[Model] Add support for MPT (#334)

README.md

@@ -46,6 +46,7 @@ vLLM seamlessly supports many Huggingface models, including the following archit
 - GPT BigCode (`bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, etc.)
 - GPT-NeoX (`EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.)
 - LLaMA (`lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.)
+- MPT (`mosaicml/mpt-7b`, `mosaicml/mpt-30b`, etc.)
 - OPT (`facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.)
 
 Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):

csrc/attention/attention_kernels.cu

@@ -395,6 +395,9 @@ void single_query_cached_kv_attention_launcher(
     case 96:
       LAUNCH_ATTENTION_KERNEL(T, 96, BLOCK_SIZE, NUM_THREADS);
       break;
+    case 112:
+      LAUNCH_ATTENTION_KERNEL(T, 112, BLOCK_SIZE, NUM_THREADS);
+      break;
     case 128:
       LAUNCH_ATTENTION_KERNEL(T, 128, BLOCK_SIZE, NUM_THREADS);
       break;

docs/source/models/supported_models.rst

@@ -29,6 +29,9 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`LlamaForCausalLM`
     - LLaMA, Vicuna, Alpaca, Koala, Guanaco
     - :code:`openlm-research/open_llama_13b`, :code:`lmsys/vicuna-13b-v1.3`, :code:`young-geng/koala`, :code:`JosephusCheung/Guanaco`, etc.
+  * - :code: `MPTForCausalLM`
+    - MPT, MPT-Instruct, MPT-Chat, MPT-StoryWriter
+    - :code:`mosaicml/mpt-7b`, :code:`mosaicml/mpt-7b-storywriter`, :code:`mosaicml/mpt-30b`, etc.
   * - :code:`OPTForCausalLM`
     - OPT, OPT-IML
     - :code:`facebook/opt-66b`, :code:`facebook/opt-iml-max-30b`, etc.

vllm/config.py

 from typing import Optional
 
 import torch
-from transformers import AutoConfig, PretrainedConfig
+from transformers import PretrainedConfig
 
 from vllm.logger import init_logger
+from vllm.transformers_utils.config import get_config
 from vllm.utils import get_cpu_memory
 
 logger = init_logger(__name__)
@@ -49,7 +50,7 @@ class ModelConfig:
         self.use_dummy_weights = use_dummy_weights
         self.seed = seed
 
-        self.hf_config: PretrainedConfig = AutoConfig.from_pretrained(model)
+        self.hf_config = get_config(model)
         self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
         self._verify_tokenizer_mode()
 

vllm/model_executor/layers/attention.py

@@ -12,7 +12,7 @@ from vllm import cache_ops
 from vllm import pos_encoding_ops
 from vllm.model_executor.input_metadata import InputMetadata
 
-_SUPPORTED_HEAD_SIZES = [64, 80, 96, 128]
+_SUPPORTED_HEAD_SIZES = [64, 80, 96, 112, 128]
 
 
 class PagedAttention(nn.Module):

vllm/model_executor/model_loader.py

@@ -16,7 +16,8 @@ _MODEL_REGISTRY = {
     "GPTBigCodeForCausalLM": GPTBigCodeForCausalLM,
     "GPTNeoXForCausalLM": GPTNeoXForCausalLM,
     "LlamaForCausalLM": LlamaForCausalLM,
-    "LLaMAForCausalLM": LlamaForCausalLM,
+    "LLaMAForCausalLM": LlamaForCausalLM,  # For decapoda-research/llama-*
+    "MPTForCausalLM": MPTForCausalLM,
     "OPTForCausalLM": OPTForCausalLM,
 }
 

vllm/model_executor/models/__init__.py

@@ -3,6 +3,7 @@ from vllm.model_executor.models.gpt2 import GPT2LMHeadModel
 from vllm.model_executor.models.gpt_bigcode import GPTBigCodeForCausalLM
 from vllm.model_executor.models.gpt_neox import GPTNeoXForCausalLM
 from vllm.model_executor.models.llama import LlamaForCausalLM
+from vllm.model_executor.models.mpt import MPTForCausalLM
 from vllm.model_executor.models.opt import OPTForCausalLM
 
 __all__ = [
@@ -11,5 +12,6 @@ __all__ = [
     "GPTBigCodeForCausalLM",
     "GPTNeoXForCausalLM",
     "LlamaForCausalLM",
+    "MPTForCausalLM",
     "OPTForCausalLM",
 ]

vllm/model_executor/models/mpt.py

+# Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main
+import math
+from typing import Dict, 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 PagedAttentionWithALiBi
+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.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):
+        super().__init__()
+        self.d_model = config.d_model
+        self.total_num_heads = config.n_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"]
+        assert not config.attn_config["prefix_lm"]
+        assert config.attn_config["alibi"]
+
+        self.qkv_proj = ColumnParallelLinear(
+            self.d_model,
+            3 * self.d_model,
+            bias=not config.no_bias,
+            gather_output=False,
+            perform_initialization=False,
+        )
+        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,
+            input_is_parallel=True,
+            perform_initialization=False,
+        )
+
+        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
+
+        # 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 = PagedAttentionWithALiBi(self.num_heads, self.head_dim,
+                                            scaling, alibi_slopes)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        del position_ids  # unused.
+        qkv, _ = self.qkv_proj(hidden_states)
+        if self.clip_qkv is not None:
+            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
+        q, k, v = qkv.chunk(chunks=3, 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,
+                                cache_event)
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class MPTMLP(nn.Module):
+
+    def __init__(self, config: MPTConfig):
+        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,
+                                            gather_output=False,
+                                            perform_initialization=False)
+        self.act = get_act_fn("gelu")
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=not config.no_bias,
+                                           input_is_parallel=True,
+                                           perform_initialization=False)
+
+    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):
+        super().__init__()
+        hidden_size = config.d_model
+        self.norm_1 = nn.LayerNorm(hidden_size)
+        self.attn = MPTAttention(config)
+        self.norm_2 = nn.LayerNorm(hidden_size)
+        self.ffn = MPTMLP(config)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> 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,
+            cache_event=cache_event,
+        )
+        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):
+        super().__init__()
+        assert config.embedding_fraction == 1.0
+        assert config.norm_type == "low_precision_layernorm"
+
+        self.wte = VocabParallelEmbedding(config.vocab_size,
+                                          config.d_model,
+                                          perform_initialization=False)
+        self.blocks = nn.ModuleList(
+            [MPTBlock(config) 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"):
+                    if 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,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.wte(input_ids)
+        for i in range(len(self.blocks)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            block = self.blocks[i]
+            hidden_states = block(
+                position_ids,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.norm_f(hidden_states)
+        return hidden_states
+
+
+class MPTForCausalLM(nn.Module):
+
+    def __init__(self, config: MPTConfig):
+        super().__init__()
+        self.config = config
+        assert config.tie_word_embeddings
+
+        self.transformer = MPTModel(config)
+        # TODO(zhuohan): create a new weight after implementing pipeline
+        #                parallelism
+        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,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> Dict[int, SequenceOutputs]:
+        hidden_states = self.transformer(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 = ["wte.weight", "up_proj.weight", "up_proj.bias"]
+    _row_parallel_weights = ["out_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_world_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        state_dict = self.state_dict()
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, use_np_cache):
+            if "Wqkv" in name:
+                # NOTE(woosuk): MPT's fused QKV has the shape of
+                # [3 * num_heads * head_size, hidden_size].
+                # When tensor model parallelism is used, we need to shard
+                # the weight along the hidden dimension.
+                total_num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // total_num_heads
+                num_heads = total_num_heads // tp_world_size
+                head_start = tp_rank * num_heads
+                head_end = (tp_rank + 1) * num_heads
+
+                if name.endswith(".weight"):
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size, hidden_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :, :]
+                    loaded_weight = loaded_weight.reshape(-1, hidden_size)
+                elif name.endswith(".bias"):
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :]
+                    loaded_weight = loaded_weight.reshape(-1)
+                else:
+                    raise ValueError(f"Unexpected parameter name {name}")
+                name = name.replace("Wqkv", "qkv_proj")
+            param = state_dict[name]
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights, tp_rank)

vllm/transformers_utils/config.py

+from transformers import AutoConfig, PretrainedConfig
+
+from vllm.transformers_utils.configs import *  # pylint: disable=wildcard-import
+
+_CONFIG_REGISTRY = {
+    "mpt": MPTConfig,
+}
+
+
+def get_config(model: str) -> PretrainedConfig:
+    config = AutoConfig.from_pretrained(model, trust_remote_code=True)
+    if config.model_type in _CONFIG_REGISTRY:
+        config_class = _CONFIG_REGISTRY[config.model_type]
+        config = config_class.from_pretrained(model)
+    return config

vllm/transformers_utils/configs/__init__.py

+from vllm.transformers_utils.configs.mpt import MPTConfig
+
+__all__ = [
+    "MPTConfig",
+]

vllm/transformers_utils/configs/mpt.py

+# Adapted from
+# https://huggingface.co/mosaicml/mpt-7b/blob/main/configuration_mpt.py
+from typing import Any, Dict, Optional, Union
+
+from transformers import PretrainedConfig
+
+_ATTN_CONFIG_DEFAULTS = {
+    "attn_type": "multihead_attention",
+    "attn_pdrop": 0.0,
+    "attn_impl": "triton",
+    "qk_ln": False,
+    "clip_qkv": None,
+    "softmax_scale": None,
+    "prefix_lm": False,
+    "attn_uses_sequence_id": False,
+    "alibi": False,
+    "alibi_bias_max": 8,
+}
+
+
+class MPTConfig(PretrainedConfig):
+    model_type = "mpt"
+    attribute_map = {
+        "hidden_size": "d_model",
+        "num_attention_heads": "n_heads",
+        "num_hidden_layers": "n_layers",
+    }
+
+    def __init__(
+        self,
+        d_model: int = 2048,
+        n_heads: int = 16,
+        n_layers: int = 24,
+        expansion_ratio: int = 4,
+        max_seq_len: int = 2048,
+        vocab_size: int = 50368,
+        resid_pdrop: float = 0.0,
+        emb_pdrop: float = 0.0,
+        learned_pos_emb: bool = True,
+        attn_config: Optional[Dict[str, Any]] = None,
+        init_device: str = "cpu",
+        logit_scale: Optional[Union[float, str]] = None,
+        no_bias: bool = False,
+        verbose: int = 0,
+        embedding_fraction: float = 1.0,
+        norm_type: str = "low_precision_layernorm",
+        use_cache: bool = False,
+        **kwargs,
+    ) -> None:
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.expansion_ratio = expansion_ratio
+        self.max_seq_len = max_seq_len
+        self.vocab_size = vocab_size
+        self.resid_pdrop = resid_pdrop
+        self.emb_pdrop = emb_pdrop
+        self.learned_pos_emb = learned_pos_emb
+        if attn_config is None:
+            self.attn_config = _ATTN_CONFIG_DEFAULTS
+        else:
+            self.attn_config = attn_config
+        self.init_device = init_device
+        self.logit_scale = logit_scale
+        self.no_bias = no_bias
+        self.verbose = verbose
+        self.embedding_fraction = embedding_fraction
+        self.norm_type = norm_type
+        self.use_cache = use_cache
+        if "name" in kwargs:
+            del kwargs["name"]
+        if "loss_fn" in kwargs:
+            del kwargs["loss_fn"]
+        super().__init__(**kwargs)