Context Parallel:在序列维度 seq_len 做“切分”,对模型单层输出做“聚合”; Tensor Parallel:在维度 hidden_dim 做“切分”,对模型单层输出做“聚合”; Pipeline Parallel:在模型层数维度 N 做“切分”,对模型最终输出做聚合。 本文将尝试从零开始实现张量并行 (Tensor Parallel, TP)。由于作者也是初学者,文章内...
Megatron-LM NVIDIA Megatron-LM 是一个基于 PyTorch 的分布式训练框架,用来训练基于Transformer的大型语言模型。Megatron-LM 综合应用了数据并行(Data Parallelism),张量并行(Tensor Parallelism)和流水线并行(Pipeline Parallelism)。很多大模型的训练过程都采用它,例如bloom、opt、智源等。 torch.distributed(dist) 为运行...
gather_from_sequence_parallel_region:是对_GatherFromSequenceParallelRegion类使用的封装,_GatherFromSequenceParallelRegion继承自torch.autograd.Function的自定义Function,在parallel并行前向进行all_gather操作,反向是梯度reduce_scatter输出。对应Pipeline Parallel Linear Layer中的g函数。 class _GatherFromSequenceParallelRe...
tokenizer='/data/wjc/Qwen/Qwen2-7B-Instruct/', quantization=None, tensor_parallel_size=2, n=1, use_beam_search=False, num_prompts=1000, seed=0, hf_max_batch_size=None, trust_remote_code=False, max_model_len=
Tensors and Dynamic neural networks in Python with strong GPU acceleration - Specifying device_id in init_process_group causes tensor parallel + pipeline parallel to fail · pytorch/pytorch@d765077
Tensor parallelism is a type of model parallelism in which specific model weights, gradients, and optimizer states are split across devices. In contrast to pipeline parallelism, which keeps individual weights intact but partitions the set of weights, gradients, or optimizer across devices, tensor para...
Tensor parallelism takes place at the level of nn.Modules; it partitions specific modules in the model across tensor parallel ranks. This is in addition to the existing partition of the set of modules used in pipeline parallelism. When a module is partitioned through tensor parallelism, i...
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager You can also easily start a service using SGLang python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --trust-remote-code --tp 2 Usage...
R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3...
CUDA cores support high-precision math and work better for tasks where accuracy can’t be compromised. Parallel Workloads CUDA cores run many small tasks in parallel. Tensor cores process large matrix operations all at once. CUDA is best for tasks like simulations or preprocessing pipelines. ...