code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def test_verify_signal_handler(self, SignalMock, ThreadingMock):
"""
Verify the internal implementation of the Signal Handler
"""
is_debugging = True # We are debugging. So setup signal
SignalMock.SIGTERM = "sigterm"
# Fake the real method with a Lambda. Also run the ha... |
Verify the internal implementation of the Signal Handler
| test_verify_signal_handler | python | aws/aws-sam-cli | tests/unit/local/lambdafn/test_runtime.py | https://github.com/aws/aws-sam-cli/blob/master/tests/unit/local/lambdafn/test_runtime.py | Apache-2.0 |
def test_must_return_a_valid_file(self, unzip_file_mock, shutil_mock, os_mock):
"""
Input is a file that exists, but is not a zip/jar file
"""
code_path = "foo.exe"
os_mock.path.isfile.return_value = True
result = self.runtime._get_code_dir(code_path)
# code pat... |
Input is a file that exists, but is not a zip/jar file
| test_must_return_a_valid_file | python | aws/aws-sam-cli | tests/unit/local/lambdafn/test_runtime.py | https://github.com/aws/aws-sam-cli/blob/master/tests/unit/local/lambdafn/test_runtime.py | Apache-2.0 |
def test_download_layer_that_was_template_defined(self, create_cache_patch, resolve_code_path_patch):
"""
when the template is not lcoated in working directory, layer's codeuri needs to be adjusted
"""
stack_path_mock = Mock()
stack_template_location = "./some/path/template.yaml"... |
when the template is not lcoated in working directory, layer's codeuri needs to be adjusted
| test_download_layer_that_was_template_defined | python | aws/aws-sam-cli | tests/unit/local/layers/test_download_layers.py | https://github.com/aws/aws-sam-cli/blob/master/tests/unit/local/layers/test_download_layers.py | Apache-2.0 |
def patched_get_checkpoint_shard_files(
pretrained_model_name_or_path, index_filename, *args, **kwargs
) -> Tuple[List[str], dict]:
"""Same as modeling_utils.get_checkpoint_shard_files(), but does not download shards for the ignored keys."""
should_ignore_keys = _ignored_keys.get() is not None
tempdir_... | Same as modeling_utils.get_checkpoint_shard_files(), but does not download shards for the ignored keys. | patched_get_checkpoint_shard_files | python | bigscience-workshop/petals | src/petals/client/from_pretrained.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/from_pretrained.py | MIT |
async def create(
cls,
config: ClientConfig,
p2p: P2P,
span: RemoteSpanInfo,
uid: ModuleUID,
rpc_info: RPCInfo,
**metadata,
) -> _ServerInferenceSession:
"""Create a new session for a given remote module. This code is meant to be run inside RemoteExper... | Create a new session for a given remote module. This code is meant to be run inside RemoteExpertWorker | create | python | bigscience-workshop/petals | src/petals/client/inference_session.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/inference_session.py | MIT |
def step(
self,
inputs: torch.Tensor,
prompts: torch.Tensor,
hypo_ids: torch.LongTensor,
*,
step_id: str,
) -> torch.Tensor:
"""
Inference step: send a chunk of input tensors and receive a chunk of outputs
:prompts: optional DEEP prompts, added... |
Inference step: send a chunk of input tensors and receive a chunk of outputs
:prompts: optional DEEP prompts, added to a prefix of each layer's outputs,
if specified, deep prompts should have shape [num_layers, batch_size, prefix_len, hid_size]
| step | python | bigscience-workshop/petals | src/petals/client/inference_session.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/inference_session.py | MIT |
async def _step(self, inputs_serialized: runtime_pb2.ExpertRequest) -> runtime_pb2.ExpertResponse:
"""Inference step on serialized data. This code is meant to be run inside RemoteExpertWorker"""
await self._inputs_queue.put(inputs_serialized)
self.stepped = True
return await asyncio.wait... | Inference step on serialized data. This code is meant to be run inside RemoteExpertWorker | _step | python | bigscience-workshop/petals | src/petals/client/inference_session.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/inference_session.py | MIT |
def close(self):
"""Finish a given inference session, close the underlying connection"""
if self._outputs_stream is None:
return # already closed
RemoteExpertWorker.run_coroutine(self._aclose_stream())
self._outputs_stream = self._inputs_queue = None
self.closed = Tr... | Finish a given inference session, close the underlying connection | close | python | bigscience-workshop/petals | src/petals/client/inference_session.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/inference_session.py | MIT |
async def _aclose_stream(self):
"""Close the inference session. This code is meant to be run inside RemoteExpertWorker"""
if self._outputs_stream is None:
return # already closed
if self.stepped:
await self._inputs_queue.put(runtime_pb2.ExpertRequest()) # empty request ... | Close the inference session. This code is meant to be run inside RemoteExpertWorker | _aclose_stream | python | bigscience-workshop/petals | src/petals/client/inference_session.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/inference_session.py | MIT |
def chunked_forward(self, hidden_states):
"""Splits word embeddings on chunks and iteratively casts them into fp32 to perform matmul more efficiently on CPU.
chunked_forward_step: provides trade-off between efficiency and extra memory consumption.
"""
assert self.chunked_forward_step > 0... | Splits word embeddings on chunks and iteratively casts them into fp32 to perform matmul more efficiently on CPU.
chunked_forward_step: provides trade-off between efficiency and extra memory consumption.
| chunked_forward | python | bigscience-workshop/petals | src/petals/client/lm_head.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/lm_head.py | MIT |
def force_non_empty_weights():
"""
This context manager allows to bypass the accelerate.init_empty_weights() context manager
(that forces all nn.Parameters to be PyTorch's meta tensors) used when low_cpu_mem_usage=True.
The transformers library should replace all meta tensors by empty tensors by itself
... |
This context manager allows to bypass the accelerate.init_empty_weights() context manager
(that forces all nn.Parameters to be PyTorch's meta tensors) used when low_cpu_mem_usage=True.
The transformers library should replace all meta tensors by empty tensors by itself
but this feature does not work due... | force_non_empty_weights | python | bigscience-workshop/petals | src/petals/client/ptune.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/ptune.py | MIT |
async def run_remote_forward(
uid: ModuleUID,
stub: StubBase,
rpc_info: RPCInfo,
*inputs: torch.Tensor,
config: ClientConfig,
metadata: Optional[bytes] = None,
**kwargs,
) -> Tuple[torch.Tensor, ...]:
"""
Serializes input tensors and calls "rpc_forward" on a remote server.
Mostly... |
Serializes input tensors and calls "rpc_forward" on a remote server.
Mostly adapted from https://github.com/learning-at-home/hivemind/blob/7a7c93aefffc9494c39e7b170c07cb06d8c09c4c/hivemind/moe/client/expert.py#L198
but without RemoteExpertWorker.run_coroutine() call that leads to deadlock here.
| run_remote_forward | python | bigscience-workshop/petals | src/petals/client/remote_forward_backward.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/remote_forward_backward.py | MIT |
async def run_remote_backward(
uid: ModuleUID,
stub: StubBase,
rpc_info: RPCInfo,
*inputs_and_grad_outputs: torch.Tensor,
config: ClientConfig,
metadata: Optional[bytes] = None,
**kwargs,
) -> Sequence[torch.Tensor]:
"""
Serializes grad outputs and calls "rpc_backward" on a remote se... |
Serializes grad outputs and calls "rpc_backward" on a remote server.
Mostly adapted from https://github.com/learning-at-home/hivemind/blob/7a7c93aefffc9494c39e7b170c07cb06d8c09c4c/hivemind/moe/client/expert.py#L221
but without RemoteExpertWorker.run_coroutine() call that leads to deadlock here.
| run_remote_backward | python | bigscience-workshop/petals | src/petals/client/remote_forward_backward.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/remote_forward_backward.py | MIT |
def use_session(self, session: Optional[InferenceSession]) -> InferenceSession:
"""Inside this context, forward() will use an _existing_ InferenceSession provided as the argument."""
token = self._active_session.set(session)
try:
yield session
finally:
self._acti... | Inside this context, forward() will use an _existing_ InferenceSession provided as the argument. | use_session | python | bigscience-workshop/petals | src/petals/client/remote_sequential.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/remote_sequential.py | MIT |
async def sequential_forward(
inputs: torch.Tensor,
prompts: torch.Tensor,
sequence_manager: RemoteSequenceManager,
start_index: int = 0,
end_index: Optional[int] = None,
) -> Tuple[torch.Tensor, Sequence[torch.Tensor], Sequence[RemoteSpanInfo]]:
"""
Constructs a routing path from <start_ind... |
Constructs a routing path from <start_index> to <end_index>.
Performs chained forward for each subsequence of blocks on the path.
If some subsequence fails, reconstructs the remaining path and tries to finish the forward.
| sequential_forward | python | bigscience-workshop/petals | src/petals/client/sequential_autograd.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/sequential_autograd.py | MIT |
async def sequential_backward(
grad_outputs: Sequence[torch.Tensor],
intermediate_inputs: List[torch.Tensor],
prompts: torch.Tensor,
forward_sequences: List[RemoteSpanInfo],
sequence_manager: RemoteSequenceManager,
) -> Tuple[Sequence[torch.Tensor], torch.Tensor]:
"""
Performs chained backwa... |
Performs chained backward for each forward subsequence.
If some subsequence fails, reconstructs the particular sub-path and recovers the backward.
| sequential_backward | python | bigscience-workshop/petals | src/petals/client/sequential_autograd.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/sequential_autograd.py | MIT |
async def _gather_forward(input_batches, prompt_batches, sequence_manager):
"""Wrapper for asyncio.gather to perform parallel sequential forwards"""
return await asyncio.gather(
*[
sequential_forward(input_batch, prompt_batch, sequence_manager)
for input_batch, prompt_batch in zi... | Wrapper for asyncio.gather to perform parallel sequential forwards | _gather_forward | python | bigscience-workshop/petals | src/petals/client/sequential_autograd.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/sequential_autograd.py | MIT |
async def _gather_backward(
grad_output_batches, intermediate_input_batches, prompt_batches, forward_sequences, sequence_manager
):
"""Wrapper for asyncio.gather to perform parallel sequential backwards"""
return await asyncio.gather(
*[
sequential_backward((grad_output,), input_batch, p... | Wrapper for asyncio.gather to perform parallel sequential backwards | _gather_backward | python | bigscience-workshop/petals | src/petals/client/sequential_autograd.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/sequential_autograd.py | MIT |
def __getitem__(self, ix: Union[int, slice]) -> RemoteSequenceManager:
"""Get a RemoteSequenceManager for a sub-sequence of blocks"""
assert isinstance(ix, (int, slice))
if not isinstance(ix, slice):
ix = slice(int(ix), int(ix) + 1, 1)
return type(self)(self.config, self.bloc... | Get a RemoteSequenceManager for a sub-sequence of blocks | __getitem__ | python | bigscience-workshop/petals | src/petals/client/routing/sequence_manager.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/routing/sequence_manager.py | MIT |
def update(self, *, wait: bool):
"""Run an asynchronous update in background as soon as possible"""
self.ready.clear()
self._thread.trigger.set()
if wait:
self.ready.wait() | Run an asynchronous update in background as soon as possible | update | python | bigscience-workshop/petals | src/petals/client/routing/sequence_manager.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/routing/sequence_manager.py | MIT |
def _update(self):
"""Perform an immediate and synchronous refresh, may take time"""
new_block_infos = get_remote_module_infos(
self.dht, self.block_uids, active_adapter=self.config.active_adapter, latest=True
)
for block_info in new_block_infos:
# Apply allow a... | Perform an immediate and synchronous refresh, may take time | _update | python | bigscience-workshop/petals | src/petals/client/routing/sequence_manager.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/routing/sequence_manager.py | MIT |
def on_request_failure(self, peer_id: Optional[PeerID]):
"""remove a given peer from the routing table. If the routing is no longer possible, trigger an update"""
if peer_id is not None:
logger.debug(f"Peer {peer_id} did not respond, banning it temporarily")
self.state.banned_pee... | remove a given peer from the routing table. If the routing is no longer possible, trigger an update | on_request_failure | python | bigscience-workshop/petals | src/petals/client/routing/sequence_manager.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/routing/sequence_manager.py | MIT |
def rpc_info(self):
"""Return the rpc_info queried from one of the servers that hold the first block"""
if self.state.rpc_info is not None:
return self.state.rpc_info
with self._thread_start_lock:
if not self.is_alive():
self._thread.start()
for ... | Return the rpc_info queried from one of the servers that hold the first block | rpc_info | python | bigscience-workshop/petals | src/petals/client/routing/sequence_manager.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/routing/sequence_manager.py | MIT |
def get_request_metadata(
self, protocol: str, args_structure: Any = None, *args, **kwargs
) -> Optional[Dict[str, Any]]:
"""
:param protocol: one of "rpc_forward", "rpc_backward" or "rpc_inference"
:param args_structure: the structure of flattened tensors from pack_args_kwargs in pe... |
:param protocol: one of "rpc_forward", "rpc_backward" or "rpc_inference"
:param args_structure: the structure of flattened tensors from pack_args_kwargs in petals.utils.packaging
:param args: request-specific inputs, typically block uids and input tensors
:param kwargs: additional reque... | get_request_metadata | python | bigscience-workshop/petals | src/petals/client/routing/sequence_manager.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/client/routing/sequence_manager.py | MIT |
def get_inference_cache_descriptors(self, batch_size: int, max_length: int) -> Sequence[TensorDescriptor]:
"""Create tensor descriptors for attention cache tensors used during inference_step"""
head_dim = self.config.hidden_size // self.config.num_attention_heads
cache_tensors = []
for d... | Create tensor descriptors for attention cache tensors used during inference_step | get_inference_cache_descriptors | python | bigscience-workshop/petals | src/petals/server/backend.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/backend.py | MIT |
def _reorder_cache_inplace(self, cache_tensors: torch.Tensor, hypo_ids: torch.Tensor):
"""If hypo_ids is specified, reorder elements of each cache tensor in-place by taking indices from hypo_ids"""
if not is_dummy(hypo_ids):
for cache_tensor in cache_tensors:
cache_tensor[...... | If hypo_ids is specified, reorder elements of each cache tensor in-place by taking indices from hypo_ids | _reorder_cache_inplace | python | bigscience-workshop/petals | src/petals/server/backend.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/backend.py | MIT |
def _select_layer_past(self, cache_tensors: Sequence[torch.Tensor], prefix_length: int) -> Sequence[torch.Tensor]:
"""Extract first {prefix_length} tokens and reshape them such that they can be used as layer_past"""
key_cache, value_cache = list(cache_tensors[0::2]), list(cache_tensors[1::2])
fo... | Extract first {prefix_length} tokens and reshape them such that they can be used as layer_past | _select_layer_past | python | bigscience-workshop/petals | src/petals/server/backend.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/backend.py | MIT |
def _update_cache_inplace(
self, cache_tensors: Sequence[torch.Tensor], new_kvs: Sequence[torch.Tensor], prefix_length: int
):
"""Writes new key/value tensors back into cache, works in-place"""
_batch_size_times_num_kv_heads, head_dim, new_length = new_kvs[0].shape
for cache_key, new... | Writes new key/value tensors back into cache, works in-place | _update_cache_inplace | python | bigscience-workshop/petals | src/petals/server/backend.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/backend.py | MIT |
def merge_inference_pools_inplace(backends: Dict[ExpertUID, TransformerBackend]):
"""Replace each backend's rpc_inference pools with a combined pool runs multiple blocks in one call"""
assert len(backends) != 0 and all(isinstance(b, TransformerBackend) for b in backends.values())
first_pool = next(iter(back... | Replace each backend's rpc_inference pools with a combined pool runs multiple blocks in one call | merge_inference_pools_inplace | python | bigscience-workshop/petals | src/petals/server/backend.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/backend.py | MIT |
async def run_rpc_forward(
*flat_tensors: torch.Tensor,
requested_backends: Sequence[TransformerBackend],
active_adapter: str = "",
prioritizer: TaskPrioritizerBase,
points: int = 0,
args_structure: Any = None,
) -> torch.Tensor:
"""
Run forward pass on deserialized inputs and prompts, u... |
Run forward pass on deserialized inputs and prompts, used by rpc_forward and rpc_forward_stream
:param flat_tensors: a list of tensors that includes first layer inputs, optional prompts and extra tensors
:note: some input tensors can be missing, in which case they will be replaced with dummy tensors (see ... | run_rpc_forward | python | bigscience-workshop/petals | src/petals/server/block_functions.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/block_functions.py | MIT |
def resolve_block_dtype(config: PretrainedConfig, dtype: Union[str, torch.dtype]) -> torch.dtype:
"""If dtype is "auto", resolves it using BloomConfig. Returns `dtype` intact otherwise."""
if dtype not in ("auto", None):
return dtype
if config.torch_dtype not in ("auto", None, torch.float32):
... | If dtype is "auto", resolves it using BloomConfig. Returns `dtype` intact otherwise. | resolve_block_dtype | python | bigscience-workshop/petals | src/petals/server/block_utils.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/block_utils.py | MIT |
def get_model_block(config, layer_idx: int = 0):
"""
The function to create a model block based on the block class
kwargs argument **only** is necessary for specific classes, like Mixtral.
They will not be passed to other block constructors.
"""
if config.block_class == WrappedMixtralBlock:
... |
The function to create a model block based on the block class
kwargs argument **only** is necessary for specific classes, like Mixtral.
They will not be passed to other block constructors.
| get_model_block | python | bigscience-workshop/petals | src/petals/server/block_utils.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/block_utils.py | MIT |
async def rpc_inference(
self,
requests: AsyncIterator[runtime_pb2.ExpertRequest],
context: P2PContext,
) -> AsyncIterator[runtime_pb2.ExpertResponse]:
"""Compute a single step of inference using attention cache; update attention cache accordingly."""
async with timeout(self.... | Compute a single step of inference using attention cache; update attention cache accordingly. | rpc_inference | python | bigscience-workshop/petals | src/petals/server/handler.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/handler.py | MIT |
async def rpc_push(self, request: runtime_pb2.ExpertRequest, context: P2PContext) -> runtime_pb2.ExpertResponse:
"""Directly push activation tensors from one server to another"""
requested_uids = self._check_uids(request.uid)
metadata = MSGPackSerializer.loads(request.metadata)
session_... | Directly push activation tensors from one server to another | rpc_push | python | bigscience-workshop/petals | src/petals/server/handler.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/handler.py | MIT |
def _serialize_outputs(
self,
hidden_states: torch.Tensor,
requested_backends: Sequence[TransformerBackend],
metadata: Dict[str, Any],
) -> Sequence[runtime_pb2.Tensor]:
"""Serialize forward outputs using either outputs_schema or custom user-specified schema"""
assert... | Serialize forward outputs using either outputs_schema or custom user-specified schema | _serialize_outputs | python | bigscience-workshop/petals | src/petals/server/handler.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/handler.py | MIT |
def _serialize_grads(
self,
grads: Sequence[torch.Tensor],
requested_backends: Sequence[TransformerBackend],
metadata: Dict[str, Any],
) -> Sequence[runtime_pb2.Tensor]:
"""Serialize backward gradients w.r.t. inputs using either default schema or custom user-specified schema"... | Serialize backward gradients w.r.t. inputs using either default schema or custom user-specified schema | _serialize_grads | python | bigscience-workshop/petals | src/petals/server/handler.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/handler.py | MIT |
def _check_uids(self, uids: str) -> Tuple[ModuleUID, ...]:
"""Check that the first request to rpc_inference is valid"""
uids = (uids or "").split(CHAIN_DELIMITER)
if not uids:
raise RuntimeError("User did not provide any uids")
for uid in uids:
if uid not in self.... | Check that the first request to rpc_inference is valid | _check_uids | python | bigscience-workshop/petals | src/petals/server/handler.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/handler.py | MIT |
async def _allocate_cache(
self,
backends: Sequence[TransformerBackend],
*,
batch_size: int,
max_length: int,
timeout: Optional[float],
) -> Sequence[Sequence[Handle]]:
"""
Allocate memory cache for all transformer blocks, return cache handle
:... |
Allocate memory cache for all transformer blocks, return cache handle
:returns: a list of {len(backends)} elements, where i-th element is a tuple of cache handles for i-th backend
| _allocate_cache | python | bigscience-workshop/petals | src/petals/server/handler.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/handler.py | MIT |
async def rpc_info(self, request: runtime_pb2.ExpertUID, context: P2PContext) -> runtime_pb2.ExpertInfo:
"""Return metadata about stored block uids and current load"""
backend = self.module_backends[request.uid] if request.uid else next(iter(self.module_backends.values()))
result = {
... | Return metadata about stored block uids and current load | rpc_info | python | bigscience-workshop/petals | src/petals/server/handler.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/handler.py | MIT |
def get_allocation_size(*descriptors: TensorDescriptor) -> int:
"""Return the memory size (bytes) to be allocated on a device. If there are many devices, return maximum"""
alloc_size_by_device = {}
for descr in descriptors:
tensor_size = descr.numel() * get_size_in_bytes(descr.dtype)... | Return the memory size (bytes) to be allocated on a device. If there are many devices, return maximum | get_allocation_size | python | bigscience-workshop/petals | src/petals/server/memory_cache.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/memory_cache.py | MIT |
async def _schedule_alloc(
self, alloc_size: int, *descriptors: TensorDescriptor, timeout: Optional[float]
) -> Sequence[Handle]:
"""
This method should be called inside asyncio.shield() because:
- hivemind.utils.enter_asynchronously() does not always release the lock on cancella... |
This method should be called inside asyncio.shield() because:
- hivemind.utils.enter_asynchronously() does not always release the lock on cancellation
| _schedule_alloc | python | bigscience-workshop/petals | src/petals/server/memory_cache.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/memory_cache.py | MIT |
def use_cache(self, *handles: Handle) -> Sequence[torch.Tensor]:
"""
Return one or more tensors previously allocated with allocate_cache,
:note: This method is called by ModuleBackend in runtime: a single process with NO process parallelism.
However, runtime may call use_cache concurren... |
Return one or more tensors previously allocated with allocate_cache,
:note: This method is called by ModuleBackend in runtime: a single process with NO process parallelism.
However, runtime may call use_cache concurrently with one or more connection handlers calling allocate_cache
| use_cache | python | bigscience-workshop/petals | src/petals/server/memory_cache.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/memory_cache.py | MIT |
def validate_reachability(peer_id, wait_time: float = 7 * 60, retry_delay: float = 15) -> None:
"""verify that your peer is reachable from a (centralized) validator, whether directly or through a relay"""
for attempt_no in range(math.floor(wait_time / retry_delay) + 1):
try:
r = requests.get... | verify that your peer is reachable from a (centralized) validator, whether directly or through a relay | validate_reachability | python | bigscience-workshop/petals | src/petals/server/reachability.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/reachability.py | MIT |
def check_direct_reachability(max_peers: int = 5, threshold: float = 0.5, **kwargs) -> Optional[bool]:
"""test if your peer is accessible by others in the swarm with the specified network options in **kwargs"""
async def _check_direct_reachability():
target_dht = await DHTNode.create(client_mode=True, ... | test if your peer is accessible by others in the swarm with the specified network options in **kwargs | check_direct_reachability | python | bigscience-workshop/petals | src/petals/server/reachability.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/reachability.py | MIT |
async def call_check(self, remote_peer: PeerID, *, check_peer: PeerID) -> Optional[bool]:
"""Returns True if remote_peer can reach check_peer, False if it cannot, None if it did not respond"""
try:
request = dht_pb2.PingRequest(peer=dht_pb2.NodeInfo(node_id=check_peer.to_bytes()))
... | Returns True if remote_peer can reach check_peer, False if it cannot, None if it did not respond | call_check | python | bigscience-workshop/petals | src/petals/server/reachability.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/reachability.py | MIT |
async def rpc_check(self, request: dht_pb2.PingRequest, context: P2PContext) -> dht_pb2.PingResponse:
"""Help another peer to check its reachability"""
response = dht_pb2.PingResponse(available=True)
check_peer = PeerID(request.peer.node_id)
if check_peer != context.local_id: # remote p... | Help another peer to check its reachability | rpc_check | python | bigscience-workshop/petals | src/petals/server/reachability.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/reachability.py | MIT |
def __init__(
self,
*,
initial_peers: List[str],
dht_prefix: Optional[str],
converted_model_name_or_path: str,
public_name: Optional[str] = None,
throughput: Union[float, str],
num_blocks: Optional[int] = None,
block_indices: Optional[str] = None,
... | Create a server with one or more bloom blocks. See run_server.py for documentation. | __init__ | python | bigscience-workshop/petals | src/petals/server/server.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/server.py | MIT |
def run_in_background(self, await_ready=True, timeout=None):
"""
Starts ModuleContainer in a background thread. if await_ready, this method will wait until the container
is ready to process incoming requests or for :timeout: seconds max.
"""
self.start()
if await_ready an... |
Starts ModuleContainer in a background thread. if await_ready, this method will wait until the container
is ready to process incoming requests or for :timeout: seconds max.
| run_in_background | python | bigscience-workshop/petals | src/petals/server/server.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/server.py | MIT |
def shutdown(self):
"""
Gracefully terminate the container, process-safe.
Please note that terminating container otherwise (e.g. by killing processes) may result in zombie processes.
If you did already cause a zombie outbreak, your only option is to kill them with -9 (SIGKILL).
"... |
Gracefully terminate the container, process-safe.
Please note that terminating container otherwise (e.g. by killing processes) may result in zombie processes.
If you did already cause a zombie outbreak, your only option is to kill them with -9 (SIGKILL).
| shutdown | python | bigscience-workshop/petals | src/petals/server/server.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/server.py | MIT |
def run(self):
"""Read tasks from incoming queue and put them into a local priority queue"""
while True:
task = self.submitted_tasks.get()
if task is None:
logger.debug("Shutting down prioritizer thread")
break
self._ordered_tasks.put(... | Read tasks from incoming queue and put them into a local priority queue | run | python | bigscience-workshop/petals | src/petals/server/task_pool.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/task_pool.py | MIT |
def submit_task(self, *args: Any, priority: float = 0.0) -> MPFuture:
"""Add task to this pool's queue, return Future for its output"""
future = MPFuture()
# Remove shmem from MPFuture. This disables the .cancel() feature but
# saves the server from "could not unlink the shared memory fi... | Add task to this pool's queue, return Future for its output | submit_task | python | bigscience-workshop/petals | src/petals/server/task_pool.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/task_pool.py | MIT |
def get_task_size(self, task: Task) -> int:
"""compute task processing complexity; defaults to the total number of tokens"""
if task.args and task.args[0].ndim >= 2:
return task.args[0].shape[0] * task.args[0].shape[1]
return 1 | compute task processing complexity; defaults to the total number of tokens | get_task_size | python | bigscience-workshop/petals | src/petals/server/task_pool.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/task_pool.py | MIT |
def send_outputs_from_runtime(self, uid: int, batch_outputs: List[torch.Tensor]):
"""send results for a processed batch, previously loaded through load_batch_to_runtime"""
batch_outputs = [_move_to_device_if_tensor(output, device="cpu", share_memory=True) for output in batch_outputs]
task = self... | send results for a processed batch, previously loaded through load_batch_to_runtime | send_outputs_from_runtime | python | bigscience-workshop/petals | src/petals/server/task_pool.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/server/task_pool.py | MIT |
async def shield_and_wait(task):
"""
Works like asyncio.shield(), but waits for the task to finish before raising CancelledError to the caller.
"""
if not isinstance(task, asyncio.Task):
task = asyncio.create_task(task)
cancel_exc = None
while True:
try:
result = aw... |
Works like asyncio.shield(), but waits for the task to finish before raising CancelledError to the caller.
| shield_and_wait | python | bigscience-workshop/petals | src/petals/utils/asyncio.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/utils/asyncio.py | MIT |
def make_inference_graphed_callable(callable: callable, sample_args, num_warmup_iters=3):
"""Similar to torch.cuda.make_graphed_callables, but takes only one function and does not build a graph for the backward pass"""
assert not isinstance(callable, torch.nn.Module)
if torch.is_autocast_enabled() and torch... | Similar to torch.cuda.make_graphed_callables, but takes only one function and does not build a graph for the backward pass | make_inference_graphed_callable | python | bigscience-workshop/petals | src/petals/utils/cuda_graphs.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/utils/cuda_graphs.py | MIT |
def declare_active_modules(
dht: DHT,
uids: Sequence[ModuleUID],
server_info: ServerInfo,
expiration_time: DHTExpiration,
wait: bool = True,
) -> Union[Dict[ModuleUID, bool], MPFuture[Dict[ModuleUID, bool]]]:
"""
Declare that your node serves the specified modules; update timestamps if decla... |
Declare that your node serves the specified modules; update timestamps if declared previously
:param uids: a list of module ids to declare
:param wait: if True, awaits for declaration to finish, otherwise runs in background
:param throughput: specify your performance in terms of compute throughput
... | declare_active_modules | python | bigscience-workshop/petals | src/petals/utils/dht.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/utils/dht.py | MIT |
def initialize_logs():
"""Initialize Petals logging tweaks. This function is called when you import the `petals` module."""
# Env var PETALS_LOGGING=False prohibits Petals do anything with logs
if os.getenv("PETALS_LOGGING", "True").lower() in ("false", "0"):
return
hm_logging.use_hivemind_log... | Initialize Petals logging tweaks. This function is called when you import the `petals` module. | initialize_logs | python | bigscience-workshop/petals | src/petals/utils/logging.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/utils/logging.py | MIT |
def pack_args_kwargs(*args, **kwargs) -> Tuple[List[torch.Tensor], Any]:
"""
Check the function's arguments and pack all tensors into different flattened lists.
:returns: a flattened list of tensors and args and kwargs, where tensors were masked
"""
masked_flat_values, flat_tensors, tensor_to_index ... |
Check the function's arguments and pack all tensors into different flattened lists.
:returns: a flattened list of tensors and args and kwargs, where tensors were masked
| pack_args_kwargs | python | bigscience-workshop/petals | src/petals/utils/packaging.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/utils/packaging.py | MIT |
def unpack_args_kwargs(flat_tensors: List[torch.Tensor], args_structure: Any):
"""
Restore arguments after `pack_args_kwargs` function.
:returns: list of args and dict of kwargs
"""
return nested_pack(
(
value if not _is_masked_tensor(value) else flat_tensors[_get_tensor_index(va... |
Restore arguments after `pack_args_kwargs` function.
:returns: list of args and dict of kwargs
| unpack_args_kwargs | python | bigscience-workshop/petals | src/petals/utils/packaging.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/utils/packaging.py | MIT |
def estimate_adapter_memory_per_block(
block_config: transformers.PretrainedConfig,
torch_dtype: Optional[torch.dtype],
adapters: Sequence[str],
**load_peft_kwargs,
) -> int:
"""Get the number of extra bytes used to store a set of adapters per given block"""
with init_empty_weights(include_buffe... | Get the number of extra bytes used to store a set of adapters per given block | estimate_adapter_memory_per_block | python | bigscience-workshop/petals | src/petals/utils/peft.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/utils/peft.py | MIT |
def _sift(self, fileslist, **arguments):
"""
a filter for time, size, name, head, tail, include, exclude, shuffle
support regular expression
"""
# for shuffle
if 's' in args.type_:
random.shuffle(fileslist)
return fileslist
# for time
... |
a filter for time, size, name, head, tail, include, exclude, shuffle
support regular expression
| _sift | python | PeterDing/iScript | pan.baidu.com.py | https://github.com/PeterDing/iScript/blob/master/pan.baidu.com.py | MIT |
def __init__(self, config: Config) -> None:
"""
Initialize the API.
Parameters
----------
config : Config
The configuration.
"""
self.config = config
self.stream_diffusion = StreamDiffusionWrapper(
mode=config.mode,
mod... |
Initialize the API.
Parameters
----------
config : Config
The configuration.
| __init__ | python | cumulo-autumn/StreamDiffusion | demo/realtime-txt2img/main.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/demo/realtime-txt2img/main.py | Apache-2.0 |
async def _predict(self, inp: PredictInputModel) -> PredictResponseModel:
"""
Predict an image and return.
Parameters
----------
inp : PredictInputModel
The input.
Returns
-------
PredictResponseModel
The prediction result.
... |
Predict an image and return.
Parameters
----------
inp : PredictInputModel
The input.
Returns
-------
PredictResponseModel
The prediction result.
| _predict | python | cumulo-autumn/StreamDiffusion | demo/realtime-txt2img/main.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/demo/realtime-txt2img/main.py | Apache-2.0 |
def _pil_to_base64(self, image: Image.Image, format: str = "JPEG") -> bytes:
"""
Convert a PIL image to base64.
Parameters
----------
image : Image.Image
The PIL image.
format : str
The image format, by default "JPEG".
Returns
--... |
Convert a PIL image to base64.
Parameters
----------
image : Image.Image
The PIL image.
format : str
The image format, by default "JPEG".
Returns
-------
bytes
The base64 image.
| _pil_to_base64 | python | cumulo-autumn/StreamDiffusion | demo/realtime-txt2img/main.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/demo/realtime-txt2img/main.py | Apache-2.0 |
def _base64_to_pil(self, base64_image: str) -> Image.Image:
"""
Convert a base64 image to PIL.
Parameters
----------
base64_image : str
The base64 image.
Returns
-------
Image.Image
The PIL image.
"""
if "base64," ... |
Convert a base64 image to PIL.
Parameters
----------
base64_image : str
The base64 image.
Returns
-------
Image.Image
The PIL image.
| _base64_to_pil | python | cumulo-autumn/StreamDiffusion | demo/realtime-txt2img/main.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/demo/realtime-txt2img/main.py | Apache-2.0 |
def main(
input: str,
output: str = os.path.join(CURRENT_DIR, "..", "..", "images", "outputs", "output.mp4"),
model_id: str = "KBlueLeaf/kohaku-v2.1",
lora_dict: Optional[Dict[str, float]] = None,
prompt: str = "1girl with brown dog ears, thick frame glasses",
scale: float = 1.0,
acceleratio... |
Process for generating images based on a prompt using a specified model.
Parameters
----------
input : str, optional
The input video name to load images from.
output : str, optional
The output video name to save images to.
model_id_or_path : str
The name of the model to... | main | python | cumulo-autumn/StreamDiffusion | demo/vid2vid/app.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/demo/vid2vid/app.py | Apache-2.0 |
def run(
iterations: int = 100,
model_id_or_path: str = "KBlueLeaf/kohaku-v2.1",
lora_dict: Optional[Dict[str, float]] = None,
prompt: str = "1girl with brown dog hair, thick glasses, smiling",
negative_prompt: str = "bad image , bad quality",
use_lcm_lora: bool = True,
use_tiny_vae: bool = ... |
Initializes the StreamDiffusionWrapper.
Parameters
----------
iterations : int, optional
The number of iterations to run, by default 100.
model_id_or_path : str
The model id or path to load.
lora_dict : Optional[Dict[str, float]], optional
The lora_dict to load, by defa... | run | python | cumulo-autumn/StreamDiffusion | examples/benchmark/multi.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/benchmark/multi.py | Apache-2.0 |
def main(
input: str = os.path.join(CURRENT_DIR, "..", "..", "images", "inputs"),
output: str = os.path.join(CURRENT_DIR, "..", "..", "images", "outputs"),
model_id_or_path: str = "KBlueLeaf/kohaku-v2.1",
lora_dict: Optional[Dict[str, float]] = None,
prompt: str = "1girl with brown dog hair, thick g... |
Initializes the StreamDiffusionWrapper.
Parameters
----------
input : str, optional
The input directory to load images from.
output : str, optional
The output directory to save images to.
model_id_or_path : str
The model id or path to load.
lora_dict : Optional[Dict... | main | python | cumulo-autumn/StreamDiffusion | examples/img2img/multi.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/img2img/multi.py | Apache-2.0 |
def main(
input: str = os.path.join(CURRENT_DIR, "..", "..", "images", "inputs", "input.png"),
output: str = os.path.join(CURRENT_DIR, "..", "..", "images", "outputs", "output.png"),
model_id_or_path: str = "KBlueLeaf/kohaku-v2.1",
lora_dict: Optional[Dict[str, float]] = None,
prompt: str = "1girl w... |
Initializes the StreamDiffusionWrapper.
Parameters
----------
input : str, optional
The input image file to load images from.
output : str, optional
The output image file to save images to.
model_id_or_path : str
The model id or path to load.
lora_dict : Optional[Di... | main | python | cumulo-autumn/StreamDiffusion | examples/img2img/single.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/img2img/single.py | Apache-2.0 |
def update_image(image_data: Image.Image, labels: List[tk.Label]) -> None:
"""
Update the image displayed on a Tkinter label.
Parameters
----------
image_data : Image.Image
The image to be displayed.
labels : List[tk.Label]
The list of labels where the image will be updated.
... |
Update the image displayed on a Tkinter label.
Parameters
----------
image_data : Image.Image
The image to be displayed.
labels : List[tk.Label]
The list of labels where the image will be updated.
| update_image | python | cumulo-autumn/StreamDiffusion | examples/optimal-performance/multi.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/optimal-performance/multi.py | Apache-2.0 |
def image_generation_process(
queue: Queue,
fps_queue: Queue,
prompt: str,
model_id_or_path: str,
batch_size: int = 10,
acceleration: Literal["none", "xformers", "tensorrt"] = "tensorrt",
) -> None:
"""
Process for generating images based on a prompt using a specified model.
Paramet... |
Process for generating images based on a prompt using a specified model.
Parameters
----------
queue : Queue
The queue to put the generated images in.
fps_queue : Queue
The queue to put the calculated fps.
prompt : str
The prompt to generate images from.
model_id_or... | image_generation_process | python | cumulo-autumn/StreamDiffusion | examples/optimal-performance/multi.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/optimal-performance/multi.py | Apache-2.0 |
def _receive_images(
queue: Queue, fps_queue: Queue, labels: List[tk.Label], fps_label: tk.Label
) -> None:
"""
Continuously receive images from a queue and update the labels.
Parameters
----------
queue : Queue
The queue to receive images from.
fps_queue : Queue
The queue t... |
Continuously receive images from a queue and update the labels.
Parameters
----------
queue : Queue
The queue to receive images from.
fps_queue : Queue
The queue to put the calculated fps.
labels : List[tk.Label]
The list of labels to update with images.
fps_label :... | _receive_images | python | cumulo-autumn/StreamDiffusion | examples/optimal-performance/multi.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/optimal-performance/multi.py | Apache-2.0 |
def receive_images(queue: Queue, fps_queue: Queue) -> None:
"""
Setup the Tkinter window and start the thread to receive images.
Parameters
----------
queue : Queue
The queue to receive images from.
fps_queue : Queue
The queue to put the calculated fps.
"""
root = tk.Tk(... |
Setup the Tkinter window and start the thread to receive images.
Parameters
----------
queue : Queue
The queue to receive images from.
fps_queue : Queue
The queue to put the calculated fps.
| receive_images | python | cumulo-autumn/StreamDiffusion | examples/optimal-performance/multi.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/optimal-performance/multi.py | Apache-2.0 |
def main(
prompt: str = "cat with sunglasses and a hat, photoreal, 8K",
model_id_or_path: str = "stabilityai/sd-turbo",
batch_size: int = 12,
acceleration: Literal["none", "xformers", "tensorrt"] = "tensorrt",
) -> None:
"""
Main function to start the image generation and viewer processes.
"... |
Main function to start the image generation and viewer processes.
| main | python | cumulo-autumn/StreamDiffusion | examples/optimal-performance/multi.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/optimal-performance/multi.py | Apache-2.0 |
def image_generation_process(
queue: Queue,
fps_queue: Queue,
prompt: str,
model_id_or_path: str,
acceleration: Literal["none", "xformers", "tensorrt"] = "tensorrt",
) -> None:
"""
Process for generating images based on a prompt using a specified model.
Parameters
----------
que... |
Process for generating images based on a prompt using a specified model.
Parameters
----------
queue : Queue
The queue to put the generated images in.
fps_queue : Queue
The queue to put the calculated fps.
prompt : str
The prompt to generate images from.
model_id_or... | image_generation_process | python | cumulo-autumn/StreamDiffusion | examples/optimal-performance/single.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/optimal-performance/single.py | Apache-2.0 |
def image_generation_process(
queue: Queue,
fps_queue: Queue,
close_queue: Queue,
model_id_or_path: str,
lora_dict: Optional[Dict[str, float]],
prompt: str,
negative_prompt: str,
frame_buffer_size: int,
width: int,
height: int,
acceleration: Literal["none", "xformers", "tenso... |
Process for generating images based on a prompt using a specified model.
Parameters
----------
queue : Queue
The queue to put the generated images in.
fps_queue : Queue
The queue to put the calculated fps.
model_id_or_path : str
The name of the model to use for image ge... | image_generation_process | python | cumulo-autumn/StreamDiffusion | examples/screen/main.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/screen/main.py | Apache-2.0 |
def main(
output: str = os.path.join(CURRENT_DIR, "..", "..", "images", "outputs",),
model_id_or_path: str = "KBlueLeaf/kohaku-v2.1",
lora_dict: Optional[Dict[str, float]] = None,
prompt: str = "1girl with brown dog hair, thick glasses, smiling",
width: int = 512,
height: int = 512,
frame_bu... |
Process for generating images based on a prompt using a specified model.
Parameters
----------
output : str, optional
The output image file to save images to.
model_id_or_path : str
The name of the model to use for image generation.
lora_dict : Optional[Dict[str, float]], optio... | main | python | cumulo-autumn/StreamDiffusion | examples/txt2img/multi.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/txt2img/multi.py | Apache-2.0 |
def numpy_to_pil(images: np.ndarray) -> PIL.Image.Image:
"""
Convert a NumPy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
if images.shape[-1] == 1:
# special case for grayscale (sing... |
Convert a NumPy image or a batch of images to a PIL image.
| numpy_to_pil | python | cumulo-autumn/StreamDiffusion | src/streamdiffusion/image_utils.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/src/streamdiffusion/image_utils.py | Apache-2.0 |
def update_image(image_data: Image.Image, label: tk.Label) -> None:
"""
Update the image displayed on a Tkinter label.
Parameters
----------
image_data : Image.Image
The image to be displayed.
label : tk.Label
The labels where the image will be updated.
"""
width = 512
... |
Update the image displayed on a Tkinter label.
Parameters
----------
image_data : Image.Image
The image to be displayed.
label : tk.Label
The labels where the image will be updated.
| update_image | python | cumulo-autumn/StreamDiffusion | utils/viewer.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/viewer.py | Apache-2.0 |
def _receive_images(
queue: Queue, fps_queue: Queue, label: tk.Label, fps_label: tk.Label
) -> None:
"""
Continuously receive images from a queue and update the labels.
Parameters
----------
queue : Queue
The queue to receive images from.
fps_queue : Queue
The queue to put t... |
Continuously receive images from a queue and update the labels.
Parameters
----------
queue : Queue
The queue to receive images from.
fps_queue : Queue
The queue to put the calculated fps.
label : tk.Label
The label to update with images.
fps_label : tk.Label
... | _receive_images | python | cumulo-autumn/StreamDiffusion | utils/viewer.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/viewer.py | Apache-2.0 |
def __init__(
self,
model_id_or_path: str,
t_index_list: List[int],
lora_dict: Optional[Dict[str, float]] = None,
mode: Literal["img2img", "txt2img"] = "img2img",
output_type: Literal["pil", "pt", "np", "latent"] = "pil",
lcm_lora_id: Optional[str] = None,
... |
Initializes the StreamDiffusionWrapper.
Parameters
----------
model_id_or_path : str
The model id or path to load.
t_index_list : List[int]
The t_index_list to use for inference.
lora_dict : Optional[Dict[str, float]], optional
The lo... | __init__ | python | cumulo-autumn/StreamDiffusion | utils/wrapper.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/wrapper.py | Apache-2.0 |
def prepare(
self,
prompt: str,
negative_prompt: str = "",
num_inference_steps: int = 50,
guidance_scale: float = 1.2,
delta: float = 1.0,
) -> None:
"""
Prepares the model for inference.
Parameters
----------
prompt : str
... |
Prepares the model for inference.
Parameters
----------
prompt : str
The prompt to generate images from.
num_inference_steps : int, optional
The number of inference steps to perform, by default 50.
guidance_scale : float, optional
The... | prepare | python | cumulo-autumn/StreamDiffusion | utils/wrapper.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/wrapper.py | Apache-2.0 |
def __call__(
self,
image: Optional[Union[str, Image.Image, torch.Tensor]] = None,
prompt: Optional[str] = None,
) -> Union[Image.Image, List[Image.Image]]:
"""
Performs img2img or txt2img based on the mode.
Parameters
----------
image : Optional[Unio... |
Performs img2img or txt2img based on the mode.
Parameters
----------
image : Optional[Union[str, Image.Image, torch.Tensor]]
The image to generate from.
prompt : Optional[str]
The prompt to generate images from.
Returns
-------
U... | __call__ | python | cumulo-autumn/StreamDiffusion | utils/wrapper.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/wrapper.py | Apache-2.0 |
def txt2img(
self, prompt: Optional[str] = None
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Performs txt2img.
Parameters
----------
prompt : Optional[str]
The prompt to generate images from.
Returns
-------
... |
Performs txt2img.
Parameters
----------
prompt : Optional[str]
The prompt to generate images from.
Returns
-------
Union[Image.Image, List[Image.Image]]
The generated image.
| txt2img | python | cumulo-autumn/StreamDiffusion | utils/wrapper.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/wrapper.py | Apache-2.0 |
def img2img(
self, image: Union[str, Image.Image, torch.Tensor], prompt: Optional[str] = None
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Performs img2img.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
T... |
Performs img2img.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
The image to generate from.
Returns
-------
Image.Image
The generated image.
| img2img | python | cumulo-autumn/StreamDiffusion | utils/wrapper.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/wrapper.py | Apache-2.0 |
def preprocess_image(self, image: Union[str, Image.Image]) -> torch.Tensor:
"""
Preprocesses the image.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
The image to preprocess.
Returns
-------
torch.Tensor
The ... |
Preprocesses the image.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
The image to preprocess.
Returns
-------
torch.Tensor
The preprocessed image.
| preprocess_image | python | cumulo-autumn/StreamDiffusion | utils/wrapper.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/wrapper.py | Apache-2.0 |
def postprocess_image(
self, image_tensor: torch.Tensor, output_type: str = "pil"
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Postprocesses the image.
Parameters
----------
image_tensor : torch.Tensor
The image tensor to post... |
Postprocesses the image.
Parameters
----------
image_tensor : torch.Tensor
The image tensor to postprocess.
Returns
-------
Union[Image.Image, List[Image.Image]]
The postprocessed image.
| postprocess_image | python | cumulo-autumn/StreamDiffusion | utils/wrapper.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/wrapper.py | Apache-2.0 |
def _load_model(
self,
model_id_or_path: str,
t_index_list: List[int],
lora_dict: Optional[Dict[str, float]] = None,
lcm_lora_id: Optional[str] = None,
vae_id: Optional[str] = None,
acceleration: Literal["none", "xformers", "tensorrt"] = "tensorrt",
warmup... |
Loads the model.
This method does the following:
1. Loads the model from the model_id_or_path.
2. Loads and fuses the LCM-LoRA model from the lcm_lora_id if needed.
3. Loads the VAE model from the vae_id if needed.
4. Enables acceleration if needed.
5. Prepares... | _load_model | python | cumulo-autumn/StreamDiffusion | utils/wrapper.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/utils/wrapper.py | Apache-2.0 |
def pvkblob_to_pkcs1(key):
"""
modified from impacket dpapi.py
parse private key into pkcs#1 format
:param key:
:return:
"""
modulus = bytes_to_long(key["modulus"][::-1]) # n
prime1 = bytes_to_long(key["prime1"][::-1]) # p
prime2 = bytes_to_long(key["prime2"][::-1]) # q
_ = by... |
modified from impacket dpapi.py
parse private key into pkcs#1 format
:param key:
:return:
| pvkblob_to_pkcs1 | python | zblurx/dploot | dploot/lib/crypto.py | https://github.com/zblurx/dploot/blob/master/dploot/lib/crypto.py | MIT |
def getUsersProfiles(self) -> dict[str, str] | None:
"""Returns the list of user profiles (from registry) in a dict
Each subkey of HKLM/SOFTWARE/Microsoft/Windows NT/CurrentVersion/ProfileList is a user SID,
and the ProfileImagePath value inside is the path to the user's profile
:return... | Returns the list of user profiles (from registry) in a dict
Each subkey of HKLM/SOFTWARE/Microsoft/Windows NT/CurrentVersion/ProfileList is a user SID,
and the ProfileImagePath value inside is the path to the user's profile
:return: dict of user_sid: path_to_profile
| getUsersProfiles | python | zblurx/dploot | dploot/lib/smb.py | https://github.com/zblurx/dploot/blob/master/dploot/lib/smb.py | MIT |
def _create_clone_schema_function(self):
"""
Creates a postgres function `clone_schema` that copies a schema and its
contents. Will replace any existing `clone_schema` functions owned by the
`postgres` superuser.
"""
cursor = connection.cursor()
db_user = setting... |
Creates a postgres function `clone_schema` that copies a schema and its
contents. Will replace any existing `clone_schema` functions owned by the
`postgres` superuser.
| _create_clone_schema_function | python | django-tenants/django-tenants | django_tenants/clone.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/clone.py | MIT |
def clone_schema(
self, base_schema_name, new_schema_name, clone_mode="DATA", set_connection=True
):
"""
Creates a new schema `new_schema_name` as a clone of an existing schema
`old_schema_name`.
"""
if set_connection:
connection.set_schema_to_public()
... |
Creates a new schema `new_schema_name` as a clone of an existing schema
`old_schema_name`.
| clone_schema | python | django-tenants/django-tenants | django_tenants/clone.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/clone.py | MIT |
def __enter__(self):
"""
Syntax sugar which helps in celery tasks, cron jobs, and other scripts
Usage:
with Tenant.objects.get(schema_name='test') as tenant:
# run some code in tenant test
# run some code in previous tenant (public probably)
"""
... |
Syntax sugar which helps in celery tasks, cron jobs, and other scripts
Usage:
with Tenant.objects.get(schema_name='test') as tenant:
# run some code in tenant test
# run some code in previous tenant (public probably)
| __enter__ | python | django-tenants/django-tenants | django_tenants/models.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/models.py | MIT |
def pre_drop(self):
"""
This is a routine which you could override to backup the tenant schema before dropping.
:return:
""" |
This is a routine which you could override to backup the tenant schema before dropping.
:return:
| pre_drop | python | django-tenants/django-tenants | django_tenants/models.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/models.py | MIT |
def delete(self, force_drop=False, *args, **kwargs):
"""
Deletes this row. Drops the tenant's schema if the attribute
auto_drop_schema set to True.
"""
self._drop_schema(force_drop)
return super().delete(*args, **kwargs) |
Deletes this row. Drops the tenant's schema if the attribute
auto_drop_schema set to True.
| delete | python | django-tenants/django-tenants | django_tenants/models.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/models.py | MIT |
def create_schema(self, check_if_exists=False, sync_schema=True,
verbosity=1):
"""
Creates the schema 'schema_name' for this tenant. Optionally checks if
the schema already exists before creating it. Returns true if the
schema was created, false otherwise.
"... |
Creates the schema 'schema_name' for this tenant. Optionally checks if
the schema already exists before creating it. Returns true if the
schema was created, false otherwise.
| create_schema | python | django-tenants/django-tenants | django_tenants/models.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/models.py | MIT |
def get_primary_domain(self):
"""
Returns the primary domain of the tenant
"""
try:
domain = self.domains.get(is_primary=True)
return domain
except get_tenant_domain_model().DoesNotExist:
return None |
Returns the primary domain of the tenant
| get_primary_domain | python | django-tenants/django-tenants | django_tenants/models.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/models.py | MIT |
def reverse(self, request, view_name):
"""
Returns the URL of this tenant.
"""
http_type = 'https://' if request.is_secure() else 'http://'
domain = get_current_site(request).domain
url = ''.join((http_type, self.schema_name, '.', domain, reverse(view_name)))
r... |
Returns the URL of this tenant.
| reverse | python | django-tenants/django-tenants | django_tenants/models.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/models.py | MIT |
def app_in_list(self, app_label, apps_list):
"""
Is 'app_label' present in 'apps_list'?
apps_list is either settings.SHARED_APPS or settings.TENANT_APPS, a
list of app names.
We check the presence of the app's name or the full path to the apps's
AppConfig class.
... |
Is 'app_label' present in 'apps_list'?
apps_list is either settings.SHARED_APPS or settings.TENANT_APPS, a
list of app names.
We check the presence of the app's name or the full path to the apps's
AppConfig class.
https://docs.djangoproject.com/en/1.8/ref/applications/... | app_in_list | python | django-tenants/django-tenants | django_tenants/routers.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/routers.py | MIT |
def get_dynamic_tenant_prefixed_urlconf(urlconf, dynamic_path):
"""
Generates a new URLConf module with all patterns prefixed with tenant.
"""
from types import ModuleType
from django.utils.module_loading import import_string
class LazyURLConfModule(ModuleType):
def __getattr__(self, at... |
Generates a new URLConf module with all patterns prefixed with tenant.
| get_dynamic_tenant_prefixed_urlconf | python | django-tenants/django-tenants | django_tenants/urlresolvers.py | https://github.com/django-tenants/django-tenants/blob/master/django_tenants/urlresolvers.py | MIT |
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