mirror of https://github.com/hpcaitech/ColossalAI
[example] upload auto parallel gpt2 demo (#2354)
YuliangLiu0306
GitHub
parent
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commit
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# Auto-Parallelism with GPT2
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## Requirements
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Before you can launch training, you need to install the following requirements.
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### Install PyTorch
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```bash
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#conda
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conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
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#pip
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pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113
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```
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### Install [Colossal-AI v0.2.0](https://colossalai.org/download/) From Official Website
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```bash
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pip install colossalai==0.2.0+torch1.12cu11.3 -f https://release.colossalai.org
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```
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### Install transformers
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```bash
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pip install transformers
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```
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### Install pulp and coin-or-cbc
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```bash
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pip install pulp
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conda install -c conda-forge coin-or-cbc
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```
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## Dataset
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For simplicity, the input data is randonly generated here.
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## Training
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```bash
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#Run the auto parallel resnet example with 4 GPUs with a dummy dataset.
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colossalai run --nproc_per_node 4 auto_parallel_with_gpt.py
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```
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from functools import partial
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from time import time
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from typing import Dict, Optional, Tuple, Union
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import psutil
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import torch
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import torch.multiprocessing as mp
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import torch.nn as nn
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import transformers
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from gpt_modules import GPT2LMHeadModel, GPTLMLoss
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from torch.fx import GraphModule
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from colossalai.auto_parallel.tensor_shard.initialize import autoparallelize, initialize_model
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from colossalai.core import global_context as gpc
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from colossalai.device.device_mesh import DeviceMesh
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from colossalai.initialize import launch_from_torch
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from colossalai.logging import disable_existing_loggers, get_dist_logger
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BATCH_SIZE = 8
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SEQ_LENGTH = 128
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HIDDEN_DIM = 3072
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NUM_HEADS = 16
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NUM_LAYERS = 1
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VOCAB_SIZE = 50257
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NUM_STEPS = 10
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FP16 = False
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def get_cpu_mem():
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return psutil.Process().memory_info().rss / 1024**2
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def get_gpu_mem():
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return torch.cuda.memory_allocated() / 1024**2
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def get_mem_info(prefix=''):
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return f'{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB'
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def get_tflops(model_numel, batch_size, seq_len, step_time):
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# Tflops_per_GPU = global_batch * global_numel * seq_len * 8 / #gpu
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return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12) / 4
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# Randomly Generated Data
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def get_data(batch_size, seq_len, vocab_size):
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input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
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attention_mask = torch.ones_like(input_ids)
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return input_ids, attention_mask
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def main():
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disable_existing_loggers()
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launch_from_torch(config={})
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logger = get_dist_logger()
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config = transformers.GPT2Config(n_position=SEQ_LENGTH, n_layer=NUM_LAYERS, n_head=NUM_HEADS, n_embd=HIDDEN_DIM)
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if FP16:
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model = GPT2LMHeadModel(config=config).half().to('cuda')
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else:
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model = GPT2LMHeadModel(config=config).to('cuda')
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global_numel = sum([p.numel() for p in model.parameters()])
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meta_input_sample = {
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'input_ids': torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64).to('meta'),
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'attention_mask': torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64).to('meta'),
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}
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# Both device mesh initialization and model initialization will be integrated into autoparallelize
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physical_mesh_id = torch.arange(0, 4)
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mesh_shape = (2, 2)
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device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
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# Enable auto-parallel
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gm, solution = initialize_model(model, meta_input_sample, device_mesh, return_solution=True)
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# print solution on rank 0
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if gpc.get_global_rank() == 0:
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for node_strategy in solution:
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print(node_strategy)
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# build criterion
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criterion = GPTLMLoss()
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optimizer = torch.optim.Adam(gm.parameters(), lr=0.01)
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logger.info(get_mem_info(prefix='After init model, '), ranks=[0])
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get_tflops_func = partial(get_tflops, global_numel, BATCH_SIZE, SEQ_LENGTH)
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torch.cuda.synchronize()
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model.train()
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for n in range(10):
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# we just use randomly generated data here
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input_ids, attn_mask = get_data(BATCH_SIZE, SEQ_LENGTH, VOCAB_SIZE)
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optimizer.zero_grad()
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start = time()
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outputs = gm(input_ids, attn_mask)
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loss = criterion(outputs, input_ids)
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loss.backward()
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optimizer.step()
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torch.cuda.synchronize()
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step_time = time() - start
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logger.info(
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f'[{n+1}/{NUM_STEPS}] Loss:{loss.item():.3f}, Step time: {step_time:.3f}s, TFLOPS: {get_tflops_func(step_time):.3f}',
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ranks=[0])
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torch.cuda.synchronize()
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if __name__ == '__main__':
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main()
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from typing import Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from transformers.activations import ACT2FN
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from transformers.models.gpt2.modeling_gpt2 import BaseModelOutputWithPastAndCrossAttentions, GPT2PreTrainedModel
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from transformers.pytorch_utils import Conv1D
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class GPT2MLP(nn.Module):
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def __init__(self, intermediate_size, config):
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super().__init__()
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embed_dim = config.hidden_size
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self.c_fc = Conv1D(intermediate_size, embed_dim)
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self.c_proj = Conv1D(embed_dim, intermediate_size)
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self.act = ACT2FN[config.activation_function]
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self.dropout = nn.Dropout(config.resid_pdrop)
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def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
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hidden_states = self.c_fc(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.c_proj(hidden_states)
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return hidden_states
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# The reason Why we don't import GPT2Attention from transformers directly is that:
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# 1. The tracer will not work correctly when we feed meta_args and concrete_args at same time,
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# so we have to build the customized GPT2Attention class and remove the conditional branch manually.
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# 2. The order of split and view op has been changed in the customized GPT2Attention class, the new
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# order is same as megatron-lm gpt model.
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class GPT2Attention(nn.Module):
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def __init__(self, config, layer_idx=None):
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super().__init__()
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max_positions = config.max_position_embeddings
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self.register_buffer(
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"bias",
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torch.tril(torch.ones((max_positions, max_positions),
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dtype=torch.uint8)).view(1, 1, max_positions, max_positions),
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)
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self.register_buffer("masked_bias", torch.tensor(-1e4))
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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self.split_size = self.embed_dim
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self.scale_attn_weights = config.scale_attn_weights
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# Layer-wise attention scaling, reordering, and upcasting
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self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
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self.layer_idx = layer_idx
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self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
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self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
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self.attn_dropout = nn.Dropout(config.attn_pdrop)
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self.resid_dropout = nn.Dropout(config.resid_pdrop)
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self.pruned_heads = set()
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def _attn(self, query, key, value, attention_mask=None, head_mask=None):
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attn_weights = torch.matmul(query, key.transpose(-1, -2))
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if self.scale_attn_weights:
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attn_weights = attn_weights / (value.size(-1)**0.5)
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# Layer-wise attention scaling
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if self.scale_attn_by_inverse_layer_idx:
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attn_weights = attn_weights / float(self.layer_idx + 1)
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# if only "normal" attention layer implements causal mask
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query_length, key_length = query.size(-2), key.size(-2)
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causal_mask = self.bias[:, :, key_length - query_length:key_length, :key_length].to(torch.bool)
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attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype))
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if attention_mask is not None:
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# Apply the attention mask
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = attn_weights.type(value.dtype)
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# Mask heads if we want to
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if head_mask is not None:
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attn_weights = attn_weights * head_mask
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attn_output = torch.matmul(attn_weights, value)
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return attn_output, attn_weights
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def _split_heads(self, tensor, num_heads, attn_head_size):
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new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
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tensor = tensor.view(new_shape)
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return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
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def _merge_heads(self, tensor, num_heads, attn_head_size):
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tensor = tensor.permute(0, 2, 1, 3).contiguous()
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new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
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return tensor.view(new_shape)
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def forward(
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self,
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hidden_states: Optional[Tuple[torch.FloatTensor]],
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
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qkv = self.c_attn(hidden_states)
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query, key, value = self._split_heads(qkv, self.num_heads, 3 * self.head_dim).split(self.head_dim, dim=3)
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present = (key, value)
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attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
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attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
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attn_output = self.c_proj(attn_output)
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return attn_output
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class GPT2Block(nn.Module):
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def __init__(self, config, layer_idx=None):
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super().__init__()
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hidden_size = config.hidden_size
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inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
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self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.attn = GPT2Attention(config, layer_idx=layer_idx)
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self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.mlp = GPT2MLP(inner_dim, config)
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def forward(
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self,
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hidden_states: Optional[Tuple[torch.FloatTensor]],
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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attn_outputs = self.attn(
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hidden_states,
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attention_mask=attention_mask,
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head_mask=head_mask,
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)
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# residual connection
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hidden_states = attn_outputs + residual
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residual = hidden_states
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hidden_states = self.ln_2(hidden_states)
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feed_forward_hidden_states = self.mlp(hidden_states)
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# residual connection
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hidden_states = residual + feed_forward_hidden_states
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return hidden_states
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class GPT2Model(GPT2PreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.embed_dim = config.hidden_size
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self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
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self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
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self.drop = nn.Dropout(config.embd_pdrop)
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self.h = nn.ModuleList([GPT2Block(config, layer_idx=i) for i in range(config.num_hidden_layers)])
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self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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batch_size = input_ids.shape[0]
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device = input_ids.device
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past_length = 0
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past_key_values = tuple([None] * len(self.h))
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position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
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position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
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# GPT2Attention mask.
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attention_mask = attention_mask.view(batch_size, -1)
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attention_mask = attention_mask[:, None, None, :]
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attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
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attention_mask = (1.0 - attention_mask) * -10000.0
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encoder_attention_mask = None
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# Prepare head mask if needed
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# 1.0 in head_mask indicate we keep the head
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# attention_probs has shape bsz x n_heads x N x N
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# head_mask has shape n_layer x batch x n_heads x N x N
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head_mask = self.get_head_mask(head_mask, self.config.n_layer)
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inputs_embeds = self.wte(input_ids)
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position_embeds = self.wpe(position_ids)
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hidden_states = inputs_embeds + position_embeds
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output_shape = input_shape + (hidden_states.size(-1),)
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for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
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outputs = block(hidden_states, attention_mask=attention_mask, head_mask=head_mask[i])
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hidden_states = outputs
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hidden_states = self.ln_f(hidden_states)
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hidden_states = hidden_states.view(output_shape)
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return hidden_states
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class GPT2LMHeadModel(GPT2PreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.transformer = GPT2Model(config)
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self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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):
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transformer_outputs = self.transformer(
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input_ids=input_ids,
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attention_mask=attention_mask,
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)
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lm_logits = self.lm_head(transformer_outputs)
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return lm_logits
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class GPTLMLoss(nn.Module):
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def __init__(self):
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super().__init__()
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self.loss_fn = nn.CrossEntropyLoss()
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def forward(self, logits, labels):
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
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@ -0,0 +1,4 @@
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colossalai >= 0.1.12
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torch >= 1.8.1
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transformers >= 4.231
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PuLP >= 2.7.0
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