mirror of https://github.com/InternLM/InternLM
53 lines
2.5 KiB
ReStructuredText
53 lines
2.5 KiB
ReStructuredText
TF32训练
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==================
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InternLM支持使用TF32训练模型。TensorFloat-32（TF32）是Nvidia在Ampere架构GPU上推出的专门运用于TensorCore的一种计算格式。其与其他常用数据格式的比较如下图：
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InternLM supports training models using TF32. TensorFloat-32 (TF32) is a computational format introduced by Nvidia for TensorCores on Ampere architecture GPUs. Here's a comparison of TF32 with other data formats:
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.. figure:: ../../imgs/tf32.png
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:scale: 50%
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:class: with-border
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使用TF32的前置条件：
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Prerequisites for using TF32:
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input data must be of type FP32 (single-precision floating-point) and the computations should be matrix multiplication, convolution and so on.
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1. 输入数据类型为FP32，且计算为矩阵乘法及卷积相关运算，才可以使用TF32作为TensorCore的中间计算类型。
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Ampere GPU
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2. Ampere架构的GPU。
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值得注意的是，TF32仅仅是在使用TensorCore时的一种中间计算格式，并不是一个完全的数据类型。因此，为了区分不同的精度与计算格式（ ``BF16`` 、``FP16`` 、``FP32`` 、``TF32`` ），InternLM支持用户在 ``model config`` 中传入 ``torch.tf32`` 来表示想要使用TF32加速运算，本质上数据类型依旧为 ``FP32``。
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It is noticed that TF32 is an intermediate calculation format when employing TensorCores. InternLM allows users to speficy ``torch.tf32`` in the model config to using TF32 acceleration while dtype is still ``torch.float32``.
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.. code-block:: python
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model = dict(
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checkpoint=False, # The proportion of layers for activation aheckpointing, the optional value are True/False/[0-1]
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num_attention_heads=NUM_ATTENTION_HEAD,
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embed_split_hidden=True,
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vocab_size=VOCAB_SIZE,
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embed_grad_scale=1,
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parallel_output=True,
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hidden_size=HIDDEN_SIZE,
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num_layers=NUM_LAYER,
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mlp_ratio=MLP_RATIO,
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apply_post_layer_norm=False,
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dtype="torch.tf32", # Support: "torch.float16", "torch.half", "torch.bfloat16", "torch.float32", "torch.tf32"
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norm_type="rmsnorm",
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layer_norm_epsilon=1e-5,
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use_flash_attn=True,
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num_chunks=1, # if num_chunks > 1, interleaved pipeline scheduler is used.
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)
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InternLM会根据 ``model config`` 中的 ``dtype`` 字符串来判断真正的数据类型。InternLM通过设置以下变量来开启TF32训练。
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InternLM enables TF32 training by setting the following variables.
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.. code-block:: python
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torch.backends.cudnn.allow_tf32 = True
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torch.backends.cuda.matmul.allow_tf32 = True
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