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d8f6d3790a
* update everywhere * style * pipelines * switch it everywhere in tests * switch it everywhere in docs * switch in converters everywhere * update in examples * update in model docstrings * style * warnings * style * Update configuration_utils.py * fix * Update configuration_utils.py * fixes and add first test * add pipeline tests * Update test_pipelines_common.py * add config test * Update test_modeling_common.py * add new ones * post rebase * add new * post rebase adds
95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import torch
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import torch.distributed as dist
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from torch.distributed.device_mesh import init_device_mesh
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from torch.distributed.tensor.experimental import context_parallel
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from torch.nn.attention import SDPBackend, sdpa_kernel
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from torch.nn.parallel import DistributedDataParallel as DDP
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from transformers import AutoModelForCausalLM
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from transformers.loss.loss_utils import ForCausalLMLoss
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world_size = int(os.environ.get("WORLD_SIZE", "1"))
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cp_mesh = init_device_mesh("cuda", (world_size,))
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rank = torch.distributed.get_node_local_rank()
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device = "cuda"
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dtype = torch.bfloat16
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sdpa_backend = SDPBackend.FLASH_ATTENTION
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# prepare inputs
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batch_size = 1
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seq_len = 128
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input_ids = torch.randint(low=8, high=64, size=(batch_size, seq_len), device=device)
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ignore_index = -100
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# When using CP, we need to use `shift_labels`
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shift_labels = torch.nn.functional.pad(input_ids, (0, 1), value=ignore_index)
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shift_labels = shift_labels[..., 1:].contiguous()
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position_ids = (
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torch.cumsum(torch.ones(size=input_ids.size(), dtype=input_ids.dtype, device=input_ids.device), dim=1) - 1
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)
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# sync input as they are created randomly
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dist.broadcast(input_ids, src=0)
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dist.broadcast(shift_labels, src=0)
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dist.broadcast(position_ids, src=0)
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# model and optimizer
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repo_id = "Qwen/Qwen2.5-Coder-0.5B-Instruct"
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model = AutoModelForCausalLM.from_pretrained(repo_id, dtype=dtype, device_map=device)
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
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model.train()
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model.zero_grad()
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optimizer.zero_grad()
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# For loss
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vocab_size = model.config.vocab_size
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# so training could be synced
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model = DDP(model, device_ids=[rank])
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# prepare for CP
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buffers = (input_ids, shift_labels, position_ids)
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buffer_seq_dims = (1, 1, 1)
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# `no_restore_buffers=set(buffers)` is required if `loss.backward` is outside `context_parallel`.
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# no_restore_buffers = set(buffers)
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no_restore_buffers = None
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# run with CP
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with sdpa_kernel(sdpa_backend):
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with context_parallel(
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cp_mesh,
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buffers=buffers,
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buffer_seq_dims=buffer_seq_dims,
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no_restore_buffers=no_restore_buffers,
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):
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outputs = model(input_ids, shift_labels=shift_labels, position_ids=position_ids)
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print(outputs.logits.shape)
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# So far we need to compute `loss` outside `model.forward` when using `shift_labels`
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# loss = outputs.loss
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loss = ForCausalLMLoss(logits=outputs.logits, labels=None, shift_labels=shift_labels, vocab_size=vocab_size)
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# This could be outside `context_parallel` context if `no_restore_buffers` is specified
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loss.backward()
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optimizer.step()
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