How can I specify symbolic constraints to avoid shape mismatch errors in JAX

Implicit Constraints

You can add implicit constraints by modifying the symbolic shape specifications. For example, using `2*b` as a dimension constrains it to be even and greater than or equal to 2. Similarly, `b + 15` ensures the dimension is at least 16.

python
# Example of implicit constraints
from jax.experimental import export

# Define a shape with implicit constraints
b = export.symbolic_shape("b + 15")[0]

# Use this shape in a function
def my_function(x):
    return x[0:16]

# Export the function with the constrained shape
export.export(jax.jit(my_function))(jax.ShapeDtypeStruct((b,), dtype=np.int32))

Explicit Constraints

Explicit constraints can be specified using the `constraints` parameter of `jax.export.symbolic_shape`. These constraints are used in conjunction with implicit constraints to further restrict the possible values of symbolic dimensions.

python
# Example of explicit constraints
from jax.experimental import export

# Define dimensions with explicit constraints
a, b = export.symbolic_shape("a, b", constraints=("a >= b", "b >= 16"))

# Use these dimensions in a function
def my_function(x):
    return x[:x.shape[0], :16]

# Export the function with the constrained shape
export.export(jax.jit(my_function))(jax.ShapeDtypeStruct((a, b), dtype=np.int32))

Managing Symbolic Scopes

Symbolic expressions must be created within the same scope to be used together in arithmetic operations. You can reuse scopes by passing the `scope` parameter to `jax.export.symbolic_shape`.

python
# Example of managing symbolic scopes
from jax.experimental import export

# Create a scope
my_scope = export.SymbolicScope()

# Define dimensions within the same scope
a = export.symbolic_shape("a", scope=my_scope)[0]
b = export.symbolic_shape("b", scope=my_scope)[0]

# Now a and b can be used together
print(a + b)

By carefully managing symbolic constraints and scopes, you can avoid shape mismatch errors and ensure that JAX operations work as expected with dynamic shapes.