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# .gdbinit inside the project folder
# Enable auto-loading of Python scripts in the project directory
add-auto-load-safe-path .
# Add the current directory to the Python sys.path
python
import sys
sys.path.insert(0, ".") # Add current directory to sys.path if not already
end
# Register type summaries for Tensor using the pretty_print function from tensor.py
python
import gdb
import re
from tools.pretty_print_lldb.parse_tensor import parse_string
import struct
import sys, os
render_outer_limit = os.environ["RL_TOOLS_LLDB_RENDER_OUTER_LIMIT"] if "RL_TOOLS_LLDB_RENDER_OUTER_LIMIT" in os.environ else 20
print(f"RLtools Tensor renderer outer limit: {render_outer_limit}")
render_inner_limit = os.environ["RL_TOOLS_LLDB_RENDER_INNER_LIMIT"] if "RL_TOOLS_LLDB_RENDER_INNER_LIMIT" in os.environ else 500
print(f"RLtools Tensor renderer inner limit: {render_inner_limit}")
def pad_number(number, length):
return str(number).rjust(length)
def get_val(target, offset, float_type):
memory = target.read_memory(offset, float_type.sizeof)
if float_type.code == gdb.TYPE_CODE_FLT:
if float_type.sizeof == 4:
val = struct.unpack('f', memory)[0]
elif float_type.sizeof == 8:
val = struct.unpack('d', memory)[0]
else:
val = f"Unsupported float size: {float_type.sizeof}"
elif float_type.code == gdb.TYPE_CODE_BOOL:
val = struct.unpack('?', memory)[0]
else:
val = f"Unsupported type: {str(float_type)}"
return val
def render_tensor(target, float_type, ptr, shape, stride, title="", use_title=False, outer_limit=render_outer_limit, inner_limit=render_inner_limit, base_offset=0):
if len(shape) == 1:
output = "["
for element_i in range(shape[0]):
pos = element_i * stride[0]
offset = int(ptr) + base_offset + pos * float_type.sizeof
print(f"pointer: {int(ptr)}, base_offset: {base_offset}, pos: {pos} offset: {offset} type size: {float_type.sizeof}")
val = get_val(target, offset, float_type)
output += str(val) + ", "
if shape[0] > 0:
output = output[:-2]
return output + "]"
elif len(shape) == 2:
output = "[ " + (("\\\\ Subtensor: " + title + f"({shape[0]}x{shape[1]})") if use_title and len(title) > 0 else "") + "\n"
for row_i in range(shape[0]) if shape[0] < inner_limit else list(range(inner_limit // 2)) + ["..."] + list(range(shape[0] - inner_limit // 2, shape[0])):
if row_i == "...":
output += "...\n"
continue
output += "[" if shape[0] < inner_limit else f"{row_i}: ["
for col_i in range(shape[1]) if shape[1] < inner_limit else list(range(inner_limit // 2)) + ["..."] + list(range(shape[1] - inner_limit // 2, shape[1])):
if col_i == "...":
output += "..., "
continue
pos = row_i * stride[0] + col_i * stride[1]
offset = int(ptr) + base_offset + pos * float_type.sizeof
val = get_val(target, offset, float_type)
output += str(val) + ", "
if shape[1] > 0:
output = output[:-2]
output += "], \n"
if shape[0] > 0:
output = output[:-3]
output += "\n"
return output + "]\n"
else:
output = "[" + ("\n" if len(shape) == 3 else "")
for i in range(shape[0]) if shape[0] < outer_limit else list(range(outer_limit // 2)) + ["..."] + list(range(shape[0] - outer_limit // 2, shape[0])):
if i != "...":
current_title = title + pad_number(i, 10) + " | "
new_base_offset = base_offset + i * stride[0] * float_type.sizeof
output += render_tensor(target, float_type, ptr, shape[1:], stride[1:], title=current_title, use_title=use_title, base_offset=new_base_offset) + ", \n"
else:
output += "...\n"
output += "...\n"
output += "...\n"
if shape[0] > 0:
output = output[:-3]
return output + "]"
class TensorPrinter:
"""Pretty-printer for rl_tools::Tensor types."""
def __init__(self, val):
self.val = val
def to_string(self):
float_ptr = self.val["_data"]
float_type = float_ptr.type.target()
target = gdb.selected_inferior()
typename = str(gdb.types.get_basic_type(self.val.type))
print(f"float_type is: {float_type}")
print(f"Typename is: {typename}")
tensor = parse_string(str(float_ptr), typename)
if tensor is None:
print(f"Parse error on: {typename}")
return typename
else:
return str(tensor) + "\n" + render_tensor(target, float_type, float_ptr, tensor.shape, tensor.stride)
import gdb
import re
import struct
import json
def decode_row_major(typename):
regex = r"^\s*(?:const|\s*)\s*rl_tools\s*::\s*Matrix\s*<\s*rl_tools\s*::\s*matrix\s*::\s*Specification\s*<\s*([^,]+)\s*,\s*([^,]+)\s*,\s*([^,]+)\s*,\s*([^,]+)\s*,\s*([^,]+)\s*,\s*rl_tools\s*::\s*matrix\s*::\s*layouts\s*::\s*RowMajorAlignment\s*<\s*([^,]+)\s*,\s*([^,]+)\s*>\s*,\s*([^,]+)\s*>\s*>\s*(&|\s*)\s*$"
result = re.match(regex, typename)
if result is None:
return None
else:
meta = dict(zip(["T", "TI", "ROWS", "COLS", "DYNAMIC_ALLOCATION", "TI2", "ROW_MAJOR_ALIGNMENT", "CONST"], result.groups()))
meta["ROWS"] = int(meta["ROWS"])
meta["COLS"] = int(meta["COLS"])
meta["ROW_MAJOR_ALIGNMENT"] = int(meta["ROW_MAJOR_ALIGNMENT"])
ALIGNMENT = meta["ROW_MAJOR_ALIGNMENT"]
meta["ROW_PITCH"] = ((meta["COLS"] + ALIGNMENT - 1) // ALIGNMENT) * ALIGNMENT
return meta
def decode_fixed(typename):
regex = r"^\s*(?:const|\s*)\s*rl_tools\s*::\s*Matrix\s*<\s*rl_tools\s*::\s*matrix\s*::\s*Specification\s*<\s*([^,]+)\s*,\s*([^,]+)\s*,\s*([^,]+)\s*,\s*([^,]+)\s*,\s*([^,]+)\s*,\s*rl_tools\s*::\s*matrix\s*::\s*layouts\s*::\s*Fixed\s*<\s*([^,]+)\s*,\s*([^,]+)\s*,\s*([^,]+)\s*>\s*,\s*([^,]+)\s*>\s*>\s*(&|\s*)\s*$"
result = re.match(regex, typename)
if result is None:
return None
else:
meta = dict(zip(["T", "TI", "ROWS", "COLS", "DYNAMIC_ALLOCATION", "TI2", "ROW_PITCH", "COL_PITCH", "CONST"], result.groups()))
meta["ROWS"] = int(meta["ROWS"])
meta["COLS"] = int(meta["COLS"])
meta["ROW_PITCH"] = int(meta["ROW_PITCH"])
meta["COL_PITCH"] = int(meta["COL_PITCH"])
return meta
def render_matrix(target, float_type, ptr, meta, layout):
all_data = {"meta": meta, "data": []}
for row_i in range(meta["ROWS"]):
current_row = []
for col_i in range(meta["COLS"]):
if layout == "row_major":
pos = row_i * meta["ROW_PITCH"] + col_i
elif layout == "fixed":
pos = row_i * meta["ROW_PITCH"] + col_i * meta["COL_PITCH"]
else:
continue
offset = int(ptr) + pos * float_type.sizeof
val = get_val(target, offset, float_type)
current_row.append(val)
all_data["data"].append(current_row)
return json.dumps(all_data)
class MatrixPrinter:
"""Pretty-printer for rl_tools::Matrix types."""
def __init__(self, val):
self.val = val
def to_string(self):
typename = str(gdb.types.get_basic_type(self.val.type))
_data = self.val["_data"]
data_type_code = _data.type.code
if data_type_code == gdb.TYPE_CODE_PTR:
float_ptr = _data
elif data_type_code == gdb.TYPE_CODE_ARRAY:
float_ptr = _data[0].address
else:
raise ValueError(f"Unexpected _data type: {_data.type}")
float_type = float_ptr.type.target()
target = gdb.selected_inferior()
# Determine the matrix layout and decode metadata
if "RowMajorAlignment" in typename:
meta = decode_row_major(typename)
layout = "row_major"
elif "Fixed" in typename:
meta = decode_fixed(typename)
layout = "fixed"
else:
return f"Unsupported matrix type: {typename}"
# If metadata could not be parsed, return an error message
if meta is None:
print(f"Meta could not be parsed for: {typename}")
return f"Matrix type could not be parsed: {typename}"
# Render the matrix and return the result as a string
print(f"Rendering matrix with meta: {meta} type {float_type} ptr {float_ptr}")
return render_matrix(target, float_type, float_ptr, meta, layout)
# Register the Tensor pretty-printer for specific type patterns
def register_tensor_pretty_printer():
pp = gdb.printing.RegexpCollectionPrettyPrinter("tensor_pretty_printer")
pp.add_printer("Tensor", "^rl_tools::Tensor.*>$", TensorPrinter)
pp.add_printer("Tensor", "^rl_tools::Tensor.*>\\s&\\s$", TensorPrinter)
pp.add_printer("Tensor", "^const\\srl_tools::Tensor.*>$", TensorPrinter)
pp.add_printer("Tensor", "^const\\srl_tools::Tensor.*>\\s&\\s$", TensorPrinter)
pp.add_printer("Matrix", "^rl_tools::Matrix<rl_tools::matrix::Specification<.*RowMajorAlignment.*>$", MatrixPrinter)
pp.add_printer("Matrix", "^rl_tools::Matrix<rl_tools::matrix::Specification<.*Fixed.*>$", MatrixPrinter)
pp.add_printer("Matrix", "^const\\srl_tools::Matrix<rl_tools::matrix::Specification<.*RowMajorAlignment.*>$", MatrixPrinter)
pp.add_printer("Matrix", "^const\\srl_tools::Matrix<rl_tools::matrix::Specification<.*Fixed.*>$", MatrixPrinter)
gdb.printing.register_pretty_printer(gdb.current_objfile(), pp)
register_tensor_pretty_printer()