[Infer] Add TPInferEngine and fix file path

colossalai/inference/tensor_parallel/__init__.py

@@ -0,0 +1,4 @@
+from .engine import TPInferEngine
+from .kvcache_manager import MemoryManager
+
+__all__ = ['MemoryManager', 'TPInferEngine']

colossalai/shardformer/inference/batch_infer_state.py → colossalai/inference/tensor_parallel/batch_infer_state.py

@@ -21,6 +21,7 @@ class BatchInferState:
     block_loc: torch.Tensor = None
     start_loc: torch.Tensor = None
     seq_len: torch.Tensor = None
+    past_key_values_len: int = None
 
     is_context_stage: bool = False
     context_mem_index: torch.Tensor = None
@@ -34,7 +35,9 @@ class BatchInferState:
 
     @property
     def total_token_num(self):
-        return self.batch_size * self.max_len_in_batch
+        # return self.batch_size * self.max_len_in_batch
+        assert self.seq_len is not None and self.seq_len.size(0) > 0
+        return int(torch.sum(self.seq_len))
 
     def set_cache_manager(self, manager: MemoryManager):
         self.cache_manager = manager

colossalai/inference/tensor_parallel/engine.py

@@ -0,0 +1,254 @@
+from typing import Any, Callable, Dict, List, Optional, Set, Union
+
+import torch
+import torch.nn as nn
+from transformers import BloomForCausalLM, LlamaForCausalLM
+from transformers.generation import GenerationConfig
+from transformers.generation.stopping_criteria import StoppingCriteriaList
+from transformers.tokenization_utils_base import BatchEncoding
+
+from colossalai.cluster import ProcessGroupMesh
+from colossalai.shardformer import ShardConfig, ShardFormer
+from colossalai.shardformer.policies.auto_policy import get_autopolicy
+
+from .batch_infer_state import BatchInferState
+from .kvcache_manager import MemoryManager
+
+DP_AXIS, PP_AXIS, TP_AXIS = 0, 1, 2
+
+_supported_models = ['LlamaForCausalLM', 'BloomForCausalLM']
+
+
+class TPInferEngine:
+
+    def __init__(self,
+                 model: nn.Module,
+                 max_batch_size: int,
+                 max_input_len: int,
+                 max_output_len: int,
+                 dtype: torch.dtype = torch.float16,
+                 device: torch.device = torch.cuda.current_device()) -> None:
+        self.model = model
+        self.sharded_model = None
+
+        self.max_batch_size = max_batch_size
+        self.max_input_len = max_input_len
+        self.max_output_len = max_output_len
+        self.max_total_token_num = self.max_batch_size * (self.max_input_len + self.max_output_len)
+
+        # Constraints relatable with specs of devices
+        assert self.max_batch_size <= 64, "Max batch size exceeds the constraint"
+        assert self.max_input_len + self.max_output_len <= 2048, "Max length exceeds the constraint"
+
+        self.device = device
+        self.dtype = dtype
+
+        self.head_dim = self.model.config.hidden_size // self.model.config.num_attention_heads
+        self.head_num = self.model.config.num_attention_heads
+        self.layer_num = self.model.config.num_hidden_layers
+
+        self.tp_size = -1    # to be set with given shard config in self.prepare_shard_config
+        self.cache_manager = None
+
+    def _init_manager(self) -> None:
+        assert self.tp_size >= 1, "TP size not initialized without providing a valid ShardConfig"
+        assert self.head_num % self.tp_size == 0, f"Cannot shard {self.head_num} heads with tp size {self.tp_size}"
+        self.head_num //= self.tp_size    # update sharded number of heads
+        self.cache_manager = MemoryManager(self.max_total_token_num, self.dtype, self.head_num, self.head_dim,
+                                           self.layer_num)
+
+    def prepare_with_shard_config(self, shard_config: Optional[ShardConfig] = None) -> ShardConfig:
+        """ Prepare the engine with a given ShardConfig, or create a default one with tp size 1 """
+        self.tp_size = 1
+        if shard_config is None:
+            shard_config = ShardConfig(
+                tensor_parallel_process_group=None,
+                pipeline_stage_manager=None,
+                enable_tensor_parallelism=False,
+                enable_fused_normalization=False,
+                enable_all_optimization=False,
+                enable_flash_attention=False,
+                enable_jit_fused=False,
+                inference_only=True,
+            )
+        else:
+            shard_config.inference_only = True
+            shard_config.pipeline_stage_manager = None
+            if shard_config.enable_tensor_parallelism:
+                self.tp_size = shard_config.tensor_parallel_size
+        self._init_manager()
+
+        return shard_config
+
+    def shard_model_by(self, shardformer: ShardFormer) -> None:
+        """ Shard the model and store the sharded model by given ShardFormer """
+        assert self.tp_size == shardformer.shard_config.tensor_parallel_size, \
+            "Discrepancy between the tp size of TPInferEngine and the tp size of shard config"
+        model_name = self.model.__class__.__name__
+        assert model_name in self._supported_models(), f"Unsupported model cls {model_name} for TP inference."
+        policy = get_autopolicy(self.model, inference_only=True)
+        self.sharded_model, _ = shardformer.optimize(self.model, policy)
+        self.sharded_model = self.sharded_model.to(self.device)
+
+    @staticmethod
+    def _supported_models() -> List[str]:
+        return _supported_models
+
+    def generate(self, input_tokens, generate_kwargs) -> torch.Tensor:
+        if isinstance(input_tokens, torch.Tensor):
+            input_tokens = dict(input_ids=input_tokens, attention_mask=torch.ones_like(input_tokens, dtype=torch.bool))
+        if self.sharded_model is not None:
+            return self.generate_by_set_infer_state(input_tokens, generate_kwargs)
+
+        return self.model.generate(**input_tokens, **generate_kwargs)
+
+    @torch.no_grad()
+    def generate_by_set_infer_state(self, input_tokens, generate_kwargs) -> torch.Tensor:
+        """
+        Generate output tokens by setting BatchInferState as an attribute to the model and calling model.generate
+
+        Args:
+            inputs: should be one of the following types
+                1. BatchEncoding or dict (e.g. tokenizer batch_encode)
+                2. list of input token ids (e.g. appended result of tokenizer encode)
+                3. torch.Tensor (e.g. tokenizer encode with return_tensors='pt')
+        """
+
+        # for testing, always use sharded model
+        assert self.sharded_model is not None, "sharded model does not exist"
+
+        batch_infer_state = self.prepare_batch_state(input_tokens)
+        assert batch_infer_state.max_len_in_batch <= self.max_input_len, "max length in batch exceeds limit"
+
+        # set BatchInferState for the current batch as attr to model
+        # NOTE this is not an expectable way to pass BatchInferState during inference
+        #   we might want to rewrite generate function (e.g. generate_by_pass_infer_state)
+        #   and pass BatchInferState via model forward
+        model = self.sharded_model
+        if isinstance(model, LlamaForCausalLM):
+            model = self.sharded_model.model
+        elif isinstance(model, BloomForCausalLM):
+            model = self.sharded_model.transformer
+        setattr(model, 'infer_state', batch_infer_state)
+
+        generate_kwargs.update(max_new_tokens=self.max_output_len)
+
+        if isinstance(input_tokens, torch.Tensor):
+            input_tokens = dict(input_ids=input_tokens)
+        for t in input_tokens:
+            if torch.is_tensor(input_tokens[t]):
+                input_tokens[t] = input_tokens[t].to(self.device)
+
+        outputs = self.sharded_model.generate(**input_tokens, **generate_kwargs, early_stopping=False)
+
+        print(f"outputs.shape {outputs.shape}")
+        return outputs
+
+    def prepare_batch_state(self, inputs) -> BatchInferState:
+        """
+        Create and prepare BatchInferState used for inference during model forwrad,
+        by processing each sequence of the given inputs
+
+        Args:
+            inputs: should be one of the following types
+                1. BatchEncoding or dict (e.g. tokenizer batch_encode)
+                2. list of input token ids (e.g. appended result of tokenizer encode)
+                3. torch.Tensor (e.g. tokenizer encode with return_tensors='pt')
+                NOTE For torch.Tensor inputs representing a batch of inputs, we are unable to retrieve
+                    the actual length (e.g. number of tokens) of each input without attention mask
+                    Hence, for torch.Tensor with shape [bs, l] where bs > 1, we will assume
+                    all the inputs in the batch has the maximum length l
+        Returns:
+            BatchInferState: the states for the current batch during inference
+        """
+        if not isinstance(inputs, (BatchEncoding, dict, list, torch.Tensor)):
+            raise TypeError(f"inputs type {type(inputs)} is not supported in prepare_batch_state")
+
+        if isinstance(inputs, (BatchEncoding, dict)):
+            attn_masks = inputs['attention_mask']
+            batch_size = attn_masks.shape[0]
+            max_len_in_batch = attn_masks.shape[1]
+        elif isinstance(inputs, list):
+            batch_size = len(inputs)
+        else:
+            batch_size = inputs.shape[0]
+
+        seq_start_indexes = torch.zeros(batch_size, dtype=torch.int32, device=self.device)
+        seq_lengths = torch.zeros(batch_size, dtype=torch.int32, device=self.device)
+        start_index = 0
+
+        max_len_in_batch = -1
+        if isinstance(inputs, (BatchEncoding, dict)):
+            for i, attn_mask in enumerate(attn_masks):
+                curr_seq_len = int(torch.sum(attn_mask))
+                seq_lengths[i] = curr_seq_len
+                seq_start_indexes[i] = start_index
+                start_index += curr_seq_len
+                max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
+        else:
+            for i, input_ids in enumerate(inputs):
+                curr_seq_len = len(input_ids)
+                seq_lengths[i] = curr_seq_len
+                seq_start_indexes[i] = start_index
+                start_index += curr_seq_len
+                max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
+
+        print(" 666 ", max_len_in_batch)
+
+        block_loc = torch.empty((batch_size, self.max_input_len + self.max_output_len),
+                                dtype=torch.long,
+                                device=self.device)
+        batch_infer_state = BatchInferState(batch_size, max_len_in_batch)
+        batch_infer_state.seq_len = seq_lengths.to(self.device)    # might want to assign specific device
+        batch_infer_state.start_loc = seq_start_indexes.to(self.device)
+        batch_infer_state.block_loc = block_loc
+        batch_infer_state.decode_layer_id = 0
+        batch_infer_state.past_key_values_len = 0
+        batch_infer_state.is_context_stage = True
+        batch_infer_state.set_cache_manager(self.cache_manager)
+        return batch_infer_state
+
+    # TODO might want to implement the func that generates output tokens by passing BatchInferState
+    #      as an arg into model.forward
+    #      requires rewriting model generate and replacing model forward
+    @torch.no_grad()
+    def generate_by_pass_infer_state(self,
+                                     input_tokens,
+                                     max_out_length: int,
+                                     generation_config: Optional[GenerationConfig] = None,
+                                     stopping_criteria: Optional[StoppingCriteriaList] = None,
+                                     prepare_inputs_fn: Optional[Callable[[torch.Tensor, Any], dict]] = None,
+                                     **model_kwargs) -> torch.Tensor:
+        # if batch_size >= 4:
+        #     assert self.sharded_model is not None, "sharded model does not exist"
+        #     batch_infer_state = self.prepare_batch_state(input_tokens)
+        #     batch_size = batch_infer_state.batch_size
+        #     assert batch_infer_state.max_len_in_batch <= self.max_input_len
+        #     # record sequences finish status, add early stopping, etc,
+        #     for _ in range(min(max_out_length, self.max_output_len)):
+        #         # ...
+        #         self.sharded_model.forward(..., **model_kwargs)
+        # else:
+        #     Use original model to generate
+        raise NotImplementedError("generate by passing BatchInferState is not implemented.")
+
+    # NOTE might want to use in rewritten generate method: use after model.forward
+    # BatchInferState is created and kept during generation
+    # after each iter of model forward, we should update BatchInferState
+    def update_batch_state(self, infer_state: Optional[BatchInferState]) -> None:
+        batch_size = infer_state.batch_size
+        device = infer_state.start_loc.device
+        infer_state.start_loc = infer_state.start_loc + torch.arange(0, batch_size, dtype=torch.int32, device=device)
+        infer_state.seq_len += 1
+
+    # TODO might want to create a sequence pool
+    #   add a single request/sequence/input text at a time and record its length
+    #   In other words, store the actual length of input tokens representing a single input text
+    #   E.g. "Introduce landmarks in Beijing"
+    #       => add request
+    #       => record token length and other necessary information to be used
+    #       => engine hold all these necessary information until `generate` (or other name) is called,
+    #       => put information already recorded in batchinferstate and pass it to model forward
+    #       => clear records in engine
+    def add_request():
+        raise NotImplementedError()

colossalai/shardformer/policies/auto_policy.py

@@ -134,7 +134,9 @@ class PolicyLocation:
 _INFER_POLICY_LIST = {
     # LlaMa
     "transformers.models.llama.modeling_llama.LlamaModel":
-        PolicyLocation(file_name="llama", class_name="LlamaModelInferPolicy")
+        PolicyLocation(file_name="llama", class_name="LlamaModelInferPolicy"),
+    "transformers.models.llama.modeling_llama.LlamaForCausalLM":
+        PolicyLocation(file_name="llama", class_name="LlamaModelInferPolicy"),
 }
 
 

tests/test_infer/test_infer_engine.py

@@ -0,0 +1,70 @@
+import pytest
+import torch
+from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM
+
+import colossalai
+from colossalai.inference.tensor_parallel import TPInferEngine
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer import ShardConfig, ShardFormer
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
+
+TP_SIZE = 2
+BATCH_SIZE = 4
+MAX_INPUT_LEN = 16
+MAX_OUTPUT_LEN = 8
+
+
+def test_orig_generate():
+    input_ids = torch.randint(low=10, high=1000, size=(BATCH_SIZE, MAX_INPUT_LEN))
+
+    model_config = LlamaConfig()
+    model = LlamaForCausalLM(model_config)
+    shard_config = ShardConfig(enable_tensor_parallelism=False)
+
+    # init TPInferEngine and
+    infer_engine = TPInferEngine(model, BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
+    infer_engine.prepare_with_shard_config(shard_config)
+
+    # original model generate
+    generate_kwargs = dict(do_sample=False)
+    infer_engine.generate(input_ids, generate_kwargs)
+
+
+def run():
+    model_config = LlamaConfig()
+    model = LlamaForCausalLM(model_config)
+    shard_config = ShardConfig(enable_tensor_parallelism=True, inference_only=True)
+    shardformer = ShardFormer(shard_config=shard_config)
+
+    infer_engine = TPInferEngine(model, BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
+    infer_engine.prepare_with_shard_config(shard_config=shard_config)
+    infer_engine.shard_model_by(shardformer)
+
+    assert infer_engine.cache_manager is not None
+    assert infer_engine.tp_size == TP_SIZE
+    assert infer_engine.head_num == model_config.num_attention_heads // TP_SIZE
+
+    # TODO After adding forward replacement for CausalLM,
+    #      uncomment these lines to test sharded model generate
+    # generate_kwargs = dict(do_sample=False)
+    # infer_engine.generate(input_ids, generate_kwargs)
+
+    torch.cuda.empty_cache()
+
+
+def check_engine(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_engine_infer():
+    spawn(check_engine, TP_SIZE)
+
+
+if __name__ == '__main__':
+    test_orig_generate()
+    test_engine_infer()

tests/test_infer/test_kvcache_manager.py

@@ -3,8 +3,8 @@
 import pytest
 import torch
 
+from colossalai.inference.tensor_parallel import MemoryManager
 from colossalai.logging import disable_existing_loggers
-from colossalai.shardformer.inference import MemoryManager
 from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 BATCH_SIZE = 4