mirror of https://github.com/InternLM/InternLM
101 lines
4.1 KiB
Python
101 lines
4.1 KiB
Python
import torch
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from internlm.core.context import ParallelMode
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from internlm.core.context import global_context as gpc
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from internlm.model.linear import FeedForward
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from internlm.utils.logger import get_logger
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from internlm.utils.registry import MODEL_INITIALIZER
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# global llm logger
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logger = get_logger(__file__)
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class MoE(torch.nn.Module):
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"""Initialize an MoE layer.
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Arguments:
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hidden_size (int): the hidden dimension of the model, importantly this is also the input and output dimension.
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num_experts (int, optional): default=1, the total number of experts per layer.
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ep_size (int, optional): default=1, number of ranks in the expert parallel world or group.
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k (int, optional): default=1, top-k gating value, only supports k=1 or k=2.
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capacity_factor (float, optional): default=1.0, the capacity of the expert at training time.
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eval_capacity_factor (float, optional): default=1.0, the capacity of the expert at eval time.
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min_capacity (int, optional): default=4, the minimum capacity per expert regardless of the capacity_factor.
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noisy_gate_policy (str, optional): default=None, noisy gate policy, valid options are 'Jitter', 'RSample'
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or 'None'.
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using_default_moe (bool, optional): default=True, whether to use the default MoE layer.
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drop_tokens (bool, optional): default=True, whether to drop tokens - (setting to False is equivalent to
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infinite capacity).
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use_rts (bool, optional): default=True, whether to use Random Token Selection.
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moe_use_residual (bool, optional): default=False, make this MoE layer a Residual MoE
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(https://arxiv.org/abs/2201.05596) layer.
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residual_mlp (torch.nn.Module, optional): default=None, the torch module that defines the residual MLP.
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"""
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def __init__(
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self,
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hidden_size,
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num_experts=1,
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ep_group=None,
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ep_size=1,
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device=None,
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dtype=None,
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):
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super().__init__()
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if not hasattr(gpc.config, "moe"):
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gpc.config.moe = dict()
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self.moe_layer = MODEL_INITIALIZER.get_module(module_name=gpc.config.model.moe_type)(
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hidden_size=hidden_size,
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num_experts=num_experts,
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ep_group=ep_group,
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ep_size=ep_size,
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device=device,
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dtype=dtype,
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**(gpc.config.moe)
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)
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# residual network, see https://arxiv.org/pdf/2201.05596.pdf, seems useful for convergence
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self.use_residual = gpc.config.model.moe_use_residual
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if self.use_residual:
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self.residual_mlp = FeedForward(
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hidden_size,
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int(hidden_size * gpc.config.model.mlp_ratio),
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out_features=hidden_size,
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process_group=gpc.get_group(ParallelMode.TENSOR),
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bias=False,
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device=device,
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dtype=dtype,
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)
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# coefficient is used for weighted sum of the output of expert and residual mlp
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self.coefficient = torch.nn.Linear(hidden_size, 2)
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def forward(self, hidden_states, used_token=None):
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"""MoE forward
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Arguments:
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hidden_states (Tensor): input to the layer
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used_token (Tensor, optional): default: None, mask only used tokens
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Returns:
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A tuple including output, gate loss, and expert count.
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* output (Tensor): output of the model
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* l_aux (Tensor): gate loss value
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* exp_counts (int): expert count
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"""
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output = self.moe_layer(hidden_states, used_token)
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if self.use_residual:
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# Residual MoE
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output_mlp = self.residual_mlp(hidden_states)
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if isinstance(output_mlp, tuple):
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output_mlp = output_mlp[0] # Ignore the bias term for now
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coef = self.coefficient(hidden_states)
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coef = torch.nn.functional.softmax(coef, dim=-1)
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output = output * coef[..., 0:1] + output_mlp * coef[..., 1:]
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return output, self.moe_layer.l_aux, self.moe_layer.exp_counts
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