Add Internlm2 (#2666)

README.md

@@ -73,6 +73,7 @@ vLLM seamlessly supports many Hugging Face models, including the following archi
 - GPT-J (`EleutherAI/gpt-j-6b`, `nomic-ai/gpt4all-j`, etc.)
 - GPT-NeoX (`EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.)
 - InternLM (`internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc.)
+- InternLM2 (`internlm/internlm2-7b`, `internlm/internlm2-chat-7b`, etc.)
 - LLaMA & LLaMA-2 (`meta-llama/Llama-2-70b-hf`, `lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.)
 - Mistral (`mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc.)
 - Mixtral (`mistralai/Mixtral-8x7B-v0.1`, `mistralai/Mixtral-8x7B-Instruct-v0.1`, etc.)

docs/source/models/supported_models.rst

@@ -47,6 +47,9 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`InternLMForCausalLM`
     - InternLM
     - :code:`internlm/internlm-7b`, :code:`internlm/internlm-chat-7b`, etc.
+  * - :code:`InternLM2ForCausalLM`
+    - InternLM2
+    - :code:`internlm/internlm2-7b`, :code:`internlm/internlm2-chat-7b`, etc.
   * - :code:`LlamaForCausalLM`
     - LLaMA, LLaMA-2, Vicuna, Alpaca, Koala, Guanaco
     - :code:`meta-llama/Llama-2-13b-hf`, :code:`meta-llama/Llama-2-70b-hf`, :code:`openlm-research/open_llama_13b`, :code:`lmsys/vicuna-13b-v1.3`, :code:`young-geng/koala`, etc.

vllm/model_executor/models/__init__.py

@@ -25,6 +25,7 @@ _MODELS = {
     "GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"),
     "GPTNeoXForCausalLM": ("gpt_neox", "GPTNeoXForCausalLM"),
     "InternLMForCausalLM": ("internlm", "InternLMForCausalLM"),
+    "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),
     "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
     # For decapoda-research/llama-*
     "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),

vllm/model_executor/models/internlm2.py

+# -*- 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)