
+            hidden_states = input_embeds
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions, hidden_states, input_metadata, residual
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class QuantMixtralForCausalLM(nn.Module):
+    def __init__(
+        self,
+        config: MixtralConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = MixtralModel(config, quant_config=quant_config)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.logits_processor = LogitsProcessor(config)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_metadata: InputMetadata,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head.weight, input_metadata
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip experts that are not assigned to this worker.
+                if "block_sparse_moe.experts." in name and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = QuantMixtralForCausalLM

python/sglang/srt/server_args.py

@@ -15,6 +15,7 @@ class ServerArgs:
     chat_template: Optional[str] = None
     trust_remote_code: bool = True
     context_length: Optional[int] = None
+    quantization: Optional[str] = None
 
     # Port
     host: str = "127.0.0.1"
@@ -135,6 +136,12 @@ class ServerArgs:
             default=ServerArgs.context_length,
             help="The model's maximum context length. Defaults to None (will use the value from the model's config.json instead).",
         )
+        parser.add_argument(
+            "--quantization",
+            type=str,
+            default=ServerArgs.quantization,
+            help="The quantization method.",
+        )
         parser.add_argument(
             "--mem-fraction-static",
             type=float,

python/sglang/srt/utils.py

@@ -106,6 +106,7 @@ def get_available_gpu_memory(gpu_id, distributed=True):
             "which may cause useless memory allocation for torch CUDA context.",
         )
 
+    torch.cuda.empty_cache()
     free_gpu_memory, _ = torch.cuda.mem_get_info(gpu_id)
 
     if distributed: