Support Yi model (#1567)

vllm/model_executor/model_loader.py

@@ -33,6 +33,7 @@ _MODEL_REGISTRY = {
     "OPTForCausalLM": OPTForCausalLM,
     "QWenLMHeadModel": QWenLMHeadModel,
     "RWForCausalLM": FalconForCausalLM,
+    "YiForCausalLM": YiForCausalLM,
 }
 
 # FIXME(woosuk): Remove this once all models support quantization.

vllm/model_executor/models/__init__.py

@@ -13,6 +13,7 @@ from vllm.model_executor.models.mistral import MistralForCausalLM
 from vllm.model_executor.models.mpt import MptForCausalLM
 from vllm.model_executor.models.opt import OPTForCausalLM
 from vllm.model_executor.models.qwen import QWenLMHeadModel
+from vllm.model_executor.models.yi import YiForCausalLM
 
 __all__ = [
     "AquilaForCausalLM",
@@ -30,4 +31,5 @@ __all__ = [
     "OPTForCausalLM",
     "QWenLMHeadModel",
     "MistralForCausalLM",
+    "YiForCausalLM",
 ]

vllm/model_executor/models/yi.py

@@ -0,0 +1,426 @@
+# 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 Yi model (https://01.ai) 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 Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+from vllm.transformers_utils.configs.yi import YiConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.quantized_linear import ParallelLinear
+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.layers import VocabParallelEmbedding
+from vllm.model_executor.quantization_utils import QuantizationConfig
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_tensor_parallel_weights, load_padded_tensor_parallel_vocab)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class YiMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = ParallelLinear.column(hidden_size,
+                                                  2 * intermediate_size,
+                                                  bias=False,
+                                                  gather_output=False,
+                                                  quant_config=quant_config)
+        self.down_proj = ParallelLinear.row(intermediate_size,
+                                            hidden_size,
+                                            bias=False,
+                                            input_is_parallel=True,
+                                            quant_config=quant_config)
+        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 YiAttention(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,
+        quant_config: Optional[QuantizationConfig] = 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)
+        num_kv_heads_replicas = max(1, tp_size // self.total_num_kv_heads)
+        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 = ParallelLinear.column(
+            hidden_size,
+            (self.total_num_heads +
+             2 * self.total_num_kv_heads * num_kv_heads_replicas) *
+            self.head_dim,
+            bias=False,
+            gather_output=False,
+            quant_config=quant_config,
+        )
+        self.o_proj = ParallelLinear.row(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            quant_config=quant_config,
+        )
+        self.attn = PagedAttentionWithRoPE(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            base=self.rope_theta,
+            max_position=self.max_position_embeddings,
+            rotary_dim=self.head_dim,
+            num_kv_heads=self.num_kv_heads,
+            rope_scaling=rope_scaling)
+
+    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 YiDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: YiConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = YiAttention(
+            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,
+            quant_config=quant_config,
+        )
+        self.mlp = YiMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+        )
+        self.ln1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.ln2 = 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,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.ln1(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.ln2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class YiModel(nn.Module):
+
+    def __init__(
+        self,
+        config: YiConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        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(
+            vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            YiDecoderLayer(config, quant_config)
+            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,
+        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 YiForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: YiConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = YiModel(config, quant_config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        # NOTE: The LM head is not quantized.
+        self.lm_head = ParallelLinear.column(config.hidden_size,
+                                             vocab_size,
+                                             bias=False,
+                                             gather_output=False,
+                                             quant_config=None)
+        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]],
+    ) -> SamplerOutput:
+        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_layers = []
+    _row_parallel_layers = ["o_proj", "down_proj"]
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        if self.quant_config is None:
+            col_weight_suffixes = ["weight"]
+            row_weight_suffixes = ["weight"]
+        else:
+            col_weight_suffixes = (
+                self.quant_config.get_col_parallel_tensor_names())
+            row_weight_suffixes = (
+                self.quant_config.get_row_parallel_tensor_names())
+
+        column_parallel_weights: List[str] = []
+        for layer in self._column_parallel_layers:
+            for suffix in col_weight_suffixes:
+                column_parallel_weights.append(f"{layer}.{suffix}")
+        row_parallel_weights: List[str] = []
+        for layer in self._row_parallel_layers:
+            for suffix in row_weight_suffixes:
+                row_parallel_weights.append(f"{layer}.{suffix}")
+
+        tp_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        q_proj_shard_size = (self.config.hidden_size // tp_size)
+        num_kv_heads_replicas = max(1,
+                                    tp_size // self.config.num_key_value_heads)
+        num_kv_heads_per_gpu = max(1,
+                                   self.config.num_key_value_heads // tp_size)
+        kv_proj_shard_size = (self.config.hidden_size //
+                              self.config.num_attention_heads *
+                              num_kv_heads_per_gpu)
+        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, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            packed_dim = None
+            is_transposed = False
+            if self.quant_config is not None:
+                packed_dim = self.quant_config.get_packed_dim(name)
+                is_transposed = self.quant_config.is_transposed(name)
+            if is_transposed:
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                loaded_weight = loaded_weight.T
+
+            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")]
+                if is_transposed:
+                    param = param.T
+
+                if packed_dim is not None:
+                    shard_dim = 0 if not is_transposed else 1
+                    if packed_dim == shard_dim:
+                        shard_size //= self.quant_config.pack_factor
+                        offset //= self.quant_config.pack_factor
+
+                if weight_name in ["k_proj", "v_proj"]:
+                    shard_id = tp_rank // num_kv_heads_replicas
+                else:
+                    shard_id = tp_rank
+                loaded_weight = loaded_weight[shard_size *
+                                              shard_id:shard_size *
+                                              (shard_id + 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")]
+                if is_transposed:
+                    param = param.T
+
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
+                                              (tp_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]
+            if is_transposed:
+                param = param.T
+
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tp_rank)
+                continue
+
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         column_parallel_weights,
+                                         row_parallel_weights, tp_rank)

vllm/transformers_utils/config.py

@@ -11,6 +11,7 @@ _CONFIG_REGISTRY = {
     "qwen": QWenConfig,
     "RefinedWeb": RWConfig,  # For tiiuae/falcon-40b(-instruct)
     "RefinedWebModel": RWConfig,  # For tiiuae/falcon-7b(-instruct)
+    "yi": YiConfig,
 }
 
 

vllm/transformers_utils/configs/__init__.py

@@ -5,10 +5,12 @@ from vllm.transformers_utils.configs.qwen import QWenConfig
 # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
 # `FalconConfig` class from the official HuggingFace transformers library.
 from vllm.transformers_utils.configs.falcon import RWConfig
+from vllm.transformers_utils.configs.yi import YiConfig
 
 __all__ = [
     "BaiChuanConfig",
     "AquilaConfig",
     "QWenConfig",
     "RWConfig",
+    "YiConfig",
 ]

vllm/transformers_utils/configs/yi.py

@@ -0,0 +1,64 @@
+""" Yi model configuration"""
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+Yi_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class YiConfig(PretrainedConfig):
+    r"""
+        Reference:
+        https://huggingface.co/01-ai/Yi-6B/blob/main/configuration_yi.py
+    """
+    model_type = "Yi"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=64000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=4,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        output_attentions=False,
+        rope_theta=5000000.0,
+        **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
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.output_attentions = output_attentions
+        self.rope_theta = rope_theta
+
+        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,
+        )