chore: import upstream snapshot with attribution

2026-07-13 13:23:58 +08:00
commit 770d92cb1f
694 changed files with 114634 additions and 0 deletions
@@ -0,0 +1,149 @@
+"""
+This file specifies how MLC's Deepseek parameter maps from other formats, for example HuggingFace
+PyTorch, HuggingFace safetensors.
+"""
+
+import functools
+
+import numpy as np
+
+from mlc_llm.loader import ExternMapping
+from mlc_llm.quantization import Quantization
+
+from .deepseek_model import DeepseekConfig, DeepseekForCausalLM
+
+
+def huggingface(model_config: DeepseekConfig, quantization: Quantization) -> ExternMapping:
+    """Returns a parameter mapping that maps from the names of MLC LLM parameters to
+    the names of HuggingFace PyTorch parameters.
+
+    Parameters
+    ----------
+    model_config : MiniCPMConfig
+        The configuration of the MiniCPM model.
+
+    quantization : Quantization
+        The quantization configuration.
+
+    Returns
+    -------
+    param_map : ExternMapping
+        The parameter mapping from MLC to HuggingFace PyTorch.
+    """
+    model = DeepseekForCausalLM(model_config)
+    if quantization is not None:
+        model.to(quantization.model_dtype)
+    _, _named_params, _ = model.export_tvm(
+        spec=model.get_default_spec(),
+        allow_extern=True,
+    )
+    named_parameters = dict(_named_params)
+
+    mapping = ExternMapping()
+
+    for i in range(model_config.num_hidden_layers):
+        # map attention weight
+        attn = f"model.layers.{i}.self_attn"
+        for weight_type in ["weight"]:
+            mlc_name = f"{attn}.wqkv_pack.{weight_type}"
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{attn}.q_proj.{weight_type}",
+                    f"{attn}.k_proj.{weight_type}",
+                    f"{attn}.v_proj.{weight_type}",
+                ],
+                functools.partial(
+                    lambda q, k, v, dtype: np.concatenate([q, k, v], axis=0).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+
+    for i in range(model_config.num_hidden_layers):
+        if i >= model_config.first_k_dense_replace and i % model_config.moe_layer_freq == 0:
+            # map mlp shared expert weight
+            mlp = f"model.layers.{i}.mlp"
+            shared_expert = f"{mlp}.shared_experts"
+            mlc_name = f"{shared_expert}.gate_up_proj.weight"
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{shared_expert}.gate_proj.weight",
+                    f"{shared_expert}.up_proj.weight",
+                ],
+                functools.partial(
+                    lambda gate, up, dtype: np.concatenate([gate, up], axis=0).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+            # map mlp moe gate and up weight
+            mlc_name = f"{mlp}.moe_gate_up_proj.weight"
+
+            def combine_expert_gate_up(*hf_params, dtype):
+                stack = []
+                for i in range(0, len(hf_params), 2):
+                    stack.append(np.concatenate([hf_params[i], hf_params[i + 1]], axis=0))
+                return np.stack(stack, axis=0).astype(dtype)
+
+            mapping.add_mapping(
+                mlc_name,
+                functools.reduce(
+                    lambda a, b: a + b,
+                    [
+                        [
+                            f"{mlp}.experts.{expert_id}.gate_proj.weight",
+                            f"{mlp}.experts.{expert_id}.up_proj.weight",
+                        ]
+                        for expert_id in range(model_config.n_routed_experts)
+                    ],
+                ),
+                functools.partial(
+                    combine_expert_gate_up,
+                    dtype=mlc_param.dtype,
+                ),
+            )
+
+            # map mlp moe gate and up weight
+            mlc_name = f"{mlp}.moe_down_proj.weight"
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{mlp}.experts.{expert_id}.down_proj.weight"
+                    for expert_id in range(model_config.n_routed_experts)
+                ],
+                functools.partial(
+                    lambda *hf_params, dtype: np.stack(hf_params, axis=0).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+        else:
+            # map mlp weight
+            mlp = f"model.layers.{i}.mlp"
+            mlc_name = f"{mlp}.gate_up_proj.weight"
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{mlp}.gate_proj.weight",
+                    f"{mlp}.up_proj.weight",
+                ],
+                functools.partial(
+                    lambda gate, up, dtype: np.concatenate([gate, up], axis=0).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+
+    for mlc_name, mlc_param in named_parameters.items():
+        if mlc_name not in mapping.param_map:
+            mapping.add_mapping(
+                mlc_name,
+                [mlc_name],
+                functools.partial(
+                    lambda x, dtype: x.astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+    return mapping