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