[megatron] fix: Fix error in megatron workers (#4832)

### What does this PR do?
There is a bug in megatron_workers.py, 745 line is redundant and
introduces a bug. It overwrites the estimated_flops and promised_flops
calculated on lines 742-744.
Also, the condition "vl" in func.__name__ is brittle as it relies on a
naming convention. This could lead to silent miscalculations of MFU if a
new vision-language model's estimation function is named differently. A
more robust approach is to attempt calling the function with the extra
arguments and handle the TypeError if it doesn't support them.

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Author: zhihaofang1017

verl/utils/flops_counter.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import inspect
+
 import torch
 from transformers import PretrainedConfig
 
@@ -540,8 +542,8 @@ def estimate_flops(self, batch_seqlens, delta_time, **kargs):
         """
         tokens_sum = sum(batch_seqlens)
         func = ESTIMATE_FUNC.get(self.config.model_type, _estimate_unknown_flops)
-        images_seqlens = kargs.get("images_seqlens", None)
-        if images_seqlens is not None and "vl" in func.__name__:
+        sig = inspect.signature(func)
+        if any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()):
             estimated_flops = func(self.config, tokens_sum, batch_seqlens, delta_time, **kargs)
         else:
             estimated_flops = func(self.config, tokens_sum, batch_seqlens, delta_time)

verl/workers/megatron_workers.py

@@ -742,7 +742,6 @@ def update_actor(self, data: DataProto):
         estimated_flops, promised_flops = self.flops_counter.estimate_flops(
             global_num_tokens, delta_time, images_seqlens=images_seqlens
         )
-        estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time)
         metrics["perf/mfu/actor"] = estimated_flops * self.config.actor.ppo_epochs / promised_flops / self.world_size
         metrics["perf/max_memory_allocated_gb"] = get_torch_device().max_memory_allocated() / (1024**3)
         metrics["perf/max_memory_reserved_gb"] = get_torch_device().max_memory_reserved() / (1024**3)