Added support for Jais models

README.md

@@ -76,6 +76,7 @@ vLLM seamlessly supports many Hugging Face models, including the following archi
 - 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.)
+- Jais (`core42/jais-13b`, `core42/jais-13b-chat`, `core42/jais-30b-v3`, `core42/jais-30b-chat-v3`, 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

@@ -66,7 +66,11 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`InternLM2ForCausalLM`
     - InternLM2
     - :code:`internlm/internlm2-7b`, :code:`internlm/internlm2-chat-7b`, etc.
-    - 
+    -
+  * - :code:`JAISLMHeadModel`
+    - Jais
+    - :code:`core42/jais-13b`, :code:`core42/jais-13b-chat`, :code:`core42/jais-30b-v3`, :code:`core42/jais-30b-chat-v3`, etc.
+    -
   * - :code:`LlamaForCausalLM`
     - LLaMA, LLaMA-2, Vicuna, Alpaca, Yi
     - :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:`01-ai/Yi-6B`, :code:`01-ai/Yi-34B`, etc.

vllm/model_executor/models/__init__.py

@@ -27,6 +27,7 @@
     "GPTNeoXForCausalLM": ("gpt_neox", "GPTNeoXForCausalLM"),
     "InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
     "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),
+    "JAISLMHeadModel": ("jais", "JAISLMHeadModel"),
     "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
     # For decapoda-research/llama-*
     "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),

vllm/model_executor/models/gpt2.py

@@ -242,8 +242,7 @@ def sample(
         logits: torch.Tensor,
         sampling_metadata: SamplingMetadata,
     ) -> Optional[SamplerOutput]:
-        next_tokens = self.sampler(self.lm_head_weight, logits,
-                                   sampling_metadata)
+        next_tokens = self.sampler(logits, sampling_metadata)
         return next_tokens
 
     def load_weights(self,

vllm/model_executor/models/jais.py

@@ -0,0 +1,354 @@
+# coding=utf-8
+# Adapted from
+# https://huggingface.co/core42/jais-30b-chat-v3/blob/main/modeling_jais.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 the Jais authors and HuggingFace Inc. team.  All rights
+# reserved.
+# Copyright 2023 Cerebras Systems.
+#
+# 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 Jais model compatible with HuggingFace weights."""
+
+import math
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from vllm.transformers_utils.configs import JAISConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    LinearMethodBase,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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,
+    get_tensor_model_parallel_rank,
+)
+from vllm.model_executor.weight_utils import (
+    default_weight_loader,
+    hf_model_weights_iterator,
+)
+from vllm.sequence import SamplerOutput
+from vllm.model_executor.sampling_metadata import (
+    SamplingMetadata,
+    SamplingTensors,
+)
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class SwiGLUActivation(nn.Module):
+
+    def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
+        return x1 * nn.functional.silu(x2)
+
+
+def _get_alibi_slopes(n):
+
+    def get_slopes_power_of_2(n):
+        start = 2**(-(2**-(math.log2(n) - 3)))
+        ratio = start
+        return [start * ratio**i for i in range(n)]
+
+    if math.log2(n).is_integer():
+        return get_slopes_power_of_2(n)
+    else:
+        closest_power_of_2 = 2**math.floor(math.log2(n))
+        return (get_slopes_power_of_2(closest_power_of_2) + _get_alibi_slopes(
+            2 * closest_power_of_2)[0::2][:n - closest_power_of_2])
+
+
+class JAISAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: JAISConfig,
+        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
+        if hasattr(config, "scale_qk_dot_by_d"):
+            config.mup_scale_qk_dot_by_d = config.scale_qk_dot_by_d
+        self.attn_scale_power = 1.0 if config.mup_scale_qk_dot_by_d else 0.5
+        self.scale = self.head_dim**-self.attn_scale_power
+
+        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,
+        )
+
+        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(total_num_heads)
+        alibi_slopes = alibi_slopes[head_start:head_end]
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            scale=self.scale,
+            alibi_slopes=alibi_slopes,
+        )
+
+    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 JAISMLP(nn.Module):
+
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: JAISConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.swiglu = config.activation_function == "swiglu"
+        self.c_fc = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=True,
+            linear_method=linear_method,
+        )
+        self.c_fc2 = (ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=True,
+            linear_method=linear_method,
+        ) if self.swiglu else None)
+        self.c_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=True,
+            linear_method=linear_method,
+        )
+
+        self.act = SwiGLUActivation()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.swiglu:
+            hidden_states2, _ = self.c_fc2(hidden_states)
+        hidden_states, _ = self.c_fc(hidden_states)
+        hidden_states = (self.act(hidden_states, hidden_states2)
+                         if self.swiglu else self.act(hidden_states))
+        hidden_states, _ = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class JAISBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: JAISConfig,
+        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 = JAISAttention(config, linear_method)
+        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = JAISMLP(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 JAISModel(nn.Module):
+
+    def __init__(
+        self,
+        config: JAISConfig,
+        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)
+                    if config.position_embedding_type != "alibi" else None)
+        if hasattr(config, "embeddings_scale"):
+            self.embeddings_scale = config.embeddings_scale
+        else:
+            self.embeddings_scale = config.mup_embeddings_scale
+        self.h = nn.ModuleList([
+            JAISBlock(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)
+        if self.wpe is not None:
+            position_embeds = self.wpe(position_ids)
+            hidden_states = inputs_embeds + position_embeds
+        else:
+            hidden_states = inputs_embeds
+        hidden_states *= torch.tensor(float(self.embeddings_scale),
+                                      dtype=hidden_states.dtype)
+
+        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 JAISLMHeadModel(nn.Module):
+
+    def __init__(
+        self,
+        config: JAISConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.transformer = JAISModel(config, linear_method)
+        self.lm_head_weight = self.transformer.wte.weight
+        if hasattr(config, "width_scale"):
+            self.output_logits_scale = config.width_scale
+        else:
+            self.output_logits_scale = (config.mup_output_alpha *
+                                        config.mup_width_scale)
+        self.logits_processor = LogitsProcessor(vocab_size=config.vocab_size,
+                                               scale=self.output_logits_scale)
+        self.sampler = Sampler()
+
+    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 compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head_weight, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, 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 "relative_pe" in name:
+                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)