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[SLM] Add support for InternLM2 architecture (#2608)
This commit introduces the InternLM2 model support.
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# pylint: disable=W0611 | ||
""" | ||
This file specifies how MLC's InternLM2 parameter maps from other formats, for example HuggingFace | ||
PyTorch, HuggingFace safetensors. | ||
""" | ||
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import functools | ||
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import numpy as np | ||
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from mlc_llm.loader import ExternMapping | ||
from mlc_llm.quantization import Quantization | ||
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from .internlm2_model import InternLM2Config, InternLM2ForCausalLM | ||
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def huggingface(model_config: InternLM2ForCausalLM, 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 : InternLM2Config | ||
The configuration of the InternLM2 model. | ||
quantization : Quantization | ||
The quantization configuration. | ||
Returns | ||
------- | ||
param_map : ExternMapping | ||
The parameter mapping from MLC to HuggingFace PyTorch. | ||
""" | ||
model = InternLM2ForCausalLM(model_config) | ||
if quantization is not None: | ||
model.to(quantization.model_dtype) | ||
_, _named_params, _ = model.export_tvm( # type: ignore[misc] | ||
spec=model.get_default_spec(), | ||
allow_extern=True, | ||
) | ||
named_parameters = dict(_named_params) | ||
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mapping = ExternMapping() | ||
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def _convert_wqkv_layout(wqkv, dtype): | ||
config = model_config | ||
kv_groups = config.num_attention_heads // config.num_key_value_heads | ||
head_dim = config.hidden_size // config.num_attention_heads | ||
wqkv = wqkv.reshape(-1, 2 + kv_groups, head_dim, wqkv.shape[-1]) | ||
wq, wk, wv = np.split(wqkv, [kv_groups, kv_groups + 1], axis=1) # pylint: disable=W0632 | ||
wq = wq.reshape(-1, wq.shape[-1]) | ||
wk = wk.reshape(-1, wk.shape[-1]) | ||
wv = wv.reshape(-1, wv.shape[-1]) | ||
return np.concatenate([wq, wk, wv], axis=0).astype(dtype) | ||
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for i in range(model_config.num_hidden_layers): | ||
# Add gates in MLP | ||
mlp = f"model.layers.{i}.feed_forward" | ||
mlc_name = f"{mlp}.gate_up_proj.weight" | ||
mlc_param = named_parameters[mlc_name] | ||
mapping.add_mapping( | ||
mlc_name, | ||
[ | ||
f"{mlp}.w1.weight", | ||
f"{mlp}.w3.weight", | ||
], | ||
functools.partial( | ||
lambda w1, w3, dtype: np.concatenate([w1, w3], axis=0).astype(dtype), | ||
dtype=mlc_param.dtype, | ||
), | ||
) | ||
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mlc_name = f"model.layers.{i}.attention.wqkv.weight" | ||
mlc_param = named_parameters[mlc_name] | ||
mapping.add_mapping( | ||
mlc_name, | ||
[mlc_name], | ||
functools.partial( | ||
_convert_wqkv_layout, | ||
dtype=mlc_param.dtype, | ||
), | ||
) | ||
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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, | ||
), | ||
) | ||
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return mapping |
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