mirror of https://github.com/hpcaitech/ColossalAI
221 lines
8.0 KiB
Python
221 lines
8.0 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.init as init
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import math
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from torch import Tensor
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from torch.nn.parameter import Parameter
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from typing import Tuple
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from colossalai.context import seed, ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.registry import LAYERS
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from colossalai.utils import get_current_device
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from .._common_utils import divide, ACT2FN
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from .._parallel_utilities import reduce_grad, reduce_input, gather_forward_split_backward, \
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split_forward_gather_backward
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from ..base_layer import ParallelLayer
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from .layers import Linear1D_Col, Linear1D_Row
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from .layers import MixedFusedLayerNorm1D as LayerNorm1D
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@LAYERS.register_module
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class TransformerMLP1D(ParallelLayer):
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"""MLP.
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MLP will take the input with h hidden state, project it to 4*h
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hidden dimension, perform nonlinear transformation, and project the
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state back into h hidden dimension.
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"""
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def __init__(self,
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in_features: int,
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mlp_ratio: int = 4.0,
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act_func: str = 'gelu',
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dropout_prob: float = 0.,
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dtype=None,
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skip_bias_add: bool = False
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):
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super(TransformerMLP1D, self).__init__()
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self.in_features = in_features
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self.mlp_ratio = mlp_ratio
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self.skip_bias_add = skip_bias_add
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# Project to h * mlp_ratio.
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self.dense_1 = Linear1D_Col(
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self.in_features,
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int(self.mlp_ratio * self.in_features),
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bias=not skip_bias_add,
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dtype=dtype,
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gather_output = False,
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)
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assert act_func in ACT2FN.keys(), f'Invalid value for argument act_func, ' \
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f'activation function can only be {list(ACT2FN.keys())}'
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self.activation_func = ACT2FN[act_func]
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# Project back to h.
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self.dense_2 = Linear1D_Row(
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int(self.mlp_ratio * self.in_features),
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self.in_features,
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bias=not skip_bias_add,
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dtype=dtype,
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parallel_input = True,
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)
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self.dropout = nn.Dropout(dropout_prob)
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# self.layernorm = LayerNorm1D(in_features, dtype=dtype)
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self.layernorm = nn.LayerNorm(in_features, dtype=dtype)
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def forward(self, x):
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if self.skip_bias_add:
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intermediate_output, _ = self.dense_1(x)
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else:
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intermediate_output = self.dense_1(x)
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intermediate_output = self.activation_func(intermediate_output)
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if self.skip_bias_add:
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output, _ = self.dense_2(intermediate_output)
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else:
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output = self.dense_2(intermediate_output)
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with seed(ParallelMode.TENSOR):
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output = self.dropout(output)
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output = self.layernorm(x + output)
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return output
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@LAYERS.register_module
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class TransformerSelfAttention1D(ParallelLayer):
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"""Self attention layer for 1D parallel Transformer
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:param hidden_size: hidden size
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:type hidden_size: int
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:param num_attention_heads: number of attention heads
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:type num_attention_heads: int
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:param attention_dropout_prob: dropout probability for attention layer
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:type attention_dropout_prob: float
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:param hidden_dropout_prob: dropout probability for hidden layer
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:type hidden_dropout_prob: float
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:param dtype: dtype of parameters, defaults to None
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:type dtype: torch.dtype, optional
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"""
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def __init__(self,
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hidden_size: int,
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num_attention_heads: int,
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attention_dropout_prob: float,
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hidden_dropout_prob: float,
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dtype=None,
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.num_attention_heads = divide(num_attention_heads, gpc.tensor_parallel_size)
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self.attention_head_size = divide(hidden_size, num_attention_heads)
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self.hidden_size_per_partition = divide(hidden_size, gpc.tensor_parallel_size)
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self.query_key_value = Linear1D_Col(
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hidden_size,
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3 * hidden_size,
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dtype=dtype,
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)
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self.attention_dropout = nn.Dropout(attention_dropout_prob)
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self.dense = Linear1D_Row(
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hidden_size,
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hidden_size,
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dtype=dtype,
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parallel_input=True,
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)
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self.dropout = nn.Dropout(hidden_dropout_prob)
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# need to re-enable torch grad to enable fused optimization.
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# self.layernorm = LayerNorm1D(
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# hidden_size,
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# dtype=dtype)
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self.layernorm = nn.LayerNorm(
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hidden_size,
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dtype=dtype)
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def forward(self, hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
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query_key_value = self.query_key_value(hidden_states)
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new_qkv_shape = query_key_value.shape[:-1] + \
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(self.num_attention_heads, 3 * self.attention_head_size)
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query_key_value = query_key_value.view(new_qkv_shape)
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query_key_value = query_key_value.permute((0, 2, 1, 3))
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query_layer, key_layer, value_layer = torch.chunk(
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query_key_value, 3, dim=-1)
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attention_scores = torch.matmul(
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query_layer, key_layer.transpose(-1, -2))
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attention_scores = attention_scores / \
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math.sqrt(self.attention_head_size)
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attention_scores = attention_scores + attention_mask
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attention_probs = nn.Softmax(dim=-1)(attention_scores)
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with seed(ParallelMode.TENSOR):
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attention_probs = self.attention_dropout(attention_probs)
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute((0, 2, 1, 3)).contiguous()
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new_context_layer_shape = context_layer.size()[
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:-2] + (self.hidden_size_per_partition,)
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context_layer = context_layer.view(*new_context_layer_shape)
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output = self.dense(context_layer)
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with seed(ParallelMode.TENSOR):
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output = self.dropout(output)
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attention_output = self.layernorm(hidden_states + output)
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return attention_output
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@LAYERS.register_module
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class TransformerLayer1D(ParallelLayer):
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"""Transformer layer which contains a self-attention layer and a MLP layer
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:param hidden_size: hidden size
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:type hidden_size: int
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:param num_attention_heads: number of attention heads
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:type num_attention_heads: int
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:param act_func: activation function, defaults to 'gelu'
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:type act_func: str, optional
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:param mlp_ratio: hidden size of MLP divided by embedding dim, defaults to 4.0
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:type mlp_ratio: float, optional
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:param attention_dropout_prob: dropout probability for attention layer, defaults to 0.
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:type attention_dropout_prob: float, optional
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:param hidden_dropout_prob: dropout probability for attention layer, defaults to 0.
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:type hidden_dropout_prob: float, optional
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:param dtype: dtype of parameters, defaults to None
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:type dtype: torch.dtype, optional
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"""
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def __init__(self,
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hidden_size: int,
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num_attention_heads: int,
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act_func: str = 'gelu',
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mlp_ratio: float = 4.0,
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attention_dropout_prob: float = 0.,
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hidden_dropout_prob: float = 0.,
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dtype=None,
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):
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super().__init__()
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self.attention = TransformerSelfAttention1D(
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hidden_size=hidden_size,
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num_attention_heads=num_attention_heads,
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attention_dropout_prob=attention_dropout_prob,
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hidden_dropout_prob=hidden_dropout_prob,
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dtype=dtype,
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)
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self.mlp = TransformerMLP1D(
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in_features=hidden_size,
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dropout_prob=hidden_dropout_prob,
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act_func=act_func,
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mlp_ratio=mlp_ratio,
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dtype=dtype,
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)
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def forward(self, hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
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attention_output = self.attention(hidden_states, attention_mask)
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output = self.mlp(attention_output)
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return output
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