# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.framework import in_dygraph_mode from paddle.fluid.data_feeder import check_variable_and_dtype, check_dtype from paddle.fluid import core, dygraph_utils from paddle import _C_ops __all__ = [] def _verify_dropout_rate(dropout_rate): if not isinstance(dropout_rate, (float, int)): raise TypeError("dropout_rate argument should be a number") if dropout_rate < 0 or dropout_rate > 1: raise ValueError("dropout_rate argument should between 0 and 1") def fused_feedforward(x, linear1_weight, linear2_weight, linear1_bias=None, linear2_bias=None, ln1_scale=None, ln1_bias=None, ln2_scale=None, ln2_bias=None, dropout1_rate=0.5, dropout2_rate=0.5, activation="relu", ln1_epsilon=1e-5, ln2_epsilon=1e-5, pre_layer_norm=False, name=None): """ This is a fusion operator to compute feed forward layer in transformer model architecture. This operator only supports running on GPU. The function of the operator is consistent with the following pseudo code: .. code-block:: python residual = src; if pre_layer_norm: src = layer_norm(src) src = linear(dropout(activation(dropout(linear(src))))) if not pre_layer_norm: src = layer_norm(out) Args: x (Tensor): the input tensor could be 3-D tensor, the input data type could be float16, float32 or float64, the shape is`[batch\_size, sequence\_length, d_model]`. linear1_weight (Tensor): The weight of first linear, the data type is same as `x`, the shape is `[d\_model, dim\_feedforward]`. linear2_weight (Tensor): The weight of second linear, the data type is same as `x`, the shape is `[dim\_feedforward, d\_model]`. linear1_bias (Tensor, optional): The bias of first linear, the data type is same as `x`, the shape is `[dim_feedforward]`. Default None. linear2_bias (Tensor, optional): The bias of second linear, the data type is same as `x`, the shape is `[d_model]`. Default None. ln1_scale (Tensor, optional): the weight of first layer_norm, the data type is float32 or float64, the shape is same as `x`. Default None. ln1_bias (Tensor, optional): The bias of first layer_norm, the data type is float32 or float64, the shape is `[d\_model]`. Default None. ln2_scale (Tensor, optional): The weight of second layer_norm, the data type is float32 or float64, the shape is same as `x`. Default None. ln2_bias (Tensor, optional): The bias of second layer_norm, the data type is float32 or float64, the shape is `[d\_model]`. Default None. dropout1_rate (float, optional): The first dropout probability of setting units to zero. Default 0.5. dropout2_rate (float, optional): The second dropout probability of setting units to zero. Default 0.5. activation (str, optional): The activation. Default "relu". ln1_epsilon (float, optional): Small float of first layer_norm added to denominator to avoid dividing by zero. Default is 1e-5. ln2_epsilon (float, optional): Small float of second layer_norm added to denominator to avoid dividing by zero. Default is 1e-5. pre_layer_norm (bool, optional): add layer_norm in the pre-processing stage or post-processing state. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The output Tensor, the data type and shape is same as `x`. Examples: .. code-block:: python # required: gpu import paddle import numpy as np x_data = np.random.random((1, 8, 8)).astype("float32") linear1_weight_data = np.random.random((8, 8)).astype("float32") linear2_weight_data = np.random.random((8, 8)).astype("float32") x = paddle.to_tensor(x_data) linear1_weight = paddle.to_tensor(linear1_weight_data) linear2_weight = paddle.to_tensor(linear2_weight_data) out = paddle.incubate.nn.functional.fused_feedforward(x, linear1_weight, linear2_weight) print(out.numpy().shape) # (1, 8, 8) """ _verify_dropout_rate(dropout1_rate) _verify_dropout_rate(dropout2_rate) if in_dygraph_mode(): out, _, _, _, _, _, _, _, _, _, _ = _C_ops.fused_feedforward( x, None, None, linear1_weight, linear1_bias, linear2_weight, linear2_bias, ln1_scale, ln1_bias, ln2_scale, ln2_bias, 'pre_layer_norm', pre_layer_norm, 'ln1_epsilon', ln1_epsilon, 'ln2_epsilon', ln2_epsilon, 'act_method', activation, 'dropout1_rate', dropout1_rate, 'dropout2_rate', dropout2_rate) return out helper = LayerHelper("fused_feedforward") dtype = x.dtype check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'fused_feedforward') check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'], 'fused_feedforward') out = helper.create_variable_for_type_inference(x.dtype) dropout1_mask = helper.create_variable_for_type_inference( 'uint8', stop_gradient=True) dropout2_mask = helper.create_variable_for_type_inference( 'uint8', stop_gradient=True) ln1_mean = helper.create_variable_for_type_inference( x.dtype, stop_gradient=True) ln1_variance = helper.create_variable_for_type_inference( x.dtype, stop_gradient=True) ln2_mean = helper.create_variable_for_type_inference( x.dtype, stop_gradient=True) ln2_variance = helper.create_variable_for_type_inference( x.dtype, stop_gradient=True) linear1_out = helper.create_variable_for_type_inference( x.dtype, stop_gradient=True) ln1_out = helper.create_variable_for_type_inference( x.dtype, stop_gradient=True) dropout1_out = helper.create_variable_for_type_inference( x.dtype, stop_gradient=True) dropout2_out = helper.create_variable_for_type_inference( x.dtype, stop_gradient=True) helper.append_op( type='fused_feedforward', inputs={ 'X': x, 'Linear1Weight': linear1_weight, 'Linear1Bias': linear1_bias, 'Linear2Weight': linear2_weight, 'Linear2Bias': linear2_bias, 'Ln1Scale': ln1_scale, 'Ln1Bias': ln1_bias, 'Ln2Scale': ln2_scale, 'Ln2Bias': ln2_bias, }, outputs={ 'Out': out, 'Dropout1Mask': dropout1_mask, 'Dropout2Mask': dropout2_mask, 'Ln1Mean': ln1_mean, 'Ln1Variance': ln1_variance, 'Ln2Mean': ln2_mean, 'Ln2Variance': ln2_variance, 'Linear1Out': linear1_out, 'Ln1Out': ln1_out, 'Dropout1Out': dropout1_out, 'Dropout2Out': dropout2_out, }, attrs={ 'dropout1_rate': dropout1_rate, 'dropout2_rate': dropout2_rate, 'act_method': activation, 'pre_layer_norm': pre_layer_norm, 'ln1_epsilon': ln1_epsilon, 'ln2_epsilon': ln2_epsilon, }) return out def fused_multi_head_attention(x, qkv_weight, linear_weight, pre_layer_norm=False, pre_ln_scale=None, pre_ln_bias=None, ln_scale=None, ln_bias=None, pre_ln_epsilon=1e-05, qkv_bias=None, linear_bias=None, attn_mask=None, dropout_rate=0.5, attn_dropout_rate=0.5, ln_epsilon=1e-05, name=None): """ Attention mapps queries and a set of key-value pairs to outputs, and Multi-Head Attention performs multiple parallel attention to jointly attending to information from different representation subspaces. This API only support self_attention. The pseudo code is as follows: if pre_layer_norm: out = layer_norm(x); out = linear(out) + qkv)bias else: out = linear(x) + bias; out = transpose(out, perm=[2, 0, 3, 1, 4]); # extract q, k and v from out. q = out[0:1,::] k = out[1:2,::] v = out[2:3,::] out = q * k^t; out = attn_mask + out; out = softmax(out); out = dropout(out); out = out * v; out = transpose(out, perm=[0, 2, 1, 3]); out = out_linear(out); out = layer_norm(x + dropout(linear_bias + out)); Parameters: x (Tensor): The input tensor of fused_multi_head_attention. The shape is `[batch\_size, sequence\_len, embed\_dim]`. qkv_weight (Tensor): The qkv weight tensor. The shape is `[3, num_head, dim_head, dim_embed]`. linear_weight (Tensor): The linear weight tensor. The shape is `[embed_dim, embed_dim]`. pre_layer_norm (bool, optional): whether it is pre_layer_norm (True) or post_layer_norm architecture (False). Default False. pre_ln_scale (Tensor, optional): The weight tensor of pre layernorm. Default None. pre_ln_bias (Tensor, optional): The bias tensor of pre layernorm. Default None. ln_scale (Tensor, optional): The weight tensor of layernorm. Default None. ln_bias (Tensor, optional): The bias tensor of layernorm. Default None. pre_ln_epsilon (float, optional): Small float value added to denominator of the pre layer_norm to avoid dividing by zero. Default is 1e-5. qkv_bias (Tensor, optional): The bias of qkv computation. The shape is `[3, num_head, dim_head]`. Default None. linear_bias (Tensor, optional): The bias of linear. The shape is `[embed_dim]`. Default None. attn_mask (Tensor, optional): A tensor used in multi-head attention to prevents attention to some unwanted positions, usually the paddings or the subsequent positions. It is a tensor with shape broadcasted to `[batch_size, n_head, sequence_length, sequence_length]`. When the data type is bool, the unwanted positions have `False` values and the others have `True` values. When the data type is int, the unwanted positions have 0 values and the others have 1 values. When the data type is float, the unwanted positions have `-INF` values and the others have 0 values. It can be None when nothing wanted or needed to be prevented attention to. Default None. dropout_rate (float, optional): The dropout probability used on attention weights to drop some attention targets for the dropout after attention. 0 for no dropout. Default 0.5. attn_dropout_rate (float, optional): The dropout probability used on attention weights to drop some attention targets for the dropout in attention. 0 for no dropout. Default 0.5. ln_epsilon (float, optional): Small float value added to denominator of layer_norm to avoid dividing by zero. Default is 1e-5. Examples: .. code-block:: python # required: gpu import paddle import paddle.incubate.nn.functional as F # input: [batch_size, seq_len, embed_dim] x = paddle.rand(shape=(2, 4, 128), dtype="float32") # qkv_weight: [3, num_head, head_dim, embed_dim] qkv_weight = paddle.rand(shape=(3, 4, 32, 128), dtype="float32") # qkv_bias: [3, num_head, head_dim] qkv_bias = paddle.rand(shape=(3, 4, 32), dtype="float32") # linear_weight: [embed_dim, embed_dim] linear_weight = paddle.rand(shape=(128, 128), dtype="float32") # linear_bias: [embed_dim] linear_bias = paddle.rand(shape=[128], dtype="float32") # self attention mask: [batch_size, num_heads, seq_len, seq_len] attn_mask = paddle.rand(shape=(2, 4, 4, 4), dtype="float32") # output: [batch_size, seq_len, embed_dim] output = F.fused_multi_head_attention( x, qkv_weight, linear_weight, False, None, None, None, None, 1e-5, qkv_bias, linear_bias, attn_mask) # [2, 4, 128] print(output.shape) """ if in_dygraph_mode(): # pre_ln_mean, pre_ln_variance, pre_ln_out, qkv_out, qkv_bias_out, transpose_out, qk_out, # qktv_out, softmax_out, attn_dropout_mask_out, attn_dropout_out, attn_mask_out, fmha_out, # linear_out, dropout_mask_out, ln_mean_out, ln_var_out, bias_dropout_residual_out, final_out assert len(qkv_weight.shape ) == 4, "The dims of the shape of qkv_weight should be 4." assert qkv_weight.shape[ 0] == 3, "The shape of qkv_weight should be [3, num_head, head_dim, embed_dim]." assert qkv_weight.shape[3] == x.shape[ 2], "The 3rd dim of qkv_weight and 2nd dim of x should be the same, i.e., embed_dim." _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, final_out = _C_ops.fused_attention( x, pre_ln_scale, pre_ln_bias, qkv_weight, qkv_bias, attn_mask, linear_weight, linear_bias, ln_scale, ln_bias, 'pre_layer_norm', pre_layer_norm, 'epsilon', pre_ln_epsilon, 'dropout_rate', dropout_rate, 'attn_dropout_rate', attn_dropout_rate, 'ln_epsilon', ln_epsilon) return final_out else: helper = LayerHelper('fused_multi_head_attention', **locals()) dtype = x.dtype # check dtypes check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'fused_multihead_attention') check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'], 'fused_multi_head_attention') # set inputs inputs = dict() inputs['X'] = [x] if pre_ln_scale: inputs['LnScale'] = [pre_ln_scale] if pre_ln_bias: inputs['LnBias'] = [pre_ln_bias] inputs['QKVW'] = [qkv_weight] inputs['QKVBias'] = [qkv_bias] inputs['SrcMask'] = attn_mask inputs['OutLinearW'] = [linear_weight] inputs['OutLinearBias'] = [linear_bias] if ln_scale: inputs['Ln2Scale'] = [ln_scale] if ln_bias: inputs['Ln2Bias'] = [ln_bias] # set attrs attrs = { 'pre_layer_norm': pre_layer_norm, 'epsilon': pre_ln_epsilon, 'ln_epsilon': ln_epsilon, 'dropout_rate': dropout_rate, 'attn_dropout_rate': attn_dropout_rate } # set outputs pre_ln_mean_out = helper.create_variable_for_type_inference( dtype=dtype, stop_gradient=True) pre_ln_variance_out = helper.create_variable_for_type_inference( dtype=dtype, stop_gradient=True) pre_ln_out = helper.create_variable_for_type_inference(dtype=dtype) qkv_out = helper.create_variable_for_type_inference(dtype=dtype) qkv_bias_out = helper.create_variable_for_type_inference(dtype=dtype) transpose_out = helper.create_variable_for_type_inference(dtype=dtype) qk_out = helper.create_variable_for_type_inference(dtype=dtype) qktv_out = helper.create_variable_for_type_inference(dtype=dtype) softmax_out = helper.create_variable_for_type_inference(dtype=dtype) attn_dropout_mask_out = helper.create_variable_for_type_inference( dtype=core.VarDesc.VarType.UINT8, stop_gradient=True) attn_dropout_out = helper.create_variable_for_type_inference( dtype=dtype) attn_mask_out = helper.create_variable_for_type_inference(dtype=dtype) fmha_out = helper.create_variable_for_type_inference(dtype=dtype) out_linear_out = helper.create_variable_for_type_inference(dtype=dtype) dropout_mask_out = helper.create_variable_for_type_inference( dtype=core.VarDesc.VarType.UINT8, stop_gradient=True) ln_mean_out = helper.create_variable_for_type_inference( dtype=dtype, stop_gradient=True) ln_variance_out = helper.create_variable_for_type_inference( dtype=dtype, stop_gradient=True) bias_dropout_residual_out = helper.create_variable_for_type_inference( dtype=dtype) final_out = helper.create_variable_for_type_inference(dtype=dtype) helper.append_op( type='fused_attention', inputs=inputs, outputs={ "LnMean": pre_ln_mean_out, "LnVariance": pre_ln_variance_out, "LnOut": pre_ln_out, "QKVOut": qkv_out, "QKVBiasOut": qkv_bias_out, "TransposeOut2": transpose_out, "QKOut": qk_out, "QKTVOut": qktv_out, "SoftmaxOut": softmax_out, "AttnDropoutMaskOut": attn_dropout_mask_out, "AttnDropoutOut": attn_dropout_out, "SrcMaskOut": attn_mask_out, "FMHAOut": fmha_out, "OutLinearOut": out_linear_out, "DropoutMaskOut": dropout_mask_out, "Ln2Mean": ln_mean_out, "Ln2Variance": ln_variance_out, "BiasDropoutResidualOut": bias_dropout_residual_out, 'Y': final_out }, attrs=attrs) return final_out