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_with_shell_env_value(self):
"""
Given values for the variables from shell environment
"""
expected = {
"AWS_SAM_LOCAL": "true",
"AWS_LAMBDA_FUNCTION_MEMORY_SIZE": "1024",
"AWS_LAMBDA_FUNCTION_TIMEOUT": "123",
"AWS_LAMBDA_FUNCTION_... |
Given values for the variables from shell environment
| test_with_shell_env_value | python | aws/aws-sam-cli | tests/unit/local/lambdafn/test_env_vars.py | https://github.com/aws/aws-sam-cli/blob/master/tests/unit/local/lambdafn/test_env_vars.py | Apache-2.0 |
def test_with_overrides_value(self):
"""
Given values for the variables from user specified overrides
"""
expected = {
"AWS_SAM_LOCAL": "true",
"AWS_LAMBDA_FUNCTION_MEMORY_SIZE": "1024",
"AWS_LAMBDA_FUNCTION_TIMEOUT": "123",
"AWS_LAMBDA_FU... |
Given values for the variables from user specified overrides
| test_with_overrides_value | python | aws/aws-sam-cli | tests/unit/local/lambdafn/test_env_vars.py | https://github.com/aws/aws-sam-cli/blob/master/tests/unit/local/lambdafn/test_env_vars.py | Apache-2.0 |
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_verify_timer_handler(self, SignalMock, ThreadingMock):
"""
Verify the internal implementation of the Signal Handler
"""
is_debugging = False
def fake_timer(timeout, handler, args):
handler()
return Mock()
# Fake the real method with a La... |
Verify the internal implementation of the Signal Handler
| test_verify_timer_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 close(self, *exc_details):
"""Finish a given inference session, close the underlying connection"""
if not self._closed:
self._exit_server_sessions(self._server_sessions)
self._server_sessions.clear()
self._closed = True | 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 |
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 forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[boo... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values... | forward | python | bigscience-workshop/petals | src/petals/models/llama/block.py | https://github.com/bigscience-workshop/petals/blob/master/src/petals/models/llama/block.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 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/single.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/benchmark/single.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 main(
prompt: str = "cat with sunglasses and a hat, photoreal, 8K",
model_id_or_path: str = "stabilityai/sd-turbo",
acceleration: Literal["none", "xformers", "tensorrt"] = "tensorrt",
) -> None:
"""
Main function to start the image generation and viewer processes.
"""
ctx = get_context('... |
Main function to start the image generation and viewer processes.
| main | 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(
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 = "low quality, bad quality, blurry, low resolution",
frame_buffer_size: int = 1,
width: int = 512,
... |
Main function to start the image generation and viewer processes.
| main | 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 main(
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 with brown dog hair, thick glasses, smiling",
width: int = 512,
height: int = 512,... |
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/single.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/txt2img/single.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 | examples/vid2vid/main.py | https://github.com/cumulo-autumn/StreamDiffusion/blob/master/examples/vid2vid/main.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 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 | 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 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.