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 +1,115 @@
+"""
+This file specifies how MLC's Cohere 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.loader.standard_loader import make_standard_hf_loader
+from mlc_llm.quantization import Quantization, make_awq_quant
+
+from .cohere_model import CohereConfig, CohereForCausalLM
+
+awq_quant = make_awq_quant(CohereForCausalLM)
+
+
+def _cohere_name_transform(name: str) -> str:
+    if "out_proj." in name:
+        return name.replace("out_proj.", "o_proj.")
+    return name
+
+
+huggingface = make_standard_hf_loader(
+    model_cls=CohereForCausalLM,
+    include_gate_up=False,
+    name_transform=_cohere_name_transform,
+)
+
+
+# https://huggingface.co/alijawad07/aya-23-8B-AWQ-GEMM/tree/main
+def awq(model_config: CohereConfig, quantization: Quantization) -> ExternMapping:
+    """Returns a parameter mapping that maps from the names of MLC LLM parameters to
+    the names of AWQ parameters.
+    Parameters
+    ----------
+    model_config : CohereConfig
+        The configuration of the Cohere model.
+
+    quantization : Quantization
+        The quantization configuration.
+
+    Returns
+    -------
+    param_map : ExternMapping
+        The parameter mapping from MLC to AWQ.
+    """
+    model, _ = awq_quant(model_config, quantization)
+    _, _named_params, _ = model.export_tvm(
+        spec=model.get_default_spec(),
+        allow_extern=True,
+    )
+    named_parameters = dict(_named_params)
+
+    mapping = ExternMapping()
+
+    def _add(mlc_name, hf_name):
+        mapping.add_mapping(
+            mlc_name,
+            [hf_name],
+            functools.partial(
+                lambda x, dtype: x.astype(dtype),
+                dtype=named_parameters[mlc_name].dtype,
+            ),
+        )
+
+    for i in range(model_config.num_hidden_layers):
+        # Add QKV in self attention
+        attn = f"model.layers.{i}.self_attn"
+        for quantize_suffix in ["qweight", "qzeros", "scales"]:
+            mlc_name = f"{attn}.qkv_proj.{quantize_suffix}"
+            assert mlc_name in named_parameters
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{attn}.q_proj.{quantize_suffix}",
+                    f"{attn}.k_proj.{quantize_suffix}",
+                    f"{attn}.v_proj.{quantize_suffix}",
+                ],
+                functools.partial(
+                    lambda q, k, v, dtype: np.concatenate(
+                        [q, k, v],
+                        axis=1,  # AWQ GEMM would transpose the weight
+                    ).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+            _add(f"{attn}.out_proj.{quantize_suffix}", f"{attn}.o_proj.{quantize_suffix}")
+
+        # Concat gate and up in MLP
+        mlp = f"model.layers.{i}.mlp"
+        for quantize_suffix in ["qweight", "qzeros", "scales"]:
+            _add(f"{mlp}.up_proj.{quantize_suffix}", f"{mlp}.up_proj.{quantize_suffix}")
+            _add(
+                f"{mlp}.gate_proj.{quantize_suffix}",
+                f"{mlp}.gate_proj.{quantize_suffix}",
+            )
+            _add(
+                f"{mlp}.down_proj.{quantize_suffix}",
+                f"{mlp}.down_proj.{quantize_suffix}",
+            )
+
+        # inv_freq is not used in the model
+        # mapping.add_unused(f"{attn}.rotary_emb.inv_freq")
+
+    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