[Refactor] Integrated some lightllm kernels into token-attention (hpc…

…aitech#4946) * add some req for inference * clean codes * add codes * add some lightllm deps * clean codes * hello * delete rms files * add some comments * add comments * add doc * add lightllm deps * add lightllm cahtglm2 kernels * add lightllm cahtglm2 kernels * replace rotary embedding with lightllm kernel * add some commnets * add some comments * add some comments * add * replace fwd kernel att1 * fix a arg * add * add * fix token attention * add some comments * clean codes * modify comments * fix readme * fix bug * fix bug --------- Co-authored-by: cuiqing.li <[email protected]> Co-authored-by: CjhHa1 <[email protected]>
flybird11111 · Nov 10, 2023 · 3e81321 · 3e81321
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diff --git a/colossalai/inference/README.md b/colossalai/inference/README.md
@@ -4,7 +4,7 @@
 
 ## Introduction
 
-`Colossal Inference` is a module that contains colossal-ai designed inference framework, featuring high performance, steady and easy usability. `Colossal Inference` incorporated the advantages of the latest open-source inference systems, including TGI, vLLM, FasterTransformer, LightLLM and flash attention. while combining the design of Colossal AI, especially Shardformer, to reduce the learning curve for users.
+`Colossal Inference` is a module that contains colossal-ai designed inference framework, featuring high performance, steady and easy usability. `Colossal Inference` incorporated the advantages of the latest open-source inference systems, including LightLLM, TGI, vLLM, FasterTransformer and flash attention. while combining the design of Colossal AI, especially Shardformer, to reduce the learning curve for users.
 
 ## Design
 
@@ -62,6 +62,12 @@ triton==2.0.0.dev20221202
 vllm
 # for install flash-attention, please use commit hash: 67ae6fd74b4bc99c36b2ce524cf139c35663793c
 flash-attention
+
+# install lightllm since we depend on lightllm triton kernels
+git clone https://github.com/ModelTC/lightllm 
+git checkout 28c1267cfca536b7b4f28e921e03de735b003039
+cd lightllm
+pip3 install -e .
 ```
 
 ### Docker
@@ -73,6 +79,17 @@ You can use docker run to use docker container to set-up environment
 docker pull hpcaitech/colossalai-inference:v2
 docker run -it --gpus all --name ANY_NAME -v $PWD:/workspace -w /workspace hpcaitech/colossalai-inference:v2 /bin/bash
 
+# enter into docker container
+cd /path/to/CollossalAI
+pip install -e .
+
+# install lightllm
+git clone https://github.com/ModelTC/lightllm 
+git checkout 28c1267cfca536b7b4f28e921e03de735b003039
+cd lightllm
+pip3 install -e .
+
+
 ```
 
 ### Dive into fast-inference!

diff --git a/colossalai/inference/tensor_parallel/batch_infer_state.py b/colossalai/inference/tensor_parallel/batch_infer_state.py
@@ -5,7 +5,7 @@
 
 from .kvcache_manager import MemoryManager
 
-
+# adapted from: lightllm/server/router/model_infer/infer_batch.py
 @dataclass
 class BatchInferState:
     r"""
@@ -41,6 +41,7 @@ def total_token_num(self):
     def set_cache_manager(self, manager: MemoryManager):
         self.cache_manager = manager
 
+    # adapted from: https://github.com/ModelTC/lightllm/blob/28c1267cfca536b7b4f28e921e03de735b003039/lightllm/common/infer_utils.py#L1
     @staticmethod
     def init_block_loc(
         b_loc: torch.Tensor, seq_len: torch.Tensor, max_len_in_batch: int, alloc_mem_index: torch.Tensor

diff --git a/colossalai/inference/tensor_parallel/kvcache_manager.py b/colossalai/inference/tensor_parallel/kvcache_manager.py
@@ -1,7 +1,9 @@
-# Adapted from lightllm/common/mem_manager.py
-# of the ModelTC/lightllm GitHub repository
-# https://github.com/ModelTC/lightllm/blob/050af3ce65edca617e2f30ec2479397d5bb248c9/lightllm/common/mem_manager.py
-
+"""
+Refered/Modified from lightllm/common/mem_manager.py
+of the ModelTC/lightllm GitHub repository
+https://github.com/ModelTC/lightllm/blob/050af3ce65edca617e2f30ec2479397d5bb248c9/lightllm/common/mem_manager.py
+we slightly changed it to make it suitable for our colossal-ai shardformer TP-engine design.
+"""
 import torch
 from transformers.utils import logging
 

diff --git a/colossalai/inference/tensor_parallel/modeling/chatglm2.py b/colossalai/inference/tensor_parallel/modeling/chatglm2.py
@@ -6,8 +6,6 @@
 from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 
 from colossalai.inference.tensor_parallel.batch_infer_state import BatchInferState
-from colossalai.kernel.triton.context_attention import llama2_context_attn_fwd
-from colossalai.kernel.triton.rotary_embedding_kernel import Llama2Forwards
 from colossalai.kernel.triton.token_attention_kernel import Llama2TokenAttentionForwards
 from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import (
     ChatGLMForConditionalGeneration,
@@ -20,6 +18,14 @@
 
 from ._utils import copy_kv_to_mem_cache
 
+try:
+    from lightllm.models.llama2.triton_kernel.context_flashattention_nopad import context_attention_fwd as lightllm_llama2_context_attention_fwd
+    from lightllm.models.chatglm2.triton_kernel.rotary_emb import rotary_emb_fwd as chatglm2_rotary_emb_fwd
+    HAS_LIGHTLLM_KERNEL = True
+except:
+    print("please install lightllm from source to run inference: https://github.com/ModelTC/lightllm")
+    HAS_LIGHTLLM_KERNEL = False
+
 
 # This func is same as Llama model init_to_get_rotary, we should move them into _utils.py
 def _init_to_get_rotary(self, base=10000):
@@ -433,17 +439,17 @@ def chatglm_flash_attn_kvcache_forward(
 
         cos, sin = infer_state.position_cos, infer_state.position_sin
 
-        Llama2Forwards.rotary_emb_fwd(
+        chatglm2_rotary_emb_fwd(
             query_layer.view(-1, self.num_attention_heads_per_partition, self.hidden_size_per_attention_head), cos, sin
         )
         if self.multi_query_attention:
-            Llama2Forwards.rotary_emb_fwd(
+            chatglm2_rotary_emb_fwd(
                 key_layer.view(-1, self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head),
                 cos,
                 sin,
             )
         else:
-            Llama2Forwards.rotary_emb_fwd(
+            chatglm2_rotary_emb_fwd(
                 key_layer.view(-1, self.num_attention_heads_per_partition, self.hidden_size_per_attention_head),
                 cos,
                 sin,
@@ -474,7 +480,7 @@ def chatglm_flash_attn_kvcache_forward(
             attn_output = torch.empty_like(query_layer.view(-1, self.projection_size))
 
             # NOTE: no bug in context attn fwd (del it )
-            llama2_context_attn_fwd(
+            lightllm_llama2_context_attention_fwd(
                 query_layer,
                 key_layer,
                 value_layer,

diff --git a/colossalai/inference/tensor_parallel/modeling/llama.py b/colossalai/inference/tensor_parallel/modeling/llama.py
@@ -5,12 +5,7 @@
 from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel, LlamaRMSNorm
 
 from colossalai.inference.tensor_parallel.batch_infer_state import BatchInferState
-from colossalai.kernel.triton import (
-    llama2_context_attn_fwd,
-    llama_context_attn_fwd,
-    rotary_embedding_fwd,
-    token_attention_fwd,
-)
+from colossalai.kernel.triton import llama_context_attn_fwd, token_attention_fwd
 from colossalai.kernel.triton.token_attention_kernel import Llama2TokenAttentionForwards
 
 from ._utils import copy_kv_to_mem_cache
@@ -29,6 +24,17 @@
     )
     HAS_VLLM_KERNERL = False
 
+try:
+    from lightllm.models.llama2.triton_kernel.context_flashattention_nopad import (
+        context_attention_fwd as lightllm_llama2_context_attention_fwd,
+    )
+    from lightllm.models.llama.triton_kernel.rotary_emb import rotary_emb_fwd as llama_rotary_embedding_fwd
+
+    HAS_LIGHTLLM_KERNEL = True
+except:
+    print("please install lightllm from source to run inference: https://github.com/ModelTC/lightllm")
+    HAS_LIGHTLLM_KERNEL = False
+
 
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
@@ -280,8 +286,8 @@ def llama_flash_attn_kvcache_forward(
         cos, sin = infer_state.position_cos, infer_state.position_sin
         # print("shape ", cos.shape, query_states.view(-1, self.num_heads, self.head_dim).shape, )
 
-        rotary_embedding_fwd(query_states.view(-1, self.num_heads, self.head_dim), cos, sin)
-        rotary_embedding_fwd(key_states.view(-1, self.num_key_value_heads, self.head_dim), cos, sin)
+        llama_rotary_embedding_fwd(query_states.view(-1, self.num_heads, self.head_dim), cos, sin)
+        llama_rotary_embedding_fwd(key_states.view(-1, self.num_key_value_heads, self.head_dim), cos, sin)
 
         query_states = query_states.reshape(-1, self.num_heads, self.head_dim)
         key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim)
@@ -312,7 +318,7 @@ def llama_flash_attn_kvcache_forward(
                     infer_state.cache_manager.past_key_values_length,
                 )
             else:
-                llama2_context_attn_fwd(
+                lightllm_llama2_context_attention_fwd(
                     query_states,
                     key_states,
                     value_states,
@@ -371,6 +377,7 @@ def llama_flash_attn_kvcache_forward(
                     infer_state.cache_manager.past_key_values_length,
                     infer_state.other_kv_index,
                 )
+
         attn_output = attn_output.view(bsz, q_len, self.hidden_size)
 
         attn_output = self.o_proj(attn_output)

diff --git a/colossalai/inference/tensor_parallel/policies/llama.py b/colossalai/inference/tensor_parallel/policies/llama.py
@@ -12,8 +12,7 @@
 from ..modeling.llama import LlamaInferenceForwards, get_llama_vllm_rmsnorm_forward
 
 try:
-    from colossalai.kernel.triton import rmsnorm_forward
-
+    from lightllm.models.llama.triton_kernel.rmsnorm import rmsnorm_forward as lightllm_rmsnorm_forward
     HAS_TRITON_RMSNORM = True
 except:
     print("you should install triton from https://github.com/openai/triton")
@@ -22,9 +21,8 @@
 
 def get_triton_rmsnorm_forward():
     if HAS_TRITON_RMSNORM:
-
         def _triton_rmsnorm_forward(self: LlamaRMSNorm, hidden_states: torch.Tensor):
-            return rmsnorm_forward(hidden_states, self.weight.data, self.variance_epsilon)
+            return lightllm_rmsnorm_forward(hidden_states, self.weight.data, self.variance_epsilon)
 
         return _triton_rmsnorm_forward
     else:

diff --git a/colossalai/kernel/triton/__init__.py b/colossalai/kernel/triton/__init__.py
@@ -9,26 +9,21 @@
 
 # There may exist import error even if we have triton installed.
 if HAS_TRITON:
-    from .context_attention import bloom_context_attn_fwd, llama2_context_attn_fwd, llama_context_attn_fwd
+    from .context_attention import bloom_context_attn_fwd, llama_context_attn_fwd
     from .copy_kv_cache_dest import copy_kv_cache_to_dest
     from .fused_layernorm import layer_norm
     from .gptq_triton import gptq_fused_linear_triton
     from .int8_rotary_embedding_kernel import int8_rotary_embedding_fwd
-    from .rms_norm import rmsnorm_forward
-    from .rotary_embedding_kernel import rotary_embedding_fwd
     from .smooth_attention import smooth_llama_context_attn_fwd, smooth_token_attention_fwd
     from .softmax import softmax
     from .token_attention_kernel import token_attention_fwd
 
     __all__ = [
         "llama_context_attn_fwd",
-        "llama2_context_attn_fwd",
         "bloom_context_attn_fwd",
         "softmax",
         "layer_norm",
-        "rmsnorm_forward",
         "copy_kv_cache_to_dest",
-        "rotary_embedding_fwd",
         "token_attention_fwd",
         "gptq_fused_linear_triton",
         "int8_rotary_embedding_fwd",