Bert MoE

examples/fairseq/README.md

@@ -70,7 +70,7 @@ You can quickly get started with our processed vocabulary files: [sentencepiece.
 spm_export_vocab --model=sentencepiece.bpe.model | sed 's/\t/ /g' | tail -n +4 > dict.txt
 ```
 
-### Training Command
+### Dense Model
 ```bash
 cd examples/fairseq/
 python -m torch.distributed.launch --nproc_per_node=8 --nnodes=8 train.py ${PATH_TO_DATA} \
@@ -117,6 +117,58 @@ python -m torch.distributed.launch --nproc_per_node=8 --nnodes=8 train.py ${PATH
         --deepnorm
 ```
 
+### Sparse (MoE) Model
+```bash
+cd examples/fairseq/
+python -m torch.distributed.launch --nproc_per_node=8 --nnodes=8 train.py ${PATH_TO_DATA} \
+        --task pretraining  \
+        --tokens-per-sample 512  \
+        --mask-prob 0.15  \
+        --span-length 3.0  \
+        --leave-unmasked-prob 0.0  \
+        --random-token-prob 0.0 \
+        --criterion masked_lm  \
+        --arch mlm_base  \
+        --share-encoder-input-output-embed \
+        --required-batch-size-multiple 8 \
+        --spm-model ${PATH_TO_DATA}/sentencepiece.bpe.model \
+        --dict-file ${PATH_TO_DATA}/dict.txt \
+        --optimizer adam  \
+        --adam-betas '(0.9,0.98)'  \
+        --adam-eps 1e-6  \
+        --clip-norm 2.0 \
+        --lr-scheduler polynomial_decay  \
+        --lr 0.0005  \
+        --warmup-updates 10000  \
+        --total-num-update 125000 \
+        --max-update 125000 \
+        --max-sentences 32  \
+        --update-freq 1 \
+        --log-format simple  \
+        --log-interval 100 \
+        --disable-validation \
+        --save-interval-updates 5000 \
+        --no-epoch-checkpoints \
+        --fp16 \
+        --fp16-init-scale 4 \
+        --fp16-scale-window 256 \
+        --min-loss-scale 0.0001 \
+        --seed 1 \
+        --save-dir ${PATH_TO_CKPT} \
+        --ddp-backend=no_c10d \
+        --distributed-no-spawn \
+        --reset-dataloader \
+        --batch-read-ahead 10000 \
+        --rel-pos-buckets 32 \
+        --max-rel-pos 128 \
+        --deepnorm \
+        --moe-expert-count 64 --moe-freq 2 \
+        --moe-gating-use-fp32 --moe-second-expert-policy random --moe-normalize-gate-prob-before-dropping \
+        --moe-eval-capacity-token-fraction -1.0 \
+        --criterion moe_cross_entropy --moe-gate-loss-wt 0.01 --moe-gate-loss-combine-method sum \
+        --use-xmoe
+```
+
 ## Example: GPT Pretraining
 
 ### Data Format

examples/fairseq/models/bert.py

@@ -350,6 +350,18 @@ class BertModel(BaseFairseqModel):
                     logger.info("Overwriting " + prefix + "classification_heads." + k)
                     state_dict[prefix + "classification_heads." + k] = v
     
+    def get_normalized_probs_scriptable(
+        self,
+        net_output,
+        log_probs,
+        sample = None,
+    ):
+        logits = net_output[0]
+        if log_probs:
+            return utils.log_softmax(logits, dim=-1)
+        else:
+            return utils.softmax(logits, dim=-1)
+
     def forward(
         self,
         src_tokens=None,