JAX Scan: Fix bug when recurring trace matches final requested size

pytensor/link/jax/dispatch/scan.py

@@ -10,7 +10,7 @@ from pytensor.scan.op import Scan
 
 
 @jax_funcify.register(Scan)
-def jax_funcify_Scan(op: Scan, **kwargs):
+def jax_funcify_Scan(op: Scan, node, **kwargs):
     # Note: This implementation is different from the internal PyTensor Scan op.
     # In particular, we don't make use of the provided buffers for recurring outputs (MIT-SOT, SIT-SOT)
     # These buffers include the initial state and enough space to store as many intermediate results as needed.
@@ -219,6 +219,8 @@ def jax_funcify_Scan(op: Scan, **kwargs):
                     if trace.shape[0] > buffer_size:
                         # Trace is longer than buffer, keep just the last `buffer.shape[0]` entries
                         partial_trace = trace[-buffer_size:]
+                    elif trace.shape[0] == buffer_size:
+                        partial_trace = trace
                     else:
                         # Trace is shorter than buffer, this happens when we keep the initial_state
                         if init_state.ndim < buffer.ndim:

tests/link/jax/test_scan.py

@@ -10,8 +10,8 @@ from pytensor.configdefaults import config
 from pytensor.graph import Apply, Op
 from pytensor.scan import until
 from pytensor.scan.basic import scan
-from pytensor.scan.op import Scan
-from pytensor.tensor import random
+from pytensor.scan.op import Scan, ScanInfo
+from pytensor.tensor import as_tensor, empty, random
 from pytensor.tensor.math import gammaln, log
 from pytensor.tensor.type import dmatrix, dvector, matrix, scalar, vector
 from tests.link.jax.test_basic import compare_jax_and_py
@@ -631,3 +631,37 @@ def test_scan_benchmark(model, mode, gradient_backend, benchmark):
 
 def test_higher_order_derivatives():
     ScanCompatibilityTests.check_higher_order_derivative(mode="JAX")
+
+
+def test_trace_truncation_regression_bug():
+    # Regression bug for a case where the final recurring trace size matched exactly with the number of steps
+    n_steps = as_tensor(7, dtype=int)
+    x0 = scalar("x0")
+    x0_buffer = empty((n_steps,))[0].set(x0)
+
+    # I don't know how to create such a Scan naturally, so we use the internal API
+    xtm1 = x0.type()
+    scan_op = Scan(
+        inputs=[xtm1],
+        outputs=[xtm1 + 1],
+        info=ScanInfo(
+            n_seqs=0,
+            mit_mot_in_slices=(),
+            mit_mot_out_slices=(),
+            mit_sot_in_slices=(),
+            sit_sot_in_slices=((-1,),),
+            n_nit_sot=0,
+            n_untraced_sit_sot_outs=0,
+            n_non_seqs=0,
+            as_while=False,
+        ),
+    )
+
+    xs_with_x0 = scan_op(n_steps, x0_buffer)
+
+    compare_jax_and_py(
+        [x0],
+        [xs_with_x0],
+        [np.array(0)],
+        jax_mode="JAX",
+    )