init

vllm/model_executor/models/__init__.py

@@ -0,0 +1,107 @@
+import importlib
+from typing import List, Optional, Type
+
+import torch.nn as nn
+
+from vllm.logger import init_logger
+from vllm.utils import is_hip, is_neuron
+
+logger = init_logger(__name__)
+
+# Architecture -> (module, class).
+_MODELS = {
+    "AquilaModel": ("llama", "LlamaForCausalLM"),
+    "AquilaForCausalLM": ("llama", "LlamaForCausalLM"),  # AquilaChat2
+    "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),  # baichuan-7b
+    "BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"),  # baichuan-13b
+    "BloomForCausalLM": ("bloom", "BloomForCausalLM"),
+    "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),
+    "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
+    "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"),
+    "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"),
+    "FalconForCausalLM": ("falcon", "FalconForCausalLM"),
+    "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
+    "GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),
+    "GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"),
+    "GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"),
+    "GPTNeoXForCausalLM": ("gpt_neox", "GPTNeoXForCausalLM"),
+    "InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
+    "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),
+    "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
+    "SQLlamaForCausalLM": ("llama_smooth","LlamaForCausalLM"),
+    "CPMDragonflyForCausalLM": ("cpm", "CPMDragonflyForCausalLM"),
+    
+    # For decapoda-research/llama-*
+    "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
+    "MistralForCausalLM": ("llama", "LlamaForCausalLM"),
+    "MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"),
+    "QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"),
+    # transformers's mpt class has lower case
+    "MptForCausalLM": ("mpt", "MPTForCausalLM"),
+    "MPTForCausalLM": ("mpt", "MPTForCausalLM"),
+    "OLMoForCausalLM": ("olmo", "OLMoForCausalLM"),
+    "OPTForCausalLM": ("opt", "OPTForCausalLM"),
+    "OrionForCausalLM": ("orion", "OrionForCausalLM"),
+    "PhiForCausalLM": ("phi", "PhiForCausalLM"),
+    "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
+    "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
+    "RWForCausalLM": ("falcon", "FalconForCausalLM"),
+    "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
+    "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
+    "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
+}
+
+# Models not supported by ROCm.
+_ROCM_UNSUPPORTED_MODELS = []
+
+# Models partially supported by ROCm.
+# Architecture -> Reason.
+_ROCM_PARTIALLY_SUPPORTED_MODELS = {
+    "Qwen2ForCausalLM":
+    "Sliding window attention is not yet supported in ROCm's flash attention",
+    "MistralForCausalLM":
+    "Sliding window attention is not yet supported in ROCm's flash attention",
+    "MixtralForCausalLM":
+    "Sliding window attention is not yet supported in ROCm's flash attention",
+}
+
+# Models not supported by Neuron.
+_NEURON_SUPPORTED_MODELS = {"LlamaForCausalLM": "neuron.llama"}
+
+
+class ModelRegistry:
+
+    @staticmethod
+    def load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]:
+        if model_arch not in _MODELS:
+            return None
+        if is_hip():
+            if model_arch in _ROCM_UNSUPPORTED_MODELS:
+                raise ValueError(
+                    f"Model architecture {model_arch} is not supported by "
+                    "ROCm for now.")
+            if model_arch in _ROCM_PARTIALLY_SUPPORTED_MODELS:
+                logger.warning(
+                    f"Model architecture {model_arch} is partially supported "
+                    "by ROCm: " + _ROCM_PARTIALLY_SUPPORTED_MODELS[model_arch])
+        elif is_neuron():
+            if model_arch not in _NEURON_SUPPORTED_MODELS:
+                raise ValueError(
+                    f"Model architecture {model_arch} is not supported by "
+                    "Neuron for now.")
+
+        module_name, model_cls_name = _MODELS[model_arch]
+        if is_neuron():
+            module_name = _NEURON_SUPPORTED_MODELS[model_arch]
+        module = importlib.import_module(
+            f"vllm.model_executor.models.{module_name}")
+        return getattr(module, model_cls_name, None)
+
+    @staticmethod
+    def get_supported_archs() -> List[str]:
+        return list(_MODELS.keys())
+
+
+__all__ = [
+    "ModelRegistry",
+]

vllm/model_executor/models/baichuan.py

@@ -0,0 +1,386 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only BaiChuan model compatible with HuggingFace weights."""
+import math
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
+    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
+    base = torch.tensor(
+        2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+        dtype=torch.float32,
+    )
+    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != total_num_heads:
+        extra_base = torch.tensor(
+            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+            dtype=torch.float32,
+        )
+        num_remaining_heads = min(closest_power_of_2,
+                                  total_num_heads - closest_power_of_2)
+        extra_powers = torch.arange(start=1,
+                                    end=1 + 2 * num_remaining_heads,
+                                    step=2,
+                                    dtype=torch.int32)
+        slopes = torch.cat(
+            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
+    return slopes
+
+
+class BaiChuanMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           linear_method=linear_method)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class BaiChuanAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        position_embedding: str,
+        rope_theta: float = 10000,
+        max_position_embeddings: int = 8192,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
+        )
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.postion_embedding = position_embedding
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        # pylint: disable=invalid-name
+        self.W_pack = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+        # Create the alibi slopes and slice them.
+        if self.postion_embedding == "ALIBI":
+            tp_rank = get_tensor_model_parallel_rank()
+            head_start = tp_rank * self.num_heads
+            head_end = (tp_rank + 1) * self.num_heads
+            alibi_slopes = _get_alibi_slopes(self.total_num_heads)
+            alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+
+            scaling = self.head_dim**-0.5
+            self.attn = PagedAttention(self.num_heads,
+                                       self.head_dim,
+                                       scaling,
+                                       alibi_slopes=alibi_slopes)
+        else:
+            self.rotary_emb = get_rope(
+                self.head_dim,
+                rotary_dim=self.head_dim,
+                max_position=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+            self.scaling = self.head_dim**-0.5
+            self.attn = PagedAttention(self.num_heads, self.head_dim,
+                                       self.scaling)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.W_pack(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        if self.postion_embedding != "ALIBI":
+            q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class BaiChuanDecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 position_embedding: str,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = BaiChuanAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            position_embedding=position_embedding,
+            rope_theta=rope_theta,
+            max_position_embeddings=max_position_embeddings,
+            linear_method=linear_method,
+        )
+        self.mlp = BaiChuanMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            linear_method=linear_method,
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class BaiChuanModel(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 position_embedding: str,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            BaiChuanDecoderLayer(config, position_embedding, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class BaiChuanBaseForCausalLM(nn.Module):
+
+    def __init__(self,
+                 config,
+                 position_embedding: str,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = BaiChuanModel(config, position_embedding, linear_method)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if name == "lm_head.weight":
+                # Unlike Baichuan, Baichuan2 normalizes the head weights. Refer to:
+                # https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
+                # Distinguish between Baichuan and Baichuan2 by checking the
+                # vocab size. This is suggested by
+                # https://github.com/vllm-project/vllm/pull/1022#discussion_r1325652704
+                is_baichuan2 = self.config.vocab_size == 125696
+                if is_baichuan2:
+                    loaded_weight = torch.nn.functional.normalize(
+                        loaded_weight)
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
+    """Baichuan 13B and Baichuan2 7B/13B."""
+
+    def __init__(self,
+                 config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        if config.hidden_size in [4096,6656]:  # baichuan2 7b 33b
+            super().__init__(config, "ROPE", linear_method)
+        else:  # baichuan 13b, baichuan2 13b
+            super().__init__(config, "ALIBI", linear_method)
+
+
+class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
+    """Baichuan 7B."""
+
+    def __init__(self,
+                 config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__(config, "ROPE", linear_method)

vllm/model_executor/models/bloom.py

@@ -0,0 +1,330 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/bloom/modeling_bloom.py
+# Copyright 2023 The CacheFlow team.
+# Copyright 2022 HuggingFace Inc. team and BigScience workshop.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only BLOOM model compatible with HuggingFace weights."""
+import math
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import BloomConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
+    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
+    base = torch.tensor(
+        2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+        dtype=torch.float32,
+    )
+    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != total_num_heads:
+        extra_base = torch.tensor(
+            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+            dtype=torch.float32,
+        )
+        num_remaining_heads = min(closest_power_of_2,
+                                  total_num_heads - closest_power_of_2)
+        extra_powers = torch.arange(start=1,
+                                    end=1 + 2 * num_remaining_heads,
+                                    step=2,
+                                    dtype=torch.int32)
+        slopes = torch.cat(
+            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
+    return slopes
+
+
+class BloomAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: BloomConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.total_num_heads = config.n_head
+        self.head_dim = self.hidden_size // self.total_num_heads
+        assert self.head_dim * self.total_num_heads == self.hidden_size
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+
+        self.query_key_value = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.dense = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            linear_method=linear_method,
+        )
+
+        # Create the alibi slopes and slice them.
+        tp_rank = get_tensor_model_parallel_rank()
+        head_start = tp_rank * self.num_heads
+        head_end = (tp_rank + 1) * self.num_heads
+        alibi_slopes = _get_alibi_slopes(self.total_num_heads)
+        alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+
+        scaling = self.head_dim**-0.5
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   scaling,
+                                   alibi_slopes=alibi_slopes)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        del position_ids  # Unused.
+        qkv, _ = self.query_key_value(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class BloomMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: BloomConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.dense_h_to_4h = ColumnParallelLinear(
+            hidden_size,
+            4 * hidden_size,
+            linear_method=linear_method,
+        )
+        quant_config = getattr(linear_method, "quant_config", None)
+        self.gelu_impl = get_act_fn("gelu", quant_config, 4 * hidden_size)
+        self.dense_4h_to_h = RowParallelLinear(
+            4 * hidden_size,
+            hidden_size,
+            linear_method=linear_method,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.dense_h_to_4h(x)
+        x = self.gelu_impl(x)
+        x, _ = self.dense_4h_to_h(x)
+        return x
+
+
+class BloomBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: BloomConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+
+        self.input_layernorm = nn.LayerNorm(hidden_size,
+                                            eps=config.layer_norm_epsilon)
+        self.self_attention = BloomAttention(config, linear_method)
+        self.post_attention_layernorm = nn.LayerNorm(
+            hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = BloomMLP(config, linear_method)
+        self.apply_residual_connection_post_layernorm = (
+            config.apply_residual_connection_post_layernorm)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        # Layer norm at the beginning of the transformer layer.
+        layernorm_output = self.input_layernorm(hidden_states)
+
+        # Layer norm post the self attention.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = hidden_states
+
+        # Self attention.
+        attention_output = self.self_attention(
+            position_ids=position_ids,
+            hidden_states=layernorm_output,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        attention_output = attention_output + residual
+        layernorm_output = self.post_attention_layernorm(attention_output)
+
+        # Get residual
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = attention_output
+
+        # MLP.
+        output = self.mlp(layernorm_output) + residual
+        return output
+
+
+class BloomModel(nn.Module):
+
+    def __init__(
+        self,
+        config: BloomConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+
+        # Embedding + LN Embedding
+        self.word_embeddings = VocabParallelEmbedding(
+            config.vocab_size,
+            self.embed_dim,
+        )
+        self.word_embeddings_layernorm = nn.LayerNorm(
+            self.embed_dim, eps=config.layer_norm_epsilon)
+
+        # Transformer blocks
+        self.h = nn.ModuleList([
+            BloomBlock(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+
+        # Final Layer Norm
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.word_embeddings(input_ids)
+        hidden_states = self.word_embeddings_layernorm(hidden_states)
+        for i in range(len(self.h)):
+            layer = self.h[i]
+            hidden_states = layer(
+                position_ids,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+            )
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class BloomForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: BloomConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.transformer = BloomModel(config, linear_method)
+        self.lm_head_weight = self.transformer.word_embeddings.weight
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if name == "lm_head.weight":
+                continue
+            if not name.startswith("transformer."):
+                name = "transformer." + name
+            param = params_dict[name]
+
+            if "query_key_value" in name:
+                # NOTE: BLOOM's fused QKV's output_dim has the shape of
+                # (num_heads * 3 * head_size), while the
+                # required shape is (3 * num_heads * head_size).
+                # Thus, we need weight conversion.
+                output_dim = getattr(param, "output_dim", None)
+                num_heads = self.config.num_attention_heads
+                if output_dim is not None:
+                    loaded_weight_shape = loaded_weight.shape
+                    loaded_weight = loaded_weight.view(
+                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
+                        loaded_weight_shape[output_dim + 1:])
+                    loaded_weight = loaded_weight.transpose(
+                        output_dim, output_dim + 1)
+                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)
+
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)

vllm/model_executor/models/chatglm.py

@@ -0,0 +1,396 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/THUDM/ChatGLM2-6B
+"""Inference-only ChatGLM model compatible with THUDM weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from torch.nn import LayerNorm
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs import ChatGLMConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class GLMAttention(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        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.multi_query_attention = config.multi_query_attention
+        self.total_num_kv_heads = (config.multi_query_group_num
+                                   if config.multi_query_attention else
+                                   config.num_attention_heads)
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = config.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+
+        self.query_key_value = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=config.add_bias_linear or config.add_qkv_bias,
+            linear_method=linear_method,
+        )
+        self.dense = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            config.hidden_size,
+            bias=config.add_bias_linear,
+            linear_method=linear_method,
+        )
+
+        # https://huggingface.co/THUDM/chatglm3-6b-32k/blob/e210410255278dd9d74463cf396ba559c0ef801c/modeling_chatglm.py#L141
+        rope_ratio = getattr(config, "rope_ratio", 1.0)
+        max_positions = getattr(config, "seq_length", 8192)
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim // 2,
+            max_position=max_positions,
+            base=10000 * rope_ratio,
+            is_neox_style=False,
+        )
+        self.attn = PagedAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.query_key_value(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(position_ids, q, k)
+        key_cache, value_cache = kv_cache
+        context_layer = self.attn(
+            q,
+            k,
+            v,
+            key_cache,
+            value_cache,
+            input_metadata,
+        )
+        attn_output, _ = self.dense(context_layer)
+        return attn_output
+
+
+class GLMMLP(nn.Module):
+    """MLP.
+
+    MLP will take the input with h hidden state, project it to 4*h
+    hidden dimension, perform nonlinear transformation, and project the
+    state back into h hidden dimension.
+    """
+
+    def __init__(
+        self,
+        config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+
+        self.add_bias = config.add_bias_linear
+
+        # Project to 4h.
+        self.dense_h_to_4h = MergedColumnParallelLinear(
+            config.hidden_size,
+            [config.ffn_hidden_size] * 2,
+            bias=config.add_bias_linear,
+            linear_method=linear_method,
+        )
+
+        self.activation_func = SiluAndMul()
+
+        # Project back to h.
+        self.dense_4h_to_h = RowParallelLinear(
+            config.ffn_hidden_size,
+            config.hidden_size,
+            bias=config.add_bias_linear,
+            linear_method=linear_method,
+        )
+
+    def forward(self, hidden_states):
+        # [s, b, 4hp]
+        intermediate_parallel, _ = self.dense_h_to_4h(hidden_states)
+        intermediate_parallel = self.activation_func(intermediate_parallel)
+        # [s, b, h]
+        output, _ = self.dense_4h_to_h(intermediate_parallel)
+        return output
+
+
+class GLMBlock(nn.Module):
+    """A single transformer layer.
+
+    Transformer layer takes input with size [s, b, h] and returns an
+    output of the same size.
+    """
+
+    def __init__(
+        self,
+        config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.apply_residual_connection_post_layernorm = (
+            config.apply_residual_connection_post_layernorm)
+
+        self.fp32_residual_connection = config.fp32_residual_connection
+
+        layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
+        # Layernorm on the input data.
+        self.input_layernorm = layer_norm_func(config.hidden_size,
+                                               eps=config.layernorm_epsilon)
+
+        # Self attention.
+        self.self_attention = GLMAttention(config, linear_method)
+        self.hidden_dropout = config.hidden_dropout
+
+        # Layernorm on the attention output
+        self.post_attention_layernorm = layer_norm_func(
+            config.hidden_size, eps=config.layernorm_epsilon)
+
+        # MLP
+        self.mlp = GLMMLP(config, linear_method)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        # hidden_states: [num_tokens, h]
+        # Layer norm at the beginning of the transformer layer.
+        layernorm_output = self.input_layernorm(hidden_states)
+        # Self attention.
+        attention_output = self.self_attention(
+            hidden_states=layernorm_output,
+            position_ids=position_ids,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = hidden_states
+
+        layernorm_input = residual + attention_output
+
+        # Layer norm post the self attention.
+        layernorm_output = self.post_attention_layernorm(layernorm_input)
+
+        # Second residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = layernorm_input
+
+        output = self.mlp(layernorm_output) + residual
+
+        return output
+
+
+class GLMTransformer(nn.Module):
+    """Transformer class."""
+
+    def __init__(
+        self,
+        config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.post_layer_norm = config.post_layer_norm
+
+        # Number of layers.
+        self.num_layers = config.num_layers
+
+        # Transformer layers.
+        self.layers = nn.ModuleList(
+            [GLMBlock(config, linear_method) for i in range(self.num_layers)])
+
+        if self.post_layer_norm:
+            layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
+            # Final layer norm before output.
+            self.final_layernorm = layer_norm_func(
+                config.hidden_size, eps=config.layernorm_epsilon)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            hidden_states = layer(
+                hidden_states=hidden_states,
+                position_ids=position_ids,
+                kv_cache=kv_caches[i],
+                input_metadata=input_metadata,
+            )
+        # Final layer norm.
+        if self.post_layer_norm:
+            hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states
+
+
+class ChatGLMModel(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+
+        self.embedding = VocabParallelEmbedding(config.padded_vocab_size,
+                                                config.hidden_size)
+
+        self.num_layers = config.num_layers
+        self.multi_query_group_num = config.multi_query_group_num
+        self.kv_channels = config.kv_channels
+        self.encoder = GLMTransformer(config, linear_method)
+
+        self.output_layer = ParallelLMHead(config.padded_vocab_size,
+                                           config.hidden_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        inputs_embeds = self.embedding(input_ids)
+
+        # Run encoder.
+        hidden_states = self.encoder(
+            hidden_states=inputs_embeds,
+            position_ids=position_ids,
+            kv_caches=kv_caches,
+            input_metadata=input_metadata,
+        )
+        return hidden_states
+
+
+class ChatGLMForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: ChatGLMConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config: ChatGLMConfig = config
+        self.linear_method = linear_method
+        self.transformer = ChatGLMModel(config, linear_method)
+        self.lm_head_weight = self.transformer.output_layer.weight
+        self.sampler = Sampler(config.padded_vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        try:
+            # torch < 2.0 do not have "remove_duplicate=False" in named_parameters
+            params_dict = dict(self.named_parameters(remove_duplicate=False))
+            for name, loaded_weight in hf_model_weights_iterator(
+                    model_name_or_path, cache_dir, load_format, revision):
+                if "rotary_pos_emb.inv_freq" in name:
+                    continue
+                if "word_embeddings" in name:
+                    name = name.replace(".word_embeddings", "")
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+        except:
+            params_dict = dict(self.named_parameters())
+            for name, loaded_weight in hf_model_weights_iterator(
+                    model_name_or_path, cache_dir, load_format, revision):
+                if "rotary_pos_emb.inv_freq" in name:
+                    continue
+                if "word_embeddings" in name:
+                    name = name.replace(".word_embeddings", "")
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                try:
+                    param = params_dict[name]
+                except:
+                    assert name == "transformer.output_layer.weight"
+                    param = self.transformer.output_layer.weight
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/cpm.py

@@ -0,0 +1,368 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only LLaMA model compatible with HuggingFace weights."""
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import (RMSNorm)
+
+
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, 
+    ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs import CPMDragonflyConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class CPMMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           linear_method=linear_method)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class CPMAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = PagedAttention(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: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class CPMDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: CPMDragonflyConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = CPMAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            linear_method=linear_method,
+        )
+        self.mlp = CPMMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            linear_method=linear_method,
+        )
+        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)
+        self.scale_states = config.scale_states # hidden_states
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual, scale = self.scale_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual, scale = self.scale_states)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class CPMModel(nn.Module):
+
+    def __init__(
+        self,
+        config: CPMDragonflyConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = getattr(config,"pad_token_id",None)
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            CPMDecoderLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.scale = self.config.scale
+        self.scale_emb = self.config.scale_emb # embeding
+        self.scale_states = self.config.scale_states # hidden_states
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        if self.scale:
+            hidden_states = self.embed_tokens(input_ids) * self.scale_emb
+        else:
+            hidden_states = self.embed_tokens(input_ids)
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual, scale=self.scale_states)
+        return hidden_states
+
+
+class CPMDragonflyForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: CPMDragonflyConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = CPMModel(config, linear_method)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+        
+        self.apply_inf = False
+        self.sampler_weight = None
+        self.scale_width = self.config.scale_width # output logits
+        
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        self.apply_inf = input_metadata.is_prompt
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> SamplerOutput:
+        # next_tokens = self.sampler(self.sampler_weight,
+        #                            hidden_states,
+        #                            sampling_metadata,
+        #                            apply_inf = self.apply_inf,
+        #                            index=1,
+        #                            skip_prompt=True,  # skip prompt tokens when apply _apply_penalties function
+        #                            logits_scale=self.scale_width, # apply scale in sampler to avoid 
+        #                            )                              # an elementwise op on all outputs
+        next_tokens = self.sampler(self.sampler_weight,
+                                   hidden_states,
+                                   sampling_metadata,
+                                   logits_scale=self.scale_width,
+                                   )
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+        self.sampler_weight = self.model.embed_tokens.weight if self.config.tie_lm_head == False else self.lm_head.weight

vllm/model_executor/models/decilm.py

@@ -0,0 +1,127 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 DeciAI Research Team. All rights reserved.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on MistralAI GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only DeciLM model compatible with HuggingFace weights."""
+
+from typing import Optional
+
+import torch
+from transformers import PretrainedConfig
+
+from vllm.config import LoRAConfig
+from vllm.model_executor.layers.linear import LinearMethodBase
+from vllm.model_executor.models.llama import LlamaForCausalLM
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+
+
+class DeciLMForCausalLM(LlamaForCausalLM):
+    """
+    Implementation for https://huggingface.co/Deci/DeciLM-7b-instruct.
+    Based on the llama executor.
+
+    The main difference is that DeciLM uses Variable Grouped Query Attention.
+    The constant number of GQA heads in the decoder is overridden with a value
+    per layer.
+
+    Usually, in the HuggingFace implementation, instead of
+    "config.num_key_value_heads", we use
+    "config.num_key_value_heads_per_layer[i]" which varies.
+
+    Currently, PagedAttention does not work well with variable GQA, so we
+    normalize the weights upon loading, and use uniform GQA with the max value
+    instead.
+    """
+
+    def __init__(
+        self,
+        config: Optional[PretrainedConfig] = None,
+        linear_method: Optional[LinearMethodBase] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        config.num_key_value_heads = max(config.num_key_value_heads_per_layer)
+        delattr(config, "num_key_value_heads_per_layer")
+        super().__init__(config=config,
+                         linear_method=linear_method,
+                         lora_config=lora_config)
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            if "k_proj" in name or "v_proj" in name:
+                loaded_weight = self._degroup_weight(loaded_weight)
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+    def _degroup_weight(self, loaded_weight: torch.Tensor) -> torch.Tensor:
+        hidden_size = self.config.hidden_size
+        head_size = self.config.hidden_size // self.config.num_attention_heads
+        target_num_kv_heads = self.config.num_key_value_heads
+        num_kv_heads = loaded_weight.shape[0] // head_size
+        n_repeats = target_num_kv_heads / num_kv_heads
+        assert n_repeats == int(n_repeats)
+
+        n_repeats = int(n_repeats)
+        loaded_weight = loaded_weight.view(num_kv_heads, head_size,
+                                           hidden_size)
+        loaded_weight = torch.repeat_interleave(loaded_weight,
+                                                repeats=n_repeats,
+                                                dim=0)
+        loaded_weight = loaded_weight.reshape(target_num_kv_heads * head_size,
+                                              hidden_size)
+
+        return loaded_weight

vllm/model_executor/models/deepseek.py

@@ -0,0 +1,444 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Deepseek model."""
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.fused_moe import fused_moe
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               ReplicatedLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.communication_op import (
+    tensor_model_parallel_all_reduce)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class DeepseekMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        linear_method: Optional[LinearMethodBase] = None,
+        reduce_results: bool = True,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           linear_method=linear_method,
+                                           reduce_results=reduce_results)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class DeepseekMoE(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.rank = get_tensor_model_parallel_rank()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.n_routed_experts = config.n_routed_experts
+        self.top_k = config.num_experts_per_tok
+        if self.tp_size > self.n_routed_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {self.n_routed_experts}.")
+
+        self.experts = nn.ModuleList([
+            DeepseekMLP(hidden_size=config.hidden_size,
+                        intermediate_size=config.moe_intermediate_size,
+                        hidden_act=config.hidden_act,
+                        linear_method=linear_method,
+                        reduce_results=False)
+            for idx in range(self.n_routed_experts)
+        ])
+        self.pack_params()
+
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     self.n_routed_experts,
+                                     bias=False,
+                                     linear_method=None)
+
+        if config.n_shared_experts is not None:
+            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+            self.shared_experts = DeepseekMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                linear_method=linear_method,
+                reduce_results=False,
+            )
+
+    def pack_params(self):
+        w1 = []
+        w2 = []
+        for expert in self.experts:
+            w1.append(expert.gate_up_proj.weight)
+            w2.append(expert.down_proj.weight)
+        self.w1 = torch._utils._flatten_dense_tensors(w1)
+        w1s = torch._utils._unflatten_dense_tensors(self.w1, w1)
+        for data, param in zip(w1s, w1):
+            param.data = data
+        self.w1 = self.w1.view(len(w1), *w1s[0].shape)
+
+        self.w2 = torch._utils._flatten_dense_tensors(w2)
+        w2s = torch._utils._unflatten_dense_tensors(self.w2, w2)
+        for data, param in zip(w2s, w2):
+            param.data = data
+
+        self.w2 = self.w2.view(len(w2), *w2s[0].shape)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        if self.config.n_shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = fused_moe(hidden_states,
+                                        self.w1,
+                                        self.w2,
+                                        router_logits,
+                                        self.top_k,
+                                        renormalize=self.config.norm_topk_prob,
+                                        inplace=True)
+
+        if self.config.n_shared_experts is not None:
+            final_hidden_states = final_hidden_states + shared_output
+        final_hidden_states = tensor_model_parallel_all_reduce(
+            final_hidden_states)
+
+        return final_hidden_states.view(batch_size, sequence_length,
+                                        hidden_dim)
+
+
+class DeepseekAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = PagedAttention(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: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class DeepseekDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_idx: int,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = DeepseekAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            linear_method=linear_method,
+        )
+        if (config.n_routed_experts is not None and  \
+            layer_idx >= config.first_k_dense_replace and layer_idx % config.moe_layer_freq == 0):
+            self.mlp = DeepseekMoE(config=config, linear_method=linear_method)
+        else:
+            self.mlp = DeepseekMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                linear_method=linear_method,
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class DeepseekModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            DeepseekDecoderLayer(config,
+                                 layer_idx,
+                                 linear_method=linear_method)
+            for layer_idx in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(positions, hidden_states,
+                                            kv_caches[i], input_metadata,
+                                            residual)
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class DeepseekForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = DeepseekModel(config, linear_method)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: Optional[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path,
+                cache_dir,
+                load_format,
+                revision,
+                fall_back_to_pt=False):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip experts that are not assigned to this worker.
+                if (("mlp.experts." in name or "mlp.shared_experts." in name)
+                        and name not in params_dict):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip experts that are not assigned to this worker.
+                if (("mlp.experts." in name or "mlp.shared_experts." in name)
+                        and name not in params_dict):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/falcon.py

@@ -0,0 +1,447 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/a5cc30d72ae2dc19af534e4b35c986cc28db1275/src/transformers/models/falcon/modeling_falcon.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 the Falcon authors and HuggingFace Inc. team.  All rights
+# reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Falcon model."""
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import LayerNorm
+from transformers import FalconConfig as HF_FalconConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.communication_op import (
+    tensor_model_parallel_all_reduce)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs import RWConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+FalconConfig = Union[HF_FalconConfig, RWConfig]
+
+
+def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
+    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
+    base = torch.tensor(2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+                        dtype=torch.float32)
+    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != total_num_heads:
+        extra_base = torch.tensor(
+            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+            dtype=torch.float32)
+        num_remaining_heads = min(closest_power_of_2,
+                                  total_num_heads - closest_power_of_2)
+        extra_powers = torch.arange(1,
+                                    1 + 2 * num_remaining_heads,
+                                    2,
+                                    dtype=torch.int32)
+        slopes = torch.cat(
+            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
+
+    return slopes
+
+
+class FalconAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: FalconConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+
+        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.head_dim = self.hidden_size // self.total_num_heads
+        assert self.head_dim * self.total_num_heads == self.hidden_size
+
+        self.new_decoder_architecture = config.new_decoder_architecture
+        self.multi_query = config.multi_query
+
+        if self.new_decoder_architecture:
+            self.total_num_kv_heads = config.num_kv_heads
+        elif self.multi_query:
+            self.total_num_kv_heads = 1
+        else:
+            self.total_num_kv_heads = self.total_num_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.query_key_value = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=config.bias,
+            skip_bias_add=True,
+            linear_method=linear_method,
+        )
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        # Layer-wise attention scaling
+        self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
+        self.reduce_row_parallel_results = not (config.new_decoder_architecture
+                                                or config.parallel_attn)
+        self.dense = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=config.bias,
+            skip_bias_add=True,
+            linear_method=linear_method,
+            reduce_results=self.reduce_row_parallel_results)
+
+        self.use_rotary = config.rotary
+        self.use_alibi = config.alibi
+        assert not (self.use_rotary and self.use_alibi), (
+            "Rotary and alibi are mutually exclusive.")
+
+        if self.use_rotary:
+            rope_theta = getattr(config, "rope_theta", 10000)
+            max_position_embeddings = getattr(config,
+                                              "max_position_embeddings", 8192)
+            self.rotary_emb = get_rope(
+                self.head_dim,
+                rotary_dim=self.head_dim,
+                max_position=max_position_embeddings,
+                base=rope_theta,
+            )
+            self.attn = PagedAttention(self.num_heads,
+                                       self.head_dim,
+                                       self.inv_norm_factor,
+                                       num_kv_heads=self.num_kv_heads)
+        elif self.use_alibi:
+            tp_rank = get_tensor_model_parallel_rank()
+            head_start = tp_rank * self.num_heads
+            head_end = (tp_rank + 1) * self.num_heads
+            alibi_slopes = (_get_alibi_slopes(self.total_num_heads) *
+                            self.inv_norm_factor)
+            alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+            self.attn = PagedAttention(self.num_heads,
+                                       self.head_dim,
+                                       self.inv_norm_factor,
+                                       num_kv_heads=self.num_kv_heads,
+                                       alibi_slopes=alibi_slopes)
+        else:
+            self.attn = PagedAttention(self.num_heads,
+                                       self.head_dim,
+                                       scale=self.inv_norm_factor,
+                                       num_kv_heads=self.num_kv_heads)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, bias = self.query_key_value(hidden_states)
+        if bias is not None:
+            qkv += bias
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.use_rotary:
+            q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        attn_output, bias = self.dense(attn_output)
+        return attn_output, bias
+
+
+class FalconMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: FalconConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+
+        self.dense_h_to_4h = ColumnParallelLinear(hidden_size,
+                                                  4 * hidden_size,
+                                                  bias=config.bias,
+                                                  skip_bias_add=True,
+                                                  linear_method=linear_method)
+        quant_config = getattr(linear_method, "quant_config", None)
+        self.act = get_act_fn("gelu", quant_config, 4 * hidden_size)
+        self.reduce_row_parallel_results = not (config.new_decoder_architecture
+                                                or config.parallel_attn)
+        self.dense_4h_to_h = RowParallelLinear(
+            4 * hidden_size,
+            hidden_size,
+            bias=config.bias,
+            skip_bias_add=True,
+            reduce_results=self.reduce_row_parallel_results,
+            linear_method=linear_method)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # NOTE(zhuohan): Following huggingface, we do not fuse bias add here.
+        x, bias = self.dense_h_to_4h(x)
+        if bias is not None:
+            x += bias
+        x = self.act(x)
+        x, bias = self.dense_4h_to_h(x)
+        return x, bias
+
+
+class FalconDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: FalconConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.self_attention = FalconAttention(config, linear_method)
+        self.mlp = FalconMLP(config, linear_method)
+        self.config = config
+
+        if config.new_decoder_architecture:
+            # The layer norm before self-attention
+            self.ln_attn = LayerNorm(hidden_size,
+                                     eps=config.layer_norm_epsilon)
+            # The layer norm before the MLP
+            self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        else:
+            self.input_layernorm = LayerNorm(hidden_size,
+                                             eps=config.layer_norm_epsilon)
+            if not config.parallel_attn:
+                self.post_attention_layernorm = LayerNorm(
+                    hidden_size, eps=config.layer_norm_epsilon)
+
+        self.reduce_row_parallel_results = not (config.new_decoder_architecture
+                                                or config.parallel_attn)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        if self.config.new_decoder_architecture:
+            attention_layernorm_out = self.ln_attn(hidden_states)
+            mlp_layernorm_out = self.ln_mlp(hidden_states)
+        else:
+            attention_layernorm_out = self.input_layernorm(hidden_states)
+
+        # Self attention.
+        attention_output, attention_bias = self.self_attention(
+            positions=positions,
+            hidden_states=attention_layernorm_out,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        if self.reduce_row_parallel_results and attention_bias is not None:
+            attention_output += attention_bias
+
+        if not self.config.new_decoder_architecture:
+            if self.config.parallel_attn:
+                mlp_layernorm_out = attention_layernorm_out
+            else:
+                residual += attention_output
+                mlp_layernorm_out = self.post_attention_layernorm(residual)
+
+        # MLP.
+        mlp_output, mlp_bias = self.mlp(mlp_layernorm_out)
+        if self.reduce_row_parallel_results and mlp_bias is not None:
+            mlp_output += mlp_bias
+
+        if not self.reduce_row_parallel_results:
+            # When MLP and Attention layers are parallel, we can use
+            # only one all-reduce operator to reduce the results from
+            # both MLP and Attention layers.
+            mlp_output += attention_output
+            mlp_output = tensor_model_parallel_all_reduce(mlp_output)
+            if attention_bias is not None:
+                mlp_output += attention_bias
+            if mlp_bias is not None:
+                mlp_output += mlp_bias
+
+        output = mlp_output + residual
+        return output
+
+
+class FalconModel(nn.Module):
+
+    def __init__(
+        self,
+        config: FalconConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.use_alibi = config.alibi
+
+        # Embedding + LN Embedding
+        self.word_embeddings = VocabParallelEmbedding(
+            config.vocab_size,
+            self.embed_dim,
+        )
+
+        # Transformer blocks
+        self.h = nn.ModuleList([
+            FalconDecoderLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+
+        # Final Layer Norm
+        self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.word_embeddings(input_ids)
+        for i in range(len(self.h)):
+            layer = self.h[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+            )
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class FalconForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: FalconConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.transformer = FalconModel(config, linear_method)
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(
+            input_ids,
+            positions,
+            kv_caches,
+            input_metadata,
+        )
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        total_num_heads = self.config.num_attention_heads
+        if self.config.new_decoder_architecture:
+            total_num_kv_heads = self.config.num_kv_heads
+        elif self.config.multi_query:
+            total_num_kv_heads = 1
+        else:
+            total_num_kv_heads = total_num_heads
+        num_query_heads_per_kv_head = total_num_heads // total_num_kv_heads
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            param = params_dict[name]
+            if "query_key_value" in name:
+                output_dim = getattr(param, "output_dim", None)
+                loaded_weight_shape = loaded_weight.shape
+                if output_dim is not None:
+                    loaded_weight = loaded_weight.view(
+                        loaded_weight_shape[:output_dim] +
+                        (total_num_kv_heads, num_query_heads_per_kv_head + 2,
+                         -1) + loaded_weight_shape[output_dim + 1:])
+                    wq = loaded_weight.narrow(
+                        output_dim + 1, 0,
+                        num_query_heads_per_kv_head).reshape(
+                            *loaded_weight_shape[:output_dim], -1,
+                            *loaded_weight_shape[output_dim + 1:])
+                    wk = loaded_weight.narrow(
+                        output_dim + 1, num_query_heads_per_kv_head,
+                        1).reshape(*loaded_weight_shape[:output_dim], -1,
+                                   *loaded_weight_shape[output_dim + 1:])
+                    wv = loaded_weight.narrow(
+                        output_dim + 1, num_query_heads_per_kv_head + 1,
+                        1).reshape(*loaded_weight_shape[:output_dim], -1,
+                                   *loaded_weight_shape[output_dim + 1:])
+                    loaded_weight = torch.cat([wq, wk, wv], dim=output_dim)
+
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)

vllm/model_executor/models/gemma.py

@@ -0,0 +1,346 @@
+# coding=utf-8
+# Copyright 2023 The vLLM team.
+# Copyright (c) Google Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Gemma model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import GemmaConfig
+
+from vllm.config import LoRAConfig
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import GeluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class GemmaMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           linear_method=linear_method)
+        self.act_fn = GeluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class GemmaAttention(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 head_dim: int,
+                 max_position_embeddings: int = 8192,
+                 rope_theta: float = 10000,
+                 linear_method: Optional[LinearMethodBase] = None) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=self.rope_theta,
+            is_neox_style=True,
+        )
+        self.attn = PagedAttention(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: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class GemmaDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: GemmaConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = GemmaAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            head_dim=config.head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+            rope_theta=config.rope_theta,
+            linear_method=linear_method,
+        )
+        self.mlp = GemmaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            linear_method=linear_method,
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class GemmaModel(nn.Module):
+
+    def __init__(
+        self,
+        config: GemmaConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            GemmaDecoderLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        # Normalize the embedding by sqrt(hidden_size)
+        hidden_states *= self.config.hidden_size**0.5
+
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class GemmaForCausalLM(nn.Module):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+    ]
+    # Gemma does not apply LoRA to the embedding layer.
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+    def __init__(
+        self,
+        config: GemmaConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        del lora_config  # Unused.
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = GemmaModel(config, linear_method)
+        self.sampler = Sampler(config.vocab_size)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.model.embed_tokens.weight,
+                                   hidden_states, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params = set()
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            for (param_name, shard_name, shard_id) in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                name = name.replace(shard_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # GemmaRMSNorm is different from Llama's in that it multiplies
+                # (1 + weight) to the output, instead of just weight.
+                if "norm.weight" in name:
+                    loaded_weight += 1.0
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        unloaded_params = params_dict.keys() - loaded_params
+        if unloaded_params:
+            raise RuntimeError(
+                "Some weights are not initialized from checkpoints: "
+                f"{unloaded_params}")

vllm/model_executor/models/gpt2.py

@@ -0,0 +1,273 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
+# Copyright 2023 The vLLM team.
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GPT-2 model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import GPT2Config
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class GPT2Attention(nn.Module):
+
+    def __init__(
+        self,
+        config: GPT2Config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        total_num_heads = config.num_attention_heads
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        assert total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = total_num_heads // tensor_model_parallel_world_size
+        self.head_dim = self.hidden_size // total_num_heads
+        self.scale = self.head_dim**-0.5
+
+        self.c_attn = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            total_num_heads,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.c_proj = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   scale=self.scale)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.c_attn(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        key_cache, value_cache = kv_cache
+        attn_output = self.attn(q, k, v, key_cache, value_cache,
+                                input_metadata)
+        attn_output, _ = self.c_proj(attn_output)
+        return attn_output
+
+
+class GPT2MLP(nn.Module):
+
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: GPT2Config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.c_fc = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.c_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=True,
+            linear_method=linear_method,
+        )
+        quant_config = getattr(linear_method, "quant_config", None)
+        self.act = get_act_fn(config.activation_function, quant_config,
+                              intermediate_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class GPT2Block(nn.Module):
+
+    def __init__(
+        self,
+        config: GPT2Config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        inner_dim = (config.n_inner if config.n_inner is not None else 4 *
+                     hidden_size)
+
+        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.attn = GPT2Attention(config, linear_method)
+        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = GPT2MLP(inner_dim, config, linear_method)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_output = self.attn(
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        # residual connection
+        hidden_states = attn_output + residual
+
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        # residual connection
+        hidden_states = residual + feed_forward_hidden_states
+        return hidden_states
+
+
+class GPT2Model(nn.Module):
+
+    def __init__(
+        self,
+        config: GPT2Config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        assert not config.add_cross_attention
+        assert not config.scale_attn_by_inverse_layer_idx
+        assert not config.reorder_and_upcast_attn
+        self.embed_dim = config.hidden_size
+        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
+        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
+        self.h = nn.ModuleList([
+            GPT2Block(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = inputs_embeds + position_embeds
+
+        for i in range(len(self.h)):
+            layer = self.h[i]
+            hidden_states = layer(hidden_states, kv_caches[i], input_metadata)
+
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class GPT2LMHeadModel(nn.Module):
+
+    def __init__(
+        self,
+        config: GPT2Config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.transformer = GPT2Model(config, linear_method)
+        self.lm_head_weight = self.transformer.wte.weight
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "lm_head.weight" in name:
+                # GPT-2 ties the weights of the embedding layer and the final
+                # linear layer.
+                continue
+            if ".attn.bias" in name or ".attn.masked_bias" in name:
+                # Skip attention mask.
+                # NOTE: "c_attn.bias" should not be skipped.
+                continue
+            if not name.startswith("transformer."):
+                name = "transformer." + name
+            param = params_dict[name]
+            # The HF's GPT-2 implementation uses Conv1D instead of Linear.
+            # Because of this, we need to transpose the weights.
+            # Note(zhuohan): the logic below might break quantized models.
+            for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
+                if conv1d_weight_name not in name:
+                    continue
+                if not name.endswith(".weight"):
+                    continue
+                loaded_weight = loaded_weight.t()
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)

vllm/model_executor/models/gpt_bigcode.py

@@ -0,0 +1,279 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 CTranslate2, and Michael Feil
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GPTBigCode model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import GPTBigCodeConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class GPTBigCodeAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTBigCodeConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        total_num_heads = config.num_attention_heads
+        self.tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        assert total_num_heads % self.tensor_model_parallel_world_size == 0
+        self.num_heads = (total_num_heads //
+                          self.tensor_model_parallel_world_size)
+        self.head_dim = self.hidden_size // total_num_heads
+        self.scale = self.head_dim**-0.5
+
+        self.multi_query = config.multi_query
+        if self.multi_query:
+            total_num_kv_heads = 1
+            self.num_kv_heads = 1
+        else:
+            total_num_kv_heads = total_num_heads
+            self.num_kv_heads = self.num_heads
+        self.kv_dim = self.head_dim * self.num_kv_heads
+        self.c_attn = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            total_num_heads,
+            total_num_kv_heads,
+            bias=True,
+            linear_method=linear_method,
+        )
+
+        self.c_proj = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   scale=self.scale,
+                                   num_kv_heads=self.num_kv_heads)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.c_attn(hidden_states)
+        q, k, v = qkv.split(
+            [
+                self.hidden_size // self.tensor_model_parallel_world_size,
+                self.kv_dim, self.kv_dim
+            ],
+            dim=-1,
+        )
+        key_cache, value_cache = kv_cache
+        attn_output = self.attn(q, k, v, key_cache, value_cache,
+                                input_metadata)
+        attn_output, _ = self.c_proj(attn_output)
+        return attn_output
+
+
+class GPTBigMLP(nn.Module):
+
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: GPTBigCodeConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.c_fc = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.c_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=True,
+            linear_method=linear_method,
+        )
+        quant_config = getattr(linear_method, "quant_config", None)
+        self.act = get_act_fn(config.activation_function, quant_config,
+                              intermediate_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class GPTBigCodeBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTBigCodeConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        inner_dim = (config.n_inner if config.n_inner is not None else 4 *
+                     hidden_size)
+
+        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.attn = GPTBigCodeAttention(config, linear_method)
+        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = GPTBigMLP(inner_dim, config, linear_method)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_output = self.attn(
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        # residual connection
+        hidden_states = attn_output + residual
+
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        # residual connection
+        hidden_states = residual + feed_forward_hidden_states
+        return hidden_states
+
+
+class GPTBigCodeModel(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTBigCodeConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        assert not config.add_cross_attention
+
+        self.embed_dim = config.hidden_size
+
+        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
+        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
+        self.h = nn.ModuleList([
+            GPTBigCodeBlock(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = inputs_embeds + position_embeds
+
+        for i in range(len(self.h)):
+            layer = self.h[i]
+            hidden_states = layer(hidden_states, kv_caches[i], input_metadata)
+
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class GPTBigCodeForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTBigCodeConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.transformer = GPTBigCodeModel(config, linear_method)
+        self.lm_head_weight = self.transformer.wte.weight
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "lm_head.weight" in name:
+                continue
+            if ".attn.bias" in name:
+                # Skip attention mask.
+                # NOTE: "c_attn.bias" should not be skipped.
+                continue
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)

vllm/model_executor/models/gpt_j.py

@@ -0,0 +1,284 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gptj/modeling_gptj.py
+# Copyright 2023 The vLLM team.
+# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GPT-J model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import GPTJConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class GPTJAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTJConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.total_num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.total_num_heads
+
+        self.qkv_proj = QKVParallelLinear(
+            config.hidden_size,
+            self.head_size,
+            self.total_num_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.out_proj = RowParallelLinear(
+            config.hidden_size,
+            config.hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+
+        scaling = self.head_size**-0.5
+        assert getattr(config, "rotary", True)
+        assert config.rotary_dim % 2 == 0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.rotary_emb = get_rope(
+            self.head_size,
+            rotary_dim=config.rotary_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            is_neox_style=False,
+        )
+        self.attn = PagedAttention(self.num_heads, self.head_size, scaling)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(position_ids, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        attn_output, _ = self.out_proj(attn_output)
+        return attn_output
+
+
+class GPTJMLP(nn.Module):
+
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: GPTJConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.n_embd
+        self.fc_in = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            linear_method=linear_method,
+        )
+        self.fc_out = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            linear_method=linear_method,
+        )
+        quant_config = getattr(linear_method, "quant_config", None)
+        self.act = get_act_fn(config.activation_function, quant_config,
+                              intermediate_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.fc_out(hidden_states)
+        return hidden_states
+
+
+class GPTJBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTJConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        inner_dim = 4 * config.n_embd if config.n_inner is None else config.n_inner
+        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.attn = GPTJAttention(config, linear_method)
+        self.mlp = GPTJMLP(inner_dim, config, linear_method)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_output = self.attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        mlp_output = self.mlp(hidden_states)
+        hidden_states = attn_output + mlp_output + residual
+        return hidden_states
+
+
+class GPTJModel(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTJConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.n_embd
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            self.embed_dim,
+        )
+        self.h = nn.ModuleList(
+            [GPTJBlock(config, linear_method) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.wte(input_ids)
+        for i in range(len(self.h)):
+            layer = self.h[i]
+            hidden_states = layer(
+                position_ids,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+            )
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class GPTJForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTJConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        assert not config.tie_word_embeddings
+        self.transformer = GPTJModel(config, linear_method)
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.n_embd,
+            bias=True,
+        )
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata, self.lm_head.bias)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "attn.bias" in name or "attn.masked_bias" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/gpt_neox.py

@@ -0,0 +1,294 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GPT-NeoX model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import GPTNeoXConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class GPTNeoXAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTNeoXConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.total_num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.total_num_heads
+        self.bias = getattr(config, "attention_bias", True)
+
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+
+        self.query_key_value = QKVParallelLinear(
+            config.hidden_size,
+            self.head_size,
+            self.total_num_heads,
+            bias=self.bias,
+            linear_method=linear_method,
+        )
+        self.dense = RowParallelLinear(
+            config.hidden_size,
+            config.hidden_size,
+            bias=self.bias,
+            linear_method=linear_method,
+        )
+        scaling = self.head_size**-0.5
+        rotary_dim = int(self.head_size * config.rotary_pct)
+        assert rotary_dim % 2 == 0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.rotary_emb = get_rope(
+            self.head_size,
+            rotary_dim=rotary_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+        )
+        self.attn = PagedAttention(self.num_heads, self.head_size, scaling)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.query_key_value(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(position_ids, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class GPTNeoXMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTNeoXConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.dense_h_to_4h = ColumnParallelLinear(
+            config.hidden_size,
+            config.intermediate_size,
+            linear_method=linear_method,
+        )
+        self.dense_4h_to_h = RowParallelLinear(
+            config.intermediate_size,
+            config.hidden_size,
+            linear_method=linear_method,
+        )
+        quant_config = getattr(linear_method, "quant_config", None)
+        self.act = get_act_fn(config.hidden_act, quant_config,
+                              config.intermediate_size)
+
+    def forward(self, hidden_states):
+        hidden_states, _ = self.dense_h_to_4h(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.dense_4h_to_h(hidden_states)
+        return hidden_states
+
+
+class GPTNeoXLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTNeoXConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.use_parallel_residual = config.use_parallel_residual
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     eps=config.layer_norm_eps)
+        self.attention = GPTNeoXAttention(config, linear_method)
+        self.mlp = GPTNeoXMLP(config, linear_method)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        attn_input = self.input_layernorm(hidden_states)
+        attn_output = self.attention(
+            position_ids=position_ids,
+            hidden_states=attn_input,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        if self.use_parallel_residual:
+            # pseudocode:
+            # x = x + attn(ln1(x)) + mlp(ln2(x))
+            mlp_input = self.post_attention_layernorm(hidden_states)
+            mlp_output = self.mlp(mlp_input)
+            hidden_states = mlp_output + attn_output + hidden_states
+        else:
+            # pseudocode:
+            # x = x + attn(ln1(x))
+            # x = x + mlp(ln2(x))
+            attn_output = attn_output + hidden_states
+            mlp_input = self.post_attention_layernorm(attn_output)
+            mlp_output = self.mlp(mlp_input)
+            hidden_states = mlp_output + attn_output
+        return hidden_states
+
+
+class GPTNeoXModel(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTNeoXConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+
+        self.embed_in = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            GPTNeoXLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.final_layer_norm = nn.LayerNorm(config.hidden_size,
+                                             eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_in(input_ids)
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states = layer(
+                position_ids,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+            )
+        hidden_states = self.final_layer_norm(hidden_states)
+        return hidden_states
+
+
+class GPTNeoXForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.gpt_neox = GPTNeoXModel(config, linear_method)
+        self.embed_out = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.gpt_neox(input_ids, positions, kv_caches,
+                                      input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.embed_out.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if ("attention.bias" in name or "attention.masked_bias" in name
+                    or "rotary_emb.inv_freq" in name):
+                continue
+            param = params_dict[name]
+
+            if "query_key_value" in name:
+                # NOTE: GPT-NeoX's fused QKV's output_dim has the shape of
+                # (num_heads * 3 * head_size), while the
+                # required shape is (3 * num_heads * head_size).
+                # Thus, we need weight conversion.
+                output_dim = getattr(param, "output_dim", None)
+                num_heads = self.config.num_attention_heads
+                if output_dim is not None:
+                    loaded_weight_shape = loaded_weight.shape
+                    loaded_weight = loaded_weight.view(
+                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
+                        loaded_weight_shape[output_dim + 1:])
+                    loaded_weight = loaded_weight.transpose(
+                        output_dim, output_dim + 1)
+                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)
+
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)

vllm/model_executor/models/internlm2.py

@@ -0,0 +1,325 @@
+# -*- coding: utf-8 -*-
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class InternLM2MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method)
+        self.w2 = RowParallelLinear(intermediate_size,
+                                    hidden_size,
+                                    bias=False,
+                                    linear_method=linear_method)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.w2(x)
+        return x
+
+
+class InternLM2Attention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.wqkv = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.wo = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = PagedAttention(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: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.wqkv(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)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.wo(attn_output)
+        return output
+
+
+class InternLMDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.attention = InternLM2Attention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            linear_method=linear_method,
+        )
+        self.feed_forward = InternLM2MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            linear_method=linear_method,
+        )
+        self.attention_norm = RMSNorm(config.hidden_size,
+                                      eps=config.rms_norm_eps)
+        self.ffn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.attention_norm(hidden_states)
+        else:
+            hidden_states, residual = self.attention_norm(
+                hidden_states, residual)
+        hidden_states = self.attention(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.ffn_norm(hidden_states, residual)
+        hidden_states = self.feed_forward(hidden_states)
+        return hidden_states, residual
+
+
+class InternLM2Model(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.tok_embeddings = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            InternLMDecoderLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.tok_embeddings(input_ids)
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class InternLM2ForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = InternLM2Model(config, linear_method)
+        self.output = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.output.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "w1", 0),
+            ("gate_up_proj", "w3", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                if "wqkv" in name:
+                    config = self.config
+                    kv_groups = config.num_attention_heads // config.num_key_value_heads
+                    head_dim = config.hidden_size // config.num_attention_heads
+                    loaded_weight = loaded_weight.view(-1, 2 + kv_groups,
+                                                       head_dim,
+                                                       loaded_weight.shape[-1])
+                    wq, wk, wv = torch.split(loaded_weight, [kv_groups, 1, 1],
+                                             dim=1)
+                    wq = wq.reshape(-1, wq.shape[-1])
+                    wk = wk.reshape(-1, wk.shape[-1])
+                    wv = wv.reshape(-1, wv.shape[-1])
+                    weight_loader = param.weight_loader
+                    weight_loader(param, wq, 'q')
+                    weight_loader(param, wk, 'k')
+                    weight_loader(param, wv, 'v')
+                else:
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)

vllm/model_executor/models/llama.py

@@ -0,0 +1,391 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only LLaMA model compatible with HuggingFace weights."""
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import LlamaConfig
+
+from vllm.config import LoRAConfig
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead, DEFAULT_VOCAB_PADDING_SIZE)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class LlamaMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           linear_method=linear_method)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class LlamaAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        linear_method: Optional[LinearMethodBase] = None,
+        bias: bool = False,
+        sliding_window: Optional[int] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=bias,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=bias,
+            linear_method=linear_method,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   self.scaling,
+                                   num_kv_heads=self.num_kv_heads,
+                                   sliding_window=sliding_window)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class LlamaDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        sliding_window = getattr(config, "sliding_window", None)
+        self.self_attn = LlamaAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=getattr(config, "num_key_value_heads",
+                                 config.num_attention_heads),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            linear_method=linear_method,
+            bias=getattr(config, "bias", False),
+            sliding_window=sliding_window,
+        )
+        self.mlp = LlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            linear_method=linear_method,
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class LlamaModel(nn.Module):
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+        self.layers = nn.ModuleList([
+            LlamaDecoderLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class LlamaForCausalLM(nn.Module):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+        "embed_tokens",
+        "lm_head",
+    ]
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = LlamaModel(config, linear_method, lora_config=lora_config)
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+        )
+        self.sampler = Sampler(self.unpadded_vocab_size, config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/llama_smooth.py

@@ -0,0 +1,409 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only LLaMA model compatible with HuggingFace weights."""
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import LlamaConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import DequantSiluAndMulQuant
+from vllm.model_executor.layers.attention import DequantPagedAttention
+from vllm.model_executor.layers.layernorm import (RMSNorm, 
+                                                  RMSNormQuant, 
+                                                  AddResidualRMSNormQuant,
+                                                  DequantAddResidualRMSNormQuant)
+
+from vllm.model_executor.layers.quantization.smoothquant import SmoothLinearMethod
+
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               QuantMergedColumnParallelLinear,
+                                               QuantQKVParallelLinear,
+                                               QuantRowParallelLinear)
+from vllm.model_executor.layers.rotary_embedding import get_dequant_rope
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding, 
+    ParallelLMHead)
+from vllm.model_executor.layers.layernorm import DequantAddResidual, AddResidual
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class QuantLlamaMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = QuantMergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method,
+            skip_bias_add=True)
+        self.down_proj = QuantRowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           linear_method=linear_method,
+                                           skip_bias_add=True)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = DequantSiluAndMulQuant()
+
+    def forward(self, x):
+        scale = None
+        # int, half -> int32
+        gate_up, _ = self.gate_up_proj(x)
+        # int32 -> int, scale
+        x, *scale = self.act_fn(gate_up)
+        scale = scale[0] if scale is not None else None
+        # int8, scale -> int32(when tp > 1, to half, scale for dequant before all reduce)
+        x, _ = self.down_proj(x, scale)
+        return x, scale
+
+
+class QuantLlamaAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        linear_method: Optional[LinearMethodBase] = None,
+        
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QuantQKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            linear_method=linear_method,
+            skip_bias_add=True,
+        )
+        self.o_proj = QuantRowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+            skip_bias_add=True,
+        )
+
+        self.rotary_emb = get_dequant_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = DequantPagedAttention(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: KVCache,
+        input_metadata: InputMetadata
+    ) -> torch.Tensor:
+        # int8 -> int32
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        # int32 -> half
+        q, k, v = self.rotary_emb(positions, q, k, v, 
+                               self.qkv_proj.q_dequant_scale.item(),
+                               self.qkv_proj.k_dequant_scale.item(),
+                               self.qkv_proj.v_dequant_scale.item())
+        k_cache, v_cache = kv_cache
+        scale = None
+        # half - > int8, scale, 添加一个per channel 量化，并返回统计的scale
+        attn_output, *scale = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        scale = scale[0] if scale is not None else None
+        # int8, scale -> int32(when tp > 1, to half, scale for dequant before all reduce)
+        output, _ = self.o_proj(attn_output, scale)
+        return output, scale
+
+
+class QuantLlamaDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = QuantLlamaAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            linear_method=linear_method,
+        )
+        self.mlp = QuantLlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            linear_method=linear_method,
+        )
+        self.apply_dequant_in_post = not linear_method.apply_dequant_after_row
+        self.input_layernorm = RMSNormQuant(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        if self.apply_dequant_in_post:
+            self.post_attention_layernorm = DequantAddResidualRMSNormQuant(config.hidden_size,
+                                                    eps=config.rms_norm_eps)
+            self.finally_add_residual = DequantAddResidual()
+        else:
+            self.post_attention_layernorm = AddResidualRMSNormQuant(config.hidden_size,
+                                                    eps=config.rms_norm_eps)
+            self.finally_add_residual = AddResidual()
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # half
+        residual = hidden_states
+        # half -> int8
+        hidden_states = self.input_layernorm(hidden_states)
+        # int8 -> int32 ,scale (when tp > 1,to half, scale, this scale is useless) 
+        hidden_states, scale = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata
+        )
+
+        # to = 1: int32, half, scale -> int8, half (scale for dequant)
+        # tp > 1: half, half, scale -> int8, half
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual, scale)
+        # int8 -> int32, scale (when tp > 1,to half, scale, this scale is useless) 
+        hidden_states, scale = self.mlp(hidden_states)
+        # ine32, half, scale -> half (when tp > 1, half, half, scale -> half)
+        hidden_states = self.finally_add_residual(hidden_states, residual, scale)
+        return hidden_states
+
+
+class QuantLlamaModel(nn.Module):
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            QuantLlamaDecoderLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata
+    ) -> torch.Tensor:
+        # half
+        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],
+                input_metadata
+            )
+        # int32 , half, scale -> int8
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class LlamaForCausalLM(nn.Module):
+    
+    def __init__(
+        self,
+        config: LlamaConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = QuantLlamaModel(config, linear_method)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> SamplerOutput:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+    
+    
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # process special params first
+            ("qkv_proj.q_dequant_scale", "q_proj.dequant_scale", "-1"),
+            ("qkv_proj.k_dequant_scale", "k_proj.dequant_scale", "-1"),
+            ("qkv_proj.v_dequant_scale", "v_proj.dequant_scale", "-1"),
+            ("act_fn.gate_dequant_scale", "gate_proj.dequant_scale", "-1"),
+            ("act_fn.up_dequant_scale", "up_proj.dequant_scale", "-1"),
+            
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        special_params_mapping = [
+            ("post_attention_layernorm.dequant_scale", "self_attn.o_proj.dequant_scale"),
+            ("finally_add_residual.dequant_scale","mlp.down_proj.dequant_scale")
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if 'bias' in name:
+                    continue
+                param = params_dict[name.replace(weight_name, param_name)]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                if weight_loader is default_weight_loader:
+                    weight_loader(param, loaded_weight)
+                else:
+                    weight_loader(param, loaded_weight,shard_id)
+                break
+            else:
+                for (param_name, weight_name) in special_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    # used in o_prof and down_proj when world_size > 1
+                    if get_tensor_model_parallel_world_size() > 1:
+                        param = params_dict[name]
+                        weight_loader = getattr(param, "weight_loader",
+                                                default_weight_loader)
+                        if weight_loader is default_weight_loader:
+                            weight_loader(param, loaded_weight)
+                        else:
+                            weight_loader(param, loaded_weight,shard_id)
+                    else:
+                        param = params_dict[name.replace(weight_name, param_name)]
+                        weight_loader = getattr(param, "weight_loader",
+                                                default_weight_loader)
+                        if weight_loader is default_weight_loader:
+                            weight_loader(param, loaded_weight)
+                        else:
+                            weight_loader(param, loaded_weight,shard_id)
+                    break
+                else:
+                    if 'bias' not in name:
+                        param = params_dict[name]
+                        weight_loader = getattr(param, "weight_loader",
+                                                default_weight_loader)
+                        weight_loader(param, loaded_weight)

vllm/model_executor/models/mixtral.py

@@ -0,0 +1,454 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Mixtral model."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import MixtralConfig
+
+from vllm.config import LoRAConfig
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.fused_moe import fused_moe
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead, DEFAULT_VOCAB_PADDING_SIZE)
+from vllm.model_executor.parallel_utils.communication_op import (
+    tensor_model_parallel_all_reduce)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class MixtralMoE(nn.Module):
+    """A tensor-parallel MoE implementation for Mixtral that shards each expert
+    across all ranks.
+
+    Each expert's weights are sharded across all ranks and a fused MoE
+    kernel is used for the forward pass, and finally we reduce the outputs
+    across ranks.
+    """
+
+    def __init__(
+        self,
+        num_experts: int,
+        top_k: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: Optional[torch.dtype] = None,
+        tp_size: Optional[int] = None,
+    ):
+        super().__init__()
+        self.tp_size = tp_size or get_tensor_model_parallel_world_size()
+        self.num_total_experts = num_experts
+        self.top_k = top_k
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size // self.tp_size
+
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.params_dtype = params_dtype
+
+        self.gate = ReplicatedLinear(self.hidden_size,
+                                     self.num_total_experts,
+                                     bias=False,
+                                     params_dtype=self.params_dtype,
+                                     linear_method=None)
+
+        self.ws = nn.Parameter(
+            torch.empty(self.num_total_experts,
+                        2 * self.intermediate_size,
+                        self.hidden_size,
+                        device="cuda",
+                        dtype=self.params_dtype))
+        self.w2s = nn.Parameter(
+            torch.empty(self.num_total_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.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]
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = fused_moe(hidden_states,
+                                        self.ws,
+                                        self.w2s,
+                                        router_logits,
+                                        self.top_k,
+                                        renormalize=True,
+                                        inplace=True)
+
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
+
+        return final_hidden_states.view(batch_size, sequence_length,
+                                        hidden_size)
+
+
+class MixtralAttention(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 max_position: int = 4096 * 32,
+                 rope_theta: float = 10000,
+                 linear_method: Optional[LinearMethodBase] = None,
+                 sliding_window: Optional[int] = None) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.sliding_window = sliding_window
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+        self.attn = PagedAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            sliding_window=self.sliding_window,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MixtralDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        self.self_attn = MixtralAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            sliding_window=config.sliding_window,
+            linear_method=linear_method)
+        self.block_sparse_moe = MixtralMoE(
+            num_experts=config.num_local_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size)
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.block_sparse_moe(hidden_states)
+        return hidden_states, residual
+
+
+class MixtralModel(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.padding_idx = config.pad_token_id
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+        self.layers = nn.ModuleList([
+            MixtralDecoderLayer(config, linear_method=linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(positions, hidden_states,
+                                            kv_caches[i], input_metadata,
+                                            residual)
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class MixtralForCausalLM(nn.Module):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "embed_tokens",
+        "lm_head",
+    ]
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = MixtralModel(config,
+                                  linear_method,
+                                  lora_config=lora_config)
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+        )
+        self.sampler = Sampler(self.unpadded_vocab_size, config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: Optional[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        expert_params_mapping = [
+            # (param_name, weight_name, expert_id)
+            ("ws" if weight_name in ["w1", "w3"] else "w2s",
+             f"experts.{expert_id}.{weight_name}.weight", expert_id)
+            for expert_id in range(self.config.num_local_experts)
+            for weight_name in ["w1", "w2", "w3"]
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path,
+                cache_dir,
+                load_format,
+                revision,
+                fall_back_to_pt=False):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for param_name, weight_name, expert_id in expert_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  weight_name,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)

vllm/model_executor/models/mixtral_quant.py

@@ -0,0 +1,412 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Mixtral model."""
+from typing import List, Optional, Tuple
+
+import numpy as np
+
+import torch
+import torch.nn.functional as F
+
+from torch import nn
+from transformers import MixtralConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               ReplicatedLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.communication_op import (
+    tensor_model_parallel_all_reduce)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class MixtralMLP(nn.Module):
+
+    def __init__(
+        self,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size: int,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.num_experts = num_experts
+        self.ffn_dim = intermediate_size
+        self.hidden_dim = hidden_size
+
+        self.w1 = ReplicatedLinear(self.hidden_dim,
+                                   self.ffn_dim,
+                                   bias=False,
+                                   linear_method=linear_method)
+        self.w2 = ReplicatedLinear(self.ffn_dim,
+                                   self.hidden_dim,
+                                   bias=False,
+                                   linear_method=linear_method)
+        self.w3 = ReplicatedLinear(self.hidden_dim,
+                                   self.ffn_dim,
+                                   bias=False,
+                                   linear_method=linear_method)
+
+        # TODO: Use vllm's SiluAndMul
+        self.act_fn = nn.SiLU()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        w1_out, _ = self.w1(hidden_states)
+        w1_out = self.act_fn(w1_out)
+        w3_out, _ = self.w3(hidden_states)
+        current_hidden_states = w1_out * w3_out
+        current_hidden_states, _ = self.w2(current_hidden_states)
+        return current_hidden_states
+
+
+class MixtralMoE(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.rank = get_tensor_model_parallel_rank()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_total_experts = config.num_local_experts
+        self.top_k = config.num_experts_per_tok
+        if self.tp_size > self.num_total_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {self.num_total_experts}.")
+        # Split experts equally between ranks
+        self.expert_indicies = np.array_split(range(
+            self.num_total_experts), self.tp_size)[self.rank].tolist()
+        if not self.expert_indicies:
+            raise ValueError(
+                f"Rank {self.rank} has no experts assigned to it.")
+
+        self.experts = nn.ModuleList([
+            MixtralMLP(self.num_total_experts,
+                       config.hidden_size,
+                       config.intermediate_size,
+                       linear_method=linear_method)
+            if idx in self.expert_indicies else None
+            for idx in range(self.num_total_experts)
+        ])
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     self.num_total_experts,
+                                     bias=False,
+                                     linear_method=None)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights,
+                                                       self.top_k,
+                                                       dim=-1)
+        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+
+        final_hidden_states = None
+        for expert_idx in self.expert_indicies:
+            expert_layer = self.experts[expert_idx]
+            expert_mask = (selected_experts == expert_idx)
+            expert_weights = (routing_weights * expert_mask).sum(dim=-1,
+                                                                 keepdim=True)
+
+            current_hidden_states = expert_layer(hidden_states).mul_(
+                expert_weights)
+            if final_hidden_states is None:
+                final_hidden_states = current_hidden_states
+            else:
+                final_hidden_states.add_(current_hidden_states)
+
+        return tensor_model_parallel_all_reduce(final_hidden_states).view(
+            batch_size, sequence_length, hidden_dim)
+
+
+class MixtralAttention(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 max_position: int = 4096 * 32,
+                 rope_theta: float = 10000,
+                 linear_method: Optional[LinearMethodBase] = None,
+                 sliding_window: Optional[int] = None) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.sliding_window = sliding_window
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+        self.attn = PagedAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            sliding_window=self.sliding_window,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MixtralDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        self.self_attn = MixtralAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            sliding_window=config.sliding_window,
+            linear_method=linear_method)
+        self.block_sparse_moe = MixtralMoE(config=config,
+                                           linear_method=linear_method)
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.block_sparse_moe(hidden_states)
+        return hidden_states, residual
+
+
+class MixtralModel(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            MixtralDecoderLayer(config, linear_method=linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(positions, hidden_states,
+                                            kv_caches[i], input_metadata,
+                                            residual)
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class MixtralForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = MixtralModel(config, linear_method)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: Optional[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path,
+                cache_dir,
+                load_format,
+                revision,
+                fall_back_to_pt=False):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip experts that are not assigned to this worker.
+                if ("block_sparse_moe.experts." in name
+                        and name not in params_dict):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/mpt.py

@@ -0,0 +1,298 @@
+# coding=utf-8
+# Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main
+import math
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs.mpt import MPTConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+def _get_alibi_slopes(
+    total_num_heads: int,
+    alibi_bias_max: int,
+) -> torch.Tensor:
+    next_power_of_2 = 2**math.ceil(math.log2(total_num_heads))
+    m = torch.arange(1, next_power_of_2 + 1, dtype=torch.float32)
+    m = m.mul(alibi_bias_max / next_power_of_2)
+    slopes = 1.0 / torch.pow(2, m)
+    if next_power_of_2 != total_num_heads:
+        slopes = torch.concat([slopes[1::2], slopes[::2]])[:total_num_heads]
+    return slopes
+
+
+class MPTAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: MPTConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.d_model = config.d_model
+        self.total_num_heads = config.n_heads
+        self.head_dim = self.d_model // self.total_num_heads
+        self.clip_qkv = config.attn_config["clip_qkv"]
+        self.qk_ln = config.attn_config["qk_ln"]
+        self.alibi_bias_max = config.attn_config["alibi_bias_max"]
+        if "kv_n_heads" in config.attn_config:
+            self.total_num_kv_heads = config.attn_config['kv_n_heads']
+        else:
+            self.total_num_kv_heads = self.total_num_heads
+        assert not config.attn_config["prefix_lm"]
+        assert config.attn_config["alibi"]
+
+        # pylint: disable=invalid-name
+        self.Wqkv = QKVParallelLinear(
+            self.d_model,
+            self.d_model // self.total_num_heads,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=not config.no_bias,
+            linear_method=linear_method,
+        )
+        if self.qk_ln:
+            self.q_ln = nn.LayerNorm(self.d_model)
+            self.k_ln = nn.LayerNorm(self.d_model)
+        self.out_proj = RowParallelLinear(
+            self.d_model,
+            self.d_model,
+            bias=not config.no_bias,
+            linear_method=linear_method,
+        )
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+
+        if self.total_num_kv_heads >= tp_world_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_world_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_world_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_world_size)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        # Create the alibi slopes and slice them.
+        tp_rank = get_tensor_model_parallel_rank()
+        head_start = tp_rank * self.num_heads
+        head_end = (tp_rank + 1) * self.num_heads
+        alibi_slopes = _get_alibi_slopes(self.total_num_heads,
+                                         self.alibi_bias_max)
+        alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+
+        self.head_dim = self.d_model // self.total_num_heads
+        scaling = self.head_dim**-0.5
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   scaling,
+                                   alibi_slopes=alibi_slopes,
+                                   num_kv_heads=self.num_kv_heads)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        del position_ids  # unused.
+        qkv, _ = self.Wqkv(hidden_states)
+        if self.clip_qkv is not None:
+            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.qk_ln:
+            q = self.q_ln(q)
+            k = self.k_ln(k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class MPTMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: MPTConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.d_model
+        expansion_ratio = config.expansion_ratio
+        intermediate_size = expansion_ratio * hidden_size
+        self.up_proj = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=not config.no_bias,
+            linear_method=linear_method,
+        )
+        quant_config = getattr(linear_method, "quant_config", None)
+        self.act = get_act_fn("gelu", quant_config, intermediate_size)
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=not config.no_bias,
+            linear_method=linear_method,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.up_proj(x)
+        x = self.act(x)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class MPTBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: MPTConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.d_model
+        self.norm_1 = nn.LayerNorm(hidden_size)
+        self.attn = MPTAttention(config, linear_method)
+        self.norm_2 = nn.LayerNorm(hidden_size)
+        self.ffn = MPTMLP(config, linear_method)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        x = self.norm_1(hidden_states)
+        x = self.attn(
+            position_ids=position_ids,
+            hidden_states=x,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        hidden_states = hidden_states + x
+        x = self.norm_2(hidden_states)
+        x = self.ffn(x)
+        hidden_states = hidden_states + x
+        return hidden_states
+
+
+class MPTModel(nn.Module):
+
+    def __init__(
+        self,
+        config: MPTConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        assert config.embedding_fraction == 1.0
+        assert config.norm_type == "low_precision_layernorm"
+
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            config.d_model,
+        )
+        self.blocks = nn.ModuleList(
+            [MPTBlock(config, linear_method) for _ in range(config.n_layers)])
+        self.norm_f = nn.LayerNorm(config.d_model)
+        if config.no_bias:
+            for module in self.modules():
+                if hasattr(module, "bias") and isinstance(
+                        module.bias, nn.Parameter):
+                    # Remove the bias term in Linear and LayerNorm.
+                    module.register_parameter("bias", None)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.wte(input_ids)
+        for i in range(len(self.blocks)):
+            block = self.blocks[i]
+            hidden_states = block(
+                position_ids,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+            )
+        hidden_states = self.norm_f(hidden_states)
+        return hidden_states
+
+
+class MPTForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: MPTConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        assert config.tie_word_embeddings
+        self.linear_method = linear_method
+
+        self.transformer = MPTModel(config, linear_method)
+        self.lm_head_weight = self.transformer.wte.weight
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)

vllm/model_executor/models/olmo.py

@@ -0,0 +1,380 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/allenai/OLMo/blob/v0.2.4/olmo/model.py and
+# https://github.com/allenai/OLMo/blob/v0.2.4/hf_olmo/modeling_olmo.py
+# Copyright 2023 The vLLM team.
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+#
+# BSD 3-Clause License
+#
+# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""Inference-only OLMo model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    LinearMethodBase,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+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 VocabParallelEmbedding
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size, )
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (
+    default_weight_loader,
+    hf_model_weights_iterator,
+)
+from vllm.sequence import SamplerOutput
+
+# this model must need this dependency
+from hf_olmo import OLMoConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class SwiGLU(nn.Module):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, gate = x.chunk(2, dim=-1)
+        return F.silu(gate) * x
+
+    @property
+    def output_multiplier(self) -> float:
+        return 0.5
+
+
+class OlmoAttention(nn.Module):
+    """
+    This is the attention block where the output is computed as ``Attention(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
+    (plus another skip connection).
+    """
+
+    def __init__(
+        self,
+        config: OLMoConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.d_model
+        assert config.d_model % config.n_heads == 0
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
+        )
+        self.total_num_heads = self.config.n_heads
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
+        self.head_dim = self.hidden_size // self.total_num_heads
+
+        # Layer norms.
+        self.attn_norm = nn.LayerNorm(config.d_model,
+                                      elementwise_affine=False,
+                                      bias=False)
+        # Attention input projection. Projects x -> (q, k, v)
+        self.att_proj = QKVParallelLinear(
+            config.d_model,
+            self.head_dim,
+            self.total_num_heads,
+            bias=config.include_bias,
+            linear_method=linear_method,
+        )
+
+        # Rotary embeddings.
+        if self.config.rope:
+            rope_theta = getattr(config, "rope_theta", 10000)
+            max_position_embeddings = getattr(config,
+                                              "max_position_embeddings", 8192)
+            self.rotary_emb = get_rope(
+                self.head_dim,
+                rotary_dim=self.head_dim,
+                max_position=max_position_embeddings,
+                base=rope_theta,
+            )
+        self.scaling = self.head_dim**-0.5
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   scale=self.scaling)
+
+        # Attention output projection.
+        self.attn_out = RowParallelLinear(
+            config.d_model,
+            config.d_model,
+            bias=config.include_bias,
+            linear_method=linear_method,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.attn_norm(hidden_states)
+        qkv, _ = self.att_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        if self.config.rope:
+            q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.attn_out(attn_output)
+        return output
+
+
+class OlmoMLP(nn.Module):
+    """
+    This is the MLP block where the output is computed as ``MLP(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
+    (plus another skip connection).
+    """
+
+    def __init__(
+        self,
+        config: OLMoConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = (config.mlp_hidden_size if config.mlp_hidden_size
+                            is not None else config.mlp_ratio * config.d_model)
+
+        # Layer norms.
+        self.ff_norm = nn.LayerNorm(config.d_model,
+                                    elementwise_affine=False,
+                                    bias=False)
+
+        # Feed-forward input projection.
+        self.ff_proj = ColumnParallelLinear(
+            config.d_model,
+            self.hidden_size,
+            bias=config.include_bias,
+            linear_method=linear_method,
+        )
+
+        # Activation function.
+        # self.act = SiluAndMul()
+        # self.act.output_multiplier = 0.5
+        self.act = SwiGLU()
+        assert (self.act.output_multiplier * self.hidden_size) % 1 == 0
+
+        # Feed-forward output projection.
+        self.ff_out = RowParallelLinear(
+            int(self.act.output_multiplier * self.hidden_size),
+            config.d_model,
+            bias=config.include_bias,
+            linear_method=linear_method,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        # Add feed-forward projection.
+        # shape: (batch_size, seq_len, d_model)
+        og_x = x
+        x = self.ff_norm(x)
+        x, _ = self.ff_proj(x)
+        x = self.act(x)
+        x, _ = self.ff_out(x)
+        x = og_x + x
+
+        return x
+
+
+class OlmoBlock(nn.Module):
+    """
+    This is a typical transformer block where the output is computed as ``MLP(LN(x + Attention(LN(x))))``
+    (plus another skip connection).
+    """
+
+    def __init__(self,
+                 config: OLMoConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        # Attention block.
+        self.attn = OlmoAttention(config, linear_method)
+
+        # MLP block.
+        self.mlp = OlmoMLP(config, linear_method)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        # Attention block.
+        og_x = hidden_states
+        x = self.attn(positions, hidden_states, kv_cache, input_metadata)
+        x = x + og_x
+
+        # MLP block.
+        hidden_states = self.mlp(x)
+        return hidden_states
+
+
+class OlmoModel(nn.Module):
+
+    def __init__(self,
+                 config: OLMoConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+
+        self.transformer = nn.ModuleDict(
+            dict(
+                wte=VocabParallelEmbedding(
+                    config.embedding_size or config.vocab_size,
+                    config.d_model,
+                ),
+                ln_f=nn.LayerNorm(config.d_model,
+                                  elementwise_affine=False,
+                                  bias=False),
+            ))
+
+        blocks = [
+            OlmoBlock(config, linear_method) for i in range(config.n_layers)
+        ]
+        if self.config.block_group_size > 1:
+            raise NotImplementedError("Block group size > 1 not supported yet")
+        else:
+            self.transformer.update({"blocks": nn.ModuleList(blocks)})
+
+        if not config.weight_tying:
+            self.transformer.update({
+                "ff_out":
+                ColumnParallelLinear(
+                    config.d_model,
+                    config.embedding_size or config.vocab_size,
+                    bias=config.include_bias,
+                    linear_method=linear_method,
+                )
+            })
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        """
+        :param input_ids: A tensor of shape `(batch_size, seq_len)`.
+        """
+        # Get embeddings of input.
+        # shape: (batch_size, seq_len, d_model)
+        x = self.transformer.wte(input_ids)  # type: ignore
+
+        # Apply blocks one-by-one.
+        for block_idx, block in enumerate(self.transformer.blocks):
+            # shape: (batch_size, seq_len, d_model)
+            x = block(
+                positions,
+                x,
+                kv_caches[block_idx],
+                input_metadata,
+            )
+
+        # Apply final layer norm.
+        # shape: (batch_size, seq_len or 1, d_model)
+        x = self.transformer.ln_f(x)  # type: ignore
+        return x
+
+
+class OLMoForCausalLM(nn.Module):
+    """
+    Extremely barebones HF model wrapper.
+    """
+
+    def __init__(self,
+                 config: OLMoConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = OlmoModel(config, linear_method)
+        self.lm_head_weight = (self.model.transformer.wte.weight
+                               if config.weight_tying else
+                               self.model.transformer.ff_out.weight)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            kv_caches=kv_caches,
+            input_metadata=input_metadata,
+        )
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(
+        self,
+        model_name_or_path: str,
+        cache_dir: Optional[str] = None,
+        load_format: str = "auto",
+        revision: Optional[str] = None,
+    ):
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            # attention
+            if ".att" in name:
+                name = name.replace(".att", ".attn.att")
+            # mlp
+            if ".ff" in name and "transformer.ff_out" not in name:
+                name = name.replace(".ff", ".mlp.ff")
+            # there is no bias in olmo
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)

vllm/model_executor/models/opt.py

@@ -0,0 +1,354 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights
+# reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only OPT model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import OPTConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class OPTLearnedPositionalEmbedding(nn.Embedding):
+
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        # OPT is set up so that if padding_idx is specified then offset the
+        # embedding ids by 2 and adjust num_embeddings appropriately. Other
+        # models don't have this hack
+        self.offset = 2
+        super().__init__(num_embeddings + self.offset, embedding_dim)
+
+    def forward(self, positions: torch.Tensor):
+        return super().forward(positions + self.offset)
+
+
+class OPTAttention(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        bias: bool = True,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        total_num_heads = num_heads
+        assert num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = total_num_heads // tensor_model_parallel_world_size
+        self.head_dim = embed_dim // total_num_heads
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            embed_dim,
+            self.head_dim,
+            total_num_heads,
+            bias=bias,
+            linear_method=linear_method,
+        )
+        self.out_proj = RowParallelLinear(
+            embed_dim,
+            embed_dim,
+            bias=bias,
+            linear_method=linear_method,
+        )
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   scale=self.scaling)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        key_cache, value_cache = kv_cache
+        attn_output = self.attn(q, k, v, key_cache, value_cache,
+                                input_metadata)
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class OPTDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: OPTConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.self_attn = OPTAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.num_attention_heads,
+            bias=config.enable_bias,
+            linear_method=linear_method,
+        )
+        self.do_layer_norm_before = config.do_layer_norm_before
+
+        self.self_attn_layer_norm = nn.LayerNorm(
+            self.embed_dim,
+            elementwise_affine=config.layer_norm_elementwise_affine)
+        self.fc1 = ColumnParallelLinear(
+            self.embed_dim,
+            config.ffn_dim,
+            bias=config.enable_bias,
+            linear_method=linear_method,
+        )
+        quant_config = getattr(linear_method, "quant_config", None)
+        self.activation_fn = get_act_fn(config.activation_function,
+                                        quant_config, config.ffn_dim)
+        self.fc2 = RowParallelLinear(
+            config.ffn_dim,
+            self.embed_dim,
+            bias=config.enable_bias,
+            linear_method=linear_method,
+        )
+        self.final_layer_norm = nn.LayerNorm(
+            self.embed_dim,
+            elementwise_affine=config.layer_norm_elementwise_affine)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+        if self.do_layer_norm_before:
+            hidden_states = self.self_attn_layer_norm(hidden_states)
+        hidden_states = self.self_attn(hidden_states=hidden_states,
+                                       kv_cache=kv_cache,
+                                       input_metadata=input_metadata)
+        hidden_states = residual + hidden_states
+        # 350m applies layer norm AFTER attention
+        if not self.do_layer_norm_before:
+            hidden_states = self.self_attn_layer_norm(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+        if self.do_layer_norm_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        hidden_states = residual + hidden_states
+        # 350m applies layer norm AFTER attention
+        if not self.do_layer_norm_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+        return hidden_states
+
+
+class OPTDecoder(nn.Module):
+
+    def __init__(
+        self,
+        config: OPTConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.max_target_positions = config.max_position_embeddings
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.word_embed_proj_dim,
+        )
+        # Positional embeddings are replicated (not sharded).
+        self.embed_positions = OPTLearnedPositionalEmbedding(
+            config.max_position_embeddings, config.hidden_size)
+
+        # Project out & in will be replicated if they exist.
+        if config.word_embed_proj_dim != config.hidden_size:
+            self.project_out = ReplicatedLinear(config.hidden_size,
+                                                config.word_embed_proj_dim,
+                                                bias=False,
+                                                linear_method=linear_method)
+        else:
+            self.project_out = None
+
+        if config.word_embed_proj_dim != config.hidden_size:
+            self.project_in = ReplicatedLinear(config.word_embed_proj_dim,
+                                               config.hidden_size,
+                                               bias=False,
+                                               linear_method=linear_method)
+        else:
+            self.project_in = None
+
+        # Note that the only purpose of `config._remove_final_layer_norm` is to
+        # keep backward compatibility with checkpoints that have been fine-tuned
+        # before transformers v4.20.1
+        # see https://github.com/facebookresearch/metaseq/pull/164
+        if config.do_layer_norm_before and not config._remove_final_layer_norm:
+            self.final_layer_norm = nn.LayerNorm(
+                config.hidden_size,
+                elementwise_affine=config.layer_norm_elementwise_affine)
+        else:
+            self.final_layer_norm = None
+
+        self.layers = nn.ModuleList([
+            OPTDecoderLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        inputs_embeds = self.embed_tokens(input_ids)
+        pos_embeds = self.embed_positions(positions)
+        if self.project_in is not None:
+            inputs_embeds, _ = self.project_in(inputs_embeds)
+        hidden_states = inputs_embeds + pos_embeds
+
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states = layer(hidden_states, kv_caches[i], input_metadata)
+
+        if self.final_layer_norm is not None:
+            hidden_states = self.final_layer_norm(hidden_states)
+        if self.project_out is not None:
+            hidden_states, _ = self.project_out(hidden_states)
+        return hidden_states
+
+
+class OPTModel(nn.Module):
+
+    def __init__(
+        self,
+        config: OPTConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.decoder = OPTDecoder(config, linear_method)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        return self.decoder(input_ids, positions, kv_caches, input_metadata)
+
+
+class OPTForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = OPTModel(config, linear_method)
+        self.lm_head_weight = self.model.decoder.embed_tokens.weight
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "lm_head.weight" in name:
+                continue
+            if name.startswith("decoder."):
+                name = "model." + name
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/phi.py

@@ -0,0 +1,305 @@
+# coding=utf-8
+# Adapted from
+# https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_phi.py
+# Copyright 2023 The vLLM team.
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+#
+# BSD 3-Clause License
+#
+# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""Inference-only Phi-1.5 model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class PhiAttention(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.total_num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.total_num_heads
+
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+
+        # pylint: disable=C0103
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_size,
+            self.total_num_heads,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.dense = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            linear_method=linear_method,
+        )
+
+        scaling = self.head_size**-0.5
+        rotary_dim = int(config.partial_rotary_factor *
+                         (config.hidden_size // config.num_attention_heads))
+        assert rotary_dim % 2 == 0
+
+        # pylint: disable=C0301
+        # Refer to:
+        # https://huggingface.co/microsoft/phi-1_5/blob/d212a789620c380ff32ca1d1ee9943a777360987/modeling_phi.py#L518
+        rope_theta = 10000
+        max_position_embeddings = getattr(config, "n_positions", 2048)
+        self.rotary_emb = get_rope(
+            self.head_size,
+            rotary_dim=rotary_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+        )
+        self.attn = PagedAttention(self.num_heads, self.head_size, scaling)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(position_ids, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class PhiMLP(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+
+        n_inner = getattr(config, "n_inner", None)
+        n_inner = n_inner if n_inner is not None else 4 * config.hidden_size
+
+        self.fc1 = ColumnParallelLinear(
+            config.hidden_size,
+            n_inner,
+            linear_method=linear_method,
+        )
+        self.fc2 = RowParallelLinear(
+            n_inner,
+            config.hidden_size,
+            linear_method=linear_method,
+        )
+        quant_config = getattr(linear_method, "quant_config", None)
+        self.act = get_act_fn(config.hidden_act, quant_config, n_inner)
+
+    def forward(self, hidden_states):
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        return hidden_states
+
+
+class PhiLayer(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+        self.self_attn = PhiAttention(config, linear_method)
+        self.mlp = PhiMLP(config, linear_method)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        attn_outputs = self.self_attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        hidden_states = attn_outputs + feed_forward_hidden_states + residual
+        return hidden_states
+
+
+class PhiModel(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
+                                                   config.hidden_size)
+        self.layers = nn.ModuleList([
+            PhiLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.final_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(self.config.num_hidden_layers):
+            layer = self.layers[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+            )
+
+        hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states
+
+
+class PhiForCausalLM(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+
+        self.model = PhiModel(config, linear_method)
+
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      bias=True)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        head = self.lm_head
+        next_tokens = self.sampler(head.weight, hidden_states,
+                                   sampling_metadata, head.bias)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v")
+        ]
+        params_dict = dict(self.named_parameters())
+
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # pylint: disable=E1136
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/qwen.py

@@ -0,0 +1,288 @@
+# coding=utf-8
+# Adapted from
+# https://huggingface.co/Qwen/Qwen-7B/blob/main/modeling_qwen.py
+# Copyright (c) Alibaba Cloud.
+# LICENSE: https://huggingface.co/Qwen/Qwen-7B/blob/main/LICENSE
+"""Inference-only QWen model compatible with HuggingFace weights."""
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class QWenMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str = "silu",
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method)
+        self.c_proj = RowParallelLinear(intermediate_size,
+                                        hidden_size,
+                                        bias=False,
+                                        linear_method=linear_method)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.c_proj(x)
+        return x
+
+
+class QWenAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        max_position_embeddings: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
+        )
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.c_attn = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.c_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.scaling = self.head_dim**-0.5
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.c_attn(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+
+        output, _ = self.c_proj(attn_output)
+        return output
+
+
+class QWenBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        self.attn = QWenAttention(config.hidden_size,
+                                  config.num_attention_heads,
+                                  config.max_position_embeddings,
+                                  rope_theta=rope_theta,
+                                  rope_scaling=rope_scaling,
+                                  linear_method=linear_method)
+
+        self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        self.mlp = QWenMLP(config.hidden_size,
+                           config.intermediate_size // 2,
+                           linear_method=linear_method)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.ln_1(hidden_states)
+        else:
+            hidden_states, residual = self.ln_1(hidden_states, residual)
+        hidden_states = self.attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.ln_2(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class QWenModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.vocab_size = config.vocab_size
+
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.h = nn.ModuleList([
+            QWenBlock(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.wte(input_ids)
+        residual = None
+        for i in range(len(self.h)):
+            layer = self.h[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                residual,
+            )
+        hidden_states, _ = self.ln_f(hidden_states, residual)
+        return hidden_states
+
+
+class QWenLMHeadModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.transformer = QWenModel(config, linear_method)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "w2", 0),
+            ("gate_up_proj", "w1", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/qwen2.py

@@ -0,0 +1,340 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2 model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import Qwen2Config
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class Qwen2MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           linear_method=linear_method)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Qwen2Attention(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 max_position: int = 4096 * 32,
+                 rope_theta: float = 10000,
+                 use_sliding_window: bool = False,
+                 linear_method: Optional[LinearMethodBase] = None,
+                 sliding_window: Optional[int] = None) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.sliding_window = sliding_window if use_sliding_window else None
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=self.rope_theta,
+        )
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   self.scaling,
+                                   num_kv_heads=self.num_kv_heads,
+                                   sliding_window=self.sliding_window)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Qwen2DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Qwen2Config,
+        layer_idx: int,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 1000000)
+        use_sliding_window = config.use_sliding_window and layer_idx < config.max_window_layers
+        self.self_attn = Qwen2Attention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            use_sliding_window=use_sliding_window,
+            linear_method=linear_method,
+            sliding_window=config.sliding_window)
+        self.mlp = Qwen2MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            linear_method=linear_method,
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class Qwen2Model(nn.Module):
+
+    def __init__(
+        self,
+        config: Qwen2Config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            Qwen2DecoderLayer(config, layer_idx, linear_method)
+            for layer_idx in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class Qwen2ForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: Qwen2Config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = Qwen2Model(config, linear_method)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                try:
+                    param = params_dict[name]
+                except:
+                    assert name=="lm_head.weight" # for qwen1.5 0.5b,skip this
+                    continue
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/stablelm.py

@@ -0,0 +1,303 @@
+# coding=utf-8
+# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This code is based off the following work:
+# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/modeling_stablelm_epoch.py
+# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/config.json
+"""Inference-only StabeLM (https://github.com/Stability-AI/StableLM) model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+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 (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class StablelmMLP(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 linear_method: Optional[LinearMethodBase] = None) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_up_proj = MergedColumnParallelLinear(
+            config.hidden_size, [config.intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method)
+        self.down_proj = RowParallelLinear(config.intermediate_size,
+                                           config.hidden_size,
+                                           bias=False)
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class StablelmAttention(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 linear_method: Optional[LinearMethodBase] = None) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        self.num_heads = self.total_num_heads // tp_size
+
+        self.total_num_key_value_heads = config.num_key_value_heads
+        if self.total_num_key_value_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_key_value_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_key_value_heads == 0
+        self.num_key_value_heads = max(
+            1, self.total_num_key_value_heads // tp_size)
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        rope_pct = getattr(config, "rope_pct",
+                           getattr(config, "partial_rotary_factor", 1))
+        self.rotary_ndims = int(self.head_dim * rope_pct)
+        self.scaling = self.head_dim**-0.5
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_key_value_heads * self.head_dim
+        self.qkv_bias = getattr(config, "use_qkv_bias", False)
+        if (self.head_dim * self.num_heads * tp_size) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads}).")
+
+        self.qkv_proj = QKVParallelLinear(self.hidden_size,
+                                          self.head_dim,
+                                          self.total_num_heads,
+                                          self.total_num_key_value_heads,
+                                          self.qkv_bias,
+                                          linear_method=linear_method)
+        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
+                                        self.hidden_size,
+                                        bias=False,
+                                        linear_method=linear_method)
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.rotary_ndims,
+            max_position=self.config.max_position_embeddings,
+            base=self.config.rope_theta,
+        )
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   self.scaling,
+                                   num_kv_heads=self.num_key_value_heads)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class StablelmDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.self_attn = StablelmAttention(config)
+        self.mlp = StablelmMLP(config, linear_method)
+        norm_eps = getattr(config, "norm_eps",
+                           getattr(config, "layer_norm_eps", 1e-05))
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     eps=norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states, residual
+
+
+class StableLMEpochModel(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 linear_method: Optional[LinearMethodBase] = None) -> None:
+        super().__init__()
+        # self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            StablelmDecoderLayer(config, linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        norm_eps = getattr(config, "norm_eps",
+                           getattr(config, "layer_norm_eps", 1e-05))
+        self.norm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+            )
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class StablelmForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = StableLMEpochModel(config, linear_method)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)

vllm/model_executor/models/starcoder2.py

@@ -0,0 +1,310 @@
+# coding=utf-8
+# Copyright 2024 BigCode and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Starcoder2 model."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearMethodBase,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding, ParallelLMHead, DEFAULT_VOCAB_PADDING_SIZE)
+from vllm.model_executor.parallel_utils.parallel_state import get_tensor_model_parallel_world_size
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+try:
+    from transformers import Starcoder2Config
+except ImportError:
+    # fallback to PretrainedConfig
+    # NOTE: Please install transformers from source or use transformers>=4.39.0
+    from transformers import PretrainedConfig as Starcoder2Config
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class Starcoder2Attention(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+
+        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:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = config.rope_theta
+        self.max_position_embeddings = config.max_position_embeddings
+        self.use_bias = config.use_bias
+        self.sliding_window = config.sliding_window
+
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=self.use_bias,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=self.use_bias,
+            linear_method=linear_method,
+        )
+        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 = PagedAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            sliding_window=self.sliding_window,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Starcoder2MLP(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.c_fc = ColumnParallelLinear(
+            config.hidden_size,
+            config.intermediate_size,
+            bias=config.use_bias,
+            linear_method=linear_method,
+        )
+        self.c_proj = RowParallelLinear(
+            config.intermediate_size,
+            config.hidden_size,
+            bias=config.use_bias,
+            linear_method=linear_method,
+        )
+        self.act = get_act_fn(config.hidden_act,
+                              intermediate_size=config.intermediate_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class Starcoder2DecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Starcoder2Attention(config,
+                                             linear_method=linear_method)
+        self.mlp = Starcoder2MLP(config, linear_method=linear_method)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.norm_epsilon)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     eps=config.norm_epsilon)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class Starcoder2Model(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        # TODO: consider padding_idx (currently removed)
+        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
+                                                   config.hidden_size)
+        self.layers = nn.ModuleList([
+            Starcoder2DecoderLayer(config, linear_method=linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> 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],
+                                  input_metadata)
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class Starcoder2ForCausalLM(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.model = Starcoder2Model(config, linear_method=linear_method)
+        self.vocab_size = config.vocab_size
+        self.unpadded_vocab_size = config.vocab_size
+        if config.tie_word_embeddings:
+            self.lm_head_weight = self.model.embed_tokens.weight
+        else:
+            self.unpadded_vocab_size = config.vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            )
+            self.lm_head_weight = self.lm_head.weight
+        self.sampler = Sampler(self.unpadded_vocab_size, config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: Optional[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)