assert lora weight

Signed-off-by: ZX-ModelCloud <[email protected]>
ModelCloud · Feb 28, 2025 · 86a242c · 86a242c
1 parent a316cd2
commit 86a242c
Showing 1 changed file with 19 additions and 7 deletions.
diff --git a/tests/test_quant_and_eora_transformers.py b/tests/test_quant_and_eora_transformers.py
@@ -16,6 +16,8 @@
 # -- do not touch
 import os
 
+import torch
+from safetensors.torch import load_file
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from peft.tuners.lora.gptq import GPTQLoraLinear
@@ -28,7 +30,7 @@
 
 from datasets import load_dataset  # noqa: E402
 from gptqmodel import BACKEND, GPTQModel, QuantizeConfig  # noqa: E402
-from gptqmodel.adapter.adapter import Lora  # noqa: E402
+from gptqmodel.adapter.adapter import Lora, HF_ADAPTER_FILE_NAME, HF_ADAPTER_WEIGHT_KEY_PREFIX  # noqa: E402
 from gptqmodel.utils.eval import EVAL  # noqa: E402
 from gptqmodel.utils.torch import torch_empty_cache  # noqa: E402
 from lm_eval.utils import make_table  # noqa: E402
@@ -122,7 +124,7 @@ def test_quant_and_eora(self):
 
             # BACKEND.EXLLAMA_V2, BACKEND.EXLLAMA_V1, BACKEND.TRITON, BACKEND.CUDA,
             for backend in [ BACKEND.MARLIN ]: # BACKEND.IPEX, BACKEND.BITBLAS, BACKEND.EXLLAMA_V2V BACKEND.MARLIN
-                # base_bench = self.bench(path=tmpdir, backend=backend, adapter=None) # inference using qweights only
+                base_bench = self.bench(path=tmpdir, backend=backend, adapter=None) # inference using qweights only
                 eora_bench = self.bench(path=tmpdir, backend=backend, adapter=eora) # inference using eora (lora)
 
                 print('--------GPTQModel + EoRA Config ---------')
@@ -131,10 +133,10 @@ def test_quant_and_eora(self):
                 table_data = [[key, value] for key, value in config_dict.items()]
                 print(tabulate(table_data, headers=["Key", "Value"], tablefmt="grid"))
 
-                # print('--------Eval GPTQ Result---------')
-                # print(make_table(base_bench))
-                # if "groups" in base_bench:
-                #     print(make_table(base_bench, "groups"))
+                print('--------Eval GPTQ Result---------')
+                print(make_table(base_bench))
+                if "groups" in base_bench:
+                    print(make_table(base_bench, "groups"))
 
                 print('--------Eval GPTQ + EoRA Result---------')
                 print(make_table(eora_bench))
@@ -153,7 +155,17 @@ def bench(self, path: str, backend: BACKEND, adapter: Optional[Lora]):
             model.load_adapter(adapter.path)
             print("peft model", model)
 
-        assert isinstance(model.model.layers[0].self_attn.v_proj, GPTQLoraLinear)
+            adapter_weights = load_file(os.path.join(adapter.path, HF_ADAPTER_FILE_NAME))
+            origin_lora_a_weight = adapter_weights[f"{HF_ADAPTER_WEIGHT_KEY_PREFIX}model.layers.6.self_attn.v_proj.lora_A.weight"]
+            origin_lora_b_weight = adapter_weights[f"{HF_ADAPTER_WEIGHT_KEY_PREFIX}model.layers.6.self_attn.v_proj.lora_B.weight"]
+
+            module = model.model.layers[6].self_attn.v_proj
+
+            assert isinstance(module, GPTQLoraLinear)
+            assert torch.equal(origin_lora_a_weight.to(model.device), module.lora_A["default"].weight.data)
+            assert torch.equal(origin_lora_b_weight.to(model.device), module.lora_B["default"].weight.data)
+
+            del origin_lora_a_weight, origin_lora_b_weight, adapter_weights
 
         tokenizer = AutoTokenizer.from_pretrained(path)
         inp = tokenizer("Capital of France is", return_tensors="pt").to(model.device)