mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-10-25 17:11:12 +00:00
9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# the root directory of this source tree.
|
|
|
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# top-level folder for each specific model found within the models/ directory at
|
|
# the top-level of this source tree.
|
|
|
|
from typing import Any
|
|
|
|
from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear
|
|
from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region
|
|
from torch import nn
|
|
from torch.nn import functional as F
|
|
|
|
|
|
class FeedForward(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
hidden_dim: int,
|
|
do_reduce: bool = True,
|
|
):
|
|
super().__init__()
|
|
self.do_reduce = do_reduce
|
|
|
|
self.w1 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x)
|
|
self.w2 = RowParallelLinear(hidden_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x)
|
|
self.w3 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x)
|
|
self._register_load_state_dict_pre_hook(self.load_hook)
|
|
|
|
def load_hook(
|
|
self,
|
|
state_dict: dict[str, Any],
|
|
prefix: str,
|
|
local_metadata: dict[str, Any],
|
|
strict: bool,
|
|
missing_keys: list[str],
|
|
unexpected_keys: list[str],
|
|
error_msgs: list[str],
|
|
) -> None:
|
|
if prefix + "mlp.fc1_weight" in state_dict:
|
|
w1, w3 = state_dict.pop(prefix + "mlp.fc1_weight").chunk(2, dim=0)
|
|
state_dict[prefix + "w1.weight"] = w1
|
|
state_dict[prefix + "w3.weight"] = w3
|
|
state_dict[prefix + "w2.weight"] = state_dict.pop(prefix + "mlp.fc2_weight")
|
|
|
|
def forward(self, x):
|
|
x = F.silu(F.linear(x, self.w1.weight)) * F.linear(x, self.w3.weight)
|
|
out = F.linear(x, self.w2.weight)
|
|
if self.do_reduce:
|
|
return reduce_from_model_parallel_region(out)
|
|
return out
|