text stringlengths 1 93.6k |
|---|
try:
|
# Load from URL or cache if already cached
|
resolved_config_file = cached_path(
|
config_file,
|
cache_dir=cache_dir,
|
force_download=force_download,
|
proxies=proxies,
|
resume_download=resume_download,
|
local_files_only=local_files_only,
|
)
|
# Load config dict
|
if resolved_config_file is None:
|
raise EnvironmentError
|
config_file = Config.load_yaml(resolved_config_file)
|
except EnvironmentError:
|
msg = "Can't load config for"
|
raise EnvironmentError(msg)
|
if resolved_config_file == config_file:
|
print("loading configuration file from path")
|
else:
|
print("loading configuration file cache")
|
return Config.load_yaml(resolved_config_file), kwargs
|
# quick compare tensors
|
def compare(in_tensor):
|
out_tensor = torch.load("dump.pt", map_location=in_tensor.device)
|
n1 = in_tensor.numpy()
|
n2 = out_tensor.numpy()[0]
|
print(n1.shape, n1[0, 0, :5])
|
print(n2.shape, n2[0, 0, :5])
|
assert np.allclose(
|
n1, n2, rtol=0.01, atol=0.1
|
), f"{sum([1 for x in np.isclose(n1, n2, rtol=0.01, atol=0.1).flatten() if x == False])/len(n1.flatten())*100:.4f} % element-wise mismatch"
|
raise Exception("tensors are all good")
|
# Hugging face functiions below
|
def is_remote_url(url_or_filename):
|
parsed = urlparse(url_or_filename)
|
return parsed.scheme in ("http", "https")
|
def hf_bucket_url(model_id: str, filename: str, use_cdn=True) -> str:
|
endpoint = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
|
legacy_format = "/" not in model_id
|
if legacy_format:
|
return f"{endpoint}/{model_id}-{filename}"
|
else:
|
return f"{endpoint}/{model_id}/{filename}"
|
def http_get(
|
url,
|
temp_file,
|
proxies=None,
|
resume_size=0,
|
user_agent=None,
|
):
|
ua = "python/{}".format(sys.version.split()[0])
|
if _torch_available:
|
ua += "; torch/{}".format(torch.__version__)
|
if isinstance(user_agent, dict):
|
ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items())
|
elif isinstance(user_agent, str):
|
ua += "; " + user_agent
|
headers = {"user-agent": ua}
|
if resume_size > 0:
|
headers["Range"] = "bytes=%d-" % (resume_size,)
|
response = requests.get(url, stream=True, proxies=proxies, headers=headers)
|
if response.status_code == 416: # Range not satisfiable
|
return
|
content_length = response.headers.get("Content-Length")
|
total = resume_size + int(content_length) if content_length is not None else None
|
progress = tqdm(
|
unit="B",
|
unit_scale=True,
|
total=total,
|
initial=resume_size,
|
desc="Downloading",
|
)
|
for chunk in response.iter_content(chunk_size=1024):
|
if chunk: # filter out keep-alive new chunks
|
progress.update(len(chunk))
|
temp_file.write(chunk)
|
progress.close()
|
def get_from_cache(
|
url,
|
cache_dir=None,
|
force_download=False,
|
proxies=None,
|
etag_timeout=10,
|
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