[inference] Refactor inference architecture (#5057)

* [inference] support only TP (#4998) * support only tp * enable tp * add support for bloom (#5008) * [refactor] refactor gptq and smoothquant llama (#5012) * refactor gptq and smoothquant llama * fix import error * fix linear import torch-int * fix smoothquant llama import error * fix import accelerate error * fix bug * fix import smooth cuda * fix smoothcuda * [Inference Refactor] Merge chatglm2 with pp and tp (#5023) merge chatglm with pp and tp * [Refactor] remove useless inference code (#5022) * remove useless code * fix quant model * fix test import bug * mv original inference legacy * fix chatglm2 * [Refactor] refactor policy search and quant type controlling in inference (#5035) * [Refactor] refactor policy search and quant type controling in inference * [inference] update readme (#5051) * update readme * update readme * fix architecture * fix table * fix table * [inference] udpate example (#5053) * udpate example * fix run.sh * fix rebase bug * fix some errors * update readme * add some features * update interface * update readme * update benchmark * add requirements-infer --------- Co-authored-by: Bin Jia <45593998+FoolPlayer@users.noreply.github.com> Co-authored-by: Zhongkai Zhao <kanezz620@gmail.com>
2025-09-10 13:30:19 +00:00 · 2023-11-19 21:05:05 +08:00
parent bc09b95f50
commit fd6482ad8c
115 changed files with 6027 additions and 1431 deletions
--- a/colossalai/legacy/inference/hybridengine/engine.py
+++ b/colossalai/legacy/inference/hybridengine/engine.py
@@ -0,0 +1,170 @@
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from transformers.tokenization_utils_base import BatchEncoding
+
+from colossalai.cluster import ProcessGroupMesh
+from colossalai.pipeline.schedule.generate import GenerateSchedule
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.shardformer import ShardConfig, ShardFormer
+from colossalai.shardformer.policies.base_policy import Policy
+
+from ..pipeline.microbatch_manager import MicroBatchManager
+from ..tensor_parallel.kvcache_manager import MemoryManager
+
+PP_AXIS, TP_AXIS = 0, 1
+
+_supported_models = [
+    "LlamaForCausalLM",
+]
+
+
+class CaiInferEngine:
+    """
+    CaiInferEngine is a class that handles the pipeline parallel inference.
+
+    Args:
+        tp_size (int): the size of tensor parallelism.
+        pp_size (int): the size of pipeline parallelism.
+        model (`nn.Module`): the model not in pipeline style, and will be modified with `ShardFormer`.
+        model_policy (`colossalai.shardformer.policies.base_policy.Policy`): the policy to shardformer model.
+        micro_batch_size (int): the micro batch size.
+        micro_batch_buffer_size (int): the buffer size for micro batch. Normally, it should be the same as the number of pipeline stages.
+        max_batch_size (int): the maximum batch size.
+        max_input_len (int): the maximum input length.
+        max_output_len (int): the maximum output length.
+
+    Example:
+
+    ```python
+    from colossalai.inference import InferEngine
+    from colossalai.inference.pipeline.policies import LlamaModelInferPolicy
+    import colossalai
+    from transformers import LlamaForCausalLM, LlamaTokenizer
+
+    colossalai.launch_from_torch(config={})
+
+    model = LlamaForCausalLM.from_pretrained("your_path_to_model")
+    tokenizer = LlamaTokenizer.from_pretrained("/home/lczyh/share/models/llama-7b-hf")
+    # assume the model is infered with 2 pipeline stages
+    inferengine = CaiInferEngine(pp_size=2, model=model, model_policy=LlamaModelInferPolicy())
+
+    input = ["Introduce a landmark in China ","Introduce a landmark in China "]
+    data = tokenizer(input, return_tensors='pt')
+    output = inferengine.inference([data.to('cuda').data])
+
+    ```
+
+    """
+
+    def __init__(
+        self,
+        tp_size: int = 1,
+        pp_size: int = 1,
+        dtype: str = "fp16",
+        model: nn.Module = None,
+        model_policy: Policy = None,
+        micro_batch_size: int = 1,
+        micro_batch_buffer_size: int = None,
+        max_batch_size: int = 4,
+        max_input_len: int = 32,
+        max_output_len: int = 32,
+        verbose: bool = False,
+        # TODO: implement early_stopping, and various gerneration options
+        early_stopping: bool = False,
+        do_sample: bool = False,
+        num_beams: int = 1,
+    ) -> None:
+        assert model.__class__.__name__ in _supported_models, f"Model {model.__class__.__name__} is not supported."
+        assert (
+            tp_size * pp_size == dist.get_world_size()
+        ), f"TP size({tp_size}) * PP size({pp_size}) should be equal to the global world size ({dist.get_world_size()})"
+        assert model and model_policy, "Model with model_policy should be provided."
+        assert dtype in ["fp16", "fp32", "bf16"], "dtype should be one of 'fp16', 'fp32', 'bf16'"
+
+        assert max_batch_size <= 64, "Max batch size exceeds the constraint"
+        assert max_input_len + max_output_len <= 4096, "Max length exceeds the constraint"
+
+        # TODO: support only tensor parallel inference
+        assert pp_size > 1, "Not support only tensor parallel inference."
+        self.pp_size = pp_size
+        self.tp_size = tp_size
+
+        if dtype == "fp16":
+            self.dtype = torch.float16
+            model.half()
+        elif dtype == "bf16":
+            self.dtype = torch.bfloat16
+            model.to(torch.bfloat16)
+        else:
+            self.dtype = torch.float32
+
+        # Init pg mesh
+        pg_mesh = ProcessGroupMesh(pp_size, tp_size)
+
+        stage_manager = None
+        if pp_size > 1:
+            stage_manager = PipelineStageManager(pg_mesh, PP_AXIS, True)
+            self.cache_manager_list = [
+                self._init_manager(model, max_batch_size, max_input_len, max_output_len)
+                for _ in range(micro_batch_buffer_size or pp_size)
+            ]
+            self.mb_manager = MicroBatchManager(
+                stage_manager.stage,
+                micro_batch_size,
+                micro_batch_buffer_size or pp_size,
+                max_input_len,
+                max_output_len,
+                self.cache_manager_list,
+            )
+            self.verbose = verbose
+            self.schedule = GenerateSchedule(stage_manager, self.mb_manager, verbose)
+
+        self.model = self._shardformer(model, model_policy, stage_manager, pg_mesh.get_group_along_axis(TP_AXIS))
+
+    def inference(self, input_list):
+        """
+        Args:
+            input_list (list): a list of input data, each element is a `BatchEncoding` or `dict`.
+
+        Returns:
+            out (list): a list of output data, each element is a list of token.
+            timestamp (float): the time cost of the inference, only return when verbose is `True`.
+        """
+        assert isinstance(
+            input_list, (BatchEncoding, dict)
+        ), f"Only accept BatchEncoding or dict as input, but get {input_list.__class__.__name__}."
+        if isinstance(input_list, BatchEncoding):
+            input_list = input_list.data
+        out, timestamp = self.schedule.generate_step(self.model, iter([input_list]))
+        if self.verbose:
+            return out, timestamp
+        else:
+            return out
+
+    def _shardformer(self, model, model_policy, stage_manager, tp_group):
+        shardconfig = ShardConfig(
+            tensor_parallel_process_group=tp_group,
+            pipeline_stage_manager=stage_manager,
+            enable_tensor_parallelism=False,
+            enable_fused_normalization=False,
+            enable_all_optimization=False,
+            enable_flash_attention=False,
+            enable_jit_fused=False,
+            enable_sequence_parallelism=False,
+        )
+        shardformer = ShardFormer(shard_config=shardconfig)
+        shard_model, _ = shardformer.optimize(model, model_policy)
+        return shard_model.cuda()
+
+    def _init_manager(self, model, max_batch_size: int, max_input_len: int, max_output_len: int) -> None:
+        max_total_token_num = max_batch_size * (max_input_len + max_output_len)
+        head_dim = model.config.hidden_size // model.config.num_attention_heads
+        head_num = model.config.num_attention_heads
+        num_hidden_layers = (
+            model.config.num_hidden_layers if hasattr(model.config, "num_hidden_layers") else model.config.num_layers
+        )
+        layer_num = num_hidden_layers // self.pp_size
+
+        cache_manager = MemoryManager(max_total_token_num, self.dtype, head_num, head_dim, layer_num)
+        return cache_manager