Initial commit

verl/third_party/vllm/vllm_v_0_4_2/dtensor_weight_loaders.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models
+
+from typing import Dict, Iterable, Tuple
+import torch
+import torch.nn as nn
+from torch.distributed._tensor import DTensor, Shard, Replicate
+
+from vllm.model_executor.layers.linear import *
+from vllm.model_executor.models import ModelRegistry
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+
+def gemma_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+        ("gate_up_proj", "gate_proj", 0),
+        ("gate_up_proj", "up_proj", 1),
+    ]
+
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        for (param_name, shard_name, shard_id) in stacked_params_mapping:
+            if shard_name not in name:
+                continue
+            stacked_name = name.replace(shard_name, param_name)
+            # Skip loading extra bias for GPTQ models.
+            if stacked_name.endswith(".bias") and stacked_name not in params_dict:
+                continue
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[stacked_name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id)
+            break
+        else:
+            # lm_head is not used in vllm as it is tied with embed_token.
+            # To prevent errors, skip loading lm_head.weight.
+            if "lm_head.weight" in name:
+                continue
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            # GemmaRMSNorm is different from Llama's in that it multiplies
+            # (1 + weight) to the output, instead of just weight.
+            if "norm.weight" in name:
+                local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+
+                norm_weight = local_loaded_weight + 1.0
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, norm_weight.to(dtype=param.dtype))
+            else:
+                local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+
+
+def gptbigcode_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module):
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        if "lm_head.weight" in name:
+            continue
+        if ".attn.bias" in name:
+            # Skip attention mask.
+            # NOTE: "c_attn.bias" should not be skipped.
+            continue
+        local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+        param = params_dict[name]
+        weight_loader = getattr(param, "weight_loader", default_weight_loader)
+        weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+
+
+def starcoder2_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module):
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+    ]
+
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        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)
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[name]
+            weight_loader = param.weight_loader
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id)
+            break
+        else:
+            if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+            param = params_dict[name]
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+
+
+def llama_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        (".qkv_proj", ".q_proj", "q"),
+        (".qkv_proj", ".k_proj", "k"),
+        (".qkv_proj", ".v_proj", "v"),
+        (".gate_up_proj", ".gate_proj", 0),
+        (".gate_up_proj", ".up_proj", 1),
+    ]
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        if "rotary_emb.inv_freq" in name:
+            continue
+        if ("rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name):
+            # Models trained using ColossalAI may include these tensors in
+            # the checkpoint. Skip them.
+            continue
+        # With tie_word_embeddings, we can skip lm_head.weight
+        # The weight might appear unnecessarily in the files if the model is
+        # processed with quantization, LoRA, fine-tuning, etc.
+        if vllm_model.config.tie_word_embeddings and "lm_head.weight" 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
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[name]
+            weight_loader = param.weight_loader
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id)
+            break
+        else:
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight)
+
+
+def qwen2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+        ("gate_up_proj", "gate_proj", 0),
+        ("gate_up_proj", "up_proj", 1),
+    ]
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        if "rotary_emb.inv_freq" in name:
+            continue
+        if vllm_model.config.tie_word_embeddings and "lm_head.weight" 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
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[name]
+            weight_loader = param.weight_loader
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype), 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]
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+
+
+def gpt2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    pass
+
+
+def redistribute_dtensor(param_name: str, loaded_weights: DTensor, parallelize_plan: Dict = None):
+    param_name = _process_parameter_names(name=param_name)
+    if parallelize_plan is not None:
+        assert param_name in parallelize_plan.keys(), \
+            f"param name: {param_name} not in parallelize_plan :{parallelize_plan.keys()}"
+        placement = parallelize_plan[param_name]
+        local_loaded_weights = loaded_weights.redistribute(device_mesh=loaded_weights.device_mesh,
+                                                           placements=placement).to_local()
+    else:
+        local_loaded_weights = loaded_weights.full_tensor()
+    return local_loaded_weights
+
+
+def _process_parameter_names(name):
+    # Remove '.weight' if it exists at the end of the string
+    if name.endswith(".weight"):
+        name = name[:-7]
+
+    # Remove 'model.layers.x.' or 'model.' prefix
+    if "model.layers" in name:
+        parts = name.split('.')
+        # Reconstruct the string without 'model.layers.x.'
+        name = '.'.join(parts[3:])  # parts[0] is 'model', parts[1] is 'layers', parts[2] is 'x'
+    elif name.startswith("model."):
+        name = name[6:]  # Remove 'model.'
+
+    return name
+
+
+__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__ = {
+    'GPT2LMHeadModel': gpt2_dtensor_weight_loader,
+    'LlamaForCausalLM': llama_dtensor_weight_loader,
+    'LLaMAForCausalLM': llama_dtensor_weight_loader,
+    'MistralForCausalLM': llama_dtensor_weight_loader,  # mistral is the same as llama in vLLM
+    'InternLMForCausalLM': llama_dtensor_weight_loader,
+    'AquilaModel': llama_dtensor_weight_loader,
+    'AquilaForCausalLM': llama_dtensor_weight_loader,
+    'Phi3ForCausalLM': llama_dtensor_weight_loader,
+    'GemmaForCausalLM': gemma_dtensor_weight_loader,
+    'GPTBigCodeForCausalLM': gptbigcode_dtensor_load_weights,
+    'Starcoder2ForCausalLM': starcoder2_dtensor_load_weights,
+    'Qwen2ForCausalLM': qwen2_dtensor_weight_loader
+}
+
+
+# the actor model is .state_dict()
+# Load dtensor weights
+def load_dtensor_weights(actor_weights: Dict, vllm_model: nn.Module):
+    weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__)
+    weight_loader(actor_weights, vllm_model)
+    # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu
+    # after init, and we need this after sync model weights for in first iter.
+    vllm_model = vllm_model.cuda()
+
+
+def _get_model_weight_loader(arch: str):
+    if arch in __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__:
+        return __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__[arch]
+    raise ValueError(f"Model architectures {arch} are not supported for now. "
+                     f"Supported architectures: {__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__.keys()}")
+
+
+# NOTE(sgm): we use per-parameter weight loader in each vllm sub
+def update_dtensor_weight_loader():
+    pass

verl/third_party/vllm/vllm_v_0_4_2/hf_weight_loader.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models
+
+from typing import Dict, Union, Optional, Iterable, Tuple
+
+import torch
+import torch.nn as nn
+
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+
+def update_hf_weight_loader():
+    from vllm.model_executor.models.gemma import GemmaForCausalLM
+    GemmaForCausalLM.load_weights = gemma_load_weights
+
+
+def gemma_load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+        ("gate_up_proj", "gate_proj", 0),
+        ("gate_up_proj", "up_proj", 1),
+    ]
+    params_dict = dict(self.named_parameters())
+    loaded_params = set()
+    for name, loaded_weight in weights:
+        for (param_name, shard_name, shard_id) in stacked_params_mapping:
+            if shard_name not in name:
+                continue
+            name = name.replace(shard_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:
+            # lm_head is not used in vllm as it is tied with embed_token.
+            # To prevent errors, skip loading lm_head.weight.
+            if "lm_head.weight" in name:
+                continue
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            # GemmaRMSNorm is different from Llama's in that it multiplies
+            # (1 + weight) to the output, instead of just weight.
+            if "norm.weight" in name:
+                norm_weight = loaded_weight + 1.0  # prevent inplace modify actor weights
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, norm_weight)
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+        loaded_params.add(name)
+    unloaded_params = params_dict.keys() - loaded_params
+    if unloaded_params:
+        raise RuntimeError("Some weights are not initialized from checkpoints: "
+                           f"{unloaded_params}")
+
+
+def load_hf_weights(actor_weights: Dict, vllm_model: nn.Module):
+    assert isinstance(actor_weights, Dict)
+    with set_default_torch_dtype(next(vllm_model.parameters()).dtype):  # TODO
+        vllm_model.load_weights(actor_weights.items())
+    for _, module in vllm_model.named_modules():
+        quant_method = getattr(module, "quant_method", None)
+        if quant_method is not None:
+            quant_method.process_weights_after_loading(module)
+        # FIXME: Remove this after Mixtral is updated
+        # to use quant_method.
+        if hasattr(module, "process_weights_after_loading"):
+            module.process_weights_after_loading()
+    vllm_model = vllm_model.cuda()

verl/third_party/vllm/vllm_v_0_4_2/llm.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py
+
+from typing import Dict, List, Optional, Tuple, Union
+
+from tqdm import tqdm
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
+from transformers import PretrainedConfig
+import torch.nn as nn
+from .arg_utils import EngineArgs
+from .llm_engine_sp import LLMEngine
+from vllm.lora.request import LoRARequest
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import SamplingParams
+from vllm.sequence import MultiModalData
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils import Counter
+import torch
+from torch.nn.utils.rnn import pad_sequence
+from verl.workers.rollout.tokenizer import HybridEngineBaseTokenizer
+
+
+class LLM:
+    """An LLM for generating texts from given prompts and sampling parameters.
+
+    This class includes a tokenizer, a language model (possibly distributed
+    across multiple GPUs), and GPU memory space allocated for intermediate
+    states (aka KV cache). Given a batch of prompts and sampling parameters,
+    this class generates texts from the model, using an intelligent batching
+    mechanism and efficient memory management.
+
+    NOTE: This class is intended to be used for offline inference. For online
+    serving, use the `AsyncLLMEngine` class instead.
+    NOTE: For the comprehensive list of arguments, see `EngineArgs`.
+
+    Args:
+        model: A HuggingFace Transformers model instance.
+        tokenizer: A HuggingFace Transformers tokenizer instance.
+        tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
+            if available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
+        tensor_parallel_size: The number of GPUs to use for distributed
+            execution with tensor parallelism.
+        dtype: The data type for the model weights and activations. Currently,
+            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
+            the `torch_dtype` attribute specified in the model config file.
+            However, if the `torch_dtype` in the config is `float32`, we will
+            use `float16` instead.
+        quantization: The method used to quantize the model weights. Currently,
+            we support "awq". If None, we assume the model weights are not
+            quantized and use `dtype` to determine the data type of the weights.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id.
+        tokenizer_revision: The specific tokenizer version to use. It can be a
+            branch name, a tag name, or a commit id.
+        seed: The seed to initialize the random number generator for sampling.
+        gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to
+            reserve for the model weights, activations, and KV cache. Higher
+            values will increase the KV cache size and thus improve the model's
+            throughput. However, if the value is too high, it may cause out-of-
+            memory (OOM) errors.
+        swap_space: The size (GiB) of CPU memory per GPU to use as swap space.
+            This can be used for temporarily storing the states of the requests
+            when their `best_of` sampling parameters are larger than 1. If all
+            requests will have `best_of=1`, you can safely set this to 0.
+            Otherwise, too small values may cause out-of-memory (OOM) errors.
+        enforce_eager: Whether to enforce eager execution. If True, we will
+            disable CUDA graph and always execute the model in eager mode.
+            If False, we will use CUDA graph and eager execution in hybrid.
+        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode.
+        disable_custom_all_reduce: See ParallelConfig
+    """
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer],
+        model_hf_config: PretrainedConfig,
+        tokenizer_mode: str = "auto",
+        trust_remote_code: bool = False,
+        tensor_parallel_size: int = 1,
+        dtype: str = "auto",
+        quantization: Optional[str] = None,
+        revision: Optional[str] = None,
+        tokenizer_revision: Optional[str] = None,
+        seed: int = 0,
+        gpu_memory_utilization: float = 0.9,
+        swap_space: int = 4,
+        enforce_eager: bool = False,
+        max_context_len_to_capture: int = None,
+        disable_custom_all_reduce: bool = False,
+        load_format = 'auto',
+        **kwargs,
+    ) -> None:
+        if "disable_log_stats" not in kwargs:
+            kwargs["disable_log_stats"] = True
+        engine_args = EngineArgs(
+            model_hf_config=model_hf_config,
+            tensor_parallel_size=tensor_parallel_size,
+            dtype=dtype,
+            quantization=quantization,
+            revision=revision,
+            tokenizer_revision=tokenizer_revision,
+            seed=seed,
+            gpu_memory_utilization=gpu_memory_utilization,
+            swap_space=swap_space,
+            enforce_eager=enforce_eager,
+            max_context_len_to_capture=max_context_len_to_capture,
+            disable_custom_all_reduce=disable_custom_all_reduce,
+            load_format=load_format,
+            **kwargs,
+        )
+        tokenizer_cls = (PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer)
+        if not isinstance(tokenizer, tokenizer_cls):
+            raise ValueError(
+                f"Unexpected tokenizer type: {type(tokenizer)}. Must be"
+                "one of the following: PreTrainedTokenizer, PreTrainedTokenizerFast, verl.workers.rollout.HybridEngineBaseTokenizer"
+            )
+        self.llm_engine = LLMEngine.from_engine_args(model, tokenizer, engine_args)
+        self.request_counter = Counter()
+
+    def init_cache_engine(self):
+        self.llm_engine.init_cache_engine()
+
+    def free_cache_engine(self):
+        self.llm_engine.free_cache_engine()
+
+    def get_tokenizer(self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
+        return self.llm_engine.tokenizer
+
+    def set_tokenizer(
+        self,
+        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
+    ) -> None:
+        self.llm_engine.tokenizer = tokenizer
+
+    def generate(
+        self,
+        prompts: Optional[Union[str, List[str]]] = None,
+        sampling_params: Optional[Union[SamplingParams, List[SamplingParams]]] = None,
+        prompt_token_ids: Optional[List[List[int]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[LoRARequest] = None,
+        multi_modal_data: Optional[MultiModalData] = None,
+    ) -> List[RequestOutput]:
+        """Generates the completions for the input prompts.
+
+        NOTE: This class automatically batches the given prompts, considering
+        the memory constraint. For the best performance, put all of your prompts
+        into a single list and pass it to this method.
+
+        Args:
+            prompts: A list of prompts to generate completions for.
+            sampling_params: The sampling parameters for text generation. If
+                None, we use the default sampling parameters. 
+                When it is a single value, it is applied to every prompt. 
+                When it is a list, the list must have the same length as the 
+                prompts and it is paired one by one with the prompt.
+            prompt_token_ids: A list of token IDs for the prompts. If None, we
+                use the tokenizer to convert the prompts to token IDs.
+            use_tqdm: Whether to use tqdm to display the progress bar.
+            lora_request: LoRA request to use for generation, if any.
+            multi_modal_data: Multi modal data.
+
+        Returns:
+            A list of `RequestOutput` objects containing the generated
+            completions in the same order as the input prompts.
+        """
+        if prompts is None and prompt_token_ids is None:
+            raise ValueError("Either prompts or prompt_token_ids must be "
+                             "provided.")
+        if self.llm_engine.model_config.skip_tokenizer_init \
+            and prompts is not None:
+            raise ValueError("prompts must be None if skip_tokenizer_init "
+                             "is True")
+        if isinstance(prompts, str):
+            # Convert a single prompt to a list.
+            prompts = [prompts]
+        if (prompts is not None and prompt_token_ids is not None and len(prompts) != len(prompt_token_ids)):
+            raise ValueError("The lengths of prompts and prompt_token_ids "
+                             "must be the same.")
+
+        if prompts is not None:
+            num_requests = len(prompts)
+        else:
+            assert prompt_token_ids is not None
+            num_requests = len(prompt_token_ids)
+
+        if sampling_params is None:
+            # Use default sampling params.
+            sampling_params = SamplingParams()
+
+        elif isinstance(sampling_params, list) and len(sampling_params) != num_requests:
+            raise ValueError("The lengths of prompts and sampling_params "
+                             "must be the same.")
+        if multi_modal_data:
+            multi_modal_data.data = multi_modal_data.data.to(torch.float16)
+
+        # Add requests to the engine.
+        for i in range(num_requests):
+            prompt = prompts[i] if prompts is not None else None
+            token_ids = None if prompt_token_ids is None else prompt_token_ids[i]
+            if not isinstance(token_ids, list):
+                # NOTE(shengguangming): convert the rollout input into List[str]
+                token_ids = self._pre_process_inputs(token_ids)
+            self._add_request(
+                prompt,
+                sampling_params[i] if isinstance(sampling_params, list) else sampling_params,
+                token_ids,
+                lora_request=lora_request,
+                # Get ith image while maintaining the batch dim.
+                multi_modal_data=MultiModalData(type=multi_modal_data.type, data=multi_modal_data.data[i].unsqueeze(0))
+                if multi_modal_data else None,
+            )
+        return self._run_engine(use_tqdm)
+
+    def _add_request(
+        self,
+        prompt: Optional[str],
+        sampling_params: SamplingParams,
+        prompt_token_ids: Optional[List[int]],
+        lora_request: Optional[LoRARequest] = None,
+        multi_modal_data: Optional[MultiModalData] = None,
+    ) -> None:
+        request_id = str(next(self.request_counter))
+        self.llm_engine.add_request(request_id,
+                                    prompt,
+                                    sampling_params,
+                                    prompt_token_ids,
+                                    lora_request=lora_request,
+                                    multi_modal_data=multi_modal_data)
+
+    def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]:
+        # Initialize tqdm.
+        if use_tqdm:
+            num_requests = self.llm_engine.get_num_unfinished_requests()
+            pbar = tqdm(total=num_requests, desc="Processed prompts", dynamic_ncols=True)
+        # Run the engine.
+        outputs: List[RequestOutput] = []
+        while self.llm_engine.has_unfinished_requests():
+            step_outputs = self.llm_engine.step()
+            for output in step_outputs:
+                if output.finished:
+                    outputs.append(output)
+                    if use_tqdm:
+                        pbar.update(1)
+        if use_tqdm:
+            pbar.close()
+        # Sort the outputs by request ID.
+        # This is necessary because some requests may be finished earlier than
+        # its previous requests.
+        outputs = sorted(outputs, key=lambda x: int(x.request_id))
+        # TODO(shengguangming): maybe we can hack the autoregressive logics without only apply post process for better performance
+        return self._post_process_outputs(outputs)
+
+    # NOTE(shengguangming): add for verl
+    # TODO(sgm): we can optimize it by making the dataloader yield List[int] without padding.
+    def _pre_process_inputs(self, prompt_token_ids: torch.Tensor) -> List[int]:
+        # remove the left padding in the prompt token_id
+        pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id
+        non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0]
+        token_ids = prompt_token_ids[non_pad_index:].tolist()
+        return token_ids
+
+    # NOTE(shengguangming): add for verl
+    def _post_process_outputs(self, request_outputs: List[RequestOutput]) -> Tuple[torch.Tensor, torch.Tensor]:
+        output_token_ids = []
+        logprobs = []
+        for request_output in request_outputs:  # List[RequestOutput]
+            outputs = request_output.outputs
+            for output in outputs:  # List[CompletionOutput], usually len == 1
+                output_token_ids.append(torch.tensor(output.token_ids))
+                # TODO(shengguangming): can be optimzied by rewrite the Sampler._get_logprobs() logits
+                logprobs_dicts = output.logprobs
+                if logprobs_dicts is not None:
+                    logprob = []
+                    for logprobs_dict, id in zip(logprobs_dicts, output.token_ids):
+                        logprob.append(logprobs_dict[id].logprob)
+                    logprobs.append(torch.tensor(logprob))
+
+        pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id
+        output_token_ids = pad_sequence(output_token_ids, batch_first=True, padding_value=pad_token_id)
+        if len(logprobs) > 0:
+            logprobs = pad_sequence(logprobs, batch_first=True, padding_value=pad_token_id)
+        return output_token_ids, logprobs
+
+    def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None:
+        self.llm_engine.sync_model_weights(actor_weights=actor_weights, load_format=load_format)
+
+    def offload_model_weights(self) -> None:
+        self.llm_engine.offload_model_weights()

verl/third_party/vllm/vllm_v_0_4_2/llm_engine_sp.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/llm_engine.py
+
+import torch
+from typing import Dict, Optional, Union, Type
+
+import vllm
+from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig,
+                         SpeculativeConfig, VisionLanguageConfig)
+from vllm.core.scheduler import Scheduler
+from vllm.engine.output_processor.interfaces import (SequenceGroupOutputProcessor)
+from vllm.engine.output_processor.stop_checker import StopChecker
+from vllm.executor.executor_base import ExecutorBase
+from vllm.logger import init_logger
+from vllm.transformers_utils.detokenizer import Detokenizer
+from vllm.engine.metrics import StatLogger
+from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled, usage_message)
+from vllm.utils import Counter
+from vllm.engine.llm_engine import _load_generation_config_dict
+from vllm.engine.llm_engine import LLMEngine
+
+import torch.nn as nn
+from .arg_utils import EngineArgs
+from .tokenizer import TokenizerGroup
+from .config import ModelConfig, LoadConfig
+
+logger = init_logger(__name__)
+_LOCAL_LOGGING_INTERVAL_SEC = 5
+
+
+class LLMEngine(LLMEngine):
+    """An LLM engine that receives requests and generates texts.
+
+    This is the main class for the vLLM engine. It receives requests
+    from clients and generates texts from the LLM. It includes a tokenizer, a
+    language model (possibly distributed across multiple GPUs), and GPU memory
+    space allocated for intermediate states (aka KV cache). This class utilizes
+    iteration-level scheduling and efficient memory management to maximize the
+    serving throughput.
+
+    The `LLM` class wraps this class for offline batched inference and the
+    `AsyncLLMEngine` class wraps this class for online serving.
+
+    NOTE: The config arguments are derived from the `EngineArgs` class. For the
+    comprehensive list of arguments, see `EngineArgs`.
+
+    Args:
+        model: the actor model initialize outside vllm (add for verl)
+        tokenizer: the initialized tokenizer (add for verl)
+        model_config: The configuration related to the LLM model.
+        cache_config: The configuration related to the KV cache memory
+            management.
+        parallel_config: The configuration related to distributed execution.
+        scheduler_config: The configuration related to the request scheduler.
+        distributed_init_method: The initialization method for distributed
+            execution. See `torch.distributed.init_process_group` for details.
+        placement_group: Ray placement group for distributed execution.
+            Required for distributed execution.
+        log_stats: Whether to log statistics.
+    """
+
+    def __init__(
+        self,
+        # NOTE(sgm): first two arguments are added for verl
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        tokenizer: nn.Module,
+        # NOTE(sgm): vllm original arguments
+        model_config: ModelConfig,
+        cache_config: CacheConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        load_config: LoadConfig,
+        lora_config: Optional[LoRAConfig],
+        vision_language_config: Optional[VisionLanguageConfig],
+        speculative_config: Optional[SpeculativeConfig],
+        decoding_config: Optional[DecodingConfig],
+        executor_class: Type[ExecutorBase],
+        log_stats: bool,
+        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
+    ) -> None:
+        logger.info(
+            "Initializing an LLM engine (v%s) with config: "
+            "model=%r, speculative_config=%r, tokenizer=%r, "
+            "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, "
+            "tokenizer_revision=%s, trust_remote_code=%s, dtype=%s, "
+            "max_seq_len=%d, download_dir=%r, load_format=%s, "
+            "tensor_parallel_size=%d, disable_custom_all_reduce=%s, "
+            "quantization=%s, enforce_eager=%s, kv_cache_dtype=%s, "
+            "quantization_param_path=%s, device_config=%s, "
+            "decoding_config=%r, seed=%d, served_model_name=%s)",
+            vllm.__version__,
+            model_config.model,
+            speculative_config,
+            model_config.tokenizer,
+            model_config.skip_tokenizer_init,
+            # model_config.tokenizer_mode,
+            model_config.revision,
+            model_config.tokenizer_revision,
+            # model_config.trust_remote_code,
+            model_config.dtype,
+            model_config.max_model_len,
+            load_config.download_dir,
+            load_config.load_format,
+            parallel_config.tensor_parallel_size,
+            parallel_config.disable_custom_all_reduce,
+            model_config.quantization,
+            model_config.enforce_eager,
+            cache_config.cache_dtype,
+            model_config.quantization_param_path,
+            device_config.device,
+            decoding_config,
+            model_config.seed,
+            # model_config.served_model_name,
+        )
+        # TODO(woosuk): Print more configs in debug mode.
+
+        self.model_config = model_config  # TODO: currently is hfconfig
+        self.cache_config = cache_config
+        self.lora_config = lora_config
+        self.vision_language_config = vision_language_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+        self.speculative_config = speculative_config
+        self.load_config = load_config
+        self.decoding_config = decoding_config or DecodingConfig()
+        self.log_stats = log_stats
+
+        # self.model = model # should not store the model, it should be deleted
+        # TODO(shengguangming): maybe we can choose init here or from arguments
+        if not self.model_config.skip_tokenizer_init:
+            # TODO: check tokenizer class
+            self._init_tokenizer(tokenizer)
+            self.detokenizer = Detokenizer(self.tokenizer)
+        else:
+            self.detokenizer = None
+            self.tokenizer = None
+
+        self.seq_counter = Counter()
+        # TODO: don't know what's the usage
+        self.generation_config_fields = _load_generation_config_dict(model_config)
+
+        self.model_executor = executor_class(
+            model=model, # add for spmd_gpu_executor
+            model_config=model_config,
+            cache_config=cache_config,
+            parallel_config=parallel_config,
+            scheduler_config=scheduler_config,
+            device_config=device_config,
+            lora_config=lora_config,
+            vision_language_config=vision_language_config,
+            speculative_config=speculative_config,
+            load_config=load_config,
+        )
+
+        # Profile the memory usage and initialize the cache.
+        self._initialize_kv_caches()
+
+        # If usage stat is enabled, collect relevant info.
+        if is_usage_stats_enabled():
+            from vllm.model_executor.model_loader import (get_architecture_class_name)
+            usage_message.report_usage(
+                get_architecture_class_name(model_config),
+                usage_context,
+                extra_kvs={
+                    # Common configuration
+                    "dtype": str(model_config.dtype),
+                    "tensor_parallel_size": parallel_config.tensor_parallel_size,
+                    "block_size": cache_config.block_size,
+                    "gpu_memory_utilization": cache_config.gpu_memory_utilization,
+
+                    # Quantization
+                    "quantization": model_config.quantization,
+                    "kv_cache_dtype": cache_config.cache_dtype,
+
+                    # Feature flags
+                    "enable_lora": bool(lora_config),
+                    "enable_prefix_caching": cache_config.enable_prefix_caching,
+                    "enforce_eager": model_config.enforce_eager,
+                    "disable_custom_all_reduce": parallel_config.disable_custom_all_reduce,
+                })
+
+        if self.tokenizer:
+            # Ping the tokenizer to ensure liveness if it runs in a
+            # different process.
+            self.tokenizer.ping()
+
+        # Create the scheduler.
+        # NOTE: the cache_config here have been updated with the numbers of
+        # GPU and CPU blocks, which are profiled in the distributed executor.
+        # NOTE(shengguangming): each process will have independent scheduler
+        self.scheduler = Scheduler(scheduler_config, cache_config, lora_config)
+
+        # Metric Logging.
+        if self.log_stats:
+            self.stat_logger = StatLogger(local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
+                                          labels=dict(model_name=model_config.served_model_name),
+                                          max_model_len=self.model_config.max_model_len)
+            self.stat_logger.info("cache_config", self.cache_config)
+
+        # Create sequence output processor, e.g. for beam search or
+        # speculative decoding.
+        self.output_processor = (SequenceGroupOutputProcessor.create_output_processor(
+            self.scheduler_config,
+            self.detokenizer,
+            self.scheduler,
+            self.seq_counter,
+            self.get_tokenizer_for_seq,
+            stop_checker=StopChecker(
+                self.scheduler_config.max_model_len,
+                self.get_tokenizer_for_seq,
+            ),
+        ))
+
+    # TODO(sgm): add for verl but we may not tokenizer in Rollout
+    def _init_tokenizer(self, tokenizer, **tokenizer_init_kwargs):
+        init_kwargs = dict(enable_lora=bool(self.lora_config),
+                           max_num_seqs=self.scheduler_config.max_num_seqs,
+                           max_input_length=None)
+        init_kwargs.update(tokenizer_init_kwargs)
+        self.tokenizer: TokenizerGroup = TokenizerGroup(tokenizer, **init_kwargs)
+
+    def init_cache_engine(self):
+        # TODO: check whether we should rebuild the CUDAGraph every iter when offload/load KVCache
+        # Re-capture CUDAGraph would be time-consuming
+        self.model_executor.init_cache_engine()
+
+    def free_cache_engine(self):
+        self.model_executor.free_cache_engine()
+
+    # NOTE(sgm): currently, we only support GPU executor
+    # The GPUExecutor remove the Ray dependency
+    @classmethod
+    def from_engine_args(
+        cls,
+        model,
+        tokenizer,
+        engine_args: EngineArgs,
+        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
+    ) -> "LLMEngine":
+        """Creates an LLM engine from the engine arguments."""
+        # Create the engine configs.
+        engine_config = engine_args.create_engine_config()
+
+        # Initialize the cluster and specify the executor class.
+        assert engine_config.device_config.device_type == "cuda", \
+            "Currently, the vllm in verl only support running on GPU"
+
+        if engine_config.parallel_config.world_size == 1:
+            engine_config.load_config.load_format = "dummy_hf"
+
+        from .spmd_gpu_executor import SPMDGPUExecutor
+        executor_class = SPMDGPUExecutor
+
+        # Create the LLM engine.
+        engine = cls(
+            model,
+            tokenizer,
+            **engine_config.to_dict(),
+            executor_class=executor_class,
+            log_stats=not engine_args.disable_log_stats,
+            usage_context=usage_context,
+        )
+        return engine
+
+    def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None:
+        self.model_executor.sync_model_weights(actor_weights=actor_weights, load_format=load_format)
+
+    def offload_model_weights(self) -> None:
+        self.model_executor.offload_model_weights()

verl/third_party/vllm/vllm_v_0_4_2/megatron_weight_loaders.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models
+
+from typing import Dict
+import torch
+import torch.nn as nn
+
+from vllm.model_executor.layers.linear import *
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding, ParallelLMHead
+from vllm.model_executor.layers.activation import ScaledActivation
+from vllm.model_executor.models import ModelRegistry
+
+
+# NOTE(shengguangming): replace the origin weight loader function in the class
+def parallel_weight_loader(self, param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
+    """Parallel Linear weight loader."""
+    assert param.size() == loaded_weight.size(
+    ), 'the parameter size is not align with the loaded weight size, param size: {}, loaded_weight size: {}'.format(
+        param.size(), loaded_weight.size())
+    assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same"
+
+    param.data = loaded_weight.data
+
+
+def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
+    """Default weight loader."""
+    assert param.size() == loaded_weight.size()
+    assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same"
+
+    param.data = loaded_weight.data
+
+
+def gpt2_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        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 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
+            # TODO: check megatron
+            loaded_weight = loaded_weight.t()
+        weight_loader = getattr(param, "weight_loader", default_weight_loader)
+        weight_loader(param, loaded_weight)
+
+
+def llama_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_mapping = [
+        # (megatron core gpt model name, vllm model name)
+        ("embedding.word_embeddings", "model.embed_tokens"),
+        ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"),
+        ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_proj", 'self_attn.o_proj'),
+        ('pre_mlp_layernorm', 'post_attention_layernorm'),
+        ('mlp.linear_fc1.layer_norm_weight', 'post_attention_layernorm.weight'),
+        ('mlp.linear_fc1.layer_norm_bias', 'post_attention_layernorm.bias'),
+        ('mlp.linear_fc1', 'mlp.gate_up_proj'),
+        ('mlp.linear_fc2', 'mlp.down_proj'),
+        ('decoder.final_layernorm', 'model.norm'),
+        ('output_layer', 'lm_head'),
+    ]
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        name = _replace_name(name, params_mapping)
+        if name.endswith('.bias') and name not in params_dict:
+            continue
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_mapping = [
+        # (megatron core gpt model name, vllm model name)
+        ("embedding.word_embeddings", "model.embed_tokens"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_proj", 'self_attn.o_proj'),
+        (
+            'input_layernorm',
+            'input_layernorm',
+        ),
+        ('pre_mlp_layernorm', 'post_attention_layernorm'),
+        ('mlp.linear_fc1', 'mlp.gate_up_proj'),
+        ('mlp.linear_fc2', 'mlp.down_proj'),
+        ('decoder.final_layernorm', 'model.norm'),
+        ('output_layer', 'lm_head'),
+    ]
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        name = _replace_name(name, params_mapping)
+        if name.endswith('.bias') and name not in params_dict:
+            continue
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def _replace_name(megatron_name, name_mapping):
+    for m_name, v_name in name_mapping:
+        if m_name not in megatron_name:
+            continue
+        if 'layers' in megatron_name:  # deal with decoder layers
+            megatron_name = megatron_name.replace('decoder', 'model')
+            megatron_name_list = megatron_name.split('.')
+            if 'layer_norm_weight' in megatron_name_list or 'layer_norm_bias' in megatron_name_list:
+                param_name_list = megatron_name_list[:3]
+                param_name_list.append(v_name)
+                param_name = '.'.join(param_name_list)
+            else:
+                param_name_list = megatron_name_list[:3]
+                weight_or_bias = megatron_name_list[-1]
+                param_name_list.append(v_name)
+                param_name_list.append(weight_or_bias)
+                param_name = '.'.join(param_name_list)
+            return param_name
+        else:
+            param_name = megatron_name.replace(m_name, v_name)
+            return param_name
+
+
+def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_mapping = [
+        # (megatron core gpt model name, vllm model name)
+        ("embedding.word_embeddings", "model.embed_tokens"),
+        ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"),
+        ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_proj", 'self_attn.o_proj'),
+        ('pre_mlp_layernorm', 'post_attention_layernorm'),
+        ('mlp.linear_fc1.layer_norm_weight', 'post_attention_layernorm.weight'),
+        ('mlp.linear_fc1.layer_norm_bias', 'post_attention_layernorm.bias'),
+        ('mlp.linear_fc1', 'mlp.gate_up_proj'),
+        ('mlp.linear_fc2', 'mlp.down_proj'),
+        ('decoder.final_layernorm', 'model.norm'),
+        ('output_layer', 'lm_head'),
+    ]
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        name = _replace_name(name, params_mapping)
+        if name.endswith('.bias') and name not in params_dict:
+            continue
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_mapping = [
+        # (megatron core gpt model name, vllm model name)
+        ("embedding.word_embeddings", "model.embed_tokens"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_proj", 'self_attn.o_proj'),
+        (
+            'input_layernorm',
+            'input_layernorm',
+        ),
+        ('pre_mlp_layernorm', 'post_attention_layernorm'),
+        ('mlp.linear_fc1', 'mlp.gate_up_proj'),
+        ('mlp.linear_fc2', 'mlp.down_proj'),
+        ('decoder.final_layernorm', 'model.norm'),
+        ('output_layer', 'lm_head'),
+    ]
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        name = _replace_name(name, params_mapping)
+        if name.endswith('.bias') and name not in params_dict:
+            continue
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def _replace_name(megatron_name, name_mapping):
+    for m_name, v_name in name_mapping:
+        if m_name not in megatron_name:
+            continue
+        if 'layers' in megatron_name:  # deal with decoder layers
+            megatron_name = megatron_name.replace('decoder', 'model')
+            megatron_name_list = megatron_name.split('.')
+            if 'layer_norm_weight' in megatron_name_list or 'layer_norm_bias' in megatron_name_list:
+                param_name_list = megatron_name_list[:3]
+                param_name_list.append(v_name)
+                param_name = '.'.join(param_name_list)
+            else:
+                param_name_list = megatron_name_list[:3]
+                weight_or_bias = megatron_name_list[-1]
+                param_name_list.append(v_name)
+                param_name_list.append(weight_or_bias)
+                param_name = '.'.join(param_name_list)
+            return param_name
+        else:
+            param_name = megatron_name.replace(m_name, v_name)
+            return param_name
+
+
+def mistral_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    # TODO: need to implement a general way to deal with prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+__LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__ = {
+    ColumnParallelLinear: parallel_weight_loader,
+    MergedColumnParallelLinear: parallel_weight_loader,
+    QKVParallelLinear: parallel_weight_loader,
+    RowParallelLinear: parallel_weight_loader,
+    VocabParallelEmbedding: parallel_weight_loader,
+    ParallelLMHead: parallel_weight_loader
+    # "ScaledActivation.weight_loader": ScaledActivation, # TODO(shengguangming): latest commit in vllm fix awq for this function and add load_weights
+    # "default_weight_loader": default_weight_loader
+}
+
+# for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items():
+#     # setattr(layer_class, 'megatron_weight_loader', weight_loader)
+#     layer_class.weight_loader = weight_loader
+
+__MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__ = {
+    'GPT2LMHeadModel': gpt2_weight_loader,
+    'LlamaForCausalLM': llama_megatron_core_te_weight_loader,  # use te backend for open-source megatron
+    'LLaMAForCausalLM': llama_megatron_core_te_weight_loader,
+    'MistralForCausalLM': mistral_megatron_weight_loader,
+}
+
+
+# the actor model is .state_dict()
+# Load megatron weights
+def load_megatron_weights(actor_weights: Dict, vllm_model: nn.Module):
+    weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__)
+    weight_loader(actor_weights, vllm_model)
+    # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu
+    # after init, and we need this after sync model weights for in first iter.
+    vllm_model = vllm_model.cuda()
+
+
+def _get_model_weight_loader(arch: str):
+    if arch in __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__:
+        return __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__[arch]
+    raise ValueError(f"Model architectures {arch} are not supported for now. "
+                     f"Supported architectures: {ModelRegistry.get_supported_archs()}")
+
+
+def update_megatron_weight_loader():
+    for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items():
+        layer_class.weight_loader = weight_loader
+    VocabParallelEmbedding.__init__ = vocab_init
+
+
+# FIXME(shengguangming): the vLLM vocab will pad to 64, which may incur out of bounds
+# so we need to rewrite the init function of vocab
+DEFAULT_VOCAB_PADDING_SIZE = 64
+
+
+def vocab_init(self,
+               num_embeddings: int,
+               embedding_dim: int,
+               params_dtype: Optional[torch.dtype] = None,
+               org_num_embeddings: Optional[int] = None,
+               padding_size: int = DEFAULT_VOCAB_PADDING_SIZE):
+    super(VocabParallelEmbedding, self).__init__()
+
+    # Keep the input dimensions.
+    # TODO (pad to be divided by 4)
+    self.num_embeddings = num_embeddings
+    self.org_vocab_size = org_num_embeddings or num_embeddings
+
+    # self.num_embeddings_padded = pad_vocab_size(num_embeddings,
+    #                                             padding_size)
+    self.embedding_dim = embedding_dim
+    if params_dtype is None:
+        params_dtype = torch.get_default_dtype()
+    self.tp_size = get_tensor_model_parallel_world_size()
+    # Divide the weight matrix along the vocaburaly dimension.
+
+    # TODO: remove dependencies from megatron
+    from megatron.core.tensor_parallel.utils import VocabUtility
+    self.vocab_start_index, self.vocab_end_index = (VocabUtility.vocab_range_from_global_vocab_size(
+        self.num_embeddings, get_tensor_model_parallel_rank(), self.tp_size))
+    self.num_embeddings_per_partition = (self.vocab_end_index - self.vocab_start_index)
+    self.weight = Parameter(
+        torch.empty(
+            self.num_embeddings_per_partition,
+            self.embedding_dim,
+            # device=torch.cuda.current_device(),
+            dtype=params_dtype))
+    set_weight_attrs(self.weight, {"parallel_dim": 0, "weight_loader": self.weight_loader})

verl/third_party/vllm/vllm_v_0_4_2/model_loader.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader
+"""Utilities for selecting and loading models."""
+from typing import Dict, Union, Optional, Iterable, Tuple
+
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel
+
+from vllm.config import (DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig, VisionLanguageConfig)
+from vllm.model_executor.model_loader import BaseModelLoader
+from vllm.model_executor.model_loader.loader import _initialize_model
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+from vllm.distributed.communication_op import tensor_model_parallel_all_gather
+
+from .config import ModelConfig, LoadFormat, LoadConfig
+from .megatron_weight_loaders import load_megatron_weights, update_megatron_weight_loader
+from .dtensor_weight_loaders import load_dtensor_weights, update_dtensor_weight_loader
+from .hf_weight_loader import update_hf_weight_loader
+
+
+def get_model(actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, load_config: LoadConfig,
+              device_config: DeviceConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig,
+              lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig]) -> nn.Module:
+    loader = get_model_loader(load_config)
+    if load_config.load_format.startswith('dummy'):
+        return loader.load_model(model_config=model_config,
+                                 device_config=device_config,
+                                 lora_config=lora_config,
+                                 vision_language_config=vision_language_config,
+                                 parallel_config=parallel_config,
+                                 scheduler_config=scheduler_config)
+    else:
+        return loader.load_model(actor_model=actor_model,
+                                 model_config=model_config,
+                                 device_config=device_config,
+                                 lora_config=lora_config,
+                                 vision_language_config=vision_language_config,
+                                 parallel_config=parallel_config,
+                                 scheduler_config=scheduler_config)
+
+
+def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
+    """Get a model loader based on the load format."""
+
+    if isinstance(load_config.load_format, type):
+        return load_config.load_format(load_config)
+
+    if load_config.load_format == LoadFormat.AUTO:
+        update_megatron_weight_loader()
+        return MegatronLoader(load_config)
+
+    # NOTE(sgm): change the weight_loader function in runtime
+    if load_config.load_format == LoadFormat.MEGATRON:
+        update_megatron_weight_loader()
+        return MegatronLoader(load_config)
+
+    if load_config.load_format == LoadFormat.HF:
+        update_hf_weight_loader()
+        return HFLoader(load_config)
+
+    if load_config.load_format == LoadFormat.DTENSOR:
+        update_dtensor_weight_loader()
+        return DTensorLoader(load_config)
+
+    if load_config.load_format == LoadFormat.DUMMY_HF:
+        update_hf_weight_loader()
+        return DummyModelLoader(load_config)
+
+    if load_config.load_format == LoadFormat.DUMMY_MEGATRON:
+        update_megatron_weight_loader()
+        return DummyModelLoader(load_config)
+
+    if load_config.load_format == LoadFormat.DUMMY_DTENSOR:
+        update_dtensor_weight_loader()
+        return DummyModelLoader(load_config)
+
+    raise ValueError('load format not supported in verl: {}, only support {} and {}'.format(
+        load_config.load_format, LoadFormat.MEGATRON, LoadFormat.HF))
+
+
+class DummyModelLoader(BaseModelLoader):
+    """Model loader that will set model weights to random values."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(f"Model loader extra config is not supported for "
+                             f"load format {load_config.load_format}")
+
+    def load_model(self, *, model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig],
+                   vision_language_config: Optional[VisionLanguageConfig], parallel_config: ParallelConfig,
+                   scheduler_config: SchedulerConfig) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            with torch.device(device_config.device):
+                model = _initialize_model(model_config, self.load_config, lora_config, vision_language_config)
+            # NOTE(woosuk): For accurate performance evaluation, we assign
+            # random values to the weights.
+            # initialize_dummy_weights(model)
+        return model.eval()
+
+
+class MegatronLoader(BaseModelLoader):
+    """Model loader that can load the model weights from partitioned megatron model."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(f"Model loader extra config is not supported for "
+                             f"load format {load_config.load_format}")
+
+    def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]):
+        # NOTE(shengguangming) Load the weights from the actor model
+        pass
+        # if isinstance(actor_model, nn.Module):
+        #     load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model)
+        # else:
+        #     load_weights(actor_weights=actor_model, vllm_model=model)
+        # return actor_model
+
+    def load_model(self, actor_model: Union[PreTrainedModel,
+                                            Dict], model_config: ModelConfig, device_config: DeviceConfig,
+                   lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig],
+                   parallel_config: ParallelConfig, scheduler_config: SchedulerConfig) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            with torch.device(device_config.device):
+                model = _initialize_model(model_config, self.load_config, lora_config, vision_language_config)
+
+            # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm
+            if isinstance(actor_model, nn.Module):
+                load_megatron_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)),
+                                      vllm_model=model)
+            else:
+                load_megatron_weights(actor_weights=actor_model, vllm_model=model)
+
+            for _, module in model.named_modules():
+                quant_method = getattr(module, "quant_method", None)
+                if quant_method is not None:
+                    quant_method.process_weights_after_loading(module)
+                # FIXME: Remove this after Mixtral is updated
+                # to use quant_method.
+                if hasattr(module, "process_weights_after_loading"):
+                    module.process_weights_after_loading()
+        # NOTE(sgm) Some weights are point to gpu, but still need this.
+        model = model.cuda()  # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage
+        return model.eval()
+
+
+class HFLoader(BaseModelLoader):
+    """Model loader that can load the model weights from model's full params."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(f"Model loader extra config is not supported for "
+                             f"load format {load_config.load_format}")
+
+    def _get_weights_iterator(self, actor_model: Union[PreTrainedModel, Dict]):
+        if isinstance(actor_model, Dict):
+            return actor_model.items()
+        elif isinstance(actor_model, nn.Module):
+            return dict(actor_model.named_parameters()).items()
+        else:
+            raise ValueError(f'actor model should be Dict or nn.Module, but get {type(actor_model)}')
+
+    def load_model(self, actor_model: Union[PreTrainedModel,
+                                            Dict], model_config: ModelConfig, device_config: DeviceConfig,
+                   lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig],
+                   parallel_config: ParallelConfig, scheduler_config: SchedulerConfig) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            # with torch.device(device_config.device):
+            # NOTE(sgm): init the model in cpu
+            model = _initialize_model(model_config, self.load_config, lora_config, vision_language_config)
+            model.load_weights(self._get_weights_iterator(actor_model))
+            for _, module in model.named_modules():
+                quant_method = getattr(module, "quant_method", None)
+                if quant_method is not None:
+                    quant_method.process_weights_after_loading(module)
+                # FIXME: Remove this after Mixtral is updated
+                # to use quant_method.
+                if hasattr(module, "process_weights_after_loading"):
+                    module.process_weights_after_loading()
+        # NOTE(sgm) Some weights are point to gpu, but still need this.
+        model = model.cuda()  # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage
+        return model.eval()
+
+
+class DTensorLoader(BaseModelLoader):
+    """Model loader that can load the model weights from partitioned megatron model."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(f"Model loader extra config is not supported for "
+                             f"load format {load_config.load_format}")
+
+    def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]):
+        # NOTE(shengguangming) Load the weights from the actor model
+        pass
+        # if isinstance(actor_model, nn.Module):
+        #     load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model)
+        # else:
+        #     load_weights(actor_weights=actor_model, vllm_model=model)
+        # return actor_model
+
+    def load_model(self, actor_model: Union[PreTrainedModel,
+                                            Dict], model_config: ModelConfig, device_config: DeviceConfig,
+                   lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig],
+                   parallel_config: ParallelConfig, scheduler_config: SchedulerConfig) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            with torch.device(device_config.device):
+                model = _initialize_model(model_config, self.load_config, lora_config, vision_language_config)
+
+            # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm
+            if isinstance(actor_model, nn.Module):
+                load_dtensor_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)),
+                                     vllm_model=model)
+            else:
+                load_dtensor_weights(actor_weights=actor_model, vllm_model=model)
+
+            for _, module in model.named_modules():
+                quant_method = getattr(module, "quant_method", None)
+                if quant_method is not None:
+                    quant_method.process_weights_after_loading(module)
+                # FIXME: Remove this after Mixtral is updated
+                # to use quant_method.
+                if hasattr(module, "process_weights_after_loading"):
+                    module.process_weights_after_loading()
+        # NOTE(sgm) Some weights are point to gpu, but still need this.
+        model = model.cuda()  # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage
+        return model.eval()
+
+
+# FIXME(sgm): hack the _get_logits function in vllm v0.4.2
+# as they use ray, the _get_logits result will only need to return to the driver node,
+# therefore gather is enough. However, we use SPMD instead of a central scheduler,
+# all_gather is required (aligned with v0.2.6)
+def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor,
+                embedding_bias: Optional[torch.Tensor]) -> torch.Tensor:
+    # Get the logits for the next tokens.
+    logits = torch.matmul(hidden_states, embedding.t())
+    if embedding_bias is not None:
+        logits += embedding_bias
+    logits = tensor_model_parallel_all_gather(logits)
+    # Remove paddings in vocab (if any).
+    if logits is not None:
+        logits = logits[:, :self.org_vocab_size]
+    return logits
+
+
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+
+LogitsProcessor._get_logits = _get_logits

verl/third_party/vllm/vllm_v_0_4_2/model_runner.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/model_runner.py
+
+import torch
+import torch.nn as nn
+from enum import IntEnum
+from typing import Dict, List, Optional, Set, Tuple, Union
+
+from vllm.attention import (AttentionMetadata, get_attn_backend)
+from vllm.config import (DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig, VisionLanguageConfig)
+from vllm.logger import init_logger
+from vllm.lora.layers import LoRAMapping
+from vllm.lora.request import LoRARequest
+from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
+from vllm.model_executor import SamplingMetadata
+from vllm.sequence import (MultiModalData, SamplerOutput, SequenceData, SequenceGroupMetadata)
+from vllm.utils import (CudaMemoryProfiler, is_hip, is_pin_memory_available)
+from vllm.worker.model_runner import ModelRunner, CUDAGraphRunner
+
+from .model_loader import get_model
+from .config import ModelConfig, LoadConfig
+
+logger = init_logger(__name__)
+
+
+# How batches are constructed.
+class BatchType(IntEnum):
+    # Every batch is prefill.
+    PREFILL = 0
+    # Every batch is decode.
+    DECODE = 1
+    # Batch is a mixture of prefill and decode.
+    MIXED = 2
+
+
+class ModelRunner(ModelRunner):
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        load_config: LoadConfig,
+        lora_config: Optional[LoRAConfig],
+        kv_cache_dtype: Optional[str] = "auto",
+        vision_language_config: Optional[VisionLanguageConfig] = None,
+    ):
+        self.model_config = model_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.lora_config = lora_config
+        self.load_config = load_config
+
+        # model_config can be None in tests/samplers/test_sampler.py.
+        # FIXME(woosuk): This is a hack to make the tests work. Refactor this.
+        self.sliding_window = (model_config.get_sliding_window() if model_config is not None else None)
+        self.device_config = (device_config if device_config is not None else DeviceConfig())
+        self.device = self.device_config.device
+
+        # NOTE(sgm): add for verl
+        self.model = model  # this will be replaced by get_model()
+
+        # Set after load_model.
+        self.lora_manager: LRUCacheWorkerLoRAManager = None
+
+        self.graph_runners: Dict[int, CUDAGraphRunner] = {}
+        self.graph_memory_pool: Optional[Tuple[int, int]] = None  # Set during graph capture.
+
+        self.max_seq_len_to_capture = (self.model_config.max_seq_len_to_capture if self.model_config is not None else 0)
+
+        self.pin_memory = is_pin_memory_available()
+        self.kv_cache_dtype = kv_cache_dtype
+        self.vision_language_config = vision_language_config
+
+        self.attn_backend = get_attn_backend(self.model_config.dtype if model_config is not None else None)
+
+        # Lazy initialization
+        self.block_size: int  # Set after initial profiling.
+        # When using CUDA graph, the input block tables must be padded to
+        # max_seq_len_to_capture. However, creating the block table in
+        # Python can be expensive. To optimize this, we cache the block table
+        # in numpy and only copy the actual input content at every iteration.
+        # The shape of the cached block table will be
+        # (max batch size to capture, max context len to capture / block size).
+        self.graph_block_tables: torch.Tensor  # Set after initial profiling.
+
+        # Set if the backend is flashinfer.
+        self.flashinfer_workspace_buffer: torch.Tensor
+
+    # NOTE(sgm): initialize model using the actor model
+    def load_model(self) -> None:
+        with CudaMemoryProfiler() as m:
+            self.model = get_model(actor_model=self.model,
+                                   model_config=self.model_config,
+                                   device_config=self.device_config,
+                                   lora_config=self.lora_config,
+                                   load_config=self.load_config,
+                                   parallel_config=self.parallel_config,
+                                   scheduler_config=self.scheduler_config,
+                                   vision_language_config=self.vision_language_config)
+        self.model_memory_usage = m.consumed_memory
+        logger.info("Loading model weights took %.4f GB", self.model_memory_usage / float(2**30))
+
+        if self.lora_config:
+            assert hasattr(self.model, "supported_lora_modules") and self.model.supported_lora_modules, (
+                "Model does not support LoRA")
+            assert hasattr(self.model, "embedding_modules"), "Model does not have embedding_modules"
+            assert hasattr(self.model, "embedding_padding_modules"), "Model does not have embedding_padding_modules"
+            self.lora_manager = LRUCacheWorkerLoRAManager(self.scheduler_config.max_num_seqs,
+                                                          self.scheduler_config.max_num_batched_tokens, self.vocab_size,
+                                                          self.lora_config, self.device, self.model.embedding_modules,
+                                                          self.model.embedding_padding_modules)
+            self.model = self.lora_manager.create_lora_manager(self.model)
+
+        if self.kv_cache_dtype == "fp8" and is_hip():
+            # Currently scaled KV cache is only enabled on ROCm
+            if self.model_config.quantization_param_path is not None:
+                if callable(getattr(self.model, "load_kv_cache_scales", None)):
+                    self.model.load_kv_cache_scales(self.model_config.quantization_param_path)
+                else:
+                    raise RuntimeError(
+                        "Using FP8 KV cache and scaling factors provided but "
+                        "model %s does not support loading scaling factors.", self.model.__class__)
+            else:
+                logger.warning("Using FP8 KV cache but no scaling factors "
+                               "provided. Defaulting to scaling factors of 1.0. "
+                               "This may lead to less accurate results!")
+        elif self.model_config.quantization_param_path is not None:
+            logger.warning("KV cache scaling factors provided, "
+                           "but the KV cache data type is not FP8. "
+                           "KV cache scaling factors will not be used.")
+
+    def prepare_input_tensors(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+    ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata, Set[LoRARequest], LoRAMapping,
+               torch.Tensor]:
+        # NOTE(sgm): all workers prepare the input in the same way
+        prefill_reqs = []
+        decode_reqs = []
+        for seq_group_meta in seq_group_metadata_list:
+            if seq_group_meta.is_prompt:
+                prefill_reqs.append(seq_group_meta)
+            else:
+                decode_reqs.append(seq_group_meta)
+
+        # Prepare input tensors.
+        (
+            input_tokens,
+            input_positions,
+            prefill_attn_metadata,
+            seq_lens,
+            query_lens,
+            lora_index_mapping,
+            lora_prompt_mapping,
+            lora_requests,
+            multi_modal_input,
+            slot_mapping,
+        ) = self._prepare_prompt(prefill_reqs)
+        (
+            decode_input_tokens,
+            decode_input_positions,
+            decode_attn_metadata,
+            decode_lora_index_mapping,
+            decode_lora_prompt_mapping,
+            decode_lora_requests,
+            decode_slot_mapping,
+        ) = self._prepare_decode(decode_reqs)
+        sampling_metadata = SamplingMetadata.prepare(seq_group_metadata_list, seq_lens, query_lens, self.device,
+                                                     self.pin_memory)
+
+        if not self.scheduler_config.chunked_prefill_enabled:
+            assert (len(prefill_reqs) and len(decode_reqs)) == 0
+
+        num_prefills = len(seq_lens)
+        num_prefill_tokens = len(input_tokens)
+        num_decode_tokens = len(decode_input_tokens)
+
+        # Coalesce tensors. Note that attn_metadata is currently not
+        # coalesced for simplicity.
+        input_tokens.extend(decode_input_tokens)
+        input_positions.extend(decode_input_positions)
+        slot_mapping.extend(decode_slot_mapping)
+        lora_index_mapping.extend(decode_lora_index_mapping)
+        lora_prompt_mapping.extend(decode_lora_prompt_mapping)
+        lora_requests.update(decode_lora_requests)
+
+        input_tokens = torch.tensor(input_tokens, dtype=torch.long, device=self.device)
+        input_positions = torch.tensor(input_positions, dtype=torch.long, device=self.device)
+        slot_mapping = torch.tensor(slot_mapping, dtype=torch.long, device=self.device)
+
+        if self.lora_config:
+            lora_mapping = LoRAMapping(
+                lora_index_mapping,
+                lora_prompt_mapping,
+            )
+        else:
+            lora_mapping = None
+
+        # Broadcast the metadata.
+        # If batch contains both prefill and decode, it sends 2 broadcasts.
+        # If it only contains 1 type, it triggers a single broadcast.
+        if (prefill_attn_metadata is not None and decode_attn_metadata is not None):
+            batch_type = BatchType.MIXED
+        elif prefill_attn_metadata is not None:
+            batch_type = BatchType.PREFILL
+        else:
+            batch_type = BatchType.DECODE
+
+        attn_metadata = AttentionMetadata(
+            num_prefills=num_prefills,
+            slot_mapping=slot_mapping,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            prefill_metadata=prefill_attn_metadata,
+            decode_metadata=decode_attn_metadata,
+            kv_cache_dtype=self.kv_cache_dtype,
+        )
+
+        return (input_tokens, input_positions, attn_metadata, sampling_metadata, lora_requests, lora_mapping,
+                multi_modal_input)
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        kv_caches: List[torch.Tensor],
+    ) -> Optional[SamplerOutput]:
+        (input_tokens, input_positions, attn_metadata, sampling_metadata, lora_requests, lora_mapping,
+         multi_modal_input) = self.prepare_input_tensors(seq_group_metadata_list)
+
+        if self.lora_config:
+            self.set_active_loras(lora_requests, lora_mapping)
+
+        # Currently cuda graph is only supported by the decode phase.
+        prefill_meta = attn_metadata.prefill_metadata
+        decode_meta = attn_metadata.decode_metadata
+        if prefill_meta is None and decode_meta.use_cuda_graph:
+            graph_batch_size = input_tokens.shape[0]
+            model_executable = self.graph_runners[graph_batch_size]
+        else:
+            model_executable = self.model
+        execute_model_kwargs = {
+            "input_ids": input_tokens,
+            "positions": input_positions,
+            "kv_caches": kv_caches,
+            "attn_metadata": attn_metadata,
+        }
+        if self.vision_language_config:
+            execute_model_kwargs.update({"image_input": multi_modal_input})
+        hidden_states = model_executable(**execute_model_kwargs)
+
+        # Compute the logits.
+        logits = self.model.compute_logits(hidden_states, sampling_metadata)
+
+        # Only perform sampling in the driver worker.
+        # if not self.is_driver_worker:
+        #     return None
+
+        # TODO(sgm): perform sampling on rank 0
+        # Sample the next token.
+        output = self.model.sample(
+            logits=logits,
+            sampling_metadata=sampling_metadata,
+        )
+
+        return output

verl/third_party/vllm/vllm_v_0_4_2/parallel_state.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Adapted from
+# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+"""Model and data parallel groups."""
+import os
+import torch
+import torch.distributed
+from typing import Optional
+
+import vllm.distributed.parallel_state as ps
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+
+from torch.distributed.device_mesh import init_device_mesh
+
+logger = init_logger(__name__)
+"""
+This version is strongly tied with Megatron to implement HybridEngine and weight sharing between vllm and Megatron.
+- We assume the Megatron tp+dp+pp world is already established before calling this function.
+
+"""
+
+# Device mesh for using DTensor
+_DEVICE_MESH = None
+
+# Tensor model parallel group that the current rank belongs to.
+_TP_DEVICE_GROUP = None
+_TP_CPU_GROUP = None
+
+
+# This method is for initializing the ParallelGroup when using HybridEngine
+def initialize_parallel_state(
+    distributed_init_method: str = "env://",
+    backend: str = "nccl",
+    tensor_model_parallel_size: int = 1,
+    num_tp_per_train_tp: int = 1,
+    pipeline_model_parallel_size: int = 1,
+):
+    # torch.distributed.all_reduce does not free the input tensor until
+    # the synchronization point. This causes the memory usage to grow
+    # as the number of all_reduce calls increases. This env var disables
+    # this behavior.
+    # Related issue:
+    # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
+    os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+    # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN.
+    rank = int(os.getenv("RANK", "-1"))
+    local_rank = int(os.getenv("LOCAL_RANK", "0"))
+
+    # Use the world_size set by TORCHRUN
+    world_size = int(os.getenv("WORLD_SIZE", "-1"))
+    assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN"
+    ps.init_distributed_environment(world_size, rank, distributed_init_method, local_rank, backend)
+    if torch.distributed.get_world_size() > 1:
+        # NOTE: build a sepearate inference group with infer tp & micro dp
+        initialize_model_parallel_for_vllm(tensor_model_parallel_size=tensor_model_parallel_size,
+                                           num_tensor_model_parallel_groups_per_train_tp=num_tp_per_train_tp)
+    else:
+        initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend)
+
+
+def ensure_model_parallel_initialized(
+    tensor_model_parallel_size: int,
+    pipeline_model_parallel_size: int = 1,
+    backend: Optional[str] = None,
+) -> None:
+    """Helper to initialize model parallel groups if they are not initialized,
+    or ensure tensor-parallel and pipeline-parallel sizes are equal to expected
+    values if the model parallel groups are initialized.
+    """
+    # get the backend of _DEVICE_WORLD_GROUP
+    backend = backend or torch.distributed.get_backend()
+    if not model_parallel_is_initialized():
+        initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend)
+        return
+
+    assert (get_tensor_model_parallel_world_size() == tensor_model_parallel_size), (
+        "tensor parallel group already initialized, but of unexpected size: "
+        f"{get_tensor_model_parallel_world_size()=} vs. "
+        f"{tensor_model_parallel_size=}")
+    # assert (get_pipeline_model_parallel_world_size(
+    # ) == pipeline_model_parallel_size), (
+    #     "pipeline parallel group already initialized, but of unexpected size: "
+    #     f"{get_pipeline_model_parallel_world_size()=} vs. "
+    #     f"{pipeline_model_parallel_size=}")
+
+
+def model_parallel_is_initialized():
+    """Check if tensor and pipeline parallel groups are initialized."""
+    return (ps._TP_DEVICE_GROUP is not None)
+    # and _PIPELINE_MODEL_PARALLEL_GROUP is not None)
+
+
+def initialize_model_parallel_for_vllm(tensor_model_parallel_size: int,
+                                       num_tensor_model_parallel_groups_per_train_tp: int = 1) -> None:
+    from torch.distributed import new_group
+    # Get world size and rank. Ensure some consistencies.
+    assert torch.distributed.is_initialized()
+
+    assert isinstance(tensor_model_parallel_size, int)
+
+    # assert num_tensor_model_parallel_groups_per_train_tp == 1 and not different_tp_group
+    # assert num_tensor_model_parallel_groups_per_train_tp > 1 and different_tp_group
+
+    # Build the tensor model-parallel groups.
+    assert ps._TP_DEVICE_GROUP is None, ("tensor model parallel group is already initialized")
+
+    global _TP_DEVICE_GROUP
+    global _TP_CPU_GROUP
+    global _DEVICE_MESH
+
+    world_size: int = torch.distributed.get_world_size()
+
+    rank = torch.distributed.get_rank()
+
+    backend = torch.distributed.get_backend()
+
+    num_tensor_model_parallel_groups = world_size // tensor_model_parallel_size
+
+    if num_tensor_model_parallel_groups_per_train_tp == 1:
+        # if tensor_model_parallel_size == train_tensor_parallel_size:
+        # using the same tp group as Megatron/vllm
+        for i in range(num_tensor_model_parallel_groups):
+            ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)
+            group = torch.distributed.new_group(ranks, backend=backend)
+            cpu_group = torch.distributed.new_group(ranks, backend="gloo")
+            if rank in ranks:
+                _TP_DEVICE_GROUP = group
+                _TP_CPU_GROUP = cpu_group
+                ps._TP_DEVICE_GROUP = group
+                ps._TP_CPU_GROUP = cpu_group
+
+        # no _MICRO_DATA_PARALLEL_GROUP
+    else:
+        # initialize a micro_dp group and a tp group
+        # assume training tp=4, infer tp=2, then, weight is partitioned as
+        # [1], [2], [3], [4] for training and [1,2], [1,2], [3,4], [3,4] for inference
+
+        # Build the inference tp groups
+        # train_tp = train_tensor_parallel_size
+        train_tp = num_tensor_model_parallel_groups_per_train_tp * tensor_model_parallel_size
+        # num_tensor_model_parallel_groups_per_train_tp = train_tp // tensor_model_parallel_size
+        assert _TP_DEVICE_GROUP is None, ("tensor model parallel group is already initialized")
+        for i in range(num_tensor_model_parallel_groups // num_tensor_model_parallel_groups_per_train_tp):
+            start = train_tp * i
+            end = train_tp * (i + 1)
+            for j in range(num_tensor_model_parallel_groups_per_train_tp):
+                ranks = list(range(start, end, num_tensor_model_parallel_groups_per_train_tp))
+                for i in range(len(ranks)):
+                    ranks[i] += j
+                group = torch.distributed.new_group(ranks)
+                cpu_group = torch.distributed.new_group(ranks, backend='gloo')
+                if rank in ranks:
+                    _TP_DEVICE_GROUP = group
+                    _TP_CPU_GROUP = cpu_group
+                    ps._TP_DEVICE_GROUP = _TP_DEVICE_GROUP
+                    ps._TP_CPU_GROUP = cpu_group
+
+    # Build the pipeline model-parallel groups.
+    # global _PIPELINE_MODEL_PARALLEL_GROUP
+    # global _PIPELINE_GLOBAL_RANKS
+    # assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized")
+
+    # ps._PIPELINE_MODEL_PARALLEL_GROUP = mpu.get_pipeline_model_parallel_group()
+    # ps._PIPELINE_GLOBAL_RANKS = mpu.get_pipeline_model_parallel_ranks()
+
+
+def initialize_model_parallel(
+    tensor_model_parallel_size: int = 1,
+    pipeline_model_parallel_size: int = 1,
+    backend: Optional[str] = None,
+) -> None:
+    """
+    NOTE: This method is a hack from the open-sourced version without
+    asertion of world_size = tp * pp
+    
+    Initialize model parallel groups.
+
+    Arguments:
+        tensor_model_parallel_size: number of GPUs used for tensor model
+            parallelism.
+        pipeline_model_parallel_size: number of GPUs used for pipeline model
+            parallelism.
+
+    Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
+    use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
+    the model pipeline. The present function will
+    create 4 tensor model-parallel groups and 2 pipeline model-parallel groups:
+        4 tensor model-parallel groups:
+            [g0, g1], [g2, g3], [g4, g5], [g6, g7]
+        2 pipeline model-parallel groups:
+            [g0, g2, g4, g6], [g1, g3, g5, g7]
+    Note that for efficiency, the caller should make sure adjacent ranks
+    are on the same DGX box. For example if we are using 2 DGX-1 boxes
+    with a total of 16 GPUs, rank 0 to 7 belong to the first box and
+    ranks 8 to 15 belong to the second box.
+    """
+    # Get world size and rank. Ensure some consistencies.
+    assert torch.distributed.is_initialized()
+    world_size: int = torch.distributed.get_world_size()
+    # get the backend of _DEVICE_WORLD_GROUP
+    backend = backend or torch.distributed.get_backend()
+
+    # NOTE(sgm) we don't assert world_size == tp * pp
+    # DP is not managed by vllm but by the verl WorkerGroup
+
+    num_tensor_model_parallel_groups: int = (world_size // tensor_model_parallel_size)
+    num_pipeline_model_parallel_groups: int = (world_size // pipeline_model_parallel_size)
+    rank = torch.distributed.get_rank()
+
+    # Build device mesh for TP
+    if num_tensor_model_parallel_groups > 1:
+        device_mesh = init_device_mesh("cuda", (num_tensor_model_parallel_groups, tensor_model_parallel_size),
+                                       mesh_dim_names=("replicate", "tp_shard"))
+    else:
+        device_mesh = init_device_mesh("cuda", (tensor_model_parallel_size,), mesh_dim_names=["tp_shard"])
+    shard_group = device_mesh.get_group(mesh_dim="tp_shard")
+
+    # Build the tensor model-parallel groups.
+    global _TP_DEVICE_GROUP, _TP_CPU_GROUP
+    global _DEVICE_MESH
+    assert _TP_DEVICE_GROUP is None, ("tensor model parallel group is already initialized")
+    assert _DEVICE_MESH is None, ("device mesh in vllm is already initialized")
+
+    _DEVICE_MESH = device_mesh
+    # for i in range(num_tensor_model_parallel_groups):
+    #     ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)
+    # group = torch.distributed.new_group(ranks, backend=backend)
+    # cpu_group = torch.distributed.new_group(ranks, backend="gloo")
+    # assert torch.distributed.get_process_group_ranks(shard_group) == torch.distributed.get_process_group_ranks(cpu_group)
+    # ranks = torch.distributed.get_process_group_ranks(shard_group)
+    # cpu_group = torch.distributed.new_group(ranks, backend="gloo") # TODO: this will hang
+    # cpu_group = torch.distributed.new_group(, backend="gloo")
+    # if rank == 0:
+    #     print(f'rank: {rank}')
+    #     print(f'ranks: {ranks}')
+    #     print(f'torch.distributed.get_process_group_ranks(shard_group): {torch.distributed.get_process_group_ranks(shard_group)}')
+    # if rank in ranks:
+    _TP_DEVICE_GROUP = shard_group
+    ps._TP_DEVICE_GROUP = _TP_DEVICE_GROUP
+    # ps._TP_CPU_GROUP = cpu_group # TODO: will hang when used with device mesh
+
+    # TODO: init using device mesh
+    # Build the pipeline model-parallel groups.
+    assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized")
+    for i in range(num_pipeline_model_parallel_groups):
+        ranks = range(i, world_size, num_pipeline_model_parallel_groups)
+        group = torch.distributed.new_group(ranks, backend=backend)
+        if rank in ranks:
+            ps._PIPELINE_MODEL_PARALLEL_GROUP = group
+            ps._PIPELINE_GLOBAL_RANKS = ranks
+
+
+"""
+Device mesh utilities
+"""
+
+
+def get_device_mesh():
+    assert _DEVICE_MESH is not None, ("device mesh is not initialized")
+    return _DEVICE_MESH
+
+
+"""
+Tensor model parallel utilities
+"""
+
+
+def get_tensor_model_parallel_group():
+    """Get the tensor model parallel group the caller rank belongs to."""
+    assert _TP_DEVICE_GROUP is not None, ("tensor model parallel group is not initialized")
+    return _TP_DEVICE_GROUP
+
+
+def get_tensor_model_parallel_world_size():
+    """Return world size for the tensor model parallel group."""
+    return torch.distributed.get_world_size(group=get_tensor_model_parallel_group())
+
+
+def get_tensor_model_parallel_rank():
+    """Return my rank for the tensor model parallel group."""
+    return torch.distributed.get_rank(group=get_tensor_model_parallel_group())
+
+
+def get_tensor_model_parallel_src_rank():
+    """Calculate the global rank corresponding to the first local rank
+    in the tensor model parallel group."""
+    global_rank = torch.distributed.get_rank()
+    local_world_size = get_tensor_model_parallel_world_size()
+    return (global_rank // local_world_size) * local_world_size

verl/third_party/vllm/vllm_v_0_4_2/spmd_gpu_executor.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/executor/gpu_executor.py
+import os
+import socket
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+import torch
+import vllm.envs as envs
+from vllm.executor.executor_base import ExecutorBase, ExecutorAsyncBase
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.sequence import SamplerOutput, ExecuteModelRequest
+
+from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig, SpeculativeConfig,
+                         VisionLanguageConfig)
+from .config import ModelConfig, LoadConfig
+
+logger = init_logger(__name__)
+
+
+class SPMDGPUExecutor(ExecutorBase):
+    """SPMD-based multi-GPU executor implementations."""
+
+    def __init__(
+        self,
+        model, # pytorch model itself or its parameter dict
+        model_config: ModelConfig,
+        cache_config: CacheConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        load_config: LoadConfig,
+        lora_config: Optional[LoRAConfig],
+        vision_language_config: Optional[VisionLanguageConfig],
+        speculative_config: Optional[SpeculativeConfig],
+    ) -> None:
+        self.model_config = model_config
+        self.cache_config = cache_config
+        self.lora_config = lora_config
+        self.load_config = load_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+        self.vision_language_config = vision_language_config
+        self.speculative_config = speculative_config
+
+        distributed_init_method = initialize_cluster(parallel_config)
+        self._init_executor(model, distributed_init_method)
+
+    # TODO(sgm): verl not support speculative decode now
+    def _init_executor(self, model, distributed_init_method) -> None:
+        assert (not self.speculative_config), "Speculative decoding not yet supported for multi-GPU backend."
+
+        # Create the parallel worker for each GPU.
+        self._init_workers_sp(model, distributed_init_method)
+
+    def _init_workers_sp(self, model, distributed_init_method: str):
+        # Lazy import the Worker to avoid importing torch.cuda/xformers
+        # before CUDA_VISIBLE_DEVICES is set in the Worker
+        from .worker import Worker  # pylint: disable=import-outside-toplevel
+
+        rank = int(os.getenv("RANK"))
+        local_rank = int(os.getenv("LOCAL_RANK"))
+        print(f'local rank {local_rank}')
+
+        self.worker = Worker(
+            model,
+            self.model_config,
+            self.parallel_config,
+            self.scheduler_config,
+            self.device_config,
+            self.cache_config,
+            self.load_config,
+            local_rank,
+            rank,
+            distributed_init_method,
+            lora_config=self.lora_config,
+            vision_language_config=self.vision_language_config,
+        )
+
+        # NOTE(shengguangming): torch.distributed.init_process_group will be called inside the init_model()
+        self.worker.init_device()
+        self.worker.load_model()
+
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """Determine the number of available KV blocks.
+
+        This invokes `determine_num_available_blocks` on each worker and takes
+        the min of the results, guaranteeing that the selected cache sizes are
+        compatible with all workers.
+
+        Returns:
+            - tuple[num_gpu_blocks, num_cpu_blocks]
+        """
+        # Get the maximum number of blocks that can be allocated on GPU and CPU.
+        num_blocks = self.worker.determine_num_available_blocks()
+
+        # NOTE(shengguangming): Now we don't use a shared centralized controler but each process will
+        # have its own scheduler
+        num_gpu_blocks = num_blocks[0]
+        num_cpu_blocks = num_blocks[1]
+
+        return num_gpu_blocks, num_cpu_blocks
+
+    def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None:
+        """Initialize the KV cache in all workers.
+        """
+
+        # NOTE: We log here to avoid multiple logs when number of workers is
+        # greater than one. We could log in the engine, but not all executors
+        # have GPUs.
+        logger.info("# GPU blocks: %d, # CPU blocks: %d", num_gpu_blocks, num_cpu_blocks)
+
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+        if torch.distributed.get_rank() == 0:
+            print(
+                f'before init cache memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB'
+            )
+        self.worker.initialize_cache(num_gpu_blocks=num_gpu_blocks, num_cpu_blocks=num_cpu_blocks)
+        if torch.distributed.get_rank() == 0:
+            print(
+                f'after init cache memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB'
+            )
+
+    # NOTE(sgm): This will not profile & capture the model(CUDAGraph) when rebuilding KVCache
+    def init_cache_engine(self) -> None:
+        self.worker._init_cache_engine()
+
+    def free_cache_engine(self) -> None:
+        self.worker.free_cache_engine()
+
+    def execute_model(self, execute_model_req) -> List[SamplerOutput]:
+        all_outputs = self.worker.execute_model(execute_model_req=execute_model_req)
+
+        # NOTE(sgm):
+        # Each GPU in vllm under verl has its own spmd_gpu_executor, therefore all GPUs should return the outputs
+        # In vllm with ray, only the driver worker returns the sampling results.
+        return all_outputs
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        assert lora_request.lora_int_id > 0, "lora_id must be greater than 0."
+        return self.worker.add_lora(lora_request=lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        assert lora_id > 0, "lora_id must be greater than 0."
+        return self.worker.remove_lora(lora_id=lora_id)
+
+    def list_loras(self) -> Set[int]:
+        return self.worker.list_loras()
+
+    def check_health(self) -> None:
+        # SPMDExecutor will always be healthy as long as
+        # it's running.
+        return
+
+    # NOTE(sgm): add for verl
+    def offload_model_weights(self) -> None:
+        self.worker.offload_model_weights()
+
+    def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None:
+        self.worker.sync_model_weights(actor_weights=actor_weights, load_format=load_format)
+
+
+def initialize_cluster(
+    parallel_config: ParallelConfig,
+    engine_use_ray: bool = False,
+    ray_address: Optional[str] = None,
+) -> Tuple[str, Optional[None]]:
+    """Initialize the distributed cluster probably with Ray.
+
+    Args:
+        parallel_config: The configurations for parallel execution.
+
+    Returns:
+        The `distributed_init_method` is the address for initializing the
+        distributed backend.
+    """
+
+    # Initialize cluster locally.
+    port = get_open_port()
+    # We need to setup the distributed init method to make sure
+    # the distributed megatron code (e.g., get world size) works correctly.
+    # distributed_init_method = f"tcp://localhost:{port}"
+    distributed_init_method = 'env://'
+    return distributed_init_method
+
+
+def get_open_port():
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+
+# TODO(sgm): not implemented async executor yet
+class SPMDGPUExecutorAsync(SPMDGPUExecutor, ExecutorAsyncBase):
+
+    async def execute_model_async(self, execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        """Executes one model step on the given sequences."""
+        raise NotImplementedError
+
+    async def check_health_async(self) -> None:
+        """Checks if the executor is healthy. If not, it should raise an
+        exception."""
+        self.check_health()

verl/third_party/vllm/vllm_v_0_4_2/tokenizer.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/tokenizer_group/tokenizer_group.py
+
+from typing import List, Optional, Tuple, Union
+
+from transformers import (AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast)
+
+from vllm.lora.request import LoRARequest
+from vllm.utils import make_async, LRUCache
+from vllm.transformers_utils.tokenizers import *
+
+
+class TokenizerGroup:
+    """A group of tokenizers that can be used for LoRA adapters."""
+
+    def __init__(self, tokenizer: PreTrainedTokenizer, enable_lora: bool, max_num_seqs: int,
+                 max_input_length: Optional[int]):
+        self.enable_lora = enable_lora
+        self.max_input_length = max_input_length
+        self.tokenizer = tokenizer
+        self.lora_tokenizers = LRUCache[PreTrainedTokenizer](capacity=max_num_seqs) if enable_lora else None
+
+    def ping(self) -> bool:
+        """Check if the tokenizer group is alive."""
+        return True
+
+    def get_max_input_len(self, lora_request: Optional[LoRARequest] = None) -> Optional[int]:
+        """Get the maximum input length for the LoRA request."""
+        return self.max_input_length
+
+    def encode(self,
+               prompt: str,
+               request_id: Optional[str] = None,
+               lora_request: Optional[LoRARequest] = None) -> List[int]:
+        tokenizer = self.get_lora_tokenizer(lora_request)
+        return tokenizer.encode(prompt)
+
+    async def encode_async(self,
+                           prompt: str,
+                           request_id: Optional[str] = None,
+                           lora_request: Optional[LoRARequest] = None) -> List[int]:
+        tokenizer = await self.get_lora_tokenizer_async(lora_request)
+        return tokenizer.encode(prompt)
+
+    def get_lora_tokenizer(self, lora_request: Optional[LoRARequest]) -> "PreTrainedTokenizer":
+        if not lora_request or not self.enable_lora:
+            return self.tokenizer
+        if lora_request.lora_int_id not in self.lora_tokenizers:
+            # TODO(sgm): the lora tokenizer is also passed, but may be different
+            tokenizer = self.tokenizer
+            # tokenizer = (get_lora_tokenizer(
+            #     lora_request, **self.tokenizer_config) or self.tokenizer)
+            self.lora_tokenizers.put(lora_request.lora_int_id, tokenizer)
+            return tokenizer
+        else:
+            return self.lora_tokenizers.get(lora_request.lora_int_id)
+
+    # FIXME(sgm): for simplicity, we assign the special token here
+    @property
+    def pad_token_id(self):
+        return self.tokenizer.pad_token_id
+
+    @property
+    def eos_token_id(self):
+        return self.tokenizer.eos_token_id

verl/third_party/vllm/vllm_v_0_4_2/worker.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/worker.py
+"""A GPU worker class."""
+import os
+import gc
+from typing import Dict, List, Tuple, Optional, Union
+
+import torch
+import torch.distributed
+import torch.nn as nn
+
+from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig, VisionLanguageConfig)
+from vllm.model_executor import set_random_seed
+from vllm.sequence import SamplerOutput, ExecuteModelRequest
+from vllm.worker.cache_engine import CacheEngine
+from vllm.distributed.device_communicators import pynccl_utils
+from vllm.distributed.device_communicators.custom_all_reduce import (init_custom_ar)
+# TODO(sgm): check why vllm has similar file in vllm.model_executor.parallel_utils.parallel_state
+from vllm.distributed import get_tensor_model_parallel_cpu_group, init_distributed_environment, get_tensor_model_parallel_group
+from vllm.worker.worker import Worker, _check_if_gpu_supports_dtype
+
+from .model_runner import ModelRunner
+from .megatron_weight_loaders import load_megatron_weights
+from .hf_weight_loader import load_hf_weights
+from .dtensor_weight_loaders import load_dtensor_weights
+from .parallel_state import (ensure_model_parallel_initialized)
+from .config import ModelConfig, LoadConfig, LoadFormat
+
+
+class Worker(Worker):
+    """A worker class that executes (a partition of) the model on a GPU.
+
+    Each worker is associated with a single GPU. The worker is responsible for
+    maintaining the KV cache and executing the model on the GPU. In case of
+    distributed inference, each worker is assigned a partition of the model.
+    """
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        cache_config: CacheConfig,
+        load_config: LoadConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        lora_config: Optional[LoRAConfig] = None,
+        vision_language_config: Optional[VisionLanguageConfig] = None,
+        is_driver_worker: bool = False,
+    ) -> None:
+        # self.model = model  # will be replaced in the init_model
+        self.model_config = model_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+        self.cache_config = cache_config
+        self.local_rank = local_rank
+        self.rank = rank
+        self.distributed_init_method = distributed_init_method
+        self.lora_config = lora_config
+        self.load_config = load_config
+        self.is_driver_worker = is_driver_worker
+        if self.is_driver_worker:
+            assert self.rank == 0, "The driver worker must have rank 0."
+
+        self.vision_language_config = vision_language_config
+        if self.vision_language_config:
+            assert not self.lora_config, ("To be tested: vision language model with LoRA settings.")
+
+        self.model_runner = ModelRunner(
+            model,
+            model_config,
+            parallel_config,
+            scheduler_config,
+            device_config,
+            load_config=load_config,
+            lora_config=self.lora_config,
+            kv_cache_dtype=self.cache_config.cache_dtype,
+            vision_language_config=vision_language_config,
+        )
+
+        # Uninitialized cache engine. Will be initialized by
+        # init_cache_engine.
+        self.cache_engine: CacheEngine = None
+        self.gpu_cache: List[torch.Tensor] = None
+
+        # NOTE(sgm): For offloading inference engine params
+        self.cpu_model = None
+
+    def init_device(self) -> None:
+        if self.device_config.device.type == "cuda":
+            # torch.distributed.all_reduce does not free the input tensor until
+            # the synchronization point. This causes the memory usage to grow
+            # as the number of all_reduce calls increases. This env var disables
+            # this behavior.
+            # Related issue:
+            # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
+            os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+            # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN.
+            self.rank = self.rank if self.rank is not None else int(os.getenv("RANK", "-1"))
+            local_rank = int(os.getenv("LOCAL_RANK", "0"))
+            self.device = torch.device(f"cuda:{local_rank}")
+            if self.rank < 0:
+                raise ValueError("Invalid or unspecified rank.")
+            torch.cuda.set_device(self.device)
+
+            # Use the world_size set by TORCHRUN
+            world_size = int(os.getenv("WORLD_SIZE", "-1"))
+            assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN"
+            self.parallel_config.world_size = world_size
+
+            _check_if_gpu_supports_dtype(self.model_config.dtype)
+            torch.cuda.empty_cache()
+            self.init_gpu_memory = torch.cuda.mem_get_info()[0]
+        else:
+            raise RuntimeError(f"Not support device type: {self.device_config.device}")
+
+        # Initialize the distributed environment.
+        init_worker_distributed_environment(self.parallel_config, self.rank, self.distributed_init_method,
+                                            self.local_rank)
+        # Set random seed.
+        set_random_seed(self.model_config.seed)
+        # self.model = get_model(actor_model=self.model, model_config=self.model_config)
+
+    @torch.inference_mode()
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """Profiles the peak memory usage of the model to determine how many
+        KV blocks may be allocated without OOMs.
+
+        The engine will first conduct a profiling of the existing memory usage.
+        Then, it calculate the maximum possible number of GPU and CPU blocks
+        that can be allocated with the remaining free memory.
+
+        .. tip::
+            You may limit the usage of GPU memory
+            by adjusting the `gpu_memory_utilization` parameter.
+        """
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.cuda.empty_cache()
+        # torch.cuda.reset_peak_memory_stats()
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model.
+        self.model_runner.profile_run()
+
+        # Calculate the number of blocks that can be allocated with the
+        # profiled peak memory.
+        torch.cuda.synchronize()
+        free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info()
+        peak_memory = total_gpu_memory - free_gpu_memory
+
+        assert peak_memory > 0, ("Error in memory profiling. This happens when the GPU memory was "
+                                 "not properly cleaned up before initializing the vLLM instance.")
+
+        cache_block_size = self.get_cache_block_size_bytes()
+
+        # NOTE(sgm) use the remaining memory
+        num_gpu_blocks = int((free_gpu_memory * self.cache_config.gpu_memory_utilization) // cache_block_size)
+        # num_gpu_blocks = int((total_gpu_memory * self.cache_config.gpu_memory_utilization - peak_memory) // cache_block_size)
+
+        num_cpu_blocks = int(self.cache_config.swap_space_bytes // cache_block_size)
+        num_gpu_blocks = max(num_gpu_blocks, 0)
+        num_cpu_blocks = max(num_cpu_blocks, 0)
+        if self.model_runner.lora_manager:
+            self.model_runner.remove_all_loras()
+
+        # NOTE(sgm): Add for verl, synchronize number of blocks with all the rank
+        num_gpu_blocks = torch.tensor([num_gpu_blocks], device='cuda')
+        num_cpu_blocks = torch.tensor([num_cpu_blocks], device='cuda')
+        torch.distributed.all_reduce(num_gpu_blocks,
+                                     op=torch.distributed.ReduceOp.MIN,
+                                     group=get_tensor_model_parallel_group())
+        torch.distributed.all_reduce(num_cpu_blocks,
+                                     op=torch.distributed.ReduceOp.MIN,
+                                     group=get_tensor_model_parallel_group())
+        num_gpu_blocks = num_gpu_blocks.item()
+        num_cpu_blocks = num_cpu_blocks.item()
+        gc.collect()
+        torch.cuda.empty_cache()
+        return num_gpu_blocks, num_cpu_blocks
+
+    def _init_cache_engine(self):
+        if self.cache_engine is None and self.gpu_cache is None:
+            super()._init_cache_engine()
+
+    def free_cache_engine(self):
+        # ensure `enforce_eager=True`
+        self.cache_engine = None
+        self.gpu_cache = None
+
+    @torch.inference_mode()
+    def execute_model(self, execute_model_req: Optional[ExecuteModelRequest] = None) -> List[SamplerOutput]:
+
+        if execute_model_req is None:
+            seq_group_metadata_list = None
+        else:
+            seq_group_metadata_list = execute_model_req.seq_group_metadata_list
+
+        # NOTE(sgm): each SPMD rank will have identical input
+        assert seq_group_metadata_list is not None
+        assert execute_model_req is not None
+        num_seq_groups = len(seq_group_metadata_list)
+        blocks_to_swap_in = execute_model_req.blocks_to_swap_in
+        blocks_to_swap_out = execute_model_req.blocks_to_swap_out
+        blocks_to_copy = execute_model_req.blocks_to_copy
+
+        self.cache_swap(blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy)
+
+        # If there is no input, we don't need to execute the model.
+        if num_seq_groups == 0:
+            return []
+
+        output = self.model_runner.execute_model(seq_group_metadata_list, self.gpu_cache)
+
+        # Worker only supports single-step execution. Wrap the output in a list
+        # to conform to interface.
+        return [output]
+
+    # assume the input is .state_dict()
+    def sync_model_weights(self, actor_weights: Dict, load_format: str):
+        if load_format in [LoadFormat.MEGATRON, LoadFormat.AUTO]:
+            load_megatron_weights(actor_weights, self.model_runner.model)
+        elif load_format == LoadFormat.HF:
+            # full model state dict without no sharding
+            load_hf_weights(actor_weights, self.model_runner.model)
+        elif load_format == LoadFormat.DTENSOR:
+            load_dtensor_weights(actor_weights, self.model_runner.model)
+
+    def offload_model_weights(self) -> None:
+        if self.cpu_model == None:
+            self.cpu_model = {}
+            for name, params in self.model_runner.model.named_parameters():
+                self.cpu_model[name] = torch.empty_like(params, device='cpu')
+                params.data = self.cpu_model[name]
+        else:
+            for name, params in self.model_runner.model.named_parameters():
+                params.data = self.cpu_model[name]
+
+
+def init_worker_distributed_environment(
+    parallel_config: ParallelConfig,
+    rank: int,
+    distributed_init_method: Optional[str] = "env://",
+    local_rank: int = -1,
+) -> None:
+    """Initialize the distributed environment."""
+    # NOTE(sgm) use tcp://localhost:xxxx will hang in HF setting without megatron
+    init_distributed_environment(parallel_config.world_size, rank, distributed_init_method, local_rank)
+
+    ensure_model_parallel_initialized(tensor_model_parallel_size=parallel_config.tensor_parallel_size,
+                                      pipeline_model_parallel_size=parallel_config.pipeline_parallel_size)
+
+    # TODO(sgm): check whether need this
+    # if pynccl_utils.is_initialized():
+    #     pynccl_world_size = pynccl_utils.get_world_size()
+    #     if pynccl_world_size != parallel_config.world_size:
+    #         raise RuntimeError(
+    #             "pynccl is already initialized but the pynccl world "
+    #             "size does not match parallel_config.world_size "
+    #             f"({pynccl_world_size} vs. {parallel_config.world_size}).")
+    # elif parallel_config.world_size > 1:
+    #     # NOTE(woosuk): We don't initialize pynccl process group when world size
+    #     # is 1.
+    #     # NOTE(kaichao): By default, pynccl is initialized for tp group.
+    #     pynccl_utils.init_process_group(
+    #         group=get_tensor_model_parallel_cpu_group())
+
+    # # Initialize a custom fast all-reduce implementation.
+    # if not parallel_config.disable_custom_all_reduce:
+    #     init_custom_ar()
+
+    # A small all_reduce for warmup.
+    torch.distributed.all_reduce(torch.zeros(1).cuda())
+    # if pynccl_utils.is_initialized():
+    #     pynccl_utils.all_reduce(torch.zeros(1).cuda())

verl/third_party/vllm/vllm_v_0_5_4/__init__.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# 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.

verl/third_party/vllm/vllm_v_0_5_4/arg_utils.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py
+
+import os
+import argparse
+import dataclasses
+import json
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional, Tuple, Type, Union
+
+import torch.nn as nn
+
+from transformers import PretrainedConfig
+from .config import ModelConfig, LoadConfig
+
+from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoRAConfig, MultiModalConfig,
+                         ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig,
+                         TokenizerPoolConfig)
+from vllm.executor.executor_base import ExecutorBase
+from vllm.logger import init_logger
+from vllm.utils import FlexibleArgumentParser
+from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.utils import str_to_int_tuple
+
+if TYPE_CHECKING:
+    from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import (BaseTokenizerGroup)
+
+logger = init_logger(__name__)
+
+
+def nullable_str(val: str):
+    if not val or val == "None":
+        return None
+    return val
+
+
+@dataclass
+class EngineArgs:
+    """Arguments for vLLM engine."""
+    model_hf_config: PretrainedConfig = None  # for verl
+    served_model_name = None  # TODO(sgm): check this
+    # tokenizer: Optional[str] = None # TODO(sgm): check this
+    skip_tokenizer_init: bool = False
+    tokenizer_mode: str = 'auto'
+    trust_remote_code: bool = False
+    download_dir: Optional[str] = None
+    load_format: str = 'auto'
+    dtype: str = 'auto'
+    kv_cache_dtype: str = 'auto'
+    quantization_param_path: Optional[str] = None
+    seed: int = 0
+    max_model_len: Optional[int] = None
+    worker_use_ray: bool = False
+    # Note: Specifying a custom executor backend by passing a class
+    # is intended for expert use only. The API may change without
+    # notice.
+    distributed_executor_backend: Optional[Union[str, Type[ExecutorBase]]] = None
+    pipeline_parallel_size: int = 1
+    tensor_parallel_size: int = 1
+    max_parallel_loading_workers: Optional[int] = None
+    block_size: int = 16
+    enable_prefix_caching: bool = False
+    disable_sliding_window: bool = False
+    use_v2_block_manager: bool = False
+    swap_space: int = 4  # GiB
+    cpu_offload_gb: int = 0  # GiB
+    gpu_memory_utilization: float = 0.90
+    max_num_batched_tokens: Optional[int] = None
+    max_num_seqs: int = 256
+    max_logprobs: int = 20  # Default value for OpenAI Chat Completions API
+    disable_log_stats: bool = False
+    revision: Optional[str] = None
+    code_revision: Optional[str] = None
+    rope_scaling: Optional[dict] = None
+    rope_theta: Optional[float] = None
+    tokenizer_revision: Optional[str] = None
+    quantization: Optional[str] = None
+    enforce_eager: bool = False
+    max_context_len_to_capture: Optional[int] = None
+    max_seq_len_to_capture: int = 8192
+    disable_custom_all_reduce: bool = False
+    tokenizer_pool_size: int = 0
+    # Note: Specifying a tokenizer pool by passing a class
+    # is intended for expert use only. The API may change without
+    # notice.
+    tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]] = "ray"
+    tokenizer_pool_extra_config: Optional[dict] = None
+    enable_lora: bool = False
+    max_loras: int = 1
+    max_lora_rank: int = 16
+    enable_prompt_adapter: bool = False
+    max_prompt_adapters: int = 1
+    max_prompt_adapter_token: int = 0
+    fully_sharded_loras: bool = False
+    lora_extra_vocab_size: int = 256
+    long_lora_scaling_factors: Optional[Tuple[float]] = None
+    lora_dtype: str = 'auto'
+    max_cpu_loras: Optional[int] = None
+    device: str = 'auto'
+    ray_workers_use_nsight: bool = False
+    num_gpu_blocks_override: Optional[int] = None
+    num_lookahead_slots: int = 0
+    model_loader_extra_config: Optional[dict] = None
+    ignore_patterns: Optional[Union[str, List[str]]] = None
+    preemption_mode: Optional[str] = None
+
+    scheduler_delay_factor: float = 0.0
+    enable_chunked_prefill: Optional[bool] = None
+
+    guided_decoding_backend: str = 'outlines'
+    # Speculative decoding configuration.
+    speculative_model: Optional[str] = None
+    speculative_draft_tensor_parallel_size: Optional[int] = None
+    num_speculative_tokens: Optional[int] = None
+    speculative_max_model_len: Optional[int] = None
+    speculative_disable_by_batch_size: Optional[int] = None
+    ngram_prompt_lookup_max: Optional[int] = None
+    ngram_prompt_lookup_min: Optional[int] = None
+    spec_decoding_acceptance_method: str = 'rejection_sampler'
+    typical_acceptance_sampler_posterior_threshold: Optional[float] = None
+    typical_acceptance_sampler_posterior_alpha: Optional[float] = None
+    qlora_adapter_name_or_path: Optional[str] = None
+    disable_logprobs_during_spec_decoding: Optional[bool] = None
+
+    otlp_traces_endpoint: Optional[str] = None
+
+    @staticmethod
+    def add_cli_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
+        """Shared CLI arguments for vLLM engine."""
+        # Model arguments
+        # TODO(shengguangming): delete the unused args
+        parser.add_argument('--model',
+                            type=str,
+                            default='facebook/opt-125m',
+                            help='name or path of the huggingface model to use')
+        parser.add_argument('--tokenizer',
+                            type=str,
+                            default=EngineArgs.tokenizer,
+                            help='name or path of the huggingface tokenizer to use')
+        parser.add_argument('--revision',
+                            type=str,
+                            default=None,
+                            help='the specific model version to use. It can be a branch '
+                            'name, a tag name, or a commit id. If unspecified, will use '
+                            'the default version.')
+        parser.add_argument('--tokenizer-revision',
+                            type=str,
+                            default=None,
+                            help='the specific tokenizer version to use. It can be a branch '
+                            'name, a tag name, or a commit id. If unspecified, will use '
+                            'the default version.')
+        parser.add_argument('--tokenizer-mode',
+                            type=str,
+                            default=EngineArgs.tokenizer_mode,
+                            choices=['auto', 'slow'],
+                            help='tokenizer mode. "auto" will use the fast '
+                            'tokenizer if available, and "slow" will '
+                            'always use the slow tokenizer.')
+        parser.add_argument('--trust-remote-code', action='store_true', help='trust remote code from huggingface')
+        parser.add_argument('--download-dir',
+                            type=str,
+                            default=EngineArgs.download_dir,
+                            help='directory to download and load the weights, '
+                            'default to the default cache dir of '
+                            'huggingface')
+        parser.add_argument('--load-format',
+                            type=str,
+                            default=EngineArgs.load_format,
+                            choices=['auto', 'pt', 'safetensors', 'npcache', 'dummy'],
+                            help='The format of the model weights to load. '
+                            '"auto" will try to load the weights in the safetensors format '
+                            'and fall back to the pytorch bin format if safetensors format '
+                            'is not available. '
+                            '"pt" will load the weights in the pytorch bin format. '
+                            '"safetensors" will load the weights in the safetensors format. '
+                            '"npcache" will load the weights in pytorch format and store '
+                            'a numpy cache to speed up the loading. '
+                            '"dummy" will initialize the weights with random values, '
+                            'which is mainly for profiling.')
+        parser.add_argument('--dtype',
+                            type=str,
+                            default=EngineArgs.dtype,
+                            choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
+                            help='data type for model weights and activations. '
+                            'The "auto" option will use FP16 precision '
+                            'for FP32 and FP16 models, and BF16 precision '
+                            'for BF16 models.')
+        parser.add_argument('--max-model-len',
+                            type=int,
+                            default=None,
+                            help='model context length. If unspecified, '
+                            'will be automatically derived from the model.')
+        # Parallel arguments
+        parser.add_argument('--worker-use-ray',
+                            action='store_true',
+                            help='use Ray for distributed serving, will be '
+                            'automatically set when using more than 1 GPU')
+        parser.add_argument('--pipeline-parallel-size',
+                            '-pp',
+                            type=int,
+                            default=EngineArgs.pipeline_parallel_size,
+                            help='number of pipeline stages')
+        parser.add_argument('--tensor-parallel-size',
+                            '-tp',
+                            type=int,
+                            default=EngineArgs.tensor_parallel_size,
+                            help='number of tensor parallel replicas')
+        # KV cache arguments
+        parser.add_argument('--block-size',
+                            type=int,
+                            default=EngineArgs.block_size,
+                            choices=[8, 16, 32],
+                            help='token block size')
+        # TODO(woosuk): Support fine-grained seeds (e.g., seed per request).
+        parser.add_argument('--seed', type=int, default=EngineArgs.seed, help='random seed')
+        parser.add_argument('--swap-space',
+                            type=int,
+                            default=EngineArgs.swap_space,
+                            help='CPU swap space size (GiB) per GPU')
+        parser.add_argument('--gpu-memory-utilization',
+                            type=float,
+                            default=EngineArgs.gpu_memory_utilization,
+                            help='the percentage of GPU memory to be used for'
+                            'the model executor')
+        parser.add_argument('--max-num-batched-tokens',
+                            type=int,
+                            default=EngineArgs.max_num_batched_tokens,
+                            help='maximum number of batched tokens per '
+                            'iteration')
+        parser.add_argument('--max-num-seqs',
+                            type=int,
+                            default=EngineArgs.max_num_seqs,
+                            help='maximum number of sequences per iteration')
+        parser.add_argument('--disable-log-stats', action='store_true', help='disable logging statistics')
+        # Quantization settings.
+        parser.add_argument('--quantization',
+                            '-q',
+                            type=str,
+                            choices=['awq', None],
+                            default=None,
+                            help='Method used to quantize the weights')
+        return parser
+
+    @classmethod
+    def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs':
+        # Get the list of attributes of this dataclass.
+        attrs = [attr.name for attr in dataclasses.fields(cls)]
+        # Set the attributes from the parsed arguments.
+        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
+        return engine_args
+
+    def create_engine_config(
+        self,
+    ) -> EngineConfig:
+        # bitsandbytes quantization needs a specific model loader
+        # so we make sure the quant method and the load format are consistent
+        if (self.quantization == "bitsandbytes" or
+           self.qlora_adapter_name_or_path is not None) and \
+           self.load_format != "bitsandbytes":
+            raise ValueError("BitsAndBytes quantization and QLoRA adapter only support "
+                             f"'bitsandbytes' load format, but got {self.load_format}")
+
+        if (self.load_format == "bitsandbytes" or
+            self.qlora_adapter_name_or_path is not None) and \
+            self.quantization != "bitsandbytes":
+            raise ValueError("BitsAndBytes load format and QLoRA adapter only support "
+                             f"'bitsandbytes' quantization, but got {self.quantization}")
+
+        assert self.cpu_offload_gb >= 0, ("CPU offload space must be non-negative"
+                                          f", but got {self.cpu_offload_gb}")
+
+        multimodal_config = MultiModalConfig()
+        device_config = DeviceConfig(self.device)
+        # NOTE(sgm): we only modify ModelConfig, other configs are import from vllm
+        model_config = ModelConfig(hf_config=self.model_hf_config,
+                                   tokenizer_mode=self.tokenizer_mode,
+                                   trust_remote_code=self.trust_remote_code,
+                                   dtype=self.dtype,
+                                   seed=self.seed,
+                                   revision=self.revision,
+                                   code_revision=self.code_revision,
+                                   rope_scaling=self.rope_scaling,
+                                   rope_theta=self.rope_theta,
+                                   tokenizer_revision=self.tokenizer_revision,
+                                   max_model_len=self.max_model_len,
+                                   quantization=self.quantization,
+                                   quantization_param_path=self.quantization_param_path,
+                                   enforce_eager=self.enforce_eager,
+                                   max_context_len_to_capture=self.max_context_len_to_capture,
+                                   max_seq_len_to_capture=self.max_seq_len_to_capture,
+                                   max_logprobs=self.max_logprobs,
+                                   disable_sliding_window=self.disable_sliding_window,
+                                   skip_tokenizer_init=self.skip_tokenizer_init,
+                                   served_model_name=self.served_model_name,
+                                   multimodal_config=multimodal_config)
+        cache_config = CacheConfig(
+            block_size=self.block_size,
+            gpu_memory_utilization=self.gpu_memory_utilization,
+            swap_space=self.swap_space,
+            cache_dtype=self.kv_cache_dtype,
+            num_gpu_blocks_override=self.num_gpu_blocks_override,
+            sliding_window=model_config.get_sliding_window(),
+            enable_prefix_caching=self.enable_prefix_caching,
+            cpu_offload_gb=self.cpu_offload_gb,
+        )
+        parallel_config = ParallelConfig(pipeline_parallel_size=self.pipeline_parallel_size,
+                                         tensor_parallel_size=self.tensor_parallel_size,
+                                         worker_use_ray=self.worker_use_ray,
+                                         max_parallel_loading_workers=self.max_parallel_loading_workers,
+                                         disable_custom_all_reduce=self.disable_custom_all_reduce,
+                                         tokenizer_pool_config=TokenizerPoolConfig.create_config(
+                                             self.tokenizer_pool_size,
+                                             self.tokenizer_pool_type,
+                                             self.tokenizer_pool_extra_config,
+                                         ),
+                                         ray_workers_use_nsight=self.ray_workers_use_nsight,
+                                         distributed_executor_backend=self.distributed_executor_backend)
+
+        # NOTE[VERL]: Use the world_size set by TORCHRUN
+        world_size = int(os.getenv("WORLD_SIZE", "-1"))
+        assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN"
+        parallel_config.world_size = world_size
+
+        max_model_len = model_config.max_model_len
+        use_long_context = max_model_len > 32768
+        if self.enable_chunked_prefill is None:
+            # If not explicitly set, enable chunked prefill by default for
+            # long context (> 32K) models. This is to avoid OOM errors in the
+            # initial memory profiling phase.
+            if use_long_context:
+                is_gpu = device_config.device_type == "cuda"
+                use_sliding_window = (model_config.get_sliding_window() is not None)
+                use_spec_decode = self.speculative_model is not None
+                has_seqlen_agnostic_layers = (model_config.contains_seqlen_agnostic_layers(parallel_config))
+                if (is_gpu and not use_sliding_window and not use_spec_decode and not self.enable_lora and
+                        not self.enable_prompt_adapter and not self.enable_prefix_caching and
+                        not has_seqlen_agnostic_layers):
+                    self.enable_chunked_prefill = True
+                    logger.warning("Chunked prefill is enabled by default for models with "
+                                   "max_model_len > 32K. Currently, chunked prefill might "
+                                   "not work with some features or models. If you "
+                                   "encounter any issues, please disable chunked prefill "
+                                   "by setting --enable-chunked-prefill=False.")
+            if self.enable_chunked_prefill is None:
+                self.enable_chunked_prefill = False
+
+        if not self.enable_chunked_prefill and use_long_context:
+            logger.warning(
+                "The model has a long context length (%s). This may cause OOM "
+                "errors during the initial memory profiling phase, or result "
+                "in low performance due to small KV cache space. Consider "
+                "setting --max-model-len to a smaller value.", max_model_len)
+
+        # TODO: spec config
+        speculative_config = SpeculativeConfig.maybe_create_spec_config(
+            target_model_config=model_config,
+            target_parallel_config=parallel_config,
+            target_dtype=self.dtype,
+            speculative_model=self.speculative_model,
+            speculative_draft_tensor_parallel_size = \
+                self.speculative_draft_tensor_parallel_size,
+            num_speculative_tokens=self.num_speculative_tokens,
+            speculative_disable_by_batch_size=self.
+            speculative_disable_by_batch_size,
+            speculative_max_model_len=self.speculative_max_model_len,
+            enable_chunked_prefill=self.enable_chunked_prefill,
+            use_v2_block_manager=self.use_v2_block_manager,
+            disable_log_stats=self.disable_log_stats,
+            ngram_prompt_lookup_max=self.ngram_prompt_lookup_max,
+            ngram_prompt_lookup_min=self.ngram_prompt_lookup_min,
+            draft_token_acceptance_method=\
+                self.spec_decoding_acceptance_method,
+            typical_acceptance_sampler_posterior_threshold=self.
+            typical_acceptance_sampler_posterior_threshold,
+            typical_acceptance_sampler_posterior_alpha=self.
+            typical_acceptance_sampler_posterior_alpha,
+            disable_logprobs=self.disable_logprobs_during_spec_decoding,
+        )
+
+        scheduler_config = SchedulerConfig(
+            max_num_batched_tokens=self.max_num_batched_tokens,
+            max_num_seqs=self.max_num_seqs,
+            max_model_len=model_config.max_model_len,
+            use_v2_block_manager=self.use_v2_block_manager,
+            num_lookahead_slots=(self.num_lookahead_slots
+                                 if speculative_config is None else speculative_config.num_lookahead_slots),
+            delay_factor=self.scheduler_delay_factor,
+            enable_chunked_prefill=self.enable_chunked_prefill,
+            embedding_mode=model_config.embedding_mode,
+            preemption_mode=self.preemption_mode,
+        )
+        lora_config = LoRAConfig(max_lora_rank=self.max_lora_rank,
+                                 max_loras=self.max_loras,
+                                 fully_sharded_loras=self.fully_sharded_loras,
+                                 lora_extra_vocab_size=self.lora_extra_vocab_size,
+                                 long_lora_scaling_factors=self.long_lora_scaling_factors,
+                                 lora_dtype=self.lora_dtype,
+                                 max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras and self.max_cpu_loras > 0 else
+                                 None) if self.enable_lora else None
+
+        if self.qlora_adapter_name_or_path is not None and \
+            self.qlora_adapter_name_or_path != "":
+            if self.model_loader_extra_config is None:
+                self.model_loader_extra_config = {}
+            self.model_loader_extra_config["qlora_adapter_name_or_path"] = self.qlora_adapter_name_or_path
+
+        load_config = LoadConfig(
+            load_format=self.load_format,
+            download_dir=self.download_dir,
+            model_loader_extra_config=self.model_loader_extra_config,
+            ignore_patterns=self.ignore_patterns,
+        )
+
+        prompt_adapter_config = PromptAdapterConfig(
+            max_prompt_adapters=self.max_prompt_adapters,
+            max_prompt_adapter_token=self.max_prompt_adapter_token) \
+                                        if self.enable_prompt_adapter else None
+
+        decoding_config = DecodingConfig(guided_decoding_backend=self.guided_decoding_backend)
+
+        observability_config = ObservabilityConfig(otlp_traces_endpoint=self.otlp_traces_endpoint)
+
+        if (model_config.get_sliding_window() is not None and scheduler_config.chunked_prefill_enabled and
+                not scheduler_config.use_v2_block_manager):
+            raise ValueError("Chunked prefill is not supported with sliding window. "
+                             "Set --disable-sliding-window to disable sliding window.")
+
+        return EngineConfig(
+            model_config=model_config,
+            cache_config=cache_config,
+            parallel_config=parallel_config,
+            scheduler_config=scheduler_config,
+            device_config=device_config,
+            lora_config=lora_config,
+            multimodal_config=multimodal_config,
+            speculative_config=speculative_config,
+            load_config=load_config,
+            decoding_config=decoding_config,
+            observability_config=observability_config,
+            prompt_adapter_config=prompt_adapter_config,
+        )

verl/third_party/vllm/vllm_v_0_5_4/config.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py
+
+import enum
+import json
+from typing import List, Optional, Union
+from dataclasses import dataclass, field, fields
+
+import torch
+from transformers import PretrainedConfig
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization import get_quantization_config
+from vllm.transformers_utils.config import get_hf_text_config
+from vllm.utils import is_hip, print_warning_once
+# Add for verl
+from vllm.config import ModelConfig, _get_and_verify_dtype, _get_and_verify_max_len, get_served_model_name
+
+GPTQMarlinConfig = get_quantization_config("gptq_marlin")
+
+logger = init_logger(__name__)
+
+_GB = 1 << 30
+
+
+class ModelConfig(ModelConfig):
+    """Configuration for the model.
+
+    Args:
+        model: Name or path of the huggingface model to use.
+        tokenizer: Name or path of the huggingface tokenizer to use.
+        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
+            available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
+        download_dir: Directory to download and load the weights, default to the
+            default cache directory of huggingface.
+        load_format: The format of the model weights to load:
+            "auto" will try to load the weights in the safetensors format and
+                fall back to the pytorch bin format if safetensors format is
+                not available.
+            "pt" will load the weights in the pytorch bin format.
+            "safetensors" will load the weights in the safetensors format.
+            "npcache" will load the weights in pytorch format and store
+                a numpy cache to speed up the loading.
+            "dummy" will initialize the weights with random values, which is
+                mainly for profiling.
+        dtype: Data type for model weights and activations. The "auto" option
+            will use FP16 precision for FP32 and FP16 models, and BF16 precision
+            for BF16 models.
+        seed: Random seed for reproducibility.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id. If unspecified, will use the default
+            version.
+        code_revision: The specific revision to use for the model code on
+            Hugging Face Hub. It can be a branch name, a tag name, or a
+            commit id. If unspecified, will use the default version.
+        tokenizer_revision: The specific tokenizer version to use. It can be a
+            branch name, a tag name, or a commit id. If unspecified, will use
+            the default version.
+        max_model_len: Maximum length of a sequence (including prompt and
+            output). If None, will be derived from the model.
+        quantization: Quantization method that was used to quantize the model
+            weights. If None, we assume the model weights are not quantized.
+        quantization_param_path: Path to JSON file containing scaling factors.
+            Used to load KV cache scaling factors into the model when KV cache
+            type is FP8_E4M3 on ROCm (AMD GPU). In the future these will also
+            be used to load activation and weight scaling factors when the
+            model dtype is FP8_E4M3 on ROCm.
+        enforce_eager: Whether to enforce eager execution. If True, we will
+            disable CUDA graph and always execute the model in eager mode.
+            If False, we will use CUDA graph and eager execution in hybrid.
+        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode (DEPRECATED. Use max_seq_len_to_capture instead).
+        max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode
+        skip_tokenizer_init: If true, skip initialization of tokenizer and
+            detokenizer.
+        served_model_name: The model name used in metrics tag `model_name`,
+            matches the model name exposed via the APIs. If multiple model 
+            names provided, the first name will be used. If not specified, 
+            the model name will be the same as `model`.
+    """
+
+    def __init__(
+        self,
+        hf_config: PretrainedConfig,
+        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,
+        quantization: Optional[str] = None,
+        quantization_param_path: Optional[str] = None,
+        enforce_eager: bool = False,
+        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,
+        multimodal_config: Optional["MultiModalConfig"] = None,
+    ) -> None:
+        self.model = hf_config._name_or_path
+        self.tokenizer = hf_config._name_or_path
+        # NOTE(sgm): same as open-sourced
+        self.tokenizer_mode = tokenizer_mode
+        self.trust_remote_code = trust_remote_code
+        self.seed = seed
+        self.revision = revision
+        self.code_revision = code_revision
+        self.rope_scaling = rope_scaling
+        self.rope_theta = rope_theta
+        # The tokenizer version is consistent with the model version by default.
+        if tokenizer_revision is None:
+            self.tokenizer_revision = revision
+        else:
+            self.tokenizer_revision = tokenizer_revision
+        self.quantization = quantization
+        self.quantization_param_path = quantization_param_path
+        self.enforce_eager = enforce_eager
+        if max_context_len_to_capture is not None:
+            raise ValueError("`max_context_len_to_capture` is deprecated. "
+                             "Use `max_seq_len_to_capture` instead.")
+        self.max_seq_len_to_capture = max_seq_len_to_capture
+        self.max_logprobs = max_logprobs
+        self.disable_sliding_window = disable_sliding_window
+        self.skip_tokenizer_init = skip_tokenizer_init
+
+        # self.hf_config = get_config(model, trust_remote_code, revision)
+        self.hf_config = hf_config
+        self.hf_text_config = get_hf_text_config(hf_config)
+        self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
+        # self.served_model_name = get_served_model_name(model,
+        #                                                served_model_name)
+        # self._verify_load_format()
+        # self._verify_tokenizer_mode()
+        if (not self.disable_sliding_window and self.hf_text_config.model_type == "gemma2" and
+                self.hf_text_config.sliding_window is not None):
+            print_warning_once("Gemma 2 uses sliding window attention for every odd layer, "
+                               "which is currently not supported by vLLM. Disabling sliding "
+                               "window and capping the max length to the sliding window size "
+                               f"({self.hf_text_config.sliding_window}).")
+            self.disable_sliding_window = True
+
+        self.max_model_len = _get_and_verify_max_len(hf_config=self.hf_text_config,
+                                                     max_model_len=max_model_len,
+                                                     disable_sliding_window=self.disable_sliding_window,
+                                                     sliding_window_len=self.get_hf_config_sliding_window())
+        self.served_model_name = get_served_model_name(
+            self.model,  # str
+            served_model_name)
+        self.multimodal_config = multimodal_config
+
+        if not self.skip_tokenizer_init:
+            self._verify_tokenizer_mode()
+        self._verify_embedding_mode()
+        self._verify_quantization()
+        self._verify_cuda_graph()
+
+
+class LoadFormat(str, enum.Enum):
+    AUTO = 'auto'
+    MEGATRON = "megatron"
+    HF = "hf"
+    DTENSOR = 'dtensor'
+    DUMMY_HF = 'dummy_hf'
+    DUMMY_MEGATRON = 'dummy_megatron'
+    DUMMY_DTENSOR = 'dummy_dtensor'
+
+
+# TODO: check whether this is necessary
+@dataclass
+class LoadConfig:
+    """
+        download_dir: Directory to download and load the weights, default to the
+            default cache directory of huggingface.
+        load_format: The format of the model weights to load:
+            "auto" will try to load the weights in the safetensors format and
+                fall back to the pytorch bin format if safetensors format is
+                not available.
+            "pt" will load the weights in the pytorch bin format.
+            "safetensors" will load the weights in the safetensors format.
+            "npcache" will load the weights in pytorch format and store
+                a numpy cache to speed up the loading.
+            "dummy" will initialize the weights with random values, which is
+                mainly for profiling.
+            "tensorizer" will use CoreWeave's tensorizer library for
+                fast weight loading.
+            "bitsandbytes" will load nf4 type weights.
+        ignore_patterns: The list of patterns to ignore when loading the model.
+            Default to "original/**/*" to avoid repeated loading of llama's 
+            checkpoints.
+            
+    """
+
+    load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO
+    download_dir: Optional[str] = None
+    model_loader_extra_config: Optional[Union[str, dict]] = field(default_factory=dict)
+    ignore_patterns: Optional[Union[List[str], str]] = None
+
+    def __post_init__(self):
+        model_loader_extra_config = self.model_loader_extra_config or {}
+        if isinstance(model_loader_extra_config, str):
+            self.model_loader_extra_config = json.loads(model_loader_extra_config)
+        self._verify_load_format()
+
+        if self.ignore_patterns is not None and len(self.ignore_patterns) > 0:
+            logger.info("Ignoring the following patterns when downloading weights: %s", self.ignore_patterns)
+        else:
+            self.ignore_patterns = ["original/**/*"]
+
+    def _verify_load_format(self) -> None:
+        if not isinstance(self.load_format, str):
+            return
+
+        load_format = self.load_format.lower()
+        self.load_format = LoadFormat(load_format)
+
+        rocm_not_supported_load_format: List[str] = []
+        if is_hip() and load_format in rocm_not_supported_load_format:
+            rocm_supported_load_format = [
+                f for f in LoadFormat.__members__ if (f not in rocm_not_supported_load_format)
+            ]
+            raise ValueError(f"load format '{load_format}' is not supported in ROCm. "
+                             f"Supported load formats are "
+                             f"{rocm_supported_load_format}")

verl/third_party/vllm/vllm_v_0_5_4/dtensor_weight_loaders.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models
+
+from typing import Dict, Iterable, Tuple
+import torch
+import torch.nn as nn
+from torch.distributed._tensor import DTensor, Shard, Replicate
+
+from vllm.model_executor.layers.linear import *
+from vllm.model_executor.models import ModelRegistry
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.utils import is_pp_missing_parameter
+
+
+def gemma_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+        ("gate_up_proj", "gate_proj", 0),
+        ("gate_up_proj", "up_proj", 1),
+    ]
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        for (param_name, shard_name, shard_id) in stacked_params_mapping:
+            if shard_name not in name:
+                continue
+            stacked_name = name.replace(shard_name, param_name)
+            # Skip loading extra bias for GPTQ models.
+            if stacked_name.endswith(".bias") and stacked_name not in params_dict:
+                continue
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[stacked_name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id)
+            break
+        else:
+            # lm_head is not used in vllm as it is tied with embed_token.
+            # To prevent errors, skip loading lm_head.weight.
+            if "lm_head.weight" in name:
+                continue
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+
+
+def gptbigcode_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module):
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        if "lm_head.weight" in name:
+            continue
+        if ".attn.bias" in name:
+            # Skip attention mask.
+            # NOTE: "c_attn.bias" should not be skipped.
+            continue
+        local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+        param = params_dict[name]
+        weight_loader = getattr(param, "weight_loader", default_weight_loader)
+        weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+
+
+def starcoder2_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module):
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+    ]
+
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        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)
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[name]
+            weight_loader = param.weight_loader
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id)
+            break
+        else:
+            if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+            param = params_dict[name]
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+
+
+def llama_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        (".qkv_proj", ".q_proj", "q"),
+        (".qkv_proj", ".k_proj", "k"),
+        (".qkv_proj", ".v_proj", "v"),
+        (".gate_up_proj", ".gate_proj", 0),
+        (".gate_up_proj", ".up_proj", 1),
+    ]
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        if "rotary_emb.inv_freq" in name:
+            continue
+        if ("rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name):
+            # Models trained using ColossalAI may include these tensors in
+            # the checkpoint. Skip them.
+            continue
+        # With tie_word_embeddings, we can skip lm_head.weight
+        # The weight might appear unnecessarily in the files if the model is
+        # processed with quantization, LoRA, fine-tuning, etc.
+        if vllm_model.config.tie_word_embeddings and "lm_head.weight" 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
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[name]
+            weight_loader = param.weight_loader
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id)
+            break
+        else:
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight)
+
+
+def qwen2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+        ("gate_up_proj", "gate_proj", 0),
+        ("gate_up_proj", "up_proj", 1),
+    ]
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        if "rotary_emb.inv_freq" in name:
+            continue
+        if vllm_model.config.tie_word_embeddings and "lm_head.weight" 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
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            param = params_dict[name]
+            weight_loader = param.weight_loader
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype), 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]
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+
+
+from vllm.model_executor.layers.fused_moe import FusedMoE
+
+
+def deepseekv2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        ("gate_up_proj", "gate_proj", 0),
+        ("gate_up_proj", "up_proj", 1),
+    ]
+
+    # Params for weights, fp8 weight scales, fp8 activation scales
+    # (param_name, weight_name, expert_id, shard_id)
+    expert_params_mapping = FusedMoE.make_expert_params_mapping(ckpt_gate_proj_name="gate_proj",
+                                                                ckpt_down_proj_name="down_proj",
+                                                                ckpt_up_proj_name="up_proj",
+                                                                num_experts=vllm_model.config.n_routed_experts)
+
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        if "rotary_emb.inv_freq" in name:
+            continue
+        for (param_name, weight_name, shard_id) in stacked_params_mapping:
+            # Skip non-stacked layers and experts (experts handled below).
+            if weight_name not in name:
+                continue
+            # We have mlp.experts[0].gate_proj in the checkpoint.
+            # Since we handle the experts below in expert_params_mapping,
+            # we need to skip here BEFORE we update the name, otherwise
+            # name will be updated to mlp.experts[0].gate_up_proj, which
+            # will then be updated below in expert_params_mapping
+            # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+            if (("mlp.experts." in name) and name not in params_dict):
+                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
+
+            if is_pp_missing_parameter(name, vllm_model):
+                continue
+
+            param = params_dict[name]
+            local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id)
+            break
+        else:
+            for mapping in expert_params_mapping:
+                param_name, weight_name, expert_id, shard_id = mapping
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                if is_pp_missing_parameter(name, vllm_model):
+                    continue
+
+                param = params_dict[name]
+                local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param,
+                              local_loaded_weight.to(dtype=param.dtype),
+                              weight_name,
+                              shard_id=shard_id,
+                              expert_id=expert_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, vllm_model):
+                    continue
+
+                param = params_dict[name]
+                local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+
+
+def gpt2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    pass
+
+
+def redistribute_dtensor(param_name: str, loaded_weights: DTensor, parallelize_plan: Dict = None):
+    param_name = _process_parameter_names(name=param_name)
+    if parallelize_plan is not None:
+        assert param_name in parallelize_plan.keys(), \
+            f"param name: {param_name} not in parallelize_plan :{parallelize_plan.keys()}"
+        placement = parallelize_plan[param_name]
+        local_loaded_weights = loaded_weights.redistribute(device_mesh=loaded_weights.device_mesh,
+                                                           placements=placement).to_local()
+    else:
+        local_loaded_weights = loaded_weights.full_tensor()
+    return local_loaded_weights
+
+
+def _process_parameter_names(name):
+    # Remove '.weight' if it exists at the end of the string
+    if name.endswith(".weight"):
+        name = name[:-7]
+
+    # Remove 'model.layers.x.' or 'model.' prefix
+    if "model.layers" in name:
+        parts = name.split('.')
+        # Reconstruct the string without 'model.layers.x.'
+        name = '.'.join(parts[3:])  # parts[0] is 'model', parts[1] is 'layers', parts[2] is 'x'
+    elif name.startswith("model."):
+        name = name[6:]  # Remove 'model.'
+
+    return name
+
+
+__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__ = {
+    'GPT2LMHeadModel': gpt2_dtensor_weight_loader,
+    'LlamaForCausalLM': llama_dtensor_weight_loader,
+    'LLaMAForCausalLM': llama_dtensor_weight_loader,
+    'MistralForCausalLM': llama_dtensor_weight_loader,  # mistral is the same as llama in vLLM
+    'InternLMForCausalLM': llama_dtensor_weight_loader,
+    'AquilaModel': llama_dtensor_weight_loader,
+    'AquilaForCausalLM': llama_dtensor_weight_loader,
+    'Phi3ForCausalLM': llama_dtensor_weight_loader,
+    'GemmaForCausalLM': gemma_dtensor_weight_loader,
+    'Gemma2ForCausalLM': gemma_dtensor_weight_loader,
+    'GPTBigCodeForCausalLM': gptbigcode_dtensor_load_weights,
+    'Starcoder2ForCausalLM': starcoder2_dtensor_load_weights,
+    'Qwen2ForCausalLM': qwen2_dtensor_weight_loader,
+    'DeepseekV2ForCausalLM': deepseekv2_dtensor_weight_loader
+}
+
+
+# the actor model is .state_dict()
+# Load dtensor weights
+def load_dtensor_weights(actor_weights: Dict, vllm_model: nn.Module):
+    weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__)
+    weight_loader(actor_weights, vllm_model)
+    # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu
+    # after init, and we need this after sync model weights for in first iter.
+    vllm_model = vllm_model.cuda()
+
+
+def _get_model_weight_loader(arch: str):
+    if arch in __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__:
+        return __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__[arch]
+    raise ValueError(f"Model architectures {arch} are not supported for now. "
+                     f"Supported architectures: {__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__.keys()}")
+
+
+# NOTE(sgm): we use per-parameter weight loader in each vllm sub
+def update_dtensor_weight_loader():
+    pass

verl/third_party/vllm/vllm_v_0_5_4/hf_weight_loader.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models
+
+from typing import Dict, Union, Optional, Iterable, Tuple
+
+import torch
+import torch.nn as nn
+
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+
+def update_hf_weight_loader():
+    print('no hf weight loader need to be updated')
+    return
+
+
+def load_hf_weights(actor_weights: Dict, vllm_model: nn.Module):
+    assert isinstance(actor_weights, Dict)
+    with set_default_torch_dtype(next(vllm_model.parameters()).dtype):  # TODO
+        if vllm_model.config.tie_word_embeddings and "lm_head.weight" in actor_weights.keys():
+            del actor_weights["lm_head.weight"]
+        vllm_model.load_weights(actor_weights.items())
+    for _, module in vllm_model.named_modules():
+        quant_method = getattr(module, "quant_method", None)
+        if quant_method is not None:
+            quant_method.process_weights_after_loading(module)
+        # FIXME: Remove this after Mixtral is updated
+        # to use quant_method.
+        if hasattr(module, "process_weights_after_loading"):
+            module.process_weights_after_loading()
+    vllm_model = vllm_model.cuda()

verl/third_party/vllm/vllm_v_0_5_4/llm.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py
+
+from contextlib import contextmanager
+from typing import ClassVar, List, Optional, Sequence, Union, cast, overload, Dict, Tuple
+
+from tqdm import tqdm
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
+from transformers import PretrainedConfig
+import torch.nn as nn
+from .arg_utils import EngineArgs
+from .llm_engine_sp import LLMEngine
+from vllm import LLM
+from vllm.inputs import (PromptInputs, TextPrompt, TokensPrompt, parse_and_batch_prompt)
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.model_executor.guided_decoding import (GuidedDecodingRequest, get_local_guided_decoding_logits_processor)
+from vllm.model_executor.guided_decoding.guided_fields import LLMGuidedOptions
+from vllm.outputs import EmbeddingRequestOutput, RequestOutput
+from vllm.pooling_params import PoolingParams
+from vllm.prompt_adapter.request import PromptAdapterRequest
+from vllm.sampling_params import SamplingParams
+from vllm.transformers_utils.tokenizer import get_cached_tokenizer
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils import Counter, deprecate_kwargs
+import torch
+from torch.nn.utils.rnn import pad_sequence
+from verl.workers.rollout.tokenizer import HybridEngineBaseTokenizer
+
+
+class LLM(LLM):
+    """An LLM for generating texts from given prompts and sampling parameters.
+
+    This class includes a tokenizer, a language model (possibly distributed
+    across multiple GPUs), and GPU memory space allocated for intermediate
+    states (aka KV cache). Given a batch of prompts and sampling parameters,
+    this class generates texts from the model, using an intelligent batching
+    mechanism and efficient memory management.
+
+    NOTE: This class is intended to be used for offline inference. For online
+    serving, use the `AsyncLLMEngine` class instead.
+    NOTE: For the comprehensive list of arguments, see `EngineArgs`.
+
+    Args:
+        model: A HuggingFace Transformers model instance.
+        tokenizer: A HuggingFace Transformers tokenizer instance.
+        tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
+            if available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
+        tensor_parallel_size: The number of GPUs to use for distributed
+            execution with tensor parallelism.
+        dtype: The data type for the model weights and activations. Currently,
+            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
+            the `torch_dtype` attribute specified in the model config file.
+            However, if the `torch_dtype` in the config is `float32`, we will
+            use `float16` instead.
+        quantization: The method used to quantize the model weights. Currently,
+            we support "awq". If None, we assume the model weights are not
+            quantized and use `dtype` to determine the data type of the weights.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id.
+        tokenizer_revision: The specific tokenizer version to use. It can be a
+            branch name, a tag name, or a commit id.
+        seed: The seed to initialize the random number generator for sampling.
+        gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to
+            reserve for the model weights, activations, and KV cache. Higher
+            values will increase the KV cache size and thus improve the model's
+            throughput. However, if the value is too high, it may cause out-of-
+            memory (OOM) errors.
+        swap_space: The size (GiB) of CPU memory per GPU to use as swap space.
+            This can be used for temporarily storing the states of the requests
+            when their `best_of` sampling parameters are larger than 1. If all
+            requests will have `best_of=1`, you can safely set this to 0.
+            Otherwise, too small values may cause out-of-memory (OOM) errors.
+        enforce_eager: Whether to enforce eager execution. If True, we will
+            disable CUDA graph and always execute the model in eager mode.
+            If False, we will use CUDA graph and eager execution in hybrid.
+        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode.
+        disable_custom_all_reduce: See ParallelConfig
+    """
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer],
+        model_hf_config: PretrainedConfig,
+        tokenizer_mode: str = "auto",
+        trust_remote_code: bool = False,
+        skip_tokenizer_init: bool = False,
+        tensor_parallel_size: int = 1,
+        dtype: str = "auto",
+        quantization: Optional[str] = None,
+        revision: Optional[str] = None,
+        tokenizer_revision: Optional[str] = None,
+        seed: int = 0,
+        gpu_memory_utilization: float = 0.9,
+        swap_space: int = 4,
+        cpu_offload_gb: float = 0,
+        enforce_eager: bool = False,
+        max_context_len_to_capture: Optional[int] = None,
+        max_seq_len_to_capture: int = 8192,
+        disable_custom_all_reduce: bool = False,
+        load_format = 'auto',
+        **kwargs,
+    ) -> None:
+        if "disable_log_stats" not in kwargs:
+            kwargs["disable_log_stats"] = True
+        engine_args = EngineArgs(
+            model_hf_config=model_hf_config,
+            tensor_parallel_size=tensor_parallel_size,
+            dtype=dtype,
+            quantization=quantization,
+            revision=revision,
+            tokenizer_revision=tokenizer_revision,
+            seed=seed,
+            gpu_memory_utilization=gpu_memory_utilization,
+            swap_space=swap_space,
+            cpu_offload_gb=cpu_offload_gb,
+            enforce_eager=enforce_eager,
+            max_context_len_to_capture=max_context_len_to_capture,
+            max_seq_len_to_capture=max_seq_len_to_capture,
+            disable_custom_all_reduce=disable_custom_all_reduce,
+            load_format=load_format,
+            skip_tokenizer_init=skip_tokenizer_init,
+            **kwargs,
+        )
+        tokenizer_cls = (PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer)
+        if not isinstance(tokenizer, tokenizer_cls):
+            raise ValueError(
+                f"Unexpected tokenizer type: {type(tokenizer)}. Must be"
+                "one of the following: PreTrainedTokenizer, PreTrainedTokenizerFast, verl.workers.rollout.HybridEngineBaseTokenizer"
+            )
+        self.llm_engine = LLMEngine.from_engine_args(model, tokenizer, engine_args)  # TODO: check usagecontext
+        self.request_counter = Counter()
+
+    def init_cache_engine(self):
+        self.llm_engine.init_cache_engine()
+
+    def free_cache_engine(self):
+        self.llm_engine.free_cache_engine()
+
+    def get_tokenizer(self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
+        return self.llm_engine.tokenizer
+
+    def set_tokenizer(
+        self,
+        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
+    ) -> None:
+        self.llm_engine.tokenizer = tokenizer
+
+    def _run_engine(self, *, use_tqdm: bool) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
+        # Initialize tqdm.
+        if use_tqdm:
+            num_requests = self.llm_engine.get_num_unfinished_requests()
+            pbar = tqdm(
+                total=num_requests,
+                desc="Processed prompts",
+                dynamic_ncols=True,
+                postfix=(f"est. speed input: {0:.2f} toks/s, "
+                         f"output: {0:.2f} toks/s"),
+            )
+        # Run the engine.
+        outputs: List[Union[RequestOutput, EmbeddingRequestOutput]] = []
+        total_in_toks = 0
+        total_out_toks = 0
+        while self.llm_engine.has_unfinished_requests():
+            step_outputs = self.llm_engine.step()
+            for output in step_outputs:
+                if output.finished:
+                    outputs.append(output)
+                    if use_tqdm:
+                        if isinstance(output, RequestOutput):
+                            # Calculate tokens only for RequestOutput
+                            total_in_toks += len(output.prompt_token_ids)
+                            in_spd = total_in_toks / pbar.format_dict["elapsed"]
+                            total_out_toks += sum(len(stp.token_ids) for stp in output.outputs)
+                            out_spd = total_out_toks / pbar.format_dict["elapsed"]
+                            pbar.postfix = (f"est. speed input: {in_spd:.2f} toks/s, "
+                                            f"output: {out_spd:.2f} toks/s")
+                        pbar.update(1)
+        if use_tqdm:
+            pbar.close()
+        # Sort the outputs by request ID.
+        # This is necessary because some requests may be finished earlier than
+        # its previous requests.
+        outputs = sorted(outputs, key=lambda x: int(x.request_id))
+        return self._post_process_outputs(outputs)
+
+    # # NOTE(shengguangming): add for verl
+    # # TODO(sgm): we can optimize it by making the dataloader yield List[int] without padding.
+    # def _pre_process_inputs(self, prompt_token_ids: torch.Tensor) -> List[int]:
+    #     # remove the left padding in the prompt token_id
+    #     pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id
+    #     non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0]
+    #     token_ids = prompt_token_ids[non_pad_index:].tolist()
+    #     return token_ids
+
+    # NOTE(shengguangming): add for verl
+    def _post_process_outputs(self, request_outputs: List[RequestOutput]) -> Tuple[torch.Tensor, torch.Tensor]:
+        output_token_ids = []
+        logprobs = []
+        for request_output in request_outputs:  # List[RequestOutput]
+            outputs = request_output.outputs
+            for output in outputs:  # List[CompletionOutput], usually len == 1
+                output_token_ids.append(torch.tensor(output.token_ids))
+                # TODO(shengguangming): can be optimzied by rewrite the Sampler._get_logprobs() logits
+                logprobs_dicts = output.logprobs
+                if logprobs_dicts is not None:
+                    logprob = []
+                    for logprobs_dict, id in zip(logprobs_dicts, output.token_ids):
+                        logprob.append(logprobs_dict[id].logprob)
+                    logprobs.append(torch.tensor(logprob))
+
+        pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id
+        output_token_ids = pad_sequence(output_token_ids, batch_first=True, padding_value=pad_token_id)
+        if len(logprobs) > 0:
+            logprobs = pad_sequence(logprobs, batch_first=True, padding_value=pad_token_id)
+        return output_token_ids, logprobs
+
+    def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None:
+        self.llm_engine.sync_model_weights(actor_weights=actor_weights, load_format=load_format)
+
+    def offload_model_weights(self) -> None:
+        self.llm_engine.offload_model_weights()

verl/third_party/vllm/vllm_v_0_5_4/llm_engine_sp.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/llm_engine.py
+
+import torch
+from typing import Dict, Optional, Union, Type
+
+import vllm.envs as envs
+from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoRAConfig, MultiModalConfig,
+                         ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig)
+from vllm.core.scheduler import Scheduler
+from vllm.engine.output_processor.interfaces import (SequenceGroupOutputProcessor)
+from vllm.engine.output_processor.stop_checker import StopChecker
+from vllm.executor.executor_base import ExecutorBase
+from vllm.inputs import INPUT_REGISTRY, LLMInputs, PromptInputs
+from vllm.logger import init_logger
+from vllm.transformers_utils.detokenizer import Detokenizer
+from vllm.engine.metrics import (LoggingStatLogger, PrometheusStatLogger, StatLoggerBase, Stats)
+from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context, init_tracer)
+from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled, usage_message)
+from vllm.utils import Counter
+from vllm.engine.llm_engine import _load_generation_config_dict
+from vllm.engine.llm_engine import LLMEngine
+from vllm.version import __version__ as VLLM_VERSION
+
+import torch.nn as nn
+from .arg_utils import EngineArgs
+from .tokenizer import TokenizerGroup
+from .config import ModelConfig, LoadConfig
+
+logger = init_logger(__name__)
+_LOCAL_LOGGING_INTERVAL_SEC = 5
+
+
+class LLMEngine(LLMEngine):
+    """An LLM engine that receives requests and generates texts.
+
+    This is the main class for the vLLM engine. It receives requests
+    from clients and generates texts from the LLM. It includes a tokenizer, a
+    language model (possibly distributed across multiple GPUs), and GPU memory
+    space allocated for intermediate states (aka KV cache). This class utilizes
+    iteration-level scheduling and efficient memory management to maximize the
+    serving throughput.
+
+    The `LLM` class wraps this class for offline batched inference and the
+    `AsyncLLMEngine` class wraps this class for online serving.
+
+    NOTE: The config arguments are derived from the `EngineArgs` class. For the
+    comprehensive list of arguments, see `EngineArgs`.
+
+    Args:
+        model: the actor model initialize outside vllm (add for verl)
+        tokenizer: the initialized tokenizer (add for verl)
+        model_config: The configuration related to the LLM model.
+        cache_config: The configuration related to the KV cache memory
+            management.
+        parallel_config: The configuration related to distributed execution.
+        scheduler_config: The configuration related to the request scheduler.
+        distributed_init_method: The initialization method for distributed
+            execution. See `torch.distributed.init_process_group` for details.
+        placement_group: Ray placement group for distributed execution.
+            Required for distributed execution.
+        log_stats: Whether to log statistics.
+    """
+
+    def __init__(
+        self,
+        # NOTE(sgm): first two arguments are added for verl
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        tokenizer: nn.Module,
+        # NOTE(sgm): vllm original arguments
+        model_config: ModelConfig,
+        cache_config: CacheConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        load_config: LoadConfig,
+        lora_config: Optional[LoRAConfig],
+        multimodal_config: Optional[MultiModalConfig],
+        speculative_config: Optional[SpeculativeConfig],
+        decoding_config: Optional[DecodingConfig],
+        observability_config: Optional[ObservabilityConfig],
+        prompt_adapter_config: Optional[PromptAdapterConfig],
+        executor_class: Type[ExecutorBase],
+        log_stats: bool,
+        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
+        stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
+    ) -> None:
+        logger.info(
+            "Initializing an LLM engine (v%s) with config: "
+            "model=%r, speculative_config=%r, tokenizer=%r, "
+            "skip_tokenizer_init=%s, revision=%s, "
+            "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, "
+            "trust_remote_code=%s, dtype=%s, max_seq_len=%d, "
+            "download_dir=%r, load_format=%s, tensor_parallel_size=%d, "
+            "pipeline_parallel_size=%d, "
+            "disable_custom_all_reduce=%s, quantization=%s, "
+            "enforce_eager=%s, kv_cache_dtype=%s, "
+            "quantization_param_path=%s, device_config=%s, "
+            "decoding_config=%r, observability_config=%r, "
+            "seed=%d, served_model_name=%s, use_v2_block_manager=%s, "
+            "enable_prefix_caching=%s)",
+            VLLM_VERSION,
+            model_config.model,
+            speculative_config,
+            model_config.tokenizer,
+            model_config.skip_tokenizer_init,
+            model_config.revision,
+            model_config.rope_scaling,
+            model_config.rope_theta,
+            model_config.tokenizer_revision,
+            model_config.trust_remote_code,
+            model_config.dtype,
+            model_config.max_model_len,
+            load_config.download_dir,
+            load_config.load_format,
+            parallel_config.tensor_parallel_size,
+            parallel_config.pipeline_parallel_size,
+            parallel_config.disable_custom_all_reduce,
+            model_config.quantization,
+            model_config.enforce_eager,
+            cache_config.cache_dtype,
+            model_config.quantization_param_path,
+            device_config.device,
+            decoding_config,
+            observability_config,
+            model_config.seed,
+            model_config.served_model_name,
+            scheduler_config.use_v2_block_manager,
+            cache_config.enable_prefix_caching,
+        )
+        # TODO(woosuk): Print more configs in debug mode.
+
+        self.model_config = model_config
+        self.cache_config = cache_config
+        self.lora_config = lora_config
+        self.multimodal_config = multimodal_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+        self.speculative_config = speculative_config
+        self.load_config = load_config
+        self.decoding_config = decoding_config or DecodingConfig()
+        self.prompt_adapter_config = prompt_adapter_config
+        self.observability_config = observability_config or ObservabilityConfig()
+        self.log_stats = log_stats
+
+        # self.model = model # should not store the model, it should be deleted
+        # TODO(shengguangming): maybe we can choose init here or from arguments
+        if not self.model_config.skip_tokenizer_init:
+            self.tokenizer = self._init_tokenizer(tokenizer)
+            self.detokenizer = Detokenizer(self.tokenizer)
+        else:
+            self.tokenizer = None
+            self.detokenizer = None
+
+        self.seq_counter = Counter()
+        self.generation_config_fields = _load_generation_config_dict(model_config)
+
+        self.input_processor = INPUT_REGISTRY.create_input_processor(self.model_config)
+
+        self.model_executor = executor_class(
+            model=model, # add for spmd_gpu_executor
+            model_config=model_config,
+            cache_config=cache_config,
+            parallel_config=parallel_config,
+            scheduler_config=scheduler_config,
+            device_config=device_config,
+            lora_config=lora_config,
+            multimodal_config=multimodal_config,
+            speculative_config=speculative_config,
+            load_config=load_config,
+            prompt_adapter_config=prompt_adapter_config,
+        )
+
+        # Profile the memory usage and initialize the cache.
+        if not self.model_config.embedding_mode:
+            self._initialize_kv_caches()
+
+        # If usage stat is enabled, collect relevant info.
+        if is_usage_stats_enabled():
+            from vllm.model_executor.model_loader import (get_architecture_class_name)
+            usage_message.report_usage(
+                get_architecture_class_name(model_config),
+                usage_context,
+                extra_kvs={
+                    # Common configuration
+                    "dtype": str(model_config.dtype),
+                    "tensor_parallel_size": parallel_config.tensor_parallel_size,
+                    "block_size": cache_config.block_size,
+                    "gpu_memory_utilization": cache_config.gpu_memory_utilization,
+
+                    # Quantization
+                    "quantization": model_config.quantization,
+                    "kv_cache_dtype": str(cache_config.cache_dtype),
+
+                    # Feature flags
+                    "enable_lora": bool(lora_config),
+                    "enable_prompt_adapter": bool(prompt_adapter_config),
+                    "enable_prefix_caching": cache_config.enable_prefix_caching,
+                    "enforce_eager": model_config.enforce_eager,
+                    "disable_custom_all_reduce": parallel_config.disable_custom_all_reduce,
+                })
+
+        if self.tokenizer:
+            # Ping the tokenizer to ensure liveness if it runs in a
+            # different process.
+            self.tokenizer.ping()
+
+        # Create the scheduler.
+        # NOTE: the cache_config here have been updated with the numbers of
+        # GPU and CPU blocks, which are profiled in the distributed executor.
+        self.scheduler = [
+            Scheduler(scheduler_config, cache_config, lora_config, parallel_config.pipeline_parallel_size)
+            for _ in range(parallel_config.pipeline_parallel_size)
+        ]
+
+        # Metric Logging.
+        if self.log_stats:
+            if stat_loggers is not None:
+                self.stat_loggers = stat_loggers
+            else:
+                self.stat_loggers = {
+                    "logging":
+                        LoggingStatLogger(local_interval=_LOCAL_LOGGING_INTERVAL_SEC),
+                    "prometheus":
+                        PrometheusStatLogger(local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
+                                             labels=dict(model_name=model_config.served_model_name),
+                                             max_model_len=self.model_config.max_model_len),
+                }
+                self.stat_loggers["prometheus"].info("cache_config", self.cache_config)
+
+        self.tracer = None
+        if self.observability_config.otlp_traces_endpoint:
+            self.tracer = init_tracer("vllm.llm_engine", self.observability_config.otlp_traces_endpoint)
+
+        # Create sequence output processor, e.g. for beam search or
+        # speculative decoding.
+        self.output_processor = (SequenceGroupOutputProcessor.create_output_processor(
+            self.scheduler_config,
+            self.detokenizer,
+            self.scheduler,
+            self.seq_counter,
+            self.get_tokenizer_for_seq,
+            stop_checker=StopChecker(
+                self.scheduler_config.max_model_len,
+                self.get_tokenizer_for_seq,
+            ),
+        ))
+
+    # TODO(sgm): add for verl but we may not tokenizer in Rollout
+    def _init_tokenizer(self, tokenizer, **tokenizer_init_kwargs):
+        init_kwargs = dict(enable_lora=bool(self.lora_config),
+                           max_num_seqs=self.scheduler_config.max_num_seqs,
+                           max_input_length=None)
+        init_kwargs.update(tokenizer_init_kwargs)
+        return TokenizerGroup(tokenizer, **init_kwargs)
+
+    def init_cache_engine(self):
+        # TODO: check whether we should rebuild the CUDAGraph every iter when offload/load KVCache
+        # Re-capture CUDAGraph would be time-consuming
+        self.model_executor.init_cache_engine()
+
+    def free_cache_engine(self):
+        self.model_executor.free_cache_engine()
+
+    # NOTE(sgm): currently, we only support GPU executor
+    # The GPUExecutor remove the Ray dependency
+    @classmethod
+    def _get_executor_cls(cls, engine_config: EngineConfig) -> Type[ExecutorBase]:
+        assert engine_config.device_config.device_type == "cuda", \
+            "Currently, the vllm in verl only support running on GPU"
+
+        if engine_config.parallel_config.world_size == 1:
+            engine_config.load_config.load_format = "dummy_hf"
+
+        from .spmd_gpu_executor import SPMDGPUExecutor
+        executor_class = SPMDGPUExecutor
+        return executor_class
+
+    @classmethod
+    def from_engine_args(
+        cls,
+        model,
+        tokenizer,
+        engine_args: EngineArgs,
+        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
+        stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
+    ) -> "LLMEngine":
+        """Creates an LLM engine from the engine arguments."""
+        # Create the engine configs.
+        engine_config = engine_args.create_engine_config()
+        executor_class = cls._get_executor_cls(engine_config)
+        # Initialize the cluster and specify the executor class.
+        assert engine_config.device_config.device_type == "cuda", \
+            "Currently, the vllm in verl only support running on GPU"
+
+        from .spmd_gpu_executor import SPMDGPUExecutor
+        executor_class = SPMDGPUExecutor
+
+        # Create the LLM engine.
+        engine = cls(
+            model,
+            tokenizer,
+            **engine_config.to_dict(),
+            executor_class=executor_class,
+            log_stats=not engine_args.disable_log_stats,
+            usage_context=usage_context,
+            stat_loggers=stat_loggers,
+        )
+        return engine
+
+    def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None:
+        self.model_executor.sync_model_weights(actor_weights=actor_weights, load_format=load_format)
+
+    def offload_model_weights(self) -> None:
+        self.model_executor.offload_model_weights()

verl/third_party/vllm/vllm_v_0_5_4/megatron_weight_loaders.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models
+
+from typing import Dict
+import torch
+import torch.nn as nn
+
+from vllm.model_executor.layers.linear import *
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding, ParallelLMHead
+from vllm.model_executor.layers.activation import ScaledActivation
+from vllm.model_executor.models import ModelRegistry
+
+
+# NOTE(shengguangming): replace the origin weight loader function in the class
+def parallel_weight_loader(self, param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
+    """Parallel Linear weight loader."""
+    assert param.size() == loaded_weight.size(
+    ), 'the parameter size is not align with the loaded weight size, param size: {}, loaded_weight size: {}'.format(
+        param.size(), loaded_weight.size())
+    assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same"
+
+    param.data = loaded_weight.data
+
+
+def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
+    """Default weight loader."""
+    assert param.size() == loaded_weight.size()
+    assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same"
+
+    param.data = loaded_weight.data
+
+
+def gpt2_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        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 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
+            # TODO: check megatron
+            loaded_weight = loaded_weight.t()
+        weight_loader = getattr(param, "weight_loader", default_weight_loader)
+        weight_loader(param, loaded_weight)
+
+
+def llama_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_mapping = [
+        # (megatron core gpt model name, vllm model name)
+        ("embedding.word_embeddings", "model.embed_tokens"),
+        ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"),
+        ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_proj", 'self_attn.o_proj'),
+        ('pre_mlp_layernorm', 'post_attention_layernorm'),
+        ('mlp.linear_fc1.layer_norm_weight', 'post_attention_layernorm.weight'),
+        ('mlp.linear_fc1.layer_norm_bias', 'post_attention_layernorm.bias'),
+        ('mlp.linear_fc1', 'mlp.gate_up_proj'),
+        ('mlp.linear_fc2', 'mlp.down_proj'),
+        ('decoder.final_layernorm', 'model.norm'),
+        ('output_layer', 'lm_head'),
+    ]
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        name = _replace_name(name, params_mapping)
+        if name.endswith('.bias') and name not in params_dict:
+            continue
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_mapping = [
+        # (megatron core gpt model name, vllm model name)
+        ("embedding.word_embeddings", "model.embed_tokens"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_proj", 'self_attn.o_proj'),
+        (
+            'input_layernorm',
+            'input_layernorm',
+        ),
+        ('pre_mlp_layernorm', 'post_attention_layernorm'),
+        ('mlp.linear_fc1', 'mlp.gate_up_proj'),
+        ('mlp.linear_fc2', 'mlp.down_proj'),
+        ('decoder.final_layernorm', 'model.norm'),
+        ('output_layer', 'lm_head'),
+    ]
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        name = _replace_name(name, params_mapping)
+        if name.endswith('.bias') and name not in params_dict:
+            continue
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def _replace_name(megatron_name, name_mapping):
+    for m_name, v_name in name_mapping:
+        if m_name not in megatron_name:
+            continue
+        if 'layers' in megatron_name:  # deal with decoder layers
+            megatron_name = megatron_name.replace('decoder', 'model')
+            megatron_name_list = megatron_name.split('.')
+            if 'layer_norm_weight' in megatron_name_list or 'layer_norm_bias' in megatron_name_list:
+                param_name_list = megatron_name_list[:3]
+                param_name_list.append(v_name)
+                param_name = '.'.join(param_name_list)
+            else:
+                param_name_list = megatron_name_list[:3]
+                weight_or_bias = megatron_name_list[-1]
+                param_name_list.append(v_name)
+                param_name_list.append(weight_or_bias)
+                param_name = '.'.join(param_name_list)
+            return param_name
+        else:
+            param_name = megatron_name.replace(m_name, v_name)
+            return param_name
+
+
+def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_mapping = [
+        # (megatron core gpt model name, vllm model name)
+        ("embedding.word_embeddings", "model.embed_tokens"),
+        ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"),
+        ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_proj", 'self_attn.o_proj'),
+        ('pre_mlp_layernorm', 'post_attention_layernorm'),
+        ('mlp.linear_fc1.layer_norm_weight', 'post_attention_layernorm.weight'),
+        ('mlp.linear_fc1.layer_norm_bias', 'post_attention_layernorm.bias'),
+        ('mlp.linear_fc1', 'mlp.gate_up_proj'),
+        ('mlp.linear_fc2', 'mlp.down_proj'),
+        ('decoder.final_layernorm', 'model.norm'),
+        ('output_layer', 'lm_head'),
+    ]
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        name = _replace_name(name, params_mapping)
+        if name.endswith('.bias') and name not in params_dict:
+            continue
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_mapping = [
+        # (megatron core gpt model name, vllm model name)
+        ("embedding.word_embeddings", "model.embed_tokens"),
+        ("self_attention.linear_qkv", "self_attn.qkv_proj"),
+        ("self_attention.linear_proj", 'self_attn.o_proj'),
+        (
+            'input_layernorm',
+            'input_layernorm',
+        ),
+        ('pre_mlp_layernorm', 'post_attention_layernorm'),
+        ('mlp.linear_fc1', 'mlp.gate_up_proj'),
+        ('mlp.linear_fc2', 'mlp.down_proj'),
+        ('decoder.final_layernorm', 'model.norm'),
+        ('output_layer', 'lm_head'),
+    ]
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        name = _replace_name(name, params_mapping)
+        if name.endswith('.bias') and name not in params_dict:
+            continue
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def _replace_name(megatron_name, name_mapping):
+    for m_name, v_name in name_mapping:
+        if m_name not in megatron_name:
+            continue
+        if 'layers' in megatron_name:  # deal with decoder layers
+            megatron_name = megatron_name.replace('decoder', 'model')
+            megatron_name_list = megatron_name.split('.')
+            if 'layer_norm_weight' in megatron_name_list or 'layer_norm_bias' in megatron_name_list:
+                param_name_list = megatron_name_list[:3]
+                param_name_list.append(v_name)
+                param_name = '.'.join(param_name_list)
+            else:
+                param_name_list = megatron_name_list[:3]
+                weight_or_bias = megatron_name_list[-1]
+                param_name_list.append(v_name)
+                param_name_list.append(weight_or_bias)
+                param_name = '.'.join(param_name_list)
+            return param_name
+        else:
+            param_name = megatron_name.replace(m_name, v_name)
+            return param_name
+
+
+def mistral_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    # TODO: need to implement a general way to deal with prefix
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+__LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__ = {
+    ColumnParallelLinear: parallel_weight_loader,
+    MergedColumnParallelLinear: parallel_weight_loader,
+    QKVParallelLinear: parallel_weight_loader,
+    RowParallelLinear: parallel_weight_loader,
+    VocabParallelEmbedding: parallel_weight_loader,
+    ParallelLMHead: parallel_weight_loader
+    # "ScaledActivation.weight_loader": ScaledActivation, # TODO(shengguangming): latest commit in vllm fix awq for this function and add load_weights
+    # "default_weight_loader": default_weight_loader
+}
+
+# for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items():
+#     # setattr(layer_class, 'megatron_weight_loader', weight_loader)
+#     layer_class.weight_loader = weight_loader
+
+__MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__ = {
+    'GPT2LMHeadModel': gpt2_weight_loader,
+    'LlamaForCausalLM': llama_megatron_weight_loader,  # use te backend for open-source megatron
+    'LLaMAForCausalLM': llama_megatron_weight_loader,
+    'MistralForCausalLM': mistral_megatron_weight_loader,
+}
+
+
+# the actor model is .state_dict()
+# Load megatron weights
+def load_megatron_weights(actor_weights: Dict, vllm_model: nn.Module):
+    weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__)
+    weight_loader(actor_weights, vllm_model)
+    # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu
+    # after init, and we need this after sync model weights for in first iter.
+    vllm_model = vllm_model.cuda()
+
+
+def _get_model_weight_loader(arch: str):
+    if arch in __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__:
+        return __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__[arch]
+    raise ValueError(f"Model architectures {arch} are not supported for now. "
+                     f"Supported architectures: {ModelRegistry.get_supported_archs()}")
+
+
+def update_megatron_weight_loader():
+    for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items():
+        layer_class.weight_loader = weight_loader

verl/third_party/vllm/vllm_v_0_5_4/model_loader.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# 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.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader
+
+from typing import Dict, Union, Optional, Iterable, Tuple
+
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel
+
+from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, ModelConfig, MultiModalConfig,
+                         ParallelConfig, SchedulerConfig)
+from vllm.model_executor.model_loader import BaseModelLoader
+from vllm.model_executor.model_loader.loader import _initialize_model
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+from vllm.distributed.communication_op import tensor_model_parallel_all_gather
+
+from .config import ModelConfig, LoadFormat, LoadConfig
+from .megatron_weight_loaders import load_megatron_weights, update_megatron_weight_loader
+from .dtensor_weight_loaders import load_dtensor_weights, update_dtensor_weight_loader
+from .hf_weight_loader import update_hf_weight_loader
+
+
+def get_model(actor_model: Union[PreTrainedModel, Dict],
+              model_config: ModelConfig,
+              load_config: LoadConfig,
+              device_config: DeviceConfig,
+              parallel_config: ParallelConfig,
+              scheduler_config: SchedulerConfig,
+              lora_config: Optional[LoRAConfig],
+              multimodal_config: Optional[MultiModalConfig],
+              cache_config: CacheConfig = None) -> nn.Module:
+    loader = get_model_loader(load_config)
+    if load_config.load_format.startswith('dummy'):
+        return loader.load_model(model_config=model_config,
+                                 device_config=device_config,
+                                 lora_config=lora_config,
+                                 multimodal_config=multimodal_config,
+                                 parallel_config=parallel_config,
+                                 scheduler_config=scheduler_config,
+                                 cache_config=cache_config)
+    else:
+        return loader.load_model(actor_model=actor_model,
+                                 model_config=model_config,
+                                 device_config=device_config,
+                                 lora_config=lora_config,
+                                 multimodal_config=multimodal_config,
+                                 parallel_config=parallel_config,
+                                 scheduler_config=scheduler_config,
+                                 cache_config=cache_config)
+
+
+def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
+    """Get a model loader based on the load format."""
+
+    if isinstance(load_config.load_format, type):
+        return load_config.load_format(load_config)
+
+    if load_config.load_format == LoadFormat.AUTO:
+        update_megatron_weight_loader()
+        return MegatronLoader(load_config)
+
+    # NOTE(sgm): change the weight_loader function in runtime
+    if load_config.load_format == LoadFormat.MEGATRON:
+        update_megatron_weight_loader()
+        return MegatronLoader(load_config)
+
+    if load_config.load_format == LoadFormat.HF:
+        update_hf_weight_loader()
+        return HFLoader(load_config)
+
+    if load_config.load_format == LoadFormat.DTENSOR:
+        update_dtensor_weight_loader()
+        return DTensorLoader(load_config)
+
+    if load_config.load_format == LoadFormat.DUMMY_HF:
+        update_hf_weight_loader()
+        return DummyModelLoader(load_config)
+
+    if load_config.load_format == LoadFormat.DUMMY_MEGATRON:
+        update_megatron_weight_loader()
+        return DummyModelLoader(load_config)
+
+    if load_config.load_format == LoadFormat.DUMMY_DTENSOR:
+        update_dtensor_weight_loader()
+        return DummyModelLoader(load_config)
+
+    raise ValueError('load format not supported in verl: {}, only support {} and {}'.format(
+        load_config.load_format, LoadFormat.MEGATRON, LoadFormat.HF))
+
+
+class DummyModelLoader(BaseModelLoader):
+    """Model loader that will set model weights to random values."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(f"Model loader extra config is not supported for "
+                             f"load format {load_config.load_format}")
+
+    def load_model(self, *, model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig],
+                   multimodal_config: Optional[MultiModalConfig], parallel_config: ParallelConfig,
+                   scheduler_config: SchedulerConfig, cache_config: CacheConfig) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            with torch.device(device_config.device):
+                model = _initialize_model(model_config, self.load_config, lora_config, multimodal_config, cache_config,
+                                          scheduler_config)
+            # NOTE(woosuk): For accurate performance evaluation, we assign
+            # random values to the weights.
+            # initialize_dummy_weights(model)
+        return model.eval()
+
+
+class MegatronLoader(BaseModelLoader):
+    """Model loader that can load the model weights from partitioned megatron model."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(f"Model loader extra config is not supported for "
+                             f"load format {load_config.load_format}")
+
+    def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]):
+        # NOTE(shengguangming) Load the weights from the actor model
+        pass
+        # if isinstance(actor_model, nn.Module):
+        #     load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model)
+        # else:
+        #     load_weights(actor_weights=actor_model, vllm_model=model)
+        # return actor_model
+
+    def load_model(self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig,
+                   device_config: DeviceConfig, lora_config: Optional[LoRAConfig],
+                   multimodal_config: Optional[MultiModalConfig], parallel_config: ParallelConfig,
+                   scheduler_config: SchedulerConfig, cache_config: CacheConfig) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            with torch.device(device_config.device):
+                model = _initialize_model(model_config, self.load_config, lora_config, multimodal_config, cache_config,
+                                          scheduler_config)
+
+            # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm
+            if isinstance(actor_model, nn.Module):
+                load_megatron_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)),
+                                      vllm_model=model)
+            else:
+                load_megatron_weights(actor_weights=actor_model, vllm_model=model)
+
+            for _, module in model.named_modules():
+                quant_method = getattr(module, "quant_method", None)
+                if quant_method is not None:
+                    quant_method.process_weights_after_loading(module)
+                # FIXME: Remove this after Mixtral is updated
+                # to use quant_method.
+                if hasattr(module, "process_weights_after_loading"):
+                    module.process_weights_after_loading()
+        # NOTE(sgm) Some weights are point to gpu, but still need this.
+        model = model.cuda()  # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage
+        return model.eval()
+
+
+class HFLoader(BaseModelLoader):
+    """Model loader that can load the model weights from model's full params."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(f"Model loader extra config is not supported for "
+                             f"load format {load_config.load_format}")
+
+    def _get_weights_iterator(self, actor_model: Union[PreTrainedModel, Dict]):
+        if isinstance(actor_model, Dict):
+            return actor_model.items()
+        elif isinstance(actor_model, nn.Module):
+            return dict(actor_model.named_parameters()).items()
+        else:
+            raise ValueError(f'actor model should be Dict or nn.Module, but get {type(actor_model)}')
+
+    def load_model(self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig,
+                   device_config: DeviceConfig, lora_config: Optional[LoRAConfig],
+                   multimodal_config: Optional[MultiModalConfig], parallel_config: ParallelConfig,
+                   scheduler_config: SchedulerConfig, cache_config: CacheConfig) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            # with torch.device(device_config.device):
+            # NOTE(sgm): init the model in cpu
+            model = _initialize_model(model_config, self.load_config, lora_config, multimodal_config, cache_config,
+                                      scheduler_config)
+            model.load_weights(self._get_weights_iterator(actor_model))
+            for _, module in model.named_modules():
+                quant_method = getattr(module, "quant_method", None)
+                if quant_method is not None:
+                    quant_method.process_weights_after_loading(module)
+                # FIXME: Remove this after Mixtral is updated
+                # to use quant_method.
+                if hasattr(module, "process_weights_after_loading"):
+                    module.process_weights_after_loading()
+        # NOTE(sgm) Some weights are point to gpu, but still need this.
+        model = model.cuda()  # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage
+        return model.eval()
+
+
+class DTensorLoader(BaseModelLoader):
+    """Model loader that can load the model weights from partitioned megatron model."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(f"Model loader extra config is not supported for "
+                             f"load format {load_config.load_format}")
+
+    def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]):
+        # NOTE(shengguangming) Load the weights from the actor model
+        pass
+        # if isinstance(actor_model, nn.Module):
+        #     load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model)
+        # else:
+        #     load_weights(actor_weights=actor_model, vllm_model=model)
+        # return actor_model
+
+    def load_model(self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig,
+                   device_config: DeviceConfig, lora_config: Optional[LoRAConfig],
+                   multimodal_config: Optional[MultiModalConfig], parallel_config: ParallelConfig,
+                   scheduler_config: SchedulerConfig, cache_config: CacheConfig) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            with torch.device(device_config.device):
+                model = _initialize_model(model_config, self.load_config, lora_config, multimodal_config, cache_config,
+                                          scheduler_config)
+
+            # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm
+            if isinstance(actor_model, nn.Module):
+                load_dtensor_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)),
+                                     vllm_model=model)
+            else:
+                load_dtensor_weights(actor_weights=actor_model, vllm_model=model)
+
+            for _, module in model.named_modules():
+                quant_method = getattr(module, "quant_method", None)
+                if quant_method is not None:
+                    quant_method.process_weights_after_loading(module)
+                # FIXME: Remove this after Mixtral is updated
+                # to use quant_method.
+                if hasattr(module, "process_weights_after_loading"):
+                    module.process_weights_after_loading()
+        # NOTE(sgm) Some weights are point to gpu, but still need this.
+        model = model.cuda()  # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage
+        return model.eval()
+
+
+# FIXME(sgm): hack the _get_logits function in vllm v0.4.2
+# as they use ray, the _get_logits result will only need to return to the driver node,
+# therefore gather is enough. However, we use SPMD instead of a central scheduler,
+# all_gather is required (aligned with v0.2.6)
+def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor,
+                embedding_bias: Optional[torch.Tensor]) -> torch.Tensor:
+    # Get the logits for the next tokens.
+    logits = torch.matmul(hidden_states, embedding.t())
+    if embedding_bias is not None:
+        logits += embedding_bias
+    logits = tensor_model_parallel_all_gather(logits)
+    # Remove paddings in vocab (if any).
+    if logits is not None:
+        logits = logits[:, :self.org_vocab_size]
+    return logits
+
+
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+
+
+def logitsprocessor_init(self,
+                         vocab_size: int,
+                         org_vocab_size: Optional[int] = None,
+                         scale: float = 1.0,
+                         logits_as_input: bool = False,
+                         soft_cap: Optional[float] = None) -> None:
+    """
+    Args:
+        scale: A scaling factor to apply to the logits.
+    """
+    super(LogitsProcessor, self).__init__()
+    self.scale = scale
+    self.vocab_size = vocab_size
+    # Whether the input is logits (default is hidden states).
+    self.logits_as_input = logits_as_input
+    # original vocabulary size (without LoRA).
+    self.org_vocab_size = org_vocab_size or vocab_size
+    # Soft cap the logits. Used in Gemma 2.
+    self.soft_cap = soft_cap
+    # Whether to use gather or all-gather to gather the logits.
+    self.use_gather = False
+
+
+LogitsProcessor.__init__ = logitsprocessor_init  # use all_gather