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Split MoE from end_to_end test (#477)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,148 @@ | ||
| # Copyright 2024 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
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| """A DAG to run end-to-end MoE tests.""" | ||
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| import datetime | ||
| from airflow import models | ||
| from airflow.utils.task_group import TaskGroup | ||
| from dags import composer_env, test_owner | ||
| from dags.quarantined_tests import QuarantineTests | ||
| from dags.vm_resource import XpkClusters, DockerImage | ||
| from dags.multipod.configs import gke_config | ||
| from xlml.utils import name_format | ||
|
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| # Run once a day at 5 am UTC (9 pm PST) | ||
| SCHEDULED_TIME = "0 5 * * *" if composer_env.is_prod_env() else None | ||
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| with models.DAG( | ||
| dag_id="maxtext_moe_tpu_e2e", | ||
| schedule=SCHEDULED_TIME, | ||
| tags=[ | ||
| "sparsity_diffusion_devx", | ||
| "multipod_team", | ||
| "maxtext", | ||
| "tpu", | ||
| "stable", | ||
| "nightly", | ||
| ], | ||
| start_date=datetime.datetime(2024, 11, 14), | ||
| catchup=False, | ||
| ) as dag: | ||
| test_name_prefix = "maxtext" | ||
| quarantine_task_group = TaskGroup( | ||
| group_id="Quarantine", dag=dag, prefix_group_id=False | ||
| ) | ||
|
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| multicluster_test_models = { | ||
| "mixtral-8x7b": [ | ||
| { | ||
| "script_name": "tpu/mixtral/8x7b/1_test_mixtral", | ||
| "cluster": XpkClusters.CPU_M1_MEGAMEM_96_CLUSTER, | ||
| "time_out_in_min": 240, | ||
| }, | ||
| { | ||
| "script_name": "tpu/mixtral/8x7b/2_test_mixtral", | ||
| "cluster": XpkClusters.TPU_V4_128_CLUSTER, | ||
| "time_out_in_min": 60, | ||
| }, | ||
| ], | ||
| "mixtral-8x22b": [ | ||
| { | ||
| "script_name": "tpu/mixtral/8x22b/1_test_mixtral", | ||
| "cluster": XpkClusters.CPU_M1_MEGAMEM_96_CLUSTER, | ||
| "time_out_in_min": 360, | ||
| }, | ||
| { | ||
| "script_name": "tpu/mixtral/8x22b/2_test_mixtral", | ||
| "cluster": XpkClusters.TPU_V5E_256_CLUSTER, | ||
| "time_out_in_min": 60, | ||
| }, | ||
| ], | ||
| } | ||
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| def convert_checkpoint_and_run_training( | ||
| test_group_id, | ||
| test_name_prefix, | ||
| type, | ||
| docker_image, | ||
| model, | ||
| test_scripts_details, | ||
| ): | ||
| with TaskGroup(group_id=test_group_id, prefix_group_id=False) as group: | ||
| test_name = f"{test_name_prefix}-{type}-{model}" | ||
| shared_gcs_location = name_format.generate_gcs_folder_location.override( | ||
| task_id=f"{test_group_id}_generate_gcs_folder_location" | ||
| )( | ||
| gcs_subfolder, | ||
| test_group_id, | ||
| ) | ||
| conversion_cpu = gke_config.get_maxtext_cpu_end_to_end_gke_config( | ||
| time_out_in_min=test_scripts_details[0]["time_out_in_min"], | ||
| test_name=test_name, | ||
| run_model_cmds=( | ||
| f"export BASE_OUTPUT_PATH=$GCS_OUTPUT; bash end_to_end/{test_scripts_details[0]['script_name']}.sh", | ||
| ), | ||
| docker_image=docker_image, | ||
| test_owner=test_owner.RAN_R, | ||
| cluster=test_scripts_details[0]["cluster"], | ||
| ).run(gcs_location=shared_gcs_location) | ||
| training_tpu = gke_config.get_gke_config( | ||
| time_out_in_min=test_scripts_details[1]["time_out_in_min"], | ||
| test_name=test_name, | ||
| run_model_cmds=( | ||
| f"export BASE_OUTPUT_PATH=$GCS_OUTPUT; bash end_to_end/{test_scripts_details[1]['script_name']}.sh", | ||
| ), | ||
| docker_image=docker_image, | ||
| test_owner=test_owner.RAN_R, | ||
| cluster=test_scripts_details[1]["cluster"], | ||
| ).run(gcs_location=shared_gcs_location) | ||
| return conversion_cpu, training_tpu | ||
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| docker_image = { | ||
| "stable": DockerImage.MAXTEXT_TPU_JAX_STABLE_STACK.value, | ||
| "nightly": DockerImage.MAXTEXT_TPU_JAX_NIGHTLY.value, | ||
| } | ||
| tests = [] | ||
| for model, test_scripts_details in multicluster_test_models.items(): | ||
| gcs_subfolder = f"{test_owner.Team.SPARSITY_DIFFUSION_DEVX.value}/maxtext" | ||
| for type in docker_image.keys(): | ||
| test_group_id = "chained_tests" + "_" + model + "_" + type | ||
| if QuarantineTests.is_quarantined(test_group_id): | ||
| with quarantine_task_group: | ||
| mode_cpu, mode_tpu = convert_checkpoint_and_run_training( | ||
| test_group_id, | ||
| test_name_prefix, | ||
| type, | ||
| docker_image[type], | ||
| model, | ||
| test_scripts_details, | ||
| ) | ||
| else: | ||
| mode_cpu, mode_tpu = convert_checkpoint_and_run_training( | ||
| test_group_id, | ||
| test_name_prefix, | ||
| type, | ||
| docker_image[type], | ||
| model, | ||
| test_scripts_details, | ||
| ) | ||
| tests.append(mode_cpu) | ||
| tests.append(mode_tpu) | ||
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| # stable_cpu >> stable_tpu >> nightly_cpu >> nightly_tpu | ||
| for i in range(len(tests) - 1): | ||
| tests[i] << tests[i + 1] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -32,7 +32,7 @@ | |
| tags=[ | ||
| "sparsity_diffusion_devx", | ||
| "multipod_team", | ||
| "gcp_gpu", | ||
| "gpu", | ||
| "axlearn", | ||
| "bite", | ||
| ], | ||
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