Clean up the compressed-tensors integration (#37349)

clean up

src/transformers/integrations/compressed_tensors.py

@@ -1,54 +0,0 @@
-# Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from transformers.utils import is_torch_available
-
-
-if is_torch_available():
-    import torch
-    import torch.nn as nn
-
-from transformers.models.llama4.modeling_llama4 import Llama4TextMLP
-
-
-def skip(*args, **kwargs):
-    pass
-
-
-class CompressedExpertsLinear(nn.Module):
-    """
-    A module that implements a compressed version of a list of expert modules.
-    This is specifically designed to work with Llama4TextExperts in MoE layers.
-    """
-
-    def __init__(self, config):
-        # Skip random weight initialization for experts. Otherwise,
-        # the init of this module would take over minutes. For a model
-        # with tens of layers of experts, it would easily take over 20 minutes.
-        nn.init.kaiming_uniform_ = skip
-        nn.init.uniform_ = skip
-        nn.init.normal_ = skip
-        super().__init__()
-        self.num_experts = config.num_local_experts
-        self.expert_modules = nn.ModuleList([Llama4TextMLP(config) for _ in range(self.num_experts)])
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-    ) -> torch.Tensor:
-        hidden_states = hidden_states.reshape(self.num_experts, -1, hidden_states.shape[-1])
-        expert_routed_out_list = []
-        for expert_idx in range(self.num_experts):
-            expert_routed_out_list.append(self.expert_modules[expert_idx](hidden_states[expert_idx]))
-        routed_out = torch.cat(expert_routed_out_list, dim=0)
-        return routed_out