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