[Model] Allow loading from original Mistral format (#8168)

Co-authored-by: Michael Goin <michael@neuralmagic.com>

tests/models/test_mistral.py

@@ -41,3 +41,43 @@ def test_models(
         name_0="hf",
         name_1="vllm",
     )
+
+
+@pytest.mark.parametrize("model", MODELS[1:])
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [64])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_mistral_format(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    with vllm_runner(
+            model,
+            dtype=dtype,
+            tokenizer_mode="auto",
+            load_format="safetensors",
+            config_format="hf",
+    ) as hf_format_model:
+        hf_format_outputs = hf_format_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    with vllm_runner(
+            model,
+            dtype=dtype,
+            tokenizer_mode="mistral",
+            load_format="mistral",
+            config_format="mistral",
+    ) as mistral_format_model:
+        mistral_format_outputs = mistral_format_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    check_logprobs_close(
+        outputs_0_lst=hf_format_outputs,
+        outputs_1_lst=mistral_format_outputs,
+        name_0="hf",
+        name_1="mistral",
+    )

vllm/config.py

@@ -13,7 +13,7 @@ from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.model_executor.models import ModelRegistry
 from vllm.platforms import current_platform
 from vllm.tracing import is_otel_available, otel_import_error_traceback
-from vllm.transformers_utils.config import (get_config,
+from vllm.transformers_utils.config import (ConfigFormat, get_config,
                                             get_hf_image_processor_config,
                                             get_hf_text_config)
 from vllm.utils import (STR_NOT_IMPL_ENC_DEC_CUDAGRAPH, GiB_bytes,
@@ -121,35 +121,37 @@ class ModelConfig:
             override default neuron config that are specific to Neuron devices, 
             this argument will be used to configure the neuron config that 
             can not be gathered from the vllm arguments. 
+        config_format: The config format which shall be loaded.
+            Defaults to 'auto' which defaults to 'hf'.
     """
 
-    def __init__(
-            self,
-            model: str,
-            tokenizer: str,
-            tokenizer_mode: str,
-            trust_remote_code: bool,
-            dtype: Union[str, torch.dtype],
-            seed: int,
-            revision: Optional[str] = None,
-            code_revision: Optional[str] = None,
-            rope_scaling: Optional[dict] = None,
-            rope_theta: Optional[float] = None,
-            tokenizer_revision: Optional[str] = None,
-            max_model_len: Optional[int] = None,
-            spec_target_max_model_len: Optional[int] = None,
-            quantization: Optional[str] = None,
-            quantization_param_path: Optional[str] = None,
-            enforce_eager: Optional[bool] = None,
-            max_context_len_to_capture: Optional[int] = None,
-            max_seq_len_to_capture: Optional[int] = None,
-            max_logprobs: int = 20,
-            disable_sliding_window: bool = False,
-            skip_tokenizer_init: bool = False,
-            served_model_name: Optional[Union[str, List[str]]] = None,
-            limit_mm_per_prompt: Optional[Mapping[str, int]] = None,
-            use_async_output_proc: bool = True,
-            override_neuron_config: Optional[Dict[str, Any]] = None) -> None:
+    def __init__(self,
+                 model: str,
+                 tokenizer: str,
+                 tokenizer_mode: str,
+                 trust_remote_code: bool,
+                 dtype: Union[str, torch.dtype],
+                 seed: int,
+                 revision: Optional[str] = None,
+                 code_revision: Optional[str] = None,
+                 rope_scaling: Optional[dict] = None,
+                 rope_theta: Optional[float] = None,
+                 tokenizer_revision: Optional[str] = None,
+                 max_model_len: Optional[int] = None,
+                 spec_target_max_model_len: Optional[int] = None,
+                 quantization: Optional[str] = None,
+                 quantization_param_path: Optional[str] = None,
+                 enforce_eager: Optional[bool] = None,
+                 max_context_len_to_capture: Optional[int] = None,
+                 max_seq_len_to_capture: Optional[int] = None,
+                 max_logprobs: int = 20,
+                 disable_sliding_window: bool = False,
+                 skip_tokenizer_init: bool = False,
+                 served_model_name: Optional[Union[str, List[str]]] = None,
+                 limit_mm_per_prompt: Optional[Mapping[str, int]] = None,
+                 use_async_output_proc: bool = True,
+                 override_neuron_config: Optional[Dict[str, Any]] = None,
+                 config_format: ConfigFormat = ConfigFormat.AUTO) -> None:
         self.model = model
         self.tokenizer = tokenizer
         self.tokenizer_mode = tokenizer_mode
@@ -176,7 +178,8 @@ class ModelConfig:
         self.skip_tokenizer_init = skip_tokenizer_init
 
         self.hf_config = get_config(self.model, trust_remote_code, revision,
-                                    code_revision, rope_scaling, rope_theta)
+                                    code_revision, rope_scaling, rope_theta,
+                                    config_format)
         self.hf_text_config = get_hf_text_config(self.hf_config)
         self.hf_image_processor_config = get_hf_image_processor_config(
             self.model, revision)
@@ -746,6 +749,7 @@ class LoadFormat(str, enum.Enum):
     SHARDED_STATE = "sharded_state"
     GGUF = "gguf"
     BITSANDBYTES = "bitsandbytes"
+    MISTRAL = "mistral"
 
 
 @dataclass

vllm/engine/arg_utils.py

@@ -8,10 +8,10 @@ from typing import (TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Tuple,
 import torch
 
 import vllm.envs as envs
-from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
-                         EngineConfig, LoadConfig, LoadFormat, LoRAConfig,
-                         ModelConfig, ObservabilityConfig, ParallelConfig,
-                         PromptAdapterConfig, SchedulerConfig,
+from vllm.config import (CacheConfig, ConfigFormat, DecodingConfig,
+                         DeviceConfig, EngineConfig, LoadConfig, LoadFormat,
+                         LoRAConfig, ModelConfig, ObservabilityConfig,
+                         ParallelConfig, PromptAdapterConfig, SchedulerConfig,
                          SpeculativeConfig, TokenizerPoolConfig)
 from vllm.executor.executor_base import ExecutorBase
 from vllm.logger import init_logger
@@ -65,6 +65,7 @@ class EngineArgs:
     trust_remote_code: bool = False
     download_dir: Optional[str] = None
     load_format: str = 'auto'
+    config_format: str = 'auto'
     dtype: str = 'auto'
     kv_cache_dtype: str = 'auto'
     quantization_param_path: Optional[str] = None
@@ -234,6 +235,13 @@ class EngineArgs:
             'section for more information.\n'
             '* "bitsandbytes" will load the weights using bitsandbytes '
             'quantization.\n')
+        parser.add_argument(
+            '--config-format',
+            default=EngineArgs.config_format,
+            choices=[f.value for f in ConfigFormat],
+            help='The format of the model config to load.\n\n'
+            '* "auto" will try to load the config in hf format '
+            'if available else it will try to load in mistral format ')
         parser.add_argument(
             '--dtype',
             type=str,
@@ -813,7 +821,10 @@ class EngineArgs:
             served_model_name=self.served_model_name,
             limit_mm_per_prompt=self.limit_mm_per_prompt,
             use_async_output_proc=not self.disable_async_output_proc,
-            override_neuron_config=self.override_neuron_config)
+            override_neuron_config=self.override_neuron_config,
+            config_format=self.config_format,
+        )
+
         cache_config = CacheConfig(
             block_size=self.block_size if self.device != "neuron" else
             self.max_model_len,  # neuron needs block_size = max_model_len

vllm/model_executor/model_loader/loader.py

@@ -17,6 +17,7 @@ import torch
 from huggingface_hub import HfApi, hf_hub_download
 from torch import nn
 from transformers import AutoModelForCausalLM, PretrainedConfig
+from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
 
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoadFormat,
                          LoRAConfig, ModelConfig, MultiModalConfig,
@@ -241,12 +242,17 @@ class DefaultModelLoader(BaseModelLoader):
         is_local = os.path.isdir(model_name_or_path)
         load_format = self.load_config.load_format
         use_safetensors = False
+        index_file = SAFE_WEIGHTS_INDEX_NAME
         # Some quantized models use .pt files for storing the weights.
         if load_format == LoadFormat.AUTO:
             allow_patterns = ["*.safetensors", "*.bin"]
         elif load_format == LoadFormat.SAFETENSORS:
             use_safetensors = True
             allow_patterns = ["*.safetensors"]
+        elif load_format == LoadFormat.MISTRAL:
+            use_safetensors = True
+            allow_patterns = ["consolidated*.safetensors"]
+            index_file = "consolidated.safetensors.index.json"
         elif load_format == LoadFormat.PT:
             allow_patterns = ["*.pt"]
         elif load_format == LoadFormat.NPCACHE:
@@ -284,10 +290,10 @@ class DefaultModelLoader(BaseModelLoader):
             # any files not found in the index.
             if not is_local:
                 download_safetensors_index_file_from_hf(
-                    model_name_or_path, self.load_config.download_dir,
-                    revision)
+                    model_name_or_path, index_file,
+                    self.load_config.download_dir, revision)
             hf_weights_files = filter_duplicate_safetensors_files(
-                hf_weights_files, hf_folder)
+                hf_weights_files, hf_folder, index_file)
         else:
             hf_weights_files = filter_files_not_needed_for_inference(
                 hf_weights_files)

vllm/model_executor/model_loader/weight_utils.py

@@ -16,7 +16,6 @@ import torch
 from huggingface_hub import HfFileSystem, hf_hub_download, snapshot_download
 from safetensors.torch import load_file, safe_open, save_file
 from tqdm.auto import tqdm
-from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
 
 from vllm.config import LoadConfig, ModelConfig
 from vllm.distributed import get_tensor_model_parallel_rank
@@ -251,6 +250,7 @@ def download_weights_from_hf(
 
 def download_safetensors_index_file_from_hf(
     model_name_or_path: str,
+    index_file: str,
     cache_dir: Optional[str],
     revision: Optional[str] = None,
 ) -> None:
@@ -269,36 +269,37 @@ def download_safetensors_index_file_from_hf(
             # Download the safetensors index file.
             hf_hub_download(
                 repo_id=model_name_or_path,
-                filename=SAFE_WEIGHTS_INDEX_NAME,
+                filename=index_file,
                 cache_dir=cache_dir,
                 revision=revision,
                 local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
             )
         # If file not found on remote or locally, we should not fail since
-        # only some models will have SAFE_WEIGHTS_INDEX_NAME.
+        # only some models will have index_file.
         except huggingface_hub.utils.EntryNotFoundError:
-            logger.info("No %s found in remote.", SAFE_WEIGHTS_INDEX_NAME)
+            logger.info("No %s found in remote.", index_file)
         except huggingface_hub.utils.LocalEntryNotFoundError:
-            logger.info("No %s found in local cache.", SAFE_WEIGHTS_INDEX_NAME)
+            logger.info("No %s found in local cache.", index_file)
 
 
 # For models like Mistral-7B-v0.3, there are both sharded
 # safetensors files and a consolidated safetensors file.
 # Passing both of these to the weight loader functionality breaks.
-# So, we use the SAFE_WEIGHTS_INDEX_NAME to
+# So, we use the index_file to
 # look up which safetensors files should be used.
 def filter_duplicate_safetensors_files(hf_weights_files: List[str],
-                                       hf_folder: str) -> List[str]:
+                                       hf_folder: str,
+                                       index_file: str) -> List[str]:
     # model.safetensors.index.json is a mapping from keys in the
     # torch state_dict to safetensors file holding that weight.
-    index_file_name = os.path.join(hf_folder, SAFE_WEIGHTS_INDEX_NAME)
+    index_file_name = os.path.join(hf_folder, index_file)
     if not os.path.isfile(index_file_name):
         return hf_weights_files
 
     # Iterate through the weight_map (weight_name: safetensors files)
     # to identify weights that we should use.
-    with open(index_file_name) as index_file:
-        weight_map = json.load(index_file)["weight_map"]
+    with open(index_file_name, "r") as f:
+        weight_map = json.load(f)["weight_map"]
     weight_files_in_index = set()
     for weight_name in weight_map:
         weight_files_in_index.add(

vllm/model_executor/models/llama.py

@@ -375,6 +375,25 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA):
         "gate_proj": ("gate_up_proj", 0),
         "up_proj": ("gate_up_proj", 1),
     }
+    # Mistral/Llama models can also be loaded with --load-format mistral
+    # from consolidated.safetensors checkpoints
+    mistral_mapping = {
+        "layers": "model.layers",
+        "attention": "self_attn",
+        "wq": "q_proj",
+        "wk": "k_proj",
+        "wv": "v_proj",
+        "wo": "o_proj",
+        "attention_norm": "input_layernorm",
+        "feed_forward": "mlp",
+        "w1": "gate_proj",
+        "w2": "down_proj",
+        "w3": "up_proj",
+        "ffn_norm": "post_attention_layernorm",
+        "tok_embeddings": "model.embed_tokens",
+        "output": "lm_head",
+        "norm": "model.norm"
+    }
 
     def __init__(
         self,
@@ -472,6 +491,8 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA):
         ]
         params_dict = dict(self.named_parameters())
         for name, loaded_weight in weights:
+            name, loaded_weight = self.maybe_remap_mistral(name, loaded_weight)
+
             if "rotary_emb.inv_freq" in name:
                 continue
             if ("rotary_emb.cos_cached" in name
@@ -549,3 +570,33 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA):
             else:
                 raise RuntimeError("Self attention has no KV cache scaling "
                                    "factor attribute!")
+
+    # This function is used to remap the mistral format as
+    # used by Mistral and Llama <=2
+    def maybe_remap_mistral(
+            self, name: str,
+            loaded_weight: torch.Tensor) -> Tuple[str, torch.Tensor]:
+
+        def permute(w, n_heads):
+            attn_in = self.config.head_dim * n_heads
+            attn_out = self.config.hidden_size
+
+            return w.view(n_heads, attn_in // n_heads // 2, 2,
+                          attn_out).transpose(1, 2).reshape(attn_in, attn_out)
+
+        mapping = self.mistral_mapping
+        modules = name.split(".")
+
+        # rotary embeds should be sliced
+        if "wk" in modules:
+            loaded_weight = permute(loaded_weight,
+                                    self.config.num_key_value_heads)
+        elif "wq" in modules:
+            loaded_weight = permute(loaded_weight,
+                                    self.config.num_attention_heads)
+
+        for item in modules:
+            if item in mapping and mapping[item] not in name:
+                name = name.replace(item, mapping[item])
+
+        return name, loaded_weight

vllm/transformers_utils/config.py

 import contextlib
+import enum
+import json
 from pathlib import Path
 from typing import Any, Dict, Optional, Type, Union
 
+from huggingface_hub import file_exists, hf_hub_download
 from transformers import GenerationConfig, PretrainedConfig
 from transformers.models.auto.image_processing_auto import (
     get_image_processor_config)
 from transformers.models.auto.modeling_auto import (
     MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
+from transformers.utils import CONFIG_NAME as HF_CONFIG_NAME
 
 from vllm.envs import VLLM_USE_MODELSCOPE
 from vllm.logger import init_logger
@@ -27,6 +31,8 @@ if VLLM_USE_MODELSCOPE:
 else:
     from transformers import AutoConfig
 
+MISTRAL_CONFIG_NAME = "params.json"
+
 logger = init_logger(__name__)
 
 _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
@@ -53,6 +59,20 @@ for name, cls in _CONFIG_REGISTRY.items():
         AutoConfig.register(name, cls)
 
 
+class ConfigFormat(str, enum.Enum):
+    AUTO = "auto"
+    HF = "hf"
+    MISTRAL = "mistral"
+
+
+def file_or_path_exists(model: Union[str, Path], config_name, revision,
+                        token) -> bool:
+    if Path(model).exists():
+        return (Path(model) / config_name).is_file()
+
+    return file_exists(model, HF_CONFIG_NAME, revision=revision, token=token)
+
+
 def get_config(
     model: Union[str, Path],
     trust_remote_code: bool,
@@ -60,45 +80,68 @@ def get_config(
     code_revision: Optional[str] = None,
     rope_scaling: Optional[dict] = None,
     rope_theta: Optional[float] = None,
+    config_format: ConfigFormat = ConfigFormat.AUTO,
     **kwargs,
 ) -> PretrainedConfig:
-
     # Separate model folder from file path for GGUF models
+
     is_gguf = check_gguf_file(model)
     if is_gguf:
         kwargs["gguf_file"] = Path(model).name
         model = Path(model).parent
 
-    config_dict, _ = PretrainedConfig.get_config_dict(
-        model, revision=revision, code_revision=code_revision, **kwargs)
+    if config_format == ConfigFormat.AUTO:
+        if is_gguf or file_or_path_exists(model,
+                                          HF_CONFIG_NAME,
+                                          revision=revision,
+                                          token=kwargs.get("token")):
+            config_format = ConfigFormat.HF
+        elif file_or_path_exists(model,
+                                 MISTRAL_CONFIG_NAME,
+                                 revision=revision,
+                                 token=kwargs.get("token")):
+            config_format = ConfigFormat.MISTRAL
+        else:
+            raise ValueError(f"No supported config format found in {model}")
+
+    if config_format == ConfigFormat.HF:
+        config_dict, _ = PretrainedConfig.get_config_dict(
+            model, revision=revision, code_revision=code_revision, **kwargs)
+
+        # Use custom model class if it's in our registry
+        model_type = config_dict.get("model_type")
+        if model_type in _CONFIG_REGISTRY:
+            config_class = _CONFIG_REGISTRY[model_type]
+            config = config_class.from_pretrained(model,
+                                                  revision=revision,
+                                                  code_revision=code_revision)
+        else:
+            try:
+                config = AutoConfig.from_pretrained(
+                    model,
+                    trust_remote_code=trust_remote_code,
+                    revision=revision,
+                    code_revision=code_revision,
+                    **kwargs,
+                )
+            except ValueError as e:
+                if (not trust_remote_code
+                        and "requires you to execute the configuration file"
+                        in str(e)):
+                    err_msg = (
+                        "Failed to load the model config. If the model "
+                        "is a custom model not yet available in the "
+                        "HuggingFace transformers library, consider setting "
+                        "`trust_remote_code=True` in LLM or using the "
+                        "`--trust-remote-code` flag in the CLI.")
+                    raise RuntimeError(err_msg) from e
+                else:
+                    raise e
 
-    # Use custom model class if it's in our registry
-    model_type = config_dict.get("model_type")
-    if model_type in _CONFIG_REGISTRY:
-        config_class = _CONFIG_REGISTRY[model_type]
-        config = config_class.from_pretrained(model,
-                                              revision=revision,
-                                              code_revision=code_revision)
+    elif config_format == ConfigFormat.MISTRAL:
+        config = load_params_config(model, revision)
     else:
-        try:
-            config = AutoConfig.from_pretrained(
-                model,
-                trust_remote_code=trust_remote_code,
-                revision=revision,
-                code_revision=code_revision,
-                **kwargs)
-        except ValueError as e:
-            if (not trust_remote_code
-                    and "requires you to execute the configuration file"
-                    in str(e)):
-                err_msg = (
-                    "Failed to load the model config. If the model is a custom "
-                    "model not yet available in the HuggingFace transformers "
-                    "library, consider setting `trust_remote_code=True` in LLM "
-                    "or using the `--trust-remote-code` flag in the CLI.")
-                raise RuntimeError(err_msg) from e
-            else:
-                raise e
+        raise ValueError(f"Unsupported config format: {config_format}")
 
     # Special architecture mapping check for GGUF models
     if is_gguf:
@@ -108,16 +151,70 @@ def get_config(
         model_type = MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[config.model_type]
         config.update({"architectures": [model_type]})
 
-    for key, value in [("rope_scaling", rope_scaling),
-                       ("rope_theta", rope_theta)]:
+    for key, value in [
+        ("rope_scaling", rope_scaling),
+        ("rope_theta", rope_theta),
+    ]:
         if value is not None:
-            logger.info("Updating %s from %r to %r", key,
-                        getattr(config, key, None), value)
+            logger.info(
+                "Updating %s from %r to %r",
+                key,
+                getattr(config, key, None),
+                value,
+            )
             config.update({key: value})
 
     return config
 
 
+def load_params_config(model, revision) -> PretrainedConfig:
+    # This function loads a params.json config which
+    # should be used when loading models in mistral format
+
+    config_file_name = "params.json"
+
+    config_path = Path(model) / config_file_name
+
+    if not config_path.is_file():
+        config_path = Path(
+            hf_hub_download(model, config_file_name, revision=revision))
+
+    with open(config_path, "r") as file:
+        config_dict = json.load(file)
+
+    config_mapping = {
+        "dim": "hidden_size",
+        "norm_eps": "rms_norm_eps",
+        "n_kv_heads": "num_key_value_heads",
+        "n_layers": "num_hidden_layers",
+        "n_heads": "num_attention_heads",
+        "hidden_dim": "intermediate_size",
+    }
+
+    def recurse_elems(elem: Any):
+        if isinstance(elem, dict):
+            config_dict = {}
+            for key, value in elem.items():
+                key = config_mapping.get(key, key)
+                config_dict[key] = recurse_elems(value)
+            return PretrainedConfig(**config_dict)
+        else:
+            return elem
+
+    config_dict["model_type"] = config_dict.get("model_type", "transformer")
+    config_dict["hidden_act"] = config_dict.get("activation", "silu")
+    config_dict["tie_word_embeddings"] = config_dict.get(
+        "tie_embeddings", False)
+
+    if config_dict["model_type"] == "transformer":
+        if "moe" in config_dict:
+            config_dict["architectures"] = ["MixtralForCausalLM"]
+        else:
+            config_dict["architectures"] = ["MistralForCausalLM"]
+
+    return recurse_elems(config_dict)
+
+
 def get_hf_image_processor_config(
     model: Union[str, Path],
     revision: Optional[str] = None,
@@ -134,7 +231,7 @@ def get_hf_image_processor_config(
 
 def get_hf_text_config(config: PretrainedConfig):
     """Get the "sub" config relevant to llm for multi modal models.
-        No op for pure text models.
+    No op for pure text models.
     """
     if hasattr(config, "text_config"):
         # The code operates under the assumption that text_config should have