support new backend cambricon (#3002)

* [dlinfer]add camb support

* [camb] fix multiple of 8, exp raise core dump

* [camb] fix multiple of 8, exp raise core dump

* [camb] format

* [camb]pow of 2 better

* [camb]rm local_adapterids

* [camb]modify graph runner

* [camb]mock graph runner

* [camb]add requirements.txt

* [camb]post init set block_size to 16

* lint

lmdeploy/cli/utils.py

@@ -377,7 +377,7 @@ def calib_search_scale(parser):
     @staticmethod
     def device(parser,
                default: str = 'cuda',
-               choices: List[str] = ['cuda', 'ascend', 'maca']):
+               choices: List[str] = ['cuda', 'ascend', 'maca', 'camb']):
         """Add argument device to parser."""
 
         return parser.add_argument('--device',

lmdeploy/messages.py

@@ -7,6 +7,9 @@
 from pydantic.dataclasses import dataclass as pydantic_dataclass
 
 from .tokenizer import Tokenizer
+from .utils import get_logger
+
+logger = get_logger('lmdeploy')
 
 LogitsProcessor = Callable[[torch.Tensor, torch.Tensor], torch.Tensor]
 """LogitsProcessor is a function that takes a tensor of input_ids, the logits
@@ -297,13 +300,18 @@ def __post_init__(self):
         assert self.num_gpu_blocks >= 0, 'invalid num_gpu_blocks'
         assert self.quant_policy in (0, 4, 8), 'invalid quant_policy'
         assert self.device_type in [
-            'cuda', 'ascend', 'maca'
+            'cuda', 'ascend', 'maca', 'camb'
         ], (f'invalid device_type: {self.device_type}')
         if self.quant_policy > 0 and self.device_type not in [
                 'cuda', 'ascend'
         ]:
             assert False, \
                    'kv cache quantization only works for CUDA and ASCEND.'
+        if self.device_type == 'camb' and self.block_size != 16:
+            self.block_size = 16
+            logger.warning(
+                'Currently, camb device requires block size to be 16, \
+                    setting block size to 16')
 
 
 class ResponseType(enum.Enum):

lmdeploy/pytorch/backends/dlinfer/__init__.py

@@ -1,3 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .ascend import AscendOpsBackend  # noqa: F401
+from .camb import CambOpsBackend  # noqa: F401
 from .maca import MacaOpsBackend  # noqa: F401

lmdeploy/pytorch/backends/dlinfer/camb/__init__.py

@@ -0,0 +1,2 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .op_backend import CambOpsBackend  # noqa: F401

lmdeploy/pytorch/backends/dlinfer/camb/op_backend.py

@@ -0,0 +1,132 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Tuple
+
+import torch
+
+from lmdeploy.pytorch.config import BackendConfig, CacheConfig, ModelConfig
+from lmdeploy.utils import get_logger
+
+from ..op_backend import DlinferOpsBackend
+
+logger = get_logger('lmdeploy')
+
+
+class CambOpsBackend(DlinferOpsBackend):
+    """camb layer backend."""
+    total_slots = None
+
+    @staticmethod
+    def get_name() -> str:
+        """backend name."""
+        return 'camb'
+
+    @staticmethod
+    def get_k_block_shape(
+        block_size: int,
+        num_heads: int,
+        head_size: int,
+        dtype: torch.dtype,
+    ) -> Tuple[int, ...]:
+        return (
+            num_heads,
+            block_size,
+            head_size,
+        )
+
+    @staticmethod
+    def get_v_block_shape(
+        block_size: int,
+        num_heads: int,
+        head_size: int,
+        dtype: torch.dtype,
+    ) -> Tuple[int, ...]:
+        return (
+            num_heads,
+            block_size,
+            head_size,
+        )
+
+    @classmethod
+    def update_step_context(cls, step_context):
+        """update step context."""
+
+        def get_total_slots():
+            if cls.total_slots is None:
+                cls.total_slots = torch.arange(
+                    block_num * block_size,
+                    dtype=torch.int32,
+                    device=step_context.block_offsets.device)
+                cls.total_slots = cls.total_slots.view(block_num, block_size)
+            return cls.total_slots
+
+        kv_start_indices = []
+        block_num, _, block_size, _ = step_context.kv_caches[0][0].shape
+
+        is_unpaged_prefill = False
+        q_start_loc = step_context.q_start_loc
+        q_seqlens = step_context.q_seqlens
+        kv_seqlens = step_context.kv_seqlens.to(torch.int32)
+        block_offsets = step_context.block_offsets.to(torch.int32)
+        max_q_seq_len = torch.max(q_seqlens).cpu().item()
+        max_kv_seq_len = torch.max(kv_seqlens).cpu().item()
+
+        cu_seqlens = torch.cat(
+            (q_start_loc, q_seqlens.sum().unsqueeze(0))).int()
+        cu_seq_lens_kv = None
+
+        q_seqlens_list = step_context.q_seqlens.tolist()
+        kv_seqlens_list = step_context.kv_seqlens.tolist()
+        if not step_context.is_decoding:
+            is_unpaged_prefill = q_seqlens_list == kv_seqlens_list
+            # get kv_indices
+            for i in range(q_start_loc.size(0)):
+                q_seq_len = q_seqlens_list[i]
+                kv_seq_len = kv_seqlens_list[i]
+                # collect kv start indices.
+                history_length = kv_seq_len - q_seq_len
+                total_slots = get_total_slots()
+                slot_tables = total_slots[block_offsets[i]].view(-1)
+                slots = slot_tables[history_length:kv_seq_len]
+                kv_start_indices.append(slots)
+            kv_start_indices = torch.cat(kv_start_indices)
+            if not is_unpaged_prefill:
+                cu_seq_lens_kv = torch.cat(
+                    (torch.tensor([0], device=kv_seqlens.device),
+                     kv_seqlens.cumsum(0))).int()
+        else:
+            # collect kv_start_indices without using a for-loop,
+            # (fill kv-cache for just ONE token during the decoding phase)
+            idx = (step_context.kv_seqlens - 1) % block_size
+            block_num = (step_context.kv_seqlens - 1) // block_size
+            last_block = block_offsets.gather(  # dtype of gather must be int64
+                1, block_num.view(-1, 1)).view(-1)
+            kv_start_indices = (last_block * block_size + idx).to(torch.int32)
+
+        attn_meta_cls = cls.get_attention_metadata_cls()
+        attn_metadata = attn_meta_cls(
+            step_context.is_decoding,
+            block_offsets,
+            q_start_loc=cu_seqlens,
+            cu_seq_lens_kv=cu_seq_lens_kv,
+            q_seqlens=q_seqlens,
+            kv_seqlens=kv_seqlens,
+            kv_start_indices=kv_start_indices,
+            block_size=block_size,
+            attention_mask=None,
+            is_unpaged_prefill=is_unpaged_prefill,
+            max_q_seq_len=max_q_seq_len,
+            max_kv_seq_len=max_kv_seq_len,
+        )
+
+        step_context.attn_metadata = attn_metadata
+        return step_context
+
+    @staticmethod
+    def build_graph_runner(model: torch.nn.Module, model_config: ModelConfig,
+                           cache_config: CacheConfig,
+                           backend_config: BackendConfig,
+                           device: torch.device):
+        """build graph runner."""
+        from lmdeploy.pytorch.backends.cuda.graph_runner import CUDAGraphRunner
+        return CUDAGraphRunner(model, model_config, cache_config,
+                               backend_config, device)

lmdeploy/pytorch/backends/selector.py

@@ -18,5 +18,8 @@ def get_backend():
     if device_type == 'maca':
         from .dlinfer import MacaOpsBackend
         return MacaOpsBackend
+    if device_type == 'camb':
+        from .dlinfer import CambOpsBackend
+        return CambOpsBackend
     else:
         raise RuntimeError(f'Unsupported device type: {device_type}')

lmdeploy/pytorch/check_env/deeplink.py

@@ -5,6 +5,7 @@
     'ascend',
     'npu',
     'maca',
+    'camb',
 ]
 
 

lmdeploy/pytorch/models/module_map.py

@@ -6,9 +6,11 @@
 MODULE_MAP = dict()
 ASCEND_MODULE_MAP = dict()
 MACA_MODULE_MAP = dict()
+CAMB_MODULE_MAP = dict()
 
 DEVICE_SPECIAL_MODULE_MAP = dict(ascend=ASCEND_MODULE_MAP,
-                                 maca=MACA_MODULE_MAP)
+                                 maca=MACA_MODULE_MAP,
+                                 camb=CAMB_MODULE_MAP)
 
 # llama
 MODULE_MAP.update({

lmdeploy/utils.py

@@ -332,7 +332,7 @@ def get_max_batch_size(device_type: str):
     Args:
         device_type (str): the type of device
     """
-    assert device_type in ['cuda', 'ascend', 'maca']
+    assert device_type in ['cuda', 'ascend', 'maca', 'camb']
     if device_type == 'cuda':
         max_batch_size_map = {
             'a100': 256,
@@ -352,6 +352,8 @@ def get_max_batch_size(device_type: str):
         return 16
     elif device_type == 'maca':
         return 128
+    elif device_type == 'camb':
+        return 128
 
 
 def is_bf16_supported(device_type: str = 'cuda'):
@@ -387,5 +389,7 @@ def is_bf16_supported(device_type: str = 'cuda'):
         #     return False
     elif device_type == 'maca':
         return True
+    elif device_type == 'camb':
+        return True
     else:
         return False

requirements/runtime_camb.txt

@@ -0,0 +1,21 @@
+accelerate==1.2.0
+einops
+fastapi
+fire
+mmengine-lite
+numpy<2.0.0
+openai
+outlines<0.1.0
+peft<=0.11.1
+pillow
+protobuf
+pydantic>2.0.0
+pynvml
+safetensors
+sentencepiece
+shortuuid
+tiktoken
+torch==2.4.0
+torchvision<=0.19.0,>=0.15.0
+transformers
+uvicorn

requirements_camb.txt

@@ -0,0 +1,4 @@
+-r requirements/build.txt
+-r requirements/runtime_camb.txt
+-r requirements/lite.txt
+-r requirements/serve.txt