# embeddings_queue

created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: b363427b-bd0a-4310-aacb-a1ac9ff7a22f
encoding: FLOAT32
metadata:
{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"b363427b-bd0a-4310-aacb-a1ac9ff7a22f\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"c1c2ff30-4ec1-4f7f-a800-1cee0c6654e8\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"421b550240b8b636cc4c83210ddda91b43e6acab6007a3ef792cb3a62fc1c7c3\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"0f0e93b8-d535-445e-afad-7423100db430\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"57c662e2d5b645555a19baa75f67d852ec634028cd166a6c796b28d19e0a6a19\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 4082, \"end_char_idx\": 7404, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "In order of fastest and most memory-efficient:\n\n| Fastest          | Memory efficient |\n|------------------|------------------|\n| ZeRO-1           | ZeRO-3 + offload |\n| ZeRO-2           | ZeRO-3           |\n| ZeRO-2 + offload | ZeRO-2 + offload |\n| ZeRO-3           | ZeRO-2           |\n| ZeRO-3 + offload | ZeRO-1           |\n\nTo find what works best for you, start with the fastest approach and if you run out of memory, try the next stage which is slower but more memory efficient. Feel free to work in whichever direction you prefer (starting with the most memory efficient or fastest) to discover the appropriate balance between speed and memory usage.\n\nA general process you can use is (start with batch size of 1):\n\n1. enable gradient checkpointing\n2. try ZeRO-2\n3. try ZeRO-2 and offload the optimizer\n4. try ZeRO-3\n5. try ZeRO-3 and offload parameters to the CPU\n6. try ZeRO-3 and offload parameters and the optimizer to the CPU\n7. try lowering various default values like a narrower search beam if you're using the [`~GenerationMixin.generate`] method\n8. try mixed half-precision (fp16 on older GPU architectures and bf16 on Ampere) over full-precision weights\n9. add more hardware if possible or enable Infinity to offload parameters and the optimizer to a NVMe\n10. once you're not running out of memory, measure effective throughput and then try to increase the batch size as large as you can to maximize GPU efficiency\n11. lastly, try to optimize your training setup by disabling some offload features or use a faster ZeRO stage and increasing/decreasing the batch size to find the best tradeoff between speed and memory usage\n\n\n## DeepSpeed configuration file\n\nDeepSpeed works with the [`Trainer`] class by way of a config file containing all the parameters for configuring how you want setup your training run. When you execute your training script, DeepSpeed logs the configuration it received from [`Trainer`] to the console so you can see exactly what configuration was used.\n\n<Tip>\n\nFind a complete list of DeepSpeed configuration options on the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference. You can also find more practical examples of various DeepSpeed configuration examples on the [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples) repository or the main [DeepSpeed](https://github.com/microsoft/DeepSpeed) repository. To quickly find specific examples, you can:\n\n```bash\ngit clone https://github.com/microsoft/DeepSpeedExamples\ncd DeepSpeedExamples\nfind . -name '*json'\n# find examples with the Lamb optimizer\ngrep -i Lamb $(find . -name '*json')\n```\n\n</Tip>\n\nThe DeepSpeed configuration file is passed as a path to a JSON file if you're training from the command line interface or as a nested `dict` object if you're using the [`Trainer`] in a notebook setting.\n\n<hfoptions id=\"pass-config\">\n<hfoption id=\"path to file\">\n\n```py\nTrainingArguments(..., deepspeed=\"path/to/deepspeed_config.json\")\n```\n\n</hfoption>\n<hfoption id=\"nested dict\">\n\n```py\nds_config_dict = dict(scheduler=scheduler_params, optimizer=optimizer_params)\nargs = TrainingArguments(..., deepspeed=ds_config_dict)\ntrainer = Trainer(model, args, ...)\n```\n\n</hfoption>\n</hfoptions>\n\n### DeepSpeed and Trainer parameters\n\nThere are three types of configuration parameters:\n\n1."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 0f0e93b8-d535-445e-afad-7423100db430
encoding: FLOAT32
metadata:
{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"0f0e93b8-d535-445e-afad-7423100db430\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"b363427b-bd0a-4310-aacb-a1ac9ff7a22f\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"37a43302df8569b959e5693ffd140c0ab98e811cce35f7ad1191be0d5376fb1a\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"690afa49-cd42-40f4-822c-baef05576ddd\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"6a484ff399c53116700d6212e3896df029a3b4418281542312ec2188cd9fe06b\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 5725, \"end_char_idx\": 9333, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "## DeepSpeed configuration file\n\nDeepSpeed works with the [`Trainer`] class by way of a config file containing all the parameters for configuring how you want setup your training run. When you execute your training script, DeepSpeed logs the configuration it received from [`Trainer`] to the console so you can see exactly what configuration was used.\n\n<Tip>\n\nFind a complete list of DeepSpeed configuration options on the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference. You can also find more practical examples of various DeepSpeed configuration examples on the [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples) repository or the main [DeepSpeed](https://github.com/microsoft/DeepSpeed) repository. To quickly find specific examples, you can:\n\n```bash\ngit clone https://github.com/microsoft/DeepSpeedExamples\ncd DeepSpeedExamples\nfind . -name '*json'\n# find examples with the Lamb optimizer\ngrep -i Lamb $(find . -name '*json')\n```\n\n</Tip>\n\nThe DeepSpeed configuration file is passed as a path to a JSON file if you're training from the command line interface or as a nested `dict` object if you're using the [`Trainer`] in a notebook setting.\n\n<hfoptions id=\"pass-config\">\n<hfoption id=\"path to file\">\n\n```py\nTrainingArguments(..., deepspeed=\"path/to/deepspeed_config.json\")\n```\n\n</hfoption>\n<hfoption id=\"nested dict\">\n\n```py\nds_config_dict = dict(scheduler=scheduler_params, optimizer=optimizer_params)\nargs = TrainingArguments(..., deepspeed=ds_config_dict)\ntrainer = Trainer(model, args, ...)\n```\n\n</hfoption>\n</hfoptions>\n\n### DeepSpeed and Trainer parameters\n\nThere are three types of configuration parameters:\n\n1. Some of the configuration parameters are shared by [`Trainer`] and DeepSpeed, and it can be difficult to identify errors when there are conflicting definitions. To make it easier, these shared configuration parameters are configured from the [`Trainer`] command line arguments.\n\n2. Some configuration parameters that are automatically derived from the model configuration so you don't need to manually adjust these values. The [`Trainer`] uses a configuration value `auto` to determine set the most correct or efficient value. You could set your own configuration parameters explicitly, but you must take care to ensure the [`Trainer`] arguments and DeepSpeed configuration parameters agree. Mismatches may cause the training to fail in very difficult to detect ways!\n\n3. Some configuration parameters specific to DeepSpeed only which need to be manually set based on your training needs.\n\nYou could also modify the DeepSpeed configuration and edit [`TrainingArguments`] from it:\n\n1. Create or load a DeepSpeed configuration to use as the main configuration\n2. Create a [`TrainingArguments`] object based on these DeepSpeed configuration values\n\nSome values, such as `scheduler.params.total_num_steps` are calculated by the [`Trainer`] during training.\n\n### ZeRO configuration\n\nThere are three configurations, each corresponding to a different ZeRO stage. Stage 1 is not as interesting for scalability, and this guide focuses on stages 2 and 3. The `zero_optimization` configuration contains all the options for what to enable and how to configure them. For a more detailed explanation of each parameter, take a look at the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference.\n\n<Tip warning={true}>\nDeepSpeed doesn\u2019t validate parameter names and any typos fallback on the parameter's default setting. You can watch the DeepSpeed engine startup log messages to see what values it is going to use."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 690afa49-cd42-40f4-822c-baef05576ddd
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To make it easier, these shared configuration parameters are configured from the [`Trainer`] command line arguments.\n\n2. Some configuration parameters that are automatically derived from the model configuration so you don't need to manually adjust these values. The [`Trainer`] uses a configuration value `auto` to determine set the most correct or efficient value. You could set your own configuration parameters explicitly, but you must take care to ensure the [`Trainer`] arguments and DeepSpeed configuration parameters agree. Mismatches may cause the training to fail in very difficult to detect ways!\n\n3. Some configuration parameters specific to DeepSpeed only which need to be manually set based on your training needs.\n\nYou could also modify the DeepSpeed configuration and edit [`TrainingArguments`] from it:\n\n1. Create or load a DeepSpeed configuration to use as the main configuration\n2. Create a [`TrainingArguments`] object based on these DeepSpeed configuration values\n\nSome values, such as `scheduler.params.total_num_steps` are calculated by the [`Trainer`] during training.\n\n### ZeRO configuration\n\nThere are three configurations, each corresponding to a different ZeRO stage. Stage 1 is not as interesting for scalability, and this guide focuses on stages 2 and 3. The `zero_optimization` configuration contains all the options for what to enable and how to configure them. For a more detailed explanation of each parameter, take a look at the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference.\n\n<Tip warning={true}>\nDeepSpeed doesn\u2019t validate parameter names and any typos fallback on the parameter's default setting. You can watch the DeepSpeed engine startup log messages to see what values it is going to use.\n\n</Tip>\n\nThe following configurations must be setup with DeepSpeed because the [`Trainer`] doesn't provide equivalent command line arguments.\n\n<hfoptions id=\"zero-config\">\n<hfoption id=\"ZeRO-1\">\n\nZeRO-1 shards the optimizer states across GPUs, and you can expect a tiny speed up. The ZeRO-1 config can be setup like this:\n\n```yml\n{\n    \"zero_optimization\": {\n        \"stage\": 1\n    }\n}\n```\n\n</hfoption>\n<hfoption id=\"ZeRO-2\">\n\nZeRO-2 shards the optimizer and gradients across GPUs. This stage is primarily used for training since its features are not relevant to inference. Some important parameters to configure for better performance include:\n\n* `offload_optimizer` should be enabled to reduce GPU memory usage.\n* `overlap_comm` when set to `true` trades off increased GPU memory usage to lower allreduce latency. This feature uses 4.5x the `allgather_bucket_size` and `reduce_bucket_size` values. In this example, they're set to `5e8` which means it requires 9GB of GPU memory. If your GPU memory is 8GB or less, you should reduce `overlap_comm` to lower the memory requirements and prevent an out-of-memory (OOM) error.\n* `allgather_bucket_size` and `reduce_bucket_size` trade off available GPU memory for communication speed. The smaller their values, the slower communication is and the more GPU memory is available. You can balance, for example, whether a bigger batch size is more important than a slightly slower training time.\n* `round_robin_gradients` is available in DeepSpeed 0.4.4 for CPU offloading. It parallelizes gradient copying to CPU memory among ranks by fine-grained gradient partitioning."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 57571c98-8329-4405-b7eb-711ff32a28b1
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The ZeRO-1 config can be setup like this:\n\n```yml\n{\n    \"zero_optimization\": {\n        \"stage\": 1\n    }\n}\n```\n\n</hfoption>\n<hfoption id=\"ZeRO-2\">\n\nZeRO-2 shards the optimizer and gradients across GPUs. This stage is primarily used for training since its features are not relevant to inference. Some important parameters to configure for better performance include:\n\n* `offload_optimizer` should be enabled to reduce GPU memory usage.\n* `overlap_comm` when set to `true` trades off increased GPU memory usage to lower allreduce latency. This feature uses 4.5x the `allgather_bucket_size` and `reduce_bucket_size` values. In this example, they're set to `5e8` which means it requires 9GB of GPU memory. If your GPU memory is 8GB or less, you should reduce `overlap_comm` to lower the memory requirements and prevent an out-of-memory (OOM) error.\n* `allgather_bucket_size` and `reduce_bucket_size` trade off available GPU memory for communication speed. The smaller their values, the slower communication is and the more GPU memory is available. You can balance, for example, whether a bigger batch size is more important than a slightly slower training time.\n* `round_robin_gradients` is available in DeepSpeed 0.4.4 for CPU offloading. It parallelizes gradient copying to CPU memory among ranks by fine-grained gradient partitioning. Performance benefit grows with gradient accumulation steps (more copying between optimizer steps) or GPU count (increased parallelism).\n\n```yml\n{\n    \"zero_optimization\": {\n        \"stage\": 2,\n        \"offload_optimizer\": {\n            \"device\": \"cpu\",\n            \"pin_memory\": true\n        },\n        \"allgather_partitions\": true,\n        \"allgather_bucket_size\": 5e8,\n        \"overlap_comm\": true,\n        \"reduce_scatter\": true,\n        \"reduce_bucket_size\": 5e8,\n        \"contiguous_gradients\": true\n        \"round_robin_gradients\": true\n    }\n}\n```\n\n</hfoption>\n<hfoption id=\"ZeRO-3\">\n\nZeRO-3 shards the optimizer, gradient, and parameters across GPUs. Unlike ZeRO-2, ZeRO-3 can also be used for inference, in addition to training, because it allows large models to be loaded on multiple GPUs. Some important parameters to configure include:\n\n* `device: \"cpu\"` can help if you're running out of GPU memory and if you have free CPU memory available. This allows offloading model parameters to the CPU.\n* `pin_memory: true` can improve throughput, but less memory becomes available for other processes because the pinned memory is reserved for the specific process that requested it and it's typically accessed much faster than normal CPU memory.\n* `stage3_max_live_parameters` is the upper limit on how many full parameters you want to keep on the GPU at any given time. Reduce this value if you encounter an OOM error.\n* `stage3_max_reuse_distance` is a value for determining when a parameter is used again in the future, and it helps decide whether to throw the parameter away or to keep it."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"2970f3a9-1fb4-4f5d-9dcf-b59dde68699a\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"57571c98-8329-4405-b7eb-711ff32a28b1\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"df907bcab579e5281fd4298f84b9f1c9ba730994b00a786c5f07948e55586ad7\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"c2a41ce6-21ce-43c0-b5d6-72926e18468a\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"1fb4afe71567f903c105a1c70ed704cb3a5d504d30934b92f1179b12dc5d91c1\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 10849, \"end_char_idx\": 14209, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "It parallelizes gradient copying to CPU memory among ranks by fine-grained gradient partitioning. Performance benefit grows with gradient accumulation steps (more copying between optimizer steps) or GPU count (increased parallelism).\n\n```yml\n{\n    \"zero_optimization\": {\n        \"stage\": 2,\n        \"offload_optimizer\": {\n            \"device\": \"cpu\",\n            \"pin_memory\": true\n        },\n        \"allgather_partitions\": true,\n        \"allgather_bucket_size\": 5e8,\n        \"overlap_comm\": true,\n        \"reduce_scatter\": true,\n        \"reduce_bucket_size\": 5e8,\n        \"contiguous_gradients\": true\n        \"round_robin_gradients\": true\n    }\n}\n```\n\n</hfoption>\n<hfoption id=\"ZeRO-3\">\n\nZeRO-3 shards the optimizer, gradient, and parameters across GPUs. Unlike ZeRO-2, ZeRO-3 can also be used for inference, in addition to training, because it allows large models to be loaded on multiple GPUs. Some important parameters to configure include:\n\n* `device: \"cpu\"` can help if you're running out of GPU memory and if you have free CPU memory available. This allows offloading model parameters to the CPU.\n* `pin_memory: true` can improve throughput, but less memory becomes available for other processes because the pinned memory is reserved for the specific process that requested it and it's typically accessed much faster than normal CPU memory.\n* `stage3_max_live_parameters` is the upper limit on how many full parameters you want to keep on the GPU at any given time. Reduce this value if you encounter an OOM error.\n* `stage3_max_reuse_distance` is a value for determining when a parameter is used again in the future, and it helps decide whether to throw the parameter away or to keep it. If the parameter is going to be reused (if the value is less than `stage3_max_reuse_distance`), then it is kept to reduce communication overhead. This is super helpful when activation checkpointing is enabled and you want to keep the parameter in the forward recompute until the backward pass. But reduce this value if you encounter an OOM error.\n* `stage3_gather_16bit_weights_on_model_save` consolidates fp16 weights when a model is saved. For large models and multiple GPUs, this is expensive in terms of memory and speed. You should enable it if you're planning on resuming training.\n* `sub_group_size` controls which parameters are updated during the optimizer step. Parameters are grouped into buckets of `sub_group_size` and each bucket is updated one at a time. When used with NVMe offload, `sub_group_size` determines when model states are moved in and out of CPU memory from during the optimization step. This prevents running out of CPU memory for extremely large models. `sub_group_size` can be left to its default value if you aren't using NVMe offload, but you may want to change it if you:\n\n    1. Run into an OOM error during the optimizer step. In this case, reduce `sub_group_size` to reduce memory usage of the temporary buffers.\n    2. The optimizer step is taking a really long time. In this case, increase `sub_group_size` to improve bandwidth utilization as a result of increased data buffers.\n\n* `reduce_bucket_size`, `stage3_prefetch_bucket_size`, and `stage3_param_persistence_threshold` are dependent on a model's hidden size. It is recommended to set these values to `auto` and allow the [`Trainer`] to automatically assign the values."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: c2a41ce6-21ce-43c0-b5d6-72926e18468a
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"c2a41ce6-21ce-43c0-b5d6-72926e18468a\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"2970f3a9-1fb4-4f5d-9dcf-b59dde68699a\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"ca9fbaa59d0b6d0f4155d0d2188fc72dd3ff77038e3b9615f31b7f06832e381c\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"400be75a-cc67-420e-9645-23e4959f0ecf\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"4cecf0cf090bf952d33b283043aaeb74f4e5134be7d21b202cfb1b35592e6e66\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 12546, \"end_char_idx\": 15535, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "If the parameter is going to be reused (if the value is less than `stage3_max_reuse_distance`), then it is kept to reduce communication overhead. This is super helpful when activation checkpointing is enabled and you want to keep the parameter in the forward recompute until the backward pass. But reduce this value if you encounter an OOM error.\n* `stage3_gather_16bit_weights_on_model_save` consolidates fp16 weights when a model is saved. For large models and multiple GPUs, this is expensive in terms of memory and speed. You should enable it if you're planning on resuming training.\n* `sub_group_size` controls which parameters are updated during the optimizer step. Parameters are grouped into buckets of `sub_group_size` and each bucket is updated one at a time. When used with NVMe offload, `sub_group_size` determines when model states are moved in and out of CPU memory from during the optimization step. This prevents running out of CPU memory for extremely large models. `sub_group_size` can be left to its default value if you aren't using NVMe offload, but you may want to change it if you:\n\n    1. Run into an OOM error during the optimizer step. In this case, reduce `sub_group_size` to reduce memory usage of the temporary buffers.\n    2. The optimizer step is taking a really long time. In this case, increase `sub_group_size` to improve bandwidth utilization as a result of increased data buffers.\n\n* `reduce_bucket_size`, `stage3_prefetch_bucket_size`, and `stage3_param_persistence_threshold` are dependent on a model's hidden size. It is recommended to set these values to `auto` and allow the [`Trainer`] to automatically assign the values.\n\n```yml\n{\n    \"zero_optimization\": {\n        \"stage\": 3,\n        \"offload_optimizer\": {\n            \"device\": \"cpu\",\n            \"pin_memory\": true\n        },\n        \"offload_param\": {\n            \"device\": \"cpu\",\n            \"pin_memory\": true\n        },\n        \"overlap_comm\": true,\n        \"contiguous_gradients\": true,\n        \"sub_group_size\": 1e9,\n        \"reduce_bucket_size\": \"auto\",\n        \"stage3_prefetch_bucket_size\": \"auto\",\n        \"stage3_param_persistence_threshold\": \"auto\",\n        \"stage3_max_live_parameters\": 1e9,\n        \"stage3_max_reuse_distance\": 1e9,\n        \"stage3_gather_16bit_weights_on_model_save\": true\n    }\n}\n```\n\nYou can use the [`deepspeed.zero.Init`](https://deepspeed.readthedocs.io/en/latest/zero3.html#deepspeed.zero.Init) context manager to initialize a model faster:\n\n```py\nfrom transformers import T5ForConditionalGeneration, T5Config\nimport deepspeed\n\nwith deepspeed.zero.Init():\n    config = T5Config.from_pretrained(\"google-t5/t5-small\")\n    model = T5ForConditionalGeneration(config)\n```\n\nFor pretrained models, the DeepSped config file needs to have `is_deepspeed_zero3_enabled: true` setup in [`TrainingArguments`] and it needs a ZeRO configuration enabled. The [`TrainingArguments`] object must be created **before** calling the model [`~PreTrainedModel.from_pretrained`]."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 400be75a-cc67-420e-9645-23e4959f0ecf
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"400be75a-cc67-420e-9645-23e4959f0ecf\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"c2a41ce6-21ce-43c0-b5d6-72926e18468a\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"c8aab10fef9ac0e714148b7a47987dffd23e87222073763b3e4560b01d015e4c\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"921c0039-5005-4820-8738-015c504542ad\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"25f10ff421ad9688a69ac27af21316983380f40eee7aa361b57494631fa64da0\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 13964, \"end_char_idx\": 17184, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "* `reduce_bucket_size`, `stage3_prefetch_bucket_size`, and `stage3_param_persistence_threshold` are dependent on a model's hidden size. It is recommended to set these values to `auto` and allow the [`Trainer`] to automatically assign the values.\n\n```yml\n{\n    \"zero_optimization\": {\n        \"stage\": 3,\n        \"offload_optimizer\": {\n            \"device\": \"cpu\",\n            \"pin_memory\": true\n        },\n        \"offload_param\": {\n            \"device\": \"cpu\",\n            \"pin_memory\": true\n        },\n        \"overlap_comm\": true,\n        \"contiguous_gradients\": true,\n        \"sub_group_size\": 1e9,\n        \"reduce_bucket_size\": \"auto\",\n        \"stage3_prefetch_bucket_size\": \"auto\",\n        \"stage3_param_persistence_threshold\": \"auto\",\n        \"stage3_max_live_parameters\": 1e9,\n        \"stage3_max_reuse_distance\": 1e9,\n        \"stage3_gather_16bit_weights_on_model_save\": true\n    }\n}\n```\n\nYou can use the [`deepspeed.zero.Init`](https://deepspeed.readthedocs.io/en/latest/zero3.html#deepspeed.zero.Init) context manager to initialize a model faster:\n\n```py\nfrom transformers import T5ForConditionalGeneration, T5Config\nimport deepspeed\n\nwith deepspeed.zero.Init():\n    config = T5Config.from_pretrained(\"google-t5/t5-small\")\n    model = T5ForConditionalGeneration(config)\n```\n\nFor pretrained models, the DeepSped config file needs to have `is_deepspeed_zero3_enabled: true` setup in [`TrainingArguments`] and it needs a ZeRO configuration enabled. The [`TrainingArguments`] object must be created **before** calling the model [`~PreTrainedModel.from_pretrained`].\n\n```py\nfrom transformers import AutoModel, Trainer, TrainingArguments\n\ntraining_args = TrainingArguments(..., deepspeed=ds_config)\nmodel = AutoModel.from_pretrained(\"google-t5/t5-small\")\ntrainer = Trainer(model=model, args=training_args, ...)\n```\n\nYou'll need ZeRO-3 if the fp16 weights don't fit on a single GPU. If you're able to load fp16 weights, then make sure you specify `torch_dtype=torch.float16` in [`~PreTrainedModel.from_pretrained`].\n\nAnother consideration for ZeRO-3 is if you have multiple GPUs, no single GPU has all the parameters unless it's the parameters for the currently executing layer. To access all parameters from all the layers at once, such as loading pretrained model weights in [`~PreTrainedModel.from_pretrained`], one layer is loaded at a time and immediately partitioned to all GPUs. This is because for very large models, it isn't possible to load the weights on one GPU and then distribute them across the other GPUs due to memory limitations.\n\nIf you encounter a model parameter weight that looks like the following, where `tensor([1.])` or the parameter size is 1 instead of a larger multi-dimensional shape, this means the parameter is partitioned and this is a ZeRO-3 placeholder.\n\n```py\ntensor([1.0], device=\"cuda:0\", dtype=torch.float16, requires_grad=True)\n```\n\n<Tip>\n\nFor more information about initializing large models with ZeRO-3 and accessing the parameters, take a look at the [Constructing Massive Models](https://deepspeed.readthedocs.io/en/latest/zero3.html#constructing-massive-models) and [Gathering Parameters](https://deepspeed.readthedocs.io/en/latest/zero3.html#gathering-parameters) guides."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 921c0039-5005-4820-8738-015c504542ad
encoding: FLOAT32
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If you're able to load fp16 weights, then make sure you specify `torch_dtype=torch.float16` in [`~PreTrainedModel.from_pretrained`].\n\nAnother consideration for ZeRO-3 is if you have multiple GPUs, no single GPU has all the parameters unless it's the parameters for the currently executing layer. To access all parameters from all the layers at once, such as loading pretrained model weights in [`~PreTrainedModel.from_pretrained`], one layer is loaded at a time and immediately partitioned to all GPUs. This is because for very large models, it isn't possible to load the weights on one GPU and then distribute them across the other GPUs due to memory limitations.\n\nIf you encounter a model parameter weight that looks like the following, where `tensor([1.])` or the parameter size is 1 instead of a larger multi-dimensional shape, this means the parameter is partitioned and this is a ZeRO-3 placeholder.\n\n```py\ntensor([1.0], device=\"cuda:0\", dtype=torch.float16, requires_grad=True)\n```\n\n<Tip>\n\nFor more information about initializing large models with ZeRO-3 and accessing the parameters, take a look at the [Constructing Massive Models](https://deepspeed.readthedocs.io/en/latest/zero3.html#constructing-massive-models) and [Gathering Parameters](https://deepspeed.readthedocs.io/en/latest/zero3.html#gathering-parameters) guides.\n\n</Tip>\n\n</hfoption>\n</hfoptions>\n\n### NVMe configuration\n\n[ZeRO-Infinity](https://hf.co/papers/2104.07857) allows offloading model states to the CPU and/or NVMe to save even more memory. Smart partitioning and tiling algorithms allow each GPU to send and receive very small amounts of data during offloading such that a modern NVMe can fit an even larger total memory pool than is available to your training process. ZeRO-Infinity requires ZeRO-3.\n\nDepending on the CPU and/or NVMe memory available, you can offload both the [optimizer states](https://www.deepspeed.ai/docs/config-json/#optimizer-offloading) and [parameters](https://www.deepspeed.ai/docs/config-json/#parameter-offloading), just one of them, or none. You should also make sure the `nvme_path` is pointing to an NVMe device, because while it still works with a normal hard drive or solid state drive, it'll be significantly slower. With a modern NVMe, you can expect peak transfer speeds of ~3.5GB/s for read and ~3GB/s for write operations. Lastly, [run a benchmark](https://github.com/microsoft/DeepSpeed/issues/998) on your training setup to determine the optimal `aio` configuration.\n\nThe example ZeRO-3/Infinity configuration file below sets most of the parameter values to `auto`, but you could also manually add these values."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 871cb367-2013-4b80-826c-ffc3f7495344
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Smart partitioning and tiling algorithms allow each GPU to send and receive very small amounts of data during offloading such that a modern NVMe can fit an even larger total memory pool than is available to your training process. ZeRO-Infinity requires ZeRO-3.\n\nDepending on the CPU and/or NVMe memory available, you can offload both the [optimizer states](https://www.deepspeed.ai/docs/config-json/#optimizer-offloading) and [parameters](https://www.deepspeed.ai/docs/config-json/#parameter-offloading), just one of them, or none. You should also make sure the `nvme_path` is pointing to an NVMe device, because while it still works with a normal hard drive or solid state drive, it'll be significantly slower. With a modern NVMe, you can expect peak transfer speeds of ~3.5GB/s for read and ~3GB/s for write operations. Lastly, [run a benchmark](https://github.com/microsoft/DeepSpeed/issues/998) on your training setup to determine the optimal `aio` configuration.\n\nThe example ZeRO-3/Infinity configuration file below sets most of the parameter values to `auto`, but you could also manually add these values.\n\n```yml\n{\n    \"fp16\": {\n        \"enabled\": \"auto\",\n        \"loss_scale\": 0,\n        \"loss_scale_window\": 1000,\n        \"initial_scale_power\": 16,\n        \"hysteresis\": 2,\n        \"min_loss_scale\": 1\n    },\n\n    \"optimizer\": {\n        \"type\": \"AdamW\",\n        \"params\": {\n            \"lr\": \"auto\",\n            \"betas\": \"auto\",\n            \"eps\": \"auto\",\n            \"weight_decay\": \"auto\"\n        }\n    },\n\n    \"scheduler\": {\n        \"type\": \"WarmupLR\",\n        \"params\": {\n            \"warmup_min_lr\": \"auto\",\n            \"warmup_max_lr\": \"auto\",\n            \"warmup_num_steps\": \"auto\"\n        }\n    },\n\n    \"zero_optimization\": {\n        \"stage\": 3,\n        \"offload_optimizer\": {\n            \"device\": \"nvme\",\n            \"nvme_path\": \"/local_nvme\",\n            \"pin_memory\": true,\n            \"buffer_count\": 4,\n            \"fast_init\": false\n        },\n        \"offload_param\": {\n            \"device\": \"nvme\",\n            \"nvme_path\": \"/local_nvme\",\n            \"pin_memory\": true,\n            \"buffer_count\": 5,\n            \"buffer_size\": 1e8,\n            \"max_in_cpu\": 1e9\n        },\n        \"aio\": {\n            \"block_size\": 262144,\n            \"queue_depth\": 32,\n            \"thread_count\": 1,\n            \"single_submit\": false,\n            \"overlap_events\": true\n        },\n        \"overlap_comm\": true,\n        \"contiguous_gradients\": true,\n        \"sub_group_size\": 1e9,\n        \"reduce_bucket_size\": \"auto\",\n        \"stage3_prefetch_bucket_size\": \"auto\",\n        \"stage3_param_persistence_threshold\": \"auto\",\n        \"stage3_max_live_parameters\": 1e9,\n        \"stage3_max_reuse_distance\": 1e9,\n        \"stage3_gather_16bit_weights_on_model_save\": true\n    },\n\n    \"gradient_accumulation_steps\": \"auto\",\n    \"gradient_clipping\": \"auto\",\n    \"steps_per_print\": 2000,\n    \"train_batch_size\": \"auto\",\n    \"train_micro_batch_size_per_gpu\": \"auto\",\n    \"wall_clock_breakdown\": false\n}\n```\n\n## DeepSpeed features\n\nThere are a number of important parameters to specify in the DeepSpeed configuration file which are briefly described in this section."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"5a213fc0-fbc1-45bc-b249-cda996b33007\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"871cb367-2013-4b80-826c-ffc3f7495344\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"db28441c6e730e06ac2b06bb31c92ceea2bc4ed0b4ac5c876dafc1af9b96e0cf\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"f41af833-5710-46a3-a694-e1480df3a1bc\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"f61da2562c3fc580e05e27938d999e344cfb0e3768042539cf5b106e62452d0a\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 20543, \"end_char_idx\": 23457, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "### Activation/gradient checkpointing\n\nActivation and gradient checkpointing trades speed for more GPU memory which allows you to overcome scenarios where your GPU is out of memory or to increase your batch size for better performance. To enable this feature:\n\n1. For a Hugging Face model, set `model.gradient_checkpointing_enable()` or `--gradient_checkpointing` in the [`Trainer`].\n2. For a non-Hugging Face model, use the DeepSpeed [Activation Checkpointing API](https://deepspeed.readthedocs.io/en/latest/activation-checkpointing.html). You could also replace the Transformers modeling code and replace `torch.utils.checkpoint` with the DeepSpeed API. This approach is more flexible because you can offload the forward activations to the CPU memory instead of recalculating them.\n\n### Optimizer and scheduler\n\nDeepSpeed and Transformers optimizer and scheduler can be mixed and matched as long as you don't enable `offload_optimizer`. When `offload_optimizer` is enabled, you could use a non-DeepSpeed optimizer (except for LAMB) as long as it has both a CPU and GPU implementation.\n\n<Tip warning={true}>\n\nThe optimizer and scheduler parameters for the config file can be set from the command line to avoid hard to find errors. For example, if the learning rate is set to a different value in another place you can override it from the command line. Aside from the optimizer and scheduler parameters, you'll need to ensure your [`Trainer`] command line arguments match the DeepSpeed configuration.\n\n</Tip>\n\n<hfoptions id=\"opt-sched\">\n<hfoption id=\"optimizer\">\n\nDeepSpeed offers several [optimizers](https://www.deepspeed.ai/docs/config-json/#optimizer-parameters) (Adam, AdamW, OneBitAdam, and LAMB) but you can also import other optimizers from PyTorch. If you don't configure the optimizer in the config, the [`Trainer`] automatically selects AdamW and either uses the supplied values or the default values for the following parameters from the command line: `lr`, `adam_beta1`, `adam_beta2`, `adam_epsilon`, `weight_decay`.\n\nYou can set the parameters to `\"auto\"` or manually input your own desired values.\n\n```yaml\n{\n   \"optimizer\": {\n       \"type\": \"AdamW\",\n       \"params\": {\n         \"lr\": \"auto\",\n         \"betas\": \"auto\",\n         \"eps\": \"auto\",\n         \"weight_decay\": \"auto\"\n       }\n   }\n}\n```\n\nYou can also use an unsupported optimizer by adding the following to the top level configuration.\n\n```yaml\n{\n   \"zero_allow_untested_optimizer\": true\n}\n```\n\nFrom DeepSpeed==0.8.3 on, if you want to use offload, you'll also need to the following to the top level configuration because offload works best with DeepSpeed's CPU Adam optimizer.\n\n```yaml\n{\n   \"zero_force_ds_cpu_optimizer\": false\n}\n```\n\n</hfoption>\n<hfoption id=\"scheduler\">\n\nDeepSpeed supports the LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR learning rate [schedulers](https://www.deepspeed.ai/docs/config-json/#scheduler-parameters)."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: f41af833-5710-46a3-a694-e1480df3a1bc
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"f41af833-5710-46a3-a694-e1480df3a1bc\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"5a213fc0-fbc1-45bc-b249-cda996b33007\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"19066e756ee402b09881587be0d7b77013e2ede541853c46134182051f8b035a\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"e40ed3c3-0882-4ee6-a5ee-d3aa8b39c8fe\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"6157b46c7ac0d7a4fb4c6baf996d039d338e36630066789becda8eb83bd070b6\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 21897, \"end_char_idx\": 24877, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "Aside from the optimizer and scheduler parameters, you'll need to ensure your [`Trainer`] command line arguments match the DeepSpeed configuration.\n\n</Tip>\n\n<hfoptions id=\"opt-sched\">\n<hfoption id=\"optimizer\">\n\nDeepSpeed offers several [optimizers](https://www.deepspeed.ai/docs/config-json/#optimizer-parameters) (Adam, AdamW, OneBitAdam, and LAMB) but you can also import other optimizers from PyTorch. If you don't configure the optimizer in the config, the [`Trainer`] automatically selects AdamW and either uses the supplied values or the default values for the following parameters from the command line: `lr`, `adam_beta1`, `adam_beta2`, `adam_epsilon`, `weight_decay`.\n\nYou can set the parameters to `\"auto\"` or manually input your own desired values.\n\n```yaml\n{\n   \"optimizer\": {\n       \"type\": \"AdamW\",\n       \"params\": {\n         \"lr\": \"auto\",\n         \"betas\": \"auto\",\n         \"eps\": \"auto\",\n         \"weight_decay\": \"auto\"\n       }\n   }\n}\n```\n\nYou can also use an unsupported optimizer by adding the following to the top level configuration.\n\n```yaml\n{\n   \"zero_allow_untested_optimizer\": true\n}\n```\n\nFrom DeepSpeed==0.8.3 on, if you want to use offload, you'll also need to the following to the top level configuration because offload works best with DeepSpeed's CPU Adam optimizer.\n\n```yaml\n{\n   \"zero_force_ds_cpu_optimizer\": false\n}\n```\n\n</hfoption>\n<hfoption id=\"scheduler\">\n\nDeepSpeed supports the LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR learning rate [schedulers](https://www.deepspeed.ai/docs/config-json/#scheduler-parameters).\n\nTransformers and DeepSpeed provide two of the same schedulers:\n\n* WarmupLR is the same as `--lr_scheduler_type constant_with_warmup` in Transformers\n* WarmupDecayLR is the same as  `--lr_scheduler_type linear` in Transformers (this is the default scheduler used in Transformers)\n\nIf you don't configure the scheduler in the config, the [`Trainer`] automatically selects WarmupDecayLR and either uses the supplied values or the default values for the following parameters from the command line: `warmup_min_lr`, `warmup_max_lr`, `warmup_num_steps`, `total_num_steps` (automatically calculated during run time if `max_steps` is not provided).\n\nYou can set the parameters to `\"auto\"` or manually input your own desired values.\n\n```yaml\n{\n   \"scheduler\": {\n         \"type\": \"WarmupDecayLR\",\n         \"params\": {\n             \"total_num_steps\": \"auto\",\n             \"warmup_min_lr\": \"auto\",\n             \"warmup_max_lr\": \"auto\",\n             \"warmup_num_steps\": \"auto\"\n         }\n     }\n}\n```\n\n</hfoption>\n</hfoptions>\n\n### Precision\n\nDeepspeed supports fp32, fp16, and bf16 mixed precision.\n\n<hfoptions id=\"precision\">\n<hfoption id=\"fp32\">\n\nIf your model doesn't work well with mixed precision, for example if it wasn't pretrained in mixed precision, you may encounter overflow or underflow issues which can cause NaN loss. For these cases, you should use full fp32 precision by explicitly disabling the default fp16 mode."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: e40ed3c3-0882-4ee6-a5ee-d3aa8b39c8fe
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For these cases, you should use full fp32 precision by explicitly disabling the default fp16 mode.\n\n```yaml\n{\n    \"fp16\": {\n        \"enabled\": false\n    }\n}\n```\n\nFor Ampere GPUs and PyTorch > 1.7, it automatically switches to the more efficient [tf32](https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) format for some operations but the results are still in fp32. You can control it from the [`Trainer`] by setting `--tf32` to enable it, and `--tf32 0` or `--no_tf32` to disable it.\n\n</hfoption>\n<hfoption id=\"fp16\">\n\nTo configure PyTorch AMP-like fp16 mixed precision reduces memory usage and accelerates training speed. [`Trainer`] automatically enables or disables fp16 based on the value of `args.fp16_backend`, and the rest of the config can be set by you. fp16 is enabled from the command line when the following arguments are passed: `--fp16`, `--fp16_backend amp` or `--fp16_full_eval`.\n\n```yaml\n{\n    \"fp16\": {\n        \"enabled\": \"auto\",\n        \"loss_scale\": 0,\n        \"loss_scale_window\": 1000,\n        \"initial_scale_power\": 16,\n        \"hysteresis\": 2,\n        \"min_loss_scale\": 1\n    }\n}\n```\n\nFor additional DeepSpeed fp16 training options, take a look at the [FP16 Training Options](https://www.deepspeed.ai/docs/config-json/#fp16-training-options) reference.\n\nTo configure Apex-like fp16 mixed precision, setup the config as shown below with `\"auto\"` or your own values. [`Trainer`] automatically configure `amp` based on the values of `args.fp16_backend` and `args.fp16_opt_level`. It can also be enabled from the command line when the following arguments are passed: `--fp16`, `--fp16_backend apex` or `--fp16_opt_level 01`."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"43de3e67-158c-4e9b-8ad6-dfdb6b45c41d\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"e40ed3c3-0882-4ee6-a5ee-d3aa8b39c8fe\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"f9ca4a2a3a72331c41025fab4cbf5613ed5f8d0a4aeba6224ff36b518d5c2484\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"3c21e308-0af4-42cb-8c12-8b8e828bf5cb\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"00d03e2729c3b10f3482d59bccd16eec56cbeece104fad2ed83a5d018a6821b5\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 25180, \"end_char_idx\": 28218, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "You can control it from the [`Trainer`] by setting `--tf32` to enable it, and `--tf32 0` or `--no_tf32` to disable it.\n\n</hfoption>\n<hfoption id=\"fp16\">\n\nTo configure PyTorch AMP-like fp16 mixed precision reduces memory usage and accelerates training speed. [`Trainer`] automatically enables or disables fp16 based on the value of `args.fp16_backend`, and the rest of the config can be set by you. fp16 is enabled from the command line when the following arguments are passed: `--fp16`, `--fp16_backend amp` or `--fp16_full_eval`.\n\n```yaml\n{\n    \"fp16\": {\n        \"enabled\": \"auto\",\n        \"loss_scale\": 0,\n        \"loss_scale_window\": 1000,\n        \"initial_scale_power\": 16,\n        \"hysteresis\": 2,\n        \"min_loss_scale\": 1\n    }\n}\n```\n\nFor additional DeepSpeed fp16 training options, take a look at the [FP16 Training Options](https://www.deepspeed.ai/docs/config-json/#fp16-training-options) reference.\n\nTo configure Apex-like fp16 mixed precision, setup the config as shown below with `\"auto\"` or your own values. [`Trainer`] automatically configure `amp` based on the values of `args.fp16_backend` and `args.fp16_opt_level`. It can also be enabled from the command line when the following arguments are passed: `--fp16`, `--fp16_backend apex` or `--fp16_opt_level 01`.\n\n```yaml\n{\n    \"amp\": {\n        \"enabled\": \"auto\",\n        \"opt_level\": \"auto\"\n    }\n}\n```\n\n</hfoption>\n<hfoption id=\"bf16\">\n\nTo use bf16, you'll need at least DeepSpeed==0.6.0. bf16 has the same dynamic range as fp32 and doesn\u2019t require loss scaling. However, if you use [gradient accumulation](#gradient-accumulation) with bf16, gradients are accumulated in bf16 which may not be desired because this format's low precision can lead to lossy accumulation.\n\nbf16 can be setup in the config file or enabled from the command line when the following arguments are passed: `--bf16` or `--bf16_full_eval`.\n\n```yaml\n{\n    \"bf16\": {\n        \"enabled\": \"auto\"\n    }\n}\n```\n\n</hfoption>\n</hfoptions>\n\n### Batch size\n\nThe batch size can be auto-configured or explicitly set. If you choose to use the `\"auto\"` option, [`Trainer`] sets `train_micro_batch_size_per_gpu` to the value of args.`per_device_train_batch_size` and `train_batch_size` to `args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps`.\n\n```yaml\n{\n    \"train_micro_batch_size_per_gpu\": \"auto\",\n    \"train_batch_size\": \"auto\"\n}\n```\n\n### Gradient accumulation\n\nGradient accumulation can be auto-configured or explicitly set. If you choose to use the `\"auto\"` option, [`Trainer`] sets it to the value of `args.gradient_accumulation_steps`.\n\n```yaml\n{\n    \"gradient_accumulation_steps\": \"auto\"\n}\n\n```\n\n### Gradient clipping\n\nGradient clipping can be auto-configured or explicitly set. If you choose to use the `\"auto\"` option, [`Trainer`] sets it to the value of `args.max_grad_norm`.\n\n```yaml\n{\n    \"gradient_clipping\": \"auto\"\n}\n```\n\n### Communication data type\n\nFor communication collectives like reduction, gathering and scattering operations, a separate data type is used."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 3c21e308-0af4-42cb-8c12-8b8e828bf5cb
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However, if you use [gradient accumulation](#gradient-accumulation) with bf16, gradients are accumulated in bf16 which may not be desired because this format's low precision can lead to lossy accumulation.\n\nbf16 can be setup in the config file or enabled from the command line when the following arguments are passed: `--bf16` or `--bf16_full_eval`.\n\n```yaml\n{\n    \"bf16\": {\n        \"enabled\": \"auto\"\n    }\n}\n```\n\n</hfoption>\n</hfoptions>\n\n### Batch size\n\nThe batch size can be auto-configured or explicitly set. If you choose to use the `\"auto\"` option, [`Trainer`] sets `train_micro_batch_size_per_gpu` to the value of args.`per_device_train_batch_size` and `train_batch_size` to `args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps`.\n\n```yaml\n{\n    \"train_micro_batch_size_per_gpu\": \"auto\",\n    \"train_batch_size\": \"auto\"\n}\n```\n\n### Gradient accumulation\n\nGradient accumulation can be auto-configured or explicitly set. If you choose to use the `\"auto\"` option, [`Trainer`] sets it to the value of `args.gradient_accumulation_steps`.\n\n```yaml\n{\n    \"gradient_accumulation_steps\": \"auto\"\n}\n\n```\n\n### Gradient clipping\n\nGradient clipping can be auto-configured or explicitly set. If you choose to use the `\"auto\"` option, [`Trainer`] sets it to the value of `args.max_grad_norm`.\n\n```yaml\n{\n    \"gradient_clipping\": \"auto\"\n}\n```\n\n### Communication data type\n\nFor communication collectives like reduction, gathering and scattering operations, a separate data type is used.\n\nAll gather and scatter operations are performed in the same data type the data is in. For example, if you're training with bf16, the data is also gathered in bf16 because gathering is a non-lossy operation.\n\nReduce operations are lossy, for example when gradients are averaged across multiple GPUs. When the communication is done in fp16 or bf16, it is more likely to be lossy because adding multiple numbers in low precision isn't exact. This is especially the case with bf16 which has a lower precision than fp16. For this reason, fp16 is the default for reduction operations because the loss is minimal when averaging gradients.\n\nYou can choose the communication data type by setting the `communication_data_type` parameter in the config file. For example, choosing fp32 adds a small amount of overhead but ensures the reduction operation is accumulated in fp32 and when it is ready, it is downcasted to whichever half-precision dtype you're training in.\n\n```yaml\n{\n    \"communication_data_type\": \"fp32\"\n}\n```\n\n## Deployment\n\nDeepSpeed can be deployed by different launchers such as [torchrun](https://pytorch.org/docs/stable/elastic/run.html), the `deepspeed` launcher, or [Accelerate](https://huggingface.co/docs/accelerate/basic_tutorials/launch#using-accelerate-launch). To deploy, add `--deepspeed ds_config.json` to the [`Trainer`] command line. It\u2019s recommended to use DeepSpeed\u2019s [`add_config_arguments`](https://deepspeed.readthedocs.io/en/latest/initialize.html#argument-parsing) utility to add any necessary command line arguments to your code.\n\nThis guide will show you how to deploy DeepSpeed with the `deepspeed` launcher for different training setups."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: c737af41-c062-4151-afc6-bf1873c41331
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For example, if you're training with bf16, the data is also gathered in bf16 because gathering is a non-lossy operation.\n\nReduce operations are lossy, for example when gradients are averaged across multiple GPUs. When the communication is done in fp16 or bf16, it is more likely to be lossy because adding multiple numbers in low precision isn't exact. This is especially the case with bf16 which has a lower precision than fp16. For this reason, fp16 is the default for reduction operations because the loss is minimal when averaging gradients.\n\nYou can choose the communication data type by setting the `communication_data_type` parameter in the config file. For example, choosing fp32 adds a small amount of overhead but ensures the reduction operation is accumulated in fp32 and when it is ready, it is downcasted to whichever half-precision dtype you're training in.\n\n```yaml\n{\n    \"communication_data_type\": \"fp32\"\n}\n```\n\n## Deployment\n\nDeepSpeed can be deployed by different launchers such as [torchrun](https://pytorch.org/docs/stable/elastic/run.html), the `deepspeed` launcher, or [Accelerate](https://huggingface.co/docs/accelerate/basic_tutorials/launch#using-accelerate-launch). To deploy, add `--deepspeed ds_config.json` to the [`Trainer`] command line. It\u2019s recommended to use DeepSpeed\u2019s [`add_config_arguments`](https://deepspeed.readthedocs.io/en/latest/initialize.html#argument-parsing) utility to add any necessary command line arguments to your code.\n\nThis guide will show you how to deploy DeepSpeed with the `deepspeed` launcher for different training setups. You can check out this [post](https://github.com/huggingface/transformers/issues/8771#issuecomment-759248400) for more practical usage examples.\n\n\n<hfoptions id=\"deploy\">\n<hfoption id=\"multi-GPU\">\n\nTo deploy DeepSpeed on multiple GPUs, add the `--num_gpus` parameter. If you want to use all available GPUs, you don't need to add `--num_gpus`. The example below uses 2 GPUs.\n\n```bash\ndeepspeed --num_gpus=2 examples/pytorch/translation/run_translation.py \\\n--deepspeed tests/deepspeed/ds_config_zero3.json \\\n--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \\\n--output_dir output_dir --overwrite_output_dir --fp16 \\\n--do_train --max_train_samples 500 --num_train_epochs 1 \\\n--dataset_name wmt16 --dataset_config \"ro-en\" \\\n--source_lang en --target_lang ro\n```\n\n</hfoption>\n<hfoption id=\"single-GPU\">\n\nTo deploy DeepSpeed on a single GPU, add the `--num_gpus` parameter. It isn't necessary to explicitly set this value if you only have 1 GPU because DeepSpeed deploys all GPUs it can see on a given node.\n\n```bash\ndeepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \\\n--deepspeed tests/deepspeed/ds_config_zero2.json \\\n--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \\\n--output_dir output_dir --overwrite_output_dir --fp16 \\\n--do_train --max_train_samples 500 --num_train_epochs 1 \\\n--dataset_name wmt16 --dataset_config \"ro-en\" \\\n--source_lang en --target_lang ro\n```\n\nDeepSpeed is still useful with just 1 GPU because you can:\n\n1."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
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If you want to use all available GPUs, you don't need to add `--num_gpus`. The example below uses 2 GPUs.\n\n```bash\ndeepspeed --num_gpus=2 examples/pytorch/translation/run_translation.py \\\n--deepspeed tests/deepspeed/ds_config_zero3.json \\\n--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \\\n--output_dir output_dir --overwrite_output_dir --fp16 \\\n--do_train --max_train_samples 500 --num_train_epochs 1 \\\n--dataset_name wmt16 --dataset_config \"ro-en\" \\\n--source_lang en --target_lang ro\n```\n\n</hfoption>\n<hfoption id=\"single-GPU\">\n\nTo deploy DeepSpeed on a single GPU, add the `--num_gpus` parameter. It isn't necessary to explicitly set this value if you only have 1 GPU because DeepSpeed deploys all GPUs it can see on a given node.\n\n```bash\ndeepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \\\n--deepspeed tests/deepspeed/ds_config_zero2.json \\\n--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \\\n--output_dir output_dir --overwrite_output_dir --fp16 \\\n--do_train --max_train_samples 500 --num_train_epochs 1 \\\n--dataset_name wmt16 --dataset_config \"ro-en\" \\\n--source_lang en --target_lang ro\n```\n\nDeepSpeed is still useful with just 1 GPU because you can:\n\n1. Offload some computations and memory to the CPU to make more GPU resources available to your model to use a larger batch size or fit a very large model that normally won't fit.\n2. Minimize memory fragmentation with it's smart GPU memory management system which also allows you to fit bigger models and data batches.\n\n<Tip>\n\nSet the `allgather_bucket_size` and `reduce_bucket_size` values to 2e8 in the [ZeRO-2](#zero-configuration) configuration file to get better performance on a single GPU.\n\n</Tip>\n\n</hfoption>\n</hfoptions>\n\n### Multi-node deployment\n\nA node is one or more GPUs for running a workload. A more powerful setup is a multi-node setup which can be launched with the `deepspeed` launcher. For this guide, let's assume there are two nodes with 8 GPUs each. The first node can be accessed `ssh hostname1` and the second node with `ssh hostname2`. Both nodes must be able to communicate with each other locally over ssh without a password.\n\nBy default, DeepSpeed expects your multi-node environment to use a shared storage. If this is not the case and each node can only see the local filesystem, you need to adjust the config file to include a [`checkpoint`](https://www.deepspeed.ai/docs/config-json/#checkpoint-options) to allow loading without access to a shared filesystem:\n\n```yaml\n{\n  \"checkpoint\": {\n    \"use_node_local_storage\": true\n  }\n}\n```\n\nYou could also use the [`Trainer`]'s `--save_on_each_node` argument to automatically add the above `checkpoint` to your config.\n\n<hfoptions id=\"multinode\">\n<hfoption id=\"torchrun\">\n\nFor [torchrun](https://pytorch.org/docs/stable/elastic/run.html), you have to ssh to each node and run the following command on both of them. The launcher waits until both nodes are synchronized before launching the training."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"0dab27f9-44ea-4d0c-b012-dcd757ab3ac6\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"b6a204ac-4b55-4743-b613-238ac1579114\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"c18d650e62ebb7703dd4f1e9384d0f77ce5383b6160343df5b6e649617af5e10\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"bf1a66e0-7f6d-4a8c-980c-ce13523617ee\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"6700ab39d5b5a7bf4cb4580fc20616a9a663fb33e6871c17725f4588b307e8d8\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 31563, \"end_char_idx\": 34355, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "2. Minimize memory fragmentation with it's smart GPU memory management system which also allows you to fit bigger models and data batches.\n\n<Tip>\n\nSet the `allgather_bucket_size` and `reduce_bucket_size` values to 2e8 in the [ZeRO-2](#zero-configuration) configuration file to get better performance on a single GPU.\n\n</Tip>\n\n</hfoption>\n</hfoptions>\n\n### Multi-node deployment\n\nA node is one or more GPUs for running a workload. A more powerful setup is a multi-node setup which can be launched with the `deepspeed` launcher. For this guide, let's assume there are two nodes with 8 GPUs each. The first node can be accessed `ssh hostname1` and the second node with `ssh hostname2`. Both nodes must be able to communicate with each other locally over ssh without a password.\n\nBy default, DeepSpeed expects your multi-node environment to use a shared storage. If this is not the case and each node can only see the local filesystem, you need to adjust the config file to include a [`checkpoint`](https://www.deepspeed.ai/docs/config-json/#checkpoint-options) to allow loading without access to a shared filesystem:\n\n```yaml\n{\n  \"checkpoint\": {\n    \"use_node_local_storage\": true\n  }\n}\n```\n\nYou could also use the [`Trainer`]'s `--save_on_each_node` argument to automatically add the above `checkpoint` to your config.\n\n<hfoptions id=\"multinode\">\n<hfoption id=\"torchrun\">\n\nFor [torchrun](https://pytorch.org/docs/stable/elastic/run.html), you have to ssh to each node and run the following command on both of them. The launcher waits until both nodes are synchronized before launching the training.\n\n```bash\ntorchrun --nproc_per_node=8 --nnode=2 --node_rank=0 --master_addr=hostname1 \\\n--master_port=9901 your_program.py <normal cl args> --deepspeed ds_config.json\n```\n\n</hfoption>\n<hfoption id=\"deepspeed\">\n\nFor the `deepspeed` launcher, start by creating a `hostfile`.\n\n```bash\nhostname1 slots=8\nhostname2 slots=8\n```\n\nThen you can launch the training with the following command. The `deepspeed` launcher automatically launches the command on both nodes at once.\n\n```bash\ndeepspeed --num_gpus 8 --num_nodes 2 --hostfile hostfile --master_addr hostname1 --master_port=9901 \\\nyour_program.py <normal cl args> --deepspeed ds_config.json\n```\n\nCheck out the [Resource Configuration (multi-node)](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node) guide for more details about configuring multi-node compute resources.\n\n</hfoption>\n</hfoptions>\n\n### SLURM\n\nIn a SLURM environment, you'll need to adapt your SLURM script to your specific SLURM environment. An example SLURM script may look like:\n\n```bash\n#SBATCH --job-name=test-nodes        # name\n#SBATCH --nodes=2                    # nodes\n#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!"}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: bf1a66e0-7f6d-4a8c-980c-ce13523617ee
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created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: ed8442fd-4d08-45c8-be48-714b9579b9d4
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created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: fac84311-ae40-47ac-a583-c4acf1ef03ac
encoding: FLOAT32
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However, this only works for 1 GPU. If you want to use more than 1 GPU, you must use a multi-process environment for DeepSpeed to work. This means you have to use the `deepspeed` launcher which can't be emulated as shown here.\n\n```py\n# DeepSpeed requires a distributed environment even when only one process is used.\n# This emulates a launcher in the notebook\nimport os\n\nos.environ[\"MASTER_ADDR\"] = \"localhost\"\nos.environ[\"MASTER_PORT\"] = \"9994\"  # modify if RuntimeError: Address already in use\nos.environ[\"RANK\"] = \"0\"\nos.environ[\"LOCAL_RANK\"] = \"0\"\nos.environ[\"WORLD_SIZE\"] = \"1\"\n\n# Now proceed as normal, plus pass the DeepSpeed config file\ntraining_args = TrainingArguments(..., deepspeed=\"ds_config_zero3.json\")\ntrainer = Trainer(...)\ntrainer.train()\n```\n\nIf you want to create the config file on the fly in the notebook in the current directory, you could have a dedicated cell.\n\n```py\n%%bash\ncat <<'EOT' > ds_config_zero3.json\n{\n    \"fp16\": {\n        \"enabled\": \"auto\",\n        \"loss_scale\": 0,\n        \"loss_scale_window\": 1000,\n        \"initial_scale_power\": 16,\n        \"hysteresis\": 2,\n        \"min_loss_scale\": 1\n    },\n\n    \"optimizer\": {\n        \"type\": \"AdamW\",\n        \"params\": {\n            \"lr\": \"auto\",\n            \"betas\": \"auto\",\n            \"eps\": \"auto\",\n            \"weight_decay\": \"auto\"\n        }\n    },\n\n    \"scheduler\": {\n        \"type\": \"WarmupLR\",\n        \"params\": {\n            \"warmup_min_lr\": \"auto\",\n            \"warmup_max_lr\": \"auto\",\n            \"warmup_num_steps\": \"auto\"\n        }\n    },\n\n    \"zero_optimization\": {\n        \"stage\": 3,\n        \"offload_optimizer\": {\n            \"device\": \"cpu\",\n            \"pin_memory\": true\n        },\n        \"offload_param\": {\n            \"device\": \"cpu\",\n            \"pin_memory\": true\n        },\n        \"overlap_comm\": true,\n        \"contiguous_gradients\": true,\n        \"sub_group_size\": 1e9,\n        \"reduce_bucket_size\": \"auto\",\n        \"stage3_prefetch_bucket_size\": \"auto\",\n        \"stage3_param_persistence_threshold\": \"auto\",\n        \"stage3_max_live_parameters\": 1e9,\n        \"stage3_max_reuse_distance\": 1e9,\n        \"stage3_gather_16bit_weights_on_model_save\": true\n    },\n\n    \"gradient_accumulation_steps\": \"auto\",\n    \"gradient_clipping\": \"auto\",\n    \"steps_per_print\": 2000,\n    \"train_batch_size\": \"auto\",\n    \"train_micro_batch_size_per_gpu\": \"auto\",\n    \"wall_clock_breakdown\": false\n}\nEOT\n```\n\nIf the training script is in a file and not in a notebook cell, you can launch `deepspeed` normally from the shell in a notebook cell."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 63fff344-5914-4f92-b9a0-0ed9f7617a1b
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"63fff344-5914-4f92-b9a0-0ed9f7617a1b\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"fac84311-ae40-47ac-a583-c4acf1ef03ac\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"db478fb565afdac90847ce2a6e155c6f36b0ede83a0376646e78194de49314a2\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"9d098ecc-9800-4ea9-a7b4-49ae6820b283\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"20888e50e29d7df9606751a329a30ed75fd4e57255c9e46abc727aa5d583ac93\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 37837, \"end_char_idx\": 40898, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "For example, to launch `run_translation.py`:\n\n```py\n!git clone https://github.com/huggingface/transformers\n!cd transformers; deepspeed examples/pytorch/translation/run_translation.py ...\n```\n\nYou could also use `%%bash` magic and write multi-line code to run the shell program, but you won't be able to view the logs until training is complete. With `%%bash` magic, you don't need to emulate a distributed environment.\n\n```py\n%%bash\n\ngit clone https://github.com/huggingface/transformers\ncd transformers\ndeepspeed examples/pytorch/translation/run_translation.py ...\n```\n\n## Save model weights\n\nDeepSpeed stores the main full precision fp32 weights in custom checkpoint optimizer files (the glob pattern looks like `global_step*/*optim_states.pt`) and are saved under the normal checkpoint.\n\n<hfoptions id=\"save\">\n<hfoption id=\"fp16\">\n\nA model trained with ZeRO-2 saves the pytorch_model.bin weights in fp16. To save the model weights in fp16 for a model trained with ZeRO-3, you need to set `\"stage3_gather_16bit_weights_on_model_save\": true` because the model weights are partitioned across multiple GPUs. Otherwise, the [`Trainer`] won't save the weights in fp16 and it won't create a pytorch_model.bin file. This is because DeepSpeed's state_dict contains a placeholder instead of the real weights and you won't be able to load them.\n\n```yaml\n{\n    \"zero_optimization\": {\n        \"stage3_gather_16bit_weights_on_model_save\": true\n    }\n}\n```\n\n</hfoption>\n<hfoption id=\"fp32\">\n\nThe full precision weights shouldn't be saved during training because it can require a lot of memory. It is usually best to save the fp32 weights offline after training is complete. But if you have a lot of free CPU memory, it is possible to save the fp32 weights during training. This section covers both online and offline approaches.\n\n### Online\n\nYou must have saved at least one checkpoint to load the latest checkpoint as shown in the following:\n\n```py\nfrom transformers.trainer_utils import get_last_checkpoint\nfrom deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint\n\ncheckpoint_dir = get_last_checkpoint(trainer.args.output_dir)\nfp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)\n```\n\nIf you've enabled the `--load_best_model_at_end` parameter to track the best checkpoint in [`TrainingArguments`], you can finish training first and save the final model explicitly. Then you can reload it as shown below:\n\n```py\nfrom deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint\n\ncheckpoint_dir = os.path.join(trainer.args.output_dir, \"checkpoint-final\")\ntrainer.deepspeed.save_checkpoint(checkpoint_dir)\nfp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)\n```\n\n<Tip>\n\nOnce `load_state_dict_from_zero_checkpoint` is run, the model is no longer usable in DeepSpeed in the context of the same application. You'll need to initialize the DeepSpeed engine again since `model.load_state_dict(state_dict)` removes all the DeepSpeed magic from it. Only use this at the very end of training."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 9d098ecc-9800-4ea9-a7b4-49ae6820b283
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created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 5e01cd34-2939-4f2a-8bdd-8ef6256f47ca
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"5e01cd34-2939-4f2a-8bdd-8ef6256f47ca\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"9d098ecc-9800-4ea9-a7b4-49ae6820b283\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"133f7875b3818a0ed84f617e9e96072cf6038e3229eb090eda2a5d03f8cd8347\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"be0ad5b6-727f-4c80-8038-2d6d57605972\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"7f929c4725a79004d526026ec489672acf83bdc611dc090fbe4379bb3b0d1971\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 39598, \"end_char_idx\": 42455, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "This section covers both online and offline approaches.\n\n### Online\n\nYou must have saved at least one checkpoint to load the latest checkpoint as shown in the following:\n\n```py\nfrom transformers.trainer_utils import get_last_checkpoint\nfrom deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint\n\ncheckpoint_dir = get_last_checkpoint(trainer.args.output_dir)\nfp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)\n```\n\nIf you've enabled the `--load_best_model_at_end` parameter to track the best checkpoint in [`TrainingArguments`], you can finish training first and save the final model explicitly. Then you can reload it as shown below:\n\n```py\nfrom deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint\n\ncheckpoint_dir = os.path.join(trainer.args.output_dir, \"checkpoint-final\")\ntrainer.deepspeed.save_checkpoint(checkpoint_dir)\nfp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)\n```\n\n<Tip>\n\nOnce `load_state_dict_from_zero_checkpoint` is run, the model is no longer usable in DeepSpeed in the context of the same application. You'll need to initialize the DeepSpeed engine again since `model.load_state_dict(state_dict)` removes all the DeepSpeed magic from it. Only use this at the very end of training.\n\n</Tip>\n\nYou can also extract and load the state_dict of the fp32 weights:\n\n```py\nfrom deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint\n\nstate_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir)  # already on cpu\nmodel = model.cpu()\nmodel.load_state_dict(state_dict)\n```\n\n### Offline\n\nDeepSpeed provides a zero_to_fp32.py script at the top-level of the checkpoint folder for extracting weights at any point. This is a standalone script and you don't need a configuration file or [`Trainer`].\n\nFor example, if your checkpoint folder looked like this:\n\n```bash\n$ ls -l output_dir/checkpoint-1/\n-rw-rw-r-- 1 stas stas 1.4K Mar 27 20:42 config.json\ndrwxrwxr-x 2 stas stas 4.0K Mar 25 19:52 global_step1/\n-rw-rw-r-- 1 stas stas   12 Mar 27 13:16 latest\n-rw-rw-r-- 1 stas stas 827K Mar 27 20:42 optimizer.pt\n-rw-rw-r-- 1 stas stas 231M Mar 27 20:42 pytorch_model.bin\n-rw-rw-r-- 1 stas stas  623 Mar 27 20:42 scheduler.pt\n-rw-rw-r-- 1 stas stas 1.8K Mar 27 20:42 special_tokens_map.json\n-rw-rw-r-- 1 stas stas 774K Mar 27 20:42 spiece.model\n-rw-rw-r-- 1 stas stas 1.9K Mar 27 20:42 tokenizer_config.json\n-rw-rw-r-- 1 stas stas  339 Mar 27 20:42 trainer_state.json\n-rw-rw-r-- 1 stas stas 2.3K Mar 27 20:42 training_args.bin\n-rwxrw-r-- 1 stas stas 5.5K Mar 27 13:16 zero_to_fp32.py*\n```\n\nTo reconstruct the fp32 weights from the DeepSpeed checkpoint (ZeRO-2 or ZeRO-3) subfolder `global_step1`, run the following command to create and consolidate the full fp32 weights from multiple GPUs into a single pytorch_model.bin file."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: be0ad5b6-727f-4c80-8038-2d6d57605972
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"be0ad5b6-727f-4c80-8038-2d6d57605972\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"5e01cd34-2939-4f2a-8bdd-8ef6256f47ca\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"7808c651345a3767cf0cd4603bda6bd726d832ff032be1c40aae795cacdfaab6\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"f9b22969-0056-44fe-bf59-e8c3cc9af4ca\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"3cf560e95490b20f80fddaaba9ac8641a7b08ab155af421cbdacb264135fc3eb\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 42456, \"end_char_idx\": 45633, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "The script automatically discovers the subfolder containing the checkpoint.\n\n```py\npython zero_to_fp32.py . pytorch_model.bin\n```\n\n<Tip>\n\nRun `python zero_to_fp32.py -h` for more usage details. The script requires 2x the general RAM of the final fp32 weights.\n\n</Tip>\n\n</hfoption>\n</hfoptions>\n\n## ZeRO Inference\n\n[ZeRO Inference](https://www.deepspeed.ai/2022/09/09/zero-inference.html) places the model weights in CPU or NVMe memory to avoid burdening the GPU which makes it possible to run inference with huge models on a GPU. Inference doesn't require any large additional amounts of memory for the optimizer states and gradients so you can fit much larger batches and/or sequence lengths on the same hardware.\n\nZeRO Inference shares the same configuration file as [ZeRO-3](#zero-configuration), and ZeRO-2 and ZeRO-1 configs won't work because they don't provide any benefits for inference.\n\nTo run ZeRO Inference, pass your usual training arguments to the [`TrainingArguments`] class and add the `--do_eval` argument.\n\n```bash\ndeepspeed --num_gpus=2 your_program.py <normal cl args> --do_eval --deepspeed ds_config.json\n```\n\n## Non-Trainer DeepSpeed integration\n\nDeepSpeed also works with Transformers without the [`Trainer`] class. This is handled by the [`HfDeepSpeedConfig`] which only takes care of gathering ZeRO-3 parameters and splitting a model across multiple GPUs when you call [`~PreTrainedModel.from_pretrained`].\n\n<Tip>\n\nIf you want everything automatically taken care of for you, try using DeepSpeed with the [`Trainer`]! You'll need to follow the [DeepSpeed documentation](https://www.deepspeed.ai/), and manually configure the parameter values in the config file (you can't use the `\"auto\"` value).\n\n</Tip>\n\nTo efficiently deploy ZeRO-3, you must instantiate the [`HfDeepSpeedConfig`] object before the model and keep that object alive:\n\n<hfoptions id=\"models\">\n<hfoption id=\"pretrained model\">\n\n```py\nfrom transformers.integrations import HfDeepSpeedConfig\nfrom transformers import AutoModel\nimport deepspeed\n\nds_config = {...}  # deepspeed config object or path to the file\n# must run before instantiating the model to detect zero 3\ndschf = HfDeepSpeedConfig(ds_config)  # keep this object alive\nmodel = AutoModel.from_pretrained(\"openai-community/gpt2\")\nengine = deepspeed.initialize(model=model, config_params=ds_config, ...)\n```\n\n</hfoption>\n<hfoption id=\"non-pretrained model\">\n\n[`HfDeepSpeedConfig`] is not required for ZeRO-1 or ZeRO-2.\n\n```py\nfrom transformers.integrations import HfDeepSpeedConfig\nfrom transformers import AutoModel, AutoConfig\nimport deepspeed\n\nds_config = {...}  # deepspeed config object or path to the file\n# must run before instantiating the model to detect zero 3\ndschf = HfDeepSpeedConfig(ds_config)  # keep this object alive\nconfig = AutoConfig.from_pretrained(\"openai-community/gpt2\")\nmodel = AutoModel.from_config(config)\nengine = deepspeed.initialize(model=model, config_params=ds_config, ...)\n```\n\n</hfoption>\n</hfoptions>\n\n### Non-Trainer ZeRO Inference\n\nTo run ZeRO Inference without the [`Trainer`] in cases where you can\u2019t fit a model onto a single GPU, try using additional GPUs or/and offloading to CPU memory."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: f9b22969-0056-44fe-bf59-e8c3cc9af4ca
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"f9b22969-0056-44fe-bf59-e8c3cc9af4ca\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"be0ad5b6-727f-4c80-8038-2d6d57605972\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"128c82e8405b2a01668997f40bbebd457c5972aa8ef4c4d73dbf4300eb0c620d\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"825bdde4-db58-43eb-b4d7-7289fa3ccd65\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"bc502238b44027b2e5282dd3a487ae1c8d8123140144a8359ae32f17957e2782\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 44178, \"end_char_idx\": 47184, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "</Tip>\n\nTo efficiently deploy ZeRO-3, you must instantiate the [`HfDeepSpeedConfig`] object before the model and keep that object alive:\n\n<hfoptions id=\"models\">\n<hfoption id=\"pretrained model\">\n\n```py\nfrom transformers.integrations import HfDeepSpeedConfig\nfrom transformers import AutoModel\nimport deepspeed\n\nds_config = {...}  # deepspeed config object or path to the file\n# must run before instantiating the model to detect zero 3\ndschf = HfDeepSpeedConfig(ds_config)  # keep this object alive\nmodel = AutoModel.from_pretrained(\"openai-community/gpt2\")\nengine = deepspeed.initialize(model=model, config_params=ds_config, ...)\n```\n\n</hfoption>\n<hfoption id=\"non-pretrained model\">\n\n[`HfDeepSpeedConfig`] is not required for ZeRO-1 or ZeRO-2.\n\n```py\nfrom transformers.integrations import HfDeepSpeedConfig\nfrom transformers import AutoModel, AutoConfig\nimport deepspeed\n\nds_config = {...}  # deepspeed config object or path to the file\n# must run before instantiating the model to detect zero 3\ndschf = HfDeepSpeedConfig(ds_config)  # keep this object alive\nconfig = AutoConfig.from_pretrained(\"openai-community/gpt2\")\nmodel = AutoModel.from_config(config)\nengine = deepspeed.initialize(model=model, config_params=ds_config, ...)\n```\n\n</hfoption>\n</hfoptions>\n\n### Non-Trainer ZeRO Inference\n\nTo run ZeRO Inference without the [`Trainer`] in cases where you can\u2019t fit a model onto a single GPU, try using additional GPUs or/and offloading to CPU memory. The important nuance to understand here is that the way ZeRO is designed, you can process different inputs on different GPUs in parallel.\n\nMake sure to:\n\n* disable CPU offload if you have enough GPU memory (since it slows things down).\n* enable bf16 if you have an Ampere or newer GPU to make things faster. If you don\u2019t have one of these GPUs, you may enable fp16 as long as you don\u2019t use a model pretrained in bf16 (T5 models) because it may lead to an overflow error.\n\nTake a look at the following script to get a better idea of how to run ZeRO Inference without the [`Trainer`] on a model that won't fit on a single GPU.\n\n```py\n#!/usr/bin/env python\n\n# This script demonstrates how to use Deepspeed ZeRO in an inference mode when one can't fit a model\n# into a single GPU\n#\n# 1. Use 1 GPU with CPU offload\n# 2. Or use multiple GPUs instead\n#\n# First you need to install deepspeed: pip install deepspeed\n#\n# Here we use a 3B \"bigscience/T0_3B\" model which needs about 15GB GPU RAM - so 1 largish or 2\n# small GPUs can handle it. or 1 small GPU and a lot of CPU memory.\n#\n# To use a larger model like \"bigscience/T0\" which needs about 50GB, unless you have an 80GB GPU -\n# you will need 2-4 gpus. And then you can adapt the script to handle more gpus if you want to\n# process multiple inputs at once.\n#\n# The provided deepspeed config also activates CPU memory offloading, so chances are that if you\n# have a lot of available CPU memory and you don't mind a slowdown you should be able to load a\n# model that doesn't normally fit into a single GPU."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"825bdde4-db58-43eb-b4d7-7289fa3ccd65\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"f9b22969-0056-44fe-bf59-e8c3cc9af4ca\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"4ffd85ad8f839ff906ce900ba683f9d3a8a194534074f661ec678bbd1203554f\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"31c9b82c-cb47-425b-ba02-6d15e03a29a1\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"7415ba0d0d9abd3b32a52e28afbde2a793d58b977230237603673bf09f5e7749\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 45773, \"end_char_idx\": 48464, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "Make sure to:\n\n* disable CPU offload if you have enough GPU memory (since it slows things down).\n* enable bf16 if you have an Ampere or newer GPU to make things faster. If you don\u2019t have one of these GPUs, you may enable fp16 as long as you don\u2019t use a model pretrained in bf16 (T5 models) because it may lead to an overflow error.\n\nTake a look at the following script to get a better idea of how to run ZeRO Inference without the [`Trainer`] on a model that won't fit on a single GPU.\n\n```py\n#!/usr/bin/env python\n\n# This script demonstrates how to use Deepspeed ZeRO in an inference mode when one can't fit a model\n# into a single GPU\n#\n# 1. Use 1 GPU with CPU offload\n# 2. Or use multiple GPUs instead\n#\n# First you need to install deepspeed: pip install deepspeed\n#\n# Here we use a 3B \"bigscience/T0_3B\" model which needs about 15GB GPU RAM - so 1 largish or 2\n# small GPUs can handle it. or 1 small GPU and a lot of CPU memory.\n#\n# To use a larger model like \"bigscience/T0\" which needs about 50GB, unless you have an 80GB GPU -\n# you will need 2-4 gpus. And then you can adapt the script to handle more gpus if you want to\n# process multiple inputs at once.\n#\n# The provided deepspeed config also activates CPU memory offloading, so chances are that if you\n# have a lot of available CPU memory and you don't mind a slowdown you should be able to load a\n# model that doesn't normally fit into a single GPU. If you have enough GPU memory the program will\n# run faster if you don't want offload to CPU - so disable that section then.\n#\n# To deploy on 1 gpu:\n#\n# deepspeed --num_gpus 1 t0.py\n# or:\n# python -m torch.distributed.run --nproc_per_node=1 t0.py\n#\n# To deploy on 2 gpus:\n#\n# deepspeed --num_gpus 2 t0.py\n# or:\n# python -m torch.distributed.run --nproc_per_node=2 t0.py\n\nfrom transformers import AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM\nfrom transformers.integrations import HfDeepSpeedConfig\nimport deepspeed\nimport os\nimport torch\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"  # To avoid warnings about parallelism in tokenizers\n\n# distributed setup\nlocal_rank = int(os.getenv(\"LOCAL_RANK\", \"0\"))\nworld_size = int(os.getenv(\"WORLD_SIZE\", \"1\"))\ntorch.cuda.set_device(local_rank)\ndeepspeed.init_distributed()\n\nmodel_name = \"bigscience/T0_3B\"\n\nconfig = AutoConfig.from_pretrained(model_name)\nmodel_hidden_size = config.d_model\n\n# batch size has to be divisible by world_size, but can be bigger than world_size\ntrain_batch_size = 1 * world_size\n\n# ds_config notes\n#\n# - enable bf16 if you use Ampere or higher GPU - this will run in mixed precision and will be\n# faster.\n#\n# - for older GPUs you can enable fp16, but it'll only work for non-bf16 pretrained models - e.g."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 31c9b82c-cb47-425b-ba02-6d15e03a29a1
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created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 7ccb325d-9905-40d3-958c-aaf7caa6b2fa
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created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"453457f3-8031-4b6b-aeba-4ad6ecf8b3c3\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"7ccb325d-9905-40d3-958c-aaf7caa6b2fa\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"b7c1a89ebc04e39594f0a10e2492914db55cb7a60ce6ecc4aac6a7ec3b81bbe3\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"9ee03198-8c08-48e5-a15d-9dc3d0a12f9f\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"6d6b78dfbb1294847f21e9191f445c51d71268caba472c165cafab67fc07bdc8\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 49919, \"end_char_idx\": 52815, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "#\n# **it has to be run before loading the model AutoModelForSeq2SeqLM.from_pretrained(model_name)**\n#\n# otherwise the model will first be loaded normally and only partitioned at forward time which is\n# less efficient and when there is little CPU RAM may fail\ndschf = HfDeepSpeedConfig(ds_config)  # keep this object alive\n\n# now a model can be loaded.\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n\n# initialise Deepspeed ZeRO and store only the engine object\nds_engine = deepspeed.initialize(model=model, config_params=ds_config)[0]\nds_engine.module.eval()  # inference\n\n# Deepspeed ZeRO can process unrelated inputs on each GPU. So for 2 gpus you process 2 inputs at once.\n# If you use more GPUs adjust for more.\n# And of course if you have just one input to process you then need to pass the same string to both gpus\n# If you use only one GPU, then you will have only rank 0.\nrank = torch.distributed.get_rank()\nif rank == 0:\n    text_in = \"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy\"\nelif rank == 1:\n    text_in = \"Is this review positive or negative? Review: this is the worst restaurant ever\"\n\ntokenizer = AutoTokenizer.from_pretrained(model_name)\ninputs = tokenizer.encode(text_in, return_tensors=\"pt\").to(device=local_rank)\nwith torch.no_grad():\n    outputs = ds_engine.module.generate(inputs, synced_gpus=True)\ntext_out = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(f\"rank{rank}:\\n   in={text_in}\\n  out={text_out}\")\n```\n\nSave the script as t0.py and launch it:\n\n```bash\n$ deepspeed --num_gpus 2 t0.py\nrank0:\n   in=Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy\n  out=Positive\nrank1:\n   in=Is this review positive or negative? Review: this is the worst restaurant ever\n  out=negative\n```\n\nThis is a very basic example and you'll want to adapt it to your use case.\n\n### Generate\n\nUsing multiple GPUs with ZeRO-3 for generation requires synchronizing the GPUs by setting `synced_gpus=True` in the [`~GenerationMixin.generate`] method. Otherwise, if one GPU is finished generating before another one, the whole system hangs because the remaining GPUs haven't received the weight shard from the GPU that finished first.\n\nFor Transformers>=4.28, if `synced_gpus` is automatically set to `True` if multiple GPUs are detected during generation.\n\n## Troubleshoot\n\nWhen you encounter an issue, you should consider whether DeepSpeed is the cause of the problem because often it isn't (unless it's super obviously and you can see DeepSpeed modules in the exception)! The first step should be to retry your setup without DeepSpeed, and if the problem persists, then you can report the issue. If the issue is a core DeepSpeed problem and unrelated to the Transformers integration, open an Issue on the [DeepSpeed repository](https://github.com/microsoft/DeepSpeed)."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 9ee03198-8c08-48e5-a15d-9dc3d0a12f9f
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Review: this is the worst restaurant ever\n  out=negative\n```\n\nThis is a very basic example and you'll want to adapt it to your use case.\n\n### Generate\n\nUsing multiple GPUs with ZeRO-3 for generation requires synchronizing the GPUs by setting `synced_gpus=True` in the [`~GenerationMixin.generate`] method. Otherwise, if one GPU is finished generating before another one, the whole system hangs because the remaining GPUs haven't received the weight shard from the GPU that finished first.\n\nFor Transformers>=4.28, if `synced_gpus` is automatically set to `True` if multiple GPUs are detected during generation.\n\n## Troubleshoot\n\nWhen you encounter an issue, you should consider whether DeepSpeed is the cause of the problem because often it isn't (unless it's super obviously and you can see DeepSpeed modules in the exception)! The first step should be to retry your setup without DeepSpeed, and if the problem persists, then you can report the issue. If the issue is a core DeepSpeed problem and unrelated to the Transformers integration, open an Issue on the [DeepSpeed repository](https://github.com/microsoft/DeepSpeed).\n\nFor issues related to the Transformers integration, please provide the following information:\n\n* the full DeepSpeed config file\n\n* the command line arguments of the [`Trainer`], or [`TrainingArguments`] arguments if you're scripting the [`Trainer`] setup yourself (don't dump the [`TrainingArguments`] which has dozens of irrelevant entries)\n\n* the outputs of:\n\n```bash\npython -c 'import torch; print(f\"torch: {torch.__version__}\")'\npython -c 'import transformers; print(f\"transformers: {transformers.__version__}\")'\npython -c 'import deepspeed; print(f\"deepspeed: {deepspeed.__version__}\")'\n```\n\n* a link to a Google Colab notebook to reproduce the issue\n\n* if impossible, a standard and non-custom dataset we can use and also try to use an existing example to reproduce the issue with\n\nThe following sections provide a guide for resolving two of the most common issues.\n\n### DeepSpeed process killed at startup\n\nWhen the DeepSpeed process is killed during launch without a traceback, that usually means the program tried to allocate more CPU memory than your system has or your process tried to allocate more CPU memory than allowed leading the OS kernel to terminate the process. In this case, check whether your configuration file has either `offload_optimizer`, `offload_param` or both configured to offload to the CPU. \n\nIf you have NVMe and ZeRO-3 setup, experiment with offloading to the NVMe ([estimate](https://deepspeed.readthedocs.io/en/latest/memory.html) the memory requirements for your model).\n\n### NaN loss\n\nNaN loss often occurs when a model is pretrained in bf16 and then you try to use it with fp16 (especially relevant for TPU trained models). To resolve this, use fp32 or bf16 if your hardware supports it (TPU, Ampere GPUs or newer).\n\nThe other issue may be related to using fp16."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: b521c736-88f0-4c0a-b5a8-51298ba5acc9
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In this case, check whether your configuration file has either `offload_optimizer`, `offload_param` or both configured to offload to the CPU. \n\nIf you have NVMe and ZeRO-3 setup, experiment with offloading to the NVMe ([estimate](https://deepspeed.readthedocs.io/en/latest/memory.html) the memory requirements for your model).\n\n### NaN loss\n\nNaN loss often occurs when a model is pretrained in bf16 and then you try to use it with fp16 (especially relevant for TPU trained models). To resolve this, use fp32 or bf16 if your hardware supports it (TPU, Ampere GPUs or newer).\n\nThe other issue may be related to using fp16. For example, if this is your fp16 configuration:\n\n```yaml\n{\n    \"fp16\": {\n        \"enabled\": \"auto\",\n        \"loss_scale\": 0,\n        \"loss_scale_window\": 1000,\n        \"initial_scale_power\": 16,\n        \"hysteresis\": 2,\n        \"min_loss_scale\": 1\n    }\n}\n```\n\nYou might see the following `OVERFLOW!` messages in the logs:\n\n```bash\n0%|                                                                                                                             | 0/189 [00:00<?, ?it/s]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 262144\n  1%|\u258c                                                                                                                    | 1/189 [00:00<01:26,  2.17it/s]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 131072.0\n  1%|\u2588\u258f\n [...]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1\n 14%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c                                                                                                   | 27/189 [00:14<01:13,  2.21it/s]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1\n 15%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f                                                                                                  | 28/189 [00:14<01:13,  2.18it/s]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1\n 15%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                                                                  | 29/189 [00:15<01:13,  2.18it/s]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1\n[...]\n```\n\nThis means the DeepSpeed loss scaler is unable to find a scaling coefficient to overcome loss overflow. To fix it, try a higher `initial_scale_power` value (32 usually works).\n\n## Resources\n\nDeepSpeed ZeRO is a powerful technology for training and loading very large models for inference with limited GPU resources, making it more accessible to everyone."}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 4a5a73f4-35d8-42a2-9667-6b85d5ffe303
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{"url": "https://huggingface.co/docs/transformers/deepspeed/", "title": "DeepSpeed", "tokens": 14117, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"4a5a73f4-35d8-42a2-9667-6b85d5ffe303\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"af39070b-9107-4e33-8b63-6eb78f0af7cd\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"9408a27b37bc8218b970c759bc1d0b849eb9bb86f1b728bc154460e07619e3f6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"b521c736-88f0-4c0a-b5a8-51298ba5acc9\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/deepspeed/\", \"title\": \"DeepSpeed\", \"tokens\": 14117, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"7a6df9c0a5eee03e25db33a3c12f1130d660c6e0952edea7d417b674e4c73584\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 55130, \"end_char_idx\": 57280, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "ref_doc_id": "af39070b-9107-4e33-8b63-6eb78f0af7cd", "chroma:document": "Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 262144\n  1%|\u258c                                                                                                                    | 1/189 [00:00<01:26,  2.17it/s]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 131072.0\n  1%|\u2588\u258f\n [...]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1\n 14%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c                                                                                                   | 27/189 [00:14<01:13,  2.21it/s]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1\n 15%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f                                                                                                  | 28/189 [00:14<01:13,  2.18it/s]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1\n 15%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                                                                  | 29/189 [00:15<01:13,  2.18it/s]\n [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1\n[...]\n```\n\nThis means the DeepSpeed loss scaler is unable to find a scaling coefficient to overcome loss overflow. To fix it, try a higher `initial_scale_power` value (32 usually works).\n\n## Resources\n\nDeepSpeed ZeRO is a powerful technology for training and loading very large models for inference with limited GPU resources, making it more accessible to everyone. To learn more about DeepSpeed, feel free to read the [blog posts](https://www.microsoft.com/en-us/research/search/?q=deepspeed), [documentation](https://www.deepspeed.ai/getting-started/), and [GitHub repository](https://github.com/microsoft/deepspeed). \n\nThe following papers are also a great resource for learning more about ZeRO:\n\n* [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://hf.co/papers/1910.02054)\n* [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://hf.co/papers/2101.06840)\n* [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://hf.co/papers/2104.07857)"}


created_at: 2024-07-27 21:51:38
topic: persistent://default/default/75f2c05a-8be8-4722-bb1a-d317079d4e8a
id: 4e2d127b-08a8-4093-ab21-d9f4508fc5f8
encoding: FLOAT32
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{"url": "https://huggingface.co/docs/transformers/index/", "title": "\ud83e\udd17 Transformers", "tokens": 9691, "retrieve_doc": false, "source": "HF_Transformers", "_node_content": "{\"id_\": \"4e2d127b-08a8-4093-ab21-d9f4508fc5f8\", \"embedding\": null, \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/index/\", \"title\": \"\\ud83e\\udd17 Transformers\", \"tokens\": 9691, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"6be8a835-f94c-4ace-ba6b-6014b9db48e9\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://huggingface.co/docs/transformers/index/\", \"title\": \"\\ud83e\\udd17 Transformers\", \"tokens\": 9691, \"retrieve_doc\": false, \"source\": \"HF_Transformers\"}, \"hash\": \"74e62e3b26e0ca81c101a9d08be91c6bea0a2b38629c57fd39d4f9e277eacf7a\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"ea616166-bf63-41ad-a22e-4274205c8666\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"4bfdfc4013aa0effcda4de666acc0791525346a31cc0c328fa5f3c808473584d\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 0, \"end_char_idx\": 3402, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "6be8a835-f94c-4ace-ba6b-6014b9db48e9", "doc_id": "6be8a835-f94c-4ace-ba6b-6014b9db48e9", "ref_doc_id": "6be8a835-f94c-4ace-ba6b-6014b9db48e9", "chroma:document": "# \ud83e\udd17 Transformers\n\nState-of-the-art Machine Learning for [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), and [JAX](https://jax.readthedocs.io/en/latest/).\n\n\ud83e\udd17 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:\n\n\ud83d\udcdd **Natural Language Processing**: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.<br>\n\ud83d\uddbc\ufe0f **Computer Vision**: image classification, object detection, and segmentation.<br>\n\ud83d\udde3\ufe0f **Audio**: automatic speech recognition and audio classification.<br>\n\ud83d\udc19 **Multimodal**: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.\n\n\ud83e\udd17 Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. This provides the flexibility to use a different framework at each stage of a model's life; train a model in three lines of code in one framework, and load it for inference in another. Models can also be exported to a format like ONNX and TorchScript for deployment in production environments.\n\nJoin the growing community on the [Hub](https://huggingface.co/models), [forum](https://discuss.huggingface.co/), or [Discord](https://discord.com/invite/JfAtkvEtRb) today!\n\n## If you are looking for custom support from the Hugging Face team\n\n<a target=\"_blank\" href=\"https://huggingface.co/support\">\n    <img alt=\"HuggingFace Expert Acceleration Program\" src=\"https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png\" style=\"width: 100%; max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);\">\n</a>\n\n## Contents\n\nThe documentation is organized into five sections:\n\n- **GET STARTED** provides a quick tour of the library and installation instructions to get up and running.\n- **TUTORIALS** are a great place to start if you're a beginner. This section will help you gain the basic skills you need to start using the library.\n- **HOW-TO GUIDES** show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model.\n- **CONCEPTUAL GUIDES** offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of \ud83e\udd17 Transformers.\n- **API** describes all classes and functions:\n\n  - **MAIN CLASSES** details the most important classes like configuration, model, tokenizer, and pipeline.\n  - **MODELS** details the classes and functions related to each model implemented in the library.\n  - **INTERNAL HELPERS** details utility classes and functions used internally.\n\n\n## Supported models and frameworks\n\nThe table below represents the current support in the library for each of those models, whether they have a Python\ntokenizer (called \"slow\"). A \"fast\" tokenizer backed by the \ud83e\udd17 Tokenizers library, whether they have support in Jax (via\nFlax), PyTorch, and/or TensorFlow.\n\n<!--This table is updated automatically from the auto modules with _make fix-copies_. Do not update manually!-->\n\n|"}


created_at: 2024-07-27 21:51:38
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created_at: 2024-07-27 21:51:38
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created_at: 2024-07-27 21:51:38
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created_at: 2024-07-27 21:51:38
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created_at: 2024-07-27 21:51:38
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created_at: 2024-07-27 21:51:38
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created_at: 2024-07-27 21:51:38
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