Source code for modelzoo.transformers.pytorch.layers_api_demo.model

# Copyright 2022 Cerebras Systems.
#
# 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.

import torch

from modelzoo.common.pytorch.model_utils.GPTLMHeadModelLoss import (
    GPTLMHeadModelLoss,
)
from modelzoo.transformers.pytorch.layers_api_demo.cb_transformer import (
    TransformerModel,
)
from modelzoo.transformers.pytorch.layers_api_demo.pytorch_transformer import (
    generate_square_subsequent_mask,
)


[docs]class TransformerBaseModel(torch.nn.Module):
[docs] def __init__(self, params): super().__init__() model_params = params["model"].copy() self.model = self.build_model(model_params)
def build_model(self, model_params): self.ntokens = model_params.pop("vocab_size") emsize = model_params.pop("embedding_size") nhead = model_params.pop("num_heads") d_hid = model_params.pop("hidden_size") nlayers = model_params.pop("num_hidden_layers") dropout = model_params.pop("dropout") activation = model_params.pop("nonlinearity") self.seq_len = model_params.pop("seq_len") model = TransformerModel( self.ntokens, emsize, nhead, d_hid, nlayers, dropout, activation, self.seq_len, ) self.loss_fn = GPTLMHeadModelLoss(self.ntokens, 1.0 / self.ntokens,) return model def forward(self, data): input_ids = data["input_ids"] target_ids = data["target_ids"] attention_mask = data["attention_mask"] src_mask = generate_square_subsequent_mask( self.seq_len, input_ids.device ) """ alternatively, you can use helper functions to create the masks from transformers/pytorch/transformer_utils.py # from modelzoo.transformers.pytorch.transformer_utils import (create_2D_autoregressive_mask, NEGATIVE_INFINITY,) # src_mask = create_2D_autoregressive_mask( # self.seq_len, # self.seq_len, # device=input_ids.device, # ) * NEGATIVE_INFINITY """ output = self.model(input_ids, src_mask) loss = self.loss_fn(output, target_ids, attention_mask) return loss