add autotuner example and args

examples/aquila/conf/config_auto_tuner.yaml

@@ -0,0 +1,43 @@
+defaults:
+  - train: demo 
+  - _self_
+
+experiment:
+  exp_name: aquila2
+  exp_dir: ./outputs
+  task:
+    type: train
+    backend: megatron
+    entrypoint: ./flagscale/train/train_aquila.py
+  runner:
+    backend: torchrun
+    nnodes: 1 
+    nproc_per_node: 8 
+  envs:
+    CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+    CUDA_DEVICE_MAX_CONNECTIONS: 1
+  auto_tuner:
+    space:
+      data_parallel_size: "auto"
+      use_distributed_optimizer: [true, false]
+      tensor_model_parallel_size: [2, 4, 8]
+      sequence_parallel: [true]
+      pipeline_model_parallel_size: "auto"
+      num_layers_per_virtual_pipeline_stage: [1]
+      context_parallel_size: "auto"
+      expert_model_parallel_size: [1]
+      micro_batch_size: "auto"
+      use_recompute: [true]
+      recompute_method: "auto"
+      recompute_granularity: "auto"
+      recompute_num_layers: "auto"
+    control:
+      max_time_per_task: 300
+      train_iters: 5
+      max_time: 600
+
+action: run
+
+hydra:
+  run:
+    dir: ${experiment.exp_dir}/hydra 

flagscale/auto_tuner/generate.py

@@ -7,8 +7,8 @@ class Generator:
     def __init__(self, config):
         self.config = config
         # TODO: Just a temporary solution, need to be configurated by user
-        if "args_mapping" in config.auto_tuner:
-            self.args_mapping = config.auto_tuner.args_mapping
+        if "args_mapping" in config.experiment.auto_tuner:
+            self.args_mapping = config.experiment.auto_tuner.args_mapping
         else:
             self.args_mapping = {
                 "data_parallel_size": "data_parallel_size",
@@ -50,8 +50,8 @@ def gen(self, strategy):
         config.experiment.runner.tee = 3
         config.experiment.runner.redirects = 3
 
-        # FLAGSCALE_AUTOTUNER should be true, it will not save ckpt when train ended and report memory every iteration
-        config.experiment.envs.FLAGSCALE_AUTOTUNER = True
+        # auto_tune should be true, it will not save ckpt when train ended and report memory every iteration
+        config.system.auto_tune = True
 
         # Del lr_warmup_samples and train_samples to run megatron.
         assert "optimizer" in config.train.model
@@ -79,8 +79,8 @@ def gen(self, strategy):
                 config.train.system.checkpoint.save_interval = 2000
 
         # Set train_iters of each task
-        if "control" in config.auto_tuner:
-            config.train.model.train_iters = config.auto_tuner.control.get(
+        if "control" in config.experiment.auto_tuner:
+            config.train.model.train_iters = config.experiment.auto_tuner.control.get(
                 "train_iters", 5)
         else:
             config.train.model.train_iters = 5

flagscale/auto_tuner/record/recorder.py

@@ -15,15 +15,15 @@ def __init__(self, config):
             "history.csv",
         )
         # Metric to grep in the last rank of last node log file
-        if "auto_tuner" in self.config and "performance" in self.config.auto_tuner:
-            self.metric = self.config.auto_tuner.performance.get(
+        if "auto_tuner" in self.config and "performance" in self.config.experiment.auto_tuner:
+            self.metric = self.config.experiment.auto_tuner.performance.get(
                 "name", "elapsed time per iteration \(ms\):")
         else:
             self.metric = "elapsed time per iteration \(ms\):"
 
         # Sort order of performance, order just in [ascend, and descend], default ascend
-        if "auto_tuner" in self.config and "performance" in self.config.auto_tuner:
-            self.sorted_order = self.config.auto_tuner.performance.get(
+        if "auto_tuner" in self.config and "performance" in self.config.experiment.auto_tuner:
+            self.sorted_order = self.config.experiment.auto_tuner.performance.get(
                 "order", "ascend")
         else:
             self.sorted_order = "ascend"
@@ -66,8 +66,8 @@ def record(self, task, strategy):
             strategy["error"] = None
 
         # Pass back to platform if need
-        if ("airs_switch" in self.config.auto_tuner.platform
-                and self.config.auto_tuner.platform.airs_switch
+        if ("airs_switch" in self.config.experiment.auto_tuner.platform
+                and self.config.experiment.auto_tuner.platform.airs_switch
                 and strategy["performance"]):
             self.pass_back_to_platform(strategy)
 

flagscale/auto_tuner/search/searcher.py

@@ -83,33 +83,33 @@ def _sort(self, key, dim, priority=None):
     def build_space(self, config):
         """Set value of each dim and sort."""
         space = {}
-        cards = config.auto_tuner.cards
-        cards_per_node = config.auto_tuner.nproc_per_node
+        cards = config.experiment.auto_tuner.cards
+        cards_per_node = config.experiment.auto_tuner.nproc_per_node
         num_layers = config.train.model.num_layers
         gbs = config.train.model.global_batch_size
-        if "space" not in config.auto_tuner:
-            config.auto_tuner.space = {}
+        if "space" not in config.experiment.auto_tuner:
+            config.experiment.auto_tuner.space = {}
 
-        if "algo" not in self.config.auto_tuner:
-            self.config.auto_tuner.algo = {"name": "grid", "priority": None}
-        priority = config.auto_tuner.algo.get("priority", None)
-        if config.auto_tuner.platform.get("airs_switch", False):
+        if "algo" not in self.config.experiment.auto_tuner:
+            self.config.experiment.auto_tuner.algo = {"name": "grid", "priority": None}
+        priority = config.experiment.auto_tuner.algo.get("priority", None)
+        if config.experiment.auto_tuner.platform.get("airs_switch", False):
             priority = "memory"
         # Set data parallel degree
         space["data_parallel_size"] = (
             [i for i in range(1, cards + 1)]
-            if "data_parallel_size" not in config.auto_tuner.space
-            or config.auto_tuner.space.data_parallel_size == "auto"
-            else config.auto_tuner.space.data_parallel_size
+            if "data_parallel_size" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.data_parallel_size == "auto"
+            else config.experiment.auto_tuner.space.data_parallel_size
         )
         self._sort("data_parallel_size", space["data_parallel_size"], priority)
 
         # Set distributed optimizer
         space["use_distributed_optimizer"] = (
             [True, False]
-            if "use_distributed_optimizer" not in config.auto_tuner.space
-            or config.auto_tuner.space.use_distributed_optimizer == "auto"
-            else config.auto_tuner.space.use_distributed_optimizer
+            if "use_distributed_optimizer" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.use_distributed_optimizer == "auto"
+            else config.experiment.auto_tuner.space.use_distributed_optimizer
         )
         self._sort(
             "use_distributed_optimizer", space["use_distributed_optimizer"], priority
@@ -118,9 +118,9 @@ def build_space(self, config):
         # Set tensor parallel degree
         space["tensor_model_parallel_size"] = (
             [i for i in range(1, cards_per_node + 1)]
-            if "tensor_model_parallel_size" not in config.auto_tuner.space
-            or config.auto_tuner.space.tensor_model_parallel_size == "auto"
-            else config.auto_tuner.space.tensor_model_parallel_size
+            if "tensor_model_parallel_size" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.tensor_model_parallel_size == "auto"
+            else config.experiment.auto_tuner.space.tensor_model_parallel_size
         )
         self._sort(
             "tensor_model_parallel_size", space["tensor_model_parallel_size"], priority
@@ -129,18 +129,18 @@ def build_space(self, config):
         # Set sequence parallel
         space["sequence_parallel"] = (
             [True, False]
-            if "sequence_parallel" not in config.auto_tuner.space
-            or config.auto_tuner.space.sequence_parallel == "auto"
-            else config.auto_tuner.space.sequence_parallel
+            if "sequence_parallel" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.sequence_parallel == "auto"
+            else config.experiment.auto_tuner.space.sequence_parallel
         )
         self._sort("sequence_parallel", space["sequence_parallel"], priority)
 
         # Set pipeline parallel degree
         space["pipeline_model_parallel_size"] = (
             [i for i in range(1, cards + 1)]
-            if "pipeline_model_parallel_size" not in config.auto_tuner.space
-            or config.auto_tuner.space.pipeline_model_parallel_size == "auto"
-            else config.auto_tuner.space.pipeline_model_parallel_size
+            if "pipeline_model_parallel_size" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.pipeline_model_parallel_size == "auto"
+            else config.experiment.auto_tuner.space.pipeline_model_parallel_size
         )
         self._sort(
             "pipeline_model_parallel_size",
@@ -151,9 +151,9 @@ def build_space(self, config):
         # Set virtual pipeline parallel degree
         space["num_layers_per_virtual_pipeline_stage"] = (
             [i for i in range(1, num_layers + 1)]
-            if "num_layers_per_virtual_pipeline_stage" not in config.auto_tuner.space
-            or config.auto_tuner.space.num_layers_per_virtual_pipeline_stage == "auto"
-            else config.auto_tuner.space.num_layers_per_virtual_pipeline_stage
+            if "num_layers_per_virtual_pipeline_stage" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.num_layers_per_virtual_pipeline_stage == "auto"
+            else config.experiment.auto_tuner.space.num_layers_per_virtual_pipeline_stage
         )
         self._sort(
             "num_layers_per_virtual_pipeline_stage",
@@ -164,63 +164,63 @@ def build_space(self, config):
         # Set use recompute
         space["use_recompute"] = (
             [True, False]
-            if "use_recompute" not in config.auto_tuner.space
-            or config.auto_tuner.space.use_recompute == "auto"
-            else config.auto_tuner.space.use_recompute
+            if "use_recompute" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.use_recompute == "auto"
+            else config.experiment.auto_tuner.space.use_recompute
         )
         self._sort("use_recompute", space["use_recompute"], priority)
         # Set recompute method
         space["recompute_method"] = (
             ["uniform", "block"]
-            if "recompute_method" not in config.auto_tuner.space
-            or config.auto_tuner.space.recompute_method == "auto"
-            else config.auto_tuner.space.recompute_method
+            if "recompute_method" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.recompute_method == "auto"
+            else config.experiment.auto_tuner.space.recompute_method
         )
         self._sort("recompute_method", space["recompute_method"], priority)
 
         # Set recompute granularity
         space["recompute_granularity"] = (
             ["full", "selective"]
-            if "recompute_granularity" not in config.auto_tuner.space
-            or config.auto_tuner.space.recompute_granularity == "auto"
-            else config.auto_tuner.space.recompute_granularity
+            if "recompute_granularity" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.recompute_granularity == "auto"
+            else config.experiment.auto_tuner.space.recompute_granularity
         )
         self._sort("recompute_granularity", space["recompute_granularity"], priority)
 
         # Set recompute num layers
         space["recompute_num_layers"] = (
             [i for i in range(1, num_layers + 1)]
-            if "recompute_num_layers" not in config.auto_tuner.space
-            or config.auto_tuner.space.recompute_num_layers == "auto"
-            else config.auto_tuner.space.recompute_num_layers
+            if "recompute_num_layers" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.recompute_num_layers == "auto"
+            else config.experiment.auto_tuner.space.recompute_num_layers
         )
         self._sort("recompute_num_layers", space["recompute_num_layers"], priority)
 
         # Set micro batch size
         space["micro_batch_size"] = (
             [i for i in range(1, gbs + 1)]
-            if "micro_batch_size" not in config.auto_tuner.space
-            or config.auto_tuner.space.micro_batch_size == "auto"
-            else config.auto_tuner.space.micro_batch_size
+            if "micro_batch_size" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.micro_batch_size == "auto"
+            else config.experiment.auto_tuner.space.micro_batch_size
         )
         self._sort("micro_batch_size", space["micro_batch_size"], priority)
 
         # Set context parallel degree
         space["context_parallel_size"] = (
             [i for i in range(1, cards + 1)]
-            if "context_parallel_size" not in config.auto_tuner.space
-            or config.auto_tuner.space.context_parallel_size == "auto"
-            else config.auto_tuner.space.context_parallel_size
+            if "context_parallel_size" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.context_parallel_size == "auto"
+            else config.experiment.auto_tuner.space.context_parallel_size
         )
         self._sort("context_parallel_size", space["context_parallel_size"], priority)
 
         # Set expert parallel degree
         # NOTE: Expert parallel degree is not supported now
         space["expert_model_parallel_size"] = (
             [1]
-            if "expert_model_parallel_size" not in config.auto_tuner.space
-            or config.auto_tuner.space.expert_model_parallel_size == "auto"
-            else config.auto_tuner.space.expert_model_parallel_size
+            if "expert_model_parallel_size" not in config.experiment.auto_tuner.space
+            or config.experiment.auto_tuner.space.expert_model_parallel_size == "auto"
+            else config.experiment.auto_tuner.space.expert_model_parallel_size
         )
         self._sort(
             "expert_model_parallel_size", space["expert_model_parallel_size"], priority
@@ -240,7 +240,7 @@ def build_strategies(self, space, config):
         return recompute_part
 
     def build_algo(self, strategies, config):
-        name = self.config.auto_tuner.algo.name
+        name = self.config.experiment.auto_tuner.algo.name
         if name == "grid":
             from .algorithm import GridAlgo
 
@@ -251,7 +251,7 @@ def build_algo(self, strategies, config):
     def _product_parallel_dims(self, space, config):
         # Avoid space explosion after product
         product_parallelism_dims = []
-        cards = config.auto_tuner.cards
+        cards = config.experiment.auto_tuner.cards
         for data_parallel_size in space["data_parallel_size"]:
             dims = {}
             if not divisible(cards, data_parallel_size):