diff --git a/examples/language/gpt/README.md b/examples/language/gpt/README.md
index 8fdf6be3b..7e6acb3d3 100644
--- a/examples/language/gpt/README.md
+++ b/examples/language/gpt/README.md
@@ -39,9 +39,15 @@ If you want to test ZeRO1 and ZeRO2 in Colossal-AI, you need to ensure Colossal-
For simplicity, the input data is randonly generated here.
## Training
-We provide two solutions. One utilizes the hybrid parallel strategies of Gemini, DDP/ZeRO, and Tensor Parallelism.
-The other one uses Pipeline Parallelism Only.
-In the future, we are going merge them together and they can be used orthogonally to each other.
+We provide two stable solutions.
+One utilizes the Gemini to implement hybrid parallel strategies of Gemini, DDP/ZeRO, and Tensor Parallelism for a huggingface GPT model.
+The other one use [Titans](https://github.com/hpcaitech/Titans), a distributed executed model zoo maintained by ColossalAI,to implement the hybrid parallel strategies of TP + ZeRO + PP.
+
+We recommend using Gemini to qucikly run your model in a distributed manner.
+It doesn't require significant changes to the model structures, therefore you can apply it on a new model easily.
+And use Titans as an advanced weapon to pursue a more extreme performance.
+Titans has included the some typical models, such as Vit and GPT.
+However, it requires some efforts to start if facing a new model structure.
### GeminiDPP/ZeRO + Tensor Parallelism
```bash
@@ -56,6 +62,11 @@ The `train_gpt_demo.py` provides three distributed plans, you can choose the pla
- Pytorch DDP
- Pytorch ZeRO
+### Titans (Tensor Parallelism) + ZeRO + Pipeline Parallelism
+
+Titans provides a customized GPT model, which uses distributed operators as building blocks.
+In [./titans/README.md], we provide a hybrid parallelism of ZeRO, TP and PP.
+You can switch parallel strategies using a config file.
## Performance
diff --git a/examples/language/gpt/titans/LICENSE b/examples/language/gpt/titans/LICENSE
new file mode 100644
index 000000000..261eeb9e9
--- /dev/null
+++ b/examples/language/gpt/titans/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
diff --git a/examples/language/gpt/titans/README.md b/examples/language/gpt/titans/README.md
new file mode 100644
index 000000000..fe1854c9f
--- /dev/null
+++ b/examples/language/gpt/titans/README.md
@@ -0,0 +1,48 @@
+# Run GPT With Colossal-AI
+
+## How to Prepare Webtext Dataset
+
+You can download the preprocessed sample dataset for this demo via our [Google Drive sharing link](https://drive.google.com/file/d/1QKI6k-e2gJ7XgS8yIpgPPiMmwiBP_BPE/view?usp=sharing).
+
+
+You can also avoid dataset preparation by using `--use_dummy_dataset` during running.
+
+## Run this Demo
+
+Use the following commands to install prerequisites.
+
+```bash
+# assuming using cuda 11.3
+pip install -r requirements.txt
+```
+
+Use the following commands to execute training.
+
+```Bash
+#!/usr/bin/env sh
+# if you want to use real dataset, then remove --use_dummy_dataset
+# export DATA=/path/to/small-gpt-dataset.json'
+
+# run on a single node
+colossalai run --nproc_per_node=<num_gpus> train_gpt.py --config configs/<config_file> --from_torch --use_dummy_dataset
+
+# run on multiple nodes with slurm
+colossalai run --nproc_per_node=<num_gpus> \
+ --master_addr <hostname> \
+ --master_port <port-number> \
+ --hosts <list-of-hostname-separated-by-comma> \
+ train_gpt.py \
+ --config configs/<config_file> \
+ --from_torch \
+ --use_dummy_dataset
+
+# run on multiple nodes with slurm
+srun python \
+ train_gpt.py \
+ --config configs/<config_file> \
+ --host <master_node> \
+ --use_dummy_dataset
+
+```
+
+You can set the `<config_file>` to any file in the `configs` folder. To simply get it running, you can start with `gpt_small_zero3_pp1d.py` on a single node first. You can view the explanations in the config file regarding how to change the parallel setting.
diff --git a/examples/language/gpt/titans/configs/gpt2_small_zero3_pp1d.py b/examples/language/gpt/titans/configs/gpt2_small_zero3_pp1d.py
new file mode 100644
index 000000000..8ef81cb0a
--- /dev/null
+++ b/examples/language/gpt/titans/configs/gpt2_small_zero3_pp1d.py
@@ -0,0 +1,31 @@
+from model import GPT2_small_pipeline_hybrid
+
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.zero.shard_utils import TensorShardStrategy
+
+BATCH_SIZE = 8
+NUM_EPOCHS = 10
+SEQ_LEN = 1024
+NUM_MICRO_BATCHES = 4
+HIDDEN_SIZE = 768
+TENSOR_SHAPE = (BATCH_SIZE // NUM_MICRO_BATCHES, SEQ_LEN, HIDDEN_SIZE)
+
+# if you do no want zero, just comment out this dictionary
+zero = dict(model_config=dict(tensor_placement_policy='cuda', shard_strategy=TensorShardStrategy()),
+ optimizer_config=dict(initial_scale=2**16))
+
+optimizer = dict(
+ type=HybridAdam,
+ lr=0.00015,
+ weight_decay=1e-2,
+)
+
+model = dict(type=GPT2_small_pipeline_hybrid, checkpoint=True, num_chunks=1)
+
+# pipeline parallel: modify integer value for the number of pipeline stages
+# tensor parallel: modify size to set the tensor parallel size, usually the number of GPUs per node
+# for the current model implementation, mode can only be 1D or None
+parallel = dict(
+ pipeline=1,
+ tensor=dict(size=2, mode='1d'),
+)
diff --git a/examples/language/gpt/titans/configs/gpt3_zero3_pp1d.py b/examples/language/gpt/titans/configs/gpt3_zero3_pp1d.py
new file mode 100644
index 000000000..9f9816b30
--- /dev/null
+++ b/examples/language/gpt/titans/configs/gpt3_zero3_pp1d.py
@@ -0,0 +1,31 @@
+from model import GPT3_pipeline_hybrid
+
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.zero.shard_utils import TensorShardStrategy
+
+BATCH_SIZE = 192
+NUM_EPOCHS = 60
+SEQ_LEN = 2048
+NUM_MICRO_BATCHES = 192
+HIDDEN_SIZE = 12288
+TENSOR_SHAPE = (BATCH_SIZE // NUM_MICRO_BATCHES, SEQ_LEN, HIDDEN_SIZE)
+
+# if you do no want zero, just comment out this dictionary
+zero = dict(model_config=dict(tensor_placement_policy='cuda', shard_strategy=TensorShardStrategy()),
+ optimizer_config=dict(initial_scale=2**16))
+
+optimizer = dict(
+ type=HybridAdam,
+ lr=0.00015,
+ weight_decay=1e-2,
+)
+
+model = dict(type=GPT3_pipeline_hybrid, checkpoint=True, num_chunks=1)
+
+# pipeline parallel: modify integer value for the number of pipeline stages
+# tensor parallel: modify size to set the tensor parallel size, usually the number of GPUs per node
+# for the current model implementation, mode can only be 1D or None
+parallel = dict(
+ pipeline=1,
+ tensor=dict(size=2, mode='1d'), # for the current model implementation, mode can only be 1D or None
+)
diff --git a/examples/language/gpt/titans/model/__init__.py b/examples/language/gpt/titans/model/__init__.py
new file mode 100644
index 000000000..eec48ef89
--- /dev/null
+++ b/examples/language/gpt/titans/model/__init__.py
@@ -0,0 +1,3 @@
+from .embed import vocab_parallel_cross_entropy
+from .gpt1d import *
+from .pipeline_gpt1d import *
diff --git a/examples/language/gpt/titans/model/embed.py b/examples/language/gpt/titans/model/embed.py
new file mode 100644
index 000000000..6369b9f8c
--- /dev/null
+++ b/examples/language/gpt/titans/model/embed.py
@@ -0,0 +1,599 @@
+import torch
+import torch.nn.init as init
+from torch import Tensor
+from torch import distributed as dist
+from torch import nn as nn
+from torch.nn import functional as F
+from torch.nn.parameter import Parameter
+
+from colossalai.context import ParallelMode, seed
+from colossalai.core import global_context as gpc
+from colossalai.nn.layer.base_layer import ParallelLayer
+from colossalai.nn.layer.parallel_1d._utils import gather_forward_split_backward, reduce_grad, reduce_input
+from colossalai.nn.layer.parallel_1d.layers import Linear1D_Row
+from colossalai.nn.layer.utils import divide
+from colossalai.registry import LAYERS, LOSSES, MODELS
+from colossalai.utils import get_current_device
+
+
+class VocabParallelEmbedding(torch.nn.Module):
+ """Language model embeddings.
+
+ Arguments:
+ hidden_size: hidden size
+ vocab_size: vocabulary size
+ max_sequence_length: maximum size of sequence. This
+ is used for positional embedding
+ embedding_dropout_prob: dropout probability for embeddings
+ init_method: weight initialization method
+ num_tokentypes: size of the token-type embeddings. 0 value
+ will ignore this embedding
+ """
+
+ def __init__(self,
+ hidden_size,
+ vocab_size,
+ max_sequence_length,
+ embedding_dropout_prob,
+ num_tokentypes=0,
+ dtype=torch.float):
+ super(VocabParallelEmbedding, self).__init__()
+
+ self.hidden_size = hidden_size
+ self.num_tokentypes = num_tokentypes
+
+ # Word embeddings (parallel).
+ self.word_embeddings = VocabParallelEmbedding1D(vocab_size, self.hidden_size, dtype=dtype)
+ self._word_embeddings_key = 'word_embeddings'
+
+ # Position embedding (serial).
+ self.position_embeddings = torch.nn.Embedding(max_sequence_length, self.hidden_size, dtype=dtype)
+ self._position_embeddings_key = 'position_embeddings'
+ # Initialize the position embeddings.
+ # self.init_method(self.position_embeddings.weight)
+
+ # Token type embedding.
+ # Add this as an optional field that can be added through
+ # method call so we can load a pretrain model without
+ # token types and add them as needed.
+ self._tokentype_embeddings_key = 'tokentype_embeddings'
+ if self.num_tokentypes > 0:
+ self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes, self.hidden_size, dtype=dtype)
+ # Initialize the token-type embeddings.
+ # self.init_method(self.tokentype_embeddings.weight)
+ else:
+ self.tokentype_embeddings = None
+
+ # Embeddings dropout
+ self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)
+
+ def zero_parameters(self):
+ """Zero out all parameters in embedding."""
+ self.word_embeddings.weight.data.fill_(0)
+ self.word_embeddings.weight.shared = True
+ self.position_embeddings.weight.data.fill_(0)
+ self.position_embeddings.weight.shared = True
+ if self.num_tokentypes > 0:
+ self.tokentype_embeddings.weight.data.fill_(0)
+ self.tokentype_embeddings.weight.shared = True
+
+ def add_tokentype_embeddings(self, num_tokentypes):
+ """Add token-type embedding. This function is provided so we can add
+ token-type embeddings in case the pretrained model does not have it.
+ This allows us to load the model normally and then add this embedding.
+ """
+ if self.tokentype_embeddings is not None:
+ raise Exception('tokentype embeddings is already initialized')
+ if torch.distributed.get_rank() == 0:
+ print('adding embedding for {} tokentypes'.format(num_tokentypes), flush=True)
+ self.num_tokentypes = num_tokentypes
+ self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes, self.hidden_size)
+ # Initialize the token-type embeddings.
+ # self.init_method(self.tokentype_embeddings.weight)
+
+ def forward(self, input_ids, position_ids=None, tokentype_ids=None):
+ # Embeddings.
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ words_embeddings = self.word_embeddings(input_ids)
+
+ if position_ids is not None:
+ position_ids = position_ids.view(-1, input_shape[-1])
+ if position_ids is None:
+ position_ids = torch.arange(0, input_shape[-1] + 0, dtype=torch.long, device=get_current_device())
+ position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
+ position_embeddings = self.position_embeddings(position_ids)
+
+ embeddings = words_embeddings + position_embeddings
+
+ # Dropout.
+ with seed(ParallelMode.TENSOR):
+ embeddings = self.embedding_dropout(embeddings)
+ return embeddings
+
+ def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False):
+ """For easy load."""
+
+ state_dict_ = {}
+ state_dict_[self._word_embeddings_key] \
+ = self.word_embeddings.state_dict(destination, prefix, keep_vars)
+ state_dict_[self._position_embeddings_key] \
+ = self.position_embeddings.state_dict(
+ destination, prefix, keep_vars)
+ if self.num_tokentypes > 0:
+ state_dict_[self._tokentype_embeddings_key] \
+ = self.tokentype_embeddings.state_dict(
+ destination, prefix, keep_vars)
+
+ return state_dict_
+
+ def load_state_dict(self, state_dict, strict=True):
+ """Customized load."""
+
+ # Word embedding.
+ if self._word_embeddings_key in state_dict:
+ state_dict_ = state_dict[self._word_embeddings_key]
+ else:
+ # for backward compatibility.
+ state_dict_ = {}
+ for key in state_dict.keys():
+ if 'word_embeddings' in key:
+ state_dict_[key.split('word_embeddings.')[1]] \
+ = state_dict[key]
+ self.word_embeddings.load_state_dict(state_dict_, strict=strict)
+
+ # Position embedding.
+ if self._position_embeddings_key in state_dict:
+ state_dict_ = state_dict[self._position_embeddings_key]
+ else:
+ # for backward compatibility.
+ state_dict_ = {}
+ for key in state_dict.keys():
+ if 'position_embeddings' in key:
+ state_dict_[key.split('position_embeddings.')[1]] \
+ = state_dict[key]
+ self.position_embeddings.load_state_dict(state_dict_, strict=strict)
+
+ # Tokentype embedding.
+ if self.num_tokentypes > 0:
+ state_dict_ = {}
+ if self._tokentype_embeddings_key in state_dict:
+ state_dict_ = state_dict[self._tokentype_embeddings_key]
+ else:
+ # for backward compatibility.
+ for key in state_dict.keys():
+ if 'tokentype_embeddings' in key:
+ state_dict_[key.split('tokentype_embeddings.')[1]] \
+ = state_dict[key]
+ if len(state_dict_.keys()) > 0:
+ self.tokentype_embeddings.load_state_dict(state_dict_, strict=strict)
+ else:
+ print('***WARNING*** expected tokentype embeddings in the '
+ 'checkpoint but could not find it',
+ flush=True)
+
+
+class VocabParallelEmbedding1D(torch.nn.Module):
+ """Embedding parallelized in the vocabulary dimension.
+
+ This is mainly adapted from torch.nn.Embedding and all the default
+ values are kept.
+ Arguments:
+ num_embeddings: vocabulary size.
+ embedding_dim: size of hidden state.
+ init_method: method to initialize weights.
+ """
+
+ def __init__(self, num_embeddings, embedding_dim, dtype=None, init_method=None):
+ super(VocabParallelEmbedding1D, self).__init__()
+ # Keep the input dimensions.
+ self.num_embeddings = num_embeddings
+ self.embedding_dim = embedding_dim
+ # Set the details for compatibility.
+ self.padding_idx = None
+ self.max_norm = None
+ self.norm_type = 2.
+ self.scale_grad_by_freq = False
+ self.sparse = False
+ self._weight = None
+ self.tensor_model_parallel_size = gpc.tensor_parallel_size
+ # Divide the weight matrix along the vocabulary dimension.
+ self.vocab_start_index, self.vocab_end_index = \
+ VocabUtility.vocab_range_from_global_vocab_size(
+ self.num_embeddings, gpc.get_local_rank(ParallelMode.PARALLEL_1D),
+ self.tensor_model_parallel_size)
+ self.num_embeddings_per_partition = self.vocab_end_index - \
+ self.vocab_start_index
+
+ # Allocate weights and initialize.
+ factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
+ self.weight = Parameter(torch.empty(self.num_embeddings_per_partition, self.embedding_dim, **factory_kwargs))
+ init.uniform_(self.weight, -1, 1)
+
+ def forward(self, input_):
+ if self.tensor_model_parallel_size > 1:
+ # Build the mask.
+ input_mask = (input_ < self.vocab_start_index) | \
+ (input_ >= self.vocab_end_index)
+ # Mask the input.
+ masked_input = input_.clone() - self.vocab_start_index
+ masked_input[input_mask] = 0
+ else:
+ masked_input = input_
+ # Get the embeddings.
+ output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, self.max_norm, self.norm_type,
+ self.scale_grad_by_freq, self.sparse)
+ # Mask the output embedding.
+ if self.tensor_model_parallel_size > 1:
+ output_parallel[input_mask, :] = 0.0
+ # Reduce across all the model parallel GPUs.
+ output = output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D)
+ return output
+
+
+@LOSSES.register_module
+class vocab_parallel_cross_entropy(nn.Module):
+
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, vocab_parallel_logits, target):
+ """Helper function for the cross entropy."""
+ vocab_parallel_logits = vocab_parallel_logits[..., :-1, :].contiguous()
+ target = target[..., 1:].contiguous()
+ return _VocabParallelCrossEntropy.apply(vocab_parallel_logits.view(-1, vocab_parallel_logits.size(-1)),
+ target.view(-1))
+
+
+class _VocabParallelCrossEntropy(torch.autograd.Function):
+
+ @staticmethod
+ def forward(ctx, vocab_parallel_logits, target):
+
+ # Maximum value along vocab dimension across all GPUs.
+ logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
+ torch.distributed.all_reduce(logits_max,
+ op=torch.distributed.ReduceOp.MAX,
+ group=gpc.get_group(ParallelMode.PARALLEL_1D))
+ # Subtract the maximum value.
+ vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1))
+
+ # Get the partition's vocab indices
+ get_vocab_range = VocabUtility.vocab_range_from_per_partition_vocab_size
+ partition_vocab_size = vocab_parallel_logits.size()[-1]
+ rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+ world_size = gpc.tensor_parallel_size
+ vocab_start_index, vocab_end_index = get_vocab_range(partition_vocab_size, rank, world_size)
+
+ # Create a mask of valid vocab ids (1 means it needs to be masked).
+ target_mask = (target < vocab_start_index) | (target >= vocab_end_index)
+ masked_target = target.clone() - vocab_start_index
+ masked_target[target_mask] = 0
+
+ # Get predicted-logits = logits[target].
+ # For Simplicity, we convert logits to a 2-D tensor with size
+ # [*, partition-vocab-size] and target to a 1-D tensor of size [*].
+ logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size)
+ masked_target_1d = masked_target.view(-1)
+ arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device)
+ predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
+ predicted_logits_1d = predicted_logits_1d.clone().contiguous()
+ predicted_logits = predicted_logits_1d.view_as(target)
+ predicted_logits[target_mask] = 0.0
+ # All reduce is needed to get the chunks from other GPUs.
+ torch.distributed.all_reduce(predicted_logits,
+ op=torch.distributed.ReduceOp.SUM,
+ group=gpc.get_group(ParallelMode.PARALLEL_1D))
+
+ # Sum of exponential of logits along vocab dimension across all GPUs.
+ exp_logits = vocab_parallel_logits
+ torch.exp(vocab_parallel_logits, out=exp_logits)
+ sum_exp_logits = exp_logits.sum(dim=-1)
+ torch.distributed.all_reduce(sum_exp_logits,
+ op=torch.distributed.ReduceOp.SUM,
+ group=gpc.get_group(ParallelMode.PARALLEL_1D))
+
+ # Loss = log(sum(exp(logits))) - predicted-logit.
+ loss = torch.log(sum_exp_logits) - predicted_logits
+ loss = loss.mean()
+ # Store softmax, target-mask and masked-target for backward pass.
+ exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
+ ctx.save_for_backward(exp_logits, target_mask, masked_target_1d)
+ return loss
+
+ @staticmethod
+ def backward(ctx, grad_output):
+
+ # Retreive tensors from the forward path.
+ softmax, target_mask, masked_target_1d = ctx.saved_tensors
+
+ # All the inputs have softmax as their gradient.
+ grad_input = softmax
+ # For simplicity, work with the 2D gradient.
+ partition_vocab_size = softmax.size()[-1]
+ grad_2d = grad_input.view(-1, partition_vocab_size)
+
+ # Add the gradient from matching classes.
+ arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device)
+ grad_2d[arange_1d, masked_target_1d] -= (1.0 - target_mask.view(-1).float())
+
+ # Finally elementwise multiplication with the output gradients.
+ grad_input.mul_(grad_output.unsqueeze(dim=-1))
+
+ return grad_input, None
+
+
+class VocabUtility:
+ """Split the vocabulary into `world_size` chunks amd return the
+ first and last index of the vocabulary belonging to the `rank`
+ partition: Note that indices in [fist, last)"""
+
+ @staticmethod
+ def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank, world_size):
+ index_f = rank * per_partition_vocab_size
+ index_l = index_f + per_partition_vocab_size
+ return index_f, index_l
+
+ @staticmethod
+ def vocab_range_from_global_vocab_size(global_vocab_size, rank, world_size):
+ per_partition_vocab_size = divide(global_vocab_size, world_size)
+ return VocabUtility.vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank, world_size)
+
+
+class VocabParallelGPTLMHead1D(ParallelLayer):
+ """
+ Language model head that shares the same parameters with the embedding matrix.
+ """
+
+ def __init__(self, embed=None, vocab_size=None, dtype=None, embed_dim=None):
+ super().__init__()
+ if embed is not None:
+ self.head = embed
+ else:
+ self.head = VocabParallelEmbedding1D(vocab_size, embed_dim, dtype=dtype)
+
+ def forward(self, x: Tensor) -> Tensor:
+ x = reduce_grad(x, ParallelMode.PARALLEL_1D)
+ x = F.linear(x, self.head.weight)
+ return x
+
+
+###################################
+
+
+class HiddenParallelEmbedding(torch.nn.Module):
+ """Language model embeddings.
+
+ Arguments:
+ hidden_size: hidden size
+ vocab_size: vocabulary size
+ max_sequence_length: maximum size of sequence. This
+ is used for positional embedding
+ embedding_dropout_prob: dropout probability for embeddings
+ init_method: weight initialization method
+ num_tokentypes: size of the token-type embeddings. 0 value
+ will ignore this embedding
+ """
+
+ def __init__(
+ self,
+ hidden_size,
+ vocab_size,
+ max_sequence_length,
+ embedding_dropout_prob,
+ dtype=torch.float,
+ padding_idx: int = 0,
+ num_tokentypes=0,
+ ):
+ super(HiddenParallelEmbedding, self).__init__()
+
+ self.hidden_size = hidden_size
+ self.num_tokentypes = num_tokentypes
+
+ # Word embeddings (parallel).
+ self.word_embeddings = HiddenParallelEmbedding1D(vocab_size, hidden_size, dtype, padding_idx)
+ self._word_embeddings_key = 'word_embeddings'
+
+ # Position embedding (serial).
+ self.position_embeddings = torch.nn.Embedding(max_sequence_length, self.hidden_size)
+ self._position_embeddings_key = 'position_embeddings'
+ # Initialize the position embeddings.
+ # self.init_method(self.position_embeddings.weight)
+
+ # Token type embedding.
+ # Add this as an optional field that can be added through
+ # method call so we can load a pretrain model without
+ # token types and add them as needed.
+ self._tokentype_embeddings_key = 'tokentype_embeddings'
+ if self.num_tokentypes > 0:
+ self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes, self.hidden_size)
+ # Initialize the token-type embeddings.
+ # self.init_method(self.tokentype_embeddings.weight)
+ else:
+ self.tokentype_embeddings = None
+
+ # Embeddings dropout
+ self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)
+
+ def zero_parameters(self):
+ """Zero out all parameters in embedding."""
+ self.word_embeddings.weight.data.fill_(0)
+ self.word_embeddings.weight.shared = True
+ self.position_embeddings.weight.data.fill_(0)
+ self.position_embeddings.weight.shared = True
+ if self.num_tokentypes > 0:
+ self.tokentype_embeddings.weight.data.fill_(0)
+ self.tokentype_embeddings.weight.shared = True
+
+ def add_tokentype_embeddings(self, num_tokentypes):
+ """Add token-type embedding. This function is provided so we can add
+ token-type embeddings in case the pretrained model does not have it.
+ This allows us to load the model normally and then add this embedding.
+ """
+ if self.tokentype_embeddings is not None:
+ raise Exception('tokentype embeddings is already initialized')
+ if torch.distributed.get_rank() == 0:
+ print('adding embedding for {} tokentypes'.format(num_tokentypes), flush=True)
+ self.num_tokentypes = num_tokentypes
+ self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes, self.hidden_size)
+ # Initialize the token-type embeddings.
+ # self.init_method(self.tokentype_embeddings.weight)
+
+ def forward(self, input_ids, position_ids=None, tokentype_ids=None):
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ words_embeddings = self.word_embeddings(input_ids)
+
+ if position_ids is not None:
+ position_ids = position_ids.view(-1, input_shape[-1])
+ if position_ids is None:
+ position_ids = torch.arange(0, input_shape[-1] + 0, dtype=torch.long, device=get_current_device())
+ position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
+ position_embeddings = self.position_embeddings(position_ids)
+
+ embeddings = words_embeddings + position_embeddings
+
+ # Dropout.
+ with seed(ParallelMode.TENSOR):
+ embeddings = self.embedding_dropout(embeddings)
+ return embeddings
+
+ def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False):
+ """For easy load."""
+
+ state_dict_ = {}
+ state_dict_[self._word_embeddings_key] \
+ = self.word_embeddings.state_dict(destination, prefix, keep_vars)
+ state_dict_[self._position_embeddings_key] \
+ = self.position_embeddings.state_dict(
+ destination, prefix, keep_vars)
+ if self.num_tokentypes > 0:
+ state_dict_[self._tokentype_embeddings_key] \
+ = self.tokentype_embeddings.state_dict(
+ destination, prefix, keep_vars)
+
+ return state_dict_
+
+ def load_state_dict(self, state_dict, strict=True):
+ """Customized load."""
+
+ # Word embedding.
+ if self._word_embeddings_key in state_dict:
+ state_dict_ = state_dict[self._word_embeddings_key]
+ else:
+ # for backward compatibility.
+ state_dict_ = {}
+ for key in state_dict.keys():
+ if 'word_embeddings' in key:
+ state_dict_[key.split('word_embeddings.')[1]] \
+ = state_dict[key]
+ self.word_embeddings.load_state_dict(state_dict_, strict=strict)
+
+ # Position embedding.
+ if self._position_embeddings_key in state_dict:
+ state_dict_ = state_dict[self._position_embeddings_key]
+ else:
+ # for backward compatibility.
+ state_dict_ = {}
+ for key in state_dict.keys():
+ if 'position_embeddings' in key:
+ state_dict_[key.split('position_embeddings.')[1]] \
+ = state_dict[key]
+ self.position_embeddings.load_state_dict(state_dict_, strict=strict)
+
+ # Tokentype embedding.
+ if self.num_tokentypes > 0:
+ state_dict_ = {}
+ if self._tokentype_embeddings_key in state_dict:
+ state_dict_ = state_dict[self._tokentype_embeddings_key]
+ else:
+ # for backward compatibility.
+ for key in state_dict.keys():
+ if 'tokentype_embeddings' in key:
+ state_dict_[key.split('tokentype_embeddings.')[1]] \
+ = state_dict[key]
+ if len(state_dict_.keys()) > 0:
+ self.tokentype_embeddings.load_state_dict(state_dict_, strict=strict)
+ else:
+ print('***WARNING*** expected tokentype embeddings in the '
+ 'checkpoint but could not find it',
+ flush=True)
+
+
+class HiddenParallelEmbedding1D(torch.nn.Module):
+ """Embedding parallelized in the vocabulary dimension.
+
+ This is mainly adapted from torch.nn.Embedding and all the default
+ values are kept.
+ Arguments:
+ num_embeddings: vocabulary size.
+ embedding_dim: size of hidden state.
+ init_method: method to initialize weights.
+ """
+
+ def __init__(self, num_embeddings, embedding_dim, dtype=torch.float, padding_idx: int = None, init_method=None):
+ super(HiddenParallelEmbedding1D, self).__init__()
+ # Keep the input dimensions.
+ self.num_embeddings = num_embeddings
+ self.embedding_dim = embedding_dim
+ embed_dim_per_partition = divide(embedding_dim, gpc.tensor_parallel_size)
+ # Set the details for compatibility.
+ self.padding_idx = padding_idx
+ self.max_norm = None
+ self.norm_type = 2.
+ self.scale_grad_by_freq = False
+ self.sparse = False
+ self._weight = None
+
+ # Allocate weights and initialize.
+ factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
+ self.weight = Parameter(torch.empty(num_embeddings, embed_dim_per_partition, **factory_kwargs))
+ init.uniform_(self.weight, -1, 1)
+
+ def forward(self, input_):
+
+ # Get the embeddings.
+ output_parallel = F.embedding(input_, self.weight, self.padding_idx, self.max_norm, self.norm_type,
+ self.scale_grad_by_freq, self.sparse)
+
+ # Reduce across all the model parallel GPUs.
+ output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1)
+ return output
+
+
+@LAYERS.register_module
+class HiddenParallelGPTLMHead1D(ParallelLayer):
+ """
+ Language model head that shares the same parameters with the embedding matrix.
+ """
+
+ def __init__(
+ self,
+ embed=None,
+ embed_dim=None,
+ vocab_size=None,
+ dtype=None,
+ ):
+ super().__init__()
+ if embed is not None:
+ self.head = embed
+ self.synced_embed = True
+ else:
+ # self.embedding = HiddenParallelEmbedding1D(vocab_size, hidden_size, dtype, padding_idx)
+ # (hidden_size/q, vocab_size)
+ self.synced_embed = False
+ self.head = Linear1D_Row(in_features=embed_dim,
+ out_features=vocab_size,
+ bias=False,
+ dtype=dtype,
+ parallel_input=False)
+
+ def forward(self, x: Tensor) -> Tensor:
+ if self.synced_embed:
+ x = F.linear(x, self.head.weight)
+ else:
+ x = self.head(x)
+
+ return x
diff --git a/examples/language/gpt/titans/model/gpt1d.py b/examples/language/gpt/titans/model/gpt1d.py
new file mode 100644
index 000000000..2edd03606
--- /dev/null
+++ b/examples/language/gpt/titans/model/gpt1d.py
@@ -0,0 +1,349 @@
+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+
+import math
+
+import torch
+from torch import Tensor
+from torch import nn as nn
+
+from colossalai import kernel
+from colossalai import nn as col_nn
+from colossalai.core import global_context as gpc
+from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType
+from colossalai.nn.layer import Linear1D_Col, Linear1D_Row
+from colossalai.nn.layer.base_layer import ParallelLayer
+from colossalai.nn.layer.utils import ACT2FN, divide
+from colossalai.utils import checkpoint
+from colossalai.utils.activation_checkpoint import checkpoint
+
+__all__ = [
+ 'GPTMLP1D', 'GPTSelfAttention1D', 'GPTTransformerLayer1D', 'FusedGPTSelfAttention1D', 'FusedGPTTransformerLayer1D'
+]
+
+
+class GPTMLP1D(ParallelLayer):
+
+ def __init__(
+ self,
+ in_features: int,
+ mlp_ratio: int,
+ act_func: str = 'gelu',
+ dropout_prob: float = 0.,
+ dtype=None,
+ checkpoint: bool = False,
+ skip_bias_add: bool = False,
+ ):
+ super().__init__()
+
+ self.in_features = in_features
+ self.mlp_ratio = mlp_ratio
+ self.checkpoint = checkpoint
+ self.skip_bias_add = skip_bias_add
+
+ self.act = ACT2FN[act_func]
+ skip_dense_1_add_bias = False
+
+ # Project to mlp_ratio * h.
+ self.dense_1 = Linear1D_Col(
+ self.in_features,
+ int(self.mlp_ratio * self.in_features),
+ dtype=dtype,
+ gather_output=False,
+ skip_bias_add=skip_dense_1_add_bias,
+ )
+
+ # Project back to h.
+ self.dense_2 = Linear1D_Row(
+ int(self.mlp_ratio * self.in_features),
+ self.in_features,
+ dtype=dtype,
+ parallel_input=True,
+ )
+
+ self.dropout = col_nn.Dropout(dropout_prob)
+
+ def _forward(self, hidden_states: Tensor) -> Tensor:
+ intermediate_output = self.dense_1(hidden_states)
+ intermediate_output = self.act(intermediate_output)
+
+ output = self.dense_2(intermediate_output)
+ output = self.dropout(output)
+ return output
+
+ def _checkpoint_forward(self, hidden_states: Tensor) -> Tensor:
+ return checkpoint(self._forward, False, hidden_states)
+
+ def forward(self, hidden_states: Tensor) -> Tensor:
+ if self.checkpoint:
+ return self._checkpoint_forward(hidden_states)
+ else:
+ return self._forward(hidden_states)
+
+
+class GenericGPTSelfAttention1D(ParallelLayer):
+
+ def __init__(
+ self,
+ hidden_size: int,
+ num_attention_heads: int,
+ attention_dropout_prob: float,
+ hidden_dropout_prob: float,
+ dtype=None,
+ checkpoint: bool = False,
+ max_position_embeddings=1024,
+ ):
+ super().__init__()
+ self.hidden_size = hidden_size
+ self.attention_head_size = divide(hidden_size, num_attention_heads)
+ self.num_attention_heads_per_partition = divide(num_attention_heads, gpc.tensor_parallel_size)
+ self.hidden_size_per_partition = divide(hidden_size, gpc.tensor_parallel_size)
+ self.checkpoint = checkpoint
+ self.query_key_value = Linear1D_Col(
+ hidden_size,
+ 3 * hidden_size,
+ dtype=dtype,
+ )
+ self.attention_dropout = col_nn.Dropout(attention_dropout_prob)
+ self.dense = Linear1D_Row(
+ hidden_size,
+ hidden_size,
+ dtype=dtype,
+ parallel_input=True,
+ )
+ self.dropout = col_nn.Dropout(hidden_dropout_prob)
+
+ def softmax_forward(self, attention_scores, attention_mask, query_layer, key_layer):
+ raise NotImplementedError
+
+ def _forward(self, hidden_states: Tensor, attention_mask=None) -> Tensor:
+ query_key_value = self.query_key_value(hidden_states)
+ new_qkv_shape = query_key_value.shape[:-1] + \
+ (self.num_attention_heads_per_partition, 3 * self.attention_head_size)
+ query_key_value = query_key_value.view(new_qkv_shape)
+ query_key_value = query_key_value.permute((0, 2, 1, 3))
+ query_layer, key_layer, value_layer = torch.chunk(query_key_value, 3, dim=-1)
+
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+ attention_scores = self.softmax_forward(attention_scores, attention_mask, query_layer, key_layer)
+
+ attention_scores = attention_scores.type(value_layer.dtype)
+
+ attention_probs = self.attention_dropout(attention_scores)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.transpose(1, 2)
+ new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
+ context_layer = context_layer.reshape(new_context_layer_shape)
+ output = self.dense(context_layer)
+ output = self.dropout(output)
+
+ return output
+
+ def _checkpoint_forward(self, hidden_states: Tensor, attention_mask=None) -> Tensor:
+ return checkpoint(self._forward, False, hidden_states, attention_mask)
+
+ def forward(self, hidden_states: Tensor, attention_mask=None) -> Tensor:
+ if self.checkpoint:
+ return self._checkpoint_forward(hidden_states, attention_mask)
+ else:
+ return self._forward(hidden_states, attention_mask)
+
+
+class GPTSelfAttention1D(GenericGPTSelfAttention1D):
+
+ def __init__(self,
+ hidden_size: int,
+ num_attention_heads: int,
+ attention_dropout_prob: float,
+ hidden_dropout_prob: float,
+ dtype=None,
+ checkpoint: bool = False,
+ max_position_embeddings=1024):
+ super().__init__(hidden_size,
+ num_attention_heads,
+ attention_dropout_prob,
+ hidden_dropout_prob,
+ dtype=dtype,
+ checkpoint=checkpoint,
+ max_position_embeddings=max_position_embeddings)
+ self.softmax = nn.Softmax(dim=-1)
+ max_positions = max_position_embeddings
+ self.register_buffer(
+ "bias",
+ torch.tril(torch.ones((max_positions, max_positions),
+ dtype=torch.uint8)).view(1, 1, max_positions, max_positions),
+ )
+ self.register_buffer("masked_bias", torch.tensor(-1e4))
+
+ def softmax_forward(self, attention_scores, attention_mask, query_layer, key_layer):
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ # causal mask
+ query_length, key_length = query_layer.size(-2), key_layer.size(-2)
+ causal_mask = self.bias[:, :, key_length - query_length:key_length, :key_length].bool()
+ attention_scores = torch.where(causal_mask, attention_scores, self.masked_bias.to(attention_scores))
+ if attention_mask is not None:
+ # Apply the attention mask
+ attention_scores = attention_scores + attention_mask
+ attention_scores = self.softmax(attention_scores)
+ return attention_scores
+
+
+class FusedGPTSelfAttention1D(GenericGPTSelfAttention1D):
+
+ def __init__(self,
+ hidden_size: int,
+ num_attention_heads: int,
+ attention_dropout_prob: float,
+ hidden_dropout_prob: float,
+ dtype=None,
+ checkpoint: bool = False,
+ max_position_embeddings=1024):
+ super().__init__(hidden_size,
+ num_attention_heads,
+ attention_dropout_prob,
+ hidden_dropout_prob,
+ dtype=dtype,
+ checkpoint=checkpoint,
+ max_position_embeddings=max_position_embeddings)
+ self.softmax = kernel.FusedScaleMaskSoftmax(input_in_fp16=True,
+ input_in_bf16=False,
+ attn_mask_type=AttnMaskType.causal,
+ scaled_masked_softmax_fusion=True,
+ mask_func=None,
+ softmax_in_fp32=True,
+ scale=math.sqrt(self.attention_head_size))
+
+ def softmax_forward(self, attention_scores, attention_mask, query_layer, key_layer):
+ return self.softmax(attention_scores, attention_mask)
+
+
+class GenericGPTTransformerLayer1D(ParallelLayer):
+
+ def __init__(self,
+ hidden_size: int,
+ num_attention_heads: int,
+ act_func: str = 'gelu',
+ mlp_ratio: float = 4.0,
+ attention_dropout_prob: float = 0.,
+ hidden_dropout_prob: float = 0.,
+ dtype=None,
+ checkpoint: bool = False,
+ max_position_embeddings: int = 1024,
+ layer_norm_epsilon: float = 1e-5,
+ apply_post_layer_norm: bool = False,
+ attention=None,
+ layer_norm=None):
+ super().__init__()
+ self.checkpoint = checkpoint
+ self.dtype = dtype
+ self.norm1 = layer_norm(hidden_size, eps=layer_norm_epsilon)
+ self.apply_post_layer_norm = apply_post_layer_norm
+ self.attention = attention(
+ hidden_size=hidden_size,
+ num_attention_heads=num_attention_heads,
+ attention_dropout_prob=attention_dropout_prob,
+ hidden_dropout_prob=hidden_dropout_prob,
+ dtype=dtype,
+ max_position_embeddings=max_position_embeddings,
+ checkpoint=False,
+ )
+
+ self.norm2 = layer_norm(hidden_size, eps=layer_norm_epsilon)
+ self.mlp = GPTMLP1D(
+ in_features=hidden_size,
+ dropout_prob=hidden_dropout_prob,
+ act_func=act_func,
+ mlp_ratio=mlp_ratio,
+ dtype=dtype,
+ checkpoint=False,
+ )
+
+ def _forward(self, hidden_states, attention_mask) -> Tensor:
+ if not self.apply_post_layer_norm:
+ residual = hidden_states
+ hidden_states = self.norm1(hidden_states)
+ if self.apply_post_layer_norm:
+ residual = hidden_states
+ attention_output = self.attention(hidden_states, attention_mask)
+ hidden_states = residual + attention_output
+
+ if not self.apply_post_layer_norm:
+ residual = hidden_states
+ hidden_states = self.norm2(hidden_states)
+ if self.apply_post_layer_norm:
+ residual = hidden_states
+ feed_forward_hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + feed_forward_hidden_states
+
+ output = (hidden_states, attention_mask)
+ return output
+
+ def forward(self, hidden_states, attention_mask):
+ if self.checkpoint:
+ return checkpoint(self._forward, False, hidden_states, attention_mask)
+ else:
+ return self._forward(hidden_states, attention_mask)
+
+
+class GPTTransformerLayer1D(GenericGPTTransformerLayer1D):
+
+ def __init__(self,
+ hidden_size: int,
+ num_attention_heads: int,
+ act_func: str = 'gelu',
+ mlp_ratio: float = 4,
+ attention_dropout_prob: float = 0,
+ hidden_dropout_prob: float = 0,
+ dtype=None,
+ checkpoint: bool = False,
+ max_position_embeddings: int = 1024,
+ layer_norm_epsilon: float = 0.00001,
+ apply_post_layer_norm: bool = False):
+ attention = GPTSelfAttention1D
+ layer_norm = nn.LayerNorm
+ super().__init__(hidden_size,
+ num_attention_heads,
+ act_func=act_func,
+ mlp_ratio=mlp_ratio,
+ attention_dropout_prob=attention_dropout_prob,
+ hidden_dropout_prob=hidden_dropout_prob,
+ dtype=dtype,
+ checkpoint=checkpoint,
+ max_position_embeddings=max_position_embeddings,
+ layer_norm_epsilon=layer_norm_epsilon,
+ apply_post_layer_norm=apply_post_layer_norm,
+ attention=attention,
+ layer_norm=layer_norm)
+
+
+class FusedGPTTransformerLayer1D(GenericGPTTransformerLayer1D):
+
+ def __init__(self,
+ hidden_size: int,
+ num_attention_heads: int,
+ act_func: str = 'gelu',
+ mlp_ratio: float = 4,
+ attention_dropout_prob: float = 0,
+ hidden_dropout_prob: float = 0,
+ dtype=None,
+ checkpoint: bool = False,
+ max_position_embeddings: int = 1024,
+ layer_norm_epsilon: float = 0.00001,
+ apply_post_layer_norm: bool = False):
+ attention = FusedGPTSelfAttention1D
+ layer_norm = kernel.LayerNorm
+ super().__init__(hidden_size,
+ num_attention_heads,
+ act_func=act_func,
+ mlp_ratio=mlp_ratio,
+ attention_dropout_prob=attention_dropout_prob,
+ hidden_dropout_prob=hidden_dropout_prob,
+ dtype=dtype,
+ checkpoint=checkpoint,
+ max_position_embeddings=max_position_embeddings,
+ layer_norm_epsilon=layer_norm_epsilon,
+ apply_post_layer_norm=apply_post_layer_norm,
+ attention=attention,
+ layer_norm=layer_norm)
diff --git a/examples/language/gpt/titans/model/pipeline_gpt1d.py b/examples/language/gpt/titans/model/pipeline_gpt1d.py
new file mode 100644
index 000000000..30180285b
--- /dev/null
+++ b/examples/language/gpt/titans/model/pipeline_gpt1d.py
@@ -0,0 +1,322 @@
+import inspect
+
+# import model_zoo.gpt.gpt as col_gpt
+import titans.model.gpt.gpt as col_gpt
+import torch
+import torch.nn as nn
+
+from colossalai import kernel
+from colossalai import nn as col_nn
+from colossalai.context.parallel_mode import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.logging import get_dist_logger
+from colossalai.nn.layer.wrapper import PipelineSharedModuleWrapper
+from colossalai.pipeline.utils import partition_uniform
+
+from .embed import HiddenParallelEmbedding, HiddenParallelGPTLMHead1D, VocabParallelEmbedding, VocabParallelGPTLMHead1D
+from .gpt1d import FusedGPTTransformerLayer1D, GPTTransformerLayer1D
+
+__all__ = [
+ 'GPT2_small_pipeline_1D',
+ 'GPT2_exlarge_pipeline_1D',
+ 'GPT3_pipeline_1D',
+ 'GPT2_exlarge_pipeline_hybrid',
+ 'GPT2_small_pipeline_hybrid',
+ 'GPT3_pipeline_hybrid',
+]
+
+
+class GenericPipelineGPT(nn.Module):
+
+ def __init__(self, embedding=None, blocks=None, norm=None, head=None) -> None:
+ super().__init__()
+ self.embedding = embedding
+ self.blocks = blocks
+ self.norm = norm
+ self.head = head
+ assert blocks is not None
+ if norm is not None or head is not None:
+ assert norm is not None and head is not None
+
+ def forward(self, hidden_states=None, input_ids=None, attention_mask=None):
+ if self.embedding is not None:
+ hidden_states = self.embedding(input_ids=input_ids)
+ batch_size = hidden_states.shape[0]
+ attention_mask = attention_mask.view(batch_size, -1)
+ attention_mask = attention_mask[:, None, None, :]
+ attention_mask = attention_mask.to(dtype=hidden_states.dtype) # fp16 compatibility
+ attention_mask = (1.0 - attention_mask) * -10000.0
+ for block in self.blocks:
+ hidden_states, attention_mask = block(hidden_states, attention_mask)
+ if self.norm is not None:
+ hidden_states = self.head(self.norm(hidden_states))
+ return hidden_states
+
+
+class PipelineGPT1D(GenericPipelineGPT):
+
+ def __init__(self,
+ num_layers: int = 12,
+ hidden_size: int = 768,
+ num_attention_heads: int = 12,
+ vocab_size: int = 50304,
+ embed_drop_rate: float = 0.,
+ act_func: str = 'gelu',
+ mlp_ratio: int = 4.0,
+ attn_drop_rate: float = 0.,
+ drop_rate: float = 0.,
+ dtype: torch.dtype = torch.float,
+ checkpoint: bool = False,
+ max_position_embeddings: int = 1024,
+ layer_norm_epsilon: float = 1e-5,
+ apply_post_layer_norm: bool = False,
+ first: bool = False,
+ last: bool = False,
+ embed_split_hidden=False):
+ embedding = None
+ norm = None
+ head = None
+ embed_cls = VocabParallelEmbedding
+ head_cls = VocabParallelGPTLMHead1D
+ if embed_split_hidden:
+ embed_cls = HiddenParallelEmbedding
+ head_cls = HiddenParallelGPTLMHead1D
+ if first:
+ embedding = embed_cls(hidden_size, vocab_size, max_position_embeddings, embed_drop_rate, dtype=dtype)
+ blocks = nn.ModuleList([
+ GPTTransformerLayer1D(hidden_size,
+ num_attention_heads,
+ act_func=act_func,
+ mlp_ratio=mlp_ratio,
+ attention_dropout_prob=attn_drop_rate,
+ hidden_dropout_prob=drop_rate,
+ dtype=dtype,
+ checkpoint=checkpoint,
+ max_position_embeddings=max_position_embeddings,
+ layer_norm_epsilon=layer_norm_epsilon,
+ apply_post_layer_norm=apply_post_layer_norm) for _ in range(num_layers)
+ ])
+ if last:
+ norm = nn.LayerNorm(hidden_size, eps=layer_norm_epsilon)
+ head = head_cls(vocab_size=vocab_size, embed_dim=hidden_size, dtype=dtype)
+ super().__init__(embedding=embedding, blocks=blocks, norm=norm, head=head)
+
+
+class FusedPipelineGPT1D(GenericPipelineGPT):
+
+ def __init__(self,
+ num_layers: int = 12,
+ hidden_size: int = 768,
+ num_attention_heads: int = 12,
+ vocab_size: int = 50304,
+ embed_drop_rate: float = 0.,
+ act_func: str = 'gelu',
+ mlp_ratio: int = 4.0,
+ attn_drop_rate: float = 0.,
+ drop_rate: float = 0.,
+ dtype: torch.dtype = torch.float,
+ checkpoint: bool = False,
+ max_position_embeddings: int = 1024,
+ layer_norm_epsilon: float = 1e-5,
+ apply_post_layer_norm: bool = False,
+ first: bool = False,
+ last: bool = False,
+ embed_split_hidden=False):
+ embedding = None
+ norm = None
+ head = None
+ embed_cls = VocabParallelEmbedding
+ head_cls = VocabParallelGPTLMHead1D
+ if embed_split_hidden:
+ embed_cls = HiddenParallelEmbedding
+ head_cls = HiddenParallelGPTLMHead1D
+ if first:
+ embedding = embed_cls(hidden_size, vocab_size, max_position_embeddings, embed_drop_rate, dtype=dtype)
+ blocks = nn.ModuleList([
+ FusedGPTTransformerLayer1D(hidden_size,
+ num_attention_heads,
+ act_func=act_func,
+ mlp_ratio=mlp_ratio,
+ attention_dropout_prob=attn_drop_rate,
+ hidden_dropout_prob=drop_rate,
+ dtype=dtype,
+ checkpoint=checkpoint,
+ max_position_embeddings=max_position_embeddings,
+ layer_norm_epsilon=layer_norm_epsilon,
+ apply_post_layer_norm=apply_post_layer_norm) for _ in range(num_layers)
+ ])
+ if last:
+ norm = kernel.LayerNorm(hidden_size, eps=layer_norm_epsilon)
+ head = head_cls(vocab_size=vocab_size, embed_dim=hidden_size, dtype=dtype)
+ super().__init__(embedding=embedding, blocks=blocks, norm=norm, head=head)
+
+ def forward(self, hidden_states=None, input_ids=None, attention_mask=None):
+ if self.embedding is not None:
+ hidden_states = self.embedding(input_ids=input_ids)
+ attention_mask = attention_mask.to(dtype=hidden_states.dtype) # fp16 compatibility
+ for block in self.blocks:
+ hidden_states, attention_mask = block(hidden_states, attention_mask)
+ if self.norm is not None:
+ hidden_states = self.head(self.norm(hidden_states))
+ return hidden_states
+
+
+class PipelineGPTHybrid(GenericPipelineGPT):
+
+ def __init__(self,
+ num_layers: int = 12,
+ hidden_size: int = 768,
+ num_attention_heads: int = 12,
+ vocab_size: int = 50304,
+ embed_drop_rate: float = 0.,
+ act_func: str = 'gelu',
+ mlp_ratio: int = 4,
+ attn_drop_rate: float = 0.,
+ drop_rate: float = 0.,
+ dtype: torch.dtype = torch.float,
+ checkpoint: bool = False,
+ max_position_embeddings: int = 1024,
+ layer_norm_epsilon: float = 1e-5,
+ apply_post_layer_norm: bool = False,
+ first: bool = False,
+ last: bool = False,
+ embed_split_hidden=False):
+ embedding = None
+ norm = None
+ head = None
+ if first:
+ embedding = col_gpt.GPTEmbedding(hidden_size,
+ vocab_size,
+ max_position_embeddings,
+ dropout=embed_drop_rate,
+ dtype=dtype)
+ blocks = nn.ModuleList([
+ col_gpt.GPTBlock(hidden_size,
+ num_attention_heads,
+ mlp_ratio=mlp_ratio,
+ attention_dropout=attn_drop_rate,
+ dropout=drop_rate,
+ dtype=dtype,
+ checkpoint=checkpoint,
+ activation=nn.functional.gelu) for _ in range(num_layers)
+ ])
+ if last:
+ norm = col_nn.LayerNorm(hidden_size, eps=layer_norm_epsilon)
+ # head = col_gpt.GPTLMHead(vocab_size=vocab_size,
+ # hidden_size=hidden_size,
+ # dtype=dtype,
+ # bias=False)
+ head = col_nn.Classifier(hidden_size, vocab_size, dtype=dtype, bias=False)
+ super().__init__(embedding=embedding, blocks=blocks, norm=norm, head=head)
+
+
+def _filter_kwargs(func, kwargs):
+ sig = inspect.signature(func)
+ return {k: v for k, v in kwargs.items() if k in sig.parameters}
+
+
+def _build_generic_gpt_pipeline_1d(module_cls, num_layers, num_chunks, device=torch.device('cuda'), **kwargs):
+ logger = get_dist_logger()
+
+ if gpc.is_initialized(ParallelMode.PIPELINE):
+ pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE)
+ pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE)
+ else:
+ pipeline_size = 1
+ pipeline_rank = 0
+ rank = gpc.get_global_rank()
+
+ if pipeline_size > 1:
+ wrapper = PipelineSharedModuleWrapper([0, pipeline_size - 1])
+ else:
+ wrapper = None
+ parts = partition_uniform(num_layers, pipeline_size, num_chunks)[pipeline_rank]
+ models = []
+ for start, end in parts:
+ kwargs['num_layers'] = end - start
+ kwargs['first'] = start == 0
+ kwargs['last'] = end == num_layers
+ logger.info(f'Rank{rank} build layer {start}-{end}, {end-start}/{num_layers} layers')
+ chunk = module_cls(**_filter_kwargs(module_cls.__init__, kwargs)).to(device)
+
+ if wrapper is not None:
+ if start == 0:
+ wrapper.register_module(chunk.embedding.word_embeddings)
+ elif end == num_layers:
+ wrapper.register_module(chunk.head)
+ models.append(chunk)
+ if len(models) == 1:
+ model = models[0]
+ else:
+ model = nn.ModuleList(models)
+
+ numel = 0
+ for _, param in model.named_parameters(recurse=True):
+ numel += param.numel()
+ logger.info(f'Rank{rank}/{pipeline_rank} model size = {numel * 2 / 1e9} GB')
+ return model
+
+
+def _build_gpt_pipeline_1d(num_layers, num_chunks, device=torch.device('cuda'), fused=False, **kwargs):
+ model = FusedPipelineGPT1D if fused else PipelineGPT1D
+ return _build_generic_gpt_pipeline_1d(model, num_layers, num_chunks, device, **kwargs)
+
+
+def _build_gpt_pipeline_hybrid(num_layers, num_chunks, device=torch.device('cuda'), **kwargs):
+ return _build_generic_gpt_pipeline_1d(PipelineGPTHybrid, num_layers, num_chunks, device, **kwargs)
+
+
+def GPT2_small_pipeline_1D(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False, fused=False):
+ cfg = dict(hidden_size=768,
+ num_attention_heads=12,
+ checkpoint=checkpoint,
+ dtype=dtype,
+ embed_split_hidden=embed_split_hidden)
+ return _build_gpt_pipeline_1d(12, num_chunks, fused=fused, **cfg)
+
+
+def GPT2_exlarge_pipeline_1D(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False, fused=False):
+ cfg = dict(hidden_size=1600,
+ num_attention_heads=32,
+ checkpoint=checkpoint,
+ dtype=dtype,
+ embed_split_hidden=embed_split_hidden)
+ return _build_gpt_pipeline_1d(48, num_chunks, fused=fused, **cfg)
+
+
+def GPT3_pipeline_1D(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False, fused=False):
+ cfg = dict(hidden_size=12288,
+ num_attention_heads=96,
+ checkpoint=checkpoint,
+ max_position_embeddings=2048,
+ dtype=dtype,
+ embed_split_hidden=embed_split_hidden)
+ return _build_gpt_pipeline_1d(96, num_chunks, fused=fused, **cfg)
+
+
+def GPT2_exlarge_pipeline_hybrid(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False):
+ cfg = dict(hidden_size=1600,
+ num_attention_heads=32,
+ checkpoint=checkpoint,
+ dtype=dtype,
+ embed_split_hidden=embed_split_hidden)
+ return _build_gpt_pipeline_hybrid(48, num_chunks, **cfg)
+
+
+def GPT2_small_pipeline_hybrid(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False):
+ cfg = dict(hidden_size=768,
+ num_attention_heads=12,
+ checkpoint=checkpoint,
+ dtype=dtype,
+ embed_split_hidden=embed_split_hidden)
+ return _build_gpt_pipeline_hybrid(12, num_chunks, **cfg)
+
+
+def GPT3_pipeline_hybrid(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False):
+ cfg = dict(hidden_size=12288,
+ num_attention_heads=96,
+ checkpoint=checkpoint,
+ max_position_embeddings=2048,
+ dtype=dtype,
+ embed_split_hidden=embed_split_hidden)
+ return _build_gpt_pipeline_hybrid(96, num_chunks, **cfg)
diff --git a/examples/language/gpt/titans/requirements.txt b/examples/language/gpt/titans/requirements.txt
new file mode 100644
index 000000000..64ff7a4ab
--- /dev/null
+++ b/examples/language/gpt/titans/requirements.txt
@@ -0,0 +1,4 @@
+torch==1.12.1
+titans==0.0.7
+colossalai==0.2.0+torch1.12cu11.3
+-f https://release.colossalai.org
diff --git a/examples/language/gpt/titans/run.sh b/examples/language/gpt/titans/run.sh
new file mode 100644
index 000000000..157bd377a
--- /dev/null
+++ b/examples/language/gpt/titans/run.sh
@@ -0,0 +1,2 @@
+export DATA=/data/scratch/gpt_data/small-gpt-dataset.json
+colossalai run --nproc_per_node=4 train_gpt.py --config ./configs/gpt2_small_zero3_pp1d.py --from_torch
diff --git a/examples/language/gpt/titans/test_ci.sh b/examples/language/gpt/titans/test_ci.sh
new file mode 100644
index 000000000..7cb24c1a4
--- /dev/null
+++ b/examples/language/gpt/titans/test_ci.sh
@@ -0,0 +1 @@
+colossalai run --nproc_per_node=4 train_gpt.py --config ./configs/gpt2_small_zero3_pp1d.py --from_torch --use_dummy_dataset
diff --git a/examples/language/gpt/titans/train_gpt.py b/examples/language/gpt/titans/train_gpt.py
new file mode 100644
index 000000000..1380b4b3a
--- /dev/null
+++ b/examples/language/gpt/titans/train_gpt.py
@@ -0,0 +1,148 @@
+import contextlib
+import os
+
+import torch
+import torch.nn as nn
+from titans.model.gpt import GPTLMLoss
+
+import colossalai
+import colossalai.utils as utils
+from colossalai.context.parallel_mode import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.logging import disable_existing_loggers, get_dist_logger
+from colossalai.nn import LinearWarmupLR
+from colossalai.trainer import Trainer, hooks
+from colossalai.utils import colo_set_process_memory_fraction, is_using_pp
+from colossalai.utils.timer import MultiTimer
+from colossalai.zero.init_ctx import ZeroInitContext
+
+
+def calc_local_model_size(model: torch.nn.Module):
+ numel_per_device = 0
+ for p in model.parameters():
+ numel_per_device += p.numel()
+ return numel_per_device
+
+
+VOCAB_SIZE = 50257
+
+
+def main():
+ parser = colossalai.get_default_parser()
+ parser.add_argument('--from_torch', default=False, action='store_true')
+ parser.add_argument('--use_dummy_dataset', default=True, action='store_true')
+ args = parser.parse_args()
+ disable_existing_loggers()
+ if args.from_torch:
+ colossalai.launch_from_torch(config=args.config)
+ else:
+ colossalai.launch_from_slurm(config=args.config, host=args.host, port=29500, seed=42)
+ logger = get_dist_logger()
+
+ if not args.use_dummy_dataset:
+ data_path = os.environ['DATA']
+ logger.info(f'Build data loader from path {data_path}', ranks=[0])
+ from dataset.webtext import WebtextDataset
+ train_ds = WebtextDataset(os.environ['DATA'], seq_len=gpc.config.SEQ_LEN)
+ train_dataloader = utils.get_dataloader(train_ds,
+ seed=42,
+ batch_size=gpc.config.BATCH_SIZE,
+ pin_memory=True,
+ shuffle=True,
+ drop_last=True)
+ else:
+ # build a dummy train_dataloader
+ logger.info('Build data loader using dummy data', ranks=[0])
+
+ def get_data(batch_size, seq_len, vocab_size):
+ input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
+ attention_mask = torch.ones_like(input_ids)
+ return input_ids, attention_mask
+
+ # 10 iterations
+ input_ids, attn_mask = get_data(gpc.config.BATCH_SIZE * 10, gpc.config.SEQ_LEN, VOCAB_SIZE)
+ from torch.utils.data import DataLoader, Dataset
+
+ class TextSamplerDataset(Dataset):
+
+ def __init__(self, data, seq_len):
+ super().__init__()
+ self.data = data
+ self.seq_len = seq_len
+
+ def __getitem__(self, index):
+ rand_start = torch.randint(0, self.data.size(0) - self.seq_len, (1,))
+ full_seq = self.data[rand_start:rand_start + self.seq_len + 1].long()
+ return full_seq.cuda()
+
+ def __len__(self):
+ return self.data.size(0) // self.seq_len
+
+ def cycle(loader):
+ while True:
+ for data in loader:
+ yield data
+
+ train_dataset = TextSamplerDataset(input_ids, gpc.config.SEQ_LEN)
+ train_dataloader = DataLoader(train_dataset, batch_size=gpc.config.BATCH_SIZE)
+
+ logger.info('Build model', ranks=[0])
+ use_pipeline = is_using_pp()
+ use_interleaved = hasattr(gpc.config.model, 'num_chunks')
+ use_zero3 = hasattr(gpc.config, 'zero')
+ ctx = contextlib.nullcontext()
+ if use_zero3:
+ ctx = ZeroInitContext(target_device=torch.cuda.current_device(),
+ shard_strategy=gpc.config.zero.model_config.shard_strategy,
+ shard_param=True)
+ with ctx:
+ model = gpc.config.model.pop('type')(**gpc.config.model)
+ if use_pipeline and use_interleaved and not isinstance(model, nn.ModuleList):
+ model = nn.ModuleList([model])
+
+ if use_zero3:
+ numel = ctx.model_numel_tensor.item()
+ else:
+ numel = calc_local_model_size(model)
+
+ tflop = numel * gpc.config.BATCH_SIZE * gpc.config.SEQ_LEN \
+ * gpc.get_world_size(ParallelMode.MODEL) * gpc.get_world_size(ParallelMode.DATA) * 8 / (1024 ** 4)
+
+ criterion = getattr(gpc.config, 'loss_fn', None)
+ if criterion is not None:
+ criterion = criterion.type()
+ else:
+ criterion = GPTLMLoss()
+ logger.info('Build optimizer', ranks=[0])
+ optimizer = gpc.config.optimizer.pop('type')(model.parameters(), **gpc.config.optimizer)
+ lr_scheduler = LinearWarmupLR(optimizer, total_steps=gpc.config.NUM_EPOCHS, warmup_steps=5)
+ engine, train_dataloader, _, lr_scheduler = colossalai.initialize(model,
+ optimizer,
+ criterion,
+ train_dataloader=train_dataloader,
+ lr_scheduler=lr_scheduler)
+ global_batch_size = gpc.config.BATCH_SIZE * \
+ gpc.get_world_size(ParallelMode.DATA) * getattr(gpc.config, "gradient_accumulation", 1)
+ logger.info(f'Init done, global batch size = {global_batch_size}', ranks=[0])
+ timier = MultiTimer()
+ trainer = Trainer(engine=engine, logger=logger, timer=timier)
+ hook_list = [
+ hooks.LossHook(),
+ hooks.LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=True),
+ hooks.LogMetricByEpochHook(logger),
+ hooks.ThroughputHook(ignored_steps=10, tflop_per_step=tflop),
+ hooks.LogMetricByStepHook(),
+ hooks.LogMemoryByEpochHook(logger),
+ # hooks.LogMemoryByEpochHook(logger),
+ # hooks.LogTimingByEpochHook(timer, logger),
+ ]
+ trainer.fit(train_dataloader=train_dataloader,
+ epochs=gpc.config.NUM_EPOCHS,
+ test_interval=1,
+ hooks=hook_list,
+ display_progress=True,
+ return_output_label=False)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/examples/language/palm/train.py b/examples/language/palm/train.py
index a334ea951..2f012780d 100644
--- a/examples/language/palm/train.py
+++ b/examples/language/palm/train.py
@@ -11,13 +11,12 @@ import tqdm
from packaging import version
from palm_pytorch import PaLM
from palm_pytorch.autoregressive_wrapper import AutoregressiveWrapper
-from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
import colossalai
from colossalai.logging import disable_existing_loggers, get_dist_logger
from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
-from colossalai.nn.parallel import GeminiDDP, ZeroDDP
+from colossalai.nn.parallel import ZeroDDP
from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec
from colossalai.utils import MultiTimer, get_current_device
from colossalai.utils.model.colo_init_context import ColoInitContext