optimize paged attention on triton3

.github/workflows/unit-test.yml

@@ -53,7 +53,7 @@ jobs:
       - name: Install pytorch
         run: |
           python3 -m pip cache dir
-          python3 -m pip install torch==2.1.0 torchvision==0.16.0 --index-url https://download.pytorch.org/whl/cu118
+          python3 -m pip install torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
       - name: Build lmdeploy
         run: |
           python3 -m pip install cmake
@@ -77,7 +77,7 @@ jobs:
         run: |
           python3 -m pip install pynvml packaging protobuf transformers_stream_generator
           # manually install flash attn
-          python3 -m pip install /root/packages/flash_attn-2.3.6+cu118torch2.1cxx11abiFALSE-cp38-cp38-linux_x86_64.whl
+          python3 -m pip install /root/packages/flash_attn-2.6.3+cu118torch2.3cxx11abiFALSE-cp38-cp38-linux_x86_64.whl
           python3 -m pip install -r requirements.txt -r requirements/test.txt
           python3 -m pip install .
       - name: Check env

lmdeploy/pytorch/check_env/__init__.py

@@ -93,15 +93,13 @@ def check_env_triton(device: str):
 
     if device == 'cuda':
         device_cap = torch.cuda.get_device_capability()
-        TRITON_VER_220 = version.parse('2.2.0')
         TRITON_VER_231 = version.parse('2.3.1')
 
         if device_cap[0] <= 7:
-            if (triton_version >= TRITON_VER_220
-                    and triton_version <= TRITON_VER_231):
+            if triton_version <= TRITON_VER_231:
                 err = RuntimeError(
                     'Attention triton kernel does not fully support '
-                    'triton[2.2.0~2.3.1] on device with capability<8. '
+                    'triton<3.0.0 on device with capability<8. '
                     'Please upgrade your triton version.')
                 _handle_exception(err, 'Triton', logger)
 
@@ -142,7 +140,8 @@ def check_awq(hf_config):
 
 
 def check_transformers_version(model_path: str,
-                               trust_remote_code: bool = True):
+                               trust_remote_code: bool = True,
+                               dtype: str = 'auto'):
     """check transformers version."""
     from packaging import version
     logger = get_logger('lmdeploy')
@@ -206,7 +205,8 @@ def __check_model_dtype_support(config):
 
         try:
             model_config = ModelConfig.from_hf_config(config,
-                                                      model_path=model_path)
+                                                      model_path=model_path,
+                                                      dtype=dtype)
             if model_config.dtype == torch.bfloat16:
                 assert torch.cuda.is_bf16_supported(), (
                     'bf16 is not supported on your device')
@@ -229,11 +229,13 @@ def __check_model_dtype_support(config):
     check_awq(config)
 
 
-def check_model(model_path: str, trust_remote_code: bool = True):
+def check_model(model_path: str,
+                trust_remote_code: bool = True,
+                dtype: str = 'auto'):
     """check model requirements."""
     logger = get_logger('lmdeploy')
     logger.info('Checking model.')
-    check_transformers_version(model_path, trust_remote_code)
+    check_transformers_version(model_path, trust_remote_code, dtype)
 
 
 def check_adapter(path: str):

lmdeploy/pytorch/engine/engine.py

@@ -96,7 +96,7 @@ def __init__(self,
         else:
             engine_config = copy.deepcopy(engine_config)
         check_env(engine_config.device_type)
-        check_model(model_path, trust_remote_code)
+        check_model(model_path, trust_remote_code, engine_config.dtype)
         if engine_config.max_batch_size is None:
             engine_config.max_batch_size = get_max_batch_size(
                 engine_config.device_type)

lmdeploy/pytorch/kernels/cuda/fill_kv_cache.py

@@ -378,12 +378,21 @@ def fill_kv_cache(k_states: Tensor,
                   block_offsets: Tensor,
                   k_scales_zeros: Tensor = None,
                   v_scales_zeros: Tensor = None,
-                  quant_policy: Literal[0, 4, 8] = 0):
+                  quant_policy: Literal[0, 4, 8] = 0,
+                  kv_layout: str = 'bshd'):
     """fill key/value state to cache for paged attention."""
+    if kv_layout == 'bshd':
+        b_dim, s_dim, h_dim, d_dim = (0, 1, 2, 3)
+    elif kv_layout == 'bhsd':
+        b_dim, s_dim, h_dim, d_dim = (0, 2, 1, 3)
+    else:
+        raise RuntimeError('Unsupported layout.')
 
     block_offsets = block_offsets.contiguous()
     batch_size = block_offsets.size(0)
-    block_size, num_heads, head_dim = k_caches.size()[1:]
+    block_size = k_caches.size(s_dim)
+    num_heads = k_caches.size(h_dim)
+    head_dim = k_caches.size(d_dim)
     head_dim_v = v_states.size(-1)
     max_num_blocks = triton.cdiv(max_q_seq_length, block_size) + 1
 
@@ -412,14 +421,14 @@ def fill_kv_cache(k_states: Tensor,
             stride_vss=v_states.stride(-3),
             stride_vsh=v_states.stride(-2),
             stride_vsd=v_states.stride(-1),
-            stride_kcn=k_caches.stride(0),
-            stride_kcb=k_caches.stride(1),
-            stride_kch=k_caches.stride(2),
-            stride_kcd=k_caches.stride(3),
-            stride_vcn=v_caches.stride(0),
-            stride_vcb=v_caches.stride(1),
-            stride_vch=v_caches.stride(2),
-            stride_vcd=v_caches.stride(3),
+            stride_kcn=k_caches.stride(b_dim),
+            stride_kcb=k_caches.stride(s_dim),
+            stride_kch=k_caches.stride(h_dim),
+            stride_kcd=k_caches.stride(d_dim),
+            stride_vcn=v_caches.stride(b_dim),
+            stride_vcb=v_caches.stride(s_dim),
+            stride_vch=v_caches.stride(h_dim),
+            stride_vcd=v_caches.stride(d_dim),
             stride_boff=block_offsets.stride(0),
             BLOCK=BLOCK,
             BLOCK_D=BLOCK_D,
@@ -450,22 +459,22 @@ def fill_kv_cache(k_states: Tensor,
             stride_vss=v_states.stride(-3),
             stride_vsh=v_states.stride(-2),
             stride_vsd=v_states.stride(-1),
-            stride_kcn=k_caches.stride(0),
-            stride_kcb=k_caches.stride(1),
-            stride_kch=k_caches.stride(2),
-            stride_kcd=k_caches.stride(3),
-            stride_vcn=v_caches.stride(0),
-            stride_vcb=v_caches.stride(1),
-            stride_vch=v_caches.stride(2),
-            stride_vcd=v_caches.stride(3),
-            stride_kszn=k_scales_zeros.stride(0),
-            stride_kszb=k_scales_zeros.stride(1),
-            stride_kszh=k_scales_zeros.stride(2),
-            stride_kszd=k_scales_zeros.stride(3),
-            stride_vszn=v_scales_zeros.stride(0),
-            stride_vszb=v_scales_zeros.stride(1),
-            stride_vszh=v_scales_zeros.stride(2),
-            stride_vszd=v_scales_zeros.stride(3),
+            stride_kcn=k_caches.stride(b_dim),
+            stride_kcb=k_caches.stride(s_dim),
+            stride_kch=k_caches.stride(h_dim),
+            stride_kcd=k_caches.stride(d_dim),
+            stride_vcn=v_caches.stride(b_dim),
+            stride_vcb=v_caches.stride(s_dim),
+            stride_vch=v_caches.stride(h_dim),
+            stride_vcd=v_caches.stride(d_dim),
+            stride_kszn=k_scales_zeros.stride(b_dim),
+            stride_kszb=k_scales_zeros.stride(s_dim),
+            stride_kszh=k_scales_zeros.stride(h_dim),
+            stride_kszd=k_scales_zeros.stride(d_dim),
+            stride_vszn=v_scales_zeros.stride(b_dim),
+            stride_vszb=v_scales_zeros.stride(s_dim),
+            stride_vszh=v_scales_zeros.stride(h_dim),
+            stride_vszd=v_scales_zeros.stride(d_dim),
             quant_policy=quant_policy,
             stride_boff=block_offsets.stride(0),
             BLOCK=BLOCK,