support qqq(w4a8) for lmdeploy

fix llama_kernels compile issue on windows

fix conflicts

format

lmdeploy/cli/utils.py

@@ -106,9 +106,10 @@ def model_format(parser, default: str = None):
             '--model-format',
             type=str,
             default=default,
-            choices=['hf', 'llama', 'awq'],
+            choices=['hf', 'llama', 'awq', 'qqq'],
             help='The format of input model. `hf` meaning `hf_llama`, `llama` '
-            'meaning `meta_llama`, `awq` meaning the quantized model by awq')
+            'meaning `meta_llama`, `awq` meaning the quantized model by awq, '
+            '`qqq` meaning the quantized model by qqq')
 
     @staticmethod
     def revision(parser, default: str = None):

lmdeploy/messages.py

@@ -115,8 +115,9 @@ class TurbomindEngineConfig:
     """TurboMind Engine config.
 
     Args:
-        model_format (str): the layout of the deployed model. It can be one of the following values [hf, meta_llama, awq],
-            `hf` meaning huggingface model(.bin, .safetensors), `meta_llama` being meta llama's format(.pth), awq` meaning the quantized model by AWQ.
+        model_format (str): the layout of the deployed model. It can be one of the following values [hf, meta_llama, awq, qqq],
+            `hf` meaning huggingface model(.bin, .safetensors), `meta_llama` being meta llama's format(.pth), `awq` meaning the quantized model by AWQ,
+            `qqq` meaning the quantized model by QQQ.
         tp (int): the number of GPU cards used in tensor parallelism, default to 1
         session_len (int): the max session length of a sequence, default to None
         max_batch_size (int): the max batch size during inference, default to 128

lmdeploy/turbomind/deploy/converter.py

@@ -15,7 +15,7 @@
 from .source_model.base import INPUT_MODELS
 from .target_model.base import OUTPUT_MODELS, TurbomindModelConfig
 
-SUPPORTED_FORMATS = ['meta_llama', 'hf', 'awq', None]
+SUPPORTED_FORMATS = ['meta_llama', 'hf', 'awq', 'qqq', None]
 logger = get_logger('lmdeploy')
 
 
@@ -26,12 +26,14 @@ def get_input_model_registered_name(model_path: str, model_format: str):
     Args:
         model_path (str): the path of the input model
         model_format (str): the format of the model, which can be one of
-            ['meta_llama',  'hf', 'awq']
+            ['meta_llama', 'hf', 'awq', 'qqq']
     """
     arch = get_model_arch(model_path)[0]
     register_name = SUPPORTED_ARCHS[arch]
     if model_format == 'awq':
         register_name = register_name + '-awq'
+    elif model_format == 'qqq':
+        register_name = register_name + '-qqq'
     return register_name
 
 
@@ -92,8 +94,9 @@ def get_output_model_registered_name_and_config(model_path: str,
     Args:
         model_path (str): the path of the input model
         model_format (str): the format of the model, which can be one of
-            ['meta_llama',  'hf', 'awq']
-        group_size (int): the size of group used by awq model
+            ['meta_llama', 'hf', 'awq', 'qqq']
+        group_size (int): the size of group used by quantization methods,
+        including `awq` and `qqq`
     """
     register_name = 'fp16'
     turbomind_model_arch = 'llama'
@@ -113,6 +116,15 @@ def get_output_model_registered_name_and_config(model_path: str,
             register_name = 'plora-w4' \
                 if turbomind_model_arch == 'xcomposer2' else 'w4'
             group_size = 128 if group_size == 0 else group_size
+            config.quantization = 'awq'
+        elif model_format == 'qqq':
+            weight_type = 'int4'
+            register_name = 'qqq-w4'
+            from transformers import AutoConfig
+            quant_config = AutoConfig.from_pretrained(
+                model_path).quantization_config
+            group_size = quant_config['group_size']
+            config.quantization = 'qqq'
         else:
             torch_dtype = getattr(model_config, 'torch_dtype', 'float16')
             TORCH_DTYPE_MAP = {torch.bfloat16: 'bf16', torch.float16: 'fp16'}
@@ -212,17 +224,19 @@ def main(model_name: str,
         model_name (str): unused any longer
         model_path (str): the directory path of the model
         model_format (str): the format of the model, should choose from
-            ['meta_llama', 'hf', 'awq', None]. 'meta_llama' stands for META's
-            llama format, 'hf' means huggingface llama format, and 'awq' means
-            llama(hf) model quantized by lmdeploy/lite/quantization/awq.py.
-            The default value is None
-        chat_template (str): the name of the built-in chat template.
+            ['meta_llama', 'hf', 'awq', 'qqq', None]. 'meta_llama' stands for
+            META's llama format, 'hf' means huggingface llama format,
+            'awq' means llama(hf) model quantized by
+            lmdeploy/lite/quantization/awq.py,
+            and 'qqq' means llama(hf) model quantized by the repo
+            https://github.com/HandH1998/QQQ,
+            the default value is None
         tokenizer_path (str): the path of tokenizer model
         dst_path (str): the destination path that saves outputs
         tp (int): the number of GPUs used for tensor parallelism, should be 2^n
         quant_path (str): Path of the quantized model, which can be None.
-        group_size (int): a parameter used in AWQ to quantize fp16 weights
-            to 4 bits
+        group_size (int): a parameter used in AWQ or QQQ to quantize fp16
+        weights to 4 bits
         revision (str): The specific model version to use. It can be a branch
             name, a tag name, or a commit id. If unspecified, will use
             the default version.

lmdeploy/turbomind/deploy/source_model/__init__.py

@@ -9,6 +9,7 @@
 from .internvl import InternVLModel  # noqa: F401
 from .llama import LlamaModel  # noqa: F401
 from .llama_awq import LlamaAwqModel  # noqa: F401
+from .llama_qqq import LlamaQQQModel  # noqa: F401
 from .meta_llama import MetaLlamaModel  # noqa: F401
 from .minicpmv import MiniCPMVModel  # noqa: F401
 from .minicpmv_awq import MiniCPMVAwqModel  # noqa: F401

lmdeploy/turbomind/deploy/source_model/llama_qqq.py

@@ -0,0 +1,69 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from .base import INPUT_MODELS
+from .llama import LlamaModel, LlamaReader
+
+
+def ensure_dtype(tensors: torch.Tensor, dtype: torch.dtype):
+    """Ensure tensors in the specified dytpe."""
+    result = []
+    for tensor in tensors:
+        if tensor is not None and tensor.numel() > 0:
+            if tensor.dtype in [torch.float16, torch.float32, torch.bfloat16]:
+                result.append(tensor.to(dtype))
+            else:
+                assert tensor.dtype == torch.int32
+                result.append(tensor)
+        else:
+            result.append(None)
+    return (*result, )
+
+
+class LlamaQQQReader(LlamaReader):
+    """LlamaQQQReader."""
+
+    def __init__(self, new_params: dict, unused_params: dict, last_bin: bool,
+                 model_cfg: dict):
+        super().__init__(new_params, unused_params, last_bin, model_cfg)
+
+    def attn(self, i: int):
+        """Get q, k, v, o qweight for layer i."""
+        return ensure_dtype(self._attn(i, 'B'), torch.int32)
+
+    def attn_scale_group(self, i: int):
+        """Get q, k, v, o per-group scales for layer i."""
+        return ensure_dtype(self._attn(i, 's_group'), torch.float16)
+
+    def attn_scale_channel(self, i: int):
+        """Get q, k, v, o per-channel scales for layer i."""
+        return ensure_dtype(self._attn(i, 's_channel'), torch.float32)
+
+    def ffn(self, i: int):
+        """Get ffn qweight for layer i."""
+        return ensure_dtype(self._ffn(i, 'B'), torch.int32)
+
+    def ffn_scale_group(self, i: int):
+        """Get ffn per-group scales for layer i."""
+        return ensure_dtype(self._ffn(i, 's_group'), torch.float16)
+
+    def ffn_scale_channel(self, i: int):
+        """Get ffn per-channel scales for layer i."""
+        return ensure_dtype(self._ffn(i, 's_channel'), torch.float32)
+
+
+@INPUT_MODELS.register_module(name='llama-qqq')
+class LlamaQQQModel(LlamaModel):
+    """Llama QQQ model in hf format."""
+
+    Reader = LlamaQQQReader
+
+    def __init__(self,
+                 model_path: str,
+                 tokenizer_path: str,
+                 ckpt_path: str = None,
+                 **kwargs):
+        super().__init__(model_path,
+                         tokenizer_path,
+                         ckpt_path=ckpt_path,
+                         **kwargs)

lmdeploy/turbomind/deploy/target_model/__init__.py

@@ -2,4 +2,5 @@
 from .fp import TurbomindModel  # noqa: F401
 from .plora import TurbomindPloraModel  # noqa: F401
 from .plora_w4 import TurbomindPloraW4Model  # noqa: F401
+from .qqq_w4 import TurbomindQQQW4Model  # noqa: F401
 from .w4 import TurbomindW4Model  # noqa: F401

lmdeploy/turbomind/deploy/target_model/base.py

@@ -66,6 +66,7 @@ class TurbomindModelConfig:
     max_prefill_iters: int = 1
     use_context_fmha: int = 1
     quant_policy: int = 0
+    quantization: str = ''
     max_position_embeddings: int = 0
     original_max_position_embeddings: int = 0
     rope_scaling_type: str = ''