Fixed CI/CD for other packages

Signed-off-by: romit <romit@ibm.com>

Author: romitjain

plugins/accelerated-moe/src/fms_acceleration_moe/utils/checkpoint_utils.py

@@ -113,7 +113,7 @@ def save_fsdp_optimizer(
         )
     sd_options = _prepare_sd_options(fsdp_plugin)
     # get the state dicts for model and optimize
-    (model_state_dict, optimizer_state_dict) = get_state_dict(
+    model_state_dict, optimizer_state_dict = get_state_dict(
         model, optimizer, options=sd_options
     )
 

plugins/accelerated-moe/src/fms_acceleration_moe/utils/scattermoe.py

@@ -389,7 +389,7 @@ def _maybe_scatter(
 
         # expect these products to be produced by an earlier
         # all-to-all gather call
-        (send_counts, recv_counts, bins, sorted_expert_idxs, sorted_scattered_idxs) = (
+        send_counts, recv_counts, bins, sorted_expert_idxs, sorted_scattered_idxs = (
             gather_products
         )
 
@@ -421,7 +421,7 @@ def forward(self, hidden_states: torch.Tensor):
         # compute the routing logits, weights, and expert assigments
         # - router_logits: will be passed out of forward, used for computing
         #   routing loss.
-        (router_logits, routing_weights, selected_experts) = (
+        router_logits, routing_weights, selected_experts = (
             self._compute_routing_weights(hidden_states)
         )
 

plugins/accelerated-moe/src/fms_acceleration_moe/utils/scattermoe_state_dict.py

@@ -188,7 +188,7 @@ def _maybe_reshape_scattermoe_expert_weights(
     num_experts: int,
     intermediate_size: int,
 ):
-    (_is_w1, _is_w2, _is_w3) = [
+    _is_w1, _is_w2, _is_w3 = [
         f"{x}.weight" in scatter_key for x in PARAM_NAME_WEIGHT_SCATTERMOE
     ]
 

plugins/accelerated-peft/src/fms_acceleration_peft/gptqmodel/utils/peft.py

@@ -163,10 +163,8 @@ def get_gptq_peft_model(
                     model.model, model_id, adapter_name
                 )
         except Exception as exc:
-            raise NotImplementedError(
-                f"{model.__class__.__name__} not support \
-                    {peft_config.peft_type.value} peft type yet."
-            ) from exc
+            raise NotImplementedError(f"{model.__class__.__name__} not support \
+                    {peft_config.peft_type.value} peft type yet.") from exc
 
     return peft_model
 

plugins/accelerated-peft/tests/test_gptqmodel.py

@@ -297,7 +297,5 @@ def test_quantizing_pretrained_model_outputs_match(
     target = torch.nn.functional.softmax(original_logits, dim=-1)
     target = target.view(BS * SEQLEN, -1)
     error = loss_fn(input, target)
-    assert error.lt(
-        LOSS_TOLERANCE
-    ), "Model logits don't match between both libraries \
+    assert error.lt(LOSS_TOLERANCE), "Model logits don't match between both libraries \
         after quantization"