Refactor exceptions in Scan Op

aesara/scan/op.py

@@ -89,6 +89,119 @@ from aesara.tensor.var import TensorVariable
 _logger = logging.getLogger("aesara.scan.op")
 
 
+err_msg1 = (
+    "When compiling the inner function of scan (the "
+    "function called by scan in each of its iterations) "
+    "the following error has been encountered: The "
+    "%s %s (argument number %d) has dtype "
+    "%s and %d dimension(s). The corresponding variable "
+    "in the inner function of scan %s "
+    "however has dtype %s and %d dimension(s). This "
+    "variable in the inner function of scan should "
+    "have the same dtype and one fewer dimension "
+    "compared to its corresponding variable in the initial "
+    "state (outputs_info in scan nomenclature). For example, "
+    "if the inner function of scan returns a vector "
+    "of size d and scan uses the values of "
+    "the previous time-step, then the initial state in scan "
+    "should be a matrix of shape (1, d). "
+    "The first dimension of this "
+    "matrix corresponds to the number of previous time-steps "
+    "that scan uses in each of its iterations. "
+    "In order to solve this issue if the two variable currently "
+    "have the same dimensionality, you can increase the "
+    "dimensionality of the varialbe in the initial state of scan "
+    "by using dimshuffle or shape_padleft. "
+)
+err_msg2 = (
+    "When compiling the inner function of scan the "
+    "following error has been encountered: The "
+    "initial state (`outputs_info` in scan nomenclature) "
+    "of variable %s (argument number %d) "
+    "has dtype %s, while the result of the inner function "
+    "(`fn`) has dtype %s. This can happen if the inner "
+    "function of scan results in an upcast or downcast."
+)
+err_msg3 = (
+    "When compiling the inner function of scan (the "
+    "function called by scan in each of its iterations) "
+    "the following error has been encountered: The "
+    "initial state (`outputs_info` in scan nomenclature) "
+    "of variable %s (argument number %d) has %d dimension(s), "
+    "while the corresponding variable in the result of the inner "
+    "function of scan (`fn`) has %d dimension(s) (it should "
+    "be one less than the initial state). For example, "
+    "if the inner function of scan returns a vector "
+    "of size d and scan uses the values of "
+    "the previous time-step, then the initial state in scan "
+    "should be a matrix of shape (1, d). "
+    "The first dimension of this "
+    "matrix corresponds to the number of previous time-steps "
+    "that scan uses in each of its iterations. "
+    "In order to solve this issue if the two varialbe currently "
+    "have the same dimensionality, you can increase the "
+    "dimensionality of the variable in the initial state of scan "
+    "by using dimshuffle or shape_padleft. "
+)
+
+
+def check_broadcast(v1, v2):
+    """Checks that the broadcast pattern of v1 and v2.
+
+    Controls that the broadcast pattern of the variable provided as
+    input to `scan` matches the broadcast pattern provided in
+    `output_info`. It raises an error when they don't match. The
+    typical case is when the user provides either the input or the
+    `output_info` (but not both) with a dimension fixed to 1,
+    which may wrongly be interpreted as broadcastable.
+
+    """
+    if not hasattr(v1, "broadcastable") and not hasattr(v2, "broadcastable"):
+        return
+    msg = (
+        "The broadcast pattern of the output of scan (%s) is "
+        "inconsistent with the one provided in `output_info` "
+        "(%s). The output on axis %d is `%r`, but it is `%r` on "
+        "axis %d in `output_info`. This can happen if one of the "
+        "dimension is fixed to 1 in the input, while it is still "
+        "variable in the output, or vice-verca. You have to make "
+        "them consistent, e.g. using aesara.tensor."
+        "{patternbroadcast,unbroadcast,addbroadcast}."
+    )
+    size = min(len(v1.broadcastable), len(v2.broadcastable))
+    for n, (b1, b2) in enumerate(
+        zip(v1.broadcastable[-size:], v2.broadcastable[-size:])
+    ):
+        if b1 != b2:
+            a1 = n + size - len(v1.broadcastable) + 1
+            a2 = n + size - len(v2.broadcastable) + 1
+            raise TypeError(msg % (v1.type, v2.type, a1, b1, b2, a2))
+
+
+def copy_var_format(var, as_var):
+    """
+    This functions ensures that ``var`` has the same dtype as ``as_var`` as
+    well as calling `filter_variable` to make sure they are both `TensorType`
+    or `GpuArrayType`.
+
+    It internally deals with the corner case where ``inp.ndim + 1 = out.ndim``.
+
+    """
+    if not hasattr(var, "dtype"):
+        return var
+    rval = var
+    if rval.type.dtype != as_var.type.dtype:
+        rval = rval.astype(as_var.type.dtype)
+    if rval.ndim == as_var.ndim:
+        rval = as_var.type.filter_variable(rval)
+    else:
+        tmp = as_var.type.clone(
+            broadcastable=(tuple(var.broadcastable[:1]) + tuple(as_var.broadcastable))
+        )
+        rval = tmp.filter_variable(rval)
+    return rval
+
+
 @dataclasses.dataclass(frozen=True)
 class ScanInfo:
     tap_array: tuple
@@ -721,7 +834,9 @@ class Scan(Op, ScanMethodsMixin):
                           the inner function)
 
         """
-        assert np.all(isinstance(i, Variable) for i in inputs)
+        if not all(isinstance(i, Variable) for i in inputs):
+            raise TypeError("Inputs must be `Variable` instances")
+
         # Check that the number of inputs to the Scan node corresponds to
         # the number of inputs of the inner function of scan
         n_outer_ins = len(inputs) - len(self.outer_nitsot(inputs)) - 1
@@ -733,123 +848,15 @@ class Scan(Op, ScanMethodsMixin):
             + len(self.inner_shared(self.inputs))
             + len(self.inner_non_seqs(self.inputs))
         )
-        assert n_outer_ins == n_inner_ins, (
-            "The number of inputs given to the inner function of scan"
-            " does not match the number of inputs given to scan."
-        )
-        # Force the inputs to be on the CPU
-        new_inputs = [as_tensor_variable(inputs[0])]
-        # assert dtype is consistent
-        err_msg1 = (
-            "When compiling the inner function of scan (the "
-            "function called by scan in each of its iterations) "
-            "the following error has been encountered: The "
-            "%s %s (argument number %d) has dtype "
-            "%s and %d dimension(s). The corresponding variable "
-            "in the inner function of scan %s "
-            "however has dtype %s and %d dimension(s). This "
-            "variable in the inner function of scan should "
-            "have the same dtype and one fewer dimension "
-            "compared to its corresponding variable in the initial "
-            "state (outputs_info in scan nomenclature). For example, "
-            "if the inner function of scan returns a vector "
-            "of size d and scan uses the values of "
-            "the previous time-step, then the initial state in scan "
-            "should be a matrix of shape (1, d). "
-            "The first dimension of this "
-            "matrix corresponds to the number of previous time-steps "
-            "that scan uses in each of its iterations. "
-            "In order to solve this issue if the two variable currently "
-            "have the same dimensionality, you can increase the "
-            "dimensionality of the varialbe in the initial state of scan "
-            "by using dimshuffle or shape_padleft. "
-        )
-        err_msg2 = (
-            "When compiling the inner function of scan the "
-            "following error has been encountered: The "
-            "initial state (`outputs_info` in scan nomenclature) "
-            "of variable %s (argument number %d) "
-            "has dtype %s, while the result of the inner function "
-            "(`fn`) has dtype %s. This can happen if the inner "
-            "function of scan results in an upcast or downcast."
-        )
-        err_msg3 = (
-            "When compiling the inner function of scan (the "
-            "function called by scan in each of its iterations) "
-            "the following error has been encountered: The "
-            "initial state (`outputs_info` in scan nomenclature) "
-            "of variable %s (argument number %d) has %d dimension(s), "
-            "while the corresponding variable in the result of the inner "
-            "function of scan (`fn`) has %d dimension(s) (it should "
-            "be one less than the initial state). For example, "
-            "if the inner function of scan returns a vector "
-            "of size d and scan uses the values of "
-            "the previous time-step, then the initial state in scan "
-            "should be a matrix of shape (1, d). "
-            "The first dimension of this "
-            "matrix corresponds to the number of previous time-steps "
-            "that scan uses in each of its iterations. "
-            "In order to solve this issue if the two varialbe currently "
-            "have the same dimensionality, you can increase the "
-            "dimensionality of the variable in the initial state of scan "
-            "by using dimshuffle or shape_padleft. "
-        )
 
-        def check_broadcast(v1, v2):
-            """Checks that the broadcast pattern of v1 and v2.
-
-            Controls that the broadcast pattern of the variable provided as
-            input to `scan` matches the broadcast pattern provided in
-            `output_info`. It raises an error when they don't match. The
-            typical case is when the user provides either the input or the
-            `output_info` (but not both) with a dimension fixed to 1,
-            which may wrongly be interpreted as broadcastable.
-
-            """
-            if not hasattr(v1, "broadcastable") and not hasattr(v2, "broadcastable"):
-                return
-            msg = (
-                "The broadcast pattern of the output of scan (%s) is "
-                "inconsistent with the one provided in `output_info` "
-                "(%s). The output on axis %d is `%r`, but it is `%r` on "
-                "axis %d in `output_info`. This can happen if one of the "
-                "dimension is fixed to 1 in the input, while it is still "
-                "variable in the output, or vice-verca. You have to make "
-                "them consistent, e.g. using aesara.tensor."
-                "{patternbroadcast,unbroadcast,addbroadcast}."
+        if n_outer_ins != n_inner_ins:
+            raise ValueError(
+                "The number of inputs given to the inner function of scan"
+                " does not match the number of inputs given to scan."
             )
-            size = min(len(v1.broadcastable), len(v2.broadcastable))
-            for n, (b1, b2) in enumerate(
-                zip(v1.broadcastable[-size:], v2.broadcastable[-size:])
-            ):
-                if b1 != b2:
-                    a1 = n + size - len(v1.broadcastable) + 1
-                    a2 = n + size - len(v2.broadcastable) + 1
-                    raise TypeError(msg % (v1.type, v2.type, a1, b1, b2, a2))
-
-        def format(var, as_var):
-            """
-            This functions ensures that ``out`` has the same dtype as
-            ``inp`` as well as calling filter_variable to make sure
-            they are both TensorType or GpuArrayType. It internally
-            deals with the corner case where inp.ndim + 1 = out.ndim
-
-            """
-            if not hasattr(var, "dtype"):
-                return var
-            rval = var
-            if rval.type.dtype != as_var.type.dtype:
-                rval = rval.astype(as_var.type.dtype)
-            if rval.ndim == as_var.ndim:
-                rval = as_var.type.filter_variable(rval)
-            else:
-                tmp = as_var.type.clone(
-                    broadcastable=(
-                        tuple(var.broadcastable[:1]) + tuple(as_var.broadcastable)
-                    )
-                )
-                rval = tmp.filter_variable(rval)
-            return rval
+
+        # Force the inputs to be on the CPU
+        new_inputs = [as_tensor_variable(inputs[0])]
 
         # Check if input sequences and variables representing a slice of
         # them have the same dtype
@@ -858,7 +865,7 @@ class Scan(Op, ScanMethodsMixin):
             self.inner_seqs(self.inputs), self.outer_seqs(inputs)
         ):
             check_broadcast(outer_seq, inner_seq)
-            new_inputs.append(format(outer_seq, as_var=inner_seq))
+            new_inputs.append(copy_var_format(outer_seq, as_var=inner_seq))
 
         argoffset += len(self.outer_seqs(inputs))
         # Check that this 3 things have the same dtype for mit_mot:
@@ -872,7 +879,7 @@ class Scan(Op, ScanMethodsMixin):
         for idx, (itaps, otaps, _outer_mitmot) in enumerate(
             zip(self.mitmot_taps(), self.mitmot_out_taps(), self.outer_mitmot(inputs))
         ):
-            outer_mitmot = format(_outer_mitmot, as_var=inner_mitmot[ipos])
+            outer_mitmot = copy_var_format(_outer_mitmot, as_var=inner_mitmot[ipos])
             new_inputs.append(outer_mitmot)
             for k in range(len(itaps)):
                 if (
@@ -882,7 +889,7 @@ class Scan(Op, ScanMethodsMixin):
                     raise ValueError(
                         err_msg1
                         % (
-                            "initial state (outputs_info" " in scan nomenclature) ",
+                            "initial state (`outputs_info` in scan nomenclature) ",
                             str(outer_mitmot),
                             argoffset + idx,
                             outer_mitmot.type.dtype,
@@ -926,7 +933,7 @@ class Scan(Op, ScanMethodsMixin):
                 self.inner_mitsot_outs(self.outputs),
             )
         ):
-            outer_mitsot = format(_outer_mitsot, as_var=inner_mitsots[ipos])
+            outer_mitsot = copy_var_format(_outer_mitsot, as_var=inner_mitsots[ipos])
             new_inputs.append(outer_mitsot)
 
             for k in range(len(itaps)):
@@ -978,7 +985,7 @@ class Scan(Op, ScanMethodsMixin):
                 self.inner_sitsot_outs(self.outputs),
             )
         ):
-            outer_sitsot = format(_outer_sitsot, as_var=inner_sitsot)
+            outer_sitsot = copy_var_format(_outer_sitsot, as_var=inner_sitsot)
             new_inputs.append(outer_sitsot)
             if inner_sitsot.ndim != outer_sitsot.ndim - 1:
                 raise ValueError(
@@ -1025,7 +1032,7 @@ class Scan(Op, ScanMethodsMixin):
                 self.outer_shared(inputs),
             )
         ):
-            outer_shared = format(_outer_shared, as_var=inner_shared)
+            outer_shared = copy_var_format(_outer_shared, as_var=inner_shared)
             new_inputs.append(outer_shared)
             if (
                 hasattr(outer_shared, "dtype")
@@ -1071,7 +1078,7 @@ class Scan(Op, ScanMethodsMixin):
                         inner_shared.ndim,
                     )
                 )
-        # We do not need to call `format` on outer_nisot arguments.
+        # We do not need to call `copy_var_format` on outer_nisot arguments.
         # outer_nitsot stands for no input tap single output tap. This means
         # these are states that do not feed anything back in the recurrent
         # computation, and hence they do not have an initial state. The scan
@@ -1083,15 +1090,14 @@ class Scan(Op, ScanMethodsMixin):
         for inner_nonseq, _outer_nonseq in zip(
             self.inner_non_seqs(self.inputs), self.outer_non_seqs(inputs)
         ):
-            outer_nonseq = format(_outer_nonseq, as_var=inner_nonseq)
+            outer_nonseq = copy_var_format(_outer_nonseq, as_var=inner_nonseq)
             new_inputs.append(outer_nonseq)
             if inner_nonseq.type != outer_nonseq.type:
                 raise ValueError(
                     (
-                        "Argument %s given to scan node does not"
-                        " match its correspondence %s"
+                        f"Argument {outer_nonseq} given to the scan node does not"
+                        f" match its corresponding loop function variable {inner_nonseq}"
                     )
-                    % (str(outer_nonseq), str(inner_nonseq))
                 )
 
         for outer_nitsot in self.outer_nitsot(inputs):
@@ -1102,10 +1108,8 @@ class Scan(Op, ScanMethodsMixin):
                 str(outer_nitsot.type.dtype) not in integer_dtypes
                 or outer_nitsot.ndim != 0
             ):
-                raise ValueError(
-                    "For output %s you need to provide a " "scalar int !",
-                    str(outer_nitsot),
-                )
+                raise ValueError(f"A scalar int is required for output {outer_nitsot}")
+
         assert len(new_inputs) == len(inputs)
 
         # The vector_seqs and vector_outs are just a workaround
@@ -1505,24 +1509,16 @@ class Scan(Op, ScanMethodsMixin):
             # History, in the past, this was used for backward
             # scan. Now we reverse the inputs outside of scan.
             raise IndexError(
-                f"Scan was asked to run for negative number of step {int(n_steps)}"
+                f"Scan was asked to run for negative number of step {n_steps}"
             )
         elif n_steps == 0:
-            raise NotImplementedError(
-                "We didn't implemented yet the case where scan do 0 iteration"
-            )
+            raise NotImplementedError("n_steps == 0")
         else:
             for idx, seq in enumerate(inputs[1 : self.seqs_arg_offset]):
                 if seq.shape[0] < n_steps:
                     raise ValueError(
-                        (
-                            "Sequence is shorter then the required "
-                            "number of steps : (n_steps, seq, "
-                            "seq.shape):"
-                        ),
-                        n_steps,
-                        node.inputs[1 + idx],
-                        seq.shape,
+                        f"Sequence {idx} has shape {seq.shape} "
+                        f"but the Scan's required number of steps is {n_steps}"
                     )
                 seqs.append(seq)
 
@@ -2267,7 +2263,7 @@ class Scan(Op, ScanMethodsMixin):
                 if str(g_y.dtype) in integer_dtypes:
                     raise TypeError(
                         "Gradients may never be integers but g_y "
-                        "has type " + str(g_y.type)
+                        f"has type {g_y.type}"
                     )
 
             out_indices = [get_out_idx(self_outputs.index(y)) for y in y_s]

aesara/scan/scan_perform.pyx

@@ -58,7 +58,7 @@ from aesara.link.utils import raise_with_op
 
 
 def get_version():
-    return 0.299
+    return 0.300
 
 @cython.boundscheck(False)
 def perform(
@@ -206,16 +206,18 @@ def perform(
             "Scan was asked to run for negative number of step %d" %
             n_steps)
     elif n_steps == 0:
-        raise NotImplementedError(
-            "We didn't implemented yet the case where scan do 0 iteration")
+        raise NotImplementedError("n_steps == 0")
     else:
         for idx in range(n_seqs):
             if args[<unsigned int>(1+idx)].shape[0] < n_steps:
-                raise ValueError(('Sequence is shorter than the required '
-                                 'number of steps : (n_steps, seq, '
-                                  'seq.shape):'), n_steps,
-                                  args[1+idx],
-                                  args[1+idx].shape)
+                raise ValueError((
+                    "Sequence %s has shape %s "
+                    "but the Scan's required number of steps is %s"
+                ) % (
+                    idx,
+                    args[1+idx].shape,
+                    n_steps,
+                ))
     # 2. Allocate memory for the outputs. Construct the list:
     #       store_steps  -- map containing the length of each output
     #       pos          -- map containing the current position of each output

aesara/scan/scan_perform_ext.py

@@ -21,7 +21,7 @@ if not config.cxx:
 
 _logger = logging.getLogger("aesara.scan.scan_perform")
 
-version = 0.299  # must match constant returned in function get_version()
+version = 0.300  # must match constant returned in function get_version()
 
 need_reload = False