Revert "[Inference] Hot fix some bugs and typos"

colossalai/inference/tensor_parallel/engine.py

@@ -36,11 +36,11 @@ def __init__(self,
         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 and model
-        # This may change into an optional arg in the future
+        # 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 <= 4096, "Max length exceeds the constraint"
+        assert self.max_input_len + self.max_output_len <= 2048, "Max length exceeds the constraint"
 
+        torch.device(device=device)
         self.dtype = dtype
 
         self.head_dim = self.model.config.hidden_size // self.model.config.num_attention_heads
@@ -94,20 +94,20 @@ def shard_model_by(self, shardformer: ShardFormer) -> None:
     def _supported_models() -> List[str]:
         return _supported_models
 
-    def generate(self, input_tokens: Union[BatchEncoding, dict, list, torch.Tensor], **generate_kwargs) -> torch.Tensor:
+    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))
         for t in input_tokens:
             if torch.is_tensor(input_tokens[t]):
                 input_tokens[t] = input_tokens[t].cuda()
 
         if self.sharded_model is not None:
-            return self.generate_by_set_infer_state(input_tokens, **generate_kwargs)
+            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:
+    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
 
@@ -191,9 +191,8 @@ def prepare_batch_state(self, inputs) -> BatchInferState:
                 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:
-            length = max(len(input_id) for input_id in input_ids_list)
             for i, input_ids in enumerate(input_ids_list):
-                curr_seq_len = length
+                curr_seq_len = len(input_ids)
                 seq_lengths[i] = curr_seq_len
                 seq_start_indexes[i] = start_index
                 start_index += curr_seq_len

colossalai/inference/tensor_parallel/kvcache_manager.py

@@ -3,7 +3,8 @@
 # https://github.com/ModelTC/lightllm/blob/050af3ce65edca617e2f30ec2479397d5bb248c9/lightllm/common/mem_manager.py
 
 import torch
-from transformers.utils import logging
+
+from colossalai.logging import get_dist_logger
 
 
 class MemoryManager:
@@ -26,7 +27,7 @@ def __init__(self,
                  head_dim: int,
                  layer_num: int,
                  device: torch.device = torch.device('cuda')):
-        self.logger = logging.get_logger(__name__)
+        self.logger = get_dist_logger(__name__)
         self.available_size = size
         self.past_key_values_length = 0
         self._init_mem_states(size, device)

colossalai/shardformer/modeling/llama.py

@@ -7,9 +7,16 @@
     CausalLMOutputWithPast,
     SequenceClassifierOutputWithPast,
 )
-from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel
+from transformers.models.llama.modeling_llama import (
+    LlamaAttention,
+    LlamaForCausalLM,
+    LlamaForSequenceClassification,
+    LlamaModel,
+    apply_rotary_pos_emb,
+)
 from transformers.utils import logging
 
+from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention
 from colossalai.pipeline.stage_manager import PipelineStageManager
 
 
@@ -395,10 +402,6 @@ def get_llama_flash_attention_forward():
 
     from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention
 
-    from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb
-
-    from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention
-
     def forward(
         self: LlamaAttention,
         hidden_states: torch.Tensor,

colossalai/shardformer/shard/shard_config.py

@@ -29,8 +29,6 @@ class ShardConfig:
     enable_flash_attention: bool = False
     enable_jit_fused: bool = False
     inference_only: bool = False
-    enable_sequence_parallelism: bool = False
-    enable_sequence_overlap: bool = False
 
     # pipeline_parallel_size: int
     # data_parallel_size: int
@@ -60,8 +58,6 @@ def _turn_on_all_optimization(self):
         self.enable_fused_normalization = True
         self.enable_flash_attention = True
         self.enable_jit_fused = True
-        self.enable_sequence_parallelism = True
-        self.enable_sequence_overlap = True
 
     def _infer(self):
         """

tests/test_infer/test_infer_engine.py

@@ -1,9 +1,9 @@
+import pytest
 from itertools import accumulate
+from packaging import version
 
-import pytest
 import torch
 import torch.nn as nn
-from packaging import version
 from transformers import BloomConfig, BloomForCausalLM, LlamaConfig, LlamaForCausalLM
 from transformers.tokenization_utils_base import BatchEncoding
 
@@ -21,7 +21,6 @@
 
 CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.5')
 
-
 @pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
 def test_prepare_data():
     # dummy module used for testing
@@ -55,24 +54,22 @@ def __init__(self):
         attention_mask[i][max_seq_len - len(li):] = [1 for _ in range(len(li))]
     data = dict(input_ids=input_ids_list, attention_mask=attention_mask)
     inputs_batch_encoding = BatchEncoding(data=data)
+
     seq_lengths = [len(li) for li in input_ids_list]
     start_loc = list(accumulate([0] + seq_lengths[:-1]))
     seq_lengths = torch.tensor(seq_lengths, dtype=torch.int32)
     start_loc = torch.tensor(start_loc, dtype=torch.int32)
+
     # input token id list as inputs
     batch_state_out1 = infer_engine.prepare_batch_state(inputs_batch_encoding)
     # BatchEncoding as inputs
     batch_state_out2 = infer_engine.prepare_batch_state(input_ids_list)
 
     assert batch_state_out1.batch_size == batch_state_out2.batch_size == batch_size
-    assert torch.equal(batch_state_out1.seq_len, batch_state_out2.seq_len)
-
-    # The following tests are discarded for now, and will be reused after all features are added
-    # assert torch.equal(batch_state_out1.seq_len.to(seq_lengths.device), seq_lengths)
-    # assert torch.equal(batch_state_out2.seq_len.to(seq_lengths.device), seq_lengths)
-    # assert torch.equal(batch_state_out1.start_loc.to(start_loc.device), start_loc)
-    # assert torch.equal(batch_state_out2.start_loc.to(start_loc.device), start_loc)
-
+    assert torch.equal(batch_state_out1.seq_len.to(seq_lengths.device), seq_lengths)
+    assert torch.equal(batch_state_out2.seq_len.to(seq_lengths.device), seq_lengths)
+    assert torch.equal(batch_state_out1.start_loc.to(start_loc.device), start_loc)
+    assert torch.equal(batch_state_out2.start_loc.to(start_loc.device), start_loc)
 
 @pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
 def test_orig_generate():
@@ -91,7 +88,7 @@ def test_orig_generate():
 
     # original model generate
     generate_kwargs = dict(do_sample=False)
-    infer_engine.generate(input_ids, **generate_kwargs)
+    infer_engine.generate(input_ids, generate_kwargs)
 
     torch.cuda.empty_cache()