[example] titans for gpt (#2484)

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
 

examples/language/gpt/titans/LICENSE

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examples/language/gpt/titans/README.md

+# 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.

examples/language/gpt/titans/configs/gpt2_small_zero3_pp1d.py

+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'),
+)

examples/language/gpt/titans/configs/gpt3_zero3_pp1d.py

+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
+)

examples/language/gpt/titans/model/__init__.py

+from .embed import vocab_parallel_cross_entropy
+from .gpt1d import *
+from .pipeline_gpt1d import *

examples/language/gpt/titans/model/gpt1d.py

+#!/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)

examples/language/gpt/titans/model/pipeline_gpt1d.py

+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)

examples/language/gpt/titans/requirements.txt

+torch==1.12.1
+titans==0.0.7
+colossalai==0.2.0+torch1.12cu11.3
+-f https://release.colossalai.org

examples/language/gpt/titans/run.sh

+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

examples/language/gpt/titans/test_ci.sh

+colossalai run --nproc_per_node=4 train_gpt.py --config ./configs/gpt2_small_zero3_pp1d.py --from_torch --use_dummy_dataset

examples/language/gpt/titans/train_gpt.py

+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()

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