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69 lines
2.6 KiB
69 lines
2.6 KiB
import torch
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import torch.nn.functional as F
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from colossalai.utils import get_current_device
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from colossalai.context.moe_context import MOE_CONTEXT
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from .experts import FFNExperts, TPExperts
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class ForceFP32Parameter(torch.nn.Parameter):
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def half(self, memory_format=None):
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return self.data.clone()
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class NormalNoiseGenerator:
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"""Generates a random noisy mask for logtis tensor.
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All noise is generated from a normal distribution :math:`(0, 1 / E^2)`, where
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`E = the number of experts`.
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Args:
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num_experts (int): The number of experts.
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"""
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def __init__(self, num_experts: int):
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self.normal = torch.distributions.normal.Normal(loc=torch.tensor(0.0, device=get_current_device()),
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scale=torch.tensor(1.0 / num_experts**2,
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device=get_current_device())).rsample
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def __call__(self, inputs: torch.Tensor):
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noisy = self.normal(inputs.shape)
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return inputs + noisy
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class UniformNoiseGenerator:
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"""Generates a random noisy mask for logtis tensor.
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copied from mesh tensorflow:
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Multiply values by a random number between :math:`1-epsilon` and :math:`1+epsilon`.
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Makes models more resilient to rounding errors introduced by bfloat16.
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This seems particularly important for logits.
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Args:
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eps (float, optional): Epsilon in generator, defaults 1e-2.
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"""
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def __init__(self, eps: float = 1e-2):
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self.uniform = torch.distributions.uniform.Uniform(low=torch.tensor(1.0 - eps, device=get_current_device()),
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high=torch.tensor(1.0 + eps,
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device=get_current_device())).rsample
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def __call__(self, inputs: torch.Tensor):
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noisy = self.uniform(inputs.shape)
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return inputs * noisy
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def autocast_softmax(logit: torch.Tensor, dim: int):
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if logit.dtype != torch.float32:
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logit = logit.float()
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return F.softmax(logit, dim=dim)
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def build_ffn_experts(num_experts: int, d_model: int, d_ff: int, activation=None, drop_rate: float = 0):
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mep_size = MOE_CONTEXT.max_ep_size
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if num_experts % mep_size == 0 or mep_size % num_experts == 0:
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return FFNExperts(num_experts, d_model, d_ff, activation, drop_rate)
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elif d_ff % mep_size == 0:
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return TPExperts(num_experts, d_model, d_ff, activation, drop_rate)
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else:
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raise NotImplementedError(f"Can not build {num_experts} experts in {mep_size} GPUS.")
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