Add support for aquila (#663)

* add aquila
Signed-off-by: ftgreat <ftgreat@163.com>

* fix some bug
Signed-off-by: shunxing1234 <xw747777271@gmail.com>

* delete pdb
Signed-off-by: shunxing1234 <xw747777271@gmail.com>

* fix bugs
Signed-off-by: shunxing1234 <xw747777271@gmail.com>

* fix bugs
Signed-off-by: shunxing1234 <xw747777271@gmail.com>

* delete whitespace
Signed-off-by: shunxing1234 <xw747777271@gmail.com>

* format

* fix order

---------
Signed-off-by: ftgreat <ftgreat@163.com>
Signed-off-by: shunxing1234 <xw747777271@gmail.com>
Co-authored-by: ftgreat <ftgreat@163.com>

vllm/model_executor/model_loader.py

@@ -11,6 +11,7 @@ from vllm.model_executor.weight_utils import initialize_dummy_weights
 
 # TODO(woosuk): Lazy-load the model classes.
 _MODEL_REGISTRY = {
+    "AquilaModel": AquilaForCausalLM,
     "BaiChuanForCausalLM": BaiChuanForCausalLM,  # baichuan-7b
     "BaichuanForCausalLM": BaichuanForCausalLM,  # baichuan-13b
     "BloomForCausalLM": BloomForCausalLM,

vllm/model_executor/models/__init__.py

+from vllm.model_executor.models.aquila import AquilaForCausalLM
 from vllm.model_executor.models.baichuan import (BaiChuanForCausalLM,
                                                  BaichuanForCausalLM)
 from vllm.model_executor.models.bloom import BloomForCausalLM
@@ -13,6 +14,7 @@ from vllm.model_executor.models.opt import OPTForCausalLM
 from vllm.model_executor.models.qwen import QWenLMHeadModel
 
 __all__ = [
+    "AquilaForCausalLM",
     "BaiChuanForCausalLM",
     "BaichuanForCausalLM",
     "BloomForCausalLM",

vllm/model_executor/models/aquila.py

+# 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.
+
+The input of the model is flattened to a 1D tensor of tokens. The model uses
+InputMetadata to extract the original 2D shape of the input.
+"""
+from typing import 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 PagedAttentionWithRoPE
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+                                              load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
+from vllm.sequence import SequenceOutputs
+from vllm.transformers_utils.configs.aquila import AquilaConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class AquilaMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+    ):
+        super().__init__()
+        self.gate_up_proj = ColumnParallelLinear(hidden_size,
+                                                 2 * intermediate_size,
+                                                 bias=False,
+                                                 gather_output=False,
+                                                 perform_initialization=False)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           input_is_parallel=True,
+                                           perform_initialization=False)
+        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 AquilaRMSNorm(nn.Module):
+
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        AquilaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1,
+                                                               keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance +
+                                                    self.variance_epsilon)
+
+        return (self.weight * hidden_states).to(input_dtype)
+
+
+class AquilaAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+    ):
+        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
+        assert self.total_num_kv_heads % tp_size == 0
+        self.num_kv_heads = 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.qkv_proj = ColumnParallelLinear(
+            hidden_size,
+            (self.total_num_heads + 2 * self.total_num_kv_heads) *
+            self.head_dim,
+            bias=False,
+            gather_output=False,
+            perform_initialization=False,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            perform_initialization=False,
+        )
+        self.attn = PagedAttentionWithRoPE(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            rotary_dim=self.head_dim,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
+                                input_metadata, cache_event)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class AquilaDecoderLayer(nn.Module):
+
+    def __init__(self, config: AquilaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = AquilaAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_attention_heads,
+        )
+        self.mlp = AquilaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = AquilaRMSNorm(config.hidden_size,
+                                             eps=config.rms_norm_eps)
+        self.post_attention_layernorm = AquilaRMSNorm(config.hidden_size,
+                                                      eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> 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,
+            cache_event=cache_event,
+        )
+        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 AquilaModel(nn.Module):
+
+    def __init__(self, config: AquilaConfig):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        #vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            perform_initialization=False)
+        self.layers = nn.ModuleList([
+            AquilaDecoderLayer(config) for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = AquilaRMSNorm(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,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(len(self.layers)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.layers[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.norm(hidden_states)
+
+        return hidden_states
+
+
+class AquilaForCausalLM(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.model = AquilaModel(config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.lm_head = ColumnParallelLinear(config.hidden_size,
+                                            vocab_size,
+                                            bias=False,
+                                            gather_output=False,
+                                            perform_initialization=False)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> Dict[int, SequenceOutputs]:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   input_metadata)
+        return next_tokens
+
+    _column_parallel_weights = [
+        "embed_tokens.weight", "lm_head.weight", "qkv_proj.weight",
+        "gate_proj.weight", "up_proj.weight"
+    ]
+    _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     use_np_cache: bool = False):
+        tp_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        q_proj_shard_size = (self.config.hidden_size // tp_size)
+        kv_proj_shard_size = (self.config.hidden_size //
+                              self.config.num_attention_heads *
+                              self.config.num_attention_heads // tp_size)
+        attention_weight_specs = [
+            # (weight_name, shard_size, offset)
+            ("q_proj", q_proj_shard_size, 0),
+            ("k_proj", kv_proj_shard_size, q_proj_shard_size),
+            ("v_proj", kv_proj_shard_size,
+             q_proj_shard_size + kv_proj_shard_size),
+        ]
+        state_dict = self.state_dict()
+
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, use_np_cache):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            if "embed_tokens" in name or "lm_head" in name:
+                param = state_dict[name]
+                # Consider padding in the vocab size.
+                padded_vocab_size = (param.shape[0] * tp_size)
+                num_extra_rows = padded_vocab_size - self.config.vocab_size
+                extra_rows = torch.empty(num_extra_rows,
+                                         loaded_weight.shape[1])
+                extra_rows = extra_rows.to(loaded_weight)
+                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
+
+            is_attention_weight = False
+            for weight_name, shard_size, offset in attention_weight_specs:
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "qkv_proj")]
+
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[offset:offset + shard_size]
+                assert param_slice.shape == loaded_weight.shape
+
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
+                break
+            if is_attention_weight:
+                continue
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/transformers_utils/config.py

@@ -5,6 +5,7 @@ from vllm.transformers_utils.configs import *  # pylint: disable=wildcard-import
 _CONFIG_REGISTRY = {
     "mpt": MPTConfig,
     "baichuan": BaiChuanConfig,
+    "aquila": AquilaConfig,
     "qwen": QWenConfig,
     "RefinedWeb": RWConfig,  # For tiiuae/falcon-40b(-instruct)
     "RefinedWebModel": RWConfig,  # For tiiuae/falcon-7b(-instruct)

vllm/transformers_utils/configs/__init__.py

 from vllm.transformers_utils.configs.mpt import MPTConfig
 from vllm.transformers_utils.configs.baichuan import BaiChuanConfig
+from vllm.transformers_utils.configs.aquila import AquilaConfig
 from vllm.transformers_utils.configs.qwen import QWenConfig
 # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
 # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
@@ -9,6 +10,7 @@ from vllm.transformers_utils.configs.falcon import RWConfig
 __all__ = [
     "MPTConfig",
     "BaiChuanConfig",
+    "AquilaConfig",
     "QWenConfig",
     "RWConfig",
 ]

vllm/transformers_utils/configs/aquila.py

+# coding=utf-8
+# Copyright 2023 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.
+""" Aquila model configuration"""
+
+from transformers import PretrainedConfig
+
+
+class AquilaConfig(PretrainedConfig):
+    model_type = "aquila"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=100008,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.006,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )