[Pipeline Inference] Merge pp with tp

colossalai/inference/__init__.py

@@ -1,4 +1,4 @@
-from .pipeline import PPInferEngine
+from .hybridengine import CaiInferEngine
+from .hybridengine.polices import LlamaModelInferPolicy
 
-
-__all__ = ['PPInferEngine']
+__all__ = ["CaiInferEngine", "LlamaModelInferPolicy"]

colossalai/inference/hybridengine/__init__.py

@@ -0,0 +1,3 @@
+from .engine import CaiInferEngine
+
+__all__ = ["CaiInferEngine"]

colossalai/inference/pipeline/engine.py → colossalai/inference/hybridengine/engine.py

@@ -1,4 +1,5 @@
 import torch
+import torch.distributed as dist
 import torch.nn as nn
 from transformers.tokenization_utils_base import BatchEncoding
 
@@ -8,31 +9,35 @@
 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
-from .microbatch_manager import MicroBatchManager
 
+PP_AXIS, TP_AXIS = 0, 1
 
-class PPInferEngine:
+_supported_models = [
+    "LlamaForCausalLM",
+]
+
+
+class CaiInferEngine:
     """
-    PPInferEngine is a class that handles the pipeline parallel inference.
+    CaiInferEngine is a class that handles the pipeline parallel inference.
 
     Args:
-        pp_size (int): the number of pipeline stages.
-        pp_model (`nn.Module`): the model already in pipeline parallelism style.
+        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.
-        new_length (int): the new length of the input sequence.
-        early_stopping (bool): whether to stop early.
         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 PPInferEngine
+    from colossalai.inference import InferEngine
     from colossalai.inference.pipeline.policies import LlamaModelInferPolicy
     import colossalai
     from transformers import LlamaForCausalLM, LlamaTokenizer
@@ -42,7 +47,7 @@ class PPInferEngine:
     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 = PPInferEngine(pp_size=2, model=model, model_policy=LlamaModelInferPolicy(), new_length=8)
+    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')
@@ -54,12 +59,11 @@ class PPInferEngine:
 
     def __init__(
         self,
-        pp_size: int,
+        tp_size: int = 1,
+        pp_size: int = 1,
         dtype: str = "fp16",
-        pp_model: nn.Module = None,
         model: nn.Module = None,
         model_policy: Policy = None,
-        new_length: int = 32,
         micro_batch_size: int = 1,
         micro_batch_buffer_size: int = None,
         max_batch_size: int = 4,
@@ -71,12 +75,21 @@ def __init__(
         do_sample: bool = False,
         num_beams: int = 1,
     ) -> None:
-        assert pp_model or (model and model_policy), "Either pp_model or model with model_policy should be provided."
+        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'"
 
-        max_output_len = max(max_output_len, max_input_len + new_length)
+        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()
@@ -85,24 +98,29 @@ def __init__(
             model.to(torch.bfloat16)
         else:
             self.dtype = torch.float32
-        self.pg_mesh = ProcessGroupMesh(pp_size)
-        self.stage_manager = PipelineStageManager(self.pg_mesh, 0, True)
-        self.model = pp_model or self._shardformer(model, model_policy)
-        self.cache_manager_list = [
-            self._init_manager(max_batch_size, max_input_len, max_output_len)
-            for _ in range(micro_batch_buffer_size or pp_size)
-        ]
-        self.mb_manager = MicroBatchManager(
-            self.stage_manager.stage,
-            new_length,
-            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(self.stage_manager, self.mb_manager, verbose)
+
+        # 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):
         """
@@ -124,10 +142,10 @@ def inference(self, input_list):
         else:
             return out
 
-    def _shardformer(self, model, model_policy):
+    def _shardformer(self, model, model_policy, stage_manager, tp_group):
         shardconfig = ShardConfig(
-            tensor_parallel_process_group=None,
-            pipeline_stage_manager=self.stage_manager,
+            tensor_parallel_process_group=tp_group,
+            pipeline_stage_manager=stage_manager,
             enable_tensor_parallelism=False,
             enable_fused_normalization=False,
             enable_all_optimization=False,
@@ -139,14 +157,12 @@ def _shardformer(self, model, model_policy):
         shard_model, _ = shardformer.optimize(model, model_policy)
         return shard_model.cuda()
 
-    def _init_manager(self, max_batch_size: int, max_input_len: int, max_output_len: int) -> None:
+    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 = self.model.config.hidden_size // self.model.config.num_attention_heads
-        head_num = self.model.config.num_attention_heads
+        head_dim = model.config.hidden_size // model.config.num_attention_heads
+        head_num = model.config.num_attention_heads
         num_hidden_layers = (
-            self.model.config.num_hidden_layers
-            if hasattr(self.model.config, "num_hidden_layers")
-            else self.model.config.num_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