Default include_tkps to true (#3134)

* default true

* force e2e

* causal trainer only

* fix eval loggin [skip-ci]

* revert setup.py

* force tests

* guarding

* guarding

* fix test case

* use evaluate [skip-e2e]

* use evaluate [skip-e2e]

* kick off ci

* fixing

* reverting

src/axolotl/core/builders/base.py

@@ -36,7 +36,6 @@ from axolotl.utils.callbacks import (
     SaveModelOnFirstStepCallback,
 )
 from axolotl.utils.callbacks.profiler import PytorchProfilerCallback
-from axolotl.utils.callbacks.tokens_per_second import TokensPerSecondCallback
 from axolotl.utils.distributed import build_parallelism_config
 from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 
@@ -145,12 +144,6 @@ class TrainerBuilderBase(abc.ABC):
                     profiler_steps_start=self.cfg.profiler_steps_start,
                 )
             )
-        if self.cfg.include_tkps:
-            callbacks.append(
-                TokensPerSecondCallback(
-                    self.cfg.tensor_parallel_size, self.cfg.context_parallel_size
-                )
-            )
 
         return callbacks
 

src/axolotl/core/builders/causal.py

@@ -39,6 +39,7 @@ from axolotl.utils.collators import (
     MambaDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
+from axolotl.utils.callbacks.tokens_per_second import TokensPerSecondCallback
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
 from axolotl.utils.import_helper import get_cls_from_module_str
 from axolotl.utils.logging import get_logger
@@ -71,6 +72,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.qat:
             callbacks.append(QATCallback(self.cfg.qat))
 
+        if self.cfg.include_tkps:
+            callbacks.append(
+                TokensPerSecondCallback(
+                    self.cfg.tensor_parallel_size, self.cfg.context_parallel_size
+                )
+            )
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):

src/axolotl/core/trainers/base.py

@@ -342,10 +342,10 @@ class AxolotlTrainer(
             inputs_key = "labels" if "labels" in inputs else "input_ids"
             if hasattr(self.state, "num_tokens"):
                 self.state.num_tokens = (
-                    self.state.num_tokens + (inputs[inputs_key] != -100).sum()
+                    self.state.num_tokens + (inputs[inputs_key] != -100).sum().cpu()
                 )
             else:
-                self.state.num_tokens = (inputs[inputs_key] != -100).sum()
+                self.state.num_tokens = (inputs[inputs_key] != -100).sum().cpu()
 
         if self.args.orpo_alpha:
             return self.orpo_compute_loss(

src/axolotl/utils/callbacks/tokens_per_second.py

@@ -43,11 +43,12 @@ class TokensPerSecondCallback(TrainerCallback):
         control: TrainerControl,
         **kwargs,
     ):  # pylint: disable=unused-argument
-        step_time = time.perf_counter() - self.start_time
+        if hasattr(state, "num_tokens"):
-        num_tokens_per_device = state.num_tokens.clone()
+            step_time = time.perf_counter() - self.start_time
-        # non data parallel groups have duplicated tokens, so we avoid double-counting
+            num_tokens_per_device = state.num_tokens.clone()
-        num_tokens_per_device = num_tokens_per_device / self.non_data_parallel_size
+            # non data parallel groups have duplicated tokens, so we avoid double-counting
-        state.last_tokens_per_second = num_tokens_per_device / step_time
+            num_tokens_per_device = num_tokens_per_device / self.non_data_parallel_size
+            state.last_tokens_per_second = num_tokens_per_device / step_time
 
     def on_log(
         self,
@@ -58,5 +59,6 @@ class TokensPerSecondCallback(TrainerCallback):
         **kwargs,
     ):  # pylint: disable=unused-argument
         # after logging, clear the running metrics
-        state.last_tokens_per_second.zero_()
+        if hasattr(state, "last_tokens_per_second"):
-        state.num_tokens = 0
+            state.last_tokens_per_second.zero_()
+            state.num_tokens = torch.zeros(1)

src/axolotl/utils/schemas/config.py

@@ -855,9 +855,9 @@ class AxolotlInputConfig(
         },
     )
     include_tkps: bool | None = Field(
-        default=None,
+        default=True,
         json_schema_extra={
-            "description": "bool of whether to report tokens per second during training by measuring throughput of non-padding tokens."
+            "description": "bool of whether to report tokens per second per-gpu during training by measuring throughput of non-padding tokens."
         },
     )
     neftune_noise_alpha: float | None = Field(