Update deprecated type hinting in models (#18132)
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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@@ -20,7 +20,8 @@
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# limitations under the License.
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"""Inference-only BaiChuan model compatible with HuggingFace weights."""
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import math
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from typing import Iterable, Optional, Set, Tuple, Union
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from collections.abc import Iterable
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from typing import Optional, Union
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import torch
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from torch import nn
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@@ -230,7 +231,7 @@ class BaiChuanDecoderLayer(nn.Module):
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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) -> tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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if residual is None:
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residual = hidden_states
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@@ -320,15 +321,15 @@ class BaiChuanModel(nn.Module):
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: Set[str] = set()
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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@@ -421,8 +422,8 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
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sampling_metadata)
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return logits
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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