add support of llama_nn

vllm/model_executor/layers/linear.py

@@ -13,8 +13,10 @@ from vllm.model_executor.parallel_utils.utils import (
     divide, split_tensor_along_last_dim)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.logger import init_logger
+import os
 
 logger = init_logger(__name__)
+USE_LLAMA_NN = int(os.environ.get('LLAMA_NN', '0')) == 1
 
 
 def adjust_marlin_shard(param, shard_size, shard_offset):
@@ -76,6 +78,10 @@ class UnquantizedLinearMethod(LinearMethodBase):
             if bias:
                 return F.linear(x, weight) + bias
             return F.linear(x, weight)
+        if USE_LLAMA_NN:
+            weight = weight.reshape(weight.shape[1], -1) 
+            return torch.matmul(x, weight) 
+        else:
             return F.linear(x, weight, bias)
 
 
@@ -206,6 +212,9 @@ class ColumnParallelLinear(torch.nn.Module):
             loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                  shard_size)
         assert param_data.shape == loaded_weight.shape
+        if USE_LLAMA_NN:
+            loaded_weight = loaded_weight.transpose(0, 1)
+            loaded_weight = loaded_weight.reshape(param_data.shape[0],-1)
         param_data.copy_(loaded_weight)
 
     def forward(self, input_):
@@ -311,6 +320,10 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                 shard_size, shard_offset = adjust_marlin_shard(
                     param, shard_size, shard_offset)
 
+            if USE_LLAMA_NN:
+                param_data_ = param_data.narrow(output_dim, shard_offset,
+                                            shard_size)
+            else:
                 param_data = param_data.narrow(output_dim, shard_offset,
                                             shard_size)
             start_idx = tp_rank * shard_size
@@ -323,6 +336,14 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                     "Loading a weight without `output_dim` attribute in "
                     "MergedColumnParallelLinear, assume the weight is "
                     "the same for all partitions.")
+        
+        if USE_LLAMA_NN:
+            assert param_data_.shape == loaded_weight.shape
+            param_data_.copy_(loaded_weight)
+            if loaded_shard_id == 1:
+                param_data = param_data.transpose(0, 1)
+                param.data = param_data.reshape(param_data.shape[1], -1)
+        else:
             assert param_data.shape == loaded_weight.shape
             param_data.copy_(loaded_weight)
 
@@ -446,6 +467,10 @@ class QKVParallelLinear(ColumnParallelLinear):
                 shard_size, shard_offset = adjust_marlin_shard(
                     param, shard_size, shard_offset)
 
+            if USE_LLAMA_NN:
+                param_data_ = param_data.narrow(output_dim, shard_offset,
+                                           shard_size)
+            else:
                 param_data = param_data.narrow(output_dim, shard_offset,
                                            shard_size)
             if loaded_shard_id == "q":
@@ -462,6 +487,14 @@ class QKVParallelLinear(ColumnParallelLinear):
                     "Loading a weight without `output_dim` attribute in "
                     "QKVParallelLinear, assume the weight is the same "
                     "for all partitions.")
+        
+        if USE_LLAMA_NN:
+            assert param_data_.shape == loaded_weight.shape
+            param_data_.copy_(loaded_weight)
+            if loaded_shard_id == "v":
+                param_data = param_data.transpose(0, 1) 
+                param.data = param_data.reshape(param_data.shape[1], -1) 
+        else:
             assert param_data.shape == loaded_weight.shape
             param_data.copy_(loaded_weight)
 
@@ -552,6 +585,9 @@ class RowParallelLinear(torch.nn.Module):
             loaded_weight = loaded_weight.narrow(input_dim, start_idx,
                                                  shard_size)
         assert param_data.shape == loaded_weight.shape
+        if USE_LLAMA_NN:
+            loaded_weight = loaded_weight.transpose(0, 1)
+            loaded_weight=loaded_weight.reshape(param_data.shape[0],-1)
         param_data.copy_(loaded_weight)
 
     def forward(self, input_):