mirror of https://github.com/google/gemma.cpp.git
118 lines
4.9 KiB
Python
118 lines
4.9 KiB
Python
# Copyright 2024 Google LLC
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# WIP - DO NOT MERGE
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# Requires torch 2.2 and gemma package from https://github.com/google/gemma_pytorch
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from collections import defaultdict
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import torch
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from gemma import config
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from gemma import model as gemma_model
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import numpy as np
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def expand_qkv(qkv_proj: np.array) -> np.array:
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"""This won't be needed anymore when MQA is implemented"""
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assert qkv_proj.shape == (2560, 2048)
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qkv_proj.reshape((10, 256, 2048))
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# TODO : repeat dimensions ...
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return qkv_proj
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TRANSFORMATIONS = defaultdict(
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lambda: lambda x: x,
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{
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"embedder.weight": lambda x: np.concatenate([np.zeros([128, 2048]), x], 0),
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"self_attn.qkv_proj.weight": expand_qkv,
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"self_attn.o_proj.weight": lambda x: x, # TODO: which of the 2048 is unpacked to 8 x 256, and which is model_dim?
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"mlp.gate_proj.weight": lambda x: x,
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"mlp.up_proj.weight": lambda x: x,
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"mlp.down_proj.weight": lambda x: x,
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},
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)
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VALIDATIONS = {
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"embedder.weight": lambda x: x.shape == (256128, 2048),
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"model.norm.weight": lambda x: x.shape == (2048,),
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"self_attn.qkv_proj.weight": lambda x: x.shape == (8, 3, 256, 2048),
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"self_attn.o_proj.weight": lambda x: x.shape == (8, 2048, 256),
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"mlp.gate_proj.weight": lambda x: x.shape == (1, 16384, 2048),
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"mlp.up_proj.weight": lambda x: x.shape == (1, 16384, 2048),
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"mlp.down_proj.weight": lambda x: x.shape == (2048, 16384),
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"input_layernorm.weight": lambda x: x.shape == (2048,),
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"post_attention_layernorm.weight": lambda x: x.shape == (2048,),
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}
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def param_names():
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"""Return parameter names in the order they are expected for deserialization."""
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# note *weight_scaler params are ignored in the forward computation unless
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# quantization is being used.
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#
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# since we are working with the full precision weights as input, don't
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# include these in the parameters being iterated over.
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# fmt: off
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names = [
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("embedder.weight", ) * 2, # embedder_input_embedding (vocab=256000, model_dim=2048) -> (vocab=256128, model_dim=2048)
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("model.norm.weight", ) * 2 # final_norm_scale (model_dim=2048)
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]
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layer_params = [
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# TODO(austinvhuang): transpositions here ...
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# ( q_heads = 8 + kv = 2 ) x qkv_dim = 2560
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"self_attn.qkv_proj.weight", # qkv_einsum_w (2560, 2048) -> (heads=8, qkv=3, qkv_dim=256, model_dim=2048)
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"self_attn.o_proj.weight", # attn_vec_einsum_w (2048, 2048) -> (heads=8, model_dim=2048, qkv_dim=256)
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# these are the same without any change
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"mlp.gate_proj.weight", # gating_einsum_w (16384, 2048) => (gate/up=2, hidden=16384, model_dim=2048)
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"mlp.up_proj.weight",
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"mlp.down_proj.weight", # linear_w (2048, 16384) => (model_dim=2048, hidden=16384)
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"input_layernorm.weight", # pre_attention_norm_scale (model_dim=2048)
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"post_attention_layernorm.weight", # pre_ffw_norm_scale (model_dim=2048)
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]
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# fmt: on
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for layer in range(18):
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for layer_param in layer_params:
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names = names + [(f"model.layers.{layer}.{layer_param}", layer_param)]
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return names
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def convert_weights():
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# TODO: parameterize paths as CLI args instead of hard coding them
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output_file = "2bit-f32.sbs"
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model_config = config.get_model_config("2b")
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model_config.dtype = "float32"
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model_config.quant = "store_true"
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model_config.tokenizer = "models/tokenizer.spm"
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device = torch.device("cpu")
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torch.set_default_dtype(torch.float)
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model = gemma_model.GemmaForCausalLM(model_config)
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model.load_weights("models/gemma-2b-it.ckpt")
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model_dict = dict(model.named_parameters())
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param_order = param_names()
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print("Writing parameters ...")
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with open(output_file, "wb") as bin_handle:
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for name, layer_name in param_order:
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arr = model_dict[name].detach().numpy()
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arr = TRANSFORMATIONS[name](arr)
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check = "OK" if VALIDATIONS[layer_name](arr) else "FAILED"
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print(f" {name : <60}{str(arr.shape) : <20}{check}")
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arr.flatten().astype(np.float32).tofile(bin_handle)
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if __name__ == "__main__":
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convert_weights()
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print("Done")
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