ColossalAI/colossalai/testing/comparison.py

136 lines
5.5 KiB
Python

from typing import Any, List, OrderedDict
import torch
import torch.distributed as dist
from torch import Tensor
from torch.distributed import ProcessGroup
from torch.testing import assert_close
from torch.utils._pytree import tree_flatten
def assert_equal(a: Tensor, b: Tensor):
assert torch.all(a == b), f'expected a and b to be equal but they are not, {a} vs {b}'
def assert_not_equal(a: Tensor, b: Tensor):
assert not torch.all(a == b), f'expected a and b to be not equal but they are, {a} vs {b}'
def assert_close_loose(a: Tensor, b: Tensor, rtol: float = 1e-3, atol: float = 1e-3):
assert_close(a,
b,
rtol=rtol,
atol=atol,
msg=f"Tensor not close, shape: {a.shape} vs {b.shape}, \
dtype: {a.dtype} vs {b.dtype}")
def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None):
# all gather tensors from different ranks
world_size = dist.get_world_size(process_group)
tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
dist.all_gather(tensor_list, tensor, group=process_group)
# check if they are equal one by one
for i in range(world_size - 1):
a = tensor_list[i]
b = tensor_list[i + 1]
assert torch.all(a == b), f'expected tensors on rank {i} and {i + 1} to be equal but they are not, {a} vs {b}'
def check_state_dict_equal(d1: OrderedDict, d2: OrderedDict, ignore_device: bool = True):
assert len(list(d1.keys())) == len(list(d2.keys())), \
f"Number of keys unequal: {len(list(d1.keys()))} vs {len(list(d2.keys()))}"
for k, v1 in d1.items():
assert k in d2
v2 = d2[k]
if isinstance(v1, dict):
assert isinstance(v2, dict)
check_state_dict_equal(v1, v2, ignore_device)
elif isinstance(v1, list):
assert isinstance(v2, list)
for v1_i, v2_i in zip(v1, v2):
if isinstance(v1_i, torch.Tensor):
assert isinstance(v2_i, torch.Tensor)
if not ignore_device:
v1_i = v1_i.to("cpu")
v2_i = v2_i.to("cpu")
assert_close_loose(v1_i, v2_i)
elif isinstance(v1_i, dict):
assert isinstance(v2_i, dict)
check_state_dict_equal(v1_i, v2_i, ignore_device)
else:
assert v1_i == v2_i, f"{v1_i} not equals to {v2_i}"
elif isinstance(v1, torch.Tensor):
assert isinstance(v2, torch.Tensor)
if not ignore_device:
v1 = v1.to("cpu")
v2 = v2.to("cpu")
assert_close_loose(v1, v2)
else:
assert v1 == v2, f"{v1} not equals to {v2}"
def check_state_dict_equal_pytree(d1: OrderedDict, d2: OrderedDict, ignore_device: bool = True):
flat_d1, _ = tree_flatten(d1)
flat_d2, _ = tree_flatten(d2)
assert len(flat_d1) == len(flat_d2)
for v1, v2 in zip(flat_d1, flat_d2):
if isinstance(v1, torch.Tensor):
assert isinstance(v2, torch.Tensor)
if not ignore_device:
v1 = v1.to("cpu")
v2 = v2.to("cpu")
assert_close_loose(v1, v2)
else:
assert v1 == v2, f"{v1} not equals to {v2}"
def assert_hf_output_close(out1: Any,
out2: Any,
ignore_keys: List[str] = None,
track_name: str = "",
atol=1e-5,
rtol=1e-5):
"""
Check if two outputs from huggingface are equal.
Args:
out1 (Any): the first output
out2 (Any): the second output
ignore_keys (List[str]): the keys to ignore when comparing two dicts
track_name (str): the name of the value compared, used to track the path
"""
if isinstance(out1, dict) and isinstance(out2, dict):
# if two values are dict
# we recursively check the keys
assert set(out1.keys()) == set(out2.keys())
for k in out1.keys():
if ignore_keys is not None and k in ignore_keys:
continue
assert_hf_output_close(out1[k],
out2[k],
track_name=f"{track_name}.{k}",
ignore_keys=ignore_keys,
atol=atol,
rtol=rtol)
elif isinstance(out1, (list, tuple)) and isinstance(out2, (list, tuple)):
# if two values are list
# we recursively check the elements
assert len(out1) == len(out2)
for i in range(len(out1)):
assert_hf_output_close(out1[i],
out2[i],
track_name=f"{track_name}.{i}",
ignore_keys=ignore_keys,
atol=atol,
rtol=rtol)
elif isinstance(out1, Tensor) and isinstance(out2, Tensor):
if out1.shape != out2.shape:
raise AssertionError(f"{track_name}: shape mismatch: {out1.shape} vs {out2.shape}")
assert torch.allclose(
out1, out2, atol=atol, rtol=rtol
), f"{track_name}: tensor value mismatch\nvalue 1: {out1}\nvalue 2: {out2}, \nmean error: {torch.abs(out1 - out2).mean()}"
else:
assert out1 == out2, f"{track_name}: value mismatch.\nout1: {out1}\nout2: {out2}"