Add vllm awq loading logic (#11987)

* [ADD] Add vllm awq loading logic

* [FIX] fix the module.linear_method path

* [FIX] fix quant_config path error

python/llm/src/ipex_llm/transformers/convert.py

@@ -181,6 +181,11 @@ def is_linear_module(module):
             out_features = module.output_size
             result = True
             mp_group = None
+            # Check for attribute qweight
+            if (not _USE_VLLM_AWQ
+               and hasattr(module.quant_method, "quant_config")
+               and module.quant_method.quant_config.get_name() == "awq"):
+                _USE_VLLM_AWQ = True
             invalidInputError(module.skip_bias_add is not True, "Currently, ipex-vllm does not"
                               " support linear layers with skip_bias_add argument")
             if isinstance(module, RowParallelLinear) and tp_size >= 2:
@@ -285,6 +290,65 @@ def convert_vllm(module, qtype, in_features, out_features, mp_group, cur_qtype,
     return new_linear
 
 
+def convert_vllm_awq(module):
+    from ipex_llm.transformers.low_bit_linear import get_block_size
+    Q4_1 = get_block_size("asym_int4")
+
+    scales = module.scales
+    wf = (torch.tensor([0, 4, 1, 5, 2, 6, 3, 7],
+                       dtype=torch.int32) * 4).unsqueeze(0)
+    # vLLM only supports load 4-bits model, so this has been checked
+    bits = 4
+    group_size = module.quant_method.quant_config.group_size
+
+    zeros = torch.bitwise_right_shift(
+        torch.unsqueeze(module.qzeros, 2).expand(-1, -1, 32 // bits),
+        wf.unsqueeze(0)).to(torch.int16 if bits == 8 else torch.int8)
+    zeros = torch.bitwise_and(zeros, (2 ** bits) - 1)
+
+    g_id_map = None
+
+    zeros = zeros.reshape(scales.shape)
+
+    weight = torch.bitwise_right_shift(
+        torch.unsqueeze(module.qweight, 2).expand(-1, -1, 32 // bits),
+        wf.unsqueeze(0)).to(torch.int16 if bits == 8 else torch.int8)
+    weight = torch.bitwise_and(weight, (2 ** bits) - 1)
+    weight = weight.reshape(weight.shape[0], weight.shape[1] * weight.shape[2])
+
+    # convert weight to ggml format
+    weight = weight.reshape(weight.shape[0]//group_size, group_size, weight.shape[1])
+    weight = weight.permute(2, 0, 1).reshape(weight.shape[2], -1, 2, Q4_1//2)
+    weight = weight.transpose(2, 3)
+    weight = torch.bitwise_left_shift(weight,
+                                      torch.tensor([0, 4], dtype=torch.int8).reshape(1, 1, 1, 2))
+    weight = torch.bitwise_or(weight[:, :, :, 0], weight[:, :, :, 1]).contiguous()
+
+    # convert zeros to ggml format
+    zeros = zeros.reshape(-1, 1, zeros.shape[1]).permute(2, 0, 1)\
+        .unsqueeze(2)\
+        .expand(-1, -1, group_size//Q4_1, -1)\
+        .reshape(zeros.shape[1], -1, 1)\
+        .contiguous().to(torch.float16)
+
+    # convert scales to ggml format
+    scales = scales.reshape(-1, 1, scales.shape[1]).permute(2, 0, 1)\
+        .unsqueeze(2)\
+        .expand(-1, -1, group_size//Q4_1, -1)\
+        .reshape(scales.shape[-1], -1, 1)\
+        .contiguous().to(torch.float16)
+
+    m = -(zeros * scales)
+    d = scales
+
+    ggml_weight = torch.cat([d.view(torch.uint8),
+                             m.view(torch.uint8),
+                             weight.view(torch.uint8)], dim=-1)
+    ggml_weight = ggml_weight.reshape([-1])
+
+    return ggml_weight, g_id_map
+
+
 def convert_gptq(module, awq=False, llm_awq=False, act_order=False):
     from ipex_llm.transformers.low_bit_linear import get_block_size
     Q4_1 = get_block_size("asym_int4")
@@ -1917,6 +1981,7 @@ def safe_bmm_fwd(*args, **kwargs):
         minicpmv_generate = minicpmv_generate_wrapper(module.MiniCPMV.generate)
         model.generate = MethodType(minicpmv_generate, model)
 
+<<<<<<< HEAD
         if model.config.hidden_size == 2304 and model.config.vocab_size == 122753:
             # MiniCPM-V 2
             model.llm.config.model_type = "minicpm"
@@ -1960,3 +2025,6 @@ def safe_bmm_fwd(*args, **kwargs):
             model.chat = MethodType(minicpmv_chat, model)
 
     return model
+=======
+    return model
+>>>>>>> 56b851402b (Add vllm awq loading logic (#11987))