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

[Kernels]Updated Triton kernels into 2.1.0 and adding flash-decoding for llama token attention  (#4965)

* adding flash-decoding

* clean

* adding kernel

* adding flash-decoding

* add integration

* add

* adding kernel

* adding kernel

* adding triton 2.1.0 features for inference

* update bloom triton kernel

* remove useless vllm kernels

* clean codes

* fix

* adding files

* fix readme

* update llama flash-decoding

---------
Co-authored-by: cuiqing.li <lixx336@gmail.com>

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 @@ class TPInferEngine:
                 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 transformers.utils import logging
 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 @@ class BloomInferenceForwards:
             # 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

 from typing import List, Optional, Tuple
+import math
+import copy
 
 import torch
 from transformers.modeling_outputs import BaseModelOutputWithPast
@@ -10,24 +12,11 @@ from colossalai.kernel.triton.token_attention_kernel import Llama2TokenAttention
 
 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
@@ -35,6 +24,13 @@ 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 @@ class LlamaInferenceForwards:
             hidden_states=all_hidden_states,
             attentions=all_self_attns,
         )
-
+        
+    
     @staticmethod
     def llama_decoder_layer_forward(
         self: LlamaDecoderLayer,
@@ -247,6 +309,7 @@ class LlamaInferenceForwards:
             outputs += (present_key_value,)
 
         return outputs
+    
 
     @staticmethod
     def llama_flash_attn_kvcache_forward(
@@ -295,27 +358,8 @@ class LlamaInferenceForwards:
                 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 @@ class LlamaInferenceForwards:
                     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 @@ class LlamaInferenceForwards:
         # 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.base_policy import ModulePolicyDescription,
 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 @@ class LlamaModelInferPolicy(LlamaForCausalLMPolicy):
         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)}

colossalai/kernel/triton/context_attention.py

@@ -5,7 +5,6 @@ import torch
 try:
     import triton
     import triton.language as tl
-
     HAS_TRITON = True
 except ImportError:
     HAS_TRITON = False
@@ -155,39 +154,43 @@ if HAS_TRITON:
         num_warps = 4 if Lk <= 64 else 8
 
         tmp = torch.empty((batch, head, max_input_len + 256), device=q.device, dtype=torch.float32)
-
-        _context_flash_attention_kernel[grid](
-            q,
-            k,
-            v,
-            sm_scale,
-            b_start_loc,
-            b_seq_len,
-            tmp,
-            alibi,
-            o,
-            q.stride(0),
-            q.stride(1),
-            q.stride(2),
-            k.stride(0),
-            k.stride(1),
-            k.stride(2),
-            v.stride(0),
-            v.stride(1),
-            v.stride(2),
-            o.stride(0),
-            o.stride(1),
-            o.stride(2),
-            tmp.stride(0),
-            tmp.stride(1),
-            tmp.stride(2),
-            # manually setting this blcok num, we can use tuning config to futher speed-up
-            BLOCK_M=BLOCK,
-            BLOCK_DMODEL=Lk,
-            BLOCK_N=BLOCK,
-            num_warps=num_warps,
-            num_stages=1,
-        )
+        
+        if triton.__version__ < "2.1.0":
+            _context_flash_attention_kernel[grid](
+                q,
+                k,
+                v,
+                sm_scale,
+                b_start_loc,
+                b_seq_len,
+                tmp,
+                alibi,
+                o,
+                q.stride(0),
+                q.stride(1),
+                q.stride(2),
+                k.stride(0),
+                k.stride(1),
+                k.stride(2),
+                v.stride(0),
+                v.stride(1),
+                v.stride(2),
+                o.stride(0),
+                o.stride(1),
+                o.stride(2),
+                tmp.stride(0),
+                tmp.stride(1),
+                tmp.stride(2),
+                # manually setting this blcok num, we can use tuning config to futher speed-up
+                BLOCK_M=BLOCK,
+                BLOCK_DMODEL=Lk,
+                BLOCK_N=BLOCK,
+                num_warps=num_warps,
+                num_stages=1,
+            )
+        else:
+            raise Exception("Please install lightllm kernels from https://github.com/ModelTC/lightllm since your triton version is larger than 2.0.0")
+            
         return
 
     @torch.no_grad()
@@ -207,36 +210,40 @@ if HAS_TRITON:
         tmp = torch.empty((batch, head, max_input_len + 256), device=q.device, dtype=torch.float32)
         num_warps = 4 if Lk <= 64 else 8
         # num_warps = 4
-        _context_flash_attention_kernel[grid](
-            q,
-            k,
-            v,
-            sm_scale,
-            b_start_loc,
-            b_seq_len,
-            tmp,
-            None,
-            o,
-            q.stride(0),
-            q.stride(1),
-            q.stride(2),
-            k.stride(0),
-            k.stride(1),
-            k.stride(2),
-            v.stride(0),
-            v.stride(1),
-            v.stride(2),
-            o.stride(0),
-            o.stride(1),
-            o.stride(2),
-            tmp.stride(0),
-            tmp.stride(1),
-            tmp.stride(2),
-            BLOCK_M=BLOCK,
-            BLOCK_DMODEL=Lk,
-            BLOCK_N=BLOCK,
-            num_warps=num_warps,
-            num_stages=1,
-        )
         
+        if triton.__version__ < "2.1.0":
+            _context_flash_attention_kernel[grid](
+                q,
+                k,
+                v,
+                sm_scale,
+                b_start_loc,
+                b_seq_len,
+                tmp,
+                None,
+                o,
+                q.stride(0),
+                q.stride(1),
+                q.stride(2),
+                k.stride(0),
+                k.stride(1),
+                k.stride(2),
+                v.stride(0),
+                v.stride(1),
+                v.stride(2),
+                o.stride(0),
+                o.stride(1),
+                o.stride(2),
+                tmp.stride(0),
+                tmp.stride(1),
+                tmp.stride(2),
+                BLOCK_M=BLOCK,
+                BLOCK_DMODEL=Lk,
+                BLOCK_N=BLOCK,
+                num_warps=num_warps,
+                num_stages=1,
+            )
+        else:
+            raise Exception("Please install lightllm kernels from https://github.com/ModelTC/lightllm since your triton version is larger than 2.0.0")
+            
         return
\ No newline at end of file

examples/inference/bench_llama.py

@@ -105,8 +105,8 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("-p", "--path", type=str, help="Model path", required=True)
     parser.add_argument("-tp", "--tp_size", type=int, default=1, help="Tensor parallel size")
-    parser.add_argument("-b", "--batch_size", type=int, default=16, help="Maximum batch size")
-    parser.add_argument("--input_len", type=int, default=256, help="Maximum input length")
+    parser.add_argument("-b", "--batch_size", type=int, default=32, help="Maximum batch size")
+    parser.add_argument("--input_len", type=int, default=1024, help="Maximum input length")
     parser.add_argument("--output_len", type=int, default=128, help="Maximum output length")
     parser.add_argument(
         "--test_mode", type=str, help="Test mode", default="e2e_test", choices=["e2e_test", "decoder_test"]

tests/test_infer/test_bloom_infer.py

@@ -10,6 +10,12 @@ from colossalai.logging import disable_existing_loggers
 from colossalai.shardformer import ShardConfig
 from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
+try:
+    import lightllm 
+    HAS_LIGHTLLM_KERNEL = True
+except:
+    HAS_LIGHTLLM_KERNEL = False
+    
 TP_SIZE = 2
 MAX_BATCH_SIZE = 4
 MAX_INPUT_LEN = 16
@@ -52,7 +58,7 @@ def check_bloom(rank, world_size, port):
     run()
 
 
-@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
+@pytest.mark.skipif(not CUDA_SUPPORT or not HAS_LIGHTLLM_KERNEL, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 @clear_cache_before_run()

tests/test_infer/test_chatglm2_infer.py

@@ -12,6 +12,12 @@ from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import Ch
 from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration
 from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
+try:
+    import lightllm 
+    HAS_LIGHTLLM_KERNEL = True
+except:
+    HAS_LIGHTLLM_KERNEL = False
+    
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 TPSIZE = 1
 BATCH_SIZE = 8
@@ -61,7 +67,7 @@ def check_chatglm2(rank, world_size, port):
     run_chatglm2_test()
 
 
-@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
+@pytest.mark.skipif(not CUDA_SUPPORT or not HAS_LIGHTLLM_KERNEL, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 @clear_cache_before_run()

tests/test_infer/test_llama2_infer.py

@@ -12,6 +12,12 @@ from colossalai.logging import disable_existing_loggers
 from colossalai.shardformer import ShardConfig
 from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
+try:
+    import lightllm
+    HAS_LIGHTLLM_KERNEL = True
+except:
+    HAS_LIGHTLLM_KERNEL = False
+    
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 TPSIZE = 2
 BATCH_SIZE = 8
@@ -57,7 +63,7 @@ def check_llama(rank, world_size, port):
     run_llama_test()
 
 
-@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
+@pytest.mark.skipif(not CUDA_SUPPORT or not HAS_LIGHTLLM_KERNEL, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 @clear_cache_before_run()

tests/test_infer/test_llama_infer.py

@@ -12,6 +12,12 @@ from colossalai.logging import disable_existing_loggers
 from colossalai.shardformer import ShardConfig
 from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
+try:
+    import lightllm
+    HAS_LIGHTLLM_KERNEL = True
+except:
+    HAS_LIGHTLLM_KERNEL = False
+
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 TPSIZE = 2
 BATCH_SIZE = 8
@@ -55,7 +61,7 @@ def check_llama(rank, world_size, port):
     run_llama_test()
 
 
-@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
+@pytest.mark.skipif(not CUDA_SUPPORT or not HAS_LIGHTLLM_KERNEL, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 @clear_cache_before_run()

tests/test_infer_ops/cuda/test_vllm_rmsnorm.py

-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-import pytest
-import torch
-from torch import nn
-
-try:
-    from vllm import layernorm_ops
-
-    rms_norm = layernorm_ops.rms_norm
-    HAS_VLLM_KERNERL = True
-except:
-    print("please install vllm kernels to install rmsnorm")
-    print("install vllm from https://github.com/vllm-project/vllm to accelerate your inference")
-    HAS_VLLM_KERNERL = False
-
-
-class LlamaRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        LlamaRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-
-def cuda_rmsnorm_forward(hidden_states, weight, variance_epsilon):
-    x = hidden_states
-    out = torch.empty_like(x)
-    rms_norm(
-        out,
-        x,
-        weight,
-        variance_epsilon,
-    )
-    return out
-
-
-@pytest.mark.skipif(not HAS_VLLM_KERNERL, reason="You need to install llama supported cuda kernels to run this test")
-def test_rmsnorm():
-    data = torch.randn((1024, 64), dtype=torch.float16, device="cuda")
-    hg_rms = LlamaRMSNorm(64)
-    hg_rms = hg_rms.half().cuda()
-    out_torch = hg_rms(data)
-    out_cuda = cuda_rmsnorm_forward(data, hg_rms.weight.data, hg_rms.variance_epsilon)
-
-    check = torch.allclose(out_torch.cpu(), out_cuda.cpu(), rtol=1e-3, atol=1e-5)
-    assert check is True, "cuda rmsnorm forward is not matched with torch rmsnorm forward"
-
-
-if __name__ == "__main__":
-    test_rmsnorm()

tests/test_infer_ops/cuda/test_vllm_rotary_embedding.py

-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-from typing import Tuple
-
-import pytest
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, rotate_half
-
-try:
-    from vllm import pos_encoding_ops
-
-    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")
-    HAS_VLLM_KERNERL = False
-
-
-def rotate_half(x: torch.Tensor) -> torch.Tensor:
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    cos: torch.Tensor,
-    sin: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class RefRotaryEmbeddingNeox(nn.Module):
-    """Reference implementation of the GPT-NeoX style rotary embedding."""
-
-    def __init__(
-        self,
-        dim: int,
-        max_position_embeddings: int = 2048,
-        base: int = 10000,
-    ) -> None:
-        super().__init__()
-        self.rotary_dim = dim
-        self.max_position_embeddings = max_position_embeddings
-
-        # Create cos and sin embeddings.
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2) / dim))
-        t = torch.arange(max_position_embeddings).float()
-        freqs = torch.einsum("i,j->ij", t, inv_freq.float())
-        emb = torch.cat((freqs, freqs), dim=-1)
-        cos = emb.cos().to(dtype=inv_freq.dtype)
-        sin = emb.sin().to(dtype=inv_freq.dtype)
-        self.register_buffer("cos_cached", cos, persistent=False)
-        self.register_buffer("sin_cached", sin, persistent=False)
-
-    def forward(
-        self,
-        positions: torch.Tensor,  # [num_tokens]
-        query: torch.Tensor,  # [num_tokens, num_heads, head_size]
-        key: torch.Tensor,  # [num_tokens, num_heads, head_size]
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        query_rot = query[..., : self.rotary_dim]
-        query_pass = query[..., self.rotary_dim :]
-        key_rot = key[..., : self.rotary_dim]
-        key_pass = key[..., self.rotary_dim :]
-
-        query_rot = query_rot.transpose(0, 1)
-        key_rot = key_rot.transpose(0, 1)
-        cos = F.embedding(positions, self.cos_cached)
-        sin = F.embedding(positions, self.sin_cached)
-        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin)
-        query_rot = query_rot.transpose(0, 1).contiguous()
-        key_rot = key_rot.transpose(0, 1).contiguous()
-
-        query = torch.cat((query_rot, query_pass), dim=-1)
-        key = torch.cat((key_rot, key_pass), dim=-1)
-
-        # Output query/key shape: [num_tokens, num_tokens, head_size]
-        return query, key
-
-
-def run_rotary_embedding_neox(
-    num_tokens: int,
-    num_heads: int,
-    head_size: int,
-    max_position: int,
-    rotary_dim: int,
-    dtype: torch.dtype,
-    base: int = 10000,
-) -> None:
-    positions = torch.randint(0, max_position, (num_tokens,), device="cuda")
-    query = torch.randn(num_tokens, num_heads * head_size, dtype=dtype, device="cuda")
-    key = torch.randn(num_tokens, num_heads * head_size, dtype=dtype, device="cuda")
-
-    # Create the rotary embedding.
-    inv_freq = 1.0 / (base ** (torch.arange(0, rotary_dim, 2) / rotary_dim))
-    t = torch.arange(max_position).float()
-    freqs = torch.einsum("i,j -> ij", t, inv_freq.float())
-    cos = freqs.cos()
-    sin = freqs.sin()
-    cos_sin_cache = torch.cat((cos, sin), dim=-1)
-    cos_sin_cache = cos_sin_cache.to(dtype=dtype, device="cuda")
-
-    # Run the kernel. The kernel is in-place, so we need to clone the inputs.
-    out_query = query.clone()
-    out_key = key.clone()
-    rotary_embedding_neox(
-        positions,
-        out_query,
-        out_key,
-        head_size,
-        cos_sin_cache,
-    )
-
-    # Run the reference implementation.
-    ref_rotary_embedding = RefRotaryEmbeddingNeox(
-        dim=rotary_dim,
-        max_position_embeddings=max_position,
-        base=base,
-    ).to(dtype=dtype, device="cuda")
-    ref_query, ref_key = ref_rotary_embedding(
-        positions,
-        query.view(num_tokens, num_heads, head_size),
-        key.view(num_tokens, num_heads, head_size),
-    )
-    ref_query = ref_query.view(num_tokens, num_heads * head_size)
-    ref_key = ref_key.view(num_tokens, num_heads * head_size)
-
-    # Compare the results.
-    assert torch.allclose(out_query, ref_query, atol=1e-3, rtol=1e-5)
-    assert torch.allclose(out_key, ref_key, atol=1e-3, rtol=1e-5)
-
-
-@pytest.mark.skipif(not HAS_VLLM_KERNERL, reason="You need to install llama supported cuda kernels to run this test")
-def test_rotary_embedding():
-    run_rotary_embedding_neox(
-        num_tokens=1024,
-        num_heads=8,
-        head_size=64,
-        max_position=8192,
-        rotary_dim=64,
-        dtype=torch.float16,
-    )
-
-
-if __name__ == "__main__":
-    test_rotary_embedding()