change setting fo random seed to configuration

Signed-off-by: Brian Dellabetta <bdellabe@redhat.com>

Author: brian-dellabetta

src/llmcompressor/metrics/logger.py

@@ -829,8 +829,8 @@ def __init__(
             or SparsificationGroupLogger(
                 python=log_python,
                 name=name,
-                tensorboard=True,
-                wandb_=True,
+                tensorboard=False,
+                wandb_=False,
             ).loggers
         )
 

tests/lmeval/configs/vl_fp8_dynamic_per_token.yaml

@@ -2,6 +2,7 @@ cadence: weekly
 model: Qwen/Qwen2-VL-2B-Instruct
 model_class: TraceableQwen2VLForConditionalGeneration
 scheme: FP8_DYNAMIC
+seed: 42 #compressed model is sensitive to random seed
 lmeval:
   model: "hf-multimodal"
   model_args:

tests/lmeval/configs/vl_int8_w8a8_dynamic_per_token.yaml

@@ -5,6 +5,7 @@ scheme: INT8_dyn_per_token
 recipe: tests/e2e/vLLM/recipes/INT8/recipe_int8_channel_weight_dynamic_per_token.yaml
 dataset_id: lmms-lab/flickr30k
 dataset_split: "test[:512]"
+seed: 42 #compressed model is sensitive to random seed
 lmeval:
   model: "hf-multimodal"
   model_args:

tests/lmeval/configs/vl_w4a16_actorder_weight.yaml

@@ -5,6 +5,7 @@ recipe: tests/e2e/vLLM/recipes/actorder/recipe_w4a16_actorder_weight.yaml
 dataset_id: lmms-lab/flickr30k
 dataset_split: "test[:512]"
 scheme: W4A16_actorder_group
+seed: 42 #compressed model is sensitive to random seed
 lmeval:
   model: "hf-multimodal"
   model_args:

tests/lmeval/test_lmeval.py

@@ -1,4 +1,5 @@
 import os
+import random
 import shutil
 from pathlib import Path
 
@@ -58,8 +59,6 @@ class TestLMEval:
     """  # noqa: E501
 
     def set_up(self):
-        torch.manual_seed(42)
-
         eval_config = yaml.safe_load(Path(TEST_DATA_FILE).read_text(encoding="utf-8"))
 
         if os.environ.get("CADENCE", "commit") != eval_config.get("cadence"):
@@ -76,6 +75,12 @@ def set_up(self):
         self.quant_type = eval_config.get("quant_type")
         self.save_dir = eval_config.get("save_dir")
 
+        seed = eval_config.get("seed", None)
+        if seed is not None:
+            random.seed(seed)
+            numpy.random.seed(seed)
+            torch.manual_seed(seed)
+
         logger.info("========== RUNNING ==============")
         logger.info(self.scheme)