[Kernels]Updated Triton kernels into 2.1.0 and adding flash-decoding for llama token attention

colossalai/inference/README.md

@@ -34,11 +34,13 @@ In this section we discuss how the colossal inference works and integrates with
   - [x] policy
   - [x] context forward
   - [x] token forward
+  - [x] support flash-decoding
 - [ ] Replace the kernels with `faster-transformer` in token-forward stage
 - [ ] Support all models
   - [x] Llama
+  - [x] Llama-2
   - [x] Bloom
-  - [ ] Chatglm2
+  - [x] Chatglm2
 - [ ] Benchmarking for all models
 
 ## Get started
@@ -68,6 +70,12 @@ git clone https://github.com/ModelTC/lightllm
 git checkout 28c1267cfca536b7b4f28e921e03de735b003039
 cd lightllm
 pip3 install -e .
+
+# also, install xformers from source: 
+pip install ninja
+# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types
+pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
+
 ```
 
 ### Docker
@@ -89,7 +97,10 @@ git checkout 28c1267cfca536b7b4f28e921e03de735b003039
 cd lightllm
 pip3 install -e .
 
-
+# install xformers from source 
+pip install ninja
+# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types
+pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers 
 ```
 
 ### Dive into fast-inference!

colossalai/inference/tensor_parallel/engine.py

@@ -311,6 +311,7 @@ def prepare_batch_state(self, inputs) -> BatchInferState:
                 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
+
         block_loc = torch.empty((batch_size, self.max_input_len + self.max_output_len), dtype=torch.long, device="cuda")
         batch_infer_state = BatchInferState(batch_size, max_len_in_batch)
         batch_infer_state.seq_len = seq_lengths.to("cuda")

colossalai/inference/tensor_parallel/modeling/bloom.py

@@ -19,6 +19,12 @@
 from colossalai.inference.tensor_parallel.batch_infer_state import BatchInferState
 from colossalai.kernel.triton import bloom_context_attn_fwd, copy_kv_cache_to_dest, token_attention_fwd
 
+try:
+    from lightllm.models.bloom.triton_kernel.context_flashattention_nopad import context_attention_fwd as lightllm_bloom_context_attention_fwd
+    HAS_LIGHTLLM_KERNEL = True
+except:
+    HAS_LIGHTLLM_KERNEL = False
+
 
 def generate_alibi(n_head, dtype=torch.float16):
     """
@@ -460,7 +466,10 @@ def bloom_attention_forward(
             # output = self.output[:batch_size*q_length, :, :]
             output = torch.empty_like(q)
 
-            bloom_context_attn_fwd(q, k, v, output, b_start_loc, b_seq_len, max_input_len, alibi)
+            if HAS_LIGHTLLM_KERNEL:
+                lightllm_bloom_context_attention_fwd(q, k, v, output, alibi, b_start_loc, b_seq_len, max_input_len)
+            else:
+                bloom_context_attn_fwd(q, k, v, output, b_start_loc, b_seq_len, max_input_len, alibi)
 
             context_layer = output.view(batch_size, q_length, H * D_HEAD)
         else:

colossalai/inference/tensor_parallel/modeling/llama.py

@@ -1,4 +1,6 @@
 from typing import List, Optional, Tuple
+import math
+import copy
 
 import torch
 from transformers.modeling_outputs import BaseModelOutputWithPast
@@ -10,31 +12,25 @@
 
 from ._utils import copy_kv_to_mem_cache
 
-try:
-    from vllm import layernorm_ops, pos_encoding_ops
-
-    rms_norm = layernorm_ops.rms_norm
-    rotary_embedding_neox = pos_encoding_ops.rotary_embedding_neox
-    HAS_VLLM_KERNERL = True
-except:
-    print("fall back to original rotary_embedding_neox of huggingface")
-    print("install vllm from https://github.com/vllm-project/vllm to accelerate your inference")
-    print(
-        "if falied to install vllm, please use this branch to install: https://github.com/tiandiao123/vllm/tree/setup_branch"
-    )
-    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.context_flashattention_nopad import context_attention_fwd as lightllm_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
 
+try:
+    from flash_attn import flash_attn_with_kvcache
+    HAS_FLASH_KERNEL = True
+except:
+    HAS_FLASH_KERNEL = False
+    print("please install flash attentiom from https://github.com/Dao-AILab/flash-attention")
+
 
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
@@ -54,6 +50,71 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
     k_embed = (k * cos) + (rotate_half(k) * sin)
     return q_embed, k_embed
 
+def llama_triton_context_attention(query_states, key_states, value_states, attn_output, infer_state, num_key_value_groups=1):
+    if num_key_value_groups == 1:
+        if HAS_LIGHTLLM_KERNEL is False:
+            llama_context_attn_fwd(
+                query_states,
+                key_states,
+                value_states,
+                attn_output,
+                infer_state.start_loc,
+                infer_state.seq_len,
+                # infer_state.cache_manager.past_key_values_length,
+                infer_state.max_len_in_batch,
+            )
+        else:
+            lightllm_context_attention_fwd(
+                query_states,
+                key_states,
+                value_states,
+                attn_output,
+                infer_state.start_loc,
+                infer_state.seq_len,
+                # infer_state.cache_manager.past_key_values_length,
+                infer_state.max_len_in_batch,
+            )
+    else:
+        assert HAS_LIGHTLLM_KERNEL is True, "You have to install lightllm kernels to run llama2 model"
+        lightllm_llama2_context_attention_fwd(
+            query_states,
+            key_states,
+            value_states,
+            attn_output,
+            infer_state.start_loc,
+            infer_state.seq_len,
+            # infer_state.cache_manager.past_key_values_length,
+            infer_state.max_len_in_batch,
+        )
+
+def llama_triton_token_attention(query_states, attn_output, infer_state, num_key_value_groups=1):
+    assert HAS_LIGHTLLM_KERNEL is True, "You have to install lightllm kernel to run token attention for llama models"
+    if num_key_value_groups == 1:
+        token_attention_fwd(
+            query_states,
+            infer_state.cache_manager.key_buffer[infer_state.decode_layer_id],
+            infer_state.cache_manager.value_buffer[infer_state.decode_layer_id],
+            attn_output,
+            infer_state.block_loc,
+            infer_state.start_loc,
+            infer_state.seq_len,
+            # infer_state.cache_manager.past_key_values_length,
+            infer_state.max_len_in_batch,
+        )
+    else:
+        Llama2TokenAttentionForwards.token_attn(
+            query_states,
+            infer_state.cache_manager.key_buffer[infer_state.decode_layer_id],
+            infer_state.cache_manager.value_buffer[infer_state.decode_layer_id],
+            attn_output,
+            infer_state.block_loc,
+            infer_state.start_loc,
+            infer_state.seq_len,
+            # infer_state.cache_manager.past_key_values_length,
+            infer_state.max_len_in_batch,
+            infer_state.other_kv_index,
+        )
+
 
 class LlamaInferenceForwards:
     """
@@ -204,7 +265,8 @@ def llama_model_forward(
             hidden_states=all_hidden_states,
             attentions=all_self_attns,
         )
-
+
+
     @staticmethod
     def llama_decoder_layer_forward(
         self: LlamaDecoderLayer,
@@ -247,6 +309,7 @@ def llama_decoder_layer_forward(
             outputs += (present_key_value,)
 
         return outputs
+
 
     @staticmethod
     def llama_flash_attn_kvcache_forward(
@@ -295,27 +358,8 @@ def llama_flash_attn_kvcache_forward(
                 infer_state.cache_manager,
             )
             attn_output = torch.empty_like(query_states)
-
-            if self.num_key_value_groups == 1:
-                llama_context_attn_fwd(
-                    query_states,
-                    key_states,
-                    value_states,
-                    attn_output,
-                    infer_state.start_loc,
-                    infer_state.seq_len,
-                    infer_state.max_len_in_batch,
-                )
-            else:
-                lightllm_llama2_context_attention_fwd(
-                    query_states,
-                    key_states,
-                    value_states,
-                    attn_output,
-                    infer_state.start_loc,
-                    infer_state.seq_len,
-                    infer_state.max_len_in_batch,
-                )
+
+            llama_triton_context_attention(query_states, key_states, value_states, attn_output, infer_state, num_key_value_groups=self.num_key_value_groups)
         else:
             if infer_state.decode_is_contiguous:
                 # if decode is contiguous, then we copy to key cache and value cache in cache manager directly
@@ -337,35 +381,26 @@ def llama_flash_attn_kvcache_forward(
                     infer_state.decode_mem_index,
                     infer_state.cache_manager,
                 )
-
-            # second token and follows
-            # kv = torch.stack((key_states, value_states), dim=2)
-            # (batch_size, seqlen, nheads, headdim)
-            attn_output = torch.empty_like(query_states)
-
-            if self.num_key_value_groups == 1:
-                token_attention_fwd(
-                    query_states,
-                    infer_state.cache_manager.key_buffer[infer_state.decode_layer_id],
-                    infer_state.cache_manager.value_buffer[infer_state.decode_layer_id],
-                    attn_output,
-                    infer_state.block_loc,
-                    infer_state.start_loc,
-                    infer_state.seq_len,
-                    infer_state.max_len_in_batch,
-                )
+
+            HAS_LIGHTLLM_KERNEL = False
+            if HAS_LIGHTLLM_KERNEL:
+                attn_output = torch.empty_like(query_states)
+                llama_triton_token_attention(query_states, attn_output, infer_state, num_key_value_groups=self.num_key_value_groups)
             else:
-                Llama2TokenAttentionForwards.token_attn(
-                    query_states,
-                    infer_state.cache_manager.key_buffer[infer_state.decode_layer_id],
-                    infer_state.cache_manager.value_buffer[infer_state.decode_layer_id],
-                    attn_output,
-                    infer_state.block_loc,
-                    infer_state.start_loc,
-                    infer_state.seq_len,
-                    infer_state.max_len_in_batch,
-                    infer_state.other_kv_index,
-                )
+                heads_per_group = self.num_heads // self.num_key_value_heads
+                cache_k = infer_state.cache_manager.key_buffer[infer_state.decode_layer_id]
+                cache_v = infer_state.cache_manager.value_buffer[infer_state.decode_layer_id]
+
+                query_states = query_states.view(bsz, -1, self.num_heads, self.head_dim)
+                copy_cache_k = cache_k.view(bsz, -1, self.num_key_value_heads, self.head_dim)
+                copy_cache_v = cache_v.view(bsz, -1, self.num_key_value_heads, self.head_dim)
+
+                attn_output = flash_attn_with_kvcache(q = query_states, 
+                                                      k_cache = copy_cache_k, 
+                                                      v_cache = copy_cache_v, 
+                                                      softmax_scale = 1/ math.sqrt(self.head_dim), 
+                                                      causal = True)       
+
 
         attn_output = attn_output.view(bsz, q_len, self.hidden_size)
 
@@ -374,22 +409,3 @@ def llama_flash_attn_kvcache_forward(
         # return past_key_value as None
         return attn_output, None, None
 
-
-def get_llama_vllm_rmsnorm_forward():
-    if HAS_VLLM_KERNERL:
-
-        def _vllm_rmsnorm_forward(self: LlamaRMSNorm, hidden_states: torch.Tensor):
-            x = hidden_states
-            out = torch.empty_like(x)
-            rms_norm(
-                out,
-                x,
-                self.weight.data,
-                self.variance_epsilon,
-            )
-
-            return out
-
-        return _vllm_rmsnorm_forward
-    else:
-        return None

colossalai/inference/tensor_parallel/policies/llama.py

@@ -9,7 +9,7 @@
 from colossalai.shardformer.policies.llama import LlamaForCausalLMPolicy
 
 from ..modeling._utils import init_to_get_rotary
-from ..modeling.llama import LlamaInferenceForwards, get_llama_vllm_rmsnorm_forward
+from ..modeling.llama import LlamaInferenceForwards
 
 try:
     from lightllm.models.llama.triton_kernel.rmsnorm import rmsnorm_forward as lightllm_rmsnorm_forward
@@ -105,9 +105,6 @@ def module_policy(self):
         infer_forward = None
         if HAS_TRITON_RMSNORM:
             infer_forward = get_triton_rmsnorm_forward()
-        else:
-            # NOTE: adding rms_norm from cuda kernels caused precision issue, fix @tiandiao123
-            infer_forward = get_llama_vllm_rmsnorm_forward()
 
         if infer_forward is not None:
             method_replacement = {"forward": partial(infer_forward)}