|
| 1 | +import numpy as np |
| 2 | + |
| 3 | +from pytensor.link.numba.cache import compile_numba_function_src |
| 4 | +from pytensor.link.numba.dispatch import basic as numba_basic |
| 5 | +from pytensor.link.numba.dispatch.basic import register_funcify_default_op_cache_key |
| 6 | +from pytensor.tensor.rewriting.join_inplace import WriteJoin, WriteSplit |
| 7 | + |
| 8 | + |
| 9 | +@register_funcify_default_op_cache_key(WriteSplit) |
| 10 | +def numba_funcify_WriteSplit(op, node, **kwargs): |
| 11 | + n_splits = op.n_splits |
| 12 | + axis = op.axis |
| 13 | + |
| 14 | + slice_lines = [] |
| 15 | + offset_expr = "0" |
| 16 | + for i in range(n_splits): |
| 17 | + slice_lines.append(f" sz_{i} = s{i}.item()") |
| 18 | + idx = ", ".join( |
| 19 | + f"{offset_expr}:{offset_expr} + sz_{i}" if d == axis else ":" |
| 20 | + for d in range(node.inputs[0].type.ndim) |
| 21 | + ) |
| 22 | + slice_lines.append(f" out_{i} = buffer[{idx}]") |
| 23 | + offset_expr = f"{offset_expr} + sz_{i}" |
| 24 | + |
| 25 | + return_vars = ", ".join(f"out_{i}" for i in range(n_splits)) |
| 26 | + size_params = ", ".join(f"s{i}" for i in range(n_splits)) |
| 27 | + |
| 28 | + func_src = f""" |
| 29 | +def write_split(buffer, {size_params}): |
| 30 | +{chr(10).join(slice_lines)} |
| 31 | + return ({return_vars},) |
| 32 | +""" |
| 33 | + fn = compile_numba_function_src(func_src, "write_split", {"np": np}) |
| 34 | + return numba_basic.numba_njit(fn) |
| 35 | + |
| 36 | + |
| 37 | +@register_funcify_default_op_cache_key(WriteJoin) |
| 38 | +def numba_funcify_WriteJoin(op, node, **kwargs): |
| 39 | + n_deps = len(node.inputs) - 1 |
| 40 | + |
| 41 | + dep_params = ", ".join(f"dep{i}" for i in range(n_deps)) |
| 42 | + sig = f"buffer, {dep_params}" if dep_params else "buffer" |
| 43 | + |
| 44 | + func_src = f""" |
| 45 | +def write_join({sig}): |
| 46 | + return buffer |
| 47 | +""" |
| 48 | + fn = compile_numba_function_src(func_src, "write_join") |
| 49 | + return numba_basic.numba_njit(fn) |
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