初始化项目，由ModelHub XC社区提供模型

Model: ayh015/myLightningOPD
Source: Original Platform

slime_plugins/mbridge/glm4.py

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+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+
+from mbridge.core import LLMBridge, register_model
+
+
+@register_model("glm4")
+class GLM4Bridge(LLMBridge):
+    """
+    Bridge implementation for Qwen2 models.
+
+    This class extends LLMBridge to provide specific configurations and
+    optimizations for Qwen2 models, handling the conversion between
+    Hugging Face Qwen2 format and Megatron-Core.
+    """
+
+    _DIRECT_MAPPING = {
+        "embedding.word_embeddings.weight": "model.embed_tokens.weight",
+        "decoder.final_layernorm.weight": "model.norm.weight",
+        "output_layer.weight": "lm_head.weight",
+    }
+    _ATTENTION_MAPPING = {
+        "self_attention.linear_proj.weight": ["model.layers.{layer_number}.self_attn.o_proj.weight"],
+        "self_attention.linear_qkv.layer_norm_weight": ["model.layers.{layer_number}.input_layernorm.weight"],
+        "self_attention.q_layernorm.weight": ["model.layers.{layer_number}.self_attn.q_norm.weight"],
+        "self_attention.k_layernorm.weight": ["model.layers.{layer_number}.self_attn.k_norm.weight"],
+        "self_attention.linear_qkv.weight": [
+            "model.layers.{layer_number}.self_attn.q_proj.weight",
+            "model.layers.{layer_number}.self_attn.k_proj.weight",
+            "model.layers.{layer_number}.self_attn.v_proj.weight",
+        ],
+        "self_attention.linear_qkv.bias": [
+            "model.layers.{layer_number}.self_attn.q_proj.bias",
+            "model.layers.{layer_number}.self_attn.k_proj.bias",
+            "model.layers.{layer_number}.self_attn.v_proj.bias",
+        ],
+    }
+    _MLP_MAPPING = {
+        "mlp.linear_fc1.weight": [
+            "model.layers.{layer_number}.mlp.gate_up_proj.weight",
+        ],
+        "mlp.linear_fc1.layer_norm_weight": ["model.layers.{layer_number}.post_attention_layernorm.weight"],
+        "mlp.linear_fc2.weight": ["model.layers.{layer_number}.mlp.down_proj.weight"],
+    }
+
+    def _build_config(self):
+        """
+        Build the configuration for Qwen2 models.
+
+        Configures Qwen2-specific parameters such as QKV bias settings and
+        layer normalization options.
+
+        Returns:
+            TransformerConfig: Configuration object for Qwen2 models
+        """
+        return self._build_base_config(
+            # qwen2
+            add_qkv_bias=True,
+            qk_layernorm=False,
+            post_mlp_layernorm=True,
+            post_self_attn_layernorm=True,
+            rotary_interleaved=True,
+        )
+
+    def _get_transformer_layer_spec(self):
+        """
+        Gets the transformer layer specification.
+
+        Creates and returns a specification for the transformer layers based on
+        the current configuration.
+
+        Returns:
+            TransformerLayerSpec: Specification for transformer layers
+
+        Raises:
+            AssertionError: If normalization is not RMSNorm
+        """
+        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+            post_self_attn_layernorm=True,
+            post_mlp_layernorm=True,
+        )
+        return transformer_layer_spec
+
+    def _weight_name_mapping_mcore_to_hf(self, mcore_weights_name: str) -> list[str]:
+        """
+        Map MCore weight names to Hugging Face weight names.
+
+        Args:
+            mcore_weights_name: MCore weight name
+
+        Returns:
+            list: Corresponding Hugging Face weight names
+        """
+        assert "_extra_state" not in mcore_weights_name, "extra_state should not be loaded"
+
+        if mcore_weights_name in self._DIRECT_MAPPING:
+            return [self._DIRECT_MAPPING[mcore_weights_name]]
+
+        if "post_self_attn_layernorm" in mcore_weights_name:
+            layer_number = mcore_weights_name.split(".")[2]
+            return [f"model.layers.{layer_number}.post_self_attn_layernorm.weight"]
+        elif "post_mlp_layernorm" in mcore_weights_name:
+            layer_number = mcore_weights_name.split(".")[2]
+            return [f"model.layers.{layer_number}.post_mlp_layernorm.weight"]
+        elif "self_attention" in mcore_weights_name:
+            return self._weight_name_mapping_attention(mcore_weights_name)
+        elif "mlp" in mcore_weights_name:
+            return self._weight_name_mapping_mlp(mcore_weights_name)
+        else:
+            raise NotImplementedError(f"Unsupported parameter name: {mcore_weights_name}")