init

transformers/tests/fsdp/test_fsdp.py

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+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# 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.
+
+import itertools
+import os
+import subprocess
+import unittest
+from copy import deepcopy
+from functools import partial
+
+from parameterized import parameterized
+
+import tests.trainer.test_trainer
+from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
+from transformers import is_torch_available
+from transformers.testing_utils import (
+    TestCasePlus,
+    backend_device_count,
+    execute_subprocess_async,
+    mockenv_context,
+    require_accelerate,
+    require_fsdp,
+    require_torch_accelerator,
+    require_torch_multi_accelerator,
+    run_first,
+    slow,
+    torch_device,
+)
+from transformers.trainer_callback import TrainerState
+from transformers.trainer_utils import FSDPOption, set_seed
+from transformers.utils import (
+    is_accelerate_available,
+    is_torch_bf16_available_on_device,
+    is_torch_fp16_available_on_device,
+)
+
+
+if is_torch_available():
+    from transformers.trainer import FSDP_MODEL_NAME
+
+# default torch.distributed port
+DEFAULT_MASTER_PORT = "10999"
+
+dtypes = []
+if is_torch_bf16_available_on_device(torch_device):
+    dtypes += ["bf16"]
+if is_torch_fp16_available_on_device(torch_device):
+    dtypes += ["fp16"]
+
+sharding_strategies = ["full_shard", "shard_grad_op"]
+state_dict_types = ["FULL_STATE_DICT", "SHARDED_STATE_DICT"]
+params = list(itertools.product(sharding_strategies, dtypes))
+
+set_seed(42)
+
+
+def get_master_port(real_launcher=False):
+    """
+    When using a single gpu launcher emulation (i.e. not deepspeed or python -m torch.distributed)
+    the issue is that once the port is tied it can't be used anywhere else outside of this process,
+    since torch.dist doesn't free the port until the process exits. Therefore for the sake of being
+    able to run both emulated launcher and normal launcher tests we need 2 distinct ports.
+
+    This function will give the right port in the right context. For real launcher it'll give the
+    base port, for emulated launcher it'll give the base port + 1. In both cases a string is
+    returned.
+
+    Args:
+        `real_launcher`: whether a real launcher is going to be used, or the emulated one
+
+    """
+
+    master_port_base = os.environ.get("DS_TEST_PORT", DEFAULT_MASTER_PORT)
+    if not real_launcher:
+        master_port_base = str(int(master_port_base) + 1)
+    return master_port_base
+
+
+if is_torch_available():
+    # hack to restore original logging level pre #21700
+    get_regression_trainer = partial(tests.trainer.test_trainer.get_regression_trainer, log_level="info")
+
+if is_accelerate_available():
+    from accelerate.utils.constants import FSDP_SHARDING_STRATEGY
+
+
+FSDP2_ACCELERATE_VERSION = "1.6.0"
+require_accelerate_fsdp2 = partial(require_accelerate, min_version=FSDP2_ACCELERATE_VERSION)
+require_fsdp_v2_version = require_fsdp
+if is_accelerate_available(min_version=FSDP2_ACCELERATE_VERSION):
+    from accelerate.utils.constants import FSDP2_PYTORCH_VERSION
+
+    require_fsdp_v2_version = partial(require_fsdp, min_version=FSDP2_PYTORCH_VERSION)
+
+
+def get_launcher(distributed=False, use_accelerate=False):
+    # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
+    # - it won't be able to handle that
+    # 2. for now testing with just 2 gpus max (since some quality tests may give different
+    # results with mode gpus because we use very little data)
+    num_gpus = min(2, backend_device_count(torch_device)) if distributed else 1
+    master_port = get_master_port(real_launcher=True)
+    if use_accelerate:
+        return f"""accelerate launch
+        --num_processes {num_gpus}
+        --main_process_port {master_port}
+        --use_fsdp
+        --fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP
+        --fsdp_state_dict_type SHARDED_STATE_DICT
+        --fsdp_transformer_layer_cls_to_wrap BertLayer""".split()
+    return f"torchrun --nnodes 1 --nproc-per-node {num_gpus} --master-port {master_port}".split()
+
+
+def _parameterized_custom_name_func(func, param_num, param):
+    # customize the test name generator function as we want both params to appear in the sub-test
+    # name, as by default it shows only the first param
+    param_based_name = parameterized.to_safe_name("_".join(str(x) for x in param.args))
+    return f"{func.__name__}_{param_based_name}"
+
+
+@require_accelerate
+@require_torch_accelerator
+class TrainerIntegrationFSDP(TestCasePlus, TrainerIntegrationCommon):
+    def setUp(self):
+        super().setUp()
+        master_port = get_master_port(real_launcher=False)
+        self.dist_env_1_gpu = {
+            "MASTER_ADDR": "localhost",
+            "MASTER_PORT": master_port,
+            "RANK": "0",
+            "LOCAL_RANK": "0",
+            "WORLD_SIZE": "1",
+        }
+        self.accelerate_fsdp_config = {
+            "fsdp_activation_checkpointing": False,
+            "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+            "fsdp_backward_prefetch": "BACKWARD_PRE",
+            "fsdp_cpu_ram_efficient_loading": True,
+            "fsdp_forward_prefetch": False,
+            "fsdp_offload_params": False,
+            "fsdp_reshard_after_forward": "FULL_SHARD",
+            "fsdp_state_dict_type": "FULL_STATE_DICT",
+            "fsdp_sync_module_states": True,
+            "fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
+            "fsdp_use_orig_params": True,
+            "fsdp_version": 1,
+        }
+
+        self.fsdp_config = {
+            "backward_prefetch": "BACKWARD_PRE",
+            "forward_prefetch": "false",
+            "limit_all_gathers": "false",
+            "use_orig_params": "true",
+            "sync_module_states": "true",
+            "cpu_ram_efficient_loading": "true",
+            "activation_checkpointing": "false",
+            "min_num_params": 1,
+        }
+
+    def tearDown(self):
+        super().tearDown()
+
+    @parameterized.expand(params, name_func=_parameterized_custom_name_func)
+    def test_accelerate_fsdp_config(self, sharding_strategy, dtype):
+        output_dir = self.get_auto_remove_tmp_dir()
+        kwargs = {
+            "output_dir": output_dir,
+            "train_len": 128,
+            "save_steps": 5,
+            "learning_rate": 0.1,
+            "fsdp": f"{sharding_strategy} offload auto_wrap",
+            "fsdp_config": self.accelerate_fsdp_config,
+        }
+        kwargs[dtype] = True
+        with mockenv_context(**self.dist_env_1_gpu):
+            trainer = get_regression_trainer(**kwargs)
+            self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
+            self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
+            self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
+            for k, v in trainer.args.fsdp_config.items():
+                self.assertTrue(k in self.accelerate_fsdp_config)
+                self.assertEqual(v, self.accelerate_fsdp_config[k])
+            self.assertEqual(os.environ.get("ACCELERATE_USE_FSDP", "false"), "true")
+
+    @parameterized.expand(params, name_func=_parameterized_custom_name_func)
+    def test_fsdp_config(self, sharding_strategy, dtype):
+        output_dir = self.get_auto_remove_tmp_dir()
+        kwargs = {
+            "output_dir": output_dir,
+            "train_len": 128,
+            "save_steps": 5,
+            "learning_rate": 0.1,
+            "fsdp": f"{sharding_strategy} offload auto_wrap",
+            "fsdp_config": self.fsdp_config,
+        }
+        kwargs[dtype] = True
+        with mockenv_context(**self.dist_env_1_gpu):
+            trainer = get_regression_trainer(**kwargs)
+            self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
+            self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
+            self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
+            for k, v in trainer.args.fsdp_config.items():
+                self.assertEqual(v, self.fsdp_config[k])
+            self.assertEqual(os.environ.get("ACCELERATE_USE_FSDP", "false"), "true")
+
+    @parameterized.expand(params, name_func=_parameterized_custom_name_func)
+    def test_fsdp_config_transformers_auto_wrap(self, sharding_strategy, dtype):
+        output_dir = self.get_auto_remove_tmp_dir()
+        fsdp_config = deepcopy(self.fsdp_config)
+        del fsdp_config["min_num_params"]
+        fsdp_config["transformer_layer_cls_to_wrap"] = "BertLayer"
+        kwargs = {
+            "output_dir": output_dir,
+            "train_len": 128,
+            "save_steps": 5,
+            "learning_rate": 0.1,
+            "fsdp": f"{sharding_strategy} offload auto_wrap",
+            "fsdp_config": fsdp_config,
+        }
+        kwargs[dtype] = True
+        prefix = "FSDP_"
+        with mockenv_context(**self.dist_env_1_gpu):
+            trainer = get_regression_trainer(**kwargs)
+            self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
+            self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
+            self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
+            fsdp_sharding_strategy = str(FSDP_SHARDING_STRATEGY.index(sharding_strategy.upper()) + 1)
+            self.assertEqual(os.environ[f"{prefix}SHARDING_STRATEGY"], fsdp_sharding_strategy)
+            self.assertEqual(os.environ[f"{prefix}OFFLOAD_PARAMS"], "true")
+            self.assertEqual(os.environ[f"{prefix}AUTO_WRAP_POLICY"], "TRANSFORMER_BASED_WRAP")
+            self.assertEqual(
+                os.environ[f"{prefix}TRANSFORMER_CLS_TO_WRAP"], ",".join(fsdp_config["transformer_layer_cls_to_wrap"])
+            )
+            self.assertEqual(os.environ[f"{prefix}BACKWARD_PREFETCH"], fsdp_config["backward_prefetch"])
+            self.assertEqual(os.environ[f"{prefix}FORWARD_PREFETCH"], fsdp_config["forward_prefetch"])
+            self.assertEqual(os.environ[f"{prefix}USE_ORIG_PARAMS"], fsdp_config["use_orig_params"])
+            self.assertEqual(os.environ[f"{prefix}SYNC_MODULE_STATES"], fsdp_config["sync_module_states"])
+            self.assertEqual(
+                os.environ[f"{prefix}CPU_RAM_EFFICIENT_LOADING"], fsdp_config["cpu_ram_efficient_loading"]
+            )
+            self.assertEqual(os.environ.get("ACCELERATE_USE_FSDP", "false"), "true")
+
+    @parameterized.expand(params, name_func=_parameterized_custom_name_func)
+    @require_torch_multi_accelerator
+    @run_first
+    @slow
+    def test_basic_run(self, sharding_strategy, dtype):
+        launcher = get_launcher(distributed=True, use_accelerate=False)
+        output_dir = self.get_auto_remove_tmp_dir()
+        args = self.get_base_args(output_dir, 1, 50).split() + [f"--{dtype}"]
+        fsdp_args = ["--fsdp", f"{sharding_strategy} auto_wrap", "--fsdp_transformer_layer_cls_to_wrap", "BertLayer"]
+        script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
+        cmd = launcher + script + args + fsdp_args
+        execute_subprocess_async(cmd, env=self.get_env())
+
+    @parameterized.expand(params, name_func=_parameterized_custom_name_func)
+    @require_torch_multi_accelerator
+    @run_first
+    @slow
+    def test_basic_run_with_gradient_accumulation(self, sharding_strategy, dtype):
+        launcher = get_launcher(distributed=True, use_accelerate=False)
+        output_dir = self.get_auto_remove_tmp_dir()
+        args = self.get_base_args(output_dir, 1, 50).split() + [f"--{dtype}", "--gradient_accumulation_steps", "2"]
+        fsdp_args = ["--fsdp", f"{sharding_strategy} auto_wrap", "--fsdp_transformer_layer_cls_to_wrap", "BertLayer"]
+        script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
+        cmd = launcher + script + args + fsdp_args
+        execute_subprocess_async(cmd, env=self.get_env())
+
+    @parameterized.expand(dtypes)
+    @require_torch_multi_accelerator
+    @run_first
+    @slow
+    def test_basic_run_with_cpu_offload(self, dtype):
+        launcher = get_launcher(distributed=True, use_accelerate=False)
+        output_dir = self.get_auto_remove_tmp_dir()
+        args = self.get_base_args(output_dir, 1, 50).split() + [f"--{dtype}", "--max_steps", "10"]
+        fsdp_args = ["--fsdp", "full_shard auto_wrap offload", "--fsdp_transformer_layer_cls_to_wrap", "BertLayer"]
+        script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
+        cmd = launcher + script + args + fsdp_args
+        execute_subprocess_async(cmd, env=self.get_env())
+
+    @parameterized.expand(state_dict_types, name_func=_parameterized_custom_name_func)
+    @require_torch_multi_accelerator
+    @run_first
+    @slow
+    def test_training_and_can_resume_normally(self, state_dict_type):
+        output_dir = self.get_auto_remove_tmp_dir("./xxx", after=False)
+
+        sharding_strategy = "full_shard"
+        use_accelerate = state_dict_type == "SHARDED_STATE_DICT"
+        launcher = get_launcher(True, use_accelerate=use_accelerate)
+        args = self.get_base_args(output_dir, 2, 25).split()
+        script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
+        logs = self.run_cmd_and_get_logs(use_accelerate, sharding_strategy, launcher, script, args, output_dir)
+
+        # resume from ckpt
+        checkpoint = os.path.join(output_dir, "checkpoint-115")
+        resume_args = args + f"--resume_from_checkpoint {checkpoint}".split()
+
+        is_fsdp_ckpt = os.path.isdir(checkpoint) and (
+            # this checks the FSDP state dict when `SHARDED_STATE_DICT` is used
+            any(
+                FSDP_MODEL_NAME in folder_name
+                for folder_name in os.listdir(checkpoint)
+                if os.path.isdir(os.path.join(checkpoint, folder_name))
+            )
+            # this checks the FSDP state dict when `FULL_STATE_DICT` is used
+            or os.path.isfile(os.path.join(checkpoint, f"{FSDP_MODEL_NAME}.bin"))
+        )
+        self.assertTrue(is_fsdp_ckpt)
+
+        logs_resume = self.run_cmd_and_get_logs(
+            use_accelerate, sharding_strategy, launcher, script, resume_args, output_dir
+        )
+
+        for log, log1 in zip(logs, logs_resume):
+            if "learning_rate" in log:
+                self.assertAlmostEqual(log["learning_rate"], log1["learning_rate"], delta=1e-5)
+
+    @require_torch_multi_accelerator
+    @run_first
+    @slow
+    def test_fsdp_cpu_offloading(self):
+        # TODO: This file is missing and should be added or the test should be removed
+        if not os.path.exists("utils/testing_scripts/fsdp_cpu_offloading.py"):
+            raise unittest.SkipTest("FSDP CPU offloading script not found!")
+
+        try:
+            subprocess.run(
+                "accelerate launch utils/testing_scripts/fsdp_cpu_offloading.py --config utils/testing_scripts/dummy_fsdp_config.yml",
+                shell=True,
+                check=True,
+            )
+        except:  # noqa
+            raise AssertionError("CPU offloading failed with FSDP!")
+
+    @require_torch_multi_accelerator
+    @run_first
+    @slow
+    @require_fsdp_v2_version
+    @require_accelerate_fsdp2
+    def test_accelerate_fsdp2_integration(self):
+        output_dir = self.get_auto_remove_tmp_dir("./xxx", after=False)
+        sharding_strategy = "full_shard"
+        use_accelerate = True
+
+        num_gpus = min(2, backend_device_count(torch_device))
+        master_port = get_master_port(real_launcher=True)
+        launcher = f"""accelerate launch
+            --num_processes {num_gpus}
+            --main_process_port {master_port}
+            --use_fsdp
+            --fsdp_version 2
+            --fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP
+            --fsdp_state_dict_type SHARDED_STATE_DICT
+            --fsdp_transformer_layer_cls_to_wrap BertLayer""".split()
+        args = self.get_base_args(output_dir, 2, 25).split()
+        script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
+        logs = self.run_cmd_and_get_logs(use_accelerate, sharding_strategy, launcher, script, args, output_dir)
+
+        # resume from ckpt
+        checkpoint = os.path.join(output_dir, "checkpoint-115")
+        resume_args = args + f"--resume_from_checkpoint {checkpoint}".split()
+
+        is_fsdp_ckpt = os.path.isdir(checkpoint) and (
+            # this checks the FSDP state dict when `SHARDED_STATE_DICT` is used
+            any(
+                FSDP_MODEL_NAME in folder_name
+                for folder_name in os.listdir(checkpoint)
+                if os.path.isdir(os.path.join(checkpoint, folder_name))
+            )
+            # this checks the FSDP state dict when `FULL_STATE_DICT` is used
+            or os.path.isfile(os.path.join(checkpoint, f"{FSDP_MODEL_NAME}.bin"))
+        )
+        self.assertTrue(is_fsdp_ckpt)
+
+        logs_resume = self.run_cmd_and_get_logs(
+            use_accelerate, sharding_strategy, launcher, script, resume_args, output_dir
+        )
+
+        for log, log1 in zip(logs, logs_resume):
+            if "learning_rate" in log:
+                self.assertAlmostEqual(log["learning_rate"], log1["learning_rate"], delta=1e-5)
+
+    @require_torch_multi_accelerator
+    @run_first
+    @slow
+    @require_fsdp
+    @require_fsdp_v2_version
+    @require_accelerate_fsdp2
+    def test_fsdp2_cpu_offloading(self):
+        # TODO: This file is missing and should be added or the test should be removed
+        if not os.path.exists("utils/testing_scripts/fsdp_cpu_offloading.py"):
+            raise unittest.SkipTest("FSDP 2 CPU offloading script not found!")
+
+        try:
+            subprocess.run(
+                "accelerate launch --fsdp_version 2 utils/testing_scripts/fsdp_cpu_offloading.py --config utils/testing_scripts/dummy_fsdp_config.yml",
+                shell=True,
+                check=True,
+            )
+        except:  # noqa
+            raise AssertionError("CPU offloading failed with FSDP!")
+
+    def run_cmd_and_get_logs(self, use_accelerate, sharding_strategy, launcher, script, args, output_dir):
+        if not use_accelerate:
+            fsdp_args = [
+                "--fsdp",
+                f"{sharding_strategy} auto_wrap",
+                "--fsdp_transformer_layer_cls_to_wrap",
+                "BertLayer",
+            ]
+            cmd = launcher + script + args + fsdp_args
+        else:
+            fsdp_config = f"""
+                --fsdp_sharding_strategy {FSDP_SHARDING_STRATEGY.index(sharding_strategy.upper()) + 1}
+            """.split()
+            cmd = launcher + fsdp_config + script + args
+
+        # keep for quick debug
+        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
+        execute_subprocess_async(cmd, env=self.get_env())
+        logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
+        return logs
+
+    def get_base_args(self, output_dir, num_epochs, logging_steps):
+        return f"""
+            --model_name_or_path google-bert/bert-base-cased
+            --task_name mrpc
+            --output_dir {output_dir}
+            --overwrite_output_dir
+            --do_train
+            --max_seq_length 128
+            --per_device_train_batch_size 16
+            --learning_rate 5e-5
+            --num_train_epochs {num_epochs}
+            --lr_scheduler_type cosine
+            --logging_steps {logging_steps}
+            --save_strategy epoch
+            --do_eval
+            --eval_strategy epoch
+            --report_to none
+        """