[Model] Snowflake arctic model implementation (#4652)

Co-authored-by: Dash Desai <1723932+iamontheinet@users.noreply.github.com>
Co-authored-by: Aurick Qiao <qiao@aurick.net>
Co-authored-by: Aurick Qiao <aurick.qiao@snowflake.com>
Co-authored-by: Aurick Qiao <aurickq@users.noreply.github.com>
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>

examples/offline_inference_arctic.py

+from vllm import LLM, SamplingParams
+
+# Sample prompts.
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+# Create an LLM.
+llm = LLM(model="snowflake/snowflake-arctic-instruct",
+          quantization="deepspeedfp",
+          tensor_parallel_size=8,
+          trust_remote_code=True)
+# Generate texts from the prompts. The output is a list of RequestOutput objects
+# that contain the prompt, generated text, and other information.
+
+outputs = llm.generate(prompts, sampling_params)
+# Print the outputs.
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

vllm/model_executor/layers/fused_moe/__init__.py

 from vllm.model_executor.layers.fused_moe.fused_moe import (
-    fused_moe, get_config_file_name)
+    fused_experts, fused_moe, fused_topk, get_config_file_name)
 
 __all__ = [
     "fused_moe",
+    "fused_topk",
+    "fused_experts",
     "get_config_file_name",
 ]

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -308,60 +308,16 @@ def get_moe_configs(E: int, N: int,
     return None
 
 
-def fused_moe(
+def fused_topk(
     hidden_states: torch.Tensor,
-    w1: torch.Tensor,
-    w2: torch.Tensor,
     gating_output: torch.Tensor,
     topk: int,
     renormalize: bool,
-    inplace: bool = False,
-    override_config: Optional[Dict[str, Any]] = None,
-    use_fp8: bool = False,
-    w1_scale: Optional[torch.Tensor] = None,
-    w2_scale: Optional[torch.Tensor] = None,
-    a1_scale: Optional[torch.Tensor] = None,
-    a2_scale: Optional[torch.Tensor] = None,
-) -> torch.Tensor:
-    """
-    This function computes a Mixture of Experts (MoE) layer using two sets of
-    weights, w1 and w2, and top-k gating mechanism.
-
-    Parameters:
-    - hidden_states (torch.Tensor): The input tensor to the MoE layer.
-    - w1 (torch.Tensor): The first set of expert weights.
-    - w2 (torch.Tensor): The second set of expert weights.
-    - gating_output (torch.Tensor): The output of the gating operation
-        (before softmax).
-    - topk (int): The number of top-k experts to select.
-    - renormalize (bool): If True, renormalize the top-k weights to sum to 1.
-    - inplace (bool): If True, perform the operation in-place.
-        Defaults to False.
-    - override_config (Optional[Dict[str, Any]]): Optional override
-        for the kernel configuration.
-    - use_fp8 (bool): If True, use fp8 arithmetic to compute the inner
-        products for w1 and w2. Defaults to False.
-    - w1_scale (Optional[torch.Tensor]): Optional scale to be used for
-        w1.
-    - w2_scale (Optional[torch.Tensor]): Optional scale to be used for
-        w2.
-
-    Returns:
-    - torch.Tensor: The output tensor after applying the MoE layer.
-    """
-    # Check constraints.
+):
     assert hidden_states.shape[0] == gating_output.shape[0], (
         "Number of tokens mismatch")
-    assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
-    assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch"
-    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
-    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
-    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
-    assert hidden_states.dtype in [
-        torch.float32, torch.float16, torch.bfloat16
-    ]
+
     M, _ = hidden_states.shape
-    E, N, _ = w1.shape
 
     if is_hip():
         # The MoE kernels are not yet supported on ROCm.
@@ -393,6 +349,33 @@ def fused_moe(
         del token_expert_indicies  # Not used. Will be used in the future.
     if renormalize:
         topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+    return topk_weights, topk_ids
+
+
+def fused_experts(hidden_states: torch.Tensor,
+                  w1: torch.Tensor,
+                  w2: torch.Tensor,
+                  topk_weights: torch.Tensor,
+                  topk_ids: torch.Tensor,
+                  inplace: bool = False,
+                  override_config: Optional[Dict[str, Any]] = None,
+                  use_fp8: bool = False,
+                  w1_scale: Optional[torch.Tensor] = None,
+                  w2_scale: Optional[torch.Tensor] = None,
+                  a1_scale: Optional[torch.Tensor] = None,
+                  a2_scale: Optional[torch.Tensor] = None):
+    # Check constraints.
+    assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
+    assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
+    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
+    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
+    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
+    assert hidden_states.dtype in [
+        torch.float32, torch.float16, torch.bfloat16
+    ]
+
+    M, _ = hidden_states.shape
+    E, N, _ = w1.shape
 
     if override_config:
         config = override_config
@@ -477,3 +460,63 @@ def fused_moe(
                          out=hidden_states)
     return torch.sum(intermediate_cache3.view(*intermediate_cache3.shape),
                      dim=1)
+
+
+def fused_moe(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    inplace: bool = False,
+    override_config: Optional[Dict[str, Any]] = None,
+    use_fp8: bool = False,
+    w1_scale: Optional[torch.Tensor] = None,
+    w2_scale: Optional[torch.Tensor] = None,
+    a1_scale: Optional[torch.Tensor] = None,
+    a2_scale: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """
+    This function computes a Mixture of Experts (MoE) layer using two sets of
+    weights, w1 and w2, and top-k gating mechanism.
+
+    Parameters:
+    - hidden_states (torch.Tensor): The input tensor to the MoE layer.
+    - w1 (torch.Tensor): The first set of expert weights.
+    - w2 (torch.Tensor): The second set of expert weights.
+    - gating_output (torch.Tensor): The output of the gating operation
+        (before softmax).
+    - topk (int): The number of top-k experts to select.
+    - renormalize (bool): If True, renormalize the top-k weights to sum to 1.
+    - inplace (bool): If True, perform the operation in-place.
+        Defaults to False.
+    - override_config (Optional[Dict[str, Any]]): Optional override
+        for the kernel configuration.
+    - use_fp8 (bool): If True, use fp8 arithmetic to compute the inner
+        products for w1 and w2. Defaults to False.
+    - w1_scale (Optional[torch.Tensor]): Optional scale to be used for
+        w1.
+    - w2_scale (Optional[torch.Tensor]): Optional scale to be used for
+        w2.
+
+    Returns:
+    - torch.Tensor: The output tensor after applying the MoE layer.
+    """
+    # Check constraints.
+    assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch"
+
+    topk_weights, topk_ids = fused_topk(hidden_states, gating_output, topk,
+                                        renormalize)
+    return fused_experts(hidden_states,
+                         w1,
+                         w2,
+                         topk_weights,
+                         topk_ids,
+                         inplace=inplace,
+                         override_config=override_config,
+                         use_fp8=use_fp8,
+                         w1_scale=w1_scale,
+                         w2_scale=w2_scale,
+                         a1_scale=a1_scale,
+                         a2_scale=a2_scale)

vllm/model_executor/layers/quantization/__init__.py

@@ -4,6 +4,8 @@ from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
 from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.layers.quantization.deepspeedfp import (
+    DeepSpeedFPConfig)
 from vllm.model_executor.layers.quantization.fp8 import Fp8Config
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.gptq_marlin import (
@@ -19,6 +21,7 @@ QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "squeezellm": SqueezeLLMConfig,
     "gptq_marlin": GPTQMarlinConfig,
     "marlin": MarlinConfig,
+    "deepspeedfp": DeepSpeedFPConfig
 }
 
 

vllm/model_executor/layers/quantization/deepspeedfp.py

+from typing import Any, Dict, List, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
+
+
+class DeepSpeedFPConfig(QuantizationConfig):
+    """Config for DeepSpeed FP quantizer. It supports fp6 and fp8.
+    
+    Args: 
+        weight_bits: the target quantization bits, 6 or 8.
+        group_size: group size for quantizaiton, default to 128.
+    """
+
+    def __init__(
+        self,
+        weight_bits: int = 8,
+        group_size: int = 512,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.valid_types = [torch.bfloat16, torch.float16]
+
+        if self.weight_bits not in (6, 8):
+            raise ValueError(
+                "Currently, only 6-bit or 8-bit weight quantization are "
+                f"supported for DeepSpeed FP quantizaiton, but got "
+                f"{self.weight_bits} bits.")
+
+    def __repr__(self) -> str:
+        return (f"DeepSpeedFPConfig(weight_bits={self.weight_bits}), "
+                f"group_size={self.group_size}")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "DeepSpeedFP"
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "DeepSpeedFPConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        return cls(weight_bits=weight_bits, group_size=group_size)
+
+    def get_linear_method(self) -> "DeepSpeedFPLinearMethod":
+        return DeepSpeedFPLinearMethod(self)
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    # Need to figure it out
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return [
+            "quant_config.json",
+            "quantize_config.json",
+        ]
+
+    def get_quant_method(
+            self,
+            layer: torch.nn.Module) -> Optional["DeepSpeedFPLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return DeepSpeedFPLinearMethod(self)
+        return None
+
+
+class DeepSpeedFPLinearMethod(LinearMethodBase):
+    """Linear method for DeepSpeedFP quantizer.
+
+    Args:
+        quant_config: the DeepSpeedFP quantization config.
+    """
+
+    def __init__(self, quant_config: DeepSpeedFPConfig):
+        self.quant_config = quant_config
+        self.weight = None
+
+    def create_weights(self,
+                       layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int],
+                       input_size: int,
+                       output_size: int,
+                       params_dtype: torch.dtype,
+                       weight_loader=None,
+                       **extra_weight_attrs):
+        del output_size
+        del input_size
+        output_size_per_partition = sum(output_partition_sizes)
+        weight = DeepSpeedFPParameter(
+            torch.Size((output_size_per_partition, input_size_per_partition)),
+            params_dtype=params_dtype,
+            quant_config=self.quant_config,
+        )
+        set_weight_attrs(weight, {
+            "input_dim": 1,
+            "output_dim": 0,
+        })
+        layer.register_parameter("weight", weight)
+
+        def quant_weight_loader(param, loaded_weight, *args, **kwargs):
+            # Calls the original weight loader (if any), quantizes the result,
+            # and then loads the quantized parameter.
+            if weight_loader is not None:
+                orig_param_data = param.data
+                param.data = param.ds_dequantize()
+                weight_loader(param, loaded_weight, *args, **kwargs)
+                param.data, loaded_weight = orig_param_data, param.data
+            param.ds_quantize_(loaded_weight.cuda())
+
+        extra_weight_attrs["weight_loader"] = quant_weight_loader
+        set_weight_attrs(weight, extra_weight_attrs)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        weight = layer.weight
+        y = weight.ds_dequantize()
+        return F.linear(x, y, bias)
+
+
+class DeepSpeedFPParameter(nn.Parameter):
+    """
+    DeepSpeedFP quantized parameter class that implements fp8/fp6
+    quantization deepspeed. Weights are stored in quantized form on
+    GPUs, and can be dequantized on-the-fly when needed by the model.
+    """
+
+    def __new__(cls, orig_shape: torch.Size, params_dtype: torch.dtype,
+                quant_config: DeepSpeedFPConfig):
+        try:
+            import deepspeed
+            if deepspeed.__version__ < "0.14.2":
+                raise ImportError("deepspeed version is wrong. Please "
+                                  "install deepspeed>=0.14.2.")
+            from deepspeed.ops.fp_quantizer import FP_Quantize
+        except ImportError as err:
+            raise ImportError("Please install deepspeed>=0.14.2 via "
+                              "`pip install deepspeed>=0.14.2` to use "
+                              "deepspeedfp quantizer.") from err
+        data = torch.empty((
+            orig_shape.numel() // quant_config.group_size,
+            quant_config.group_size * quant_config.weight_bits // 8 + 4,
+        ),
+                           dtype=torch.int8)
+        self = torch.Tensor._make_subclass(cls, data, data.requires_grad)
+        self.orig_shape = orig_shape
+        self.quant_config = quant_config
+        self.fp_quantizer = FP_Quantize(group_size=quant_config.group_size)
+        self.fp_quantizer.orig_shape = orig_shape
+        self.fp_quantizer.orig_dtype = params_dtype
+        return self
+
+    def ds_quantize_(self, tensor: torch.Tensor):
+        assert tensor.device.type == "cuda" and tensor.dtype != torch.int8
+        return self.data.copy_(
+            self.fp_quantizer.quantize(
+                tensor.data,
+                q_bits=self.quant_config.weight_bits,
+            ))
+
+    def ds_dequantize(self, fp_out=None) -> torch.Tensor:
+        """
+        Return a tensor containing the dequantized weights of this parameter.
+        """
+        assert self.data.device.type == "cuda" and self.data.dtype == torch.int8
+        return self.fp_quantizer.dequantize(
+            self.data, fp_out=fp_out, q_bits=self.quant_config.weight_bits)
+
+    def ds_selective_dequantize(self, indices, fp_out=None) -> torch.Tensor:
+        """
+        Return a tensor where only the weights at `indices` are dequantized
+        (to save HBM -> SRAM bandwidth).
+        """
+        assert self.data.device.type == "cuda" and self.data.dtype == torch.int8
+        return self.fp_quantizer.selective_dequantize(
+            self.data,
+            indices,
+            fp_out=fp_out,
+            q_bits=self.quant_config.weight_bits)

vllm/model_executor/models/__init__.py

@@ -54,6 +54,7 @@ _MODELS = {
     "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
+    "ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
     "XverseForCausalLM": ("xverse", "XverseForCausalLM"),
 }
 

vllm/model_executor/models/arctic.py

+"""Inference-only Snowflake Arctic model."""
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.attention import Attention, AttentionMetadata
+from vllm.distributed import (get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.quantization.deepspeedfp import (
+    DeepSpeedFPConfig, DeepSpeedFPParameter)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import Sampler
+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.sampling_metadata import SamplingMetadata
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs.arctic import ArcticConfig
+
+logger = init_logger(__name__)
+
+
+class ArcticMLP(nn.Module):
+
+    def __init__(self,
+                 config: ArcticConfig,
+                 layer_id: int,
+                 expert_id: int = -1,
+                 is_residual_mlp: bool = False,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 reduce_results: bool = True):
+        super(ArcticMLP, self).__init__()
+        self.hidden_size = config.hidden_size
+        self.expert_id = expert_id
+        self.layer_id = layer_id
+
+        self.ffn_dim = config.intermediate_size if not is_residual_mlp \
+            else self.hidden_size
+
+        self.w13 = MergedColumnParallelLinear(self.hidden_size,
+                                              [self.ffn_dim] * 2,
+                                              bias=False,
+                                              quant_config=quant_config)
+        self.w2 = RowParallelLinear(self.ffn_dim,
+                                    self.hidden_size,
+                                    bias=False,
+                                    reduce_results=reduce_results,
+                                    quant_config=quant_config)
+        if config.hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {config.hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, hidden_states):
+        gate_up, _ = self.w13(hidden_states)
+        hidden_states = self.act_fn(gate_up)
+        hidden_states, _ = self.w2(hidden_states)
+        return hidden_states
+
+
+class ArcticMoE(nn.Module):
+    """
+    Model-parallel implementation of Arctic MoE Layer.
+    """
+
+    def __init__(self,
+                 config: ArcticConfig,
+                 layer_id: int,
+                 tp_size: Optional[int] = None,
+                 params_dtype: Optional[torch.dtype] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 reduce_results: bool = True):
+        super(ArcticMoE, self).__init__()
+
+        self.tp_size = tp_size or get_tensor_model_parallel_world_size()
+        self.hidden_size = config.hidden_size
+        self.num_experts = config.num_local_experts
+        self.layer_id = layer_id
+        self.top_k = config.num_experts_per_tok
+        self.intermediate_size = config.intermediate_size // self.tp_size
+
+        self.is_moe_layer = (layer_id + 1) % config.moe_layer_frequency == 0
+        self.is_quant = isinstance(quant_config, DeepSpeedFPConfig)
+        self.reduce_results = reduce_results
+        # Some other parameters
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.params_dtype = params_dtype
+
+        if not self.is_moe_layer:
+            self.mlp = ArcticMLP(config,
+                                 layer_id=layer_id,
+                                 quant_config=quant_config,
+                                 reduce_results=reduce_results)
+        else:
+            self.gate = ReplicatedLinear(self.hidden_size,
+                                         self.num_experts,
+                                         bias=False,
+                                         params_dtype=self.params_dtype,
+                                         quant_config=quant_config)
+            if self.is_quant:
+                self.ws = DeepSpeedFPParameter(
+                    torch.Size((self.num_experts, 2 * self.intermediate_size,
+                                self.hidden_size)),
+                    params_dtype=params_dtype,
+                    quant_config=quant_config,
+                )
+                self.w2s = DeepSpeedFPParameter(
+                    torch.Size((self.num_experts, self.hidden_size,
+                                self.intermediate_size)),
+                    params_dtype=params_dtype,
+                    quant_config=quant_config,
+                )
+            else:
+                self.ws = nn.Parameter(
+                    torch.empty(self.num_experts,
+                                2 * self.intermediate_size,
+                                self.hidden_size,
+                                device="cuda",
+                                dtype=self.params_dtype))
+                self.w2s = nn.Parameter(
+                    torch.empty(self.num_experts,
+                                self.hidden_size,
+                                self.intermediate_size,
+                                device="cuda",
+                                dtype=self.params_dtype))
+            set_weight_attrs(self.ws, {
+                "weight_loader": self.weight_loader,
+            })
+            set_weight_attrs(self.w2s, {
+                "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.ds_dequantize() if self.is_quant else 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 self.is_quant:
+            param.ds_quantize_(param_data)
+
+    def local_moe_fused(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)
+        do_normalize = self.top_k > 1
+        topk_weights, topk_ids = fused_topk(hidden_states,
+                                            router_logits,
+                                            self.top_k,
+                                            renormalize=do_normalize)
+        # topk_ids: (num_tokens, k)
+        if self.is_quant:
+            if 2 * num_tokens <= self.num_experts:
+                # If much fewer tokens than experts, use selective dequantize.
+                ws_dequantized = self.ws.ds_selective_dequantize(
+                    topk_ids.flatten())
+                w2s_dequantized = self.w2s.ds_selective_dequantize(
+                    topk_ids.flatten())
+                # We gathered the experts to the tokens so update the mapping.
+                topk_ids = torch.arange(
+                    0,
+                    topk_ids.numel(),
+                    device=topk_ids.device,
+                ).reshape(topk_ids.shape)
+            else:
+                ws_dequantized = self.ws.ds_dequantize()
+                w2s_dequantized = self.w2s.ds_dequantize()
+
+        final_hidden_states = fused_experts(
+            hidden_states,
+            ws_dequantized if self.is_quant else self.ws,
+            w2s_dequantized if self.is_quant else self.w2s,
+            topk_weights,
+            topk_ids,
+            inplace=True)
+        if self.reduce_results and self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
+        return final_hidden_states.view(num_tokens, hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor):
+        if self.is_moe_layer:
+            final_hidden_states = self.local_moe_fused(hidden_states)
+        else:
+            final_hidden_states = self.mlp(hidden_states)
+        return final_hidden_states
+
+
+class ArcticAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: ArcticConfig,
+        layer_idx: Optional[int] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_key_value_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 = 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.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(self.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,
+            self.hidden_size,
+            bias=False,
+            reduce_results=True,
+            quant_config=quant_config,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> 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, kv_cache, attn_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class ArcticDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: ArcticConfig,
+        layer_idx: int,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        is_moe_layer = (layer_idx + 1) % config.moe_layer_frequency == 0
+        self.use_residual = config.use_residual and is_moe_layer
+        self.self_attn = ArcticAttention(config,
+                                         layer_idx,
+                                         quant_config=quant_config)
+        self.block_sparse_moe = ArcticMoE(
+            config,
+            layer_id=layer_idx,
+            quant_config=quant_config,
+            reduce_results=(not self.use_residual))
+
+        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)
+
+        if self.use_residual:
+            self.residual_layernorm = RMSNorm(config.hidden_size,
+                                              eps=config.rms_norm_eps)
+            self.residual_mlp = ArcticMLP(config,
+                                          layer_id=layer_idx,
+                                          is_residual_mlp=True,
+                                          reduce_results=False)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        residual_input = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            attn_metadata=attn_metadata,
+        )
+        hidden_states = residual_input + hidden_states
+
+        residual_attn = hidden_states
+        if self.use_residual:
+            hidden_states = self.residual_layernorm(hidden_states)
+            hidden_states = self.residual_mlp(hidden_states)
+            residual_mlp = hidden_states
+            hidden_states = self.post_attention_layernorm(residual_input)
+            hidden_states = self.block_sparse_moe(hidden_states)
+            hidden_states = residual_mlp + hidden_states
+            hidden_states = tensor_model_parallel_all_reduce(hidden_states)
+            hidden_states = residual_attn + hidden_states
+        else:
+            hidden_states = self.post_attention_layernorm(hidden_states)
+            hidden_states = self.block_sparse_moe(hidden_states)
+            hidden_states = residual_attn + hidden_states
+        return hidden_states
+
+
+class ArcticModel(nn.Module):
+
+    def __init__(
+        self,
+        config: ArcticConfig,
+        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(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=self.vocab_size)
+        self.layers = nn.ModuleList([
+            ArcticDecoderLayer(config, layer_idx, quant_config=quant_config)
+            for layer_idx in range(config.num_hidden_layers)
+        ])
+        self._attn_implementation = config._attn_implementation
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states = layer(positions, hidden_states, kv_caches[i],
+                                  attn_metadata)
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class ArcticForCausalLM(nn.Module):
+
+    def __init__(self,
+                 config: ArcticConfig,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 **kwargs) -> None:
+        super().__init__()
+        self.config = config
+        self.model = ArcticModel(config, quant_config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = ParallelLMHead(
+            self.vocab_size,
+            config.hidden_size,
+        )
+        self.num_experts = config.num_local_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+        self.unpadded_vocab_size = config.vocab_size
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size)
+        self.sampler = Sampler()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   attn_metadata)
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head.weight, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: Optional[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    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"),
+        ]
+
+        mlp_params_mapping = []
+        expert_params_mapping = []
+        num_layers = self.config.num_hidden_layers
+
+        for layer in range(num_layers):
+            mlp_params_mapping.append(
+                (f"layers.{layer}.residual_mlp.w13.weight",
+                 f"layers.{layer}.residual_mlp.w1.weight", 0))
+            mlp_params_mapping.append(
+                (f"layers.{layer}.residual_mlp.w13.weight",
+                 f"layers.{layer}.residual_mlp.w3.weight", 1))
+            if layer % 2 == 0:
+                # MLP layers
+                mlp_params_mapping.append(
+                    (f"layers.{layer}.block_sparse_moe.mlp.w13.weight",
+                     f"layers.{layer}.block_sparse_moe.mlp.w1.weight", 0))
+                mlp_params_mapping.append(
+                    (f"layers.{layer}.block_sparse_moe.mlp.w13.weight",
+                     f"layers.{layer}.block_sparse_moe.mlp.w3.weight", 1))
+            else:
+                # MoE layers
+                for expert_id in range(self.config.num_local_experts):
+                    expert_params_mapping.append(
+                        ("ws", f"experts.{expert_id}.w1.weight", expert_id))
+                    expert_params_mapping.append(
+                        ("w2s", f"experts.{expert_id}.w2.weight", expert_id))
+                    expert_params_mapping.append(
+                        ("ws", f"experts.{expert_id}.w3.weight", expert_id))
+
+        params_dict = dict(self.named_parameters())
+
+        logger.info(
+            "It will take ~10 minutes loading from the 16-bit weights. "
+            "Alternatively, use the prequantized 8-bit weights of arctic "
+            "and set load-format to `sharded_state` will accelerate loading.")
+        for name, loaded_weight in weights:
+            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:
+                for param_name, weight_name, shard_id in mlp_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, shard_id)
+                    break
+                else:
+                    for param_name, weight_name, shard_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=shard_id)
+                        break
+                    else:
+                        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)

vllm/transformers_utils/configs/arctic.py

+# yapf: disable
+# ruff: noqa: E501
+# coding=utf-8
+# Copied from
+# https://huggingface.co/Snowflake/snowflake-arctic-instruct/blob/main/configuration_arctic.py
+""" Arctic model configuration"""
+
+from dataclasses import asdict, dataclass
+from typing import Any, Dict
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+ARCTIC_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "arctic": "https://huggingface.co/Snowflake/snowflake-arctic-instruct/tree/main/config.json",
+}
+
+
+@dataclass
+class ArcticLoraConfig:
+    lora_r: int = 64
+    lora_alpha: float = 16
+    shard_base_weights: bool = False
+
+
+@dataclass
+class ArcticQuantizationConfig:
+    q_bits: int = 8
+    rounding: str = "nearest"
+    mantissa_bits: int = 3
+    group_size: int = 128
+
+
+class ArcticConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ArcticModel`]. It is used to instantiate an
+    Arctic model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the #TODO(rsamdani): add what model has the default config..
+
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Arctic model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ArcticModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Arctic's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to root per-token, can be also interpreted as the `top-p` routing
+            parameter
+        num_local_experts (`int`, *optional*, defaults to 8):
+            Number of experts per Sparse MLP layer.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+
+    ```python
+    >>> from transformers import ArcticModel, ArcticConfig
+
+    >>> # Initializing a Arctic 7B style configuration TODO(rsamdani): verify which model does the default configuration correspond to.
+    >>> configuration = ArcticConfig()
+
+    >>> # Initializing a model from the Arctic 7B style configuration
+    >>> model = ArcticModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "arctic"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=1e6,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=1,
+        num_local_experts=8,
+        router_aux_loss_coef=0.001,
+        moe_layer_frequency=2,
+        parallel_attn_mlp_res=False,
+        moe_train_capacity_factor=1,
+        moe_eval_capacity_factor=1,
+        enable_expert_tensor_parallelism=False,
+        moe_min_capacity=0,
+        moe_token_dropping=True,
+        quantization=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.moe_layer_frequency = moe_layer_frequency
+        self.moe_train_capacity_factor = moe_train_capacity_factor
+        self.moe_eval_capacity_factor = moe_eval_capacity_factor
+        self.enable_expert_tensor_parallelism = enable_expert_tensor_parallelism
+        self.moe_min_capacity = moe_min_capacity
+        self.moe_token_dropping = moe_token_dropping
+        self.parallel_attn_mlp_res = parallel_attn_mlp_res
+        if isinstance(quantization, dict):
+            self.quantization = ArcticQuantizationConfig(**quantization)
+        else:
+            self.quantization = quantization
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "ArcticConfig":
+        result = super().from_dict(config_dict, **kwargs)
+        config = result[0] if isinstance(result, tuple) else result
+        if isinstance(config.quantization, dict):
+            config.quantization = ArcticQuantizationConfig(**config.quantization)
+        return result
+
+    def to_dict(self) -> Dict[str, Any]:
+        ret = super().to_dict()
+        if isinstance(ret["quantization"], ArcticQuantizationConfig):
+            ret["quantization"] = asdict(ret["quantization"])
+        return ret