add bash scripts for running diff FT strategies

peft/fft.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+
+export TF_CPP_MIN_LOG_LEVEL=2 XLA_PYTHON_CLIENT_PREALLOCATE=false
+
+python3 finetune.py \
+    --model-name="google/timesfm-1.0-200m" \
+    --backend="gpu" \
+    --horizon-len=128 \
+    --context-len=512 \
+    --freq="15min" \
+    --data-path="../datasets/ETT-small/ETTm1.csv" \
+    --num-epochs=1 \
+    --learning-rate=1e-3 \
+    --adam-epsilon=1e-7 \
+    --adam-clip-threshold=1e2 \
+    --early-stop-patience=10 \
+    --datetime-col="date" \
+    --boundaries=34560 46080 57600 \
+    --cos-initial-decay-value=1e-4 \
+    --cos-decay-steps=40000 \
+    --cos-final-decay-value=1e-5 \
+    --ema-decay=0.9999

peft/finetune.py

@@ -235,10 +235,10 @@ def finetune(
 
     @pax_fiddle.auto_config
     def build_learner() -> learners.Learner:
-        bprop_variable_inclusion = None
-        bprop_variable_exclusion = None
+        bprop_variable_inclusion = []
+        bprop_variable_exclusion = []
         if use_lora:
-            bprop_variable_inclusion = [r"^.*lora.*$"]
+            bprop_variable_inclusion.append(r"^.*lora.*$")
             if use_dora:
                 bprop_variable_inclusion.append(r"^.*dora.*$")
         elif use_linear_probing:

peft/linear_probing.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+
+export TF_CPP_MIN_LOG_LEVEL=2 XLA_PYTHON_CLIENT_PREALLOCATE=false
+
+python3 finetune.py \
+    --model-name="google/timesfm-1.0-200m" \
+    --backend="gpu" \
+    --horizon-len=128 \
+    --context-len=512 \
+    --freq="15min" \
+    --data-path="../datasets/ETT-small/ETTm1.csv" \
+    --num-epochs=100 \
+    --learning-rate=1e-2 \
+    --adam-epsilon=1e-7 \
+    --adam-clip-threshold=1e2 \
+    --early-stop-patience=10 \
+    --datetime-col="date" \
+    --boundaries=1000 46080 57600 \
+    --use-linear-probing \
+    --cos-initial-decay-value=1e-4 \
+    --cos-decay-steps=40000 \
+    --cos-final-decay-value=1e-5 \
+    --ema-decay=0.9999

peft/lora.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+
+export TF_CPP_MIN_LOG_LEVEL=2 XLA_PYTHON_CLIENT_PREALLOCATE=false
+
+python3 finetune.py \
+    --model-name="google/timesfm-1.0-200m" \
+    --backend="gpu" \
+    --horizon-len=128 \
+    --context-len=512 \
+    --freq="15min" \
+    --data-path="../datasets/ETT-small/ETTm1.csv" \
+    --num-epochs=100 \
+    --learning-rate=1e-3 \
+    --adam-epsilon=1e-7 \
+    --adam-clip-threshold=1e2 \
+    --early-stop-patience=10 \
+    --datetime-col="date" \
+    --boundaries=34560 46080 57600 \
+    --use-lora \
+    --lora-rank=1 \
+    --lora-target-modules="all" \
+    --cos-initial-decay-value=1e-4 \
+    --cos-decay-steps=40000 \
+    --cos-final-decay-value=1e-5 \
+    --ema-decay=0.9999