# Copyright 2024 Google LLC # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # WIP - DO NOT MERGE # Requires torch 2.2 and gemma package from https://github.com/google/gemma_pytorch from collections import defaultdict import torch from gemma import config from gemma import model as gemma_model import numpy as np def expand_qkv(qkv_proj: np.array) -> np.array: """This won't be needed anymore when MQA is implemented""" ## this will only be true for 2b assert qkv_proj.shape == (2560, 2048) qkv = qkv_proj.reshape((10, 256, 2048)) ## based on line 230 of ## https://github.com/google/gemma_pytorch/blob/main/gemma/model.py q_proj = qkv[:8].reshape((1,8,256,2048)) kv_proj = qkv[8:] kv_proj = kv_proj[:, np.newaxis, :, :] kv_proj = np.repeat(kv_proj, 8, axis=1) qkv = np.concatenate([q_proj, kv_proj]) qkv = np.transpose(qkv, axes=[1,0,2,3]) return qkv TRANSFORMATIONS = defaultdict( lambda: lambda x: x, { ## padding goes at end per discussion "embedder.weight": lambda x: np.concatenate([x, np.zeros([128, 2048])], 0), "self_attn.qkv_proj.weight": expand_qkv, ## based on line 234 of ## https://github.com/google/gemma_pytorch/blob/main/gemma/model.py "self_attn.o_proj.weight": lambda x: x.reshape(2048, 8, 256).transpose([1,0,2]), # TODO: which of the 2048 is unpacked to 8 x 256, and which is model_dim? "mlp.gate_proj.weight": lambda x: x[np.newaxis, :, :], "mlp.up_proj.weight": lambda x: x[np.newaxis, :, :], "mlp.down_proj.weight": lambda x: x, }, ) VALIDATIONS = { "embedder.weight": lambda x: x.shape == (256128, 2048), "model.norm.weight": lambda x: x.shape == (2048,), "self_attn.qkv_proj.weight": lambda x: x.shape == (8, 3, 256, 2048), "self_attn.o_proj.weight": lambda x: x.shape == (8, 2048, 256), "mlp.gate_proj.weight": lambda x: x.shape == (1, 16384, 2048), "mlp.up_proj.weight": lambda x: x.shape == (1, 16384, 2048), "mlp.down_proj.weight": lambda x: x.shape == (2048, 16384), "input_layernorm.weight": lambda x: x.shape == (2048,), "post_attention_layernorm.weight": lambda x: x.shape == (2048,), } def param_names(): """Return parameter names in the order they are expected for deserialization.""" # note *weight_scaler params are ignored in the forward computation unless # quantization is being used. # # since we are working with the full precision weights as input, don't # include these in the parameters being iterated over. # fmt: off names = [ ("embedder.weight", ) * 2, # embedder_input_embedding (vocab=256000, model_dim=2048) -> (vocab=256128, model_dim=2048) ("model.norm.weight", ) * 2 # final_norm_scale (model_dim=2048) ] layer_params = [ # TODO(austinvhuang): transpositions here ... "self_attn.o_proj.weight", # attn_vec_einsum_w (2048, 2048) -> (heads=8, model_dim=2048, qkv_dim=256) # # ( q_heads = 8 + kv = 2 ) x qkv_dim = 2560 "self_attn.qkv_proj.weight", # qkv_einsum_w (2560, 2048) -> (heads=8, qkv=3, qkv_dim=256, model_dim=2048) # these are the same without any change "mlp.gate_proj.weight", # gating_einsum_w (16384, 2048) => (gate/up=2, hidden=16384, model_dim=2048) "mlp.up_proj.weight", "mlp.down_proj.weight", # linear_w (2048, 16384) => (model_dim=2048, hidden=16384) "input_layernorm.weight", # pre_attention_norm_scale (model_dim=2048) "post_attention_layernorm.weight", # pre_ffw_norm_scale (model_dim=2048) ] # fmt: on for layer in range(18): for layer_param in layer_params: names = names + [(f"model.layers.{layer}.{layer_param}", layer_param)] print("names:", names) return names def convert_weights(): # TODO: parameterize paths as CLI args instead of hard coding them output_file = "2bit-f32.sbs" model_config = config.get_model_config("2b") model_config.dtype = "float32" ## this turns on int8 quantization # model_config.quant = "store_true" model_config.tokenizer = "models/tokenizer.spm" device = torch.device("cpu") torch.set_default_dtype(torch.float) model = gemma_model.GemmaForCausalLM(model_config) model.load_weights("models/gemma-2b-it.ckpt") model_dict = dict(model.named_parameters()) for layer_name in model_dict: ## Make sure we're not silently having int8 quantization turned on. print(layer_name, model_dict[layer_name].max()) assert(model_dict[layer_name].max() > 0.0) param_order = param_names() all_ok = True print("Checking transformations ...") for name, layer_name in param_order: arr = model_dict[name].detach().numpy() arr = TRANSFORMATIONS[layer_name](arr) check = "OK" if VALIDATIONS[layer_name](arr) else "FAILED" if check == "FAILED": all_ok = False print(f" {name : <60}{str(arr.shape) : <20}{check}") if all_ok: print("Writing parameters ...") gate = None with open(output_file, "wb") as bin_handle: for name, layer_name in param_order: arr = model_dict[name].detach().numpy() arr = TRANSFORMATIONS[layer_name](arr) check = "OK" if VALIDATIONS[layer_name](arr) else "FAILED" print(f" {name : <60}{str(arr.shape) : <20}{check}") if "gate_proj" in name: gate = arr elif "up_proj" in name: up = arr f = np.concatenate([gate, up]) print (f.shape) f.flatten().astype(np.float32).tofile(bin_handle) else: arr.flatten().astype(np.float32).tofile(bin_handle) if __name__ == "__main__": convert_weights() print("Done")