[Model] support telechat_12B

README.md

@@ -11,15 +11,16 @@ vLLM是一个快速且易于使用的LLM推理和服务库，使用PageAttention
 | 结构 | 模型 | 模型并行 | FP16 |
 | :------: | :------: | :------: | :------: |
 | LlamaForCausalLM      | LLaMA、LLaMA-2、LLaMA-3、Codellama、deepseek、Yi | Yes | Yes |
-| QWenLMHeadModel       | QWen                                            | Yes | Yes |
+| QWenLMHeadModel       | QWen、Qwen-VL                                   | Yes | Yes |
 | Qwen2ForCausalLM      | QWen1.5、CodeQwen1.5、QWen2                     | Yes | Yes |
 | ChatGLMModel          | chatglm2、chatglm3                              | Yes | Yes |
 | BaiChuanForCausalLM   | Baichuan、Baichuan2                             | Yes | Yes |
-| BloomForCausalLM      | BLOOM        | Yes | Yes |
-| InternLMForCausalLM   | InternLM     | Yes | Yes |
-| InternLM2ForCausalLM  | InternLM2    | Yes | Yes |
-| DeepseekV2ForCausalLM | DeepSeek-V2  | Yes | Yes |
-| MixtralForCausalLM    | Mixtral-8x7B | Yes | Yes |
+| BloomForCausalLM      | BLOOM                                           | Yes | Yes |
+| InternLMForCausalLM   | InternLM                                        | Yes | Yes |
+| InternLM2ForCausalLM  | InternLM2                                       | Yes | Yes |
+| DeepseekV2ForCausalLM | DeepSeek-V2                                     | Yes | Yes |
+| MixtralForCausalLM    | Mixtral-8x7B                                    | Yes | Yes |
+| TeleChat12BForCausalLM (#TelechatForCausalLM) | TeleChat-12B            | Yes | Yes |
 
 
 ## 安装

examples/template_telechat.jinja

+{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}
+
+{%- for message in messages -%}
+    {%- if message['role'] == 'user' -%}
+        {{- '<_user>' + message['content'] +'<_bot>' -}}
+    {%- elif message['role'] == 'assistant' -%}
+        {{- message['content'] + '<_end>' -}}
+    {%- endif -%}
+{%- endfor -%}

vllm/attention/ops/paged_attn.py

@@ -35,7 +35,7 @@ class PagedAttention:
 
     @staticmethod
     def get_supported_head_sizes() -> List[int]:
-        return [64, 80, 96, 112, 120, 128, 192, 256]
+        return [64, 80, 96, 112, 120, 128, 160, 192, 256]
 
     @staticmethod
     def get_kv_cache_shape(

vllm/distributed/device_communicators/custom_all_reduce.py

@@ -13,14 +13,10 @@ from vllm.distributed.parallel_state import in_the_same_node_as
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils import cuda_device_count_stateless
-from vllm.utils import is_hip
 
 try:
-    if (not is_hip()):
-        ops.meta_size()
-        custom_ar = True
-    else:
-        custom_ar = False
+    ops.meta_size()
+    custom_ar = True
 
 except Exception:
     # For AMD GPUs and CPUs

vllm/model_executor/layers/quantization/awq.py

@@ -157,7 +157,7 @@ class AWQLinearMethod(LinearMethodBase):
         zeros_and_scales = GroupQuantScaleParameter(data=torch.empty(
             input_size_per_partition // self.quant_config.group_size,
             output_size_per_partition,
-            dtype=params_dtype,
+            dtype=torch.int32,
         ),
                                           input_dim=0,
                                           output_dim=1,

vllm/model_executor/models/__init__.py

@@ -61,6 +61,7 @@ _GENERATION_MODELS = {
     "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
+    "TeleChat12BForCausalLM": ("telechat_12B", "TeleChat12BForCausalLM"),  # telechat12b
     "SolarForCausalLM": ("solar", "SolarForCausalLM"),
     "ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
     "XverseForCausalLM": ("xverse", "XverseForCausalLM"),

vllm/model_executor/models/telechat_12B.py

+# coding=utf-8
+# 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.
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.attention import Attention, AttentionMetadata
+from vllm.config import CacheConfig, LoRAConfig
+from vllm.distributed import (get_pp_group, get_pp_indices,
+                              get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.distributed import (divide, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              split_tensor_along_last_dim,
+                              tensor_model_parallel_all_gather,
+                              tensor_model_parallel_all_reduce)
+
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+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 (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, kv_cache_scales_loader)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors, SamplerOutput
+from vllm.utils import is_hip, print_warning_once
+
+from .interfaces import SupportsLoRA
+
+
+class TeleChatMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+    ) -> None:
+        super().__init__()
+        self.gate_proj = ColumnParallelLinear(
+            input_size=hidden_size,
+            output_size=intermediate_size,
+            bias=False,
+            quant_config=quant_config)
+        
+        self.up_proj = ColumnParallelLinear(
+            input_size=hidden_size,
+            output_size=intermediate_size,
+            bias=False,
+            quant_config=quant_config)
+        
+        self.down_proj = RowParallelLinear(input_size=intermediate_size,
+                                           output_size=hidden_size,
+                                        #    bias=bias,
+                                           bias=True,
+                                           quant_config=quant_config,
+                                           input_is_parallel=True)
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_output, _ = self.gate_proj(x)
+        up_output, _ = self.up_proj(x)
+
+        gate_output = self.act_fn(torch.cat([gate_output, up_output], dim=-1))
+
+        output, _ = self.down_proj(gate_output)
+        return output
+    
+class TeleChatAttention(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        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,
+        bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config 
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.tp_size = tp_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.query = ColumnParallelLinear(
+            input_size=hidden_size,
+            output_size=hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            gather_output=False)
+        
+        kv_projection_size = self.head_dim * self.total_num_heads
+        self.key_value = MergedColumnParallelLinear(
+            input_size=hidden_size,
+            output_sizes=[kv_projection_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            gather_output=False,)
+        
+        self.dense = RowParallelLinear(
+            input_size=hidden_size,
+            output_size=hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            input_is_parallel=True,
+        )
+
+        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 = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config)
+    
+    def split_tensor_along_last_dim(self,
+                                    tensor: torch.Tensor,
+                                    num_partitions: int,
+                                    contiguous_split_chunks: bool = False,
+                                    ):
+
+        # Get the size and dimension.
+        last_dim = tensor.dim() - 1
+        last_dim_size = tensor.size()[last_dim] // num_partitions
+        # Split.
+        tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
+
+        return tensor_list
+    
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+
+        query_layer, _ = self.query(hidden_states)
+        mixed_kv_layer, _ = self.key_value(hidden_states)
+        (key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_kv_layer, 2)
+        query_layer, key_layer = self.rotary_emb(positions, query_layer, key_layer)
+        attn_output = self.attn(query_layer, key_layer, value_layer, kv_cache, attn_metadata)
+        output, _ = self.dense(attn_output)
+
+        return output
+
+
+class TeleChatDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None and getattr(
+                config, "original_max_position_embeddings", None):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        # Support abacusai/Smaug-72B-v0.1 with attention_bias
+        # Support internlm/internlm-7b with bias
+        attention_bias = getattr(config, "attention_bias", False) or getattr(
+            config, "bias", False)
+        self.self_attention = TeleChatAttention(
+            config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=getattr(config, "num_key_value_heads",
+                                 config.num_attention_heads),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=attention_bias,
+            cache_config=cache_config,
+        )
+        self.mlp = TeleChatMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            bias=getattr(config, "mlp_bias", False),
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+        self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
+
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        residual = hidden_states
+        layernorm_output = self.input_layernorm(hidden_states)
+        attn_outputs = self.self_attention(
+            positions=positions,
+            hidden_states=layernorm_output,
+            kv_cache=kv_cache,
+            attn_metadata=attn_metadata,
+        )
+        attn_outputs = residual + attn_outputs
+        residual = attn_outputs
+        layernorm_output = self.post_attention_layernorm(attn_outputs)
+        output = residual + self.mlp(layernorm_output)
+        return output
+
+class TeleChatModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.word_embeddings = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+        self.h = nn.ModuleList(
+           [
+                TeleChatDecoderLayer(config=config,
+                                  cache_config=cache_config,
+                                  quant_config=quant_config)
+                for _ in range(self.config.num_hidden_layers)
+            ] 
+            )
+        
+        self.ln_f = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.word_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        
+        hidden_states = self.get_input_embeddings(input_ids)
+       
+        for i in range(self.config.num_hidden_layers):
+            layer = self.h[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                attn_metadata,
+            )
+
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class TeleChat12BForCausalLM(nn.Module, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens",
+        "lm_head"
+    ]
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+    bitsandbytes_stacked_params_mapping = {
+        # shard_name, weight_name, index
+        "q_proj": ("qkv_proj", 0),
+        "k_proj": ("qkv_proj", 1),
+        "v_proj": ("qkv_proj", 2),
+        "gate_proj": ("gate_up_proj", 0),
+        "up_proj": ("gate_up_proj", 1),
+    }
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        super().__init__()
+        config.intermediate_size = config.ffn_hidden_size
+        config.hidden_act = "silu"
+        config.rms_norm_eps = config.layer_norm_epsilon
+        config.tie_word_embeddings = False
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.transformer = TeleChatModel(config,
+                                cache_config,
+                                quant_config,
+                                lora_config=lora_config)
+  
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.sampler = Sampler()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        model_output = self.transformer(input_ids, positions, kv_caches,
+                                  attn_metadata)
+        return model_output
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, 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 make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+
+
+        params_dict = dict(self.named_parameters())
+
+        num_key_value_heads = self.config.num_attention_heads
+        head_dim = self.config.hidden_size // num_key_value_heads
+        for name, loaded_weight in weights:
+            if "self_attention.key_value" in name:
+                k_weight = []
+                v_weight = []
+                for i in range(num_key_value_heads):
+                    start =i * head_dim * 2
+                    k_weight.append(loaded_weight[start:start+head_dim,:])
+                    v_weight.append(loaded_weight[start+head_dim:start+2*head_dim:])
+                
+                k_weight = torch.cat(k_weight,dim=0)
+                v_weight = torch.cat(v_weight,dim=0)
+
+                loaded_weight = torch.cat([k_weight, v_weight], dim=0)
+
+            try:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            except KeyError:
+                print("key error")
+                pass
+
+    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
+        tp_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        for layer_idx, scaling_factor in kv_cache_scales_loader(
+                quantization_param_path, tp_rank, tp_size,
+                self.config.num_hidden_layers,
+                self.config.__class__.model_type):
+            if not isinstance(self.model.layers[layer_idx], nn.Identity):
+                layer_self_attn = self.model.layers[layer_idx].self_attn
+
+            if is_hip():
+                # The scaling factor convention we are assuming is
+                # quantized_value * scaling_factor ~= true_value
+                # which is consistent with the practice of setting
+                # scaling_factor = tensor_amax / FPtype_max
+                scaling_factor *= 2
+            if hasattr(layer_self_attn, "kv_scale"):
+                layer_self_attn.attn._kv_scale = scaling_factor
+            else:
+                raise RuntimeError("Self attention has no KV cache scaling "
+                                   "factor attribute!")