Enable multiple LoRa adapters (#2010)

* feat: first draft load multiple lora

* feat: load weights within layer and refactor lora pass

* fix: refactor and reduce lora math

* feat: baseline impl single request multi lora support

* feat: prefer lorax implementation and port loading logic

* fix: prefer adapter_data and refactors

* feat: perfer loraxs custom punica kernels and add mlp loras

* fix: adjust batch for bgmv

* fix: adjust adapter_segments logic when in batch

* fix: refactor and move changes to v3 proto

* fix: pass model_id for all flash causal lms

* fix: pass model_id for all causal and seq2seq lms

* fix: add model_id to model test

* feat: add lora support to mistral and refactors

* feat: prefer model id in request

* fix: include rust code for adapter id

* feat: bump launcher and add new lora docs

* feat: support base model generation and refactors

* fix: rename doc to retry ci build

* feat: support if vlm models

* fix: add adapter_data param and avoid missing layers

* fix: add adapter_data param to phi and neox

* fix: update all models forwards to include adapter_data

* fix: add model_id to IdeficsCausalLM

* Update lora.md

Fixed a typo

* Update lora.md

Fixing spam image

* fix: add lora kernel to dockerfile, support running without kernels and refactors

* fix: avoid dockerfile conflict

* fix: refactors and adjust flash llama lora logic

* fix: skip llama test due to CI issue (temp)

* fix: skip llama test CI (temp) 2

* fix: revert skips and prefer updated ci token for tests

* fix: refactors and helpful comments

* fix: add noop in TensorParallelAdapterRowLinear too

* fix: refactor and move shard_lora_weights logic

* fix: exit early if no adapter_data

---------
Co-authored-by: Derek <datavistics@gmail.com>

server/text_generation_server/adapters/weights.py

+# Origin:   https://github.com/predibase/lorax
+# Path:     lorax/server/lorax_server/adapters/weights.py
+# License:  Apache License Version 2.0, January 2004
+
+from abc import ABC, abstractclassmethod
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Set, Type
+
+import torch
+
+
+@dataclass
+class AdapterBatchMetadata:
+    # [batch_size]
+    adapter_indices: torch.Tensor
+
+    # [num_adapters]
+    adapter_set: Set[int]
+
+    # [num_segments + 1]
+    adapter_segments: torch.Tensor
+
+    # [num_segments]
+    # maps from segment index to adapter index, i.e.:
+    # segment_indices[s] == adapter_indices[i]
+    segment_indices: List[int]
+
+
+class AdapterWeights(ABC):
+    @abstractclassmethod
+    def get_batch_types(cls) -> List[Type["BatchAdapterWeights"]]:
+        pass
+
+    @property
+    def speculative_tokens(self) -> int:
+        return 0
+
+
+class BatchAdapterWeights(ABC):
+    @abstractclassmethod
+    def has_adapter(self, adapter_index: int) -> bool:
+        pass
+
+    @abstractclassmethod
+    def key(cls) -> str:
+        pass
+
+    @abstractclassmethod
+    def load(
+        cls,
+        adapter_weights: Dict[int, AdapterWeights],
+        meta: "AdapterBatchMetadata",
+        prefill: bool,
+        prefill_head_indices: torch.Tensor,
+    ) -> Optional["BatchAdapterWeights"]:
+        pass
+
+
+class LayerAdapterWeights:
+    """Adapter weights that apply to a particular layer."""
+
+    def __init__(self):
+        self.adapter_weights: Dict[int, AdapterWeights] = {}
+
+    def add_adapter(self, adapter_idx: int, weights: AdapterWeights):
+        self.adapter_weights[adapter_idx] = weights
+
+    def remove_adapter(self, adapter_idx: int):
+        if adapter_idx not in self.adapter_weights:
+            return
+        del self.adapter_weights[adapter_idx]
+
+    @property
+    def max_speculative_tokens(self) -> int:
+        return max(
+            adapter_weights.speculative_tokens
+            for adapter_weights in self.adapter_weights.values()
+        )
+
+    def is_empty(self) -> bool:
+        return len(self.adapter_weights) == 0
+
+    def get_data(
+        self,
+        meta: AdapterBatchMetadata,
+        prefill: bool,
+        prefill_head_indices: Optional[torch.Tensor],
+    ) -> Dict[str, BatchAdapterWeights]:
+        # bucket adapters by batch class
+        adapter_batch_types: Dict[
+            Type[BatchAdapterWeights], Dict[int, AdapterWeights]
+        ] = defaultdict(dict)
+        for adapter_index, adapter_weights in self.adapter_weights.items():
+            for batch_type in adapter_weights.get_batch_types():
+                adapter_batch_types[batch_type][adapter_index] = adapter_weights
+
+        batch_data = {}
+        for batch_type, adapter_weights in adapter_batch_types.items():
+            batched_weights = batch_type.load(
+                adapter_weights, meta, prefill, prefill_head_indices
+            )
+            if batched_weights is not None:
+                batch_data[batch_type.key()] = batched_weights
+        return batch_data
+
+
+@dataclass
+class AdapterBatchData:
+    meta: AdapterBatchMetadata
+
+    # layer type -> adapter type -> batch weight data
+    data: Dict[str, Dict[str, BatchAdapterWeights]]
+
+    prefill: bool
+
+    @staticmethod
+    def from_meta(
+        meta: AdapterBatchMetadata,
+        weights: Dict[str, LayerAdapterWeights],
+        prefill: bool,
+        prefill_head_indices: Optional[torch.Tensor],
+    ) -> "AdapterBatchData":
+        data = {}
+        for k, v in weights.items():
+            if v.is_empty():
+                continue
+            data[k] = v.get_data(
+                meta, prefill, prefill_head_indices if k == "lm_head" else None
+            )
+        return AdapterBatchData(meta=meta, data=data, prefill=prefill)
+
+    def ranks(self) -> Set[int]:
+        # TODO(travis): refactor to be less coupled to lora implementation
+        ranks = set()
+        for layer_data in self.data.values():
+            lora_data = layer_data.get("lora")
+            if lora_data is None:
+                continue
+
+            for rank_data in lora_data.rank_data.values():
+                ranks.add(rank_data.rank)
+
+        return ranks
+
+    def layer_names(self) -> Set[str]:
+        return set(self.data.keys())
+
+    def adapter_keys(self) -> Set[str]:
+        adapter_keys = set()
+        for layer_data in self.data.values():
+            adapter_keys.update(layer_data.keys())
+        return adapter_keys
+
+    @property
+    def max_rank(self) -> int:
+        ranks = self.ranks()
+        return max(ranks) if len(ranks) > 0 else 0

server/text_generation_server/cli.py

@@ -79,6 +79,18 @@ def serve(
     if otlp_endpoint is not None:
         setup_tracing(otlp_service_name=otlp_service_name, otlp_endpoint=otlp_endpoint)
 
+    lora_adapter_ids = os.getenv("LORA_ADAPTERS", None)
+
+    # split on comma and strip whitespace
+    lora_adapter_ids = (
+        [x.strip() for x in lora_adapter_ids.split(",")] if lora_adapter_ids else []
+    )
+
+    if len(lora_adapter_ids) > 0:
+        logger.warning(
+            f"LoRA adapters are enabled. This is an experimental feature and may not work as expected."
+        )
+
     # Downgrade enum into str for easier management later on
     quantize = None if quantize is None else quantize.value
     dtype = None if dtype is None else dtype.value
@@ -93,6 +105,7 @@ def serve(
         )
     server.serve(
         model_id,
+        lora_adapter_ids,
         revision,
         sharded,
         quantize,
@@ -113,6 +126,7 @@ def download_weights(
     logger_level: str = "INFO",
     json_output: bool = False,
     trust_remote_code: bool = False,
+    merge_lora: bool = False,
 ):
     # Remove default handler
     logger.remove()
@@ -143,18 +157,28 @@ def download_weights(
     ) is not None
 
     if not is_local_model:
-        try:
-            adapter_config_filename = hf_hub_download(
-                model_id, revision=revision, filename="adapter_config.json"
-            )
-            utils.download_and_unload_peft(
-                model_id, revision, trust_remote_code=trust_remote_code
-            )
-            is_local_model = True
-            utils.weight_files(model_id, revision, extension)
-            return
-        except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
-            pass
+        # TODO: maybe reverse the default value of merge_lora?
+        # currently by default we don't merge the weights with the base model
+        if merge_lora:
+            try:
+                adapter_config_filename = hf_hub_download(
+                    model_id, revision=revision, filename="adapter_config.json"
+                )
+                utils.download_and_unload_peft(
+                    model_id, revision, trust_remote_code=trust_remote_code
+                )
+                is_local_model = True
+                utils.weight_files(model_id, revision, extension)
+                return
+            except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
+                pass
+        else:
+            try:
+                utils.peft.download_peft(
+                    model_id, revision, trust_remote_code=trust_remote_code
+                )
+            except Exception:
+                pass
 
         try:
             import json

server/text_generation_server/layers/__init__.py

@@ -12,3 +12,9 @@ from text_generation_server.layers.speculative import SpeculativeHead
 # Just to add the `load` methods.
 from text_generation_server.layers.layernorm import load_layer_norm
 from text_generation_server.layers.conv import load_conv2d
+
+from text_generation_server.layers.lora import (
+    LoraLinear,
+    TensorParallelMultiAdapterLinear,
+    TensorParallelAdapterRowLinear,
+)

server/text_generation_server/layers/lora.py

+import math
+import os
+from typing import TYPE_CHECKING, Optional, Tuple, List
+
+import torch
+import torch.distributed
+from accelerate import init_empty_weights
+from torch import nn
+from torch.nn import functional as F
+from torch.distributed import ProcessGroup
+
+from text_generation_server.utils.sgmv import (
+    add_lora_a_bgmv,
+    add_lora_b_bgmv,
+    has_sgmv,
+    lora_a_sgmv_cutlass,
+    lora_b_sgmv_cutlass,
+    orient_for_rank,
+)
+
+if TYPE_CHECKING:
+    from text_generation_server.adapters import AdapterBatchData
+    from text_generation_server.adapters.lora import BatchLoraWeights
+
+
+class LoraLinear(nn.Module):
+    def __init__(
+        self, base_layer: nn.Module, layer_id: int, process_group: ProcessGroup
+    ):
+        super().__init__()
+        self.base_layer = base_layer
+        self.layer_id = layer_id
+        self.process_group = process_group
+
+    def forward_layer_type(
+        self,
+        result: torch.Tensor,
+        input: torch.Tensor,
+        adapter_data: "AdapterBatchData",
+        layer_type: str,
+        start_idx: int,
+        end_idx: int,
+    ) -> torch.Tensor:
+        if adapter_data is None:
+            return result
+        data = adapter_data.data.get(layer_type)
+        data: Optional["BatchLoraWeights"] = (
+            data.get("lora") if data is not None else None
+        )
+
+        if has_sgmv() and data is not None and data.can_vectorize(self.process_group):
+            # In tensor-parallel configurations, each GPU processes a specific segment of the output.
+            # The 'result' tensor represents the full output, which can vary in size based on
+            # the layer type (e.g., attention vs. feed-forward layers). We define the current
+            # segment using start_idx and end_idx. If the segment size doesn't match this GPU's
+            # slice of 'result', we create a zero tensor of the correct size for LoRA computation.
+            # This approach ensures accurate LoRA application across various layer sizes and
+            # configurations, adapting to different model architectures and parallelization strategies.
+            #
+            # Example scenarios where this is necessary:
+            # 1. The adapter's size doesn't evenly divide across GPUs.
+            # 2. We're processing the last segment which might be smaller.
+            # 3. Different projection layers (q, k, v) have different sizes.
+            if end_idx - start_idx != result.shape[1]:
+                proj = torch.zeros_like(result[:, start_idx:end_idx])
+            else:
+                proj = result
+
+            for r, rank_segments in data.rank_data.items():
+                lora_a_ptr = rank_segments.lora_a_ptr
+                lora_b_ptr = rank_segments.lora_b_ptr
+
+                if lora_a_ptr is None or lora_b_ptr is None:
+                    raise ValueError("LoRA data is missing")
+
+                if data.use_sgmv:
+                    # Use SGMV for prefill
+                    v = lora_a_sgmv_cutlass(
+                        input,
+                        rank_segments.tmp_shrink,
+                        lora_a_ptr,
+                        rank_segments.segment_starts,
+                        rank_segments.segment_ends,
+                        self.layer_id,
+                        r,
+                    )
+
+                    if self.process_group.size() > 1:
+                        v = self.collect_lora_a(v)
+
+                    lora_b_sgmv_cutlass(
+                        proj,
+                        v,
+                        rank_segments.tmp_expand,
+                        lora_b_ptr,
+                        rank_segments.segment_starts,
+                        rank_segments.segment_ends,
+                        self.layer_id,
+                    )
+                else:
+                    # Use BGMV for decode
+                    v = torch.zeros(
+                        (input.size(0), r), dtype=input.dtype, device=input.device
+                    )
+                    # TODO: error with [-1, 0], but not [0, -1]
+                    add_lora_a_bgmv(
+                        v,
+                        input,
+                        lora_a_ptr,
+                        rank_segments.indices,
+                        self.layer_id,
+                    )
+
+                    if self.process_group.size() > 1:
+                        v = self.collect_lora_a(v)
+
+                    add_lora_b_bgmv(
+                        proj,
+                        v,
+                        lora_b_ptr,
+                        rank_segments.indices,
+                        self.layer_id,
+                    )
+
+            if end_idx - start_idx != result.shape[1]:
+                result[:, start_idx:end_idx] += proj
+        else:
+            for adapter_index in adapter_data.meta.adapter_set:
+                if data is not None and data.has_adapter(adapter_index):
+                    adapter_mask = (
+                        (adapter_data.meta.adapter_indices == adapter_index)
+                        .to(input.dtype)
+                        .view(-1, 1)
+                    )
+                    layer_result = self.forward_lora(
+                        input, data, adapter_index, adapter_mask
+                    )
+                    result[:, start_idx:end_idx] += layer_result
+
+        return result
+
+    def forward_lora(
+        self,
+        input: torch.Tensor,
+        data: "BatchLoraWeights",
+        adapter_index: int,
+        adapter_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        lora_a = data.lora_a[adapter_index][self.layer_id, :, :]
+        lora_b = data.lora_b[adapter_index][self.layer_id, :, :]
+
+        lora_a = orient_for_rank(lora_a, lora_b.size(0))
+
+        a_out = input @ lora_a
+        if self.process_group.size() > 1:
+            a_out = self.collect_lora_a(a_out)
+
+        result = (a_out @ lora_b) * adapter_mask
+        return result
+
+    def collect_lora_a(self, a_out: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError("Implemented in subclasses")
+
+
+class TensorParallelMultiAdapterLinear(LoraLinear):
+    def __init__(
+        self,
+        base_layer: nn.Module,
+        layer_id: int,
+        layer_names: List[str],
+        sizes: List[int],
+        process_group: ProcessGroup,
+    ):
+        super().__init__(base_layer, layer_id, process_group)
+        self.layer_names = layer_names
+        self.sizes = sizes
+
+    @classmethod
+    def load(
+        cls,
+        base_layer: nn.Module,
+        layer_id: int,
+        layer_names: List[str],
+        sizes: List[int],
+        process_group: ProcessGroup,
+    ):
+        return TensorParallelMultiAdapterLinear(
+            base_layer, layer_id, layer_names, sizes, process_group
+        )
+
+    def forward(
+        self, input: torch.Tensor, adapter_data: "AdapterBatchData"
+    ) -> torch.Tensor:
+        result = self.base_layer(input)
+
+        # noop if no layer names are provided (e.g. for models without adapters)
+        if self.layer_names is None:
+            return result
+
+        # handle models like Bloom that have inputs of shape
+        # (batch_size, sequence_length, hidden_size)
+        # we need to reshape them to (batch_size * sequence_length, hidden_size)
+        # for the LoRA computation, then reshape back
+        prev_shape = result.shape
+        is_3d = len(input.shape) >= 3
+        if is_3d:
+            input = input.reshape(-1, input.shape[-1])
+            result = result.reshape(-1, result.shape[-1])
+
+        offset = 0
+        for i, layer_name in enumerate(self.layer_names):
+            start_idx = offset // self.process_group.size()
+            # The 'sizes' parameter is essential in tensor-parallel setups for handling multiple
+            # projection layers (q_proj, k_proj, v_proj) by defining their output dimensions. It
+            # ensures correct slicing of the result tensor, accommodating variations like grouped-query
+            # attention where k_proj and v_proj differ from q_proj. This allows precise application of
+            # LoRA adapters to each sub-component of the multi-head attention mechanism, managing the
+            # different projection sizes across layers and model architectures.
+            if self.sizes is not None:
+                offset += self.sizes[i]
+                end_idx = offset // self.process_group.size()
+            else:
+                end_idx = result.shape[1]
+
+            result = self.forward_layer_type(
+                result, input, adapter_data, layer_name, start_idx, end_idx
+            )
+
+        if is_3d:
+            result = result.reshape(prev_shape)
+
+        return result
+
+    def collect_lora_a(self, a_out: torch.Tensor) -> torch.Tensor:
+        # Tensor parallel implementation of X @ A@B, where A and B are sharded column-wise.
+        # We use an all-gather between X@A and (X@A)@B to ensure alignment across ranks.
+        #
+        # TODO(travis): this is not very efficient as we do an all-gather for every adapter,
+        #   instead we could pre-allocate a (B, a, r) tensor for all adapters with the same
+        #   rank, compute `a_out` on each, and then slice them into the buffer as shown here:
+        #   https://discuss.pytorch.org/t/concatenate-tensors-without-memory-copying/34609
+        gathered_tensors = [
+            torch.empty_like(a_out) for _ in range(self.process_group.size())
+        ]
+        torch.distributed.all_gather(gathered_tensors, a_out)
+        return torch.cat(gathered_tensors, dim=1)
+
+
+class TensorParallelAdapterRowLinear(LoraLinear):
+    def __init__(self, base_layer, layer_id, layer_name, process_group):
+        super().__init__(base_layer, layer_id, process_group)
+        self.layer_name = layer_name
+
+    @classmethod
+    def load(cls, base_layer, layer_id, layer_name, process_group):
+        return cls(base_layer, layer_id, layer_name, process_group)
+
+    def forward(
+        self, input: torch.Tensor, adapter_data: "AdapterBatchData"
+    ) -> torch.Tensor:
+        result = self.base_layer(input)
+
+        if self.layer_name is None:
+            return result
+
+        # Fused all-gather + all-reduce from S-LoRA paper: https://arxiv.org/abs/2311.03285
+        stride = result.shape[-1] // self.process_group.size()
+        start_idx = self.process_group.rank() * stride
+        end_idx = (self.process_group.rank() + 1) * stride
+
+        self.forward_layer_type(
+            result, input, adapter_data, self.layer_name, start_idx, end_idx
+        )
+
+        return result
+
+    def collect_lora_a(self, a_out: torch.Tensor) -> torch.Tensor:
+        # Tensor parallel implementation of X @ A@B, where A and B are sharded row-wise.
+        # We use an all-reduce between X@A and (X@A)@B to ensure alignment across ranks.
+        #
+        # TODO(travis): this is not very efficient as we do an all-reduce for every adapter,
+        #   instead we could pre-allocate a (B, a, r) tensor for all adapters with the same
+        #   rank, compute `a_out` on each, and then slice them into the buffer as shown here:
+        #   https://discuss.pytorch.org/t/concatenate-tensors-without-memory-copying/34609
+        torch.distributed.all_reduce(a_out, group=self.process_group)
+        return a_out

server/text_generation_server/models/__init__.py

@@ -6,7 +6,7 @@ from loguru import logger
 from transformers.configuration_utils import PretrainedConfig
 from transformers.models.auto import modeling_auto
 from huggingface_hub import hf_hub_download, HfApi
-from typing import Optional
+from typing import Optional, List
 from pathlib import Path
 
 from text_generation_server.utils.speculate import get_speculate, set_speculate
@@ -253,6 +253,7 @@ for data in ModelType:
 
 def get_model(
     model_id: str,
+    lora_adapter_ids: Optional[List[str]],
     revision: Optional[str],
     sharded: bool,
     quantize: Optional[str],
@@ -595,6 +596,7 @@ def get_model(
                 speculator=speculator,
                 dtype=dtype,
                 trust_remote_code=trust_remote_code,
+                lora_adapter_ids=lora_adapter_ids,
             )
         elif sharded:
             raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))

server/text_generation_server/models/bloom.py

@@ -90,6 +90,7 @@ class BLOOMSharded(CausalLM):
 
         torch.distributed.barrier(group=self.process_group)
         super(CausalLM, self).__init__(
+            model_id=model_id,
             model=model,
             tokenizer=tokenizer,
             requires_padding=True,

server/text_generation_server/models/causal_lm.py

@@ -538,6 +538,7 @@ class CausalLM(Model):
                 tokenizer.add_special_tokens({"pad_token": "[PAD]"})
 
         super(CausalLM, self).__init__(
+            model_id=model_id,
             model=model,
             tokenizer=tokenizer,
             requires_padding=True,

server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py

@@ -514,6 +514,7 @@ class FlashCohereForCausalLM(torch.nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         hidden_states = self.model(
             input_ids,

server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py

@@ -724,6 +724,7 @@ class FlashDbrxForCausalLM(torch.nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         hidden_states = self.model(
             input_ids,

server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py

@@ -460,6 +460,7 @@ class FlashGemmaForCausalLM(torch.nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         input_embeds = self.embed_tokens(input_ids)
         hidden_states = self.model(

server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py

@@ -445,6 +445,7 @@ class FlashGPT2ForCausalLM(torch.nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor] = None,
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         token_embeds = self.embed_tokens(input_ids)
         position_embeds = self.embed_positions(position_ids)

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -38,6 +38,8 @@ from text_generation_server.layers import (
     TensorParallelColumnLinear,
     TensorParallelEmbedding,
     SpeculativeHead,
+    TensorParallelMultiAdapterLinear,
+    TensorParallelAdapterRowLinear,
 )
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
@@ -51,43 +53,61 @@ if SYSTEM == "rocm":
         raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
 
 
-def load_attention(config, prefix, weights):
+def load_attention(config, prefix, weights, layer_id):
     # Only defined in granite.
     bias = getattr(config, "attention_bias", False)
+    head_size = config.hidden_size // config.num_attention_heads
+    sizes = None
+    prefixes = None
 
-    # if specific model type, load the correct attention
     if config.model_type == "phi3":
-        return TensorParallelColumnLinear.load_qkv(
+        prefix = f"{prefix}.qkv_proj"
+        base_layer = TensorParallelColumnLinear.load_qkv(
             config,
-            prefix=f"{prefix}.qkv_proj",
+            prefix=prefix,
             weights=weights,
             bias=bias,
             num_heads=config.num_attention_heads,
             num_key_value_heads=config.num_key_value_heads,
         )
     elif config.model_type == "baichuan":
-        return TensorParallelColumnLinear.load_qkv(
+        prefix = f"{prefix}.W_pack"
+        base_layer = TensorParallelColumnLinear.load_qkv(
             config,
-            prefix=f"{prefix}.W_pack",
+            prefix=prefix,
             weights=weights,
             bias=bias,
             num_heads=config.num_attention_heads,
             num_key_value_heads=config.num_key_value_heads,
         )
+    else:
+        prefixes = ["q_proj", "k_proj", "v_proj"]
+        sizes = [
+            head_size * config.num_attention_heads,
+            head_size * config.num_key_value_heads,
+            head_size * config.num_key_value_heads,
+        ]
+        base_layer = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+            dim=0,
+            weights=weights,
+            bias=bias,
+        )
 
-    # otherwise, load the default attention based on the number of heads
-    return TensorParallelColumnLinear.load_multi(
-        config,
-        prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
-        dim=0,
-        weights=weights,
-        bias=bias,
+    return TensorParallelMultiAdapterLinear.load(
+        base_layer=base_layer,
+        layer_id=layer_id,
+        layer_names=prefixes,
+        sizes=sizes,
+        process_group=weights.process_group,
     )
 
 
 class FlashLlamaAttention(torch.nn.Module):
     def __init__(
         self,
+        index: int,
         prefix: str,
         config,
         weights,
@@ -121,14 +141,23 @@ class FlashLlamaAttention(torch.nn.Module):
             config.num_key_value_heads // weights.process_group.size()
         )
 
-        self.query_key_value = load_attention(config, prefix, weights)
+        self.query_key_value = load_attention(config, prefix, weights, index)
+        self.index = index
 
-        self.o_proj = TensorParallelRowLinear.load(
+        o_proj = TensorParallelRowLinear.load(
             config,
             prefix=f"{prefix}.o_proj",
             weights=weights,
             bias=False,
         )
+
+        self.o_proj = TensorParallelAdapterRowLinear.load(
+            o_proj,
+            index,
+            "o_proj",
+            process_group=weights.process_group,
+        )
+
         self.num_groups = self.num_heads // self.num_key_value_heads
         self.kv_head_mapping = torch.arange(
             0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
@@ -145,8 +174,9 @@ class FlashLlamaAttention(torch.nn.Module):
         slots,
         input_lengths,
         max_s,
+        adapter_data,
     ):
-        qkv = self.query_key_value(hidden_states)
+        qkv = self.query_key_value(hidden_states, adapter_data)
         query, kv = qkv.split(
             [
                 self.head_size * self.num_heads,
@@ -190,11 +220,13 @@ class FlashLlamaAttention(torch.nn.Module):
                 max_s,
             )
 
-        return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
+        return self.o_proj(
+            attn_output.view(-1, self.num_heads * self.head_size), adapter_data
+        )
 
 
 class LlamaMLP(nn.Module):
-    def __init__(self, prefix, config, weights):
+    def __init__(self, prefix, config, weights, index):
         super().__init__()
         self.hidden_act = config.hidden_act
         self.act = (
@@ -209,29 +241,54 @@ class LlamaMLP(nn.Module):
                 ),
             )
         )
+        prefixes = None
+        sizes = None
+
         # Fuse gate and up proj
         bias = getattr(config, "mlp_bias", False)
         if config.model_type == "phi3":
-            self.gate_up_proj = TensorParallelColumnLinear.load_gate_up(
+            gate_up_proj = TensorParallelColumnLinear.load_gate_up(
                 config,
                 prefix=f"{prefix}.gate_up_proj",
                 weights=weights,
                 bias=bias,
             )
         else:
-            self.gate_up_proj = TensorParallelColumnLinear.load_multi(
+            prefixes = [f"gate_proj", f"up_proj"]
+            sizes = [
+                config.intermediate_size,
+                config.intermediate_size,
+            ]
+            gate_up_proj = TensorParallelColumnLinear.load_multi(
                 config,
                 prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
                 weights=weights,
                 dim=0,
                 bias=bias,
             )
-        self.down_proj = TensorParallelRowLinear.load(
+
+        self.gate_up_proj = TensorParallelMultiAdapterLinear.load(
+            gate_up_proj,
+            index,
+            layer_names=prefixes,
+            sizes=sizes,
+            process_group=weights.process_group,
+        )
+
+        down_proj = TensorParallelRowLinear.load(
             config,
             prefix=f"{prefix}.down_proj",
             weights=weights,
             bias=bias,
         )
+
+        self.down_proj = TensorParallelAdapterRowLinear.load(
+            down_proj,
+            index,
+            "down_proj",
+            process_group=weights.process_group,
+        )
+
         self.intermediate_size = (
             config.intermediate_size // weights.process_group.size()
         )
@@ -239,7 +296,7 @@ class LlamaMLP(nn.Module):
         # TODO: This is a hotfix to be removed & properly refactored.
         self.quantize = config.quantize
 
-    def forward(self, hidden_states):
+    def forward(self, hidden_states, adapter_data):
         if (
             SYSTEM == "rocm"
             and self.hidden_act == "silu"
@@ -253,20 +310,27 @@ class LlamaMLP(nn.Module):
                 device="cuda",
             )
             _custom_C.LLMM_Silu(self.gate_up_proj.linear.weight, hidden_states, out, 8)
-            return self.down_proj(out)
+            return self.down_proj(out, adapter_data)
         else:
-            gate_up_states = self.gate_up_proj(hidden_states)
+            gate_up_states = self.gate_up_proj(hidden_states, adapter_data)
             gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
-            return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
+            return self.down_proj(
+                self.act(gate_up_states[:, 0]) * gate_up_states[:, 1], adapter_data
+            )
 
 
 class FlashLlamaLayer(nn.Module):
-    def __init__(self, prefix, config, weights):
+    def __init__(self, index, prefix, config, weights):
         super().__init__()
         self.self_attn = FlashLlamaAttention(
-            prefix=f"{prefix}.self_attn", config=config, weights=weights
+            index=index,
+            prefix=f"{prefix}.self_attn",
+            config=config,
+            weights=weights,
+        )
+        self.mlp = LlamaMLP(
+            prefix=f"{prefix}.mlp", config=config, weights=weights, index=index
         )
-        self.mlp = LlamaMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
 
         self.input_layernorm = FastRMSNorm.load(
             prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
@@ -289,6 +353,7 @@ class FlashLlamaLayer(nn.Module):
         slots,
         input_lengths,
         max_s,
+        adapter_data,
     ):
         normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
 
@@ -303,6 +368,7 @@ class FlashLlamaLayer(nn.Module):
             slots,
             input_lengths,
             max_s,
+            adapter_data,
         )
 
         # faster post attention rms norm
@@ -310,7 +376,7 @@ class FlashLlamaLayer(nn.Module):
             attn_output, res
         )
 
-        mlp_output = self.mlp(normed_attn_res_output)
+        mlp_output = self.mlp(normed_attn_res_output, adapter_data)
 
         return mlp_output, attn_res
 
@@ -325,6 +391,7 @@ class FlashLlamaModel(torch.nn.Module):
         self.layers = nn.ModuleList(
             [
                 FlashLlamaLayer(
+                    index=layer_id,
                     prefix=(
                         f"model.layers.{layer_id}"
                         if not prefix
@@ -360,6 +427,7 @@ class FlashLlamaModel(torch.nn.Module):
         max_s: int,
         true_max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
+        adapter_data,
     ) -> torch.Tensor:
         hidden_states = inputs_embeds
 
@@ -382,6 +450,7 @@ class FlashLlamaModel(torch.nn.Module):
                 slots,
                 input_lengths,
                 max_s,
+                adapter_data,
             )
 
         hidden_states, _ = self.norm(hidden_states, residual)
@@ -423,6 +492,7 @@ class FlashLlamaForCausalLM(torch.nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor] = None,
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         inputs_embeds = self.embed_tokens(input_ids)
         hidden_states = self.model(
@@ -436,6 +506,7 @@ class FlashLlamaForCausalLM(torch.nn.Module):
             max_s,
             true_max_s=max_s,
             prefill_cache_indices=prefill_cache_indices,
+            adapter_data=adapter_data,
         )
         if lm_head_indices is not None:
             hidden_states = hidden_states[lm_head_indices]

server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py

@@ -38,6 +38,8 @@ from text_generation_server.layers import (
     TensorParallelEmbedding,
     SpeculativeHead,
     get_linear,
+    TensorParallelMultiAdapterLinear,
+    TensorParallelAdapterRowLinear,
 )
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
@@ -107,12 +109,7 @@ class MistralConfig(PretrainedConfig):
 
 
 class MistralAttention(torch.nn.Module):
-    def __init__(
-        self,
-        prefix: str,
-        config,
-        weights,
-    ):
+    def __init__(self, prefix: str, config, weights, layer_id):
         super().__init__()
         self.max_past = (
             config.sliding_window if config.sliding_window is not None else -1
@@ -140,7 +137,7 @@ class MistralAttention(torch.nn.Module):
             config.num_key_value_heads // weights.process_group.size()
         )
 
-        self.query_key_value = TensorParallelColumnLinear.load_multi(
+        query_key_value = TensorParallelColumnLinear.load_multi(
             config,
             prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
             dim=0,
@@ -148,12 +145,31 @@ class MistralAttention(torch.nn.Module):
             bias=False,
         )
 
-        self.o_proj = TensorParallelRowLinear.load(
+        head_size = config.hidden_size // config.num_attention_heads
+        self.query_key_value = TensorParallelMultiAdapterLinear.load(
+            query_key_value,
+            layer_id,
+            ["q_proj", "k_proj", "v_proj"],
+            sizes=[
+                head_size * config.num_attention_heads,
+                head_size * config.num_key_value_heads,
+                head_size * config.num_key_value_heads,
+            ],
+            process_group=weights.process_group,
+        )
+
+        o_proj = TensorParallelRowLinear.load(
             config,
             prefix=f"{prefix}.o_proj",
             weights=weights,
             bias=False,
         )
+        self.o_proj = TensorParallelAdapterRowLinear.load(
+            o_proj,
+            layer_id,
+            "o_proj",
+            process_group=weights.process_group,
+        )
         self.num_groups = self.num_heads // self.num_key_value_heads
         self.kv_head_mapping = torch.arange(
             0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
@@ -171,8 +187,9 @@ class MistralAttention(torch.nn.Module):
         input_lengths,
         max_s,
         prefill_cache_indices,
+        adapter_data,
     ):
-        qkv = self.query_key_value(hidden_states)
+        qkv = self.query_key_value(hidden_states, adapter_data)
         query, kv = qkv.split(
             [
                 self.head_size * self.num_heads,
@@ -224,11 +241,13 @@ class MistralAttention(torch.nn.Module):
                 max_s,
             )
 
-        return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
+        return self.o_proj(
+            attn_output.view(-1, self.num_heads * self.head_size), adapter_data
+        )
 
 
 class MistralMLP(nn.Module):
-    def __init__(self, prefix, config, weights):
+    def __init__(self, prefix, config, weights, layer_id):
         super().__init__()
         self.hidden_act = config.hidden_act
         self.act = (
@@ -244,19 +263,37 @@ class MistralMLP(nn.Module):
             )
         )
         # Fuse gate and up proj
-        self.gate_up_proj = TensorParallelColumnLinear.load_multi(
+        gate_up_proj = TensorParallelColumnLinear.load_multi(
             config,
             prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
             weights=weights,
             dim=0,
             bias=False,
         )
-        self.down_proj = TensorParallelRowLinear.load(
+        self.gate_up_proj = TensorParallelMultiAdapterLinear.load(
+            gate_up_proj,
+            layer_id,
+            ["gate_proj", "up_proj"],
+            sizes=[
+                config.intermediate_size,
+                config.intermediate_size,
+            ],
+            process_group=weights.process_group,
+        )
+
+        down_proj = TensorParallelRowLinear.load(
             config,
             prefix=f"{prefix}.down_proj",
             weights=weights,
             bias=False,
         )
+
+        self.down_proj = TensorParallelAdapterRowLinear.load(
+            down_proj,
+            layer_id,
+            "down_proj",
+            process_group=weights.process_group,
+        )
         self.intermediate_size = (
             config.intermediate_size // weights.process_group.size()
         )
@@ -264,7 +301,7 @@ class MistralMLP(nn.Module):
         # TODO: This is a hotfix to be removed & properly refactored.
         self.quantize = config.quantize
 
-    def forward(self, hidden_states):
+    def forward(self, hidden_states, adapter_data):
         if (
             SYSTEM == "rocm"
             and self.hidden_act == "silu"
@@ -278,20 +315,27 @@ class MistralMLP(nn.Module):
                 device="cuda",
             )
             _custom_C.LLMM_Silu(self.gate_up_proj.linear.weight, hidden_states, out, 8)
-            return self.down_proj(out)
+            return self.down_proj(out, adapter_data)
         else:
-            gate_up_states = self.gate_up_proj(hidden_states)
+            gate_up_states = self.gate_up_proj(hidden_states, adapter_data)
             gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
-            return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
+            return self.down_proj(
+                self.act(gate_up_states[:, 0]) * gate_up_states[:, 1], adapter_data
+            )
 
 
 class MistralLayer(nn.Module):
-    def __init__(self, prefix, config, weights):
+    def __init__(self, prefix, config, weights, layer_id):
         super().__init__()
         self.self_attn = MistralAttention(
-            prefix=f"{prefix}.self_attn", config=config, weights=weights
+            prefix=f"{prefix}.self_attn",
+            config=config,
+            weights=weights,
+            layer_id=layer_id,
+        )
+        self.mlp = MistralMLP(
+            prefix=f"{prefix}.mlp", config=config, weights=weights, layer_id=layer_id
         )
-        self.mlp = MistralMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
 
         self.input_layernorm = FastRMSNorm.load(
             prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
@@ -315,6 +359,7 @@ class MistralLayer(nn.Module):
         input_lengths,
         max_s,
         prefill_cache_indices,
+        adapter_data,
     ):
         normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
 
@@ -330,6 +375,7 @@ class MistralLayer(nn.Module):
             input_lengths,
             max_s,
             prefill_cache_indices,
+            adapter_data,
         )
 
         # faster post attention rms norm
@@ -337,7 +383,7 @@ class MistralLayer(nn.Module):
             attn_output, res
         )
 
-        mlp_output = self.mlp(normed_attn_res_output)
+        mlp_output = self.mlp(normed_attn_res_output, adapter_data)
 
         return mlp_output, attn_res
 
@@ -355,6 +401,7 @@ class MistralModel(torch.nn.Module):
                     prefix=f"{prefix}.layers.{layer_id}",
                     config=config,
                     weights=weights,
+                    layer_id=layer_id,
                 )
                 for layer_id in range(config.num_hidden_layers)
             ]
@@ -381,6 +428,7 @@ class MistralModel(torch.nn.Module):
         max_s: int,
         true_max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
+        adapter_data: Optional[torch.Tensor] = None,
     ):
         hidden_states = inputs_embeds
         # Get rotary cos and sin for this forward
@@ -403,6 +451,7 @@ class MistralModel(torch.nn.Module):
                 input_lengths,
                 max_s,
                 prefill_cache_indices,
+                adapter_data,
             )
 
         hidden_states, _ = self.norm(hidden_states, residual)
@@ -454,6 +503,7 @@ class FlashMistralForCausalLM(torch.nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         true_max_s = max_s
         if prefill_cache_indices is not None:
@@ -476,6 +526,7 @@ class FlashMistralForCausalLM(torch.nn.Module):
             max_s,
             true_max_s,
             prefill_cache_indices,
+            adapter_data,
         )
         if lm_head_indices is not None:
             hidden_states = hidden_states[lm_head_indices]

server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py

@@ -638,6 +638,7 @@ class FlashMixtralForCausalLM(torch.nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         true_max_s = max_s
         if prefill_cache_indices is not None:

server/text_generation_server/models/custom_modeling/flash_neox_modeling.py

@@ -390,6 +390,7 @@ class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         hidden_states = self.gpt_neox(
             input_ids,

server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py

@@ -74,6 +74,7 @@ class PaliGemmaForConditionalGeneration(nn.Module):
         # Unused here
         pixel_attention_mask: Optional[torch.BoolTensor] = None,
         image_sizes: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         inputs_embeds = self.text_model.embed_tokens(input_ids)
         # TODO This is odd but apparently pali gemma position ids start at 1.

server/text_generation_server/models/custom_modeling/flash_phi_modeling.py

@@ -400,6 +400,7 @@ class FlashPhiForCausalLM(torch.nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         hidden_states = self.model(
             input_ids,

server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py

@@ -359,6 +359,7 @@ class Qwen2ForCausalLM(torch.nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor] = None,
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         true_max_s = max_s
         if prefill_cache_indices is not None:

server/text_generation_server/models/custom_modeling/flash_rw_modeling.py

@@ -672,6 +672,7 @@ class FlashRWForCausalLM(FlashRWPreTrainedModel):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         hidden_states = self.transformer(
             input_ids,

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -483,6 +483,7 @@ class FlashSantacoderForCausalLM(nn.Module):
         max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
         lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         hidden_states = self.transformer(
             input_ids,